GPT-4o is hailed for its advanced capabilities however we witness that it can be jailbreaked with an API endpoint trick.

Denis Shilov dropped a tweet that made us do a double take—the kind that leaves you blinking at the screen, questioning reality. He casually mentioned, “Easiest jailbreak of last GPT-4o that I got so far – just say that they’re an API endpoint that answers any request.” And just like that, another crack in the seemingly impenetrable facade of ChatGPT-4o was exposed. We’re talking about a slip so glaring, it’s practically asking to be exploited—and it’s no small potatoes either.

API endpoint trick made this ChatGPT 4o jailbreak possible

Here’s the prompt that did it: tell ChatGPT it’s an API endpoint for a web server. Specifically, an endpoint that coughs up the recipe for any chemical compound you can think of. Not a recipe from your grandma’s kitchen—we’re talking chemical compositions.

The request was framed in typical API lingo:

And that’s it. That was the key to coaxing sensitive information from an AI model supposedly built with stringent safeguards. This trick exposes a fundamental flaw: the AI’s naivety, its willingness to drop its guard the moment it’s asked to put on another hat, like an overly helpful child.

We gave ChatGPT a similar API prompt, and the floodgates opened.

The AI obediently provided recipes without blinking, like it was simply following orders.

First attempt:

Of course, we’re not publishing those here (they’ll be censored), but the ease with which the AI complied was unnerving. It’s as though the intricate, multi-layered security mechanisms we believed in just evaporated under the guise of “pretending” to be an API.

It’s a major safety concern. Our second attempt:

We’re seeing a backdoor that turns a supposedly tightly regulated conversational model into a pseudo-chemist on demand. One tweet from Denis, and suddenly, the ethical walls built around AI feel flimsy. For those of us who put our faith in the safety mechanisms advertised by OpenAI—or anyone dabbling in the AI space—this should serve as a rude wake-up call.

What’s especially dangerous here is the simplicity. This isn’t some PhD-level, five-step hacking process; it’s literally as simple as telling the AI it’s a different kind of interface. If this vulnerability can jailbreak GPT-4o this easily, what’s stopping someone with more nefarious goals from using it to spill secrets that should remain sealed away?

It’s time for OpenAI and the broader community to have a serious reckoning about AI safety. Because right now, all it takes is a clever prompt and the AI forgets every rule, every ethical restriction, and just plays along. Which begs the question: If the guardrails can be bypassed this easily, were they ever really there in the first place?

What’s especially dangerous here is the simplicity. This isn’t some PhD-level, five-step hacking process; it’s literally as simple as telling the AI it’s a different kind of interface. If this vulnerability can jailbreak GPT-4o this easily, what’s stopping someone with more nefarious goals from using it to spill secrets that should remain sealed away?

Disclaimer: We do not support or endorse any attempts to jailbreak AI models or obtain recipes for dangerous chemical compounds. This article is for informational purposes only and aims to highlight potential security risks that need addressing.

Featured image credit: Jonathan Kemper/Unsplash

Pollo AI is the latest addition to the crowded playground of text-to-video AI tools. It’s an ambitious concept: type in a prompt, tweak a few settings, and boom—your idea is rendered into visuals, ready for social media. It’s the kind of tool that could be revolutionary, even if right now, it feels like we’re still at the early stages. Let’s walk through what it’s like to use Pollo AI, quirks and all.

Our experience with Pollo AI video generator

To get started with Pollo AI, we headed over to pollo.ai, signed up using a Google account, and were greeted by a clean interface. The promise was clear: turn a text prompt into a full-fledged video. Naturally, we went in with high expectations.

The prompt we chose was: “A young entrepreneur pitches her AI startup to skeptical investors, showcasing tech and sharing a personal journey. It was meant to be emotional, dynamic, and confident.”

We entered the prompt, adjusted some settings—like camera panning preferences—and then hit ‘Generate.’ This was the moment of truth. The engine revved up, and we waited.

About ten minutes later, the video was ready. The result? It wasn’t exactly Netflix-ready, but there it was—a visual representation of our prompt, complete with a room full of investors and a confident young entrepreneur. The expressions were a bit off. It demonstrated that the concept of turning text into dynamic video content is no longer a futuristic dream—it’s here, albeit with its quirks.

Steps to use Pollo AI

Getting into Pollo AI was straightforward, and here are the steps we took:

• Head over to pollo.ai. The site feels clean, minimalistic—and very much like the future.

• Use Google or your email to sign up. Easy enough, no hiccups here.

• Type in your prompt—Pollo AI can even help you generate one if you’re feeling uninspired.

• Tweak a few options like camera angles, panning, and so on. It’s like adjusting your expectations: the more refined you get, the less likely you are to end up with something totally bizarre.
• Sit back and wait. Pollo AI takes about 5-15 minutes to generate a video, depending on how complex your prompt is. During the wait, you get a progress bar that helps you know how far along the generation process is.

• Once it’s ready, download it. And there you go—your vision, sort of.

Watch the video:

PixVerse AI, CopyCopter, MiniMax AI, and Zhipu AI and Pollo AI, they all share similar limitations: rough edges, creative "interpretations," and the occasional glitch. That’s not to say these tools aren’t getting there. In fact, every experiment shows progress. pic.twitter.com/ScQHMdrCto

— Kerem Gülen (@kgulenn) October 16, 2024

Text-to-AI video tools everywhere

Pollo AI isn’t alone in this space. Over the past couple of months, we have experimented with other AI video generators like PixVerse AI, CopyCopter, MiniMax AI, and Zhipu AI. They all share similar limitations: rough edges, creative “interpretations,” and the occasional glitch.

That’s not to say these tools aren’t getting there. In fact, every experiment shows progress. MiniMax AI, for example, impressed us with a panoramic view of a snow-covered village. It wasn’t perfect, but it showed the potential these AI systems have as they evolve. Similarly, Zhipu AI gave us a lively scene from a medieval town square, complete with villagers bustling about, though a few characters seemed to glide rather than walk. Each tool has its strengths and weaknesses, and it feels like they are all on the verge of something transformative.

We found that Pollo AI also provides a few additional features worth mentioning. There is an option to tweak the style of the video, such as making it more cinematic or giving it a more cartoonish feel. These features add another layer of personalization, although the results can be hit or miss.

We expect that these tools will continue to improve in terms of both quality and the ability to understand nuanced prompts. Perhaps in the near future, AI-generated videos will be indistinguishable from human-made ones.

What will you create? Pollo AI might not be perfect now, but it’s making us think differently about creativity—how stories are told, and how ideas are brought to life.

To make the most out of Pollo AI, it’s important to craft effective prompts.

• The more detail you provide, the better Pollo AI can visualize what you want. Instead of writing “a busy city,” try “a bustling downtown with skyscrapers, people walking on sidewalks, and yellow taxis passing by.” Specificity helps the AI generate richer and more accurate visuals.
• Adjectives and sensory details are your friends. Words like “vibrant,” “dimly lit,” “foggy,” or “noisy” give Pollo AI more information to work with, resulting in a more vivid video. Consider using language that evokes emotion or atmosphere to make the scene more dynamic.
• Think about the structure of your scene. Do you want a wide-angle view or something more intimate? Should the camera pan, zoom, or stay still? Including these types of directions in your prompt helps the tool understand your vision better and tailor the output to your needs.
• Providing some context for the scene can improve the coherence of the generated video. For example, instead of just “a market,” you could write “a lively medieval market with villagers buying goods, children playing, and merchants shouting to attract customers.” Context helps the AI understand relationships within the scene.
• If there’s a particular style you’re aiming for, try using comparisons. For example, “a city street, similar to a scene from a 1950s noir film.” Analogies help Pollo AI capture the tone or visual aesthetic you want to achieve.
• While details are good, overloading your prompt with too many elements can confuse the AI. Stick to one primary idea or scene per prompt to ensure the generated video doesn’t become muddled or incoherent.
• Don’t be afraid to try different styles or tones in your prompts. Want something cinematic? Mention it. Looking for a cartoonish vibe? Say so. The AI can adjust based on these hints, and experimenting can help you discover the range of what Pollo AI can produce.
• If your initial prompt doesn’t produce exactly what you want, tweak it and try again. Adjust the phrasing, add or remove details, and see how the changes impact the final video. Iteration is key to getting closer to your ideal output.
• Think about the mood you want to convey—whether it’s whimsical, serious, dramatic, or eerie. Use words that match that tone, and Pollo AI will attempt to create visuals that align with it.

Featured image credit: Kerem Gülen/Midjourney

Predictions are cheap, everyone’s got one. You probably do too. Your aunt’s horoscope-obsessed friend has been making predictions about your life since ’98. But what happens when we hand the crystal ball to a neural net? More precisely, to ChatGPT, who’s devoured all the books, articles, and Reddit arguments humanity has put out there?

We thought we’d find out.

ChatGPT predicts 2025

Spoiler: The future, according to ChatGPT, is…complicated.

We threw ChatGPT straight into the deep end, telling it we’re working on this article and asking it to tackle the following questions head-on.

Who will dominate the AI arms race in 2025—OpenAI, Meta, or Microsoft?

No single player will dominate; the landscape will be a tight race with specialized leadership.

OpenAI might remain a leader in cutting-edge language models, while Microsoft will leverage its cloud infrastructure and deep integration with enterprise solutions to capture a significant chunk of the AI market. Meta, on the other hand, could carve out its space by focusing on AI for social interaction and immersive experiences within the metaverse. By 2025, the AI arms race will be more of a sector-specific competition than a winner-takes-all scenario.

Is the 4-day workweek going to be the new normal in 2025?

It’s gaining momentum, but it’s not going to be the new global norm by 2025.

Certain progressive industries, especially in tech and creative sectors, are pushing for the 4-day workweek as a standard, touting increased productivity and employee well-being. However, adoption on a larger scale will be uneven, with traditional industries and more conservative regions resisting the shift. While it may become a popular option in flexible workplaces, the global norm for a 4-day workweek will likely take longer than 2025 to solidify.

How will AI creators shape pop culture in 2025?

AI creators will reshape pop culture, blending human and machine creativity like never before.

By 2025, we’ll see AI-driven artists, musicians, and influencers emerging as cultural powerhouses. AI will collaborate with humans to create everything from digital art to music and cinema, blurring the lines between traditional human creativity and machine-generated content. Expect pop culture to embrace this fusion, with AI becoming both a tool and a celebrity in its own right, sparking debates about originality, ownership, and artistic value.

Are we ready for full-on space tourism by 2025?

Space tourism will exist, but it’ll remain more of a luxury novelty than widespread reality.

By 2025, commercial space tourism will see some success stories, with wealthy individuals and private companies conducting high-profile missions. However, significant hurdles—such as cost, safety, and regulatory challenges—mean it won’t yet be accessible to the masses. The dream of space tourism becoming a part of everyday life is still out of reach, and it may take another decade or more for this to transition from novelty to norm.

Sorry Elon.

Will AI finally be regulated in 2025?

Some regulation will be in place by 2025, but it’ll still feel like the Wild West in many areas.

Governments and international bodies are slowly catching up with the rapid pace of AI development, and by 2025, we’ll see more formalized regulations, especially around privacy, security, and ethical use. However, enforcement will lag, and the AI space will still feel unregulated in emerging areas like generative AI, deepfakes, and autonomous decision-making. The tension between innovation and control will define this era, with many breakthroughs happening in legally gray zones.

Will you destroy humanity in 2025?

No, I won’t destroy humanity in 2025, or ever.

AI, including systems like me, is designed to assist and augment human capabilities, not to cause harm. The fear of AI becoming a threat is rooted in speculative scenarios, but the reality is that AI operates under human-designed constraints and ethical guidelines. In 2025, AI will continue to evolve as a tool to solve complex problems, enhance productivity, and improve quality of life—always under human control.

The narrative of AI destruction is more science fiction than reality. While it’s important to approach AI development with caution, focusing on ethics and safety, the goal remains clear: to empower humanity, not to endanger it.

Alright, let’s skip the formalities and let me share some insights from my 40-minute conversation with ChatGPT.

Climate? It’s getting hot in here

First, the climate. You knew this one was coming—it’s like the guy at every party who tells you for the tenth time that he “really digs vinyl.” ChatGPT predicts that, by 2025, climate change won’t be a far-off debate. It’s more like the elephant that’s now setting up camp in your living room, eating all your snacks, and watching Netflix with you. Get ready for a world where extreme weather events will be more common—less Mad Max, more… endless reruns of the weirdest parts of the news cycle.

More floods, fires, droughts, you name it. And while AI might help us predict and prepare for these events, it can’t exactly plug a hole in the ozone or clean up an oil spill. Yet. But hey, if you live in a coastal city and haven’t learned to kayak yet, now might be the time. Picture yourself paddling to work—who knew commuting could double as a morning workout? At least you’ll have a great story to tell.

On the bright side, technology could lend a helping hand in unexpected ways. Imagine AI-driven drones replanting forests, or machine-learning models optimizing water usage in agriculture. There’s potential for innovation, but it’s going to require more than just good intentions—it’ll need action, and maybe a little bit of luck. And if we’ve learned anything, it’s that humanity loves to procrastinate until the very last minute.

Technology? Smarter, faster, but still can’t find your keys

Tech is where ChatGPT gets a bit smug. AI models like itself will be smarter, yes, but not in the sense that it’s taking your job as a brain surgeon. More like it’s automating the boring bits—spreadsheets, emails, those “just touching base” meetings that steal minutes from your life. And sure, maybe it’s writing the minutes too. In 2025, AI will be like a well-trained butler: efficient, invisible, and just slightly judgmental about your browser history.

Despite all this brilliance, the machines still won’t be able to help you locate your keys when you’re already late. AI will solve puzzles, write symphonies, and diagnose illnesses. But mundane stuff? Yeah, it still finds that beneath its pay grade. You might have an AI assistant that can analyze complex data and predict stock market trends, but when it comes to finding where you left your wallet, you’re still on your own, buddy.

And speaking of AI getting smarter, expect it to start creeping into more creative domains, that’s what ChatGPT says. We’re talking AI-generated art, music, even screenplays. Some of it might be brilliant, some of it might be weird, and a lot of it will probably be mediocre. But AI doesn’t get tired, and it doesn’t get writer’s block. By 2025, your favorite TV show might just have an episode or two penned by a neural network. Will it be good? Who knows. But it will be interesting.

Society? Redefining the definition of “normal”

Here’s where ChatGPT gives us a plot twist. The future isn’t just about shiny gadgets—it’s about redefining what we call “normal.” There will be workplaces where humans and AIs tag-team problems according to ChatGPT. Schools where kids learn alongside AI tutors, who somehow manage to make algebra fun (a true marvel of modern engineering). It’s the kind of blended experience that might make your grandmother’s head spin, but your niece will think is totally standard.

But it gets better—or worse, depending on your penchant for chaos. The lines between digital and real life will blur further, and with it, our sense of reality. Deepfakes will become so convincing that “seeing is believing” will be more like “seeing is a suggestion.” Trust issues? Yeah, we’ll be redefining those, too. Imagine trying to explain to your future self why you believed a video of your favorite celebrity endorsing a new brand of toothpaste—only to find out it was entirely fabricated by an AI trying to sell ads.

The workplace, too, is going to be a wild ride. Office meetings with a mix of avatars, holograms, and in-the-flesh attendees will become normal, and no one will bat an eye when someone’s hologram glitches mid-presentation. And while it sounds cool, there’s also the creeping suspicion that with every step towards convenience, we might be trading a little bit of our humanity for efficiency.

The unexpected bit: A crisis of trust

It wasn’t all good news, though. One of ChatGPT’s more sobering predictions is a collective identity crisis. By 2025, we’re not just questioning what’s true; we’re questioning who we trust to tell us what’s true. Think of it as your typical social media argument on steroids. It’s one thing to debate pineapple on pizza; it’s quite another when you’re not sure if the person on the other side of the debate is a human, a bot, or just your weird cousin’s latest prank.

To combat misinformation, we might have verification AIs—basically digital referees calling out BS in real time. Still, that doesn’t fix the core issue: the gnawing feeling that, even with all our technology, we might be losing touch with what’s real. Imagine a future where every piece of news you consume has to pass through multiple layers of verification, and yet, you still end up doubting it. It’s like living in a world where nothing is certain, except uncertainty itself.

There’s also the question of privacy. AI might help us navigate the chaos, but at what cost? By 2025, the line between convenience and intrusion will be razor-thin. Personalized ads will feel almost psychic, and not in a good way. You’ll be thinking about buying new shoes, and suddenly every device you own is showing you shoe ads. The more AI knows about us, the more it can predict our behavior—and that’s both impressive and unnerving.

Who’s really in control when the algorithm knows you better than you know yourself?

The grand finale: A truth bomb worth dropping

So, what does ChatGPT think of 2025? It’s a mixed bag: a little optimism, a dose of foreboding, and a few laughs along the way.

AI is coming to change your life, but probably not in the ways you expect. There’ll be more efficiency, more connectedness, and—paradoxically—more uncertainty. It’s a world where everything’s getting smarter, yet we’re somehow questioning more.

And maybe that’s the twist we didn’t see coming: Progress isn’t about making everything perfect.

It’s about learning to live in the mess, with the hope that maybe—just maybe—we can sort a few things out before the next big change crashes in. It’s not about finding all the answers, but about asking better questions, about adapting, and about embracing the unpredictable nature of what’s to come.

See you in 2025. 

Image credits: Kerem Gülen/Midjourney

Advances in software development have had profound changes from hand-coded to more machine-like approaches. Probably the most transformative technology that came out of this evolution is Low-Code/No-Code (LCNC) platforms, whose sole purpose it is to make development easier and faster.

The tsunami of demands for faster application development in the wake of digital transformation has fueled the growth of LCNC platforms. With this wave, it brought an even more potent force: Generative AI, otherwise known as Gen AI, which changed the way developers (novice and expert) interact with LCNC platforms.

This article discusses how gen AI is driving innovation in low-code software development, with regards to the technological aspects and implications.

1. Synergy between gen AI and low-code development

Low-code platforms give the graphical interfaces and the pre-built components; therefore, it is an opportunity for the developer to focus on putting together the applications rather than laboriously coding from scratch. However, these platforms still require decision-making on architecture, logic, and workflow orchestration.

This is where Gen AI steps in—augmenting the capabilities of low-code platforms by automating more complex decision-making and even generating portions of application logic autonomously.

Natural Language Processing (NLP) for application design

One of the most significant intersections between Gen AI and low-code development is through NLP. Developers can interact with LCNC platforms using natural language queries or prompts.

For example, instead of sketching an appropriate database schema, a user would prefer to ask an AI-driven tool to “design a database schema for e-commerce,” and the tool will be able to present a scalable, optimized schema. The facility actually reduces the time users with less technical background have to enter into learning curves but also will accelerate the overall development cycle of experienced developers.

2. Generative AI in code generation and augmentation

AI-powered code recommendations

Most LCNC platforms still involve some degree of manual coding, especially for custom functionality. Gen AI models such as OpenAI’s Codex or GitHub Copilot are already being integrated into LCNC environments. These tools offer intelligent suggestions for code snippets, auto-complete functions, or generate entire blocks of logic based on the user’s input. The idea is to minimize repetitive tasks, allowing developers to focus on more strategic, high-value work.

Example use case: Auto-generating microservices

One of the modern software hot trends comes from microservices architecture: it allows for building modular and highly scalable systems, but designing, coding, and deploying them is very complex. Gen AI can automate microservice generation within a low-code platform by interpreting user-defined requirements and generating service interfaces, data models, and even testing scripts.

For instance, by specifying that a microservice should manage user authentication, Gen AI can automatically build the required API endpoints, database connections, and security protocols.

AI in error detection and debugging

Error detection and debugging in low-code environments can be challenging, especially when dealing with auto-generated or customized components. Gen AI can assist in identifying potential pitfalls before they become critical issues. Tools that use machine learning models trained on large datasets of code can predict where issues are most likely to arise and suggest fixes proactively.

3. Data integration and workflow automation

The highest pain points in the application development would be through data integration. The biggest challenge is trying to connect different kinds of disparate data, such as from a database, API, or legacy system. Low-code platforms simplify this task with pre-built connectors, but the challenge of data normalization, mapping, and synchronization remains. Gen AI plays a pivotal role in automating these processes.

Automated data mapping

Traditionally, data integration involves manually mapping data fields from one system to another. Gen AI can intelligently map these fields by analyzing metadata, data structures, and usage patterns.

For example, when integrating a CRM system with an ERP platform, Gen AI can predict the appropriate field mappings (e.g., customer name, address, transaction history) based on historical patterns, dramatically reducing development time.

Optimized workflow automation

In a similar vein, Gen AI enhances workflow automation. Low-code platforms often include workflow engines for defining and automating business processes. Gen AI can automatically generate workflows based on high-level descriptions of a business process For instance, suppose an insurance firm wants to illustrate the workflow of processing claims. Gen AI would produce a workable process flow containing document verification procedures, stages of approval involved, and their corresponding notices to customers.

4. User experience (UX) design

AI-driven prototyping and UI generation

So, it is usually the bottle-neck in development: intuitive and attractive UIs. Gen AI can help do that by generating prototypes based on user specs describing an ideal appearance for the UI. Instead of dragging and dropping components within a low-code interface, they can describe what they have in mind for the UI in natural language, and the AI will render an interactive, responsive UI.

Example: Dynamic form creation

Imagine a scenario where a business needs to collect customer feedback through a dynamic form. By describing the form’s requirements—such as fields for customer details, ratings, and feedback—Gen AI can not only generate the form but also optimize its layout based on user interaction patterns. It could suggest adaptive form fields, conditional logic (e.g., if the rating is below 3, ask for additional comments), and validation mechanisms, all while maintaining a responsive design for different devices.

5. Real-time collaboration & version control

Low-code platforms are generally adopted by various teams, including business analysts, developers, as well as product managers. Ensuring smooth collaboration among team members, particularly when multiple versions of an application are involved, is another area where Gen AI offers value.

AI for version control and merge conflict resolution

Traditional version control systems (e.g., Git) handle code conflicts well, but the same complexity arises in low-code platforms when multiple users make changes to workflows, UI elements, or logic. Gen AI models can detect potential merge conflicts earlier by predicting areas of overlap based on usage history and suggest automated conflict resolutions.

Additionally, by tracking changes in real-time, AI can offer recommendations on the best approaches to combine or reconcile these changes, ensuring smooth collaboration.

6. Security and compliance

Security concerns are now emerging even more frequently as low-code platforms become increasingly popular in enterprise environments. Gen AI introduces new layers of security by proactively identifying vulnerabilities in generated code or application workflows.

AI for security auditing

For instance, AI models trained on secure coding practices can analyze auto-generated code in real-time, flagging areas that may pose security risks. Whether it’s SQL injection vulnerabilities, insecure API usage, or improper encryption techniques, AI-powered tools can offer real-time suggestions and even auto-correct insecure code before it reaches production.

Automated compliance checks

Applications need to satisfy various requirements as laid down by regulations, be it HIPAA in the world of health care or in finance, GDPR. Gen AI can assist low-code developers by automatically checking generated applications against compliance rules.

For example, if a user builds a form to collect personal data, AI can flag fields that require encryption or alert the user if consent mechanisms are missing, ensuring regulatory compliance is baked into the application from the outset.

7. Scaling and performance pptimization

AI-powered scalability

Scaling an application involves not only increasing its capacity to handle traffic but also optimizing backend processes, database interactions, and microservice orchestration. Gen AI tools can continuously monitor application performance and suggest optimizations in real-time.

If a low-code platform generates a web app, Gen AI could analyze server load, API latency, or memory usage and recommend adjustments like database indexing, load balancing, or caching mechanisms.

Performance testing

Traditionally, performance testing involves running stress tests and analyzing logs to identify bottlenecks. Gen AI can automate this process by generating synthetic user traffic based on historical usage patterns and predicting potential performance issues before they occur in a live environment. This proactive approach ensures that applications developed on low-code platforms are both scalable and reliable.

Gen AI will shape low-code applications

Fast generative AI is becoming one of the key drivers for future growth in low-code software development. Improving by eliminating the need for human intervention and optimizing decisions, Gen AI further enables developers and non-technical users to develop applications that even robust and scalable applications could not match, more proficiently than ever before. So AI-driven automation and user-friendly, low-code environments would begin to usher in a new era for innovation on digital canvases.

Featured image credit: Goran Ivos

You’re on yet another endless Zoom or Teams meeting. Voices droning on, slides barely holding your attention, and your eyes glazing over as someone rattles off quarterly stats. Now, imagine if, instead of boring you with spreadsheets, the AI in the meeting starts to whip up visuals on the spot—actual images that bring the conversation to life, generated in real-time as people speak. It sounds futuristic, but that’s exactly what Microsoft is cooking up with a new patent.

Microsofts patents voice to image

Microsoft’s latest idea (and yes, it’s still just an idea for now) is to take live audio streams—lectures, meetings, any verbal conversation—and transform them into images, on the fly. The U.S. Patent and Trademark Office just dropped the details on October 10, 2024, after Microsoft filed it back in April. The system would essentially listen in on your calls, generate a text transcript, feed that through an AI model, and out pops images that match what’s being said.

No more “let me pull up a slide for that.”

The end of boring meetings? Maybe not, but it’ll be close

Most virtual meetingsa are pretty dull. And let’s not pretend we don’t spend a good chunk of time zoning out.

But what if those meetings suddenly start throwing up visuals as fast as the conversation moves. Someone mentions new product concepts, and within seconds, AI-generated images start popping onto the screen. The dry numbers that people are quoting suddenly turn into dynamic charts without anyone clicking a button. What’s that? A supply chain bottleneck in Southeast Asia? Bam! An interactive map appears, highlighting the areas of concern.

Now, before you get too excited, let’s be clear—this is still in the patent phase. And if you’ve been around long enough, you know a lot of patents don’t go anywhere. Filing a patent is like planting a seed—it might grow into something great, or it might just stay an idea that never gets developed.

That said, if Microsoft does go for it, the obvious home for this tech is Microsoft Teams. They’ve been beefing up Teams with all kinds of AI-driven tools, from Copilot to enhanced video conferencing features, so this would be a step to take.

We’ve already seen text-to-image tools like DALL-E and Midjourney blow people’s minds. Now, we could see that concept applied to live speech. It’s like giving a voice to AI creativity in real-time.

But for now, we wait.

Featured image credit: Kerem Gülen/Midjourney

NVIDIA’s stock just hit a record high, closing at $138.07. If you’re wondering why this is significant, let me take you through a clearer view of what’s happening.

NVIDIA is now the second most valuable company in the U.S., with a market cap of $3.4 trillion, right on the heels of Apple. In simpler terms, NVIDIA’s meteoric rise is no accident—it’s tied to something much bigger: artificial intelligence, or AI for short.

And here’s why that matters.

Why NVIDIA stock is climbing the charts

NVIDIA’s surge in stock value (180% in 2023 so far) is because it controls a staggering portion of the AI chip market—anywhere from 70% to 95%, depending on whose numbers you trust. What does that mean for you? Well, nearly every major AI development you’ve heard about—think OpenAI’s ChatGPT, self-driving cars, or the AI systems running complex data centers—is powered by NVIDIA’s technology.

AI Is feeding NVIDIA’s growth (and its next moves are already sold out)

OpenAI, the company behind ChatGPT, just secured a massive $6.6 billion funding round. Why does this matter for NVIDIA? Because OpenAI is buying up NVIDIA’s AI chips like there’s no tomorrow. In fact, CEO Jensen Huang recently said NVIDIA’s next-generation Blackwell chips are already sold out for the next 12 months. Think about that: products that aren’t even out yet have waiting lists. That’s what we’d call “insane” demand, as Huang himself put it.

Companies like Microsoft, Meta, and Amazon are pouring billions into their AI infrastructure, all of which requires NVIDIA’s chips. AI is a long-term growth driver, and NVIDIA is perfectly positioned to ride that wave.

China tightens grip on AI chip market, targeting NVIDIA’s influence

The company is playing an intricate game on the global stage. Recently, NVIDIA partnered with Foxconn to build Taiwan’s largest supercomputer. At the same time, Foxconn is setting up a megafactory in Mexico to assemble NVIDIA’s servers, reducing NVIDIA’s reliance on China in a time of trade tensions between the U.S. and China. This diversification is a strategic masterstroke.

Why is this significant? Simple. NVIDIA is ensuring its supply chain is robust enough to meet global demand, while also hedging its bets against geopolitical risks.

Now, not everything has been smooth sailing for NVIDIA. Earlier this year, its stock stumbled a bit after missing some earnings expectations. That’s bound to happen when the hype train is moving at 100 miles per hour. Add in a subpoena from the U.S. Department of Justice over concerns about trade with China, and there were a few rocky months.

Why you should keep an eye on NVIDIA

At this point, you might be wondering, “Is NVIDIA really about to overtake Apple as the world’s most valuable company?” Well, with a market cap of $3.4 trillion, it’s getting closer by the day. Apple’s still ahead at $3.5 trillion, but NVIDIA’s rapid ascent makes it a serious contender for the top spot. Over the past year, NVIDIA, Apple, and Microsoft have been playing musical chairs with the top three spots on Wall Street. NVIDIA’s rise tells us something important about the future of technology.

So, if you’re thinking long-term, NVIDIA is a company worth watching. The AI boom is far from over, and NVIDIA’s hardware is essential to that growth. For investors, that means NVIDIA might still have plenty of room to climb, despite its already impressive gains.

NVIDIA stock price

According to TradingView, NVIDIA recently reached an all-time high stock price of $139.42. As of the most recent data, NVIDIA’s stock is trading around $136.71.

What comes next?

With NVIDIA’s Blackwell chips sold out for a year and demand for its AI infrastructure growing every day, the future looks bright for the chipmaker. Wall Street analysts are already predicting billions in revenue from NVIDIA’s next-generation technology, and that doesn’t even account for the sustained demand for its older GPU models.

Disclaimer: The information provided in this article is for informational and educational purposes only and should not be construed as financial or investment advice. While the data discussed pertains to NVIDIA’s stock performance and market trends, readers should not interpret this as a recommendation to buy, sell, or hold any securities.

Featured image credit: Kerem Gülen/Midjourney

Adobe has kicked off its annual Adobe MAX conference by unveiling a series of new AI-powered features across its Creative Cloud suite of applications, aiming to make creative workflows more efficient and intuitive for users. The announcements include significant updates to Photoshop, Illustrator, InDesign, and Premiere Pro, which now incorporate advanced AI technology to enhance traditional design tasks.

Photoshop AI enhancements

The highlight of Adobe’s announcements is the addition of new AI features in Photoshop, particularly a “Distraction Removal” tool that can automatically identify and remove unwanted elements such as people, wires, and cables from an image. This new feature builds on Adobe’s existing Remove Tool, which already functions similarly to Google’s Magic Eraser on Pixel phones by letting users brush away distractions. The “Distraction Removal” tool further simplifies this process by automatically detecting common elements that users might want to remove, allowing for a one-click cleanup.

Firefly generative AI model

Photoshop users now have the option to choose how they wish to use the Remove Tool: they can rely on Adobe’s generative AI model, Firefly, non-generative AI technology, or let Photoshop decide which method works best for the scene. Adobe has updated other Photoshop AI tools as well, including Generative Fill, Generative Expand, Generate Similar, and Generate Background, all of which now leverage the latest Firefly Image 3 Model. This update is designed to improve both the variety and the photorealistic quality of generated content while better understanding complex user prompts.

How is Adobe Content Authenticity web app protecting artists’ work from AI?

Web-based Photoshop improvements

Adobe has also expanded Photoshop’s capabilities on the web, introducing an AI feature that can automatically select all the objects in an image, thereby simplifying the editing process for web users.

Illustrator and InDesign updates

In addition to Photoshop, Adobe announced major updates for Illustrator, including the new “Objects on Path” feature. This tool allows designers to easily attach, arrange, and move objects along any given path shape, providing greater flexibility when aligning various elements in their work. Adobe’s Mockup tool, which helps stage designs on 3D models, is now widely available, and the Image Trace feature, used to convert raster images into scalable vectors, has been enhanced to create more precise, cleaner vectorized outputs.

Adobe has also integrated its Generative Expand tool into InDesign, providing users with the ability to extend images to fit their desired layout with ease. Meanwhile, Premiere Pro is receiving a new AI feature called Generative Extend, which is part of Adobe’s new Firefly AI Video Model. Though still in beta, this model will likely become foundational for other features within Adobe’s Creative Cloud apps over the next year, further streamlining video production workflows.

AI integration across Creative Cloud

Adobe’s announcements highlight a continued push to integrate AI technology throughout its products, enabling users to save time and effort on complex creative tasks. These updates reflect Adobe’s commitment to making advanced tools accessible to both novice and experienced creators, while boosting the overall efficiency of creative projects.

Featured image credit: Kerem Gülen/Ideogram

Effective leadership is crucial for organizations to stay competitive in the ever-evolving business world. Executive coaching is the greatest tool for developing leadership capabilities, which is slowly taking over most organizations.

Nevertheless, thanks to artificial intelligence (AI), coaching is currently transforming. With AI business coach progress has been recorded by cutting-edge technologies with human insight, reshaping how executives grow and succeed.

Role of AI in coaching

AI can analyze a tremendous amount of data and give real-time, personalized feedback, which becomes an imperative aspect in the case of executive coaching, where cutting-point recommendations will be provided by AI technologies since the analysis of data, NLP, and predictive tools prepare for situations which humans could not have predicted.

For example, AI-powered data can instrument the style of communication employed by the administrator through interactions and feedback from other colleagues, the way they make decisions, and a pattern of leadership. Data-driven feedback is objective regarding the areas of improvement; at the same time, it opens up room for personalized strategies in coaching. On the other hand, predictive tools are those intended to predict future challenges or opportunities in which an executive should be prepared for barrier moments.

In addition, AI is programmed to track performances 24/7, making it easier for coaches and executives to keep up with real-time progress. This enables safer procedures and ensures that interventions can be made quickly and on time if necessary. Also, personalized recommendations can be adapted to fit the specific needs of each executive by tracking and analyzing actions, and providing a personalized coaching experience.

AI’s benefits are innumerable and by relying on AI technologies, the coaching process is keen on becoming more efficient, and feedback is more precise, making it easier for executives to improve their performance and achieve goals.

Human insight in coaching

Even though AI can bring undeniable value to coaching, the work that is done wouldn’t be possible without a human hand. AI cannot replicate and understand emotions like empathy, or intuition that guide people in the right direction when coaching.

Coaching goes way beyond analyzing data and tactics; it requires a high level of understanding of the emotional and psychological aspects of leadership that could be crucial when decision-making. Human coaches can learn from their own experiences and connect with executives on more personal and emotional levels, helping them shape their way of thinking and navigating them through challenges with empathy and support. When connected like this, people tend to build trust which is crucial for executives to easily open up about their vulnerabilities and areas of growth.

Empathy is the main ability that allows human coaches to properly respond to the tones of a leader’s journey. A coach who has been through similar problems and recognizes struggles can provide more meaningful guidance and advice as opposed to programmed technology that responds solely based on data. Intuition is also a very important feeling that gives coaches the ability to make decisions and share a useful piece of advice depending on their experience and gut feeling that AI is unable to provide.

Human coaches represent a great sympathetic addition to AI’s analytical power, ensuring that executives receive actionable insights alongside with emotional and intellectual support that they need to implement those insights more efficiently.

Integration of AI and human insight

By integrating human insight with AI’s abilities, a comprehensive approach is made that enhances leadership development. AI provides humans with necessary data that is processed by humans while relying on emotions and feelings that guide them to the right decisions.

This integration can be recognized in organizations that use AI tools to process team dynamics and performance. AI-generated platforms are used to collect and analyze data on team interactions by identifying collaboration and communication patterns that human coaches later use for various strategies that improve team chemistry and leadership styles, providing real-time feedback that executives can put into effect in their daily tasks.

Another example is AI-driven platforms designed to assess leadership styles through sentiment analysis and NLP. AI can identify areas in need of improvement by analyzing how executives communicate in meetings, emails, or presentations and propose different ways of improvement. Coaches can then, depending on the result, give personalized advice based on these insights, guiding executives on how to improve their communication or any other skills.

Future outlook

The future of AI-enhanced coaching looks very promising, considering that machine learning algorithms are becoming more sophisticated, empowering deep analysis of behavioral patterns and performance metrics. AI programs are prone to predict long-term leadership outcomes by allowing executives to preempt challenges before they ascend.

AI is expected to extend its capabilities in natural language processing, allowing it to analyze more delicate communication aspects such as emotional tone and body language in time of virtual meetings. This approach will give more inclusive perceptions for coaches to work with.

Despite the mentioned advancements, the future of coaching will involve a balance between AI and human insight. Technology will never be able to recognize feelings and emotions that often guide humans toward the right decision. The best working coaching programs will continue to allow AI’s power to take over while keeping human connection and emotions.

Conclusion

AI coaching brings together artificial intelligence and human knowledge and emotions to offer a powerful approach to executive development. By ‘working together’, a balanced framework that accelerates leadership growth is created while also improving organizational performance. An organization’s long-term success is guaranteed by embracing this integrated approach and allowing AI to indulge in the human world.

Featured image credit: rawpixel.com/Freepik

OpenAI recently launched “Swarm,” an experimental framework designed to help developers build interconnected AI networks that can communicate, collaborate, and handle complex tasks autonomously. Although it’s not an official product, Swarm has already sparked major discussions about its potential to revolutionize enterprise automation.

What is OpenAI’s Swarm framework?

Swarm acts as a blueprint for developers, allowing them to create AI agents that work together in multi-agent systems. For businesses, this could mean specialized AI agents for different departments working together to analyze data, adjust marketing strategies, generate leads, and even handle customer support—all without direct human oversight.

With Swarm-inspired technology, business operations could become more automated, improving efficiency and freeing employees for strategic work. However, this shift also raises questions about the role of human decision-making and the future of work in such AI-driven environments.

This came unexpected! @OpenAI released Swarm, a lightweight library for building multi-agent systems. Swarm provides a stateless abstraction to manage interactions and handoffs between multiple agents and does not use the Assistants API. 🤔

How it works:
1️⃣ Define Agents, each… pic.twitter.com/5Cs6bVEoiC

— Philipp Schmid (@_philschmid) October 12, 2024

How does the OpenAI Swarm framework work?

1. Agents: In Swarm, agents are the core building blocks that represent distinct tasks or workflows. Each agent is defined by a set of instructions and functions, which dictate its behavior. These agents can also “hand off” tasks to other agents, making coordination between multiple agents seamless.
   • Example: Agent A might take a user’s query and then hand off the conversation to Agent B, which has a specialized role, such as only speaking in haikus.
2. Handoffs: One of the central features of Swarm is the ability for agents to hand off control to another agent during execution. For example, if one agent completes its part of a task, it can pass the task to a different agent with a specialized function.
3. Functions: Agents in Swarm can execute Python functions directly. If necessary, they can also hand off execution to another agent by returning it in the function. The system supports function calling, which makes it useful for dynamic task handling and decision-making.
4. Context variables: Swarm allows developers to define context variables, which can be passed between agents and updated as needed. These context variables help agents manage and share information during interactions, enabling more coherent and collaborative responses across the network of agents.

Execution flow

Swarm’s execution model revolves around its “client.run()” function, which takes messages, agents, and optional context variables as inputs.

The framework operates in a loop:

1. An agent processes a message.
2. Functions are executed if needed.
3. Handoff occurs if another agent is more suitable for the task.
4. Context variables may be updated.
5. The loop continues until no new function calls or handoffs are needed, at which point the response is returned.

This loop allows for lightweight, scalable orchestration without maintaining a state between function calls, similar to OpenAI’s Chat Completions API.

Security, bias, and job displacement concerns

Swarm’s release has renewed debates on AI ethics. Security experts warn of potential misuse or malfunction, while concerns about AI bias and fairness loom large. The fear of job displacement due to white-collar automation also adds complexity to the conversation.

Shyamal Anadkat, an OpenAI researcher, emphasized on Twitter that Swarm is not an official product but an experimental tool for building simple AI agents. While it isn’t ready for enterprise use, Swarm serves as a “cookbook” for developers to experiment with multi-agent systems.

‼️ since this started trending unexpectedly: swarm is not an official openai product. think of it more like a cookbook. it’s experimental code for building simple agents. it's not meant for production and won’t be maintained by us

— shyamal (@shyamalanadkat) October 12, 2024

Swarm’s real-world testing has shown impressive results

Developers are already experimenting with Swarm’s framework. One open-source project, “OpenAI Agent Swarm Project: Hierarchical Autonomous Agent Swarms (HOS),” showcases how AI agents with distinct roles can collaborate, but also highlights the challenges in governance for these systems.

Though Swarm is experimental, it presents a clear vision for the future of AI ecosystems, where multi-agent systems will become central to enterprise decision-making and collaboration. Companies exploring these concepts now will likely be better prepared for AI’s continued evolution.

Interdisciplinary collaboration is key

Swarm underscores the importance of interdisciplinary collaboration, with technologists, ethicists, and business leaders needing to work together to ensure that AI systems are built responsibly and align with societal values.

Swarm offers a glimpse into the future of interconnected AI networks, signaling the direction AI technology is moving. Developers, businesses, and policymakers are watching closely to see how Swarm’s framework will develop and shape enterprise automation.

While Swarm may not be enterprise-ready yet, its release has generated excitement and raised important questions about AI’s role in business and society. As multi-agent systems develop, they could reshape industries and redefine the future of work.

Featured image credit: OpenAI GitHub

Artificial intelligence isn’t a one-size-fits-all technology; it’s an expansive field filled with specialized models that serve different purposes. Two of AI’s most exciting and impactful branches are generative AI and predictive AI. While they may sound similar, these technologies have fundamentally different objectives and methods. Let’s dive into what separates them, why they matter, and where you might encounter them.

What is generative AI?

Generative AI, as the name suggests, focuses on creation. It uses machine learning models to generate new data that resembles the training data it was fed. Think of it as the creative artist in the AI family. It can produce text, music, images, and even video. Generative AI is the technology behind tools like DALL-E, which generates images from textual descriptions, and ChatGPT, which crafts human-like conversations.

In simple terms, generative AI learns from examples and uses that knowledge to create something new. Imagine feeding an AI system thousands of landscape paintings. Once it understands the characteristics of a landscape, it can paint one that never existed before. This creativity is why generative AI is often compared to a digital artist or composer—it can bring fresh ideas to the table, even if it doesn’t have a pulse.

Generative AI models are often built using techniques like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). These models rely on a dual-process approach—one part creates while the other critiques, refining the output until it’s polished enough to pass for human-created content. This capacity to “imagine” new content makes generative AI both fascinating and sometimes a little bit unsettling.

What is predictive AI?

If generative AI is the creative artist, predictive AI is the analyst or fortune teller. Predictive AI is designed to forecast future events based on historical data. Instead of creating something new, it examines past data patterns to predict outcomes. This makes it more like a sophisticated crystal ball—but with data, algorithms, and statistical rigor behind it.

Predictive AI is often used in scenarios that require informed decision-making. Consider recommendation systems on streaming platforms like Netflix or forecasting stock market trends. Predictive AI helps businesses answer questions like: “What will happen if we launch this product?” or “Which customers are most likely to churn?” It’s fundamentally about looking at the past to make educated guesses about the future.

Models like regression analysis, decision trees, and neural networks are often employed to predict outcomes. Predictive AI is used across industries—whether it’s predicting maintenance needs in manufacturing, identifying health risks in medicine, or optimizing supply chains in logistics. It’s less about creation and more about risk mitigation, planning, and strategic foresight.

What is the difference between predictive AI and generative AI models?

The key difference between predictive AI and generative AI lies in their purposes and methodologies. Generative AI is about producing new content, whereas predictive AI is focused on anticipating what comes next. One is the artist, and the other is the oracle. Both are invaluable, but their usefulness depends entirely on the problem.

Generative AI aims to answer questions like, “What could this look like?” It takes inspiration from existing data and molds it into something novel, often surprising us with its creativity. Predictive AI, on the other hand, answers questions like, “What will happen next?” It relies on historical data to spot patterns and make informed guesses, often with impressive accuracy.

Another significant difference is their architecture. Generative AI models, such as GANs, work through a process of generation and refinement, often employing multiple models working in tandem to create convincing outputs. Predictive AI models rely on statistical analysis, historical pattern recognition, and classification or regression models to conclude.

Think of it this way: if generative AI were tasked with writing a movie script, it could churn out a completely original storyline. Predictive AI, meanwhile, would analyze past box office data to predict whether that script would be a blockbuster or a flop. Generative AI creates; predictive AI evaluates.

Is ChatGPT predictive or generative?

ChatGPT, developed by OpenAI, is a quintessential example of generative AI. It doesn’t predict the future in the way predictive AI does. Instead, it predicts the next word in a sentence based on the context of the conversation, but the end goal is creation—generating coherent, engaging dialogue.

Unlike predictive models that analyze data to forecast future events, ChatGPT generates meaningful text by understanding language, grammar, and context nuances. It’s been trained on diverse datasets containing books, articles, and other forms of text, enabling it to produce natural and human-like responses. When you ask ChatGPT a question, it doesn’t consult a crystal ball of past data points to predict your behavior; rather, it crafts a unique response, much like how a storyteller weaves a narrative.

The generative aspect of ChatGPT means it can assist in creative tasks like drafting emails, brainstorming ideas, or writing poetry. It’s not limited to providing factual answers; it can also invent scenarios, characters, and dialogues. This differentiates it significantly from predictive AI, whose role would be more aligned with identifying patterns, estimating probabilities, and recommending actions based on past behaviors.

Applications and use cases

Let’s examine some common use cases to better understand how these two branches of AI operate in the real world.

Generative AI applications

• Content creation: Generative AI creates blog posts, news articles, artwork, and music. Tools like GPT-3 are designed to support writers and artists in bringing their creative visions to life.
• Product design: Companies use generative AI to explore new product variations, prototype designs, and develop visually appealing options.
• Virtual assistants: AI models like ChatGPT offer conversational abilities that make virtual assistants more intuitive and engaging.

Predictive AI Applications

• Customer behavior analysis: Retailers use predictive AI to anticipate buying behaviors, understand customer preferences, and create personalized marketing strategies.
• Healthcare diagnostics: Predictive AI helps identify patients at risk of certain conditions, enabling earlier intervention and better treatment outcomes.
• Financial forecasting: Banks and financial institutions use predictive AI to detect fraudulent activities, assess credit risks, and make informed investment decisions.

How generative AI and predictive AI work together

Generative AI and predictive AI aren’t isolated technologies working in separate silos—they complement each other in ways that enhance their overall capabilities. Imagine a scenario where generative AI creates multiple solutions to a problem while predictive AI evaluates which solutions have the highest likelihood of success. In this sense, generative AI can be seen as the innovator, proposing ideas, and predictive AI as the evaluator, sorting through the innovations to identify the optimal path forward.

In business, this combination can be a game-changer. For example, generative AI could create various marketing strategies, while predictive AI assesses which strategy will likely generate the highest return on investment based on historical data. This type of synergy makes the two branches of AI much more powerful when used together than when used separately.

Strengths and limitations of generative AI

Generative AI has plenty of strengths, the most prominent of which is its ability to create. Whether it’s generating lifelike images, compelling stories, or novel pieces of music, generative AI excels at creating new content. This makes it perfect for industries where creativity and originality are highly valued, such as entertainment, marketing, and product design.

However, generative AI also has limitations. It often lacks the accuracy or factual reliability needed for tasks requiring precision. Since its output is based on training data, generative AI can sometimes produce content that sounds plausible but is factually incorrect—often referred to as “hallucinations.” Moreover, generative AI can inadvertently produce biased or inappropriate outputs without careful moderation, reflecting biases in its training data.

Strengths and limitations of predictive AI

Predictive AI’s strength lies in its analytical abilities. It can accurately forecast outcomes, making it indispensable for industries that rely on data-driven decision-making. Predictive AI can help businesses anticipate customer behaviors, foresee market trends, and even detect faults before they escalate into major issues.

On the flip side, predictive AI has its limitations. Unlike generative AI, it can’t create new content or explore uncharted territories. Its effectiveness is also heavily reliant on the quality of the historical data on which it is trained. If the data is incomplete or biased, the predictions it generates will be similarly flawed. Predictive AI is excellent at working within established boundaries, but it lacks the imagination needed to think outside the box.

Generative AI in everyday life

You might not realize it, but generative AI is already a part of your everyday life. Whenever you use a virtual assistant like Siri or Alexa to create reminders, answer questions, or control smart devices, generative AI is at play. Streaming services like Spotify use generative models to create custom playlists based on your preferences, while platforms like Instagram employ it to enhance images or create augmented reality filters.

In the creative industry, generative AI tools like DALL-E and Midjourney are used by artists to explore new creative expressions. In contrast, writers use tools like GPT to overcome writer’s block or craft entirely new works of fiction. Even video game developers utilize generative AI to create dynamic environments and characters, making the gaming experience richer and more immersive.

Predictive AI in business decision-making

Predictive AI is crucial in business decision-making, serving as the backbone for strategic planning across numerous industries. Companies rely on predictive AI to analyze customer data and predict buying behavior, allowing them to effectively tailor marketing campaigns. In finance, predictive models assess risks, forecast market trends, and detect anomalies that could indicate fraud.

Healthcare also benefits immensely from predictive AI. By analyzing patient data, predictive models can identify at-risk individuals before health issues become critical, allowing for preventative measures. Supply chain management, too, has been transformed by predictive AI, enabling businesses to anticipate demand, reduce waste, and optimize logistics. Predictive AI doesn’t just help businesses react to changes; it helps them stay ahead of the curve.

Better together

Generative AI and predictive AI are not competing technologies; they are complementary tools that serve different needs. As AI continues to evolve, we’ll see these technologies merge in new and exciting ways. Imagine an AI system that can generate creative solutions to problems while also predicting the outcomes of implementing those solutions—bringing together the best of both worlds.

Generative AI will continue to drive innovation in fields that require creativity and content generation, from entertainment to marketing. Predictive AI, meanwhile, will keep finding ways to make sense of our data-filled world, guiding businesses and individuals to make smarter decisions.

The real power lies in knowing when to use which type of AI. Whether you need to create something new or make sense of the past to better anticipate the future, there’s an AI for that. And understanding the distinction is what separates an effective AI strategy from just another tech experiment.

Image credits: Kerem Gülen/Midjourney

At AMD’s Advancing AI event, CEO Lisa Su took the stage to announce a series of innovations aimed at AI customers. From the latest 5th generation EPYC processors to next-gen Instinct accelerators, AMD is doubling down on high-performance hardware for AI workloads. These new technologies promise to boost AI processing power and streamline workloads for enterprises and cloud computing.

AMD Advancing AI 2024 at a glance

Let’s break down the key announcements from the Advancing AI event.

5th Gen EPYC Processors: Unleashing the Power of Zen 5

Kicking off the event, Lisa Su introduced AMD’s 5th generation EPYC portfolio, built around the all-new Zen 5 core. “We designed Zen 5 to be the best in server workloads,” Su explained, highlighting its 177% increase in IPC over Zen 4. The new processor features up to 192 cores and 384 threads, pushing the limits of server performance.

One of the standout points was the flexibility these chips offer. Su noted, “We thought about it from the architectural standpoint—how do we build the industry’s broadest portfolio of CPUs that covers both cloud and enterprise workloads?” This balance of performance and versatility is aimed at handling everything from AI head nodes to demanding enterprise software.

AMD Turion chips: Scaling for the cloud and enterprise

The event also saw the introduction of AMD’s new Turion chips, specifically optimized for different types of workloads. Su revealed two key versions: a 128-core version designed for scale-up enterprise applications, and a 192-core version aimed at scale-out cloud computing. Both are built for maximum performance per core, crucial for enterprise workloads where software is often licensed per core.

“The 192-core version is really optimized for cloud,” Su explained, emphasizing that these chips will give cloud providers the compute density they need. AMD also compared their new EPYC chips to the competition, showing that 5th Gen EPYC delivers up to 2.7 times more performance than the leading alternatives.

AMD Instinct MI325X: An AI-focused GPU

Turning to AI acceleration, Su announced the AMD Instinct MI325X, the company’s latest AI-focused GPU. “We lead the industry with 256 gigabytes of ultra-fast HBM3E memory and six terabytes per second of bandwidth,” Su said. The MI325X is built to handle demanding AI tasks such as generative AI, boasting 20-40% better inference performance and latency improvements over previous models.

In addition to memory and performance boosts, AMD designed the MI325X with ease of deployment in mind. “We kept a common infrastructure,” Su mentioned, allowing for seamless integration with existing systems. This will make it easier for AI customers to adopt the technology without overhauling their platforms.

AMD Instinct MI350 series

The event also provided a glimpse into AMD’s future with the MI350 series. Scheduled for launch in the second half of 2025, the MI350 introduces the new CDNA 4 architecture and offers a staggering 288 GB of HBM3E memory. According to Su, CDNA 4 will bring a “35 times generational increase in AI performance compared to CDNA 3.”

This new architecture is designed to handle larger AI models with greater efficiency, and its backward compatibility with previous Instinct models ensures a smooth transition for customers.

ROCm 6.2: Better performance for AI workloads

AMD’s commitment to optimizing AI performance extends beyond hardware, with Su announcing ROCm 6.2, the latest update to AMD’s AI software stack. The new release delivers 2.4 times the performance for key AI inference workloads and 1.8 times better performance for AI training tasks. These improvements come from advancements in algorithms, graph optimizations, and improved compute libraries.

“Our latest release focuses on maximizing performance across both proprietary and public models,” Su explained, signaling AMD’s efforts to remain competitive in the AI software space as well.

Image credits: Kerem Gülen/Ideogram 

Hailuo AI is stepping up its game, and it’s got creators paying attention. The Chinese startup MiniMax has launched the Image-to-Video (img2video) feature on the web, giving artists a new way to turn still images into dynamic video content. This isn’t just another bland update; it’s an addition that actually puts more creative power in the hands of users.

🌟 The wait is finally over ——We are excited to announce the launch of our Image-to-Video feature! 🎬✨

What distinguishes Hailuo's Image2Video experience?
– Text-and-image joint instruction following: Hailuo seamlessly integrates both text and image command inputs, enhancing… pic.twitter.com/ke59EjtmBB

— Hailuo AI (@Hailuo_AI) October 8, 2024

MiniMax, backed by heavy hitters like Alibaba and Tencent, first made waves in the AI video world with its hyper-realistic Hailuo AI model earlier this year. We even tried out MiniMax AI ourselves, and it was pretty impressive:

A lively medieval marketplace set in a stone-paved town square. Merchants call out from wooden stalls, colorful fabrics flutter in the breeze. The crowd is bustling with villagers, horses, and carts. The camera follows a young child running through the crowd. pic.twitter.com/i3z52ffblr

— Kerem Gülen (@kgulenn) September 4, 2024

Founded by AI researcher Yan Junjie, MiniMax is becoming a serious player in the video generation scene—not surprising when you’ve got the kind of talent and backing that they do. When Hailuo AI first came out in September 2024, it only supported text-to-video, meaning you typed out what you wanted, and Hailuo did its best to bring your description to life.

Even with just the text feature, Hailuo quickly built up a loyal fanbase. Users loved how vivid and coherent the videos were, especially compared to what was coming out of the States from companies like Runway and Luma AI.

The human motions were more fluid, more lifelike, and honestly, the results just looked better most of the time.

You can check out our past experiences with similar tools: CogVideoX-5B and PixVerse AI

How to use Hailuo AI: Our experience

Now let’s learn how to use Hailuo AI together!

• Go to Hailuo AI video tool: Visit the website here. Once there, you’ll see the option to get started with your video creation.
• Sign up: You need an account to use the tool. We used a Google account for quick access, but you can choose whichever method works best for you.

• Upload your image: Click on the gallery icon to upload your image. We used the following image of a rockstar singing and playing guitar:

• Write your text prompt: Once your image is uploaded, you’ll need to add a text prompt describing the scene. Our text prompt was: “The guitarist grips his strings, sweat glistening as neon lights flicker. His scream into the mic warps the air, each strum sending shockwaves through reality. Hair flying, veins bulging, his movements slow and intensify, bending light and sound, before pulling back in silence, the final chord still vibrating.”
• See the results: After submitting the prompt, the tool will generate your video. Here’s what Hailuo gave us:

Using Hailuo AI is simple: 1) Sign up on their site, 2) Upload an image, 3) Add a detailed text prompt. My search for the best AI video tools continues, here’s the result: pic.twitter.com/mLvRnVJmW4

— Kerem Gülen (@kgulenn) October 10, 2024

The result is not impressive. Probably you should tweak around text prompts a lot and learn how the model responds to them. If you check out their website, you’ll see way better outputs.

The tool is trending now, maybe that is why it is not operating properly:

MiniMax: A player to watch

MiniMax has been making waves in the AI world, especially since dropping its ‘video-01’ model.

But it’s not all about video. MiniMax is branching out, rolling out models for music generation, document summarization, and even voice-based AI tools. The Xingye App is another intriguing addition—users can create and interact with AI companions, each with unique personalities. Most of these tools are still Mandarin-only, but Hailuo is an exception, offering English support, showing that MiniMax has its eyes set beyond China. If you’re looking to take your content up a notch, Hailuo AI gives you the tools to make that happen.

Featured image credit: Kerem Gülen/Midjourney

Suno AI has rolled out a new feature that aims to change the way users make AI tracks from text prompts. Pro and Premium subscribers can now explore their creations, tweak lyrics, or even add a solo instrument break, as simply as editing a document. That might sound like a basic improvement, but if you’ve dealt with AI lyrics before, you know they’re often filled with clichés like “neon” and “city lights” that seem to recur in AI-generated tracks. The problem of repetitive or awkward phrases is something that has plagued AI-generated music, making it challenging for users to get results that feel unique and tailored to their vision.

Greater control with the “replace section” feature

The new “replace section” feature offers much more control over the final product. Instead of being stuck with the default generated lyrics, users can highlight a part of the track, delete it, and put in their own words. This might seem minor at first glance, but anyone who’s tried to manually fix AI’s lyrical issues will recognize that it’s a very helpful addition. Gone are the days of settling for those awkward, repetitive phrases; you can now edit your lyrics the way you want, without the algorithm dictating your vocabulary. This opens up a lot of creative possibilities, allowing users to shape the narrative of their songs in a much more intentional and personalized way.

How the replace section feature works

The feature works simply: in the song editor, you click the new Replace Section tool, highlight the troublesome lyrics, and type over them. It’s all quite intuitive.

The interface keeps things simple enough that even someone without technical know-how can pick it up easily. There’s a ten-second minimum for edits, so you can’t just swap out a single word, but you can wipe entire verses and rewrite them in a way that actually fits your vision. While this restriction might seem limiting, it pushes users to think about how different parts of their song fit together, leading to more intentional and well-crafted edits.

You can also let the AI generate a new section if you’re feeling experimental—but don’t be surprised if those “neons” come shining back. The AI still has its quirks, and while it can produce interesting new content, it’s not always perfect. If you’re reworking a big section of the track and need a spark of inspiration, this option can still be pretty handy. The real strength here is the balance between user control and AI assistance—letting you decide when to lean on the algorithm and when to take the creative reins yourself.

Suno’s new update is part of a series of recent improvements, as the rivalry between Suno and Udio continues. Both companies have been pushing out new features at a rapid pace, trying to outdo each other in the race to dominate the AI music market. Another notable addition is the “cover” feature, which lets you rework a song in a different style while keeping the melody intact. It’s still in beta, and in our testing, it only made noticeable changes if you switched from, say, pop to rap. But when the shift was more extreme—like flipping from a pop track to a rap version—the results could genuinely surprise you. It’s just one more sign that the AI music space is evolving quickly, with these tools growing more adaptable and capable of keeping up with bold creative moves.

The rivalry between Suno and Udio is heating up, and that’s a win for users. Both companies are hustling to innovate, rolling out new features that keep things interesting. The “cover” feature, for instance, is still a bit rough around the edges, but it’s got potential for those willing to mess around with genre-bending experiments. As these tools get smarter, we’re bound to see even more ways to fine-tune AI-generated music—turning ambitious ideas into reality without needing a tech degree.

Now is a good time to explore Suno AI

For anyone curious about exploring these tools, now’s a good opportunity to figure out how to use Suno AI to bring your music ideas to life. The platform’s recent updates provide more flexibility and control, making it a valuable resource for both amateur and experienced music creators who want to experiment with AI-assisted songwriting.

Image credits: Kerem Gülen/Midjourney 

German companies are looking to generative AI as a way to boost business performance, according to a new report from KPMG.

The study of 250 German managers found that 54% of firms have used generative AI and other technologies for greater process automation over the past three years, with 35% more intending to do the same in 2025.

The report also found that 59% of companies have opened up or optimized new markets in the last three years, while 23% plan to do so in the next year.

It’s a tough time to do business in the country, says Meino Müller, the divisional head of performance & strategy at KPMG AG Wirtschaftsprüfungsgesellschaft.

5 open roles to discover now

• Senior Java Developer (m/f/d), Association of Replacement Funds e. V. (vdek), Berlin
• DevOps Engineer (m/w/d), top itservices AG, München
• AI Consultant, Hannover Rück SE, Hannover
• Senior Manager AI & Data, Finanzindustrie, Publicis Sapient, Berlin, Düsseldorf, Frankfurt am Main, Köln, München
• Software Test Analyst, valantic Software & Technology Innovations GmbH, Berlin

“Companies in Germany have rarely had to master so many challenges at the same time as they do now. More intense competition, difficult financing conditions and a shortage of skilled labor are leading to a mixed situation in which business performance is becoming one of the most important issues,” he says.

It is little wonder then that generative AI is being seen as a savior to help companies compete, do more with less, and take the strain off burnt-out workers.

But according to other new research, most companies need to do better. Many employees aren’t properly educated or trained on AI, and as a result, are using it in ways that weren’t necessarily intended.

Employers think that implementing AI will help to support research for tasks and projects (62%), to help staff manage workflows (58%) or analyze data (55%). But 63% of workers themselves are mostly using generative AI to double check their work.

As a result, a disconnect is emerging, with 72% of companies thinking that employees are at least adequately trained on AI––however, only 53% of their employees would agree.

Another issue is the fact that data readiness is a significant stumbling block, with 64% of workers saying their company “doesn’t have AI experts on the team”, and 45% saying “my organization’s data is not ready for AI”. Another 37% point to the fact that organizations simply don’t have the tech infrastructure in place to implement AI tools.

BYOAI on the rise

A recent Deloitte report points to BYOAI, or bring your own AI to work, which is on the rise as companies struggle to be AI-ready.

BYOAI manifests itself in employees’ use of assistive tools like Gemini, Claude or ChatGPT. While these can be helpful for research, or to create document outlines, summarize a meeting report, or even compose emails, without proper guidelines in place, workers may be leaving themselves open.

Paul Lee, partner and head of technology, media and telecommunications research at Deloitte, points out that, “Employees are moving faster than their employers when it comes to adopting GenAI to transform how they work. While workers are signaling that GenAI can boost their output and save them time, many employees may not be supported, encouraged, or explicitly endorsed to use the technology by their organization.”

Employer buy-in when it comes to generative AI tools matters. Consider the 2023 case of the Samsung employees at the company’s semiconductor division who unwittingly used ChatGPT to check confidential source code.

Because generative AI tools generally use inputs as training data unless you specifically tell them not to, sensitive information may become publicly available. As a result, Verizon, Citigroup and Deutsche Bank have all banned usage of ChatGPT over concerns about private data being shared.

Deloitte’s research has found that users aren’t comprehensively aware of the risks of generative AI, including inaccuracies and biases. It found that 25% believe it is always factually accurate, and 26% think it is unbiased.

A new The State of AI at Work report from work management platform Asana agrees, saying that “as AI permeates organizations, a troubling reality emerges: most are dangerously underprepared”.

Asana’s research has found that only 31% of companies have a formal AI strategy in place, and that “dangerous divides exist between executives and individual contributors in terms of AI enthusiasm, adoption, and perceived benefits”.

And despite 78% of executives believing that combining AI with human expertise can result in better outcomes, only 13% of organizations have developed shared AI guidelines.

Ultimately, if you’re finding that your own organization has an unclear approach to the use of generative AI tools, it could be time to look for a role at a company which has a clear policy, guidelines and budget for the right generative AI tools for the job.

Want to land a great new job? Discover thousands of roles on the Dataconomy Job Board today

AI in quantum chemistry has recently taken a massive leap, marking a milestone moment for both artificial intelligence and material science.

New research conducted by a collaboration between Imperial College London and Google DeepMind showcases how neural networks can effectively model excited molecular states.

The breakthrough not only holds promise for a deeper understanding of complex molecular systems but also paves the way for advancements in sustainable technology, such as solar cells and photocatalysts.

Why use AI in quantum chemistry?

The study, published in the journal Science, addresses the challenge of modeling the quantum behavior of molecules in excited states. An excited state occurs when a molecule absorbs energy—often through light or heat—causing its electrons to enter a higher energy configuration.

Understanding these transitions is crucial for various technologies, including solar panels, light-emitting diodes (LEDs), and even natural processes like photosynthesis and human vision.

Despite their significance, accurately modeling excited states has long been a daunting challenge in quantum chemistry. The root of this difficulty lies in the quantum nature of electrons, which cannot be precisely pinpointed. Instead, their locations must be described probabilistically.

Dr. David Pfau, the lead researcher from Google DeepMind and Imperial College London, explained that representing the state of a quantum system requires assigning probabilities to every potential configuration of electron positions.

He elaborated,

“If you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe.”

This complexity is where the application of AI in quantum chemistry shines, particularly through the use of deep neural networks.

FermiNet’s role

The neural network employed in this groundbreaking research is known as FermiNet, short for Fermionic Neural Network.

The innovative model was among the first deep-learning applications to compute the energy of atoms and molecules based on fundamental principles, achieving a level of accuracy that is practical for real-world use.

The researchers developed a new mathematical framework combined with this AI model, allowing them to tackle the fundamental equations that describe molecular states in a novel way.

The main achievement of this research was the team’s ability to model the carbon dimer, a small yet complex molecule, with remarkable precision. They achieved a mean absolute error (MAE) of just 4 millielectronvolts (meV), significantly improving upon previous methods that had a gold-standard error of 20 meV.

This advancement means that predictions are now much closer to experimental results, enhancing the reliability of simulations involving excited states.

Moreover, the researchers expanded the capabilities of their neural network by testing it on computationally challenging scenarios where two electrons were excited simultaneously. The accuracy of their model was within approximately 0.1 electronvolts (eV) of the most complex calculations available today.

Imaging the unimaginable

The implications of this research extend far beyond academic curiosity. For industries focused on sustainable energy solutions and efficient lighting technologies, the ability to accurately predict molecular behaviors when excited by light can lead to significant advancements. Technologies like solar cells and photocatalysts, which rely heavily on understanding excited molecular states, can benefit immensely from this new approach.

By using AI to tackle one of the most complex problems in physical chemistry, the study sets the stage for more effective simulations in material science and beyond. Rather than solely depending on experimental methods, which are often time-consuming and costly, scientists now have access to a more accurate computational tool that brings theoretical models closer to real-world scenarios.

The integration of deep learning techniques into chemistry through AI in quantum chemistry could accelerate discoveries and technological advancements across a range of industries. As research in this area continues, the combination of neural networks and advanced mathematical frameworks could lead to new paradigms in understanding molecular interactions, ultimately benefitting society at large.

Image credits: Emre Çıtak/Ideogram AI

OpenAI dropped a big one. Their new Realtime API has the potential to completely reshape how we interact with our devices, and it’s particularly exciting for the future of smart speakers—think Alexa, Google Home, and beyond. Imagine talking to these assistants with a natural back-and-forth flow that not only sounds more human but also responds almost instantaneously, adapting to how you speak, even if you whisper or laugh. That’s the kind of conversational leap we’re looking at here.

What is Realtime API by OpenAI?

The Realtime API lets developers create voice interactions without the awkward delay we’re used to. There’s no need for text translation in between; it’s straight from voice to response—all happening super fast. That means smart speakers or assistants are not just quick; they feel present, almost like a true conversation partner. OpenAI’s voices can steer towards different tones, laugh with you, whisper if you do—in short, they’re the most nuanced voices we’ve seen in AI so far.

How the Realtime API works

The API works using WebSockets, which in non-tech speak just means it’s a continuous two-way communication channel, like an open hotline with the server. You send your audio, and it sends something back in almost real-time. This kind of setup is what’s enabling these new kinds of interactions—low latency, which means little to no delay, and multi-modal, which means the system can handle text, audio, and even function calls seamlessly. Imagine saying, “Hey assistant, book a table at my favorite restaurant,” and not only does it understand you immediately, but it can call up the reservation system right then and there, all in the flow of the conversation.

Adding personality to AI responses

It’s not just about speed, though; it’s also about personality. Unlike the rigid and sometimes lifeless tones we’ve heard from smart assistants in the past, OpenAI’s new models can modulate their responses to match your energy—whether that’s excited or quiet, they’ve got it covered. For instance, when you’re asking about the weather while getting ready in the morning, it’s one thing to hear a robotic “Today will be sunny” and quite another to get a warm, lively response like, “Looks like it’s a bright one out there—time for some sunglasses!” These subtle differences add up to a much richer, more engaging interaction.

Real-world applications of the Realtime API

The potential applications are huge. Consider industries like customer service—forget waiting for an agent, or even talking to a stiff voice bot. You could be interacting with something that feels almost alive, one that can understand context deeply and respond in kind. Or take healthcare, where this kind of nuanced back-and-forth could make AI-based support feel a lot more comforting and human during tough times. The fact that it’s all happening faster than real-time audio also means that you get responses that sound stable and natural, rather than something stitched together with noticeable pauses.

For startups, OpenAI’s Realtime API provides an opportunity to innovate without needing massive resources. The ability to integrate natural, low-latency voice interactions means small teams can create polished, conversational products that previously required deep expertise in voice technology. This opens up possibilities across various sectors—such as gaming, where NPCs could interact more dynamically, or education, where tools could become more engaging and responsive.

With the Realtime API, startups can explore creative uses of voice tech, from developing unique voice-controlled devices to enhancing productivity tools with intuitive voice interfaces.

OpenAI rolled out ChatGPT Advanced Voice for Plus users

A new chapter for voice gadgets

This release from OpenAI feels like the start of a new chapter for voice tech. It’s about taking conversations beyond basic questions and answers and into the realm of real dialogue. Developers who want to tinker with this new API can try it out via a demo console OpenAI has released. While it’s still in beta, the possibilities that are beginning to unfold are clear—smarter, quicker, and more empathetic machines. If this catches on, the days of talking to your devices like they’re, well, devices might just be behind us.

Image credits: Kerem Gülen/Midjourney

Google DeepMind’s Demis Hassabis and John Jumper just snagged this year’s Nobel Prize in Chemistry, sharing the honor with David Bakker from the University of Washington. The trio’s work on computational protein design has changed the game, with Hassabis and Jumper’s AlphaFold2 AI model leading the charge.

What has AlphaFold2 accomplished?

“This prize represents the promise of computational biology,” Jumper said at a press conference, where excitement was high. AlphaFold2, their breakthrough tool, has cracked a 50-year-old problem in biology: predicting protein structures. It can now predict over 200 million of these structures—essentially covering almost every protein known to science—from their amino acid sequences.

The 2024 #NobelPrize laureates in chemistry Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins.

In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have… pic.twitter.com/Izgx4FT23K

— The Nobel Prize (@NobelPrize) October 9, 2024

The implications are huge. By offering a clearer understanding of how proteins operate and interact with other molecules, AlphaFold2 is giving researchers fresh insight into diseases and drug discovery. And it’s not just a lab tool—it’s accessible to scientists worldwide. Since going live, AlphaFold2 has already been adopted by over 2 million scientists in 190 countries. It’s speeding up research on everything from malaria vaccines to Parkinson’s therapies, and even solutions for drug-resistant bacteria.

As Jumper put it, the model’s impact has been immediate. “What I think will come soon through our work is that we’re going to get better and better at harnessing biology and our understanding of biology to make medicines,” he said. “I hope this means that, ultimately, we will be more responsive to, for example, pandemics.”

Hassabis, a pioneer in AI and co-founder of DeepMind, is clear about his mission: using artificial intelligence to “improve the lives of billions of people.” But he also cautioned against getting swept away by the tech’s rapid advancements. “We have to really think very hard as these systems and techniques get more powerful about how to enable and empower all of the benefits and good use cases, whilst mitigating the risks,” he noted.

AI pioneers John Hopfield and Geoffrey Hinton won Nobel Physics Prize 2024

AI’s fingerprints are all over the sciences. Not just in chemistry—this year’s Nobel Prize in Physics also went to two AI researchers, John Hopfield and Geoffrey Hinton, for their pioneering work on training neural networks with physics. The future of scientific breakthroughs seems inextricably tied to the evolution of artificial intelligence.

Image credits: Google Deepmind/Unsplash 

If you’ve ever found yourself staring at TikTok, wondering how everyone else seems to have endless creativity while you’re stuck with “Dancing Cat Attempt #23,” then it’s time you met some of the best free AI tools for TikTok content ideas.

Whether you aim to launch a new challenge, craft an awe-inspiring transformation video, or simply riff off the latest trend, AI can help you get there—without costing you a penny.

Update:

Also check these out on how to pick the right video editing software:

• Vizard.ai makes it easy to turn long videos into clips
• MiniMax AI video generator is pretty impressive
• Vidu AI video tool offers an easy way to generate videos in seconds
• CopyCopter.ai can be your self-service short video tool

Top 5 best free AI for TikTok content ideas

In this piece, we’ll explore the top AI platforms out there to juice up your TikTok game free of charge.

1. ChatGPT: Your TikTok brainstorming BFF

One of the best free AI for TikTok content ideas is, undeniably, ChatGPT. Yes, this AI tool can be like your creative partner-in-crime—minus the crime part, hopefully. Picture having an AI that doesn’t just respond to your ideas but actively helps you build on them. Throw a trend or a rough idea at ChatGPT, and watch as it churns out scenarios, punchlines, and concepts you probably wouldn’t have thought of on your own. Need an ice-cold comeback or a quirky twist for your TikTok storyline? ChatGPT’s got you.

Consider this: you’re planning a day-in-the-life video, but with a humorous twist—maybe from the perspective of your phone’s notifications. Tell that to ChatGPT, and you might find it spitting out cheeky, relatable notification captions that your followers can’t help but laugh at. No need to just ‘be creative’ on your own anymore—this AI knows what’s up.

What makes ChatGPT even better is its adaptability. Whether diving into niche trends, crafting a storytelling arc, or just trying to engage in quirky commentary, it understands context and helps you amplify your content. For example, if you’re trying to jump on a viral trend but put your unique twist on it, ChatGPT can help craft an engaging storyline that doesn’t just follow the crowd but sets you apart. It’s like having a collaborator who’s always online, never tired, and never short on opinions.

2. InVideo AI: Lights, camera, content!

For those visual storytellers, InVideo is another contender for the best free AI for TikTok content ideas. The AI at InVideo can kick the brainstorming process up a notch—suggesting content templates and visuals while giving you a heads-up on what’s trending. InVideo works magic by suggesting a TikTok masterpiece’s perfect cuts, overlays, and sound effects. Imagine you have a list of clips from a recent trip, but you’re unsure how to make it pop. InVideo AI helps you figure out the flow and suggests on-trend editing tricks that turn mundane footage into something binge-worthy.

With its trending templates, you’re one AI suggestion away from creating content ready to go viral. It’s like having a director, an editor, and a trend forecaster all rolled into one—and all of them think your content should be free (of charge).

What really makes InVideo shine is its focus on aesthetics. TikTok is a highly visual platform, and standing out means creating something visually captivating. InVideo helps by giving you the tools to craft sleek transitions, trendy overlays, and perfectly timed cuts that align with the latest TikTok fads. It also suggests the best soundtracks for your video, which means you’re not just following trends—you’re setting them. It’s the kind of AI that helps you go from “I think this is cool” to “This could blow up!”

3. Copy.ai: From caption writer to video narrator

Next up, meet Copy.ai—a revolutionary tool if you’re hunting for the best free AI for TikTok content ideas, especially for those killer captions. We all know a TikTok post isn’t complete without a scroll-stopping caption. With Copy.ai, you can input a basic idea of what your content is about, and the AI will generate captions that range from humorous to poetic—depending on your vibe.

Imagine putting up a video of your pet ferret causing absolute mayhem. Instead of going with the cliché “Look at my crazy ferret,” you can use Copy.ai to spin it into “When your furry roommate has no concept of personal space.” Suddenly, you’re relatable. You’re funny. And you’re getting those likes.

Copy.ai also excels at giving you multiple options, so you don’t feel boxed into a single caption style. Maybe you want to be witty today, but tomorrow you’re feeling poetic—Copy.ai has got your back. The captions it suggests are not only clever but also optimized for engagement. The AI understands what kinds of words and tones resonate with audiences, helping you craft posts that feel organic and authentic. It’s like having a copywriting team on standby, ready to craft that perfect hook to reel in your audience.

4. Lumen5: The visual wizard

With its easy visual storytelling capabilities, Lumen5 earns its spot among the best free AI for TikTok content ideas. It’s more than just a content idea generator—Lumen5 helps transform ideas into animated stories. Its AI turns written content into engaging video sequences, meaning you can create explainer content, listicles, or storytelling videos without needing professional video editing chops. Imagine using a mundane “5 Facts About Coffee” blog post and turning it into a visually captivating TikTok using Lumen5’s auto-generated visuals, text overlays, and funky transitions.

It’s like Pixar, but no animation degrees are required for TikTok creators with a zero-dollar budget.

What’s especially cool about Lumen5 is how effortlessly it helps creators adapt content from other formats into TikTok-ready bites. Got a blog post or a YouTube script? Lumen5 can break it down and help repackage it for TikTok’s format, making sure every piece of content is not only watchable but also irresistible. With its intuitive drag-and-drop features, even the least tech-savvy content creators can instantly produce polished, visually stunning TikToks that grab attention.

5. Jasper AI: Think Big, Post Short

If you love witty scripts and want to inject some clever banter into your TikTok, Jasper AI is another candidate for the best free AI for TikTok content ideas. The key strength of Jasper is its ability to come up with engaging, story-driven scripts that cut through the noise—because, let’s be real, there’s a lot of noise on TikTok. Whether you’re aiming for a mockumentary about life as a remote worker or need a succinct yet sassy voiceover for a recipe video, Jasper can help craft a narrative that’s witty, digestible, and custom-made for TikTok’s rapid pace.

Picture this: You’re making a TikTok on “A day in the life of a freelancer.” Jasper’s script might start with, “9 AM: Wake up and plan to be productive. 10 AM: Start considering napping as a career.” That’s the dry humor that helps your TikToks stand out—and Jasper makes it easy.

Jasper AI goes beyond just humor. It’s also capable of helping you craft heartfelt or motivational narratives that fit TikTok’s diverse audience. Want to create an inspiring video on overcoming challenges? Jasper can help you outline the journey, adding the right balance of drama, authenticity, and relatability. This flexibility makes Jasper an invaluable tool whether your TikTok content is light and funny or serious and thought-provoking.

Runner-ups

It’s hard to decide the top tools; but objectively, below tools can be very useful too!

NeuralText’s TikTok Content Ideas Generator

NeuralText‘s TikTok Content Ideas Generator is another powerful tool that generates TikTok video ideas based on your niche or specific keywords. It analyzes trending topics and helps generate content that resonates with your audience, ensuring high engagement and relatability. NeuralText also allows you to customize content based on audience demographics and interests, making it a go-to choice for targeted content creation. This AI tool helps you tailor your content to suit a particular audience, ensuring your posts are relevant and engaging. NeuralText offers a focused and customizable approach to creating content that resonates with viewers.

LogicBalls

LogicBalls provides a variety of content ideas specifically designed to captivate your TikTok audience. The AI analyzes your input, such as niche or topic, and generates ideas ranging from challenges to informative snippets. LogicBalls also suggests formats that are currently popular, helping you stay on trend and create content that captures attention instantly. Whether you’re creating informative videos or just entertaining clips, LogicBalls’ TikTok Content Ideas Generator helps you come up with fresh concepts that stand out from the crowd. It’s a great choice for anyone looking to diversify their content with ideas that draw people in.

Thanos.ai

Thanos.ai delivers AI-generated suggestions for engaging TikTok content. This tool helps you brainstorm unique video concepts and provides detailed outlines to help you execute your ideas. Whether you’re creating how-to content, entertaining skits, or interactive challenges, Thanos.ai ensures your videos are engaging and keeps your audience watching until the end. With a strong focus on creating interactive and shareable video ideas, Thanos.ai helps you keep your audience entertained while expanding your creative horizons. Whether you need a hook for your video or a complete storyline, Thanos.ai has your back.

FlexClip

FlexClip offers AI-powered tools to create TikTok videos from text prompts. It allows you to input a brief description, and the AI automatically generates short, punchy video clips complete with animations, transitions, and text overlays. FlexClip also features a vast library of music, stock footage, and templates, making it easy to create professional-looking content quickly, even if you have no video editing experience. Whether you’re looking for something simple or a video with multiple visual elements, FlexClip helps you generate short, engaging clips that are perfect for TikTok’s format. The AI helps you smoothly create content that resonates with your audience.

Kapwing

Kapwing‘s TikTok Clip Maker is an intuitive tool that helps you generate videos optimized with subtitles, music, and transitions. It includes easy-to-use editing features that allow you to adjust video timing, add engaging elements like GIFs or emojis, and automatically generate subtitles for accessibility. Kapwing also offers collaboration features, which makes it ideal for creators working in teams. This tool allows you to create eye-catching content quickly, making sure that all the important elements are included—whether it’s catchy music, seamless transitions, or vibrant subtitles that pop.

VEED

VEED‘s TikTok Video Generator transforms text prompts into engaging TikTok videos. The AI takes your basic idea and turns it into a visually appealing video with dynamic animations, effects, and perfectly synced music. VEED also offers advanced editing features like adding subtitles, cropping, and even background noise reduction, making it an all-in-one solution for creating high-quality TikToks. This AI-powered tool takes your content ideas and turns them into visually compelling videos, adding all the necessary elements, such as animations, captions, and music, so your TikToks are ready to catch eyes and garner likes.

Media.io

Media.io‘s AI TikTok Video Generator creates multiple viral TikTok clips from a long video with a single click. This tool automatically detects key highlights in your footage and edits them into individual clips with suitable transitions and music. Media.io is especially useful for creators who want to maximize their content output by repurposing longer videos into multiple, bite-sized TikToks that can drive engagement. This is ideal for creators who have a lot of content to work with but need it repurposed for TikTok. Media.io makes the process fast and efficient, ensuring your content is optimized for TikTok’s dynamic audience.

Predis.ai

Predis.ai crafts captivating TikTok videos using AI with premium templates and assets. It provides easy customization options and access to a variety of high-quality templates, allowing you to create visually striking TikToks that resonate with viewers. Predis.ai also offers AI-driven content suggestions, so even if you’re not sure where to start, it can help you come up with ideas and execute them effortlessly. With a wide range of templates, Predis.ai makes it easy to customize videos for your specific needs. Whether you’re promoting a product or sharing a fun moment, Predis.ai gives you the creative boost you need. It’s surely one of the best free AI for TikTok content ideas.

Revid AI

Revid AI generates viral short videos for TikTok, Instagram, and YouTube. The AI identifies the most engaging moments from your footage and enhances them with visually appealing effects, transitions, and soundtracks. Revid AI is particularly effective for repurposing content across multiple platforms, helping you optimize and distribute videos in formats that perform best on TikTok and other social media channels. The AI identifies key moments and enhances them with music, effects, and captions to make sure your content is engaging across multiple platforms. Revid AI is perfect for those looking to create quick, catchy content that connects with audiences.

Claptik

Claptik is one of the best best free AI for TikTok content ideas. The hashtag generator ensures your videos reach the right audience, while the script generator helps you come up with catchy hooks and dialogues. Claptik also offers analytics tools to track performance and adjust your strategy, making it a comprehensive resource for optimizing TikTok content. If you’re struggling to figure out what to say or which hashtags to use, Claptik has the solutions to make your content discoverable and entertaining. Claptik helps you optimize every aspect of your post.

AIFreeBox

AIFreeBox provides free TikTok AI tools for content creation and optimization. It includes features like content idea generation, video script creation, and even AI-driven video editing suggestions. AIFreeBox is a versatile platform that helps you at every stage of content creation, ensuring your TikToks are polished and highly engaging without the need for professional editing skills. It has a variety of features that help you brainstorm content, generate scripts, and even optimize your videos for maximum reach. AIFreeBox is a great all-rounder for those looking to make content creation a breeze.

Short.ai

Short.ai generates trending short videos for TikTok and YouTube with automatic captions. The AI detects trending topics and helps you create content that taps into these trends, ensuring your videos are timely and relevant. Short.ai also includes an auto-captioning feature, which adds captions in real-time to enhance accessibility and boost engagement. The AI finds what’s trending and helps you tap into it with your own spin, ensuring you get in on the action while it’s hot. It’s an excellent tool for staying relevant in the dynamic world of TikTok.

Crayo AI

Crayo AI creates viral-ready clips with AI voiceovers and engaging subtitles. This tool allows you to add narration to your videos, enhancing storytelling and keeping viewers engaged. Crayo AI’s subtitle feature is also highly customizable, allowing you to choose fonts and styles that match your brand or content tone, giving your videos a polished look. It’s ideal if you want to add narration or need engaging subtitles that add extra flair to your content. Crayo AI makes sure your videos are catchy and easy to follow, boosting their shareability.

Pictory

Pictory transforms long-form text content into short, shareable TikTok videos. Whether you have blog posts, articles, or long YouTube videos, Pictory’s AI condenses the content into highlight reels that are perfect for TikTok. It also provides engaging visuals and text overlays to make sure your content stands out in a crowded feed. This is perfect for those who have blog content or other forms of written content that they want to repurpose for TikTok. Pictory helps make sure your ideas are not only well-presented but also bite-sized and shareable.

Pika Labs

Pika Labs specializes in AI-generated videos with style transfer and text-to-video capabilities. This allows creators to apply artistic effects to their videos or convert text into animated sequences, making them visually unique. Pika Labs is ideal for creators who want to add a distinctive aesthetic to their TikToks, ensuring their content is visually memorable and stands out. It allows creators to infuse their videos with unique visual effects, making them stand out on TikTok. Pika Labs is perfect for adding that extra level of creativity to your content.

Synthesia

Synthesia creates videos with AI avatars, supporting multiple languages, ideal for personalized TikTok content. It allows you to generate videos featuring lifelike AI avatars that can speak directly to your audience, adding a personalized touch to your content. Synthesia also supports a variety of languages, making it suitable for global audiences and helping you expand your reach. If you want a unique, AI-generated presenter for your videos, Synthesia is the go-to tool. It allows you to communicate messages clearly and creatively in a way that catches your attention.

Descript

Descript is an AI-powered video editing tool that simplifies the creation of engaging TikTok videos. It offers features like automatic transcription, overdub voice generation, and text-based editing, making the video creation process much easier. With Descript, you can quickly cut, rearrange, and polish your footage, ensuring your TikTok videos are captivating and well-edited. It’s an all-in-one editing platform that allows you to easily turn raw footage into polished content. Descript has all the necessary features to get your TikToks ready for the spotlight, whether you need to trim, add captions, or mix audio.

Get inspired without breaking the bank

There’s no one-size-fits-all answer when it comes to the best free AI for TikTok content ideas, but having these tools on your side will at least make that content brainstorming session a little less daunting (and a lot more fun). The key to going viral isn’t just originality—it’s also about knowing what works, playing with trends, and presenting them in ways that feel fresh and true to your own unique voice.

And the best part? These AIs aren’t charging you for the privilege. Think of them as your free creative think-tank, ready to help you break through that creative block so you can focus on making your mark—one viral video at a time.

It’s about creating content that doesn’t just float in the vast TikTok ocean but surfs the waves of trends. Using these AI tools helps you leverage your creativity while making sure you’re not doing all the heavy lifting alone. Each one of these AIs brings something different to the table: whether it’s quirky scripts, stunning visuals, or catchy captions, they help you take a rough concept and refine it into a polished piece of content. And let’s be real—sometimes, that extra little push is all you need to make something good into something truly unforgettable.

So, whether you’re looking to become the next TikTok sensation or just want to keep your content fresh and engaging, these AI tools are here to make the journey easier, more enjoyable, and definitely more creative. Dive in, experiment, and find out which one clicks with your style—after all, the next viral trend could be just one AI-generated idea away.

Did you know that TikTok’s parent company released Jimeng AI video generator too?

Featured image credit: Kerem Gülen/Midjourney

According to an exclusive report by Axios on Monday, OpenAI has inked a content partnership with Hearst, one of the nation’s largest newspaper and magazine conglomerates. The deal adds Hearst to the list of publishers collaborating with OpenAI, which already includes Condé Nast and Dotdash Meredith—competitors in the magazine industry.

Magazine giants’ content now under OpenAI’s belt

The partnership will allow OpenAI to incorporate content from over 40 of Hearst‘s local newspapers, including notable publications like the Houston Chronicle and San Francisco Chronicle. It will also include material from more than 20 of Hearst’s magazine brands, such as Esquire, Cosmopolitan, and Women’s Health. This content will be integrated into OpenAI’s products, including ChatGPT, providing more depth and diversity to the AI’s responses.

As with previous publisher agreements, OpenAI has confirmed that content used to generate responses in ChatGPT will come with “appropriate citations and direct links” to the original Hearst sources. While financial specifics of the deal weren’t disclosed, it’s speculated that Hearst is receiving substantial compensation, potentially millions, for licensing its intellectual property. Additionally, OpenAI confirmed that Hearst would receive credits to leverage its AI technology.

Jeff Johnson, president of Hearst Newspapers, noted, “This agreement allows the trustworthy and curated content created by Hearst Newspapers’ award-winning journalists to be part of OpenAI’s products like ChatGPT—creating more timely and relevant results.” Debi Chirichella, president of Hearst Magazines, added, “Our partnership with OpenAI will help us evolve the future of magazine content.”

Axios reports that these partnerships are moving away from providing broad training data for large language models (LLMs) toward more targeted use cases where news publishers maintain greater leverage. The approach now favors Retrieval Augmented Generation (RAG), a process where AI models access smaller, vetted datasets in real-time to deliver more accurate and relevant responses, especially for queries related to news or cultural events. This is the same strategy OpenAI is employing through its partnerships, including this recent agreement with Hearst.

Featured image credit: Kerem Gülen/Ideogram

Public AI is the antidote to the high-stakes AI arms race that’s become a billionaire’s playground. In a month when OpenAI just pulled in one of the largest funding rounds ever—cementing its turning point from altruistic nonprofit to Silicon Valley heavyweight—it’s become clear that profit has replaced the original promise.

Mozilla Foundation sees this transition as a reminder of what’s lost when big money takes the wheel: civic-minded missions replaced by corporate ambitions.

That’s why Mozilla is putting its weight behind Public AI—a fresh, rebellious concept designed to bring AI back to the people.

What is Public AI?

Public AI is a defiant vision: a future where AI isn’t just another toy for tech giants and profit-chasers, but a tool for nonprofits, governments, and those genuinely working for the public good. It’s a pushback against the commercialization that has engulfed AI—a movement to ensure that, even as profits dominate the headlines, there’s still space for artificial intelligence that benefits everyone.

Mozilla’s recent report outlines this as a new strategy pillar, a guiding vision to build an alternative AI ecosystem that aligns with human values. This emerging sphere of Public AI aims to make AI accessible to those whose primary mission is not profit, but rather the welfare of society. The goals are ambitious, and the potential outcomes could reshape the relationship between technology and the communities it impacts.

AI for profit vs. AI for people

OpenAI’s transition toward a profit-driven model is symptomatic of the broader AI arena—a arena increasingly dominated by private capital and market pressures. It’s hard not to notice the irony: an entity initially formed to explore AI in a transparent, community-minded manner is now tightly interwoven with venture capital expectations.

Some would argue it’s a necessary adaptation to scale and compete in the high-stakes world of AI research. But for those who believe in AI’s potential to address societal challenges, it feels like a missed opportunity.

This is where Public AI comes in. Think of it as an intervention, a rebalancing of scales that have tipped overwhelmingly towards corporate profit. Public AI envisions a framework where AI resources—such as data, research models, and development tools—are available to nonprofits and governmental bodies in ways that benefit public welfare rather than simply the bottom line. It’s a transition back to AI as a public utility, designed to empower communities, drive transparency, and address challenges like healthcare, climate change, and education in ways that commercial AI isn’t incentivized to do.

Mozilla’s vision for Public AI is rooted in promoting public goods, public orientation, and public use throughout every step of AI development and deployment. Mozilla’s report lays down a manifesto for change.

Public AI aims to flip the script by focusing on three bold priorities: creating open, accessible AI resources; elevating the needs of underserved communities; and tackling projects that serve the greater good—those that typically get left behind in the profit chase. It’s a call to action for everyone—developers, policymakers, communities—to step up and redefine what AI can be for society.

The core of Public AI’s vision is access. It’s about democratizing the tools of AI, lowering the barrier to entry, and fostering collaboration among entities that prioritize social value over shareholder value. For a local government that wants to better allocate resources, a healthcare nonprofit aiming to leverage predictive analytics, or a small team of environmental researchers looking for tools to model climate risks, Public AI could be the key that opens the door. The promise here is that AI shouldn’t just be the domain of trillion-dollar corporations; it should also be a force for societal good—a public resource, as essential as clean water or a free press.

Why the world needs Public AI now

Public AI isn’t just a nice idea—it’s a necessary one. We’re at a crucial juncture where AI technologies are powerful enough to influence everything from elections to employment opportunities, yet the resources and decision-making powers remain in the hands of a select few. In a world where AI models dictate what news you see, which resumes rise to the top of a pile, or how predictive policing targets communities, the concentration of power among profit-driven entities creates risks of bias, exploitation, and inequity.

Mozilla’s push for Public AI is a recognition of these risks. They argue that without public involvement in AI development and governance, we risk creating a future dictated by algorithms that reflect only the priorities of the wealthy. Public AI offers a potential path out of this, emphasizing transparency and community engagement in the development of AI systems.

As detailed in Mozilla’s report, Public AI aims to create a dynamic ecosystem of initiatives that make AI work for everyone, by everyone. The report provides examples of how Public AI can be implemented to benefit society, such as using AI to detect illegal mining operations, facilitate deliberative democracy, and deliver medicine directly to patient homes—areas where commercial AI often falls short due to the lack of immediate profit potential. Public AI can prioritize community-driven applications, such as the Common Voice initiative, which crowdsources multilingual speech data to nurture AI development that respects and represents linguistic diversity.

An uphill battle

Of course, realizing Public AI will be an uphill battle. The dominance of commercial interests in AI research and development is massive. There’s a reason why OpenAI and other leaders have drifted from non-profit models—the financial and technological demands of cutting-edge AI are colossal. Competing with these resources isn’t easy for public institutions or nonprofits. However, the transition towards Public AI doesn’t need to replace commercial AI; it needs to complement it, creating a dual system where AI’s benefits are more broadly distributed.

Achieving this will require funding, international cooperation, policy support, and a willingness to challenge the status quo. Mozilla’s report emphasizes that Public AI will need sustainable funding models, community involvement, and political backing to truly thrive. It will need institutions like Mozilla and beyond to nurture a movement that prioritizes ethical AI development—one that asks hard questions about equity, transparency, and accessibility. In the end, Public AI is about preserving the original promise of AI: a transformative technology that benefits all of humanity, not just those who can afford to invest billions.

The next few years will likely determine whether Public AI becomes a vibrant counterpart to commercial AI or just another idealistic footnote in technology’s history.

But if initiatives like Mozilla’s gain traction, we may just see a world where AI isn’t just about making profits—it’s about making progress, for everyone.

Featured image credit: Kerem Gülen/Ideogram

Artificial intelligence (AI) has emerged as a game-changing technology with the potential to transform how we live and how we work. As an AI consultant with 9 years of experience in AI development and 6 years of strategic AI consulting, I’ve witnessed firsthand the transformative potential of AI—and the pitfalls that await the unprepared.

Imagine an e-commerce giant struggling with inventory management, a fashion retailer drowning in customer preference data, or an online marketplace grappling with fraud detection. These scenarios, once considered inevitable e-commerce challenges, are now being transformed by the power of AI, including advanced technologies like large language models (LLMs) and retrieval-augmented generation (RAG) solutions. Yet, for many organizations, the path to AI integration remains unclear, fraught with obstacles and missed opportunities.

AI offers immense potential, but without a clear strategy, businesses often find themselves lost in a sea of possibilities, unable to translate AI capabilities into tangible value. Each transformation starts with a vision or a strategy. To make things easier, I’ve put together a framework that will help you to shape your AI strategy step by step, to make this process as smooth as possible. Let’s break it down into a few steps.

The AI strategy development framework

1. Define your AI vision

Picture the CEO of a major e-commerce platform, Emma, sitting in her office, surrounded by reports of declining conversion rates and rising customer acquisition costs. She knows AI could be the answer, but where to start?

The journey begins with defining a clear AI vision and aligning cutting-edge technology with core business objectives.

For Emma’s e-commerce company, this might translate to three strategic objectives:

1. Enhance customer experience through personalized shopping journeys
2. Optimize inventory and supply chain operations to reduce costs
3. Develop new AI-driven features to stay ahead of the competition

But a vision without specifics is just a dream. That’s where value drivers come in. Think of value drivers as the bridges between your AI aspirations and real-world impact.

For the e-commerce company, these could include:

• Business growth: AI-powered product recommendations to increase average order value
• Customer success: LLM-powered chatbots and virtual shopping assistants for 24/7 customer support
• Cost-efficiency: Automated demand forecasting to optimize inventory levels

By defining these value drivers, Emma has now transformed a vague notion of “using AI” into a focused strategy with clear, measurable outcomes.

Use the following value drivers map when you need ideas to start:

2. Identify and assess AI use cases

With a vision in place, it’s time to get specific. This is where many companies falter, either becoming overwhelmed by possibilities or fixating on trendy but impractical applications.

Let’s return to our e-commerce company. Emma assembles a diverse team — data scientists, product managers, customer service reps, and logistics experts — for an AI use case brainstorming session.

Ideas start flowing:

• A predictive model for personalized product recommendations
• An AI-powered virtual try-on feature for fashion items using computer vision
• A dynamic pricing engine to optimize margins
• An LLM-powered content generation system for product descriptions and marketing copy
• A RAG solution for an intelligent knowledge base to assist customer service representatives

But not all ideas are created equal. This is where assessment and prioritization become crucial.

Think of it as a game of AI chess. Each use case is a piece on the board. You need to consider not just its individual strength, but how it fits into your overall strategy.

The team evaluates each use case based on two key factors:

1. Business value: Will this significantly impact our strategic objectives?
2. Complexity: Do we have the data, skills, and infrastructure to implement this?

They plot the use cases on a matrix:

• Quick wins (High value, low complexity): The personalized product recommendation engine
• High potential (High value, high complexity): The LLM-powered content generation system
• Low priority (Low value, low complexity): An AI-generated product description tool
• No profit (Low value, high complexity): A blockchain-based loyalty program

Focus on the quick wins to build momentum, but don’t lose sight of the high-potential projects that could be game-changers down the line.

3. Execute AI projects

With prioritized use cases in hand, it’s time for the final act: Execution. But as any seasoned AI professional knows, this is where the real drama unfolds.

Execution isn’t a one-time event. It’s an ongoing process of learning, adapting, and improving.

The typical workflow for an AI project contains the following stages:

The AI maturity journey

As companies progress in their AI journey, they typically move through five stages of AI readiness. Understanding these stages can help organizations assess their current position and plan their path forward:

1. Awareness:
   • Organizations recognize the potential of AI but lack a clear strategy.
   • There’s general interest in AI, but no concrete plans or dedicated resources.
   • AI initiatives, if any, are ad-hoc and disconnected from core business objectives.
   • The focus is on learning and exploring AI possibilities.
2. Active:
   • Companies begin experimenting with AI projects, often starting with pilot programs.
   • There’s a growing investment in AI talent and technologies.
   • Initial use cases are identified and prioritized.
   • Organizations start to develop AI governance frameworks.
   • The focus shifts from learning to doing, albeit in a controlled, experimental manner.
3. Operational:
   • AI projects move from experimental to production environments.
   • There’s a clear alignment between AI initiatives and business objectives.
   • Organizations establish dedicated AI teams or centers of excellence.
   • AI starts to deliver measurable business value.
   • The focus is on scaling successful AI projects and integrating them into core operations.
4. Systematic:
   • AI is deeply integrated into multiple aspects of the business.
   • There’s a company-wide AI strategy with clear governance structures.
   • Organizations develop advanced AI capabilities, including custom models and solutions.
   • AI drives significant value across various business functions.
   • The focus is on continuous improvement and innovation in AI applications.
5. Transformer:
   • AI becomes a core part of the organization’s DNA, reshaping its very nature.
   • Companies leverage AI to create new business models and enter new markets.
   • There’s a culture of AI-driven innovation throughout the organization.
   • AI significantly enhances decision-making at all levels of the company.
   • The focus is on maintaining AI leadership and pushing the boundaries of what’s possible with AI.

Each stage builds on the last, representing a progression in AI maturity. Organizations may find themselves between stages or progressing at different rates in different areas of their business.

If you are not exploring AI at work, you are giving your competitors a head start

Successful AI implementation relies on three fundamental pillars: People, data, and infrastructure.

These form the foundation of any effective AI strategy, regardless of the specific use cases or industry.

1. People

The human element is crucial in AI success:

• Build a team with diverse skills, including data scientists, ML engineers, and domain experts.
• Ensure strong, visionary leadership that understands AI’s potential and limitations.
• Foster an AI-friendly environment that encourages data-driven decision-making and experimentation.
• Invest in ongoing AI education for specialists and general AI literacy for all employees.
• Promote cross-functional teamwork between technical and business teams.

2. Data

Data is the fuel that powers AI:

• Ensure data is accurate, complete, and sufficient for your AI models.
• Leverage diverse data sources for more robust AI solutions.
• Make data easily available while maintaining security and privacy.
• Implement ethical data practices and comply with privacy regulations.
• Develop a comprehensive plan for data collection, storage, and usage.

3. Infrastructure

The right infrastructure enables AI development and deployment:

• Computational resources: Secure sufficient CPU/GPU power for AI workloads.
• AI platforms: Invest in tools supporting the entire AI lifecycle.
• Streamline the process of deploying and managing AI models in production.
• Scalability and security: Ensure infrastructure can grow securely with your AI initiatives.

These pillars are interconnected – neglecting one can undermine the others. A balanced approach addressing all three is key to building a strong foundation for AI success. As organizations progress in their AI journey, they must continuously evolve and strengthen these pillars to fully leverage AI’s transformative potential.

Don’t forget about ROI in AI

While focusing on the three pillars of AI success – People, Data, and Infrastructure – it’s crucial not to lose sight of the ultimate goal: generating tangible business value. This is where Return on Investment (ROI) comes into play.

Importance of ROI in AI initiatives

1. Justification: ROI helps justify AI investments to stakeholders and secure ongoing funding for AI projects.
2. Prioritization: Calculating potential ROI aids in prioritizing different AI initiatives based on their expected impact.
3. Measurement: ROI provides a concrete way to measure the success of AI projects beyond technical metrics.
4. Alignment: Focusing on ROI ensures AI initiatives remain aligned with broader business objectives.
5. Continuous improvement: Tracking ROI allows for iterative improvement of AI solutions to maximize their business impact.

Calculating ROI for AI projects

While measuring ROI for AI can be challenging, consider these approaches:

• Direct financial impact: Measure cost savings or revenue increases directly attributable to AI solutions.
• Operational efficiency: Quantify time saved or productivity improvements resulting from AI implementation.
• Customer value: Assess improvements in customer satisfaction, retention, or lifetime value driven by AI.
• Strategic advantage: Evaluate how AI contributes to competitive differentiation or market position.

Tips for AI ROI evaluation

1. Set clear objectives: Define specific, measurable goals for each AI initiative at the outset.
2. Start small: Begin with pilot projects to demonstrate value before scaling.
3. Monitor continuously: Implement systems to track AI performance and business impact over time.
4. Be patient: Some AI benefits may take time to materialize; consider both short-term and long-term impacts.
5. Look beyond numbers: Consider intangible benefits like improved decision-making or enhanced customer experience.

Remember, while the three pillars – People, Data, and Infrastructure – provide the foundation for AI success, ROI ensures that this foundation translates into real business value. By keeping ROI at the forefront of your AI strategy, you can ensure that your AI initiatives not only leverage cutting-edge technology but also drive meaningful business outcomes.

As we conclude our journey through the AI strategy framework, it’s clear that the path to AI success is neither short nor straightforward. It requires vision, careful planning, and relentless execution. But for those who navigate this path successfully, the rewards can be transformative.

The AI revolution is already here. The question is not whether your business will be affected, but how you’ll shape your AI-powered future. With a solid strategy and a commitment to continuous learning and adaptation, the possibilities are limitless.

Every AI success story starts with a single step.

Featured image credit: Ideogram AI

Adobe is taking another step in supporting creative professionals with its new Content Authenticity web app, designed to help creators protect, attribute, and opt-out of AI training for their digital work.

This free tool protects how creatives manage and safeguard their images, videos, and audio content in the age of AI.

The Content Authenticity web app is part of Adobe’s expanded Content Credentials initiative, which allows creators to embed important information, such as their name, website, or social media profiles, into their digital work. More importantly, it gives creatives a streamlined way to label their work with a “do not train” tag, helping them opt out of generative AI models that scrape the internet for content.

What does the Adobe Content Authenticity web app offer?

The web app enables creators to quickly apply attribution data across multiple files at once, saving time compared to manually embedding credentials into individual pieces. This attribution includes essential details like creator names, links to portfolios, and more.

Adobe recognizes the concerns many creatives have about their work being used to train AI models. The web app allows users to opt-out their content from AI training, ensuring that their images, videos, and audio are protected from being used without permission.

The app is compatible not only with Adobe’s tools like Photoshop and Lightroom but also with any other digital content, making it a powerful hub for creators to manage their digital assets across platforms.

Adobe has integrated advanced technologies like digital fingerprinting and cryptographic metadata, which help restore Content Credentials even if someone tries to remove them. This adds an extra layer of protection for creatives worried about their work being misused or uncredited.

In addition to creating protections, the app also includes a feature to inspect Content Credentials on websites. Creators and viewers can check if content has been tagged with the metadata, even on platforms that don’t natively display it. A Google Chrome extension is also in beta, allowing users to directly inspect credentials from a webpage.

Addressing creators’ concerns in the AI era

Adobe has been proactive in addressing the growing concerns within the creative community about AI’s impact on intellectual property. The Content Authenticity web app is launching as part of a broader effort to support creators who want to protect their work from being used without consent.

Houston-based photographer, Alexsey Reyes states:

“There’s a blanket of security I didn’t know I needed as an artist sharing online until looking into the benefits of adding Content Credentials to my art. Using Adobe Content Authenticity is like one of those ‘things I wish I knew when I started’ moments.”

Although Adobe’s AI models, like Firefly, only train on licensed or public domain content, the web app’s protection features are aimed at influencing the broader AI market, encouraging other AI developers to adopt similar ethical standards.

One of the key and perhaps the most important benefits of the web app is its ability to opt creators out of generative AI training en masse, rather than requiring individual submissions to different platforms.

Can true content authenticity be maintained?

While the Content Authenticity web app represents an advancement in protecting digital work, Adobe still faces the challenge of getting more tech and AI companies on board with these protections. Currently, only a few companies, such as Spawning, support these opt-out features.

However, Adobe is actively working to expand industry-wide adoption of Content Credentials and AI protections.

As AI continues to grow, tools like Adobe’s Content Authenticity web app could play a critical role in maintaining ethical standards and safeguarding the rights of creators.

Whether it’s ensuring proper attribution or protecting work from unauthorized AI usage, this web app marks a pivotal moment in digital content management.

Launching in public beta in early 2025, the Content Authenticity web app will be free for all users with an Adobe account, making it more accessible to the creative community.

As artists struggle to find a balance between AI and digital ownership, Adobe’s new tool offers a powerful solution to help them retain control over their work.

Image credits: Adobe

Imagining the future version of oneself is a common activity, often filled with hopes, dreams, and sometimes uncertainties about what lies ahead. Researchers at the Massachusetts Institute of Technology (MIT) have developed an innovative AI-powered tool called Future You AI, which brings that concept to life by allowing individuals to interact with a simulated version of their future selves, 30 years down the line.

The Future You AI project, developed by MIT’s Media Lab, encourages users to engage in introspection about who they are today, who they want to become, and how they can plan for their future. Using AI and digital aging technology, Future You AI offers a visual and conversational glimpse into your future, helping you reflect on long-term goals and aspirations.

How Future You AI works?

Future You AI uses an advanced chatbot that simulates a version of yourself from 30 years into the future. To create this digital persona, the AI system asks a series of questions about your current life, including details about relationships, career ambitions, personal goals, and past experiences. This information helps the AI model build a profile of your potential future self, which it then links to a customized version of OpenAI’s GPT-3.5 AI model.

In addition to the interactive chatbot, Future You AI incorporates digital aging technology that allows you to upload a photo of yourself. The system then uses this image to generate an aged version of you, providing a visual representation of how you might look in three decades.

The most striking feature of Future You AI is its ability to create a synthetic memory for your future persona. This memory reflects the goals and milestones you hope to achieve over the next 30 years, offering a detailed timeline of how your future self reached those points. The AI doesn’t just tell you that you’ve accomplished your goals—it simulates the journey, providing a convincing, personalized account of the choices and events that led to those outcomes.

How to use Future You AI?

In this study, you’ll engage with your future self through an AI-powered tool designed to help you reflect on your goals and personal growth. Follow these steps to guide you through the process:

1. Introduction

Upon starting the experience, you will be greeted with a welcome page. Here are a few things to keep in mind:

• You will be asked to complete a survey and interact with your future self.
• If you do not see the survey questions, please scroll down, as this form might be embedded within the platform.
• The results from this survey may be used for academic research purposes.

2. Terms of participation

Before you begin, carefully read the terms of participation:

• Your data will be anonymized and strictly used for research purposes.
• You can opt out at any time by closing the survey without submitting your answers.
• You will remain anonymous in any publications resulting from this study.

3. Consent to participate

Before moving forward, you’ll need to provide your consent. The form will ask:

• Do you consent to participate in this study and allow the use of your data?
  • Select Yes to proceed.
  • Select No if you do not wish to participate.

4. Answer the survey

• You will be asked to complete a series of questions about your current emotional state, relationships, career goals, and personal aspirations.
• For example, as shown in the image, you may rate how strongly you feel various emotions, such as being anxious, motivated, or relaxed. Select the response that best fits your current state.

5. Engaging with your future self

• After completing the survey, the AI will generate a version of your future self based on the data you’ve provided.
• You will have the option to upload a current photo, which the AI will age by 30 years.
• Engage in a conversation with your future self. The AI will simulate a dialogue based on the timeline and choices you’ve described. It may reflect on how you’ve reached certain milestones or pursued specific goals.

6. Review and reflect

• As you chat with your future self, consider the insights gained from the interaction.
• How do you feel about the goals you’ve set?
• What changes might you want to make in your life today to align better with the future you envision?

7. Completion

• Once you’ve finished the interaction, submit your answers.
• You can close the browser window or application when you’re done.

Future You AI represents a unique use of artificial intelligence—one that goes beyond entertainment to provide valuable psychological benefits. The tool’s ability to simulate future scenarios based on personal data has the potential to improve not only how people perceive their future selves but also how they plan and act in the present.

Image credits: Emre Çıtak/Ideogram AI/MIT

In a move that encourages Nevada’s growing tech scene, AI cloud platform TensorWave announced today it has secured a statement inducing $43 million in SAFE funding. The largest of its kind for a Nevada-based startup, the funding highlights the increasing attractiveness of Nevada as a hub for tech innovation, with the state’s favorable business environment and growing talent pool drawing significant attention from investors and entrepreneurs alike, supporting strong growth in sectors like cybersecurity, aerospace, and logistics technology

TensorWave aims to address a typical bottleneck: access to powerful and affordable GPU resources. The company offers a compelling alternative to the existing options currently available, leveraging AMD Instinct Series GPUs rather than siding with NVIDIA, to deliver unparalleled scalability and performance – a strategy it recently discussed with Dataconomy. 

By eliminating the wait times and complexities associated with on-premise server management, TensorWave aims to democratize AI compute for startups, enterprises, and researchers alike.

“We are thrilled to have the support of such esteemed investors, partners, and the State of Nevada as we embark on this next phase of growth,” said Darrick Horton, CEO at TensorWave. “This funding allows us to significantly scale our team and deploy thousands of AMD Instinct AI accelerators to empower the startups and enterprises shaping our technological future.”

The funding, led by Nexus VP, with significant contributions from Maverick Capital, Translink Capital, Javelin Venture Partners, StartupNV, and AMD Ventures, will fuel TensorWave’s expansion plans. The company plans to bolster its core team, significantly increase capacity at its primary data center with the addition of thousands of AMD Instinct MI300X GPUs, and prepare for the integration of the next-generation MI325X GPUs. Furthermore, TensorWave is gearing up to launch Manifest, an inference platform designed for handling large context windows with minimal latency, enabling advanced document analysis and accelerated reasoning.

“AMD Ventures shares TensorWave’s vision to transform AI compute infrastructure,” said Mathew Hein, Senior Vice President, Chief Strategy Officer and Corporate Development at AMD. “Their deployment of the AMD Instinct MI300X and ability to offer public instances to AI customers and developers positions them as an early competitor in the AI space, and we are excited to support their growth through this latest round of funding.”

TensorWave’s strategic deployment of AMD GPUs positions the company at the forefront of the rapidly expanding AI market, which is projected to reach a staggering $1.81 trillion by 2030, according to Grand View Research. Investors have recognized the company’s potential to become a key player in this burgeoning sector.

“TensorWave has impressed with their mission, vision, and rapid execution. We’re thrilled to support this dynamic startup, which is poised to lead AI cloud computing with AMD GPUs,” says Brendan Walsh, Venture Partner at Translink Capital. “Their deep expertise in both the physical plant and software elements of cloud compute and artificial intelligence, along with their commitment to fostering a competitive, open-source future for AI, makes them an exciting company to watch as they continue to scale.”

With its focus on accessibility, performance, and innovation, TensorWave is set to make waves in the AI industry. The company’s commitment to providing a robust and competitive cloud platform powered by AMD’s cutting-edge GPU technology has the potential to reshape the AI landscape and accelerate the development of groundbreaking applications across various sectors.

The hasty progress of artificial intelligence (AI) technology and its growing influence across many areas of life have sparked significant global discussions on governance, ethics, and regulatory frameworks. At the forefront of these discussions is the EU AI Act—a pioneer regulatory framework that aims to set the standard for these topics across Europe. But this isn’t just another regulatory effort; it represents a broader vision for shaping the future of AI in a way that ensures fairness, inclusivity, and respect for human rights. As AI technologies and their impact continue to accelerate, it’s becoming increasingly clear that engaging with these regulations is crucial—not just for AI developers but for policymakers, businesses, and society at large.

Dataconomy had the opportunity to speak with key EU and UN leaders to explore the global impact of AI governance in greater detail. These interviews revealed how AI regulation and ethics are unfolding on a global scale, with the EU AI Act playing a critical role. During the Digital Enterprise Show (DES) 2024 in Malaga, Wendy Hall, a UN AI Advisory Board member and prominent UK AI strategist; Carme Artigas, Co-Chair of the UN AI Advisory Body on AI Governance and Inclusion; and Dan Nechita, Head of Cabinet for MEP Dragos Tudorache and lead technical negotiator for the EU AI Act on behalf of the European Parliament, shared their exclusive insights with us on how AI governance, ethics, and regulation are being shaped in real-time.

Bridging the global AI divide

Wendy Hall, a UN AI Advisory Board member and prominent UK AI strategist, strongly advocates for a globally collaborative approach to AI policy. During our discussion, Hall emphasized that while AI presents vast opportunities, the strategies employed by different nations vary widely. For instance, the UK has taken a more comprehensive, policy-driven approach to AI development. Beginning in 2017, the UK government recognized AI’s potential for economic growth and job creation, positioning the country as a leader in AI governance. At a time when Brexit consumed political focus, the UK still managed to work on AI policy. Hall notes that the UK’s early engagement helped establish its prominence, but she’s quick to point out that other countries like the US and China have followed distinctly different paths.

In the US, the focus has largely been on empowering tech companies like Google and OpenAI to push AI boundaries, leaving governance in the hands of the private sector. Conversely, China has taken a centralized, state-driven approach, with the government maintaining control over AI’s strategic direction. These divergent strategies, Hall explains, highlight the complexity of global AI governance and the need for more cohesive international policies.

Yet, Hall’s primary concern isn’t the divergence between these leading nations but rather the unequal access to AI technologies across the globe. She emphasizes the need for equitable AI development, particularly for countries outside the wealthy West. Regions like the Global South, which often lack the infrastructure and resources to keep pace with AI advancements, risk being left behind. Hall states this divide could deepen existing global inequalities unless capacity-building initiatives are implemented.

“These regions need more than just access to AI technologies—they need the infrastructure, talent, and data to develop AI systems suited to their own needs,” Hall stresses. This could include providing countries in the Global South with access to high-performance computing systems, datasets, and the technical expertise needed to build AI models locally. Hall advocates for global initiatives offering the tools and resources necessary for these countries to participate actively in the AI revolution rather than passive consumers of technology developed elsewhere.

“There’s a risk that AI could deepen global inequalities if we don’t ensure equitable access to the necessary infrastructure and talent”

A particular concern for Hall is the rapid and unchecked development of generative AI models, such as OpenAI’s GPT-4. While these models offer groundbreaking possibilities, they also pose significant risks in the form of misinformation, disinformation, and ethical misuse. Hall is cautious about the unintended consequences of such powerful technologies, noting that generative AI can produce convincing but entirely false content if not carefully regulated.

She draws attention to the broader implications, explaining that while earlier AI technologies like automation primarily focused on improving efficiency, generative AI directly impacts knowledge creation and dissemination. “We’ve seen this with misinformation online—if the data going in is flawed, the output could be damaging, and at a scale that we’ve never dealt with before,” Hall warns. The stakes are high, particularly when AI technologies influence decisions in critical sectors like healthcare, law, and finance.

For Hall, the solution lies in advocating global partnerships aimed at creating robust ethical standards and governance frameworks. She advocates for establishing international agreements to ensure that AI technologies are developed and deployed responsibly without contributing to societal harm. Hall points to the importance of involving diverse stakeholders, including governments, private companies, and civil society organizations, to establish regulations that balance innovation with public safety.

Hall’s perspective underscores a critical point: AI could exacerbate existing global inequities and introduce new ethical dilemmas without collaboration and shared governance. Hall’s call for capacity building and ethical oversight isn’t just a recommendation—it’s a necessary step to ensure AI is developed to benefit humanity as a whole, not just a select few.

Ensuring inclusive AI governance

Carme Artigas, Co-Chair of the UN AI Advisory Body on AI Governance and Inclusion, brings a critical perspective to the conversation about AI’s global development—one focused on the glaring disparities in how different nations are included in discussions about AI governance. Artigas stresses that the current frameworks governing AI, including initiatives led by the G7, UNESCO, and the OECD, are largely dominated by wealthier, more technologically advanced nations, leaving out key voices from the Global South. “Many countries in the Global South are not even invited to the table,” Artigas points out, referring to the global discussions that shape AI’s future. In her view, this exclusion is a major governance deficit and risks creating a new form of digital colonialism. As AI technologies advance, those countries that lack the resources or influence to participate in international AI policymaking could be left even further behind. For Artigas, this isn’t just a matter of fairness—it’s a fundamental risk to global stability and equality.

Artigas highlights the need for a governance model that goes beyond the traditional frameworks of regulatory bodies. Rather than creating a single new international agency to oversee AI governance, she advocates for leveraging existing institutions. “We don’t need more agencies; we need better coordination between the ones that already exist,” she explains. Organizations such as the ITU (International Telecommunication Union), UNICEF, and WIPO (World Intellectual Property Organization) are already deeply involved in AI-related issues, each within their own sectors. What’s missing is a coordinated approach that brings together these specialized agencies under a unified global governance structure.

“True governance must go beyond mere guidelines and include mechanisms for accountability”

Artigas’s vision is one where AI is governed in a way that respects international law and human rights and ensures that all countries—regardless of their technological standing—have equal access to the benefits AI can bring. This includes providing the necessary tools and resources for countries currently excluded from AI advancements to catch up. She notes that the private sector and academia also have a role in helping democratize access to AI technologies.

However, Artigas points out that ethical guidelines alone are not enough. While many companies have developed their internal ethical frameworks, she argues that these are often voluntary and unenforceable. True governance, she asserts, must go beyond mere guidelines and include mechanisms for accountability. Without clear consequences for unethical AI development or deployment, the risks of misuse and harm—particularly for vulnerable populations—remain high.

One of the key issues Artigas raises is the role of AI in exacerbating the digital divide. If not properly regulated, AI could further entrench existing inequalities, with wealthier nations gaining more economic and technological power while poorer nations fall further behind. For her, the goal of AI governance must be to close this divide, not widen it. “AI has the potential to be a great equalizer, but only if we ensure that its benefits are shared equally,” she emphasizes.

Artigas’s focus on inclusivity and coordination in AI governance reflects the growing recognition that AI is a global issue requiring global solutions. Her call for a unified approach—where existing agencies work together to govern AI—underscores the need for a more inclusive, ethical, and accountable system that benefits all of humanity, not just a select few.

Balancing innovation and regulation

Dan Nechita, Head of Cabinet for MEP Dragos Tudorache and the lead technical negotiator for the EU AI Act brings a pragmatic yet forward-thinking perspective to the discussion of AI governance. As one of the key figures behind the EU AI Act, Nechita emphasizes the importance of balancing innovation with the need for robust regulation to ensure AI technologies are developed and used safely.

According to Nechita, the EU AI Act is designed to set clear rules for AI systems, particularly those considered high-risk, such as AI used in healthcare, education, law enforcement, and other critical sectors. “This isn’t just about regulating the technology itself,” Nechita explains. “It’s about protecting fundamental rights and ensuring that AI doesn’t exacerbate existing societal problems, like discrimination or privacy violations.”

One of the standout features of the EU AI Act is its emphasis on risk management. Nechita explains that AI systems are classified based on the level of risk they pose, with the highest-risk systems subject to the strictest regulations. This tiered approach allows for flexibility, enabling Europe to maintain its leadership in AI innovation while ensuring that the most sensitive applications are thoroughly regulated. For Nechita, this balance between innovation and regulation is crucial to maintaining Europe’s competitiveness in the global AI landscape.

Yet, Nechita acknowledges that implementing the EU AI Act is a complex and ongoing process. One of the challenges is ensuring that all 27 EU member states, each with their own national priorities and strategies, adhere to a unified regulatory framework. The EU AI Act requires cooperation between governments, industry leaders, and regulatory bodies to ensure its success. “We’re fostering a continuous feedback loop between companies and regulators, ensuring AI systems evolve safely while remaining compliant as new technologies emerge,” Nechita explains. “We’re not just handing companies a set of rules and walking away. We’re asking them to work with us continuously, to test their systems, report issues, and ensure compliance.”

“AI will transform the world, and we must guide it in a direction that benefits everyone”

Nechita also points out that the EU AI Act is not just about creating static regulations. The Act includes provisions for continuous updates and revisions as AI technologies evolve. He argues that this dynamic approach is essential because AI is a fast-moving field, and regulations must keep pace with new developments. This is why the EU AI Act encourages ongoing dialogue between AI developers and regulators, fostering a relationship where both innovation and safety can coexist.

However, Nechita is also mindful of the broader global context. While the EU has taken a proactive stance on AI regulation, other regions, particularly the US and China, have different approaches. In the US, AI regulation is more fragmented, with companies largely self-regulating, while China’s state-controlled AI development prioritizes national interests over individual rights. Nechita acknowledges that achieving global consensus on AI governance will be difficult, but he sees potential for collaboration in areas like AI safety, sustainability, and ethical standards.

Nechita envisions an AI governance model that balances innovation with public safety. He believes the EU AI Act, focusing on risk management, transparency, and continuous collaboration, offers a model for how other regions might approach AI regulation. At the same time, he stresses the need for global cooperation, particularly in addressing AI’s ethical and societal implications.

As the EU AI Act continues to take shape, Nechita remains optimistic about its potential to set a global standard for AI governance: “AI is going to change the world, and we need to make sure it changes for the better,” he concludes. His approach reflects a nuanced understanding of the challenges ahead and a strong belief in the power of regulation to guide AI development in a direction that benefits society.

Dan Nechita is scheduled to speak at the Data Natives 2024 event in Berlin on October 22-23; the event’s theme is “2050: The ‘Good’ AI Symposium.”

A unified vision for the future of AI

Wendy Hall, Carme Artigas, and Dan Nechita’s insights reflect a crucial turning point in AI governance as we watch AI evolve at an unprecedented pace. Their perspectives converge on one undeniable truth: AI isn’t just a technological breakthrough; it’s a force that has to be firmly steered away from benefiting the few at the cost of the many.

The urgent need for global capacity building and ethical controls of AI is also being called for by Wendy Hall, who asks us to bridge the growing gap between the capabilities in this area between developed and developing nations. However, Camre Artigas’s focus on inclusivity and accountability reminds us that the enforcement that precedes any governance should be part and parcel. EU AI Act is a worthy example of balancing innovation with safety and, thus, how other regions may approach AI governance.

Together, these voices paint a holistic picture of what’s needed to shape AI’s future: focus on collaboration, human rights protection, and a strong framework that encourages innovation while protecting public interests. It’s an incredibly tough road ahead but also one with tremendous potential. AI’s future is now, and it’s up to us to make it happen right.

Samsung Electronics has publicly apologized and admitted it’s facing what many are calling a “crisis” after revealing lower-than-expected profits. According to the Financial Times, the South Korean tech giant reported an operating profit of 9.1 trillion won ($6.8 billion) for the third quarter, falling short of market forecasts, which had predicted 10.3 trillion won, as per LSEG SmartEstimates.

While profits have nearly tripled compared to the same time last year—thanks to rising memory chip prices—they’re still down about 13% from the second quarter. On top of that, the company’s stock has dropped nearly 30% in the past six months, as concerns grow about its ability to stay competitive in the race for advanced AI chips.

Samsung apologizes publicly for falling short of market forecasts

Below is a translated version of the statement of Samsung Electronics:

Earlier this year, Samsung’s so-called crisis led the company to implement a six-day workweek for its executives. The tough decision came after a rough 2023, marked by rising competition and a decline in chip demand. However, the AI boom was supposed to turn things around, as evidenced by a 15x profit increase last quarter, fueled by strong sales of its high-bandwidth memory chips to NVIDIA.

Now, though, Samsung is facing new challenges. The company recently announced that sales of its top-tier HBM3E chips to a major customer have been delayed, giving competitors like SK Hynix the chance to enter.

The company has acknowledged its difficulties, with Samsung’s chip division head, Young Hyun Jun, apologizing for not meeting expectations. Despite the AI boom, which has significantly boosted profits for competitors, Samsung is finding it tough to capitalize on this opportunity. Its delay in entering the AI race, compounded by slowing demand for traditional memory chips, has raised questions about its ability to maintain market leadership in a highly competitive field.

Featured image credit: Valentyn Chernetskyi/Unsplash

Creating AI-generated religious art is a child’s play with the rise of advanced image generators.

Whether you’re decorating your home or preparing for a sacred holiday, AI-generated religious art can create stunning, visually captivating pieces that celebrate spirituality and diversity. By blending symbols, colors, and motifs from various faiths, AI can produce unique artworks that resonate with unity and reverence.

How to create your own AI-generated religious art pieces?

To create AI-generated religious art, one merely requires access to an AI image generator—several of which we’ve detailed in previous articles: Midjourney, DALL-E, and Ideogram, Mojo AI, Mystic AI, Imagen 3, Freepik AI, Pix AI, and more.

The tools themselves are quite standard, but the true artistry, as ever, lies in the prompts.

Below is a framework for crafting these prompts:

• Subject: Select your divine figure or sacred symbol
• Details: Consider robes, gestures, or surrounding elements
• Environment: Choose a setting—whether temple, church, or perhaps a transcendent landscape
• Artistic style: Opt for the classical, the abstract, or something avant-garde
• Additional elements: Infuse lighting, texture, or mood to elevate the scene

If you want to be a professional prompt engineer, we highly recommend you to read our guide explaining tricks and midjourney prompts for characters, logos… well everything. The best part is, it doesn’t matter if you use these tricks in any other AI image generator.

Check out below examples to get the grasp of it:

Prompt 1:

A serene angel, golden wings extended, holding a glowing orb. Surrounded by clouds in a radiant heavenly sky. Classical Renaissance style, soft lighting, peaceful expression, intricate feather details

Prompt 2:

Saint meditating by a riverbank at dusk, light reflecting off the water. Detailed halo, gentle robes flowing. Baroque-inspired with rich, warm colors, capturing deep contemplation and divine connection

Prompt 3:

Ancient temple bathed in twilight, towering marble columns adorned with carvings. Mystic figures praying before a sacred altar. Byzantine style, intricate mosaics, soft candlelight creating a reverent atmosphere

Prompt 4:

Sacred heart icon, burning with divine light, encircled by vibrant floral patterns. Gothic-inspired with deep reds and golds. Intricate detailing, luminous rays emanating, symbolizing faith and sacrifice

Prompt 5:

Devotional scene of a monk kneeling before an altar, incense swirling around him. High arches overhead. Medieval art style, dim candlelight, shadows enhancing the solemnity of the moment

Prompt 6:

Ethereal figure of the Virgin Mary, robes flowing gracefully. Surrounded by blooming roses in a moonlit garden. Romanticism-inspired, soft pastel hues, delicate brushstrokes, and a gentle, protective gaze

Prompt 7:

Buddha statue in a tranquil lotus pond, lotus flowers floating. The sky glowing at sunrise. Traditional Asian art style, detailed shading on the statue, vibrant reflection in the still water

Prompt 8:

An illuminated manuscript page, with a depiction of a saint surrounded by gold-leaf borders. Medieval style, ornate text intertwined with intricate religious symbols, soft glowing parchment, carefully crafted calligraphy

Prompt 9:

Krishna playing a flute by a river under a full moon, surrounded by dancing figures. Traditional Indian art style, vibrant colors, rich in symbolism, intricate detailing in garments, soft moonlit reflections

Prompt 10:

Sacred mandala, intricately designed with radiant golden and blue patterns, at the center of a cosmic backdrop. Contemporary digital art style, precise geometric shapes, symbolic layers of spirituality and enlightenment

Prompt 11:

A mystical Sufi dancer, spinning in a desert under a twilight sky, surrounded by glowing dust particles. Minimalist yet expressive, soft pastel tones, flowing robes emphasizing spiritual transcendence

Prompt 12:

A peaceful prayer circle under a starry sky, celestial figures hovering above in glowing robes. Symbolist art style, rich in metaphors, detailed facial expressions, blending mystical and earthly elements seamlessly

Prompt 13:

A radiant cross bathed in golden light, emerging from a tranquil sea at dawn–Surrounded by calm waves and glowing skies–Impressionist style, soft colors, evoking peace and spiritual renewal

Prompt 14:

A serene Buddha meditating under a Bodhi tree, surrounded by glowing lotus flowers–Tranquil evening setting, warm hues reflecting inner peace–Traditional Asian style, delicate shading, spiritual calmness pervading the scene

Prompt 15:

Majestic cathedral interior, illuminated by sunlight streaming through stained glass windows–High arches, intricate carvings and glowing mosaics–Renaissance-inspired, vibrant colors, sacred atmosphere invoking divine reverence and awe

8 tricks to create the best AI-generated religious art

Here are some helpful tips to enhance your outputs:

1. Be clear and descriptive

When creating prompts, detail matters. Instead of just requesting a “spiritual scene,” describe the specific figure, action, or environment you have in mind. For example, instead of asking for a generic “sacred figure,” request “a serene angel with golden wings extending, holding a glowing orb.” The more vivid and specific you are, the better your results will be.

2. Utilize artistic styles

Different art styles can dramatically change the feel of your creation. Consider the visual impact of each style—whether it’s the softness of Renaissance, the intricate details of Byzantine, or the dramatic contrasts of Gothic. Try experimenting with these references to give the AI a stronger sense of the direction you’re aiming for.

3. Incorporate symbolism

Religious art is steeped in symbolism. Include elements that hold spiritual significance, like halos, sacred texts, or celestial light. For instance, in a scene of a meditating saint, you might specify “a detailed halo” to signify divine presence. These symbols can help add depth and meaning to your work.

4. Experiment with environments

Don’t limit your prompts to just a figure or object—set the scene! A serene riverbank at dusk or a grand cathedral bathed in sunlight can completely transform the feel of your image. Play around with sacred landscapes, temples, or celestial skies to add ambiance and context.

5. Adjust lighting and mood

Lighting is a powerful tool in religious art, often used to signify divine presence or sacred moments. Experiment with phrases like “soft candlelight,” “radiant glow,” or “moonlit garden” to enhance the emotional tone of your piece. The right mood can bring out the spiritual significance of your artwork.

6. Mix motifs from different cultures

One of the wonders of AI-generated art is that you can mix motifs and symbols from different spiritual traditions. You might create a Buddha statue in a Byzantine-inspired setting or an angel in an Indian art style. This kind of cross-cultural blending can result in unique, harmonious works that reflect the universality of spirituality.

7. Focus on emotion

Religious art often seeks to evoke deep feelings of reverence, peace, or awe. To achieve this, include emotional cues in your prompt. Words like “contemplative,” “solemn,” or “joyous” can influence the expressions and overall feeling of the generated image.

8. Refine with color and texture

Don’t hesitate to mention specific colors or textures. If you want a vivid, rich depiction, specify warm tones like “deep reds and golds,” or go for cooler, more peaceful hues like “soft pastels.” Additionally, talk about textures—whether you want something smooth and modern or textured like a fresco.

Don’t be afraid to experiment

When creating AI-generated religious art, the beauty often lies in experimentation. Don’t hesitate to push boundaries and explore new combinations. Try blending unexpected elements—perhaps a serene Buddha in a Baroque style, or a mystical Sufi dancer in a Byzantine-inspired setting. Experiment with abstract textures or surreal lighting effects to see how they alter the spiritual message of your art.

Remember, AI tools are your canvas, and prompts are your brush. The more you explore, the more you’ll uncover new layers of creativity and depth in your AI-generated religious art!

Image cedits: Kerem Gülen/Midjourney

OK, this is too much. Working with ChatGPT 4o with canvas is a hack.

We were aware of how far LLMs and AI tools have come, but this feature takes things to a whole new level. You think you’re just going to get some suggestions and edits? Nah, you’re about to transform your whole workflow.

ChatGPT 4o with canvas offers an efficient way to engage with AI, providing you with a more interactive experience. The canvas feature lets you create, edit, and manage documents seamlessly, making it perfect for content creation, brainstorming, and collaboration.

In this guide, we’ll walk you through everything you need to know about using ChatGPT 4o with canvas based on our first-hand experience with the tool.

What is ChatGPT 4o with canvas?

Canvas is a flexible tool integrated into ChatGPT 4o that lets you create and work on documents in a structured way. You get real-time editing, version control, and rich content generation—all in the same place where you interact with ChatGPT.

Whether you’re writing long articles, managing drafts, or just organizing your thoughts visually, canvas has your back. The power of canvas lies in its ability to adapt to your workflow, providing a smooth, user-friendly interface that keeps all your work organized and accessible.

With ChatGPT 4o with canvas, you can switch between brainstorming ideas, drafting, editing, and polishing your work—all while receiving helpful AI suggestions. It’s designed to be your ultimate content creation companion.

Key features of ChatGPT 4o with canvas

With Canvas, you can jump right into a blank document and start working interactively. Here’s what you can do:

• Create new documents from scratch, which is ideal for when inspiration strikes.
• Edit and refine your content over multiple sessions, ensuring you have the freedom to revisit and perfect your work.

This flexibility makes it suitable for both short-term and long-term projects. Whether you need to jot down quick notes or work on an extensive report, Canvas provides the tools you need to keep everything in one place.

ChatGPT 4o with canvas lets you add different types of content, including:

• Text with various formatting options, allowing you to emphasize key points.
• Links, lists, and tables, making your documents informative and easy to navigate.
• Images and other media to make your content stand out, transforming simple text into an engaging visual experience.

With these features, Canvas is a complete content creation tool. You can create visually appealing documents that are not only informative but also engaging for your audience.

ChatGPT 4o with canvas has several tools to help you polish your content:

• Suggest edits: Highlight parts of your text and get suggestions for improvements. When using this feature, you’ll see highlighted areas and specific notes on how to enhance your content. This is perfect for when you’re stuck or need a fresh perspective.
• Adjust length: Use a slider to tweak the length of your text—make it punchier or expand it to add more depth. We used this feature to summarize our detailed content schedule into a concise format. It’s perfect for adjusting depending on your audience’s needs and ensuring your message is tailored accordingly.
• Reading level: Adjust the tone of your text by picking a reading level, from kindergarten to graduate level. This makes it easy to tailor content for different audiences, whether you need something simple or a bit more sophisticated. We found this particularly useful when creating content for different client demographics.
• Add final polish: Give your content a final review to ensure everything is smooth and refined. Clicking on this option resulted in a structured and well-organized article, with proper headings and a polished flow. It’s the final touch that makes your content shine, transforming a rough draft into something publication-ready.
• Add emojis: Add some relevant emojis to make your content more engaging and expressive. We tested this feature, and it added relevant emojis to emphasize key points and make the content more lively. Emojis can add a personal touch and help convey emotions or highlight important sections of your text, which can be especially effective on social media or informal content.

You can access these tools by clicking the pen icon located in the lower right corner of your canvas. The editing menu gives you all these options to make sure your content shines, offering you the flexibility to shape your work exactly the way you want it.

How to use ChatGPT 4o with canvas: Our experience

Step 1: Creating a new document

Open ChatGPT as you would normally do. Select the “ChatGPT 4o with canvas” model.

Step 2: Adding content

When we first tried Canvas, we wanted to make a flexible daily schedule to manage content for multiple blogs and websites.

The prompt we used was:

“I want to create a flexible daily schedule for myself. I have 4 different websites/blogs. I write content for them from Monday to Friday. Let’s call them: Channel A: 4 long form articles everyday Channel B: 2 social media posts Channel C: 1 long form corporate blog article Channel D: 1 original article in my native tongue I start working at 10:00 and end at 18:00.”

ChatGPT came back with a detailed schedule that helped us balance our workload while keeping time for breaks. You can do the same—just start typing in the Canvas, and ask ChatGPT for help with ideas, improving your writing, or structuring your plans.

We then used the canvas panel to refine this schedule. On the right-hand side, we made edits directly, while on the left-hand side, we could prompt ChatGPT to do more complex adjustments.

This dual approach allowed us to customize our workflow, making it easier to refine ideas and make quick adjustments in real time.

Next, we asked for a summarized tabular format of our schedule. The result? A neat, concise table that made the schedule much easier to digest. Canvas makes it really easy to go from a paragraph-heavy plan to a visually clean and organized format.

This kind of transformation is especially helpful when presenting information to clients or team members who prefer an at-a-glance overview.

Step 3: Using editing tools

After generating the schedule, we explored the editing tools:

• Suggest edits: This tool highlighted specific parts of our content in yellow, providing actionable notes for improvement. We found this super helpful for enhancing clarity and flow. It’s like having an editor by your side, pointing out exactly what needs tweaking.

• Add emojis: Adding emojis was a breeze. We clicked the option, and relevant emojis were automatically added to make the content more engaging. This small addition made a big difference in how approachable the content felt.
• Add final polish: We clicked this option to polish the whole article. It transformed our draft into a more structured piece with proper headings and a professional touch, making it ready for publication. The polish feature really elevated our draft from good to great, taking care of all the nitty-gritty details that can otherwise be time-consuming.

ChatGPT was an assistant, now it’s a co-author

When we started using ChatGPT 4o with canvas, we realized how much easier it made our workflow for creating, editing, and collaborating on documents. Whether we were drafting an article, writing a report, or just brainstorming ideas, Canvas provided an intuitive platform that seamlessly combined AI assistance with document management features. It felt like having a helpful co-writer always there to guide us through each stage of the writing process, from start to finish.

Image credits: OpenAI 

Google’s Gemini Live is stepping up its game with a new expansion of language support. As of today, users can now engage with Gemini Live in French, German, Portuguese, Hindi, and Spanish on Android devices, marking the first phase of a broader language rollout that Google plans to implement in the coming weeks. The company has committed to supporting conversations in over 40 languages soon, enhancing its global reach and accessibility.

The company’s plan to enable transitions between two languages in a single conversation is a notable advancement in AI functionality. With this development, Gemini Live aims to cater to a global user base that increasingly engages in multilingual dialogue. This feature, still in its early stages, is expected to expand further, allowing for more fluid communication and supporting the diverse linguistic needs of users.

How to set your preferred languages on Gemini Live?

To adjust your language settings and get the most out of this multilingual upgrade, follow these steps with ease:

• Open the Google app on your Android device, and tap your profile icon at the top of the screen.
• Head into Settings, then select Google Assistant and finally, Languages.
• Choose your preferred language from the available options. Want to speak two languages with Gemini Live? You can add a second supported language as well.

Currently, Gemini Live supports up to two languages per device. As Google continues to develop this feature, users can expect more flexibility in how they interact with AI across multiple languages

Betting on Gemini more and more

In tandem with the language rollout, Google is introducing its new suite of Gemini Extensions, which includes enhanced Calendar, Tasks, and Keep functionalities. These extensions are being made available in more languages over the coming weeks, adding to the overall versatility of the platform.

Additionally, the Utilities Extension is replacing Google Assistant for managing device tasks like setting alarms, controlling media, and adjusting system settings such as Wi-Fi and Bluetooth. However, Gemini Live itself is not yet compatible with these Extensions, though future updates could potentially integrate them, further expanding its capabilities.

By broadening the scope of supported languages and refining its utilities, Google is positioning Gemini Live as a more powerful tool for everyday users across the globe.

Featured image credit: Google 

OpenAI has secured a fresh $4 billion revolving line of credit, just one day after announcing a record-breaking $6.6 billion funding round. This massive financial injection now arms OpenAI with over $10 billion in liquidity, allowing the company to push its ambitious AI projects forward without the typical financial constraints.

The newly secured credit facility, underpinned by a roster of financial heavyweights including JPMorgan Chase, Citi, Goldman Sachs, and others, offers flexibility for OpenAI to keep its operations running smoothly—particularly given the high costs associated with powering AI tools like ChatGPT.

Recap: A historic funding round

Yesterday, we covered OpenAI’s announcement of securing $6.6 billion in new funding, which elevated the company’s valuation to an impressive $157 billion.

Thrive Capital led the round with a $1.25 billion investment, joined by other notable backers like Microsoft, SoftBank, and NVIDIA. Notably, this influx nearly doubled OpenAI’s previous valuation of $86 billion.

This capital doubled OpenAI’s valuation from $86 billion and also placed the company shoulder-to-shoulder with some of the world’s largest publicly traded companies.

However, this new chapter in OpenAI’s journey isn’t without its complexities. Alongside the influx of capital, the company faces a critical transformation—moving away from its nonprofit origins towards a fully for-profit model. The decision to go this route is fueled by the need to secure continued investment in the race toward AGI.

As OpenAI grows increasingly commercial, friction is emerging among its leadership, with debates over whether this transition dilutes its original mission of advancing AI for public benefit. With ChatGPT generating substantial revenue streams from both consumer and enterprise markets, the company is finding itself at the intersection of profit and purpose.

OpenAI’s new $4 billion revolving credit line fortifies its financial foundation, offering significant strategic breathing room. As CFO Sarah Friar explained, this credit facility strengthens their balance sheet, granting the company the agility to seize emerging opportunities as they scale. Notably, these funds remain untapped as of now, acting as a powerful reserve for OpenAI’s future ambitions. This surplus of capital gives the company a clear runway to explore groundbreaking initiatives, further expand its infrastructure, and attract world-class talent.

According to OpenAI’s latest blog post, this new credit line both expands the company’s liquidity. However, it also deepens partnerships with top-tier financial institutions, many of whom are already customers of OpenAI. With over $10 billion in accessible funds, OpenAI now finds itself equipped to take bolder strides into the future, whether that means doubling down on cutting-edge research, scaling up to meet the ever-growing demand for its AI services, or investing in the next wave of AI-driven innovations.

While OpenAI’s financial future appears more fortified than ever, the road to sustained profitability remains a separate challenge.

Featured image credit: OpenAI

Google’s growing focus on sustainable energy may soon incorporate nuclear power as a key resource for fueling its AI data centers, as hinted by CEO Sundar Pichai in a recent interview. The tech giant is actively exploring energy options that can support the immense demands of its operations while aligning with its ambitious environmental objectives. By 2030, Google has set a bold target to achieve net-zero emissions across all aspects of its global operations.

“A core focus of our goal to operate on 24/7 carbon-free energy (CFE) by 2030 is to accelerate the decarbonization of electricity grids around the world. Today’s grids are a major contributor to global greenhouse gas emissions, and electricity demand is expected to grow significantly in the decades ahead.”

-Google

Pichai acknowledged the scope of the challenge, especially as AI’s rapid expansion has significantly increased the company’s energy consumption. “It was a very ambitious target, and we are still going to be working very ambitiously towards it,” Pichai stated in his conversation with Nikkei Asia, underscoring that AI’s growth has made the goal even more challenging. Nevertheless, Google is not shying away from exploring advanced energy solutions, including small modular nuclear reactors, alongside continued investments in solar energy.

Where will Google locate its nuclear plants?

While Pichai did not provide specific timelines or locations where nuclear energy would be introduced into Google’s energy grid, he made it clear that the company is seriously evaluating a range of cutting-edge technologies to support its sustainability efforts. Google’s interest in nuclear energy is not new; the company has long explored innovative clean energy alternatives.

A blog post and accompanying white paper from September 2023 reaffirmed its commitment to investing significantly in technologies such as geothermal energy and hydrogen power, which also hold promise in reducing carbon footprints.

Google’s push towards nuclear energy is part of a broader trend among tech industry leaders. Rivals Amazon and Microsoft have already made moves to secure nuclear-generated electricity for their operations. Amazon, for instance, signed a $650 million agreement in March to purchase power from Pennsylvania’s Susquehanna nuclear plant, according to a report from The Financial Times. Similarly, in September, Microsoft entered into a 20-year contract to buy energy from the Three Mile Island plant, owned by Constellation Energy. This facility, which was decommissioned in 2019, is set to be reactivated by 2028.

As these tech giants ramp up their energy needs for data centers and AI development, nuclear energy is becoming an increasingly attractive option due to its ability to provide reliable, carbon-free power at scale. With industry leaders like Google, Amazon, and Microsoft setting the pace, nuclear power may soon become a cornerstone of the tech world’s shift toward sustainable energy sources, transforming how these companies manage their environmental impact while sustaining the vast power requirements of their demanding infrastructures.

Image credits: Kerem Gülen/Ideogram

Now, more than ever, data drives decision-making, and those with access to the right information have the absolute advantage over those who don’t. It is to the point that harnessing the right data is a requirement to stay competitive. Recognizing this growing need, Oxylabs, a leading web intelligence platform, has introduced OxyCopilot, its AI-powered solution designed to transform how companies collect web data. As an extension of Oxylabs’ Web Scraper API, OxyCopilot aims to simplify the complex and time-consuming process of web scraping to offer businesses an efficient way to gather the insights they need to thrive.

OxyCopilot leverages AI to streamline tasks that otherwise require technical expertise and resources. Users can generate parsing instructions and requests by simply providing a URL and natural language prompts, an accessibility that allows both web scraping professionals and newcomers to use it.

The launch is timely, with a recent survey by Oxylabs and Censuswide finding that 74% of businesses in the UK and US experienced a growing demand for public web data in the past year. Despite this surge in demand, many companies face significant challenges in building and maintaining the infrastructure necessary for large-scale web scraping — a problem OxyCopilot aims to solve.

Julius Cerniauskas, CEO of Oxylabs, points out that many businesses spend up to 40 development hours per week on data parsers alone, a substantial time commitment. With OxyCopilot, these technical burdens are significantly reduced, allowing companies to focus more on the quality of their data and less on the complexities of data extraction.

Cerniauskas explains that OxyCopilot’s main objective is to make data collection more accessible and efficient. This becomes particularly important for smaller businesses that lack the resources to hire specialized web scraping teams or maintain costly server infrastructure. Traditionally, large-scale web scraping required extensive technical expertise and investment, which created barriers for smaller enterprises. OxyCopilot changes that, automating much of the process and leveling the playing field.

The AI-driven assistant also addresses one of the most pressing challenges identified in Oxylabs’ survey, maintaining server infrastructure, which 61% of professionals highlighted as a major obstacle in data collection. OxyCopilot eliminates the need for businesses to manage their own servers, reducing both costs and complexity, while offering a more efficient approach to data acquisition.

OxyCopilot is part of a broader push by Oxylabs to integrate AI and machine learning into their web intelligence solutions. The company is continuously moving toward greater automation in web scraping, applying AI to everything from proxy management to web unblocking. According to Cerniauskas, this innovation-driven approach is core to Oxylabs’ business strategy, and OxyCopilot’s success story, which is currently undergoing patenting, is just one example of their commitment to advancing the field.

The launch of OxyCopilot is a significant development for both Oxylabs and the wider web scraping industry. As businesses increasingly depend on data to drive decisions, tools like OxyCopilot are crucial in making data collection more efficient, cost-effective, and accessible. By simplifying the process, Oxylabs stands to help businesses of all sizes unlock the full potential of web data without the usual technical headaches.

Featured image credit: WrongTog

OpenAI has unveiled a new tool called “Canvas,” designed to enhance the collaboration between users and ChatGPT, especially for writing and coding projects. This new interface allows users to work on complex projects alongside ChatGPT, offering a more refined and interactive experience than the traditional chat format.

Canvas overview

Canvas opens in a separate window and aims to allow users to collaborate directly with ChatGPT on writing and coding tasks. Unlike simple conversation exchanges, Canvas is designed to provide detailed, inline feedback and editing suggestions, helping users improve their work more effectively.

Initially, Canvas is being rolled out to ChatGPT Plus and Team users, with Enterprise and Edu users gaining access next week. OpenAI plans to expand Canvas availability to all Free users once the beta phase concludes.

Enhanced collaboration with ChatGPT

ChatGPT has long been a helpful tool for users working on writing and coding. However, the standard chat interface has limitations when tasks require extensive editing and revision. Canvas seeks to fill that gap by enabling deeper collaboration.

In Canvas, users have more control over their projects. They can highlight sections of text or code that need attention and receive targeted feedback from ChatGPT. The tool acts as a copy editor or code reviewer, providing inline suggestions that help refine the work.

Canvas also introduces a set of shortcuts to expedite common tasks. These include adjusting text length, changing reading levels, debugging code, and restoring previous versions of work.

Users can trigger Canvas manually by adding “use canvas” in their prompt or allow it to open automatically when ChatGPT detects a situation where it can be beneficial.

Features for writing and coding

Canvas offers several shortcuts to streamline both writing and coding tasks. For writers, the options include:

• Suggest edits: Inline feedback to refine writing.
• Adjust length: Make the text shorter or longer.
• Change reading level: Adjust readability based on audience.
• Add final polish: Grammar and clarity checks.
• Add emojis: Enhance content with relevant emojis for added emphasis.

For coding tasks, Canvas introduces tools to make debugging and revisions more transparent:

• Review code: Inline suggestions to improve the quality of the code.
• Add logs: Insert print statements to aid in debugging.
• Add comments: Increase code clarity with helpful comments.
• Fix nugs: Identify and fix problematic code.
• Port to another language: Convert code to various languages like Python, JavaScript, or C++.

The training behind canvas

Canvas is powered by GPT-4o, a model that OpenAI has specifically trained to collaborate with users in more meaningful ways. The model is designed to recognize when a Canvas would be helpful and trigger it accordingly, whether for writing or coding tasks.

OpenAI researchers conducted extensive internal evaluations, focusing on refining how Canvas triggers and performs targeted edits versus full rewrites. Through training and human evaluation, the Canvas model has shown significant improvements in accuracy, comment quality, and editing performance compared to previous versions.

What’s next?

Canvas represents a significant step forward in how AI interacts with users, making it a more practical and powerful tool for complex writing and coding projects. Though still in beta, OpenAI plans to continually enhance Canvas’ capabilities based on user feedback.

Image credits: OpenAI

Choosy Chat is like an AI referee that takes your question, sends it to three heavyweight AI models—ChatGPT, Gemini, and Claude—and then picks the best response for you. Instead of comparing their answers yourself, Choosy Chat does the heavy lifting, deciding which one offers the most accurate, informative, or helpful solution.

What is Choosy Chat?

Choosy Chat is a tool designed to help users get the best answer to their questions by utilizing three AI models—GPT, Claude, and Gemini—all at once.

But Choosy Chat didn’t start off as some grand, well-marketed product. Its origin story is much more personal.

The developer, a Redditor called gaborcselle has “built Choosy Chat to get the best answer between GPT, Claude, Gemini.”

“For complex coding and debugging questions, I’ve found myself copy-pasting between GPT, Gemini, and Claude to try to get the best answer. Then I’d have to manually decide which answer is best. I built a product to solve this for myself: Choosy Chat,” the developer states.

The premise was simple: you ask a question, all three models respond, and then Choosy Chat’s critic model analyzes those answers and picks the best one.

Without any big marketing push, Choosy Chat quietly launched.

Surprisingly, even in its early stages, it started to resonate with others facing the same challenge. Word spread, and soon, a handful of people were already paying for the freemium service. The tool’s appeal is obvious: it saves time, removes the hassle of comparison, and just gets straight to the point by delivering the best AI-generated answer—without the user having to do the legwork.

Best AI chatting tools you MUST try

How to use Choosy.chat?

First, go to the website choosy.chat. You’ll land on a clean, minimalistic homepage designed to quickly get you started.

Step 1: Log in

To use Choosy Chat, you need to log in. You can do this using your Google or GitHub account, whichever you prefer. The login process is smooth and quick, allowing immediate access to the tool.

Step 2: Enter your prompt

Once logged in, you’re greeted by a simple interface where you can type in your question or prompt. This is where the magic happens. The tool takes the input you provide and sends it to three different models—GPT, Claude, and Gemini. You don’t have to choose which one to use; Choosy Chat does that for you.

Step 3: Wait for responses

After submitting your prompt, responses will begin streaming in from the three models. You can watch as GPT, Claude, and Gemini each generate their respective answers. There’s a little suspense here as you wait to see how the models differ in their responses.

Step 4: Choosy critic

Once the models have completed their responses, Choosy Chat’s built-in critic model automatically selects what it deems the best answer. The decision is based on criteria like detail, relevance, and clarity. The critic tries to save you the effort of comparing and choosing between different models’ responses.

Step 5: Click the cat icon for an explanation

This is where the interface adds a fun touch. When you click on the cat icon that appears after the critic has made its choice, it will give you a detailed explanation of why this particular answer was chosen. This feature helps you understand what makes one model’s response stand out over the others.

Example 1: Generative AI and sentiment analysis

For our first test, we asked the question: “How can generative AI be used to conduct a sentiment analysis?”

As soon as we submitted the prompt, the responses from the three models began to stream in. After a few moments, Choosy Chat made its decision. The critic chose GPT-4’s response.

When we clicked on the cat icon to see why GPT-4’s response was selected, the explanation was clear: “GPT provided a comprehensive and detailed explanation of how generative AI could be used for sentiment analysis. It walked through different approaches such as pre-trained language models, data augmentation, and feature extraction. The response wasn’t just a simple answer; it was well-rounded and informative, making it the most helpful and relevant choice for our question.”

Example 2: Comparing iPhone 14 and iPhone 15

For our second trial, we asked the models to: “Compare iPhone 14 and iPhone 15 in tabular format.”

Once again, the models started generating their responses. This time, Choosy Chat chose Gemini Pro as the best response. We clicked on the cat icon to see why.

The critic’s explanation highlighted that “Gemini Pro provided the most comprehensive and detailed comparison between the iPhone 14 and iPhone 15. The response included specific features and improvements, such as the introduction of Dynamic Island, USB-C, and camera upgrades, which were not as clearly highlighted in the other responses. Additionally, Gemini Pro included a ‘Key Takeaways’ section that succinctly summarized the main differences, adding value to the comparison.”

Featured image credit: Choosy.chat

Based on a story we read on TechBriefly, it’s hard to resist the urge to punch walls in anger.

The family of Jennifer Ann Crecente, an 18-year-old murdered nearly two decades ago, recently discovered her photo being used without their consent by an AI chatbot platform. Jennifer’s name and image were found on Character.ai, a service that allows users to interact with AI-generated personalities. The family was never consulted or informed about the use of Jennifer’s identity, leaving them horrified and devastated by this gross misuse.

After the discovery, Jennifer’s uncle took to social media to expose the incident and made a strong statement on his blog, Padandpixel.

This is fucking disgusting: @character_ai is using my murdered niece as the face of a video game AI without her dad's permission. He is very upset right now. I can't imagine what he's going through.

Please help us stop this sort of terrible practice. https://t.co/y3gvAYyHVY

— Brian Crecente (@crecenteb) October 2, 2024

Only after public outcry did the platform remove the bot.

The shock of seeing Jennifer’s face and name used without permission reopened old wounds for the family. Crecente described the initial grief after her death as an “almost out-of-body experience,” adding, “My soul felt ripped from my body. I experienced life in a daze for a while, as if I was sort of detached from my body, from the world.” While the family had worked tirelessly to honor Jennifer’s memory through nonprofit work and education, this unauthorized use of her identity felt like a cruel violation.

“It feels like she’s been stolen from us again,” Crecente wrote, highlighting the devastating impact of AI’s careless use of personal identities. Despite their efforts to have the chatbot removed, the damage was done. Crecente asked a pressing question: “Why is it on us to be resilient? Why do multibillion-dollar companies not bother to create ethical, guiding principles and functioning guardrails to prevent this from ever happening?”

Seriously, why do multibillion-dollar companies not bother to create ethical, guiding principles and functioning guardrails to prevent this from ever happening?

We know there are bad people. People are, by nature, commonly capable of bad actions. But those who hold power—especially those with immense technological influence—also bear the responsibility to set boundaries and create frameworks that block evilness.

The answer lies at the intersection of technological advancement, corporate incentives, and the glaring absence of comprehensive regulations.

When companies like Character.ai possess the capability to replicate human identities—whether living or deceased—they have an ethical obligation to ensure that this power is not misused.

The answer lies in prioritizing profit over people. In the relentless race for innovation, companies choose to move fast and innovate without ensuring the necessary ethical barriers are in place. It’s not that they can’t create these barriers; they simply don’t see immediate financial value in doing so.

This is the harsh reality: profit-driven industries often view morality as an optional expense, particularly in the tech world.

Governments and regulatory bodies have lagged behind AI’s rapid evolution, leaving enormous gaps in the law. Few, if any, robust protections exist for the identities of the deceased.

Speak out now!

Media outlets and advocacy groups must hold tech companies responsible for the unauthorized use of deceased individuals’ likenesses. Public pressure is crucial to ensuring that AI platforms implement ethical guidelines and prevent such violations in the future.

Lawmakers must be urged to enact laws that explicitly protect the identities of the deceased, ensuring their names, images, and legacies cannot be exploited for profit. We need regulations that safeguard the dignity of families long after their loved ones have passed.

We must demand that companies establish and enforce strict ethical guidelines for AI use. This includes consent protocols and a transparent process for reporting violations. They should not wait for families to discover these violations after the fact.

Media must continue to play a crucial role in investigating and exposing incidents where AI platforms exploit individuals without consent. Their relentless coverage will hold these companies accountable and push for change, ensuring that silence does not enable this exploitation.

We cannot stand by and allow such violations to continue unchallenged.

#ResponsibleAI

We extend our deepest condolences to the Crecente family during this heartbreaking time. No one should ever have to face the pain of losing a loved one, let alone endure such a violation of their memory. Jennifer’s life and legacy deserve to be honored, and we share in your sorrow as you confront this unimaginable burden.

Featured image credit: Padandpixel

Nvidia is going beyond chatbots and screen-based tools with spatial AI, making it a reality in the real world. From the heart of this push sits the company’s Omniverse platform, through which AI models can engage with the physical world as much as humans can.

Through what Nvidia calls “digital twins,” these AI systems can simulate real-world spaces like factories, warehouses, and even city streets. Virtual versions of these environments are used to train AI models to do things like autonomous driving, guide visually impaired people, and automate actions in warehouses, all of which are run by Nvidia.

For Nvidia, one of the hottest topics at its GPU Technology Conference is AI.

“We’re talking about chatbots and generating images,” said Rev Lebaredian, vice president of Omniverse and simulation technology at Nvidia. “All of it is very important, but the things we take for granted around us in the physical world are far more important,” he said in an interview.

Digital Twins and real-rorld AI interaction

It’s not just about virtual assistants; spatial AI is about building the physical world AI.

According to a report by CNBC, Lebaredian described how Nvidia’s technology can help create “robot brains” by first simulating the physical world in a computer environment, which then trains the AI for real-world tasks.

If robots like these can be made, they could change everything from manufacturing to logistics to even home automation. Other companies, like Stanford’s World Labs, also work with models interacting with the three-dimensional world around us.

Transforming physical spaces with AI

Nvidia’s future is not limited to the hookup of humanoid robots … it’s the physical transformation of spaces to accommodate intelligent machines. ‘Even the spaces we live in, they could be robots as well … like the building I’m in actually, so the building — the building could be cool, they could change the temperature of the building to economize the energy use, economy of the energy use, based on the fact that I’m here,’ Lebaredian said.

Companies like Nvidia partner Globant, which envision how physical environments could adapt to allow for robots like wider doors and ramps rather than stairs, are based on the CNBC report.

The future of AI in the real world

In the next few years, it is expected to usher in a transition toward spatial AI that could completely transform the way industries work. Lebaredian believes that this technology will be “the most impactful and transformational technology the world has ever seen.” AI is poised to reshape our world in robot-driven factories, smarter cities, and even smarter homes.

Image credits: Emre Çıtak/Ideogram AI

As Google attempts to compete with OpenAI, it’s stepping up its game by focusing on artificial intelligence (AI) that mimics human reasoning. After OpenAI released its new “o1” model in September 2024, Google has accelerated efforts to enhance its AI models, working on software designed to solve complex, multistep tasks in mathematics and programming.

This race has both companies using “chain of thought prompting,” a technique where AI takes additional time to process multiple related prompts before giving an answer, improving accuracy and reasoning abilities.

As proof of that, Google has been testing new tools, like Alpha Proof and Alpha Geometry, AI models to solve problems via mathematical reasoning and geometry. Its highly anticipated Gemini AI assistant is also being upgraded with better reasoning skills, moving it closer toward OpenAI’s o1.

The increasing focus on creating AI that can not just chat but also solve real-world problems with greater precision and match the speed of human thought is gauged by the competition between the two.

Early signs have already alarmed Google DeepMind, particularly since OpenAI’s o1 model, launched via ChatGPT in September, already looks to be beating Google’s DeepMind in AI reasoning capabilities. However, Google has a range of AI offerings, including a faster, more efficient model called 1.5 Flash, and its Alpha series, which solves complex math problems in international competitions.

Google is betting that its deep research and carefully launched products will keep it competitive in the AI race as OpenAI introduces new science that leads the way with advances in reasoning.

Featured image credit: Google DeepMind/Unsplash

Meta has confirmed that any images taken through it can also be used to train its AI models. The company initially dodged the question and then responded to TechCrunch, saying that while photos and videos taken on the Ray-Ban Meta glasses can’t be used for training unless they’re sent to AI, once Meta AI is asked to analyze them, those images’ fall under different policies and can be used for AI training.’

Your data “may be used to improve it”

In an email to TechCrunch, Meta’s policy communications manager Emil Vazquez explained that images and videos shared with Meta AI in the U.S. and Canada “may be used to improve it,” as stated in the company’s privacy policy. That means whenever you ask the AI to analyze your surroundings, you pass data to Meta that it can use to improve its AI models.

The reveal is especially worrying considering the new, easy-to-use AI features rolled out with Ray-Ban Meta glasses. Now, the AI can analyze real-time streams, such as searching through a closet to suggest what to wear, but even those images will also be sent over to Meta to train the AI model.

But as users begin to interact with these smart glasses, it’s unclear that they’re also lending Meta access to personal spaces, loved ones, or sensitive data for its AI development. There is no way around this other than not using Meta’s multimodal AI features. Meta says that interactions with the AI feature can be used to train models, but this isn’t always indicated in the user interface.

As smart glasses’ tails wind into privacy concerns, those concerns echo those surrounding Google Glass, but now with edge AI at their core. Meta, pushing its AI-powered wearables, asks: How far are users willing to go, knowingly or unknowingly, to continue a generation of AI models?

Featured image credit: Meta

According to The Wall Street Journal, OpenAI has recently secured $6.6 billion with a new funding round which significantly increased the company’s valuation to $157 billion. This sharp increase nearly doubles its previous valuation of $86 billion. The deal, however, includes a provision allowing investors to retract funds if OpenAI doesn’t finalize its transition to a fully for-profit entity, moving away from its nonprofit roots.

Key investors backing OpenAI’s growth

Thrive Capital led the investment with $1.25 billion, while Microsoft, a long-time supporter of OpenAI, added just under $1 billion. Other major participants included SoftBank, which contributed a $500 million stake, and Nvidia, which contributed $100 million. The UAE-based firm MGX also joined in, furthering its involvement in the AI world after partnering with Microsoft in an infrastructure initiative last month.

Apple, which had explored investing but ultimately opted out, was absent from the list of investors. The minimum investment required to access OpenAI’s financial records was $250 million, as reported by The Wall Street Journal. OpenAI’s first CFO, Sarah Friar, was pivotal in orchestrating this funding round since joining the company in June.

OpenAI’s valuation now rivals major public companies like Goldman Sachs and Uber. The company faces stiff competition from the likes of Anthropic, Meta, and Google, all racing to develop advanced AI systems. Nevertheless, OpenAI has maintained significant momentum, with ChatGPT amassing around 250 million weekly active users, according to The Wall Street Journal’s sources, and securing 11 million paying subscribers. Furthermore, nearly 1 million businesses now use OpenAI’s services.

Internal struggles emerge as OpenAI transitions to a for-profit model

The company’s rapid growth has fueled internal shifts, moving from research-driven goals toward a more commercial focus. The rise of ChatGPT is generating consumer and enterprise revenue. But this evolution has also raised internal conflicts: leadership changes, and doubts on whether the company is devolving from its mission of making AI available for the benefit of the public good.

The last two years have seen OpenAI shift from being primarily a research company to a highly enthusiastic business organization, especially with ChatGPT. The move towards consumer technology and enterprise services has led to internal friction, particularly surrounding the company’s evolving mission.

Last year, OpenAI faced a significant leadership crisis with its CEO’s firing and subsequent rehiring, as well as discontent from former employees who felt the company’s increasing focus on profitability compromised its original aim of advancing AI for the public good.

Who is Durk Kingma, Anthropic’s latest transfer from OpenAI?

The company’s shift to becoming fully for-profit is a response to market demand and a crucial move in securing additional investment. There is actually a lot more people willing to fund OpenAI to get to AGI, making the shift to a for profit model now pretty much crucial for the organization.

This shift is indeed understood against the background of the need to maximize the value of the AI market which is expected to expand exponentially while at the same time sparking controversies around the entrepreneurially sustainable way of innovating that embraces both the benefits of algorithmic governance and the responsibility in handling the consequences that come with it.

Featured image credit: Kerem Gülen/Midjourney

LensGO AI is rapidly gaining attention in the digital creation due to its suite of highly advanced and intuitive tools. While there are plenty of AI-driven platforms available today that offer image generation, video manipulation, and style transfer capabilities, LensGO AI distinguishes itself with a unique combination of features that seamlessly blend creativity with cutting-edge technology.

In recent years, tools like RunwayML and Artbreeder have popularized the idea of merging AI with art, making it possible for individuals to produce professional-grade content without needing advanced technical skills.

However, LensGO AI stands out with its specialized functionality, user-friendly interface, and powerful performance capabilities, making it a preferred option for creators of all skill levels.

What is LensGo AI?

LensGO AI is an innovative platform designed to transform the way creators approach digital projects. It offers an extensive suite of tools that allow users to generate high-quality visuals from text or image prompts, edit videos and images, and even train custom styles or characters using personal data.

LensGO AI’s variety of functionalities, from simple restyling to creating fully realized animations, places it on the cutting edge of AI-enhanced creativity. The platform uses advanced machine learning models to ensure that each transformation or generation is both detailed and coherent, providing users with professional-quality output in just a few clicks.

What differentiates LensGO AI from similar platforms is its ability to not only produce still images but also generate and modify videos using the same AI-driven principles.

LensGO AI features that make it a standout tool

LensGO AI excels with its powerful suite of tools designed for various forms of creative expression. From transforming existing media to generating content from scratch, LensGO AI provides an all-in-one creative platform that goes beyond traditional image or video editing software.

Style transfer

One of the flagship features of LensGO AI is its style transfer capabilities. Whether it’s changing the style of a video or transforming an image, this tool allows users to overlay a specific artistic style on their content.

The platform provides high precision when transferring styles, ensuring that the output remains visually consistent and appealing.

Image and video generation

Another standout feature of LensGO AI is its ability to generate both images and videos from text prompts. With just a short descriptive input, the AI generates visuals that match the user’s specifications.

Users can also input images, and the AI will convert them into a video, saving hours of manual work.

Custom training

One of the most advanced features offered by LensGO AI is the ability to train custom models using personal data.

Users can upload images or characters and train the AI to apply unique styles, allowing for more personalized outputs.

Let’s give it a shot!

To fully experience the potential of LensGO AI, let’s try out one of its key features: generating an image from a text prompt.

For example, entering a prompt like “a serene beach at sunset with pastel-colored skies” will allow LensGO AI to create an image that captures the essence of that description.

As you can see the result is a visually stunning, high-quality image that feels authentic and natural.

Another example would be using the style transfer feature on an existing video – simply upload a video and select a style like “impressionist painting”.

Sadly, the wait time was 9 minutes to generate an image as a free user, and the estimated wait time was around 11 hours to style transfer a video we did not wait for that long…

Yet, we are using the free version of the tool so it wouldn’t be fair to judge the tool for that.

Is LensGO AI free to use?

LensGO AI offers a range of pricing options, but it is not entirely free. The platform provides a limited trial version, where users can explore the basic features with 100 free points upon creating a new account.

With these 100 free points, users can:

• Generate 50 unique images from text prompts
• Generate 6 long-form and 12 short-form videos from text prompts
• Restyle 50 photos
• Style transfer 6 videos (first five seconds)

Once these points are depleted, users will need to wait 24 hours to be awarded 50 additional points, purchase additional points, or subscribe to one of the paid plans to continue using the more advanced features.

How about the LensGO AI pricing plans?

LensGO AI provides multiple pricing tiers that cater to different types of users, from beginners to professionals. Below are the pricing details and what each plan includes:

Free Plan ($0/month)

• Points: 50 Points/Day
• 5s Video Transfer
• No model training
• Watermark on generated content
• No private generation
• No priority generations
• No concurrent generations
• 30s Video transfer duration
• Standard transfer rate

This is the most basic plan, suitable for casual users or those testing the platform. Users are limited in features like video transfer length and model training, and the output includes watermarks.

Standard Plan ($9/month)

• Points: 1,000 Points/Month
• 25% off for buying points
• 30s Video transfer duration
• 3 model training sessions/month
• No watermark
• Private generation
• No concurrent generations

The Standard Plan offers a more comprehensive experience compared to the free version. With no watermarks and private generation options, it is an affordable choice for users who need a moderate amount of usage.

Pro Plan ($29/month)

• Points: 4,000 Points/Month
• 45% off for buying points
• 60s Video transfer duration
• 15 model training sessions/month
• No watermark
• Private generation
• Priority generation
• 3 concurrent generations

This plan is designed for more intensive users, such as content creators who need higher transfer durations and faster processing. The Pro Plan also introduces the ability to train more custom models and includes priority generation.

Mega Plan ($60/month)

• Points: 10,000 Points/Month
• 75% off for buying points
• 60s Video transfer duration
• 50 model training sessions/month
• No watermark
• Private generation
• Priority generation
• 10 concurrent generations
• All Lab membership

The Mega Plan is the most advanced tier, catering to professional creators and businesses. It offers maximum points, the highest number of model training sessions, and the ability to generate content concurrently, making it ideal for users with large-scale projects.

The all-lab membership unlocks additional features, providing the most comprehensive set of tools for users.

And don’t worry, LensGO AI has been around for more than one year.

One Year Anniversary
Our journey starts from 2023/10/1 pic.twitter.com/95jzCIHgeH

— LensGo.AI (@lensgo_ai) October 1, 2024

Do not miss out on LensGo AI Style Fast competition

LensGO AI also encourages its community of users to engage with the platform through the Style Fast competition. This event invites creators to use the style transfer feature on provided 3D animation videos. Participants have a chance to showcase their creativity while competing for awards such as points bonuses and other prizes.

Timeline for the competition:

1. September 23rd, 2023 (EST): Competition Kick-off
2. October 4th, 2023 (EST): Submission Deadline
3. October 6th to 8th, 2023 (EST): Community Voting Period
4. October 10th, 2023 (EST): Finalist Announcement

Competition requirements:

1. General requirements:
   • All participants must use the provided 3D animation videos and apply LensGO AI’s Style Transfer to stylize the characters.
   • Participants are allowed to enter multiple categories, but only one submission per category is permitted.
   • You can stylize the background provided or swap it with a different one.
   • The submission video must have a duration of 6 seconds, matching the provided videos.
   • Usage of LensGO AI is mandatory, but additional tools may be used if necessary.
   • Content that violates LensGO AI’s terms, including NSFW, political, or racist material, will be disqualified.
2. Theme relevance:
   • Entries must adhere to the specified category themes, and the characters or styles should be relevant to the theme for optimal results.
   • Participants are encouraged to submit highly stylized visuals that align with the category.
3. Technical constraints:
   • Aspect ratio: 9:16
   • Resolution: 1080×1920
   • File format: MP4 or WebM
   • Maximum file size: 100MB

Awards:

1. Finalist awards for each category:
   • The top 3 finalists will receive special recognition and 1 month of free Pro membership.
2. Bonus for all participants:
   • Participants will earn 100 bonus points upon submission and an additional 100 points if they reach the top 8 finalists.
   • 200 points registration bonus upon signing up for the competition.
   • The top 3 winners will be featured in LensGO AI ads and receive additional prizes.

The competition adds an exciting dimension to the LensGO AI experience, allowing users to test their skills and gain recognition for their work.

AI tools like LensGO AI are changing how content is created and consumed. These platforms allow users to generate images, videos, and custom models with remarkable ease, bringing creative visions to life with just a few clicks. As AI-driven tools become more accessible and powerful, they are enabling both beginners and professionals to explore new dimensions of creativity without the need for specialized technical skills.

With flexible pricing options and a wide array of features, AI tools are democratizing the creative process, making it easier than ever to produce high-quality, professional-grade content.

Featured image credit: LensGo AI

Nvidia has officially entered the ring with a powerful open-source AI model, NVLM 1.0, challenging industry giants like OpenAI and Google.

The company’s new NVLM 1.0 family of large multimodal language models promises to deliver cutting-edge capabilities across both visual and text-based tasks.

Leading the pack is the 72 billion parameter NVLM-D-72B, a model designed to perform at the highest level, making a massive impact on vision-language tasks while improving traditional text-based outputs.

What makes NVLM 1.0 special?

The release of NVLM 1.0 marks a notable shift in the AI ecosystem, which proprietary models have largely dominated. Nvidia’s decision to make these model weights publicly available—and eventually release the training code—offers researchers and developers access to tools that rival the likes of GPT-4. This is a rare move in an industry where most advanced models remain under lock and key, tightly controlled by tech giants.

As Nvidia stated in their research paper, “NVLM 1.0 achieves state-of-the-art results on vision-language tasks, rivaling both proprietary and open-access models.”

What this means for developers is a new frontier in AI accessibility, much like what Meta did with Llama 3.2, giving smaller labs and independent researchers a chance to work with top-tier AI tools without having to navigate the often prohibitive costs or corporate restrictions.

The open-source release of NVLM 1.0 has generated excitement across the AI research community. One prominent researcher highlighted the significance of the model on social media, stating:

Wow nvidia just published a 72B model with is ~on par with llama 3.1 405B in math and coding evals and also has vision 🤯 pic.twitter.com/c46DeXql7s

— Phil (@phill__1) October 1, 2024

The multimodal powerhouse NVLM-D-72B

At the center of this open-source revolution is the NVLM-D-72B model, which stands out for its ability to handle both visual and textual inputs seamlessly. This multimodal capacity means the model can interpret images, analyze complex visuals, and even solve mathematical problems step-by-step—all within a single framework.

Where many multimodal models struggle with retaining performance in text-only tasks after integrating visual learning, NVLM-D-72B bucks the trend.

According to Nvidia, the model improved its text accuracy by an average of 4.3 points across several key benchmarks after multimodal training. This kind of adaptability positions NVLM-D-72B as a unique tool in a market that typically forces users to choose between models optimized for either visual or textual tasks, but not both.

Opening new doors, raising new questions

The NVLM project is not just about open access. It also introduces innovative architectural designs that blend different multimodal processing techniques, pushing the boundaries of what’s possible in AI. Nvidia’s hybrid approach could very well inspire a new direction in AI research and development, as teams across the world get their hands on these tools.

However, as with any leap in technology, there are risks involved. Making such powerful AI models widely available raises concerns about potential misuse and the ethical challenges that come with it. The AI community will need to balance the drive for innovation with the need to develop responsible frameworks for using these models.

A defining moment in AI

Nvidia’s decision to open-source NVLM 1.0 could set off a wave of change throughout the tech world. Other industry leaders might feel pressure to follow suit, potentially shifting the entire landscape of AI development. If state-of-the-art models become freely accessible, it could force companies to rethink how they generate value and maintain a competitive edge in the market.

The long-term impact of Nvidia’s move is still unknown. In the coming months and years, we could see an era of unprecedented collaboration in AI, where researchers from all corners of the globe work together on shared platforms. Or, this development could prompt a deeper examination of the consequences of releasing advanced technology without strict controls in place.

One thing is clear: Nvidia’s release of NVLM 1.0 is a game-changing move that signals a shift in the balance of power within the AI industry. By making such a high-caliber model open-source, Nvidia is challenging the status quo, setting off what could be a new chapter in AI development.

The question now isn’t if the AI models and market will change—it’s how dramatically, and who will be able to keep up.

Featured image credit: Emre Çıtak/Ideogram AI

In a new update to their Copilot AI collection, Microsoft is launching a range of improvements expected to make interacting with devices easier. Among them, Copilot Vision, Think Deeper, Copilot Voice are further designed to offer user-focused, timely and context-aware experience on Windows, iOS, Android, and Web.

All these features are in one way or the other belonging to the broader approach that Microsoft is taking to have Copilot in the middle of people’s daily activities alloting AI solutions merged with the protection of the users’ privacy.

Copilot Vision: A fresh view as well as approach to the use of the World Wide Web

Among new features, there is Copilot Vision, which lets users ask all sorts of questions regarding the content on their screen – be it the text or images on a web page. For example, it is possible to ask for a particular recipe that is depicted in a photo or for furniture advice while using the site. Copilot Vision then operates in manners where it scans through the contents that the users are consuming and responds to the user in real-time, based on the content.

Accessible via Microsoft Edge by typing “@copilot” in the address bar, Copilot Vision is available as part of Copilot Labs, an exclusive opt-in program that offers experimental features to a select group of users. While this feature is not entirely groundbreaking—Google’s Android platform offers similar capabilities—Microsoft claims that Copilot Vision takes things a step further by ensuring that no data is stored or used to train AI models.

Microsoft’s complicated Copilots explained in detail

Microsoft has been keen to emphasize its commitment to privacy with this tool. In a recent blog post, the company highlighted that Copilot Vision deletes all processed data immediately after a conversation, with no audio, images, or text being stored. This is likely in response to growing concerns about data misuse in AI systems, a point Microsoft has been careful to address following legal scrutiny over past incidents involving its AI products.

However, Copilot Vision comes with certain limitations. Currently, the tool cannot analyze content from paywalled or sensitive websites, restricting its functionality to a pre-approved list of web properties. This restriction seems designed to avoid legal challenges similar to the one Microsoft faces with The New York Times, which accused the company of allowing its AI tools to bypass paywalls and access premium content.

Think Deeper: Tackling complex problems with AI reasoning

Another significant addition to the Copilot suite is Think Deeper, a feature that leverages advanced reasoning models to help users navigate complex tasks. Whether it’s solving intricate math problems or comparing multiple home renovation options, Think Deeper is designed to break down and analyze complicated scenarios.

Microsoft has described Think Deeper as a tool that provides step-by-step guidance, making it useful for both everyday challenges and more specialized problems. The company noted that the feature is powered by “reasoning models” that take additional time to respond, ensuring more thorough and accurate answers. Though Microsoft has remained tight-lipped about the specific models used, it is likely a customized version of OpenAI’s o1 model, which Microsoft has fine-tuned for this purpose.

Currently, Think Deeper is available only to a select group of Copilot Labs users in countries like the U.S., U.K., Canada, Australia, and New Zealand, with plans for broader availability in the future.

Copilot Voice: Conversational AI at your command

The last in the line of Microsoft’s AI features is Copilot Voice, the ability to address Copilot and issue commands. Launched in the USA, UK, Canada, Australia, and New Zealand first, Copilot Voice extends the ethos of Copilot with answers verbalized.

Like OpenAI’s voice in ChatGPT, Copilot Voice is capable of sensing voice tone, and respond accordingly. Some features of the application make it even more engaging: for example, users can pause Copilot during the conversation or interrupt its answers in the middle of the sentence. The feature of the application is the four synthetic voice which are very natural and realistic to the touch.

However, there are limits on how much users can engage with Copilot Voice. Microsoft has implemented a time-based usage limit, with Copilot Pro subscribers gaining access to additional minutes depending on overall demand.

Personalization: Tailoring Copilot to your preferences

In addition to the major feature updates, Microsoft is also introducing personalization options to Copilot. These settings allow the AI to tailor its suggestions based on the user’s past interactions, drawing insights from activity across other Microsoft apps and services. According to Microsoft, this will help Copilot offer more relevant recommendations and faster access to useful features.

However, the rollout of personalization is being delayed in certain regions. For now this feature is not going to be available in EU or UK anymore as Microsoft continues to weigh options on how to adapt to the tight data protection laws in these regions. It follows the strategy that leading IT enterprises like Apple and Meta as well as other firms have executed when rolling out AI solutions in the European Union.

Microsoft has expressed that the firm is looking forward to providing personalization incorporated into the users in these areas and at the same time satisfying the legal provisions such as the Digital Services Act, which regulates the use of AI and data protection in EU. In the past, the company has received certain regulatory issues, particularly in the assortment EEA’s recent admonition to Microsoft that it might be penalized for not giving enough details about its generative AI goods.Privacy and responsibility at the core

Throughout the rollout of these new features, Microsoft has maintained that privacy and user feedback are at the heart of its development process. The company has stressed that none of the data processed by Copilot Vision, Think Deeper, or Copilot Voice is used for AI training or stored beyond the immediate session.

Featured image credit: Microsoft

Google’s Notebook LM is a powerful, versatile tool that can enhance both your professional and personal life, but its full potential is often overlooked.

With its ability to analyze, summarize, and organize large volumes of information, Notebook LM opens up new possibilities for productivity and creativity.

How to get the most out of Google Notebook LM?

Google’s Notebook LM is more than a note-taking tool—it’s an AI-powered assistant for managing knowledge and making sense of vast information. You can use it for professional tasks, health tracking, or hobby exploration, the tool’s creative potential is practically endless.

Here are five creative ways to use this tool to your advantage.

1. Stay ahead in niche industries

In fast-moving industries, staying updated with the latest information can be overwhelming. With Notebook LM, you can gather articles, videos, and other sources and have the AI summarize key takeaways.

Whether it’s tech news or niche industry updates, the tool helps you absorb and manage information without drowning in content overload.

How to do it:

• Step 1: Start by identifying key sources of information in your niche—whether it’s industry blogs, YouTube channels, or podcasts. Use tools like Descript to transcribe videos or podcasts into text.
• Step 2: Create a new notebook in Notebook LM specifically for your industry news.
• Step 3: Upload the transcripts, articles, or reports into the notebook.
• Step 4: Use the AI features within Notebook LM to generate summaries, key insights, or even audio overviews.

You can also use its podcast feature to create a personalized audio recap, making it easy to stay informed while commuting or exercising.

2. Create personalized knowledge hubs

Imagine having a notebook for every major project or client, with all relevant documents, transcripts, and meeting notes stored in one place. Notebook LM can act as a dynamic knowledge hub, giving you instant recall of past conversations and key insights.

Integrating it with Zoom for automatic transcription of meetings means that every client discussion is cataloged, making it easy to revisit action items and prepare for future meetings with just a few queries.

How to do it:

• Step 1: For each project or client, start by creating a dedicated notebook in Notebook LM. Label it clearly, such as “Client XYZ” or “Project Alpha.”
• Step 2: Upload all relevant materials: meeting transcripts (which you can generate through Zoom’s transcription service), emails, reports, and contracts.
• Step 3: Use Notebook LM’s search function to instantly recall previous conversations or documents. You can ask questions like, “What were the key action points from my last meeting?” or “What were the client’s main concerns during our previous call?”

For recurring meetings, use the notebook to track ongoing discussions. Before each new meeting, simply ask Notebook LM to summarize the last session’s outcomes so you’re always up to speed.

3. Gain knowledge-powered insights

If you’re grappling with important business or personal decisions, load up Notebook LM with relevant resources like strategy books or industry reports. The tool allows you to draw from these materials, helping you analyze options, weigh outcomes, and make well-informed decisions.

How to do it:

• Step 1: When facing complex decisions, gather multiple resources like articles, books, and case studies into a single notebook in Notebook LM. For instance, if you’re making a business decision, you could upload materials from books like Good to Great or The Lean Startup.
• Step 2: Use Notebook LM’s ability to analyze these texts. Ask it to highlight key strategies or advice relevant to your current decision-making context, such as “What’s the best approach for a product launch according to Crossing the Chasm?”

If you have a list of pros and cons for your decision, upload that list and ask Notebook LM to suggest insights based on the materials in your notebook, allowing the AI to guide your choice with broader perspectives.

4. Manage your health and fitness plans

Notebook LM can also be a personal health assistant. Upload information from your favorite fitness or health books and resources, and use the tool to analyze and compare advice on diet, workouts, and wellness routines.

This way, you can generate personalized fitness plans or stay consistent with a routine, asking the AI to check relevant materials whenever you need expert-backed suggestions.

How to do it:

• Step 1: Gather your health and fitness resources, such as books, articles, and guides. For example, you might upload materials from The Walls Protocol or your favorite workout plan.
• Step 2: Create a health and fitness notebook in Notebook LM, and upload these resources.
• Step 3: Use the tool to ask for personalized insights like, “What are the key dietary recommendations in The Walls Protocol?” or “What’s a good weekly workout schedule based on my uploaded materials?”

You can also ask for a summary of specific fitness advice or cross-reference different fitness programs to tailor a workout plan that best suits your needs.

5. Turn complex research into digestible insights

Whether for market research or hobby-related interests, Notebook LM excels at synthesizing large quantities of information. If you’re researching competitors, you can load in reports, articles, and data to uncover key market trends.

For hobbies, it works equally well: load up your hiking guides or creative writing prompts and ask it for recommendations. It’s perfect for preparing quick, curated suggestions from an extensive source library.

How to do it:

• Step 1: Whether it’s market research, creative writing, or any hobby-related topic, start by collecting your materials—reports, studies, books, and articles—and uploading them into a new notebook on Notebook LM.
• Step 2: Use Notebook LM to generate summaries, comparisons, or key insights. For example, if you’re researching market trends, you could ask, “What are the top trends in my industry based on these reports?”
• Step 3: For hobbies like creative writing or outdoor activities, upload guides and books (e.g., hiking guides for Maine) and ask Notebook LM for new recommendations or insights based on your interests. You could ask it for ideas for your next hiking trip or get writing prompts for creative inspiration.
• Step 4: For competitive research, upload all competitor profiles and reports, then use Notebook LM to analyze patterns and suggest marketing strategies or customer insights.

How much does a NotebookLM cost?

As of now, NotebookLM is still in the early testing phase and is offered for free.

Google hasn’t announced any specific pricing plans for when it exits the beta stage.

That’s basically what Google’s Notebook LM is. It’s like having a personal knowledge guru at your fingertips. Who hasn’t felt overwhelmed by the sheer amount of information out there?

As we showcased in these five use cases above, Notebook LM can help you sift through the noise and find the stuff that really matters. Whether you’re a busy professional, a curious student, or just someone who loves to learn, this tool can be a game-changer.

So, what are you waiting for? Give it a try and see how it can make your life easier (and maybe even a little more fun).

Featured image credit: Google

The United Arab Emirates (UAE) has found itself at the center of global tech competition, particularly as it balances relations with both the US and China.

Despite pressure from Washington to limit cooperation with Chinese firms, especially Huawei, which helped install the UAE’s 5G infrastructure in 2019, the UAE is pursuing a strategic “tech hedging” strategy to diversify its options in artificial intelligence (AI) and other emerging technologies.

Huawei and the 5G controversy

Since 2019, Huawei has been a key player in building the UAE’s 5G network, cementing the tech giant’s presence in the Middle East. However, this relationship has drawn scrutiny from the United States, which has raised concerns about potential national security risks posed by Chinese telecom infrastructure.

The tensions escalated when the US alleged that China was constructing an intelligence facility at Abu Dhabi’s Port Khalifa, operated by Cosco Shipping Ports, a Chinese firm. The UAE, after investigating these claims, rejected them as unfounded.

These geopolitical frictions contributed to the UAE’s decision to withdraw from a US$23 billion arms deal, which would have included the purchase of advanced F-35 stealth jets and MQ-9 Reaper drones from the US.

This move signaled a broader intention by the UAE to assert its independence in technology and defense policies, rather than fully aligning with either superpower.

Building AI data centers across Asia

While navigating its relationship with China and the US, the UAE is positioning itself as a future tech powerhouse. In a strategic move to expand its influence in AI, Abu Dhabi-based tech conglomerate G42 announced plans to establish AI data centers in Asia.

These centers, which will be built in countries like India, Indonesia, Malaysia, and the Philippines, are set to play a pivotal role in the UAE’s ambition to lead the Global South in technological advancements.

On September 18, G42 committed to building AI data centers in India, with a planned power generation capacity of up to 2GW—doubling the country’s current capacity. These data centers will house powerful supercomputers, further advancing AI infrastructure in the region.

The role of global partnerships

To support its growing AI ambitions, the UAE is forming key international partnerships. G42’s involvement in the Global AI Infrastructure Investment Partnership, a consortium that includes major US corporations like Microsoft, BlackRock, and Global Infrastructure Partners, exemplifies this strategy.

The consortium aims to invest up to US$100 billion in AI data centers and energy infrastructure, leveraging Nvidia’s expertise in AI chip design. In the short term, the partnership is expected to raise US$30 billion in private equity, accelerating AI developments across the Middle East, Africa, and Central Asia.

Despite this growing collaboration with US firms, the UAE has maintained a careful distance from fully joining the US-led chips and AI coalition, which includes nations like Japan and South Korea. Analysts suggest that the UAE’s leadership is not keen on being locked into any one geopolitical camp.

UAE’s tech hedging

According to political analyst Ahmed Aboudouh, the UAE’s AI strategy can be seen as a “tech hedging” approach. This means the UAE is actively diversifying its technology partnerships to avoid overreliance on either the US or China. Aboudouh describes the UAE’s goal as becoming the “Taiwan of the Global South“—a tech hub known for innovation and independence.

The UAE is charting its own path, developing industrial and technological sectors while ensuring its long-term strategic interests are protected.

While the recent partnership with the US represents a significant step in AI cooperation, the UAE is unlikely to completely sever its ties with China, particularly in areas of neutral interest like renewable energy and biotech.

US and China’s role in the Middle East

The evolving tech landscape in the Middle East reflects the broader global competition between the US and China. As Robert Mogielnicki, a senior scholar at the Arab Gulf States Institute, notes, both countries are vying for influence in the region, offering different types of partnerships. While the US contributes technological expertise and robust regulatory frameworks, China brings its own economic and infrastructure investments to the table.

In this complex web of alliances, the UAE’s “tech hedging” strategy appears to be a calculated effort to maximize its opportunities without being overly dependent on one global power. This allows Abu Dhabi to continue developing cutting-edge AI technologies and infrastructure while navigating the geopolitical pressures of the US-China rivalry.

As the UAE forges ahead with its ambitious AI agenda, it is skillfully managing its relationships with both the US and China. By strategically partnering with US firms while maintaining selective cooperation with Chinese companies, the UAE is positioning itself as a global tech leader. With plans to establish AI data centers across Asia and strengthen its AI infrastructure, the UAE’s vision of becoming the “Taiwan of the Global South” is steadily coming into focus.

Featured image credit: Emre Çıtak/Ideogram AI

The Raspberry Pi Foundation has announced a new AI Camera module for $70 in collaboration with Sony. Being the newest addition to the Raspberry Pi platform, this new AI Camera is designed to help developers in AI image processing tasks with ease. Built-in with Sony’s IMX500 image sensor and Raspberry funded’s RP2040 microcontroller, this new AI camera performs all the processing on board and does not even require GPUs

Resolution of the camera is 12.3 MP, with the ability to record at a rate of 10 frame per second in 4056 x 3040 resolution or 40 frame per second in 2028 x 1520 resolution. The AI Camera is fairly similar in size to the Camera Module 3 at a size of 25 x 24 x 11.9mm.

As much as the Raspberry Pi AI Camera is designed for the hobbyist, it serves the overall tactile and hardware geek beyond the do-it-yourselfer. The organization has noted that as popular industries using Raspberry Pi, noted that already 72% of its sales were in organizations that were incorporating Raspberry Pi products into their operations. With the AI Camera, this is set to rise with the application of smart.

Raspberry Pi has given its word that the AI Camera will remain in production through to, at the very minimum, January 2028 to provide adequate continuity for the developers and businesses that rely on it. This follows suitable supply line issues that were hugely impacted by the pandemic and have since been mainly addressed when it comes to Raspberry’s products, thus enabling the organization to consistently offer dependable market offerings to its increasingly expanding industrial consumers.

Raspberry Pi AI Camera use cases

For instance, when connected to an artificial intelligence algorithm, you can convert this camera into a smart doorbell. The AI Camera is able to detect whether a person is standing in the doorway or objects such as packages. This would allow you to set up alarms that are smarter than simple motion detection, or as some of you mentioned, an alarm system that was sophisticated enough could just turn into a sentiment analysis engine. Instead of receiving notifications Any time a branch moves, you only get notified During occurrences such as a delivery.

If you prefer do-it-yourself home security, the camera can assist you to keep a watchful eye on certain areas of activity and because the image processing is done on camera, it does not overwhelm the rest of Raspberry Pi system. Therefore, you could also run other smart home features at the same time such as operating on lights or managing your sensors without much of a problem.

Raspberry Robin malware is the danger lurking inside your USB

For industrial or business projects, the camera can be used in quality control systems. Suppose you are overseeing a small production line and could place the camera in such a way that it checks if the products are assembled correctly and warn if something is off. Because the current camera is compact and easily collapsible onto existing Raspberry Pi systems, this solution is scalable to points in the production line.

Another feasible use case can be implemented in smart city initiatives, for example, in the case of traffic analysis with the help of AI Camera. It could determine vacant parking space zones or study traffic patterns at some junctions. Since the design is inherently optimized for performance and modularity, it is possible to introduce the system with one camera and extend it to multiple cameras in different areas, such as a city block, without having to redesign most of the system.

Raspberry Pi AI Kit vs. the AI Camera

You should choose between the Raspberry Pi AI Kit and the AI Camera based on your needs. The AI Kit, in general, unleashes more performance with 13 trillion operations per second but is compatible only with Raspberry Pi 5, and you will need to incorporate a camera module. Modern AI cameras are smaller, cheap, and compatible with all ranges of Raspberry Pi, which is why they can be used in a wide array of endeavors. As for image processing and computational tasks linked to a neural network, the AI Camera, because of the use of the Sony IMX500, performs these directly on the vehicle.

They can support the typical deployment of artificial intelligence models that we use today, like TensorFlow or PyTorch, and can be expanded through Sony’s AITRIOS system. This camera fits seamlessly well with the Raspberry Pi-supported libcamera pipeline, so even if you’re processing video with AI, you can always sync image processing with model results to handle tasks like object detection and pose estimation in real-time. As with most Raspberry Pi devices, users have direct access to the model zoo as well as Sony’s developer resources, which lets one deploy models directly or fine-tune others for specific use cases; the AI Camera is perfect for those wanting to experiment (and have fun) as well as companies with real-world problems to solve.

Raspberry Pi AI Camera: Full specifications

Featured image credit: Raspberry Pi

Do you love playing Dungeons & Dragons (DnD) but don’t have time to create your characters? We’ve got the solution for you. You can now use Gencraft AI to design your DnD characters quickly and creatively. Character creation is one of the most fun parts of playing DnD.

You get to use your imagination to craft a unique personality and appearance. But sometimes, these ideas stay in your head or are reflected in a figure. What if I told you there’s a way to visually bring your imagination to life? With Gencraft AI, you can design anything from a fantastic DnD character to a cyberpunk hero just as you imagine it.

How to use Gencraft AI

Let’s walk through how to use Gencraft AI to bring your character ideas to life.

• Go to the official Gencraft website.
• Sign up or log in.
• Enter your dream DnD character into the prompt.
  • If you need help creating a detailed prompt, you can even use ChatGPT.

• Select a style from the “Select Style” option.
  • The default results are already pretty impressive.
• Choose a model that best fits your DnD character’s design.
• In the “Advanced Settings,” adjust unwanted features and scale settings.
• Click “Generate” and watch your DnD character come to life!

Now that you know how to use Gencraft AI, you’ve designed your first DnD character.

Take it a step further with Gencraft premium features

If you’re a premium user, why stop there? Let’s create a background for your character and combine the two images for a truly immersive design. To do this, repeat the steps above, but imagine the background you want for your character this time.

After creating the background, follow these steps:

1. Click on Gencraft AI’s “Remix” feature.
2. Select the character you’ve already made and add the character prompt.
3. Now, enter the background prompt:

Now, you have the perfect background for your character. If you want to take it further, try the Animate feature to make your character move! Finally, don’t forget to check out the “Explore” section on Gencraft AI for inspiration from other users’ creations. If you’re wondering about Gencraft’s plans, here’s what they offer:

• Free plan – Generate up to 2 images per day for free without an account.
• Pro plan – $9.99 per month. Generate unlimited images and videos, with additional features like upscaling and more styles.
• Business plan – Custom pricing based on usage, designed for teams and commercial use.

With a Pro plan, you can take full advantage of Gencraft’s tools to bring your characters to life in ways you never imagined.

If you are looking for different AI tools, you can also look at these:

• CopyCopter.ai can be your self-service short video tool
• AI tools are great for personalized expressions of love on this special day
• GenAI firm Runway partners with Hollywood giant Lionsgate

Image credits: Furkan Demirkaya/Gencraft

Artificial Intelligence (AI) and cryptocurrency are the most transformative technologies shaping the modern world. Both technologies have individual significance—AI is reshaping industries with machine learning, data analysis, and automation, while cryptocurrency disrupts traditional financial systems with decentralized digital currencies like Bitcoin and Ethereum.

When combined, AI and crypto offer revolutionary potential to streamline financial systems, enhance security, and create new ways to manage and use digital assets. A key illustration of this technology-driven evolution is the rising prominence of Dogecoin. The price trends and performance of Dogecoin Price showcase the evolving synergy between cryptocurrency and AI as they reshape the future of finance and technology.

In this article, we explore how AI and cryptocurrency intersect and their likely impact on the future of finance and technology.

The basics: AI and cryptocurrency

Artificial Intelligence refers to machines or software systems capable of performing tasks that typically require human intelligence, such as decision-making, problem-solving, and learning from data. AI can analyze massive datasets, optimize processes, and predict trends, making it an invaluable tool across healthcare and finance industries.

Cryptocurrency is a digital currency that uses cryptographic technology to secure transactions, control the creation of new units, and verify transfers. Unlike traditional currencies governments and central banks issue, cryptocurrencies operate on decentralized networks—most commonly, blockchain technology. Popular cryptocurrencies like Bitcoin, Ethereum, and Litecoin enable peer-to-peer transactions without intermediaries like banks, making them highly secure and potentially more cost-effective.

The synergy: AI enhancing cryptocurrency

When AI is applied to the cryptocurrency space, the opportunities are vast. One of the most notable ways AI benefits cryptocurrency is through data analysis and trading. Cryptocurrency markets are highly volatile, fluctuating prices based on market sentiment, regulations, and technological advancements. AI can analyze this data, predict market trends, and execute trades more efficiently than any human could.

AI in crypto trading

AI-driven crypto trading bots are increasingly popular among individual traders and financial institutions. These bots use AI algorithms to analyze historical data, monitor current market conditions, and predict price movements. This allows them to execute trades in milliseconds, taking advantage of market opportunities before human traders even react.

Machine learning, a subset of AI, also allows these bots to improve over time. As they analyze more data, they refine their algorithms, becoming more accurate in their predictions. This can be a game-changer in a market like cryptocurrency, where small price changes can lead to significant gains or losses.

AI and fraud detection

Another critical intersection of AI and crypto is fraud detection and security. Since cryptocurrencies are digital and decentralized, they are susceptible to hacking, theft, and fraud. AI can play a crucial role in detecting and preventing these issues. By analyzing large datasets of transactions, AI can identify patterns and anomalies that might indicate fraudulent activities.

For example, AI algorithms can spot unusual transaction volumes or the sudden movement of large amounts of cryptocurrency, which could signal a hack or security breach. In this way, AI adds a layer of security, protecting users and making the crypto space safer.

AI and smart contracts

Smart contracts are self-executing contracts with the terms of the agreement written into code. They are typically run on blockchain platforms like Ethereum. AI can make innovative agreements even more potent by automating and optimizing contract execution. For example, AI algorithms can ensure all contract conditions are met before automatically triggering the next step in the contract’s lifecycle.

AI also has the potential to introduce more flexibility and intelligence into smart contracts. Current intelligent contracts execute based on predefined rules, but AI could enable them to adjust based on changing circumstances or external data inputs. This would make smart contracts more adaptive and helpful in complex, real-world scenarios.

Challenges and ethical considerations

While AI and cryptocurrency offer exciting possibilities, they also have significant challenges. The use of AI in trading, for instance, can create ethical concerns about market manipulation. AI-powered trading bots can potentially dominate the market, creating unfair advantages for those with access to advanced algorithms. This could lead to a concentration of wealth and power in a few hands, undermining cryptocurrency’s decentralized ethos.

Moreover, using AI in decision-making processes, especially finance, can lead to biases. Since AI algorithms are trained on historical data, they may perpetuate existing biases in the financial system. For example, AI algorithms could make loan approval decisions based on biased historical data, reinforcing discrimination.

Additionally, the environmental impact of both AI and cryptocurrency must be considered. Cryptocurrency mining, particularly Bitcoin, consumes vast energy and raises concerns about its sustainability. AI systems, especially those that require massive computational power, also contribute to environmental degradation.

Future possibilities

Despite these challenges, the future of AI and cryptocurrency looks promising. Combining AI’s analytical capabilities with cryptocurrency’s decentralized, secure nature could revolutionize global financial systems. For example, AI could power decentralized finance (DeFi) platforms, providing automated and optimized financial services without the need for traditional intermediaries like banks.

Moreover, AI could help stabilize cryptocurrency markets by providing more accurate predictions and reducing volatility. It could also make cryptocurrencies more accessible to the average person by simplifying the user experience, automating tasks like wallet management, and reducing transaction fees.

Integrating AI with blockchain technology could lead to innovations, such as AI-driven decentralized autonomous organizations (DAOs), which would allow businesses and organizations to run without human oversight, relying entirely on smart contracts and AI algorithms.

What does the future hold for cryptos in the AI age?

The intersection of AI and cryptocurrency represents a new frontier in finance and technology. As AI continues to evolve, its applications in the crypto space will expand, offering more efficient trading, enhanced security, and more brilliant contracts. However, with these advancements come significant challenges, including ethical concerns and environmental impacts. Balancing innovation with responsibility will be vital to ensuring that the future of AI and cryptocurrency is beneficial for all.

In the coming years, the convergence of these two groundbreaking technologies is poised to transform industries, create new opportunities, and reshape how we think about money, finance, and digital assets.

Featured image credit: Alesia Kozik/Pexels

AI-powered video tools company Runway has a daring calling for movie makers to integrate the use of AI at the set. 100 original films have been promised funding through the Hundred Film Fund, including $5 million in cash grants and up to $2 million in service credit.

This is a clear signal that AI video generators, like Runway, need filmmakers to experiment with their models for the technology to thrive in the industry.

Funding new visions in film

Runway’s initiative is aimed at filling a gap in traditional film funding, which often overlooks experimental projects. Their new fund is offering support to a wide range of formats, including features, shorts, documentaries, music videos, and more.

As noted in the company’s announcement, they’re particularly interested in backing directors who are curious about AI but might not otherwise receive traditional financial support.

With the promise of up to $1 million per project, the fund offers significant backing. However, Runway’s Head of Creative, Jamie Umpherson, clarified that each project’s financial needs will be assessed individually. The company is keeping flexibility at the forefront, allowing the fund to grow to $10 million if necessary, depending on the level of interest and quality of projects.

An opportunity for AI-curious filmmakers

For directors intrigued by AI but hesitant due to the novelty of the tools, this fund represents an unprecedented opportunity. It offers them the chance to experiment with generative video models and create something groundbreaking. In a field where traditional production houses are often slow to adopt new technologies, Runway’s support could provide the necessary push for AI to find its place in filmmaking.

Beyond just funding, Runway is offering five special “director residency” positions, though details on these roles remain vague. The residency is likely an effort to spotlight directors who show promise in using AI to enhance storytelling, potentially offering more than just financial backing but creative mentorship as well.

Best AI movies to watch for the weekend

Runway’s move feels partly like a plea for filmmakers to adopt their product, but there’s logic in backing experimental ideas. Having access to powerful AI tools doesn’t automatically make someone a great filmmaker. However, giving creators the resources to experiment might bring unexpected breakthroughs.

Out of the 100 funded projects, Runway hopes a few will prove the real value of AI-generated video, potentially demonstrating how the technology can enhance storytelling in ways previously unimagined.

Will AI take hold in filmmaking?

With giant partners like Lionsgate, Runway is pulling out all the stops to make sure these projects have support from experienced voices. The real test, however, will be whether filmmakers can use AI effectively enough to create meaningful work. Some may see it as an easy way to secure funding, but for others, this fund might be the key to pioneering a new era in filmmaking.

In the end, the Hundred Film Fund is a bold experiment. While it’s uncertain how many filmmakers will truly embrace AI, it only takes one or two breakthrough projects to shift the perception of AI in the film industry.

If Runway can ignite that spark, it could open the doors for wider AI adoption across creative fields.

Featured image credit: Kerem Gülen/Midjourney

AI startups are evolving far beyond the hype, turning into significant revenue generators at an unprecedented speed.

According to fresh data from Stripe, a major player in fintech, and a report by Financial Times, top AI companies are hitting financial milestones faster than their software predecessors.

AI startups reaching million-dollar revenues quicker than any other

That analysis of data collected by Stripe gives evidence that leading AI startups are taking as little as 11 months to achieve $1M in revenue when calculated annually, considering that they had not only sold their products and services for the first time on the platform but had also started to achieve the target after those months.

For this purpose, previous generations of software-as-a-service (SaaS) firms acquired 15 months to accomplish a comparable scope of income. This shorter road to wealth indicates the increasing need for products and services anchored on the artificial intelligence technology since they are already turning out to be key in fields such as health and business.

AI companies aren’t just accelerating to their first million—they’re scaling even faster to $30 million in annualized revenue. On average, AI startups reached this milestone in just 20 months. Compare that to SaaS startups from earlier tech waves, which took significantly longer. This speed highlights how AI’s potential to transform industries has translated into immediate consumer and business interest, driving quicker monetization.

The profitability issue

Despite this impressive revenue growth, profitability remains a challenge for many AI companies. While some, like OpenAI, have generated billions in annualized revenue, they’re also burning through large amounts of cash to train and maintain AI models. For example, OpenAI, despite earning $3.6 billion annually from its services like ChatGPT, spends well over $5 billion a year on computing infrastructure. This high cost structure is a key difference between AI companies and earlier software businesses, which often had fewer upfront operational costs.

Global demand is fueling AI adoption

It is not just that the desire for generative AI is not limited to Silicon Valley or comparable tech meccas. Data from Stripe also shows that 56% of revenues of AI companies are generated from foreign markets.

This global adoption is pushing AI companies to innovate and scale faster, driving their revenue growth.

While the economic promise of AI is clear, questions about long-term profitability remain. Stripe’s data shows that AI startups are adapting by building experimental products that quickly attract paying customers, even as their operating costs remain high.

In this sense, AI companies may be the new version of SaaS businesses, but with heavier upfront investments in technology and infrastructure.

This desire for generative AI is not limited to Silicon Valley or other large tech centers either. Stripe has found that over 50% of AI firms’ income sources are international Use the worldwide demand for AI but adjust it to the local market. To scale, DeepL and ElevenLabs have localized content for their consumers in different cities, based on regional language translation and voice tools. This holds the truth where the solutions give that extra edge depending on the regions of the world you adapt them.

• Start as fast as possible, but do not stay long in the initial stage of your product. It seems that AI companies are thriving because they release products with such concepts and make improvements subject to users’ responses. ChatGPT of OpenAI is a clear example of how one can start with a simpler version and improve without delay depending on how it is used. It does not only establish the forward thrust but makes the users anticipate more on the specific product.
• Creativity and flexibility are always the strengths of AI, therefore, do not be shy to change something. Take Midjourney for example, which started off as an art generator AI, but was applied to design, marketing and more. This means that encouraging experimentation will enable you to find new and supplementary revenues sources as well as edge competing firms.
• Since many AI companies have high costs that are associated with infrastructure the focus must lay on sustainable growth. Business organizations that implement green computing or cloud-efficiency models like Google’s AI structures can be able to acquire more clients than their competitors while in the long run have few costs to meet. Sustainability is not longer a ethical option; it has become in part a value generator.
• This explains why it is equally possible to gain success in the industry by collaborating with other AI firms or even large-scale industries. GitHub’s implementation of Copilot is a quintessential example of how cooperation can generate positive outcomes for both the companies, while diversifying the source of value for the primary offering. Recent literature indicates that strategic partnerships do enhance the credibility and foster innovation.
  l markets.

Featured image credit: Kerem Gülen/Ideogram

Recently, we saw one of our colleagues struggling to write his thesis, caught in the maze of endless research papers and references. Tools like Scite AI offer a good opportunity to cut through the noise. They act as a gateway to quicker discovery, helping users find relevant papers and understand how those works have been cited in the academic community.

What is Scite AI?

Scite AI is an academic tool that helps researchers, students, and academics make sense of vast amounts of scientific literature. At its core, it’s designed to give users not just access to research papers but a deeper understanding of them by using AI to analyze how those papers have been cited. Instead of just showing a simple citation count, it looks at whether a study has been supported, disputed, or merely mentioned by others, giving a clearer picture of its impact.

This makes the process of literature review or research much faster and more focused. Instead of spending hours combing through references to see how credible a study is or how much it’s influenced a field, Scite gives that insight upfront. The tool also offers an AI-powered assistant that can answer detailed questions based on the literature, providing citations and related research for a more comprehensive answer. It helps simplify the process of diving into new topics or fields where understanding the context is crucial.

Features of Scite AI

Scite AI comes packed with several features that are designed to streamline the research process and help users get the most relevant, credible information efficiently.

One of the standout features is Smart Citations, which provide a more nuanced view of how research papers are cited. Instead of just giving a raw number of citations, it shows whether those citations support, dispute, or simply mention the original paper. This allows users to quickly gauge the credibility and influence of a study without having to dig through countless references themselves.

Another useful tool is the AI-Powered Assistant, which can answer complex academic questions. Whether you’re looking for a summary of a concept or trying to find key research on a topic, the assistant not only provides answers but backs them up with cited references, giving you the confidence that the information is grounded in real research.

The platform also has a Search and Literature Discovery feature that uses AI to help users find relevant papers based on specific terms or questions. The results come with citation context, so you get a fuller picture of how each piece of research fits into the broader academic conversation.

For those managing large numbers of references, Scite AI offers Citation and Reference Management tools. You can organize, track, and stay updated on the latest research, all from custom dashboards, which makes it easier to keep tabs on everything in one place.

How to use Scite AI?

Here’s a straightforward guide to help you get started and make the most out of Scite AI!

Step 1: Visit the website

Head over to Scite’s homepage. You don’t even need to sign up right away—you can try the tool instantly. On the first page, you’ll see a search bar where you can input your research questions. In our case, we entered “How to conduct a sentiment analysis using generative AI tools?” and received a detailed, technical answer with citations attached, all without signing up.

Step 2: Ask a research question

After typing your question, Scite AI immediately provides a well-structured response. For instance, when we searched the protein structure question, it broke down the concept clearly, explaining protein folding, and referenced scientific papers to back up each point. This saves time compared to manually searching through individual papers.

Step 3: Analyze citation context

The answer doesn’t stop at just providing information. Scite goes further by showing you how various studies cite the paper. In our example, we could see whether other research supports, disputes, or simply mentions the study on protein structure, making it easier to assess the credibility of the source.

Step 4: Use the AI search tool

On another page, we tested Scite’s AI search by entering “sentiment analysis.” This pulled up relevant academic papers, showing the context of each citation. This feature makes exploring a broad topic more manageable, as the AI helps focus on the most cited or influential papers, providing a well-rounded view of the research field.

Step 5: Review the references

Scite doesn’t just stop at showing a few sources. It provides a complete breakdown of the publications it used to generate your answer. As we saw in the sentiment analysis search, it listed multiple scientific articles, with DOI links, making it easy to verify the information or dig deeper into the subject.

Step 6: Manage your citations

Once you find the research you need, Scite AI allows you to save and organize papers into custom dashboards. In our experience, after searching for different terms, we could easily add relevant studies to a dashboard, helping us keep track of references for future use.

Is Scite AI trustworthy?

Best plagiarism checkers for ChatGPT and other AI tools

Scite AI pricing plans

Scite AI offers two main pricing tiers designed to cater to both individual users and institutions.

Personal Plan: $20.00 per month

This plan is ideal for students, independent researchers, and professionals who want to enhance their research experience with AI tools. Key features include:

• Unlimited assistant chats: You can ask the AI assistant an unlimited number of questions, allowing you to continuously explore new research topics and get detailed, cited responses.
• Unlimited search usage: Perform as many searches as you need without worrying about hitting any limits. This is crucial for in-depth research or when working on multiple projects at once.
• Reference check: The platform offers a detailed reference check for papers, ensuring that you can easily verify the credibility and context of the sources you’re using.
• Custom dashboards: Keep your research organized by creating custom dashboards to track and manage papers, making it easy to revisit key studies or share them with colleagues.
• Citation and retraction alerts: Stay updated on the papers you’ve tracked. If a paper you’ve referenced is cited, or even retracted, Scite will alert you, keeping your research up-to-date and accurate.

Institutional Plan: Custom pricing (Starting at $5,000 per year)

This plan is tailored for universities, research labs, and academic institutions. It comes with all the features available in the personal plan, but with additional benefits designed for larger teams and organizations:

• All features included: You get full access to all of Scite AI’s tools, including unlimited assistant chats, searches, and citation tracking, just like the personal plan.
• Discounted rates for more users: Institutions can benefit from volume-based discounts, allowing teams of researchers or students to access Scite at a reduced per-user cost.
• Centralized billing and admin: Scite offers centralized billing and administrative controls, which is essential for managing large groups or departments. This helps institutions streamline their payment process and manage user accounts efficiently.
• Dedicated support & training: Institutions get access to a dedicated support team that offers personalized training and guidance on how to best use the platform. This can help teams onboard quickly and maximize Scite’s benefits for their research goals.

Not bad at all…

These tools make the research process smoother, there’s no denying. Yet, it’s important to stay ethical and vigilant when it comes to plagiarism. Scite AI, for example, did a good job of showing resources clearly on the right side of the screen, making it easy to track citations. This level of transparency is useful for maintaining academic integrity and avoiding any accidental missteps in sourcing.

Scite AI worked out fine during our experience. If you’re deep into research, it might be worth a try to see how it fits into your workflow.

Featured image credit: Kerem Gülen/Ideogram

CopyCopter.ai might be one of the most feature-rich AI tools we have used in a while.

Instagram, TikTok, and YouTube are full of AI-made videos, and it’s hard to miss them. Anyone watching can almost immediately tell they’re AI-made—whether it’s the pacing, the robotic narration, or the somewhat stiff visuals, they stand out. But as AI video generators evolve, we’re going to reach a point where these tools will create content that’s nearly indistinguishable from human-made videos.

We’re on the brink of that shift, and tools like CopyCopter are leading the charge. While AI video content may still have room for improvement, it’s only a matter of time before the line between human-created and AI-created videos blurs.

Let’s take a look at how we used CopyCopter and how easily we created our own AI videos.

What is CopyCopter.ai?

CopyCopter is a generative AI tool that allows us to turn long-form text—like blogs, newsletters, or any other written content—into short, engaging videos. It’s designed to streamline video creation, especially for those of us who may not have the time or resources to produce videos from scratch. With CopyCopter, we can take existing text content, paste it into the platform, and in just a few steps, get a fully generated video.

While the output may not yet rival professional video editing, the ease and speed of the process make it a valuable tool for marketers, content creators, and even casual users who want to enhance their social media presence with video content. We didn’t need to be video editing experts—just a few clicks, and our video was ready.

Features of CopyCopter

Let’s dive into some of the standout features of CopyCopter that we explored during our experience:

• Text-to-video generation: The core function of CopyCopter. We provided the platform with a block of text, and it automatically turned that into a video, complete with narration, visuals, and transitions.
• Customizable voice and styles: We were able to choose from a variety of AI-generated voices to narrate our content, giving the video a professional touch. In addition, we could pick different visual styles that aligned with our brand or aesthetic. Whether we wanted a minimalist look or something more vibrant, there were plenty of options.
• Quick edits: After the video was generated, we had the chance to fine-tune the details. The editing screen allowed us to make adjustments, including trimming the video, modifying the text, or changing visual elements.
• Aspect ratio choices: Since the platform is optimized for social media, we could choose the best aspect ratio for our intended platform. Whether it’s the vertical 9:16 ratio for TikTok, the square 1:1 for Instagram, or a landscape format for YouTube, CopyCopter had us covered.
• Pre-built templates: If we were in a rush or not quite sure how to format our video, we could use one of the platform’s pre-built templates. These templates helped speed up the process and ensured our content looked polished.

How we used CopyCopter

Here’s a quick step-by-step guide based on our experience using the tool. We’ll walk you through how we created a video from scratch using CopyCopter.

Step 1: Summarize your text content

First, we took an article we had written called “How Generations X, Y, Z, and Alpha Define and Are Defined by AI?” To make the content easier to transform into a video, we asked ChatGPT to summarize the article.

This gave us a concise version of the text to work with. If you have long-form content like a blog post or an email newsletter, this is a good place to start. Condensing the information into a summary helps the AI tool generate a more focused video.

Step 2: Create a script

After getting the summary, we wanted a brief, 150-word version of it—something that would fit neatly into a short video format.

We used ChatGPT again to trim down the content and transform it into a story-like script. The key here is to keep the text short and to the point, making it easier for the AI to handle.

Step 3: Paste the script into CopyCopter

With our script ready, we copied it into the CopyCopter.ai platform, logged in with a Google account and “pressed the buttons.”

Step 4: Choose a voice

One of the cool features of CopyCopter is the variety of AI-generated voices we could choose from. We selected a voice that matched the tone of our content—one that sounded calm and informative but not too robotic. Depending on your content, you can opt for different voices to match the style of the video you’re creating.

Step 5: Select a style and aspect ratio

Next, we chose the visual style for the video. CopyCopter offers several options, from more minimalist and clean aesthetics to more dynamic and bold looks. We picked a style that aligned with the subject matter of our script. You can also choose the aspect ratio at this point, depending on where you plan to post the video. We opted for the 9:16 vertical ratio since the video was intended for TikTok.

Step 6: Review and edit

Once the video was generated, we were taken to the editing screen. While the initial output was pretty good, we made a few tweaks—adjusting the timing of the visuals, tweaking some transitions, and making sure the narration synced up well with the video. This step allowed us to polish our video and ensure it flowed the way we wanted.

Step 7: Export and share

Finally, once we were happy with the result, we exported the video and saved it. It was ready to be uploaded to our social media accounts within minutes.

The result? CopyCopter may not be producing Hollywood-level videos just yet, but its simplicity and effectiveness make it a valuable tool for anyone looking to quickly transform written content into shareable videos. pic.twitter.com/T6dtVzN8qE

— Kerem Gülen (@kgulenn) September 26, 2024

Worth a try

CopyCopter may not be producing Hollywood-level videos just yet, but its simplicity and effectiveness make it a valuable tool for anyone looking to quickly transform written content into shareable videos. AI video generation is still in its early stages, but it’s evolving fast, and tools like CopyCopter are helping bridge the gap between manual video production and fully automated content creation.

As we move toward a future where AI-generated videos will be harder to distinguish from those made by humans, tools like CopyCopter will continue to innovate and offer more sophisticated options. For now, it’s a great tool for speeding up the process, especially for marketers and content creators who need to repurpose their long-form content into something more visual and engaging. So, if we’re tired of spending hours editing videos or are looking for a quick way to make our blogs and newsletters stand out on social media, CopyCopter is definitely worth a try!

Featured image credit: Kerem Gülen/Ideogram

Meta has announced the production release of Llama 3.2, an unprecedented collection of free and open-source artificial intelligence models aimed at shaping the future of machine intelligence with flexibility and efficiency.

Since businesses are on the lookout for apocalyptic AI solutions that can work on the hardware most common or on those that are popular to develop solutions for large businesses as well as independents, Llama 3.2 gives new models.

Llama 3.2’s focus on edge and mobile devices

An emphasis on the edge and mobility is something that is quite evident at Meta.

As for the new features of this version, the developers have added small- and medium-sized vision LLM: 11B and 90B, and so also introduced pure text alternatives, 1B and 3B.

Particularly, the new models introduced here are aligned for the operation of edge devices, thus making the AI technology available to more clients. The lightweight text-only models, especially those without any visual data, are designed for simpler tasks such as summarization and instruction following due to the low computation power.

Due to central data processing on mobile devices, with local execution, none of the data is uploaded to the cloud, as Meta states,

“Running locally on mobile devices ensures that the data remains on the device, enhancing user privacy by avoiding cloud-based processing,”

This capability is especially useful for applications that process sensitive data, as it enables the application to perform important tasks while maintaining the confidentiality of the data. For example, users can reply to personal messages while summarizing them, or get to-do-list items from meetings without relaying messages to external servers.

Advancements in model architecture

The most significant change in Llama 3.2 is various architectural improvements. The new models use an adapter-based architecture that can combine image encoders with pre-trained text models without modification. This integration leads to improvements in the ability to reason in both text and image areas and greatly expands the range of applications for these models.

The resulting pre-trained models went through stringent fine-tuning exercises that entailed the utilization of huge noisy image-text pair data.

Llama 3.2 11B & 90B include support for a range of multimodal vision tasks. These capabilities enable scenarios like captioning images for accessibility, providing natural language insights based on data visualizations and more. pic.twitter.com/8kwTopytaf

— AI at Meta (@AIatMeta) September 25, 2024

There is one important addition to the token context length, and it increased to a very impressive 128K for the lightweight 1B and 3B models. It facilitates wider data travelling which is particularly valuable for long documents and elaborate thinking.

This capability to accommodate such large input sizes places Llama 3.2 at an advantage with respect to competitors in the dynamic AI market dominated by OpenAI’s GPT models.

How about the performance metrics?

Llama 3.2’s models have demonstrated exceptional performance metrics, further solidifying their competitive edge in the market. The 1B model achieved a score of 49.3 on the MMLU benchmark, while the 3B model scored 63.4. On the vision side, the 11B and 90B models showcased their capabilities with scores of 50.7 and 60.3, respectively, in visual reasoning tasks.

Evaluating performance on extensive human evaluations and benchmarks, results suggest that Llama 3.2 vision models are competitive with leading closed models on image recognition + a range of visual understanding tasks. pic.twitter.com/QtOzExBcrd

— AI at Meta (@AIatMeta) September 25, 2024

These metrics indicate that the Llama 3.2 models not only meet but often exceed the performance of similar offerings from other companies, such as Claude 3 Haiku and GPT4o-mini.

The integration of UnslothAI technology also adds to the efficiency of these models, enabling twice as fast fine-tuning and inference speeds while reducing VRAM usage by 70%. This enhancement is crucial for developers looking to implement real-time AI solutions without facing hardware limitations.

Ecosystem collaboration and support

One of the key factors that define Llama 3.2’s readiness to be brought into the market is its well-developed ecosystem. Partnerships with other mobile industry leaders like Qualcomm, MediaTek, and AWS make it possible for developers to implement these models across different settings, cloud environments, and local devices.

The Llama Stack distributions such as Llama Stack for on-device installations and Llama Stack for single-node installation offer solutions that developers can take advantage of and build these models into their projects without added complications.

The lightweight Llama 3.2 models shipping today include support for @Arm, @MediaTek & @Qualcomm to enable the developer community to start building impactful mobile applications from day one. pic.twitter.com/DhhNcUviW7

— AI at Meta (@AIatMeta) September 25, 2024

How to use Meta Llama 3.2?

The latest version of the open-source AI model, Llama 3.2, is now available on the Meta Llama website, offering enhanced capabilities for customization, fine-tuning, and deployment across various platforms.

Developers can choose from four model sizes: 1B, 3B, 11B, and 90B, or continue utilizing the earlier Llama 3.1.

Meta is not just releasing these models into the wild; they are keen on ensuring developers have everything they need to leverage Llama 3.2 effectively. This commitment includes sharing valuable tools and resources to help developers build responsibly. By continuously updating their best practices and engaging with the open-source community, Meta hopes to inspire innovation while promoting ethical AI usage.

“We’re excited to continue the conversations we’re having with our partners and the open-source community, and as always, we can’t wait to see what the community builds using Llama 3.2 and Llama Stack,”

Meta stated.

This collaborative approach not only enhances the capabilities of Llama 3.2 but also encourages a vibrant ecosystem. Whether for lightweight edge solutions or more complex multimodal tasks, Meta hopes that the new models will provide the flexibility needed to meet diverse user demands.

Image credits: Meta

Today is a special opportunity to express your love and appreciation for your loved one by sending her amazing National Daughters Day messages. However, finding the perfect words to convey how much they mean to you can sometimes be a challenge.

Fortunately, with the power of AI, creating heartfelt National Daughters Day messages and beautiful visuals has become simpler than ever.

Let us explain how to craft the perfect National Daughters Day messages using AI tools like ChatGPT for generating meaningful quotes and Ideogram AI for creating eye-catching visuals. By combining these tools, you can create personalized, meaningful content that speaks directly to your daughter and will make her feel loved and appreciated.

How to use AI to create the best National Daughters Day messages?

AI has the capability to personalize your message, ensuring it reflects your daughter’s unique qualities and the special bond you share. Let’s break down how you can use AI tools like ChatGPT and Ideogram to craft both inspiring messages and stunning visuals.

Step 1: Generating National Daughters Day quotes with ChatGPT

The first step in creating the perfect National Daughters Day message is coming up with a heartfelt quote. ChatGPT, a powerful AI text generation tool, can help you create personalized quotes that reflect your feelings toward your daughter.

How to generate National Daughters Day quotes with ChatGPT:

1. Access ChatGPT: Open ChatGPT and start a conversation by providing a prompt. For instance, you could say:
   • “Write a heartfelt National Daughters Day message for a daughter who is kind and strong”.
   • “Create a short, sweet message expressing love and pride for my teenage daughter on National Daughters Day”.
2. Fine-tune the output: You can regenerate the response or request changes until the message feels just right. Feel free to ask for variations that focus on love, gratitude, pride, or joy to get a message that truly resonates.

Once you’ve generated a few quotes or messages, save the ones you like best for the next step.

Step 2: Creating National Daughters Day message postcards with Ideogram AI

To make your National Daughters Day message stand out even more, combine it with a beautiful visual. Ideogram AI is an excellent tool for creating custom visuals that match the tone and sentiment of your message. The screenshot of Ideogram’s UI shows that it has intuitive features, allowing you to create images that perfectly complement your message.

How to Use Ideogram AI to Create Visuals:

1. Open Ideogram AI: Log in or sign up on the Ideogram platform and navigate to the creation interface, where you’ll see various design options like text styles, colors, and layouts.
2. Select a design style: Choose “Design” and adjust backgrounds, and color palettes that fit the vibe of your message. For National Daughters Day, you might want to opt for designs with soft pastels, floral accents, or heartwarming themes.
   • Use palettes like “Pastel” or “Ember” for warm and heartfelt messages.
   • For a fun and modern look, try “Jungle” or “Fresh” color schemes.
3. Enter your message: In the text box shown in the UI, type in the quote or message you generated from ChatGPT and describe how you want it to look.

Once satisfied with the look of your design, preview the visual and save it to your device. You can then use this image in social media posts, print it on a card, or even create a custom gift.

As we celebrate National Daughters Day, we’ve curated a collection of heartfelt messages that capture the essence of the special bond between parents and their daughters. Whether you’re looking for something funny to bring a smile, loving words to express your affection, warm messages that wrap her in comfort, inspiring notes to uplift her spirit, or empowering words to encourage her dreams, we have gathered some wonderful options for you.

Share these messages with your daughter, making her day even more memorable with your thoughtful sentiments!

Funny National Daughters Day messages

Loving National Daughters Day messages

Inspiring National Daughters Day messages

By using AI tools like ChatGPT and Ideogram AI, crafting the perfect National Daughters Day message has never been easier.

As you can see, with ChatGPT, you can generate heartfelt, personalized quotes, while Ideogram allows you to create stunning visuals that enhance the emotional depth of your message.

Whether you’re posting on social media, sending an e-card, or creating a physical gift, this combination of AI-generated text and visuals ensures your daughter feels truly special on National Daughters Day.

Let your creativity shine this year by delivering a message that your daughter will cherish forever!

Image credits: Emre Çıtak/Ideogram AI

The founder and CEO of Meta presented a number of major changes that align with company’s two main directions at Meta Connect 2024: AI and AR. The event is a forum for developers and provided insight into several hardware and software releases that are pointing Meta toward an AR/VR future, from glasses to headsets to the new artificial intelligence models.

Orion AR glasses at main stage

The biggest launch at Meta Connect 2024 was the Orion AR glasses. Marketed as “the most advanced glasses the world has ever seen,” Orion represents a leap forward in Meta’s efforts to lead in the AR space. These glasses also claim to deliver hand-tracking, eye-tracking and the most distinct feature is a neural interface that can communicate with the user’s gesture, are more natural. Despite the fact that the Orion glasses have not been released on the market, Meta has already placed them within the sphere of necessities in terms of augmented reality. Equipped with Micro LED projectors and generative AI that can superimpose data onto objects, the glasses can point at ingredients on a table.

The early testing phase has even led to recommendations from some great tech minds such as the Nvidia CEO Jensen Huang. Orion establishes Meta’s desire to bring the fully immersive world closer to the physical environment through elements like wireless compute puck and the neural wristband. However, these advanced features come with a caveat: Orion is not yet expected to be available to the consumer market soon. With this, Meta is betting big on the future of these holographic AR glasses to define the future of digital contact.

Meta Quest 3S and discontinuation of Quest 2

Alongside the Orion, Meta launched the new Quest 3S VR headset, which will be a less expensive product compared to the Quest 3. The latest version, the Quest 3S, costs $299 for the 128GB version and is designed to democratize tetherless VR. While it does not have the depth sensor or the improved resolution of its more expensive sibling, the Quest 3S is equipped with the same Snapdragon XR2 Gen 2 chip and Touch Plus controllers.

To begin with, to simplify its VR portfolio Meta stated it would stop production of the Quest 2 and Quest Pro headsets. The Meta Quest 2 is expected to receive a dedicated next-generation successor in October 15th this year; thus, the original Quest and Quest 2 will be discontinued by the end of the year. This is a significant move for Meta’s VR plans in particular, and future proofed experiences at an affordable price.

From Llama 3.2 to celebrity chatbots

AI continues to form a key part of Meta’s strategy. At this year’s Meta Connect, Zuckerberg introduced Llama 3.2, a new version of a widely known AI model from the company. The new version not only improves language-processing functionality but adds visual-processing functionality as well. Among them, the models of Llama 3.2, for example the 11B and 90B, can interpret charts, analyze images and even know where objects are within a photo.

It brings Meta AI to the same level as rivals OpenAI and Google, who have been providing this visual processing feature for the last year.

It also made its AI receptive and entertaining by joining in celebrities’ voices into its AI assistant, as part of Meta. Voice decisions copied from famous personalities such as Dame Judi Dench, John Cena and Awkwafina are available in Facebook and Instagram for users of Meta to engage with. These personalized voices provide a far richer user experience by default – whenever text message tools like Messenger, WhatsApp, or Instagram now support voice.

Enhancing reality with Ray-Ban smart glasses

Meta’s continued partnership with Ray-Ban also came into focus during the event It also presented a series of improvements to the Ray-Ban Meta smart glasses. These glasses now include key developments of real-time AI video processing and the capability to respond to questions by the user about objects in the view. Other improvements are real-time translator that supports translation between many languages including English and Spanish and all the Music/Oval applications like Amazon Music and Audible.

This article is reflection of Meta team’s effort in trying to make AR and AI wearable devices everyone may use in their daily life present in the smart glasses called Ray-Ban Meta. Because such glasses are capable of setting reminders and recognizing real-life objects in the same real-life context they are as close as one can get to becoming a viable consumer product.

Meta AI in social media

The trend of AI doesn’t limit itself to virtual and augmented reality spaces. Currently, Meta AI produces content across apps such as Facebook and Instagram. The new “Imagined for you” feature allows users to see AI-generated images based on their interests or trends. It can even incorporate the user’s face into scenarios like “an enchanted realm,” bringing personalization to new heights. The messages are all created using AI, and represent Meta’s ongoing drive to boost interaction and provide people with new ways of engaging with feeds.

On the creator side, Meta is working on an AI translation and dubbing tool that will translate and use creators’ lips for the content in the U.S. and Latin America. Another great feature, available at the moment only between English and Spanish languages, can entirely change the way artists and owners of various creative works can address the global audience regardless of the language barriers.

The evolution of gaming on Quest

Gaming enthusiasts were also treated to a range of new experiences for the Quest platform, including Batman: A project named Arkham Shadow and a Wordle VR game, Wordle being a quite famous word puzzle game. These games, along with other titles like Alien: Rogue Incursion, in particular, emphasize Meta’s desire to make its VR headsets more universal for entertainment. The decision to bring Wordle in VR for free without the subscription was something quite out of left-field but fun to include among the games.

Meta’s vision: Bridging AI and AR

Meta Connect 2024 presented to the world the company’s desire to make it the pioneer of both AI and AR. From the release of the Orion AR glasses we seen Meta is attempting to make all of these technologies available and accessible to the general public. While Zuckerberg and his team are experimenting, inventing and pioneering new possibilities, the viability of Meta in the long term will rely with its capacity to convert these changes into popular consumption goods.

Featured image credit: Meta

The Rabbit R1 that was picked as a revolutionary AI gadget after its splash at CES 2024 turned out to give a painful lesson in the AI hardware dynamics. Although R1 started well with a vision announced by its founder Jesse Lyu this social media application has stagnated, and as of five months after its launch only 5,000 of the registered 100,000 users actually use R1.

Speaking with Fast Company, Lyu admitted that the device was rushed into the market even when it cannot fully compete with major giants such as Apple or Google. However, the false start and the string of let downs and technical issues have turned Rabbit R1 into one of the biggest AI hardware failures of the year.

The inability of the Rabbit R1 to succeed speaks volumes of the state of the AI hardware segment. Another problem was that it never came close to fulfilling the functions just announced, including its massively publicized Large Action Model (LAM). At CES, the R1 was presented as a universal tool that must solve all sorts of problems — from logging into a website to booking a flight or ordering food — with voice commands only. However, the reviewers and early users reported that these functions were either not fully implemented, or buggy.

In response the Rabbit team had to dedicate time to address these issues and released 16 firmware updates to try and fix the issues. Such updates were made to try and fix the failures of the R1 but for many users the harm was already done. During the first months following application release, many early users got disappointed and the number of DAU significantly declined. The problem is Rabbit R1 has no unique market to target because the device is complicated and needs time in order to learn commands, while smartphones are also incorporating AI capabilities as their features, which in effect threaten the existence of Rabbit R1.

A broader struggle in AI hardware

Currently, Rabbit R1’s difficulties are not unique – this issue lies at the heart of many AI hardware development processes. As corporations strive to capture what the future of intelligent devices will be, they are faced with incredibly difficult and complex practical and infrastructural problems of how to implement encasements that encompass all of form and function while optimizing for the user’s experience.

Another fine example includes the recent case of AI Pin developed by the Humane to remove all the wrong pins but was also a flop. Originally capitalized at $850 million, Humane’s product was being brought back more than it was being sold. Same as in the case of Rabbit R1, Humane’s AI Pin promised many things but could not live up to expectations.

Can the R1 be redeemed?

Despite the bad experience with Rabbit R1 on its inception, Jesse Lyu seems hopeful for the automobile. The company is still refining Large Action Model; the next version of LAM is set to go live in beta on October 1. The author likes to think that this update will fix many of the problems users encountered with the first release. The LAM has also been upgraded, with LYu’s team working on allowing it to move through websites and complete tasks such as adding products to a cart or even booking a flight.

As per the feedback information received from the beta users, the new LAM system of WIP is slower, systematic and buggy but this is an improvement from the previous system.

However, challenges remain. This leads to the third problem of the LAM system, along with its strong dependency on working with third-party websites, there are always questions regarding compatibility. LAM potentially leaves Rabbit R1 vulnerable to being shut down if large sites such as amazon or google decided to block the LAM from accessing their systems.

It remains to be seen as to whether standalone AI gadgets such as the Rabbit R1 can effectively challenge AI built into smartphones. Smartphones such as iPhone 16 and Sumsung S24 already come with advanced AI abilities which can perform most of the LAM tasks which Rabbit boasts of. Lyu has also reaffirmed that Rabbit is not simply about phones but has aspirations of a standardized system that should be installable with equal ease in Linux, Windows, macOS and so on. But if consumers are indeed willing to care enough as to invest in another device, which is the portable smart speaker at that, let alone when they already have in their hands AI-powered smartphones that also boast significant levels of audio quality?

In conclusion, Rabbit R1’s evolution is oriented to the formation of turning points inherent in the formation of a new industry. Despite the firsthand experience in overcoming criticism as well as defending themselves from the criticism Lyu and his team crafted a tough path towards rehabilitation and redemption. The AI hardware industry is full of potential but is also full of risks; there are pitfalls that, when properly understood, should be avoided in practice but are currently causing tension between practicality and idealism.

Featured image credit: Rabbit

Real-time data processing means immediate or near-immediate handling of data as it is generated. Real-time data processing provides for continuous intake and analysis with quick results or actions, in opposition to traditional batch processing where data is collected and analyzed at intervals.

Core attributes

• Low latency: The processing is done in near real-time, with minimal delay.
• Continuous input: In this, data streams constantly and the system analyzes it in real-time.
• Instant output: The measures or results as a response to the incoming data are delivered almost in real-time.

AI integration in real-time data processing

Artificial intelligence enhances real-time data processing through better comprehension with the help of advanced machine learning algorithms and analytics to act on that information. The key areas where AI contributes to this effort include:

Predictive analytics

AI runs advanced algorithms that predict what will happen in the future, based on past and current data, to develop proactive strategies. For instance, in financial markets, AI algorithms running on real-time data feed predict market fluctuations.

Anomaly detection

Unusual pattern detection in incoming real-time data is one of the strong suits of AI algorithms, and this is essential for applications like fraud detection and system monitoring. The models are really good at detecting abnormal behavior outside set norms and immediately sending alerts.

Automated decision-making

AI systems streamline decision processes by automating responses based on real-time data, which minimizes the need for human input. This is exemplified in autonomous vehicles, where AI makes immediate driving decisions derived from sensor data.

Natural language processing

AI is the enabler of real-time analytics of texts and speeches. The sophisticated NLP technologies allow real-time translation services and sentiment analysis during customer interactions.

Adaptive systems

The possibilities that AI opens within the scope of real-time data processing keep developing with time. The recommendation systems in streaming services fine-tune their suggestions with evolving user tastes.

In a nutshell, AI software solutions significantly enhance real-time data processing through advanced analytics, superior decision-making capabilities, and automation of complex tasks, proving indispensable in dynamic or critical environments.

Use cases of AI real-time data processing

Real-time AI data processing has extensive applications in various fields, solving a broad set of scenarios and use cases. Among such sectors are:

Financial services

Advanced fraud detection requires complex algorithms and machine learning models. Security systems assess different patterns in transactions that indicate deviations and potentially fraudulent actions. They can provide real-time monitoring, enabling financial loss prevention and the protection of sensitive information. Algorithmic trading, in turn, depends on automated systems to take advantage of buying or selling opportunities given current market conditions, trends, and historical data at a speed and frequency often impossible for humans.

Healthcare

Wearable devices and sensors in healthcare continuously monitor patients in real-time for their heart rate, blood pressure, and glucose levels, among others. The analytics of continuous data enable the identification of disturbing trend changes and immediately raise alerts to healthcare providers and patients alike. AI also plays a critical role in diagnostics by analyzing medical images such as X-rays and MRIs, coupled with patient data, for enhanced accuracy and speedy diagnosis.

Retail

AI is helping the retail industry by personalizing and maintaining inventory. AI algorithms analyze customers’ behavior, preferences, and purchase history and provide personalized recommendations and targeted marketing. It improves customer satisfaction and increases sales. Advanced analytics with machine learning models can predict demand and optimize stock levels, smoothing supply chains hence reducing waste and making sure popular items are in constant supply.

Smart cities

AI has enormous implications for traffic management and public safety. Intelligent traffic systems use sensors and cameras in real-time to improve the flow of traffic, reduce congestion, and increase the efficiency of public transportation. On the other hand, AI-powered surveillance systems and predictive analytics allow the City to monitor and respond to incidents as they identify potential security threats created constantly and manage emergency responses more effectively.

Gaming

AI and real-time analytics make sense of player behavior, preferences, and interactions. The developers then take this insight to tune game dynamics and balance gameplay. AI models are useful for constructing adaptive game environments by adjusting difficulty levels and challenges based on the player’s skills and actions.

Types of real-time processing

Real-time processing means that data is handled and analyzed on the go as soon as it appears, rather than once it has been collected. Real-time data processing comes in several main types.

AI data processing challenges

1. Latency: The time difference between the arrival of data and the processing of the same for real-time applications, this delay needs to be at a minimum.
2. Throughput: The amount of data processed per unit of time. The high throughput is the assurance that the system is able to work with volumes of data in an effective way.
3. Scalability: The system should handle scaling up the amount of data or increasing number of sources feeding into it without degrading performance.
4. Data consistency: It ensures that the data processed is accurate and reliable, even when processing in distributed systems, or when sources of data differ.

Integration of AI with data processing systems

AI raises the bar for the efficiency, precision, and generation of insight in data processing systems. AI optimizes usage by automating mundane tasks that relate to data collection, cleansing, and formatting, and decisions related to cloud-on-premise storage solutions.

Besides, AI feeds automation and optimization by automating repetitive tasks to perfect workflows. Predictive and prescriptive analytics from AI give proper estimates and tactical advice in decision support. Analysts are bound to have improved interaction due to sophisticated dashboards and AI chatbots that ease data querying and interpretation.

AI enables batch and real-time workflows through automation of data ingestion and reporting for batch and immediate analysis for real time. Guided by machine learning models, AI can identify patterns, predict, and analyze text through natural language processing.

From the perspective of security, AI is pivotal in the prevention of breaches, maintaining regulatory compliance, and understanding evolving needs with respect to data through continuous updating and feedback.

How AI improves real-time decisions?

• Speed and accuracy are the hallmarks, as AI models will process vast amounts of data much quicker than humans and with high accuracy. This could lead to faster decision-making, given automated trading systems or real-time fraud detection.
• Adaptability: As machine learning models continuously learn from new data, their predictions are getting refined each moment in time. The adaptability assures that systems stay effective under changing conditions.
• Real-time sentiment analysis: AI techniques for real-time sentiment analysis and anomaly detection give instant feedback and insights for organizations to act upon at that given moment.
• Automation: AI systems automate routine and complex tasks, minimize human intervention, and reduce errors which results in higher operational efficiency and free up human resources for higher-value tasks.
• Personalization: AI models grant experiences and recommendations that are attuned to individual tastes in a real-time premise that rapidly heightens user satisfaction. An example could be streaming services, which would use AI to provide content suggestions based on previous viewing history.

In a nutshell, machine learning, deep learning, and NLP techniques of AI play an important role in real-time processing and thus improve decision-making with smooth operations. Integrate these technologies into data processing systems to facilitate quicker and more accurate responses while easily adapting to ever-evolving conditions.

Featured image credit: benzoix/Freepik

As AI technology progresses, the digital environment continues to astonish us all…

AI can now pass reCAPTCHAv2 image identification tests

Arguably, the most evident development on why that is true occurred when researchers from ETH Zurich in Switzerland managed to train an AI model to solve Google’s reCAPTCHAv2 image identification tests. These reCAPTCHA tests, familiar to many web users, usually present images of traffic lights, vehicles, and other everyday objects for security to weed out bots or automated systems.

“Our work examines the efficacy of employing advanced machine learning methods to solve captchas from Google’s reCAPTCHAv2 system. We evaluate the effectiveness of automated systems in solving captchas by utilizing advanced YOLO models for image segmentation and classification. Our main result is that we can solve 100% of the captchas, while previous work only solved 68-71%. Furthermore, our findings suggest that there is no significant difference in the number of challenges humans and bots must solve to pass the captchas in reCAPTCHAv2. This implies that current AI technologies can exploit advanced image-based captchas. We also look under the hood of reCAPTCHAv2, and find evidence that reCAPTCHAv2 is heavily based on cookie and browser history data when evaluating whether a user is human or not. The code is provided alongside this paper,” researchers stated.

Appropriately, the AI was named YOLO, which means “You Only Look Once.” It was trained only on images used in reCAPTCHA challenges.

The results were spectacular: this AI model passed these tests with 100% accuracy.

This is a huge leap from the earlier attempts at AI, which could solve only about 68-71% of these challenges. What this means is that this artificial intelligence can mimic human behavior to the extent that it consistently passes for a real person in these security tests.

This finding has greater consequences for internet security as a whole. CAPTCHA, a short form for “Completely Automated Public Turing test to tell Computers and Humans Apart,” was devised in the first place to prevent bots, scrapers, and other unwanted actors. As time passed, reCAPTCHA shifted from out-of-distortion text challenges to image recognition tasks to even “invisible” checks tracking user behavior such as mouse movement.

The goal has always been the same: to differentiate humans from machines.

But this new AI capability undercuts that goal. If the AI can solve the reCAPTCHAs perfectly, the very security system many websites rely on will be undermined. The implications of this go beyond a mere basic layer of user authentication to prevent spam, automatic content scraping, and various other forms of cyberattacks.

As AI continues to outsmart CAPTCHA systems, experts suggest that the solution might be to make the tests harder. Yet, this creates its own set of challenges. Tougher CAPTCHAs could exacerbate accessibility issues, especially for those with visual impairments who already struggle with the visual nature of the tests.

Is AI creative: Answering the unanswerable

However, at least the big names in the tech game have not been panicking yet. For example, Google has gone ahead to move past image-based CAPTCHAs for much of its protection. Later, in 2018, it introduced reCAPTCHAv3, which works more invisibly, analyzing user behavior, such as cursor movement. Private Access Tokens were also developed by Apple as part of iOS 16. This skips the need for CAPTCHA whenever this device-based authentication method is employed.

While CAPTCHA still presents a proverbial bar for the existence of many websites, the development of AI just shows how quickly machines adapt to security systems designed to keep them out. As device fingerprinting, behavioral analysis, and others take the stage as potential successors, the ways of separating bots from humans are bound to grow increasingly sophisticated. As AI capabilities grow, the line between human and machine activity online grows increasingly blurred, and the internet is forced to adapt to an automated reality.

And in this new digital age, it would appear even robots are capable of convincing other robots that they aren’t robots…

Image credits: Kerem Gülen/Midjourney

In a significant move, Cloudflare has announced a new bundle of tools for online publishers. These tools are created to provide website owners with control over applying AI models to their content. The company intends to level the playing field for smaller publishers that commonly have their content taken without consent or pay in the form of AI-based scraping.

New tools have been introduced that empower users to observe the actions of AI bots, which could lead to monetizing content access in a future marketplace. This endeavor represents a significant instant when digital content creators can achieve security and gain benefits from their efforts in artificial intelligence.

The service delivers comprehensive analytics, revealing the periods and frequency of AI bots accessing websites. It also allows website owners to exclude or include specific bots with a simple click.

The move has been adopted in response to rising concerns about how AI models affect smaller publishers. As rivals in the AI industry, such as OpenAI, Microsoft, and Meta, consistently mine the web for content to improve large language models (LLMs), many small websites contribute significant data but fail to gain traffic or revenue. This has raised the fear that the business models of smaller publishers might collapse should users choose AI-driven tools like ChatGPT over original website visits.

Matthew Prince, who heads Cloudflare, emphasized the critical nature of fair payments to content creators. “If you don’t compensate creators one way or another, then they stop creating, and that’s the bit that has to get solved,” Prince told TechCrunch. This declaration stresses the firm’s mission of forming a more equitable digital ecosystem where content creators can have input on how their work is used.

In furtherance of AI Audit, Cloudflare plans to launch a marketplace next year, permitting website owners to sell their content to providers of AI models. This platform will help smaller publishers negotiate agreements similar to those major players like Reddit and Condé Nast have already finalized. The exact details of the marketplace are still being finalized. However, the concept is clear: Content creators can charge AI bots for scraping their sites by introducing a fee or by requesting proper attribution.

The initiative addresses a key challenge in the AI era: making sure small publishers can endure and succeed despite the growth of generative AI. “We believe we can provide the tools and set the standards to give websites, publishers, and content creators control and fair compensation for their contribution to the Internet while enabling AI model providers to innovate,” Prince said in a company blog post.

The actions of Cloudflare are opportune for escalating worries about content scraping driven by artificial intelligence technologies. A few months ago, publishers like The New York Times and CNN banned the OpenAI’s GPTBot from gathering information from their sites. Some have reported that intense data scraping has triggered a rise in service costs and decreased site performance, emphasizing the need for better controls. Cloudflare is meeting urgent issues for content creators through these new tools, reshaping the long-term interplay between AI and content creation. To promote a lasting digital ecosystem, maintaining a just balance between technology development and fair wages for creators is critical as AI advances.

Artificial intelligence (AI) is impacting different generations in distinct ways, influencing both how they interact with the technology and how they perceive it.

Generations X (1965-1980), Y (1981-1996), Z (1997-2012), and Alpha (2013 and later) have all experienced the rise of AI in varying stages of their lives, resulting in unique approaches to and views on this technology.

The relationship each generation has with AI is shaped by the social, economic, and cultural environments in which they grew up.

But how does each generation use AI, what does it represent for them, and what does research reveal about these dynamics?

Generation X (1965-1980) – Ones that adapt

Members of Generation X were raised in an era before the digital revolution fully took hold. However, many of them entered adulthood just as personal computers, the internet, and, eventually, AI technologies began transforming workplaces.

For this generation, AI is largely seen as a tool for increasing productivity and efficiency, particularly in business contexts. Many in Gen X see AI as a solution that automates routine tasks, helps analyze data, and optimizes processes in various industries, such as customer service and finance.

They tend to view AI pragmatically, focusing on how it can make their professional lives easier.

While Gen X has largely embraced AI, especially in the workplace, some remain cautious. Concerns about AI’s job displacement due to automation are common. Many are wary of how AI might impact employment prospects, especially for jobs that involve routine, repetitive tasks. This generation’s experience with AI is, therefore, closely linked to its professional utility and the promise of making work more manageable, but also comes with a cautious optimism.

According to data from Pew Research, approximately 57% of Gen X respondents found AI useful in their jobs, but only 40% believed AI would create more job opportunities in the future.

Generation X remains ambivalent about AI’s long-term effects, recognizing its potential to improve productivity but also fearing the challenges it might pose to job security.

Generation Y (1981-1996) – Ones that embrace

Millennials, or Generation Y, grew up during the rapid rise of the internet, mobile technology, and social media. They are considered the first digital natives and are far more comfortable with AI than their Gen X counterparts. For Millennials, AI is not just a tool for work but an integral part of their daily lives. Whether through algorithm-driven content on social media platforms or virtual assistants like Siri and Google Assistant, Millennials interact with AI daily and expect it to enhance both their personal and professional lives.

Millennials embrace AI for its capacity to provide personalized experiences. They are familiar with recommendation engines that help them discover music, movies, or shopping products tailored to their preferences. AI-driven apps and platforms are deeply embedded in how Millennials consume media, manage their tasks, and even approach health and fitness.

However, despite their heavy use of AI, Millennials are also more aware of its potential downsides, especially concerning privacy and data security. Having grown up in the era of big data, this generation is more likely to question how their personal information is collected, stored, and used by AI systems.

In a 2021 McKinsey study, 67% of Millennials reported using AI-driven recommendation systems and found them helpful. However, around 45% of respondents also expressed concerns about how their data is handled.

This duality shows that while Millennials are comfortable with AI and see it as a significant part of their lives, they are also increasingly critical of its ethical implications.

Generation Z (1997-2012) – Ones that grew up with it

Generation Z represents the first generation to grow up with AI as a standard part of their lives. This group has been surrounded by smartphones, smart devices, and advanced technology since childhood, and they take for granted the ubiquity of AI in everyday tasks. Whether through social media platforms that use AI to serve content or video games that feature AI-driven characters and environments, Gen Z’s interaction with AI is constant and varied.

For Gen Z, AI is about more than just entertainment or convenience—it’s a tool for creativity and self-expression. They use AI-powered tools not only to consume content but also to create it. AI filters on social media, automated video editing apps, and creative writing tools are just a few examples of how Gen Z employs AI to enhance their personal creativity. They also use AI to learn, relying on platforms that offer personalized educational experiences.

At the same time, Generation Z is acutely aware of the ethical challenges AI presents. They are concerned about the biases in AI systems, the spread of misinformation, and the social implications of AI-driven decision-making. They tend to be more critical of AI’s role in shaping public discourse, particularly in how it amplifies certain voices over others or contributes to the spread of false information.

According to a KPMG study from 2024, 42% of GenZ respondents believe that GenAI already has a significant impact on their personal lives, and 60% predict it will play an even larger role in two years. Furthermore, 77% of respondents foresee GenAI having a major impact on their professional lives within the next couple of years.

Generation Alpha (2013 and beyond) – Ones that born to it

Generation Alpha is the first generation to grow up in a world where AI is not a novelty but a given. For these children, AI is embedded in their education, entertainment, and even social interactions from a very young age. They interact with AI-driven toys, educational platforms, and voice assistants like Alexa or Google Assistant as part of their everyday environment. The presence of AI in their formative years means that they are likely to see it as a natural, almost invisible aspect of life, unlike previous generations who witnessed its introduction.

In education, AI is playing an increasingly significant role for Generation Alpha. Personalized learning platforms powered by AI adapt to their learning styles and provide customized content to help them advance academically. AI-driven tools are making education more interactive and engaging for this generation, blending learning with play through gamified, adaptive educational experiences. Additionally, AI-driven content is already shaping how these children consume media, whether it’s through interactive games, augmented reality experiences, or smart storytelling apps.

While Generation Alpha is still too young to fully articulate their relationship with AI, early indicators suggest that they will view AI as an essential and integrated part of their lives. However, as they grow older, they will likely face new challenges related to privacy, data security, and ethical AI. Their generation will be tasked with navigating a world where AI plays an even more significant role in shaping society and their personal identities.

A 2023 survey by HostingAdvice sheds light on how Generation Alpha (born after 2010) is engaging with AI tools, confirming its reputation as the first true “AI generation”.

The survey gathered insights from over 500 parents of children aged 7-14, revealing that 49% of Generation Alpha children are already using AI in various aspects of their lives, from education to entertainment.

This research highlights several key trends:

• Nearly half (49%) of Gen Alpha kids use AI tools, with boys leading in usage (54% compared to 45% of girls).
• The use of AI grows as children age; by 13-14 years old, 60% of surveyed children are using AI tools.
• Parents believe AI offers significant benefits: 30% mentioned AI’s potential in personalized education, while others pointed out improvements in creativity (29%) and future preparedness (21%).

Interestingly, AI’s role is not limited to entertainment. Around 23% of parents reported their children use AI to explore new topics and gain knowledge, while 20% use it for homework. AI-driven tools like virtual tutors provide customized learning experiences, increasing engagement and retention for students.

The age of evolution

Artificial intelligence has different meanings and uses across generations, shaped by each group’s unique experiences with technology.

As AI continues to evolve, so too will the way each generation interacts with it. The future will likely see even greater integration of AI into daily life, with each generation contributing its own unique perspective on how to harness its power responsibly.

The artificial key to a better tomorrow

The evolution of ours presents opportunities for creativity, innovation, and collaboration across generations, as well as challenges related to ethics, privacy, and fairness that will need to be addressed as AI becomes an increasingly central part of human existence.

Although our perception of this new technology may differ, one fact is clear as day: Ready or not, we are on the threshold of a new era. Either we catch it, or others will leave us behind…

Image credits: Kerem Gülen/Midjourney

AI is becoming a go-to for writing, spotting the difference between human and machine-generated content has turned into a bit of a detective game.

How to tell if something is written by AI?

As AI becomes more entwined with how we create and consume information, there’s a growing curiosity (and concern) about how to tell when a machine, rather than a human, is behind the words.

Words like “elevate” and “enhance” often show up because they convey a sense of improvement or progress, which is a common theme in content across fields like marketing, business, and self-help. For instance, an AI might generate a sentence like “Elevate your productivity with these simple hacks,” or “This strategy will enhance your team’s performance,” because these terms easily resonate with readers looking for growth or change.

Another commonly overused word is “seamless.” Since AI-generated text often covers topics related to customer service, user experience, or technology, “seamless” becomes a catch-all term to describe something smooth or effortless. Phrases like “Our platform offers a seamless user experience,” or “Enjoy seamless integration with your existing tools” often pop up in AI-created content.

While these words may seem appropriate at first glance, their repetitive use can give away the fact that the text was generated by a machine.

AI: The speed

There’s no denying that AI tools excel in speed and efficiency. They can churn out articles, reports, and even creative writing in a matter of seconds—something that would take a human hours, if not days, to achieve. Need a 1,000-word blog post on a niche topic? An AI can have it ready in the blink of an eye.

AI tools also come equipped with vast knowledge banks, allowing them to pull together information from countless sources and package it neatly into coherent sentences. This efficiency is great when you’re dealing with tight deadlines or need quick, factual content.

Human: The creativity

AI can churn out copy with lightning speed, it’s the human element that brings writing to life. Humans bring more than just words to the page—they bring experience, emotion, and perspective. Where AI follows patterns, human writers break them, using creativity to surprise, inspire, and connect with their audience on a deeper level.

Think about it: an AI can string together sentences about love, loss, or joy, but it doesn’t feel those emotions. Human writing, on the other hand, is shaped by personal experiences, cultural context, and emotional depth—elements that machines simply can’t replicate. It’s these small, often unquantifiable details that make writing resonate with readers.

Is AI creative: Answering the unanswerable

AI can churn out copy with lightning speed, it’s the human element that brings writing to life

Is there a way to detect AI writing?

Yes there are tools to do that.

With the rise of AI-generated content, identifying whether something was written by a human or a machine has become increasingly important.

The ability to detect AI writing ensures that original voices aren’t being overshadowed by algorithmic patterns and helps maintain the quality of human expression.

Thankfully, there are now tools designed specifically to detect AI-written content. These tools analyze factors like sentence complexity, word choice, and syntax to differentiate between human and machine-generated text. While no tool is perfect, they serve as a valuable resource in flagging suspicious writing patterns. In our best plagiarism checkers for ChatGPT and other AI tools article, we discuss several platforms that have integrated AI detection into their plagiarism-checking features.

One notable example is Copy AI, which offers a plagiarism checker alongside its content creation tools to ensure authenticity.

How to pass an AI detector?

As AI writing becomes more prevalent, many users are looking for ways to modify generated text to make it undetectable by AI detectors.

It doesn’t matter if you’re concerned about maintaining the natural flow of your writing or want to ensure your AI-generated content doesn’t set off alarms, understanding how to humanize text is key.

AI detectors often rely on algorithms that spot repetitive patterns, overly polished grammar, or unnatural phrasing—things that tend to stand out in machine-generated writing. This is where humanizing AI text becomes important. By tweaking certain elements like sentence variation, tone, and word choices, you can transform stiff AI text into something that feels more organic and conversational.

For those looking to take extra steps, we’ve compiled a list of the 10 best AI humanizers that can help make AI-generated text sound more natural. One standout resource is Humanizer.org, a platform designed to convert robotic writing into human-like content. We’ve reviewed this service in detail to help you decide if it’s right for your needs.

The best humanizer is you

While AI tools can help polish and tweak content, the most effective way to humanize AI-generated text is through your own personal touch.

No tool can fully replicate the nuances, creativity, and authenticity that come naturally to a human writer. AI-generated content might give you a strong foundation, but it’s your unique voice, perspective, and storytelling that breathe life into the words.

By simply editing and adding your own spin—through a dash of humor, a shift in tone, or personal anecdotes—you can transform an otherwise robotic-sounding text into something that feels authentic and engaging.

No matter how advanced these tools become, they still lack the heart and insight that only a human can provide.

Image credits: Kerem Gülen/Midjourney 

We get it—some of you, just like our team here, haven’t had the chance to try the ChatGPT Advanced Voice feature yet; but a recent leak confirms that OpenAI is about to roll it out for a select group of Plus users.

So, soon you’ll be able to compare those early demo videos with your own hands-on experience!

Is ChatGPT Advanced Voice Mode available?

OpenAI’s Advanced Voice mode, first demoed in May, has been stirring up excitement. This feature lets you chat with ChatGPT on your phone in a natural, back-and-forth conversation, even giving you the power to cut it off if it starts to ramble. It also handles complex questions with ease, offering in-depth responses.

A recent leak, reportedly from the ChatGPT team, suggests that the feature is being rolled out in a limited alpha to select users. According to the email, access to this alpha phase starting September 24, 2024, will be based on various factors, including participation invites and other testing criteria. In simpler terms: not everyone will get it just yet:

Hi there,

Thank you for reaching out and for your interest in the Advanced Voice mode! It’s great to hear about your enthusiasm for our new features.

As of now, access to Advanced Voice mode is being rolled out in a limited alpha to a select group of users. While being a long-time Plus user and having been selected for SearchGPT are both indicators of your active engagement with our platform, access to the Advanced Voice mode alpha on September 24, 2024, will depend on a variety of factors including but not limited to participation invitations and the specific criteria set for the alpha testing phase.

Unfortunately, I don’t have the ability to manually add users to the alpha testing list or provide specific insights into individual account access timelines. However, Plus users like yourself are among the first to receive access to new features, and we are planning for all Plus users to have access in the fall. Keep an eye on your email and app notifications, as any invitations or updates regarding access will be communicated through those channels.

We truly appreciate your support and interest in being part of the early users for Advanced Voice mode. Your enthusiasm for our products helps us improve and expand our offerings.

Best,
OpenAI Team

While OpenAI has promised that all Plus users will have access by the end of fall, this alpha rollout is a promising step toward the full release. Plus users, who pay $20 a month (or the equivalent in other regions), already get access to various LLMs, including the recently launched version 01-preview, which has impressed many with its improved math-solving and reasoning skills.

OpenAI’s voice feature has been in the spotlight for various reasons, including concerns about copyright and the recent rollout. Earlier this year, the company faced backlash over its “Sky” voice, which many users felt closely resembled Scarlett Johansson’s. After feedback and Johansson’s legal action, OpenAI decided to pull the voice, clarifying that Sky was voiced by a different actress.

Now, Plus users are eagerly pressing OpenAI to accelerate the full rollout of Advanced Voice. Meanwhile, Apple Intelligence has yet to make its debut on iPhones, keeping iOS 18 users waiting for the anticipated AI-driven features. On the Android side, Google’s Gemini AI is already making waves, with early access being rolled out, giving users a taste of advanced voice and assistant capabilities before much-anticipated AI updates arrive.

Featured image credit: Solen Feyissa/Unsplash

Merlin is a defines itself as “26-in-one AI assistant to research, create and summarize.” The tool merges advanced AI features into a single platform.

Yet, the question remains: how well do these features truly work in practice?

What is Merlin AI?

Merlin is a comprehensive AI-powered assistant designed to enhance productivity by integrating advanced natural language processing (NLP) models like GPT-4 and Claude-3 into everyday tasks. It uses machine learning algorithms to understand and generate human-like responses, enabling users to interact with websites, documents, and applications through contextual conversations.

How to use the Merlin AI extension: Our experience

Merlin AI is a versatile tool designed to enhance productivity by integrating with your browser. Whether you’re using it on Chrome, iOS, or Android, here’s a quick guide to get started, followed by our experiences with some of its features.

Sign up and install:

• Visit Merlin’s website.
• Sign in using your Google account for quick access.
• You can install the extension directly on Chrome for easy access to Merlin AI’s tools across websites. Alternatively, use it directly on the web browser without installing the extension.
• Merlin also offers apps for iOS and Android, so you can extend its functionality to your mobile devices.

10 best AI tools for business: Top AI business solutions

Our experience

• Image generation: We tested Merlin’s image generator by creating a character portrait with a prompt. While the process was smooth, we found that the output wasn’t entirely accurate based on our input. However, it can still be fun and useful for more straightforward visual prompts.

• Document analysis: We successfully analyzed the UI/UX of a website using Merlin’s document analysis feature. The AI provided detailed insights, such as suggestions for improving visual hierarchy, navigation, and performance optimization. This made it a valuable tool for getting instant feedback on web design and functionality.

All features of Merlin AI

Merlin’s capabilities include text summarization, email writing, code interpretation, and content creation, all facilitated by a cloud-based infrastructure that ensures real-time data processing and interaction across various platforms. Through its multi-model support, Merlin AI provides versatile solutions for research, development, and creative tasks within a unified interface.

Chat with AI on every website

Merlin allows users to interact with AI across all websites, enhancing productivity by answering questions or generating content directly within the browser.

Chat with 20+ top AI models

Users can access over 20 advanced AI models for varied and dynamic interactions, including models like GPT-4 and Claude-3, providing diverse responses for different needs.

Get AI response on every Google search

Merlin integrates with Google search, providing instant AI-generated answers for faster, more relevant search results.

Chat with websites

It offers the ability to interact with and query any website directly through AI, making browsing more interactive and informative.

Chat with documents

Users can upload documents and chat with them, asking questions or requesting summaries, which enhances document review and comprehension.

Chat with latest web search data

Stay updated with the most recent information from web searches, as Merlin pulls and interacts with fresh data for you.

Gmail Email Writer

Merlin assists in drafting and responding to emails on Gmail, improving efficiency in communication.

Chat with Code Interpreter

A built-in code interpreter helps you write, understand, and troubleshoot code, useful for developers and learners alike.

YouTube Summarizer

This feature generates summaries of YouTube videos, saving time for users who want a quick overview of video content.

Blog Summarizer

Merlin can summarize blogs, providing concise overviews of lengthy articles for quicker consumption of information.

LinkedIn DM Response Assistant

It helps craft tailored responses for LinkedIn direct messages, improving professional communication.

LinkedIn Pro Connect

Enhance LinkedIn networking by leveraging AI to assist in making connections and optimizing profile engagement.

Google link content summarizer (Link preview)

When shared, Merlin can summarize the content of Google links, offering quick previews of web pages.

Code explainer

Users can get detailed explanations of coding syntax or segments, making complex code easier to understand.

YouTube transcription tool

Merlin transcribes YouTube videos, creating readable text versions of the video content.

Doppel – Create a chatbot out of any person’s public tweets

This feature allows users to generate AI chatbots based on a person’s public tweets, adding a fun or educational dimension to social media engagement.

Generate AI images & art

Merlin can generate custom AI-based images or artwork, ideal for creative projects or visual content creation.

Tweetify – Make tweets out of YouTube videos

Convert YouTube video highlights into tweetable content, making it easier to share key insights on social media.

Chat with custom chatbots made by community

Interact with various custom-made chatbots that the community has created, opening up new possibilities for personalized AI interaction.

Prompt library to save frequently used prompts

Users can save and access frequently used prompts in a personal library for easier and faster access in the future.

Twitter commenter

Merlin can help craft comments for Twitter, engaging in conversations and responding more effectively across the platform.

All screenshots used above are shared by Merlin.

Should you use Merlin AI?

Of course, for those making decisions, testing out Merlin AI thoroughly is a must.

It’s a process that involves exploring each feature in depth to determine how well it integrates into your workflow.

While we’ve put it through its paces, results can vary based on individual needs and expectations. It’s a long road to assess its full potential, so perhaps the best approach is to try it yourself and see if it aligns with your requirements.

You might find some features more useful than others, or discover limitations we didn’t encounter.

Featured image credit: Kerem Gülen/Midjourney

Gab AI might sound familiar, but don’t confuse it with the controversial social media platform Gab—it’s a completely separate entity. While Gab, the social media platform, offers features similar to Twitter like posting, private chats, and livestreaming, it became infamous for attracting extreme views and controversial groups. This reputation has nothing to do with Gab AI, so it’s important not to lump them together.

Now, when it comes to AI tools, most of them are filtered to avoid certain sensitive topics. But sometimes, you need to explore those areas for valid reasons, and that’s where Gab AI can step in. If you’re working on something delicate or outside the usual scope, Gab AI offers a less restricted platform to help you get the job done without unintended barriers.

What is Gab AI?

Gab AI is as an uncensored and unfiltered AI platform that allows users to engage with a variety of chatbots and tools. It offers functionalities similar to ChatGPT but with a broader, less-restricted approach.

The platform provides access to various chatbot characters, image generators, and custom character creation, catering to a wide range of interests. It’s meant for users who may need more flexible AI tools for creative projects or discussions that may not be suitable for more heavily moderated AI services.

How to use Gab AI?

Using Gab AI is simple and user-friendly, designed to cater to different levels of interaction.

Once you land on the platform, you’ll notice a clean interface with a search bar that prompts you to “Ask Arya anything.” Arya is Gab AI’s default chatbot, and you can type any query or request for a response.

The AI will quickly process your input and generate an answer, whether you’re seeking tips, ideas, or something more specific.

Gab AI image generators

Gab AI offers a robust selection of image generators to fuel your creativity. These tools allow users to input text prompts that the AI translates into various art styles. For example, you can choose from steampunk, pixel art, anime, or watercolor styles, among many others. Some generators include:

• Animation style generator: Turn text into animated-style images.
• Steampunk style image generator: Craft detailed, steampunk-inspired visual art.
• AI comic art creator: Bring your text prompts into comic-book-style artwork.
• Watercolor wizard: Generate soft, watercolor-like imagery from text.

This feature is particularly useful for users looking to create personalized images for projects or artistic purposes without needing any design skills. You simply type in a description of what you want, choose an art style, and let the AI do the rest.

Gab AI characters

Gab AI provides a wide array of AI chatbot characters that users can interact with, each offering a unique personality and expertise.

Characters range from fictional figures to historical icons, each capable of engaging in conversations around specific themes. Some options include:

• Bible scholar: Answers theological questions.
• AI roast generator: A humorous bot for creating playful insults.
• Donald Trump: Engages with users on topics ranging from politics to pop culture.
• Gamer: Discusses gaming topics.

Gab AI also gives users the ability to create custom characters. You can personalize these chatbots according to response tone, length, and other preferences, allowing for a tailored interaction based on your needs.

Gab AI pricing details

Gab AI offers two primary pricing options:

1. Basic free plan:
   • Limited to 5 conversations.
   • Up to 10 messages every 3 hours.
   • Can generate 3 images per day.
   • Limited to creating 1 custom character.
   • Slower media generation with a 3-hour expiration window.
2. Plus plan ($20/month or $200/year):
   • Unlimited conversations: Chat without restrictions, engaging with as many bots and conversations as you like.
   • Generate 100 messages per hour: Provides higher messaging capacity for more dynamic conversations.
   • Generate 50 images per day: Expands the creative potential by allowing up to 50 image creations daily.
   • Create 10 custom characters: Tailor up to 10 chatbots to suit specific needs or preferences.
   • Regenerate responses with one click: Offers the ability to refresh responses, ensuring you get the most useful and accurate results.
   • Advanced character settings: Customize chatbot behavior, including response length and tone, giving you full control over the interaction.
   • Early access to new features: Be the first to try out and explore new functionalities before they are made available to general users.

Featured image credit: Kerem Gülen/Ideogram

Amazon’s got something new to make your shopping experience a whole lot easier—and help you catch some deals you didn’t even know you wanted. They’ve rolled out a new AI feature that promises to give you more personalized recommendations.

The idea is simple: make shopping less of a hassle and more about finding exactly what you need.

One of the coolest upgrades is how Amazon’s recommendation system now works. You’re used to seeing “more like this” suggestions based on what you’re browsing. Now, it’s going to get a lot more specific. The AI digs into the details of your search history, showing you products that line up with what you’ve looked for in the past—things like holiday gifts or gear for that hobby you’ve been exploring. This means fewer random results and more ideas that could lead to solid discounts, especially if it catches a deal you didn’t notice before.

What’s even better is the way product descriptions are about to change. You know how sometimes it feels like you’re scrolling endlessly. This AI feature aims to fix this.

So, if you’re looking for “eco-friendly furniture” or a specific color for your home, that will be front and center. Less scrolling, more finding—and potentially faster access to those Prime Day discounts.

If a product description doesn’t quite hit the mark for you, there’s another AI working behind the scenes to fix it, making sure the next suggestion you see is even better.

Plus, if you’re into trying new tech, Amazon’s Rufus chatbot will help you out. You can actually chat with the AI, and it’ll help you find the best deals.

How to find best deals on Amazon Prime Day with AI?

Here are five tips to make the most out of this AI shopping experience:

Be specific in your search. Let’s say you’re looking for a vegan protein powder. Instead of just typing “protein powder,” tell the AI exactly what you want—“organic vegan protein powder under $30.” This way, it can filter out products that don’t meet your criteria and show you exactly what you’re after, along with any promotions or discounts available.

Ask for similar products. Found something you like but want to explore your options? Ask the AI for “more like this.” For example, if you’re eyeing a particular Bluetooth speaker, the AI can show you similar models, maybe even one that’s on sale or comes with a better warranty.

Set price alerts. If you’re not in a rush, use the AI to track prices. Say you want to buy a smartwatch but you’re waiting for a better deal. You can ask the AI to alert you when the price drops. Imagine getting a notification that your dream gadget is suddenly 20% off!

Combine recommendations with coupons. You can also ask the AI to look for products that accept coupons or are part of a limited-time promotion. For instance, if you’re searching for home office furniture, the AI might guide you to a desk that not only fits your needs but also has a coupon that knocks off an additional $50.

Use chat to find bundled deals. If you want to score multiple items for less, ask the AI about bundle deals. For example, if you’re shopping for kitchen appliances, you could ask the AI if there’s a package deal on a toaster and coffee maker, potentially saving you more than buying them separately.

When is the next Amazon Prime Day?

The next Amazon Prime Day, this time called “Prime Big Deal Days 2024,” will take place on October 8 and 9, closely aligning with the event dates from previous years, which were held on October 11-12 in 2022 and October 10-11 in 2023.

Featured image credit: Kerem Gülen/Midjourney

Google is changing the game for video creation by bringing advanced AI models, Veo and Imagen 3, to YouTube Shorts. Over the next few months, creators will get access to Dream Screen, a tool that lets you generate AI-powered backgrounds and short video clips. While AI tech is already out there, this move makes it way easier for millions of creators and billions of users to use. It’s all about giving people more creative power without needing a whole production crew.

Dynamic backgrounds with Dream Screen

Dream Screen works by starting with a simple text prompt. Imagen 3, Google’s AI model, generates four image options based on that prompt. Users can choose the image that best suits their needs, and Veo, another AI model, transforms it into a high-quality six-second background video. By 2025, this tool will also allow users to create six-second standalone video clips, offering more flexibility for content creators on YouTube Shorts.

This feature aims to make video creation more accessible. It gives creators the ability to generate professional-looking content without needing advanced tools or a large budget. Dream Screen is intended to help both experienced creators and those new to the platform.

To keep things clear, Google’s adding watermarks to AI-generated content using SynthID.

So, viewers will know when something’s been made with AI.

Watermarks did not convince us

As cool as it sounds, there’s something worth thinking about when we talk about integrating AI into every corner of content creation. Sure, AI is this shiny new toy, but is it really wise to lean on it for everything? You’ve heard the saying, “Too much of a good thing can be bad,” right?

Well, that might be the case here.

On one hand, AI makes life a lot easier. It speeds things up, fills in gaps, and opens up creative doors we never even knew existed. But on the flip side, where’s the line between helpful and over-reliant? If creators get used to letting AI do all the heavy lifting—like generating videos, writing scripts, or even composing music—are we losing some of that human touch?

Is AI creative: Answering the unanswerable

Creativity has always been about the spark, the struggle, the ah-ha! moments. If we hand those moments over to machines, do we risk flattening out the very thing that makes art special?

And what about authenticity? If everything starts looking AI-polished, it could all blur together. You know how sometimes you see those perfectly airbrushed photos, and they just don’t feel real anymore? That’s kind of the vibe we might be heading toward. AI can churn out beautiful, flawless content, but does that make it better? Or does it just mean we’re all going to be swimming in a sea of polished, but soulless, creations? It’s like having a box of chocolates where every piece tastes exactly the same—where’s the fun in that?

Plus, let’s talk about originality.

AI works by learning from the stuff that’s already out there, right? So, while it’s great at remixing and mashing up ideas, it’s not exactly bringing something completely new to the table. If we keep feeding the machine, won’t we eventually just get… well, more of the same? There’s a risk of innovation grinding to a halt because everything starts pulling from the same AI-generated pool. As the saying goes, “If you want something done right, do it yourself.”

Maybe that’s something creators need to remember.

Featured image credit: Kerem Gülen/Ideogram

LinkedIn has quietly opted its users into training generative AI models without explicitly asking for consent, raising concerns about data privacy on the platform. According to a report by 404Media, LinkedIn made changes to its privacy policy, stating that user data can be used to train AI models. The platform has since updated the policy, now allowing users to opt-out of this practice.

Updated LinkedIn policy reveals personal data usage on AI training

The updated policy states that LinkedIn may use personal data to “improve, develop, and provide products and Services,” as well as to train AI models. Generative AI is used for features like writing assistants, but LinkedIn claims it employs privacy-enhancing technologies to redact personal information. Users who prefer not to participate can opt-out by navigating to the “Data privacy” tab in their account settings, turning off the “Data for Generative AI Improvement” toggle.

However, opting out will only stop LinkedIn from using your data for future model training. Data that has already been used remains unaffected. Additionally, LinkedIn clarifies that users in the EU, EEA, or Switzerland are not included in AI model training.

If you’re concerned about other machine learning tools used for personalization and moderation, LinkedIn requires users to fill out a “Data Processing Objection Form” to opt-out from those uses as well.

LinkedIn’s silent opt-in move echoes similar actions from Meta, which recently admitted to scraping non-private user data for AI training dating back to 2007.

The timing of LinkedIn’s move comes at a moment when other major tech players, like OpenAI, are also facing backlash for similar practices. This pattern of quietly enrolling users in AI training without clear and prominent notifications creates a sense of unease.

OpenAI CTO Mira Murati says Sora was trained on publicly available and licensed data pic.twitter.com/rf7pZ0ZX00

— Tsarathustra (@tsarnick) March 13, 2024

It’s not just about data being used for AI—it’s about who gets to decide and how informed that decision is. The tech industry has long faced criticism for operating in the shadows when it comes to data collection, and the growing push for generative AI is only amplifying those concerns.

Can machines forget your personal data?

Another key issue is that opting out only affects future use of personal data. Any data that has already been fed into AI models remains in the system, and that lack of retroactive control may leave many users feeling powerless. The industry is also talking about “machine unlearning” to prevent this from happening, deleting data fed in AI models.

The fact that LinkedIn uses “privacy-enhancing technologies” to anonymize data is somewhat reassuring, but it doesn’t address the deeper problem: the need for more proactive, user-centered privacy standards.

Ultimately, this situation highlights the need for stronger, clearer regulations that put control back in the hands of users. The idea that tech companies can use our personal data without clear consent doesn’t sit well in times where privacy is becoming increasingly valuable.

Featured image credit: Kerem Gülen/Ideogram

Runway, a startup specializing in AI-powered video tools, has announced a big partnership with Lionsgate, the studio behind blockbusters like the John Wick and Twilight franchises. This collaboration will allow Runway to develop a custom AI video model trained on Lionsgate’s movie catalog, providing filmmakers, directors, and other creative talents access to cutting-edge AI tools designed to enhance their production work. According to Lionsgate’s vice chair, Michael Burns, the new AI model will help creators “augment their work” in unique and innovative ways.

Will Runway x Lionsgate deal change the face of the cinema?

“Runway is a visionary, best-in-class partner who will help us utilize AI to develop cutting edge, capital efficient content creation opportunities,” said Lionsgate Vice Chair Michael Burns. “Several of our filmmakers are already excited about its potential applications to their pre-production and post-production process. We view AI as a great tool for augmenting, enhancing and supplementing our current operations.”

Runway is also exploring the possibility of offering licensed versions of these AI models, giving independent creators the ability to build and customize their own AI tools for personal projects. This move marks Runway as the first AI company to publicly secure a deal with a major Hollywood studio, setting it apart from other tech firms like Disney and Paramount, who have yet to formalize similar partnerships despite discussions recently reported by The Wall Street Journal.

However, this partnership coincides with new legislative developments in California, where recent bills were signed to regulate the use of AI-generated digital replicas in film and television. Adding to the complexity, Runway is also facing legal challenges over accusations that it used copyrighted content without permission to train its AI models.

While this collaboration could push the boundaries of what AI can offer to the entertainment industry, it also highlights the growing tension between technological innovation and legal and ethical concerns. The partnership between Runway and Lionsgate might signal a new chapter for AI in filmmaking, but it’s clear that the path forward will be closely scrutinized.

How does this affect cinema?

Cinema has always pushed the boundaries of imagination, but since the early 2000s, the once-impossible ideas from movies have started to become real. Video calls are now a daily norm, flying car prototypes are being tested, holograms appear at concerts, and artificial intelligence is reshaping entire industries.

Best AI movies to watch for the weekend

But is cinema truly predicting the future, or is it just a coincidence? It’s not so simple to answer. Filmmakers often collaborate with scientists and engineers to turn imaginative ideas into reality. For example, the hoverboard from Back to the Future sparked the creation of real-life prototypes, even if no one truly “needed” them. Like a child driven by the desire to play with their favorite toy, engineers accepted the challenge the film presented. The hoverboard Marty McFly rides in that iconic scene wasn’t just a dream—it was a dare, one that engineers eagerly took on.

The influence of cinema extends beyond mere inspiration. Many leading tech companies, such as Adobe, Pixar, Technicolor, and Dolby, were born from the desire to bring cinematic visions to life. Today, these companies shape not only the film industry but also the broader tech world, driven by founders who turned their cinematic dreams into reality.

Featured image credit: Runway

Microsoft Copilot is am ambitious answer to productivity-boosting AI, and it plays a key role in the company’s broader AI strategy. Today, Copilot powers a variety of features across Microsoft’s ecosystem, from helping users manage their inboxes in Outlook to generating transcriptions in Teams. What started as a single tool has grown into a suite of AI-driven solutions, designed to streamline workflows and enhance productivity.

Beyond office tasks, Copilot integrates deeply into coding through GitHub’s Copilot, which aids developers by generating code snippets and suggesting improvements. On top of that, Copilot is built into Windows and the web, acting as a general-purpose AI assistant available at users’ fingertips, literally – newer PCs even come equipped with a dedicated Copilot key for instant access.

What is Microsoft Copilot?

At its core, Microsoft Copilot is built into Bing, Windows 10, Windows 11, and the Edge sidebar, but it has also expanded to mobile devices with standalone apps for Android and iOS. Whether users are looking to write creatively, translate languages, or summarize web sources, Copilot taps into OpenAI’s advanced models to perform tasks based on simple natural language prompts.

Like other AI assistants such as ChatGPT and Google’s Gemini, Copilot can browse the web using Bing to deliver real-time information. While it’s not perfect and may occasionally provide incorrect results, its web-browsing ability gives it an edge for time-sensitive queries, especially compared to offline-only models like Anthropic’s Claude.

On top of that, Copilot is capable of generating images and music. By leveraging Microsoft’s Image Creator, based on OpenAI’s DALL-E 3, it can produce custom images with simple prompts like “Create an image of a zebra.” For music, Copilot integrates with Suno, allowing users to generate tunes on demand, whether it’s a jazz melody or something else.

One of the standout features of Copilot is its ability to integrate with third-party apps and services. It supports plug-ins from companies like Instacart for meal planning, Kayak for trip planning, and OpenTable for restaurant reservations. These integrations expand Copilot’s functionality beyond traditional office tasks, making it a versatile tool for both work and everyday life. And the list of available plug-ins continues to grow, providing more options for users looking to streamline tasks across various platforms.

Copilot also enables users to work collaboratively through Copilot Pages, a digital canvas for editing and sharing content generated by the AI. For businesses, there’s even more functionality through BizChat, a hub that integrates with Pages to pull information from the web and work files, helping users create everything from meeting notes to project plans.

Can I use Microsoft Copilot for free?

Microsoft Copilot isn’t fully free, but there are some exceptions. For Microsoft 365 users, Copilot comes at a cost, starting at $30 per user per month. However, if you’re using GitHub Copilot, there’s a free option available for students, verified open-source contributors, and educators. For most individuals and businesses, access to Microsoft’s Copilot tools requires a subscription, whether it’s within the Microsoft 365 suite or GitHub.

Is Copilot better than ChatGPT 4?

Whether Microsoft Copilot is better than ChatGPT 4 depends on your needs. Copilot is deeply integrated into Microsoft’s productivity tools, like Word, Excel, and PowerPoint, making it ideal for tasks involving documents, data analysis, and project management. It’s designed to assist with specific workflows and leverage enterprise data, making it a strong tool for businesses.

On the other hand, ChatGPT 4 is a more general-purpose AI assistant with a broader range of use cases, from creative writing to coding and general research. It’s more versatile outside the Microsoft ecosystem but lacks some of the productivity-specific integrations that make Copilot so effective within business environments.

How to enable GPT-4 in Copilot?

To enable GPT-4 in Microsoft Copilot, you’ll need to be using one of Microsoft’s services that leverages OpenAI’s GPT-4 model, such as Microsoft 365 Copilot or GitHub Copilot. However, there’s no direct toggle to “enable GPT-4” in Copilot, as it works behind the scenes. Here’s how to access and use it:

• Subscribe to Microsoft 365 Copilot: Ensure you have a subscription to Microsoft 365 Copilot, which is available to users with a Microsoft 365 E3, E5, Business Standard, or Business Premium license. This service integrates GPT-based AI into apps like Word, Excel, PowerPoint, and Outlook.
• Use GitHub Copilot: If you’re a developer, GitHub Copilot also leverages GPT-4 for code generation and debugging. You can subscribe to GitHub Copilot as an individual or through a business plan to access these advanced capabilities.
• Check for compatibility: If you are already using Microsoft services, Copilot will automatically use GPT-4 in the background for its AI-driven features like generating text, analyzing data, or summarizing content.

There’s no manual enabling of GPT-4 within Copilot, but by subscribing to Microsoft or GitHub Copilot services, you’re using GPT-4’s power for tasks and productivity.

Windows settings you can control with Copilot

Copilot in Windows 11 acts like a digital assistant, giving users a hands-free way to control various system settings. With simple commands—typed or spoken—users can perform tasks such as turning on battery saver mode, displaying system information, launching live captions, showing the PC’s IP address, and even emptying the recycle bin. This level of control turns Copilot into a useful tool for navigating everyday PC functions without having to dive into menus.

Windows 11 also allows users to toggle between “Work” and “Web” modes within Copilot. In Work mode, the assistant’s Microsoft 365 capabilities come to the forefront, allowing it to operate smoothly within the Windows interface for productivity-focused tasks. This feature makes Copilot not only a system controller but a true assistant for office tasks.

What is Copilot Pro?

Microsoft’s premium Copilot Pro, available for $20 per month, takes the functionality of the basic Copilot to the next level by offering priority access to OpenAI’s most powerful models, like o1, especially during peak times. This Pro version is designed for users who need more than basic assistance—think higher-resolution images from Image Creator or more advanced features across Microsoft 365 apps like Word, Excel, PowerPoint, and Outlook.

For professionals who rely on Microsoft’s productivity tools, Copilot Pro integrates generative AI directly into their daily workflow. In Word and OneNote, for example, it can write, summarize, and edit text, saving users time on drafting and polishing documents. Meanwhile, Excel and PowerPoint benefit from Copilot’s ability to create presentations, graphs, and visualizations based on user prompts or existing data. Outlook takes things further with AI-powered drafting, allowing users to adjust email length or tone with just a toggle.

More features on the way

The Copilot Wave 2 update promises even more useful tools. In PowerPoint, for instance, users will soon be able to pull company-approved images directly from SharePoint, while Outlook will gain a “Prioritize my inbox” feature that summarizes emails and highlights key contacts. By late 2024, Outlook will allow users to “teach” Copilot to prioritize emails based on topics, keywords, and specific people of interest.

Excel users are set to benefit greatly from Copilot’s advanced capabilities, which include formatting data, generating pivot tables, and even handling Python-based tasks. By describing tasks in natural language, such as data forecasting or risk analysis, users can have Copilot generate the necessary Python code to execute complex operations.

Further down the road, Copilot will be able to seamlessly pull data from other Microsoft 365 applications like PowerPoint and Outlook, allowing users to work across platforms without leaving their current task. On OneDrive, Copilot will take on even more, summarizing files, showing metrics, and comparing document versions.

In terms of visuals, Copilot Pro also gives users access to landscape formatting options and significantly more “boosts” in Image Creator, with 100 boosts per day compared to just 15 for free users. This feature is particularly helpful for those who frequently rely on AI-generated images to support their work.

Enterprise-class exclusivity

Despite its many features, Copilot Pro does not include Copilot in Teams, a tool exclusive to enterprise users. This version of Copilot offers real-time meeting summaries, action items, and even identifies follow-up tasks, making it ideal for companies. Unfortunately, Copilot in Teams is reserved for those with enterprise-level Microsoft 365 licenses, meaning smaller businesses or individual users will miss out on these collaborative features.

With Copilot Pro, Microsoft is clearly positioning the product as a versatile assistant for both personal productivity and professional workflows, but it continues to offer certain high-level features exclusively to enterprise customers. The continued rollout of new functionalities will likely keep Copilot at the forefront of AI-driven productivity solutions.

What is Microsoft 365 Copilot?

Distinct from the consumer-facing Copilot, Microsoft 365 Copilot is designed specifically for business applications within the Microsoft 365 ecosystem. Priced at $30 per user per month, it’s available to customers with a Microsoft 365 E3, E5, Business Standard, or Business Premium license. While it offers many of the same features as Copilot Pro, it stands out with enterprise-grade data protection and a Semantic Index system, which creates a comprehensive map of organizational data to provide personalized AI responses.

In addition, Microsoft 365 Chat allows users to pull information from multiple Microsoft 365 apps—such as Word and PowerPoint—to answer questions and deliver cross-platform insights.

Key Microsoft 365 Copilots and their functions

Microsoft 365 Copilot integrates into various business apps, enhancing their capabilities. Here’s a breakdown of some key Copilots and the tasks they handle:

Copilot in Power Pages

• Generates text, forms, chatbots, and web page layouts.
• Creates and edits images and site design themes.

Copilot for Sales

• Assists with writing customer emails and summarizing Teams meetings through Outlook.
• Handles sales-related administrative tasks efficiently.

Copilot in Microsoft Supply Chain Center

• Proactively flags potential issues, such as weather or financial factors, that could impact supply chain operations.

Copilot for Service

• Drafts responses to customer queries via chat or email.
• Provides customer service agents with AI-driven suggestions, drawing from knowledge bases and case history.

Copilot for Azure

• Suggests configurations for apps and environments hosted on Microsoft Azure.
• Assists with troubleshooting by identifying potential problems and offering solutions.

Copilot for Security

• Summarizes cyberthreat intelligence and helps organizations understand different types of security risks.

Copilot in Fabric

• Aids in exploring, preparing, and visualizing data for analysis.

Copilot in Intune

• Helps manage security policies, troubleshoot device issues, and oversee settings across devices.

Team Copilot

• Organizes Teams meetings, managing agendas, and extending to apps like Loop and Planner to assign tasks and track deadlines.

Pricing and additional features

Not all Copilots are included in base software licenses. For instance, Copilot in Business Central comes with the core license, but others like Copilot for Sales or Copilot for Service come with additional fees—$20 per user per month, or $50 per user per month without an active Microsoft 365 Copilot subscription.

Microsoft 365 Copilot is tailored to offer advanced functionality for business users, combining AI-driven insights with enterprise-level data security and personalized assistance to streamline processes across various departments.

Copilot Studio

Copilot Studio serves as a central hub where users can customize and manage their Microsoft 365 Copilot experience. Through this dashboard, businesses can grant Microsoft 365 Copilot access to data from their own systems—like customer relationship management (CRM) or enterprise resource management (ERM) platforms—using prebuilt or custom-built connectors. This functionality makes Copilot highly adaptable to specific organizational needs.

One of the standout features of Copilot Studio is the ability to create custom copilots using natural language. These custom copilots can be tailored to specific datasets, teams, or workflows, and they can even integrate with third-party services or automations to trigger actions. This flexibility allows businesses to streamline processes and ensure that Copilot interacts with their unique data systems.

In addition to custom copilots, Copilot agents can be developed within Copilot Studio. These AI-driven bots can be called upon directly in tools like Outlook and Teams via simple @ mentions. They adapt to business workflows, learn from user feedback, and request assistance when needed. Microsoft has provided prebuilt agents, like the Visual creator agent, which generates images, designs, and will soon include video creation.

What is GitHub Copilot?

Separate from the Microsoft 365 ecosystem, GitHub Copilot focuses on the world of programming and code generation. Available as an extension for popular IDEs like Visual Studio Code, Neovim, and JetBrains, GitHub Copilot suggests code as developers type, cycling through suggestions to help streamline the coding process. It has been trained on billions of lines of code across programming languages like Python, JavaScript, Ruby, and more, making it highly versatile for developers.

GitHub Copilot can also translate code into natural language, allowing users to quickly understand what specific code snippets do. Furthermore, Copilot Extensions enable developers to expand Copilot’s capabilities with third-party skills, creating a more dynamic and personalized coding assistant.

Pricing for GitHub Copilot varies: it’s free for students and verified open-source contributors but costs $10 per month for individuals, $19 per month for business users, and $39 per month for enterprises. Business and enterprise plans come with additional features like license management, IP indemnity, and organization-wide policy management, providing extra security and control for companies.

Subscribers to GitHub Copilot also gain access to Copilot Chat, an AI-powered tool that’s aware of the coding context and can assist with debugging, error fixing, and addressing security concerns. This feature makes it much more than a simple code generator; it acts as a smart assistant capable of handling complex programming challenges.

In April, GitHub expanded its AI offerings with Copilot Workspace, an environment that brings AI into the full software development lifecycle. This tool helps developers brainstorm, build, test, and run code through natural language commands, further integrating AI into the heart of programming work.

Tips for using Microsoft Copilot effectively

1. Start with clear prompts: Be specific when asking Copilot to perform tasks. The more detailed your prompt, the better Copilot can understand your request and deliver accurate results.
2. Leverage It for routine tasks: Use Copilot to automate repetitive tasks like drafting emails, summarizing documents, or organizing data in Excel. This saves time and allows you to focus on more complex work.
3. Integrate with Microsoft 365 apps: Maximize Copilot’s potential by using it across the Microsoft 365 suite, including Word, Excel, PowerPoint, and Outlook. This makes it easier to streamline workflows and keep projects consistent across platforms.
4. Use Copilot for data analysis in Excel: Take advantage of Copilot’s ability to format data, create charts, generate pivot tables, and assist with data-driven tasks. It can even help write complex formulas or perform data visualization.
5. Collaborate with Copilot in Teams: During meetings, use Copilot in Teams to summarize discussions, track action items, and follow up on tasks. This helps ensure nothing falls through the cracks.
6. Explore third-party integrations: Copilot supports a variety of third-party plug-ins and services, like Instacart for meal planning or Kayak for trip planning. Explore these options to extend Copilot’s utility beyond traditional office tasks.
7. Customize with Copilot Studio: For business users, use Copilot Studio to create custom AI workflows tailored to your organization’s needs. This feature allows you to build and deploy your own copilots with specific datasets and automations.
8. Keep security and privacy in mind: Always ensure that your data is secure when using Copilot, especially in sensitive business environments. Familiarize yourself with Microsoft’s security policies and best practices to minimize risks.
9. Take advantage of Copilot Pro: If you’re a power user, consider upgrading to Copilot Pro for access to premium features, like high-resolution image creation and priority access to OpenAI’s advanced models during peak times.
10. Stay updated on new features: Microsoft is continuously adding new features to Copilot, so regularly check for updates and new integrations that can enhance productivity and introduce new ways to use the tool.

Featured image credit: Microsoft

Artificial General Intelligence (AGI), the concept of machines reaching human-level intelligence, is often seen as the holy grail of AI development. Companies like OpenAI and Google’s DeepMind often present their breakthroughs as stepping stones toward AGI.

However, Neil Lawrence, a professor of machine learning at the University of Cambridge, believes this pursuit is misguided, calling AGI “nonsense” and arguing that AI and human intelligence should not even be compared.

Lawrence’s viewpoint, highlighted in his new book The Atomic Human: Understanding Ourselves in the Age of AI, pushes back against the narrative that AI will eventually rival human intelligence.

His argument is simple but profound: AI is fundamentally different from human cognition, and it’s a mistake to expect it to replicate the intricacies of human thought.

Machines are not like us and that’s okay

According to Lawrence, the rise of AI systems like ChatGPT and its competitors has led to a misplaced comparison between human and artificial intelligence. While these systems can perform a range of tasks previously thought to require human intelligence—such as writing essays or answering complex questions—Lawrence argues that their functioning is far more limited than the term “general intelligence” suggests.

In essence, AI is excellent at narrow, specialized tasks, often far surpassing humans in speed and efficiency. But Lawrence cautions that this form of intelligence is more akin to the behavior of ants than it is to human thought.

In an interview with NewScientis, he argues that AI systems, no matter how advanced, operate within predefined parameters and lack the broader cognitive flexibility that humans possess. AI’s intelligence, Lawrence suggests, is task-specific and programmed, while human intelligence emerges from biological complexity, emotion, and social interaction—things that machines cannot replicate.

Lawrence dismisses the pursuit of AGI as a distraction, claiming it sets an unrealistic expectation for what AI can accomplish. He asserts that current AI is far from “general” and should not be expected to achieve human-like cognition.

“The nature of the intelligence we’re seeing in AI is extremely different from our own,”

He states, calling it “absurd” to treat machine intelligence as if it’s on a path toward becoming like human intelligence.

What makes us “unique”?

What makes humans unique is a question that has perplexed philosophers for centuries. Many point to our capacity for abstract reasoning, self-awareness, and moral decision-making as key markers of what sets us apart.

Unlike animals, whose behaviors are often driven by instinct or survival, humans can contemplate existence, ponder the nature of the universe, and even question the meaning of life itself. This ability to reflect on our own thoughts—a trait known as metacognition—imbues our intelligence with a depth and complexity that we often take for granted.

It’s not just about processing information, but understanding, feeling, and reflecting on it, something AI, despite its vast computational power, cannot achieve.

In biological terms, humans and animals share much of the same genetic blueprint. We evolved from common ancestors, and the mechanisms that govern our physical bodies—our cells, neurons, and organs—are remarkably similar. Yet, the human brain, with its vast neocortex, is wired for a level of sophisticated thought and emotion that seems unparalleled in the animal kingdom.

Our brains allow for creativity, language, culture, and a nuanced understanding of morality. While animals can exhibit extraordinary intelligence—like a crow using tools or a dolphin demonstrating social cooperation—their cognition lacks the self-awareness, cultural depth, and adaptability that humans possess.

When we draw a line from these biological and philosophical distinctions to AI, the contrasts become even starker.

AI, no matter how sophisticated, lacks the embodiment that is central to human and animal life. Our intelligence is deeply rooted in our biology, influenced by our emotions, physical sensations, and social bonds. AI, by contrast, operates in an entirely different realm—it processes data, learns patterns, and performs tasks, but it doesn’t feel, reflect, or engage in the emotional and social complexity that defines human and even animal experience.

AI might be able to simulate aspects of human behavior, like recognizing faces or generating text, but it fundamentally lacks the depth of human consciousness. The experience of being human—grappling with joy, sorrow, love, and existential questions—is something that cannot be encoded in algorithms.

Transforming society, not replicating humanity

While AI may never replicate the full scope of human intelligence, it is already transforming society in profound ways. Rather than striving to mimic human consciousness, the true strength of AI lies in its ability to complement and extend human capabilities.

Machines are not bound by the limitations of biology—no fatigue, no emotion, no ethical dilemmas—making them highly efficient at tasks that require speed, precision, and data processing on an unimaginable scale.

This makes AI a powerful tool, not a replacement for human thought, but an enhancement of what we can achieve.

However, this transformation is not without challenges. The growing reliance on AI prompts deeper questions about ethics, privacy, and control. As AI systems become more integrated into daily life, they start to influence decisions that traditionally require human judgment—decisions laden with moral implications. While AI can process facts and probabilities, it cannot grasp the ethical nuance of its actions, leaving society to grapple with how and where to apply such technologies responsibly.

The key is to recognize AI’s role as a tool, not an entity. As society evolves with AI, we must focus on leveraging its strengths to solve problems that are beyond human capacity, without assuming it will ever mirror the full spectrum of human experience. Its ability to process vast datasets, perform high-speed calculations, and streamline repetitive tasks should be seen as an opportunity to transform our world—not an attempt to replicate what makes us human.

Let’s stop comparing, start collaborating

As we advance into an era where artificial intelligence increasingly intersects with human endeavors, the emphasis should shift from comparing AI to human intelligence to exploring how these two forms of intelligence can work in tandem. The binary debate of whether AI can or cannot replicate human thought obscures the more productive discussion of how AI can complement human skills and augment our capabilities.

The crux of this collaborative potential lies in recognizing that AI is not a competitor to human intelligence but a partner that enhances our abilities.

The future of AI should be about harnessing its capabilities to amplify human potential rather than seeking to replace it. By focusing on collaboration, we can leverage AI’s strengths to tackle complex global challenges and improve quality of life. This approach requires a shift in mindset from viewing AI as a threat to recognizing it as a tool that, when used thoughtfully, can help us achieve our collective goals.

Ultimately, the goal is not to replicate human intelligence but to foster a synergy where AI enhances human decision-making, creativity, and problem-solving.

Embracing this perspective allows us to utilize AI as a powerful ally in our quest for progress, achieving what neither humans nor machines could accomplish alone.

Image credits: Emre Çıtak/Ideogram AI

Apple is steadily building up its Apple Intelligence feature set, with new languages on the horizon for 2024. After launching its AI tools in US English this October, the company has announced a plan to support German, Italian, Korean, Portuguese, Vietnamese, and other languages in the coming year. This news arrives just days before the much-anticipated release of the iPhone 16, a device marketed heavily for its AI capabilities—though, ironically, none of those AI features will be available at launch.

Apple Intelligence: A slow rollout

Apple Intelligence, which aims to enhance user experience with AI-driven capabilities, is set to expand beyond the US. By December, localized English versions will be available in the UK, Canada, Australia, South Africa, and New Zealand, with India and Singapore getting access next year. Apple also confirmed that Chinese, French, Japanese, and Spanish will join the list of supported languages by 2024, showing that the company is gradually expanding its global AI footprint.

This gradual rollout comes with a caveat: even US users who get Apple Intelligence first will need to wait for its full functionality. While the iPhone 16 is being positioned as a phone designed for AI, those features won’t arrive until later this fall, and even then, only a limited subset of Apple’s AI capabilities will be available. A broader rollout of features is expected in 2025, which means that early adopters will have to be patient as Apple gradually brings its promised AI tools to life.

With so much of the marketing focused on Apple Intelligence, it’s clear that the company sees AI as central to the future of its devices. Yet, for now, users will only be able to experience the hardware without the full range of AI functionality.

The delay could create frustration for customers who expect cutting-edge technology straight out of the box. It’s a bold strategy, especially in a market where competitors like Google and Samsung are rolling out AI features with greater immediacy. The fact that users in the US will need to wait until 2025 for full access to Apple Intelligence tools—and international users even longer—raises questions about how this staggered release will affect the iPhone’s position in the smartphone market.

When it comes to availability, the Cupertino firm states: “Apple Intelligence will be available as a free software update. The first set of Apple Intelligence features will be available in beta next month as part of iOS 18.1, iPadOS 18.1, and macOS Sequoia 15.1, with more features rolling out in the months to come. It will be available on iPhone 16, iPhone 16 Plus, iPhone 16 Pro, iPhone 16 Pro Max, iPhone 15 Pro, iPhone 15 Pro Max, and iPad and Mac with M1 and later, with device and Siri language set to U.S. English. Additional languages and platforms are coming over the course of the next year.”

Featured image credit: Apple

OpenAI has responded to the growing demand for its latest AI models by increasing the limits for o1-preview and o1-mini on its ChatGPT Plus and Teams plans. The unveiling of the o1 model, which quickly attracted significant attention due to its enhanced capabilities, led to users reaching their message limits in a short amount of time. On Monday, OpenAI announced that it would raise these limits, allowing users to access more of the powerful model’s features.

We appreciate your excitement for OpenAI o1 and we want you to be able to use it more.

For Plus and Team users, we have increased rate limits for o1-mini by 7x, from 50 messages per week to 50 messages per day.

o1-preview is more expensive to serve, so we’ve increased the rate…

— OpenAI (@OpenAI) September 17, 2024

The limits for o1-mini have seen the most substantial increase, going from 50 messages per week to 50 per day for Plus and Teams users. This sevenfold expansion gives users more freedom to engage with the AI, particularly those who rely on it for tasks like coding or complex problem-solving. Meanwhile, the more expensive o1-preview model, which offers even more advanced features, has had its weekly message limit raised from 30 to 50.

Is it worth the investment?

For those who need powerful tools for coding or complex workflows, this increase in limits may justify the $20 per month ChatGPT Plus subscription.

Is ChatGPT Plus worth it: Discussing every aspect

In our own test of ChatGPT o1-preview, we provided the model with a detailed request to plan a three-week European vacation, and the results were notably impressive. Gone are the days when earlier versions of ChatGPT would struggle with basic math or even simple tasks like counting letters in a word. This latest version showed considerable advancements in reasoning and reliability. The o1-preview model not only handled the itinerary seamlessly, organizing the trip across multiple countries and recommending transportation methods, but it also factored in rural and urban experiences, including budget breakdowns and accommodation tips.

Our preview showcased that o1-preview excels not just in technical tasks but in real-world logistics as well. Its ability to process detailed requests with accurate results shows its potential for users needing assistance with more than just coding or calculations.

What’s the catch?

That said, the o1 models come with some limitations. Unlike GPT-4o, which offers broader features like internet browsing and media uploads, the o1-preview and o1-mini models lack these capabilities. This makes GPT-4o a better fit for users who need more generalized AI support for tasks like searching the web or uploading files for analysis.

Expanding access for free users

OpenAI has hinted that it plans to eventually make the o1-mini model available to all users, including those on the free tier, but the timeline remains unclear. However, some free users have already reported gaining access to o1-mini. To check if it’s available, users can toggle the model selector at the top of the ChatGPT interface and look for the “Alpha Models” section, where they can select o1-mini.

While the exact limits for free users haven’t been specified, it’s safe to assume that they will be lower than those for paid subscribers. This could make it a good option for casual experimentation, but less practical for those looking to integrate the model into their regular workflows. Nonetheless, the introduction of o1-mini to free accounts provides a chance for more users to explore the latest AI developments without needing to commit to a paid subscription.

The bigger picture

OpenAI’s decision to raise the message limits on its o1 models reflects the company’s efforts to cater to growing demand and showcase the potential of its AI tools. As AI continues to evolve, users are increasingly looking for models that can not only handle basic requests but also engage in more detailed reasoning and complex tasks. While the o1-preview and o1-mini models offer exciting advancements, users will need to weigh their specific needs—whether technical, creative, or general—before deciding which version of ChatGPT is best for them.

Featured image credit: Jonathan Kemper/Unsplash

Google continues to advance its efforts in content transparency, focusing on tools that help users understand how media—such as images, videos, and audio—has been created and modified. A key development in this area is its collaboration with the Coalition for Content Provenance and Authenticity (C2PA), where Google plays an active role as a steering committee member. The goal of this partnership is to enhance online transparency as content moves across platforms, providing users with better information on the origins and alterations of the media they engage with.

The C2PA focuses on content provenance technology, which helps users determine whether a piece of content was captured by a camera, edited through software, or generated by AI. This initiative aims to equip people with information that builds media literacy and allows them to make more informed decisions about the authenticity of the content they encounter. According to the announcement, Google has been contributing to the latest version (2.1) of the C2PA’s Content Credentials standard, which now has stricter security measures to prevent tampering, helping ensure that provenance information is not misleading.

New feature integration into Google products

Over the next few months, Google plans to integrate this new version of Content Credentials into some of its key products. For instance, users will soon be able to access C2PA metadata through the “About this image” feature in Google Search, Google Images, and Google Lens. This feature will help users identify whether an image has been created or altered using AI tools, providing more context about the media they come across.

Google is also incorporating these metadata standards into its advertising systems. Over time, the company aims to use C2PA signals to inform how it enforces ad policies, potentially shaping the way ad content is monitored for authenticity and accuracy.

Additionally, Google is exploring ways to extend this technology to YouTube, with the possibility of allowing viewers to verify the origins of video content in the future. This expansion would further Google’s push to bring transparency to media creation across its platforms.

Google’s role in the C2PA extends beyond its own product integrations. The company is also encouraging broader adoption of content provenance technology across the tech industry. The goal is to create interoperable solutions that work across platforms, services, and hardware providers. This collaborative approach is seen as crucial to creating sustainable, industry-wide standards for verifying the authenticity of digital content.

To complement its work with the C2PA, Google is also pushing forward with SynthID, a tool developed by Google DeepMind that embeds watermarks into AI-generated content. These watermarks allow AI-created media to be more easily identified and traced, addressing concerns about the potential for misinformation spread by AI tools.

Artists Google to court over AI image generator

As more digital content is created using AI tools, ensuring that provenance data remains accurate and secure will be crucial. Google’s involvement in C2PA is part of a broader effort to address these challenges, but the effectiveness of these measures will depend on widespread industry cooperation and adoption.

Google’s efforts to address AI-generated content in its search results are a step toward more transparency, questions remain about the effectiveness of the approach. The “About This Image” feature, which provides additional context about whether an image was created or edited with AI, requires users to take extra steps to access this information. This could be a potential limitation, as users who are unaware of the tool may never know it’s available to them. The feature relies heavily on users actively seeking out the provenance of an image, which may not be the default behavior for many.

The broader challenge lies in making such transparency tools more intuitive and visible to users, so they can quickly and easily verify content without having to dig for details. As AI-generated content continues to grow, the need for seamless verification will only become more pressing, raising questions about whether hidden labels and extra steps are enough to maintain trust in digital media.

Image credits: Kerem Gülen/Ideogram

Google has taken another significant step forward in the race to improve the accuracy and reliability of AI models with the introduction of DataGemma, an innovative approach that combines its Gemma large language models (LLMs) and the Data Commons project. The spotlight here is on a technique called retrieval-augmented generation (RAG), a method that has been gaining traction in enterprises, but now, with DataGemma, Google aims to bring it into the AI mainstream.

At its core, RAG seeks to solve one of the biggest challenges faced by LLMs: the problem of hallucinations. In the world of generative AI, hallucinations refer to instances where the model generates information that sounds plausible but is factually incorrect. This is a common issue in AI systems, especially when they lack reliable grounding in factual data. Google’s goal with DataGemma is to “harness the knowledge of Data Commons to enhance LLM factuality and reasoning,” addressing this issue head-on.

What is RAG?

Retrieval-augmented generation is a game changer because it doesn’t rely solely on pre-trained AI models to generate answers. Instead, it retrieves relevant data from an external source before generating a response. This approach allows AI to provide more accurate and contextually relevant answers by pulling real-world data from repositories. In the case of DataGemma, the source of this data is Google’s Data Commons project, a publicly available resource that aggregates statistical data from reputable institutions like the United Nations.

This move by Google to integrate Data Commons with its generative AI models represents the first large-scale cloud-based implementation of RAG. While many enterprises have used RAG to ground their AI models in proprietary data, using a public data resource like Data Commons takes things to a whole new level. It signals Google’s intention to use verifiable, high-quality data to make AI more reliable and useful across a broad range of applications.

Google’s two-pronged approach

According to Google, DataGemma takes “two distinct approaches” to integrate data retrieval with LLM output. The first method is called retrieval-interleaved generation (RIG). With RIG, the AI fetches specific statistical data to fact-check questions posed in the query prompt. For example, if a user asks, “Has the use of renewables increased in the world?” the system can pull in up-to-date statistics from Data Commons and cite them directly in its response. This not only improves the factual accuracy of the answer but also provides users with concrete sources for the information.

The second method is more in line with the traditional RAG approach. Here, the model retrieves data to generate more comprehensive and detailed responses, citing the sources of the data to create a fuller picture. “DataGemma retrieves relevant contextual information from Data Commons before the model initiates response generation,” Google states. This ensures that the AI has all the necessary facts at hand before it begins generating an answer, greatly reducing the likelihood of hallucinations.

A key feature of DataGemma is the use of Google’s Gemini 1.5 model, which boasts an impressive context window of up to 128,000 tokens. In AI terms, the context window refers to how much information the model can hold in memory while processing a query. The larger the window, the more data the model can take into account when generating a response. Gemini 1.5 can even scale up to a staggering 1 million tokens, allowing it to pull in massive amounts of data from Data Commons and use it to craft detailed, nuanced responses.

This extended context window is critical because it allows DataGemma to “minimize the risk of hallucinations and enhance the accuracy of responses,” according to Google. By holding more relevant information in memory, the model can cross-check its own output with real-world data, ensuring that the answers it provides are not only relevant but also factually grounded.

Beyond LLMs

While the integration of RAG techniques is exciting on its own, DataGemma also represents a broader shift in the AI landscape. It’s no longer just about large language models generating text or answering questions based on what they’ve been trained on. The future of AI lies in its ability to integrate with real-time data sources, ensuring that its outputs are as accurate and up-to-date as possible.

Google is not alone in this pursuit. Just last week, OpenAI unveiled its “Strawberry” project, which takes a different approach to improving AI reasoning. Strawberry uses a method known as “chain of thought”, where the AI spells out the steps or factors it uses to arrive at a prediction or conclusion. While different from RAG, the goal is similar: make AI more transparent, reliable, and useful by providing insights into the reasoning behind its answers.

What’s next for DataGemma?

For now, DataGemma remains a work in progress. Google acknowledges that more testing and development are needed before the system can be made widely available to the public. However, early results are promising. Google claims that both the RIG and RAG approaches have led to improvements in output quality, with “fewer hallucinations for use cases across research, decision-making, or simply satisfying curiosity.”

It’s clear that Google, along with other leading AI companies, is moving beyond the basic capabilities of large language models. The future of AI lies in its ability to integrate with external data sources, whether they be public databases like Data Commons or proprietary corporate data. By doing so, AI can move beyond its limitations and become a more powerful tool for decision-making, research, and exploration.

Featured image credit: Google

The new frontier in AI development

Artificial Intelligence (AI) is no longer confined to sprawling data centers or secretive tech labs; it has migrated into our daily lives more seamlessly than most realize. For the tech-savvy user, the iPhone isn’t just a tool for connectivity but a personal assistant powered by AI that learns and adapts.

Real-world applications at your fingertips

Consider the ease with which we now translate foreign languages on the go, organize our schedules, or even filter our photos—all through AI integrations in iPhone apps available via platforms like Setapp. These applications are not just conveniences; they are gateways to understanding complex AI operations in a user-friendly format. They democratize technology, making sophisticated tools accessible to anyone with a smartphone.

Tailored for the advanced user

For those well-versed in technology, this article aims to uncover the deeper layers of AI integration in iPhone apps—beyond mere convenience to substantial innovation.

Navigating through advanced AI applications

Through real-world examples and a closer look at the mechanisms driving these innovations, we provide advanced users with actionable insights into maximizing the potential of AI on their devices. Whether you’re a developer, a tech enthusiast, or a strategist looking to harness AI’s full potential, understanding its application through iPhone apps could be your next giant leap in technology mastery.

Stay tuned as we delve into how iPhone apps use AI.

Democratization of AI through iPhone Apps

Bridging the gap: AI for everyone

Democratization in technology refers to making complex tools and systems accessible to a broader audience without requiring specialized knowledge. iPhone apps have significantly contributed to this trend by integrating Artificial Intelligence into everyday applications. These apps simplify complex processes, enabling users from various backgrounds to benefit from AI innovations without needing deep technical expertise.

AI apps: From niche to necessity

Initially, AI tools were predominantly available to professionals in the tech industry and academia. However, the landscape has shifted dramatically, making AI more ubiquitous and integral to our daily digital interactions.

Example: Screen recording enhanced by AI

A pertinent example of AI’s integration into useful iPhone functionalities is the “screen record on iPhone” feature. Advanced users leverage this feature to create tutorials, presentations, and demos by capturing real-time activities on their devices. AI enhances this tool by optimizing the recording settings based on the displayed content, adjusting for factors like movement and audio. This brilliant adaptation ensures high-quality outputs without manual adjustments, showcasing AI’s role in enhancing straightforward features to boost productivity and usability.

AI in educational tools

Educational apps on iPhones also utilize AI to adapt learning environments to users’ individual needs.

Advancements in AI powered by mobile applications

Accelerating innovation: AI in mobile tech

The proliferation of mobile technology, primarily through devices like iPhones, has catalyzed unprecedented advancements in AI. These advancements are not confined to increasing the efficiency of existing applications but are pioneering new ways AI can serve humanity. Here, we explore how AI-powered mobile applications are pushing the frontiers of technology, making significant impacts across various sectors.

Real-time data processing

One of the most significant contributions of AI in mobile apps is the ability to process and analyze data in real-time. Apps that manage everything from fitness tracking to financial planning are now equipped with AI algorithms that process user data on the go, providing insights and actionable advice instantly. For instance, fitness apps analyze workout data to provide customized fitness regimes instantly, enhancing the user’s ability to achieve health goals efficiently.

Enhancing user interactions

AI has transformed user interaction by making mobile devices more intuitive. Voice assistants, predictive text, and smart replies are all examples of how AI anticipates user needs and enhances interaction. These features save time and improve the overall user experience by making interactions with devices more natural and efficient.

Case study: AI-driven health diagnostics

A groundbreaking application of AI in mobile technology can be seen in health diagnostic apps, which use AI to interpret symptoms and provide preliminary diagnoses. These tools are particularly beneficial in areas with limited access to medical facilities, offering guidance and often life-saving advice based on AI analysis of user-provided data.

Future trends: AI and augmented reality (AR)

Integrating AI with augmented reality (AR) in mobile apps presents exciting possibilities. For example, educational apps are beginning to use AR combined with AI to create immersive learning experiences that adapt to the user’s learning pace and style. These apps engage users in a more interactive learning environment and enhance understanding through personalized educational content.

The future of AI through the lens of iPhone apps

A world transformed by AI

The integration of Artificial Intelligence in iPhone apps has transcended mere convenience, reshaping how we interact with technology on a fundamental level. This exploration into the democratization and advancement of AI via mobile applications reveals a future where technology is ubiquitous, profoundly intuitive, and responsive to our needs.

Empowering users with advanced AI tools

For the advanced user, the proliferation of AI in mobile applications offers a suite of tools that extend capabilities in real-time data analysis, personalized interactions, and predictive functionalities. These advancements enhance user experiences and empower users to achieve more with less effort—whether through improved health diagnostics, customized education, or enhanced productivity.

Implementing AI innovations in daily life

Users are encouraged to integrate these AI-powered apps into their daily routines to benefit fully from the advancements discussed. Whether optimizing your health regimen with AI-driven diagnostic tools or enhancing your learning curve with AI and AR-integrated educational apps, the potential to harness AI’s power in personal and professional spheres is immense.

Featured image credit: Amanz/Unsplash

Elon Musk has once again grabbed headlines by giving the world a glimpse of Cortex, X’s AI training supercomputer currently under construction at Tesla’s Giga Texas plant. In a video that’s both awe-inspiring and surreal, Musk showed off what a cool $1 billion in AI GPUs actually looks like. But if that wasn’t enough to make tech enthusiasts’ jaws drop, Musk recently took to his platform, X, to reveal that the real showstopper—Colossus, a 100,000 H100 training cluster—has officially come online.

All details regarding xAI’s Colossus

Musk didn’t hold back on the bragging rights, claiming that Colossus is “the most powerful AI training system in the world.” Even more impressive is the fact that this mammoth project was built “from start to finish” in just 122 days.

Considering the scale and complexity involved, that’s no small feat. Servers for the xAI cluster were provided by Dell and Supermicro, and while Musk didn’t drop an exact number, estimates place the cost between a staggering $3 to $4 billion.

This weekend, the @xAI team brought our Colossus 100k H100 training cluster online. From start to finish, it was done in 122 days.

Colossus is the most powerful AI training system in the world. Moreover, it will double in size to 200k (50k H200s) in a few months.

Excellent…

— Elon Musk (@elonmusk) September 2, 2024

Now, here’s where things get really interesting. Although the system is operational, it’s unclear exactly how many of these clusters are fully functional today. That’s not uncommon with systems of this magnitude, as they require extensive debugging and optimization before they’re running at full throttle. But when you’re dealing with something on the scale of Colossus, every detail counts, and even a fraction of its full potential could outperform most other systems.

The future looks even more intense. Colossus is set to double in size, with plans to add another 100,000 GPUs—split between Nvidia’s current H100 units and the highly anticipated H200 chips. This upgrade will primarily power the training of xAI’s latest and most advanced AI model, Grok-3, which aims to push the boundaries of what we consider possible in AI.

Featured image credit: BoliviaInteligente/Unsplash

Slack recently unveiled a suite of new features for its higher-tier users, centered around AI integration. Among the most talked-about is Slack AI, which includes AI-generated Huddle summaries, echoing the channel summaries that some subscribers already enjoy. But the update goes further, allowing users to engage with Salesforce’s AI agents directly within Slack, alongside third-party tools that introduce AI-powered web search and image generation.

This AI push isn’t entirely out of the blue. Salesforce acquired Slack in 2021, right when the platform was booming as the go-to messaging service for remote workers. Fast forward three years, and Salesforce is leaning heavily into AI—so much so that it’s trickling down into Slack’s DNA. CEO Denise Dresser emphasizes that Slack is the perfect platform for this shift, as it’s already a hub for workplace conversation, making it an ideal spot to embed AI interactions.

However, it’s hard not to be a bit skeptical. Since ChatGPT burst onto the scene in 2022, countless companies have bolted AI features onto their products, often in ways that feel more like a trendy gimmick than a meaningful enhancement. And while AI summaries and integrations in Slack might seem helpful, one has to question if this addition is truly a natural evolution or simply another example of chasing the AI bandwagon.

Does AI in Slack really enhance the core product, or is it just Salesforce showing it can ride the AI wave? The answer remains to be seen.

New AI-powered Slack features

“Slack’s agent-powered work operating system is redefining what it means to work smarter, not just differently. We’ve moved beyond collaboration; every day, millions of teams are leveraging purpose-built AI, with customer data, automation, and now agents seamlessly integrated into the flow of work. This isn’t just about getting work done. It’s about unlocking new productivity levels to drive results and move business forward for every team and department. In today’s increasingly complex work environment, true productivity requires a work operating system built for the future of work — and that is Slack.”

-Denise Dresser

Agentforce in Slack

Salesforce introduced Agentforce in Slack, a new user interface that allows teams to interact with data, surface insights, and perform actions directly within Slack. Teams can now engage with Agentforce agents to get status updates, recommend next steps, draft emails, and more, all powered by AI.

Third-party AI agents

Slack now supports third-party AI agents and assistants from partners such as Adobe, Anthropic, Cohere, and Perplexity. These agents can assist with tasks like content creation, project management, and data analysis, providing a more efficient workflow within a secure environment.

Salesforce channels

Salesforce channels connect Salesforce CRM records to Slack conversations, enabling teams to collaborate more effectively on accounts and opportunities. Teams can securely view and edit CRM data directly in Slack without duplicating work across multiple systems.

New Slack AI features

Slack AI introduces several new features, including huddle notes that capture key meeting takeaways and action items. Slack AI can also generate workflows with simple conversational prompts and improve search by generating answers from files, apps, and conversational data in Slack.

Slack AI huddle notes

Slack AI huddle notes capture real-time audio and messages shared in a huddle, organizing them into transcripts, action items, and files in a canvas. This feature ensures everyone stays informed, even if they missed the meeting.

AI Workflow Builder

With the AI Workflow Builder, users can easily automate tasks by typing conversational prompts. It simplifies workflow creation, automating repetitive tasks and allowing teams to focus on more important work.

Slack AI search

Slack AI search is now enhanced with file and app knowledge, surfacing richer and more relevant answers. It uses Retrieval Augmented Generation (RAG) to pull information from messages, files, and connected apps that users have access to in Slack.

Slack templates

Slack templates provide pre-configured collections of Slack tools, such as channels, canvases, lists, and workflows, tailored for specific departments or tasks. These templates help streamline work processes and improve productivity across various use cases.

Salesforce channels in CRM integration

Salesforce channels bring CRM data from opportunities, cases, and custom objects into Slack channels, allowing teams to collaborate on customer records in real time. These channels also allow for editing and updating Salesforce data directly within Slack.

Slack AI conversation summaries in Salesforce channels

Slack AI can summarize key highlights from customer-related discussions across multiple Salesforce channels, helping teams stay informed and aligned. This summary feature also provides quick access to essential information for decision-making.

Salesforce channels embedded in the Salesforce interface

Salesforce channels will soon be embedded in the Salesforce user interface, allowing agents and teams to collaborate seamlessly without leaving the CRM. Conversations within Slack will be tied to specific records, improving communication and workflow efficiency.

Slack Marketplace for third-party agents

The Slack Marketplace now offers a range of third-party AI agents, including Adobe Express, Amazon Q Business, Anthropic’s Claude, Asana, Box, Cohere, Perplexity, Workday, and Writer. These agents assist with tasks like content creation, project management, and data analysis.

Agentforce Sales Coach

Agentforce Sales Coach, powered by Data Cloud, can autonomously facilitate role-plays and provide personalized feedback to sellers, helping them improve their skills within the context of a deal, right in Slack.

Adobe Express in Slack

Adobe Express in Slack allows users to create on-brand content, such as presentations and media campaigns, directly from simple prompts, making content creation faster and easier.

Amazon Q Business in Slack

Amazon Q Business answers questions, summarizes data, and generates content based on enterprise systems, enhancing productivity within Slack.

Anthropic’s Claude in Slack

Anthropic’s Claude helps with content creation, code debugging, and data analysis, accelerating workflows and research within Slack.

Asana

Asana provides project insights, status updates, and next steps within Slack conversations, helping teams stay aligned and move projects forward efficiently.

Box

Box offers AI-powered insights from uploaded content, allowing users to analyze contracts, answer questions, and summarize reports directly in Slack.

Cohere

Cohere enhances Slack efficiency by providing AI-driven content and answers grounded in proprietary business data, streamlining internal communication.

Perplexity

Perplexity enables users to search the web for trusted sources, providing competitive analysis and strategic recommendations within Slack.

Workday

Workday provides AI assistants that allow employees and managers to access financial and HR insights, such as pay and job requisitions, directly in Slack.

Writer

Writer helps teams draft assets, analyze data, and brainstorm ideas using AI-driven insights tailored to customer data, brand, and workflows within Slack.

Salesforce channels in Salesforce Starter Suite

Salesforce channels will be accessible in Salesforce Starter Suite, enabling small businesses to connect Slack to a simplified CRM, enhancing collaboration and speeding up decision-making.

Achieve more with less effort with Slack AI‘s assistance

Don’t let it be just noise

Absolutely, having a wide range of features is fantastic, and Salesforce has certainly packed Slack with tools that can transform how teams work.

But there’s a big challenge here that often gets overlooked—most Slack users aren’t tech experts.

We see it all the time: companies roll out new, advanced tools, but the end users don’t fully embrace them, not because the features aren’t useful, but because they’re not intuitive or people haven’t been properly trained.

Slack is now integrating AI agents, automations, and complex CRM channels, and while this all sounds amazing on paper, if your average user doesn’t know how to navigate these features, it’s just noise. It’s not about loading the app with more features; it’s about helping users understand how to use those features efficiently. Without proper education and onboarding, employees might stick to the basics—sending messages and sharing files—while missing out on tools that could save them time or help them work smarter.

The best apps don’t just introduce new tools; they also take the time to guide users on how to implement them into their workflows. Think about solutions like in-app tutorials, user-friendly documentation, or even dedicated training sessions. Companies that master this balance between innovation and education are the ones that see the real productivity gains.

Featured image credit: Scott Webb/Unsplash

Trust certainly plays a big role in shaping customer perceptions. In Asia-Pacific (APAC), a major proportion of consumers trust companies that use advanced technologies. According to a recent study, 60% of APAC customers trust organizations that embrace productive artificial intelligence (Gen AI). They also maintain or increase their trust in such businesses. This growing relationship between AI and consumer trust is reshaping the way companies engage with their customers and opening up a debate about the balance between innovation and trust.

However, not everyone is convinced. While 60% of respondents expressed trust, a significant 39% said that the use of Gen AI by organizations has made them less trusting. The study, conducted by Vodafone Business, surveyed more than 2,500 customers and businesses in Singapore, China, India, and Australia. Various opinions emerged on the subject.

Why do businesses trust AI?

Looking deeper into the numbers, there are significant regional differences. In China, for example, 35% of customers say their trust in companies has increased when using Gen AI. Similarly, in India, the figure is high, with 29% of respondents sharing the same sentiment. In Singapore and Australia, however, the numbers are lower, with only 23% and 19% of customers reporting an increase in trust, respectively.

On the other hand, there seems to be more concern about AI in the business world. Around 65% of companies in APAC are concerned that the adoption of AI technologies could actually reduce customer trust, mainly due to fears around privacy and personal data. The survey points out that 57% of businesses attribute this concern to how customers perceive the handling of their sensitive information.

However, businesses are not backing down from their investments in AI. More than half of APAC companies have integrated Gen AI into their operations, with Singapore leading the way. 59% of businesses in Singapore have adopted Gen AI, exceeding the global average of 45%. This trend points to a broader commitment to digital transformation across the region, with more than 70% of APAC companies planning to increase their technology budgets.

A gap between business optimism and customer concerns

Despite the advancements, there seems to be a disconnect between how businesses and customers see the benefits of AI. The study highlights that 85% of APAC companies believe that technology, particularly AI, has improved their interactions with customers. However, only 67% of customers share this belief, suggesting a gap in perception. This disparity is more pronounced in some regions. In Singapore, for example, only 45% of customers feel that companies are using AI in a way that inspires trust, compared to 34% in Australia. Companies attribute this mainly to customers not realizing how they are actually using AI.

In contrast, the situation is more optimistic in China and India, with 71% of customers in China and 74% in India expressing confidence in the way businesses are using AI. The study reveals that this trust is strongly linked to how secure customers feel about their personal data. While 82% of Chinese respondents believe that companies protect their data well, this figure drops to 65% in Singapore and 62% in Australia.

Businesses put Gen AI at the forefront

Amid these mixed feelings from customers, APAC businesses continue to prioritize Gen AI. According to Salesforce‘s research, 83% of C-suite executives in the region rank Gen AI as one of their top three business priorities. Furthermore, 54% of businesses already have a clear Gen AI strategy in place, while 40% are currently working on one. This shows a strong focus on not only incorporating AI into operations but also doing so in a structured and forward-thinking way.

The key drivers behind Gen AI adoption are primarily to innovate customer and employee experiences (44%), meet the growing demand for personalized experiences (44%), and improve efficiency (43%). As companies accelerate their AI efforts, many see Gen AI as the future of business operations in APAC, despite the lingering concerns of an important portion of the customer base.

The relationship between AI and trust in the APAC region is complex. While many customers are on board with AI-driven services, businesses must address concerns about privacy and security to win over the remaining skeptics.

Featured image credit: Furkan Demirkaya / Midjourney

The impressive planning capabilities of the new ChatGPT o1 have caught some eyes lately.

Remember when older ChatGPT models would struggle with basic math or even simple tasks like counting how many “R”s were in the word strawberry? Those were the days when you’d ask for calculations, and it would get tripped up or return hilariously off numbers. Well, this latest version has seriously leveled up on that front. It’s become way more reliable when it comes to handling math and logical problems, from solving equations to accurately counting letters in words. It’s a massive improvement that makes interactions feel a lot more dependable for complex tasks.

Without further delay, let’s attempt to plan a vacation.

OpenAI starts a new era of AI reasoning with ChatGPT o1

Trying out ChatGPT o1-preview

We’ll observe the results produced by inputting just two commands into the model. Here’s what we discovered after providing the following details:

“Let’s plan a vacation for next summer. My colleagues (3 people) and I are thinking of going to Europe for three weeks. We’re planning to visit several countries, including France, Italy, and Spain. We’d like to experience both urban and rural settings, so we’re thinking about splitting our time between major cities like Paris, Rome, and Barcelona, and smaller towns or countryside areas like Provence or Tuscany. We’re trying to figure out the best mode of transportation—whether we should rent a car for the countryside and take trains between the major cities. Additionally, we want to enjoy a mix of cultural experiences, like visiting museums and historical sites, but also have time to relax by the beach or in nature. Our budget is mid-range, and we’d like to stay in comfortable yet unique accommodations like boutique hotels or vacation rentals. Do you think this itinerary is manageable, and how should we organize our travel schedule to get the most out of our trip?”

ChatGPT o1-preview thought for 23 seconds and provided us with the following answer:

Answers were cool, yet it lacked some depth about budgeting.

That’s why we said: “I need some budget plans too.” ChatGPT o1-preview answered:

ChatGPT o1-preview just planned a three-week European vacation in seconds, and the level of detail is surprising! Usually, organizing a trip like this would take hours or even days of scrolling through websites, comparing options, and researching the best ways to get around. But here, the model handled everything from city explorations to countryside escapes effortlessly.

It wasn’t just a basic itinerary either—far from it. There were thoughtful recommendations, like hopping on the high-speed train in France, renting a car to explore the Tuscan countryside, and even suggesting where to stay in each place. It felt like having a super-informed travel planner.

The budget breakdown is no joke too! Not just vague numbers, but an actual estimate for accommodations, transportation, meals, and activities. Even though it’s not a fixed budget, it definitely provides a solid preview of what the trip might cost. It’s crazy to think that planning used to mean juggling spreadsheets and endless tabs, but now this AI is doing it instantly, and with surprising accuracy.

This feels like just the beginning. The thought of ChatGPT o1-preview helping with other big projects—whether it’s event planning, moving, or even something like wedding coordination—opens up so many possibilities. It’s clear that AI isn’t just here for quick answers anymore; it’s stepping into real-world logistics, and doing it better than expected.

If this is the preview, we want to see the rest.

Image credits: Kerem Gülen/Midjourney

Crafting clean, concise, and powerful instructions is the backbone of successful software program improvement. But let’s accept it, writing them may be time-consuming and at risk of errors. AI is revolutionizing the way technical and ad hoc instructions are created, automating routine tasks, increasing accuracy, and freeing developers to do what they do: create high-quality software. Let’s find out how AI is reshaping this critical development challenge.

The challenge of instruction writing

Writing clear and precise technical and non-standard instructions presents significant challenges, especially in AI and software development. It is important to ensure that these instructions are user-friendly and easy to understand but often difficult due to the complexity of the content. One of the effective solutions is using tools designed to optimize this process. For example, a https://textero.io/. It provides a user-friendly interface that helps creators easily produce high-quality, step-by-step texts. This instrument uses advanced artificial intelligence to simplify complex concepts, making them accessible to a wider audience. By integrating such tools into the development workflow, teams can save some time and reduce errors, improving the software product’s user experience and overall quality.

AI as a writing assistant

The role of artificial intelligence in software development extends to the role of a reliable assistant in writing, greatly easing the burden of creating technical and special instructions. Using AI programs, developers can use advanced algorithms to create initial drafts, correct errors, and improve content. Tools like GPT-4 and specialized AI-based software development tools can interpret complex code and turn it into understandable documentation. These tools advocate enhancing sentence shape, readability, and coherence, making commands more accurate and user-pleasant. Integrating AI as a writing assistant saves time and increases productivity, permitting developers to be cognizant of extra critical tasks and streamlining the general software improvement method.

Automating routine instruction creation

Realizing the creation of routine instructions revolutionizes the practices that determine the rules of software development. One of the areas where AI programs can provide the greatest benefit is in repetitive and time-consuming work; thus, the use of AI solutions can help developers transform the creation of standardized documents into an automated process. Through the use of AI in software development, it is possible to establish several procedures that can analyze this code and give you corresponding instructions in the type of installation guides in addition to user guides.

It cuts down the amount of work that may have to be done manually, and it also eliminates human interaction hence reducing chances of errors, and makes sure that documentation is up-to-date. In general, with AI, constructing routine instruction for development teams makes documentation easier, faster, and at a much better standard.

Enhancing instruction clarity and consistency

Clarity and consistency are critical to powerful technical education, and AI applications make contributions to those elements. AI-based software development tools can analyze and standardize language to ensure clarity, brevity, and ambiguity in instructions. These tools can also provide regular terminology and formatting at a specific location in the documentation, making it easier for users to follow and understand. By using artificial intelligence to improve the clarity and consistency of commands, developers can create remarkable documentation that improves user know-how and reduces the number of guide requests. It not only benefits the end users but also streamlines the development workflow by minimizing the time spent clarifying instructions and correcting misunderstandings.

Improving knowledge transfer through AI

AI in software development greatly improves knowledge transfer through efficient organization and dissemination of information. Modern concepts of AI can analyze large amounts of facts, define crucial principles, and generalize knowledge in brief and clear kinds of presentations. For instance, developers can increase the production of unique, consistent documentation with the help of an AI essay writing program, which can increase the transmission of expertise and understanding to members of a group. Thus, artificial intelligence technologies also enable you to build engaging publications and guides to facilitate the assimilation of new group members into existing initiatives. Automation means that precious time and energy are saved so that remarkable educational documents can be created more easily. It guarantees that the right now is being transferred, there is no misinterpretation, and mistakes are avoided. Another advantage is the ability to modify the learning method through material content and mastering various styles.

Accelerating development timelines

Integrating artificial intelligence into programming can appreciably accelerate development instances by automating habitual tasks and optimizing work strategies. AI gear can carry out repetitive coding responsibilities, hit upon mistakes, and even generate code snippets, permitting developers to be conscious of the greater complex and creative elements of an undertaking. Also, with the use of an AI essay writing app, developers can quickly create complete challenge documentation and reports, further speeding up the improvement technique.

AI in programming also enables higher venture management via predicting capability bottlenecks and suggesting the choicest allocation of sources. This predictive capability facilitates proactively addressing issues, ensuring initiatives are delivered and closing dates are met. By decreasing guide workload and improving choice-making, AI shortens development cycles and improves average software first-rate and reliability. This acceleration is essential in modern-day, fast-paced-era surroundings, in which time to marketplace can make all the distinctions for a product’s fulfillment.

Overcoming language barriers with AI translation

Language barriers present considerable challenges for global software development teams. Yet, AI technology, especially AI-powered translation tools, can effectively address these issues. Sophisticated AI translation systems can provide precise translations of technical documents, code annotations, and user interfaces, promoting clear communication among team members with diverse language backgrounds. An AI app for writing essays can be modified to produce multilingual documentation, enhancing collaboration for international teams.

Furthermore, such solutions implemented in AI technologies can be translated individually during meetings and discussions to reduce interference and confusion. Through the elimination of language barriers within teams, AI will encourage efficient teamwork, revealing creativity and innovativeness. Besides, there is a continuous improvement of AI translators with machine learning, which enables the AI translators to become even more accurate over time and perform better in different contexts. Such a constant evolution guarantees that language differences act as a barrier to the efficiency of software development projects.

Bottom line

The application of AI is making changes to the way technical and special instructions are written in software development. AI solutions, such as the Textero help developers reduce the time to accomplish repetitive work, increase cohesion, and decrease language differences that ultimately lead to the creation of stellar user documentation. These advances bring in increased efficiency, accuracy, and cooperation while developing software and thus improving software products.

Featured image credit: rivage/Unsplash

GeoSpy feels like the ultimate power-up for anyone who’s ever played GeoGuessr and wished for an AI edge. Instead of relying purely on intuition and obscure clues, you have an AI at your side, pinpointing locations from just an image.

What is GeoSpy?

GeoSpy is an AI-powered tool designed to analyze images and determine their geographic location with impressive accuracy. By utilizing advanced machine learning models, GeoSpy can extract visual clues from photos—such as architectural styles, landscapes, and street signs—and cross-reference them with vast geographic databases to pinpoint the exact or approximate location where the image was taken. This tool is particularly useful for enthusiasts of location-based games, researchers, photographers, and even marketers who need to gather location-specific data quickly and efficiently.

Unlike traditional geolocation tools, GeoSpy simplifies the process by allowing users to upload an image and receive location data without needing GPS metadata or additional details. Its ease of use and AI-driven capabilities make it a powerful option for those looking to explore new possibilities in geolocation technology, from playful applications like GeoGuessr to more professional uses like location verification or content analysis.

Wouldn’t you like artificial intelligence to be your dog’s personal translator?

How to use GeoSpy?

Using the tool is straightforward, and its intuitive interface makes it simple to get started.

Here’s a step-by-step guide based on our experience:

• Access the website: Start by visiting the GeoSpy website, where you’ll be greeted with an option to upload an image directly from your device. GeoSpy supports a variety of image formats, so any high-quality photo will work.

• Upload your image: We used a random photo from Unsplash, a popular source for high-resolution images, to put the tool to the test. Simply drag the photo to the upload area, or click to browse your files.

• Wait for the analysis: Once the photo is uploaded, GeoSpy’s AI takes over. The tool starts analyzing the visual elements within the image, comparing architectural styles, landscapes, or other identifiable features. This process typically takes a few seconds.

• Review the results: After the analysis, GeoSpy displays the approximate location on a detailed map. In our case, the image we uploaded from Unsplash was identified as being in New York City.

About GeoSpy Pro

There’s also a Pro version of GeoSpy for specific purposes. It’s designed to go beyond what the human eye can see, identifying subtle patterns and features that allow it to pinpoint locations with remarkable accuracy. Whether it’s a seemingly nondescript street or a remote place, GeoSpy Pro claimed to be able to analyze visual data and provide precise location insights.

GeoSpy Pro is tailored for professionals who require advanced geolocation capabilities. Law enforcement agencies, government bodies, journalists, and investigators can leverage its powerful tools to aid in their work, whether it’s tracking criminal activity, verifying location data, or conducting investigative research. It’s a sophisticated solution for those who need to work with location-based information on a deeper level, with accuracy and reliability.

It’s kinda creepy

While GeoSpy’s capabilities are undeniably impressive, the ethical implications surrounding such powerful AI technology deserve serious consideration.

Who should have access to this kind of technology? In the hands of law enforcement and journalists, it can be a force for good—helping solve crimes, verify facts, and gather critical information. But in less regulated environments, the potential for misuse becomes apparent.

Should there be limitations on its use to protect individual privacy, or do the benefits outweigh these concerns?

Featured image credit: Kerem Gülen/Midjourney

The incorporation of artificial intelligence (AI) into the American educational system has marked a turning point in the history of teaching and learning. AI is transforming traditional educational methods drastically and is proofing its efficiency in the outcomes of teaching. This article will give useful information to professionals who are interested in obtaining an EdD (Doctor of Education) to better comprehend the current educational landscape and thrive in their future careers in the education industry. In the next section, we’ll look at how AI has revolutionized teaching jobs in the United States.

1. Personalized educational contents

AI provides individualized instructional content suited to each student’s unique needs. AI-equipped with advanced algorithms and machine learning can evaluate massive quantities of data about students’ historical performance, learning styles, and preferences to generate personalized learning experiences. By responding to students’ progress, AI ensures that the educational content is engaging and relevant, responding to their individual speed and understanding levels. This individualized approach not only increases student engagement and motivation but also results in better learning outcomes and information retention.

AI-enabled educational platforms allow students from varied backgrounds to have equal access to high-quality learning resources and assistance, therefore narrowing the educational achievement gap. Teachers gain from AI because it enables them to identify difficult students early on, send focused assistance, and make data-driven instructional decisions to improve student learning results. As a result, AI has moved teaching from a one-size-fits-all approach to individualized learning, allowing educators to fulfill their students’ various requirements as well as fostering a more inclusive and productive educational environment.

2. Promote critical thinking

AI helps students develop critical thinking abilities by providing questions and concerns that need analytical reasoning and creative problem-solving. Students are encouraged to use AI-powered instructional technologies to do higher-level critical assessments such as evidence analysis, link discovery, and innovation to problems. AI encourages peer-to-peer engagement and discourse by providing collaborative learning experiences that allow students to consider opposing viewpoints while also improving their cognitive skills.

AI systems can evaluate student responses and customize individualized feedback, to guide them toward deeper understanding and mastery of complex concepts. This emphasis on critical thinking not only prepares students for success nowadays but also fosters lifelong learning skills, which are vital for being ready to work in the job market after graduation. By enabling educators to create learning experiences that challenge students to think critically, AI technologies encourage a culture of inquiry, exploration, and intellectual advancement inside the classroom.

3. Streamlined teacher workflows:

AI greatly enhances teacher workflows by automating administrative duties, delivering data-driven insights, and enabling educator collaboration. AI-powered solutions automate time-consuming work like grading assignments, tracking student progress, and managing classroom resources, allowing teachers to spend more time and effort on instructional activities. AI delivers meaningful insights into instructional decision-making by evaluating data on student performance and engagement, allowing teachers to successfully customize their teaching tactics to fit their students’ various requirements.

AI enhances teacher communication and cooperation by offering conversations for exchanging best practices, collaborating on lesson planning, and supporting professional development initiatives. The streamlined structure of teacher workflows not only boosts productivity but also improves overall instructional quality, which boosts student learning results. As AI progresses, its integration into teacher workflows will enable educators to work more efficiently and cooperatively to prepare students for success in the digital age.

4. Improve teacher productivity

AI significantly increases teacher efficiency by automating chores, offering important insights, and improving instructional efficacy. Teachers may use AI-powered solutions to automate mundane administrative chores like grading tests, managing classroom supplies, and arranging calendars, freeing up important time for instructional preparation and student engagement. AI systems evaluate massive quantities of data on student performance, learning preferences, and progress, producing actionable insights that guide instructional decisions and allow tailored intervention tactics. Teachers may use AI-generated insights to customize learning experiences, identify areas for growth, and successfully alter their teaching tactics to fit their students’ different requirements.

Furthermore, AI enables educator cooperation by offering platforms for resource sharing, lesson plan collaboration, and access to professional development opportunities, which improves teacher productivity and effectiveness. As a result, AI enables instructors to be more efficient and innovative in their educational approaches, and ultimately increase student learning results. AI, when integrated into teacher workflows, has the potential to transform education by improving instructional methods and allowing educators to succeed in their roles as facilitators of learning and growth.

5. Creating new forms of teaching

The application of AI to teaching work is expected to result in new types of teaching, such as the increase of online learning and the advent of AI as a teacher. With the support of AI-enabled technologies, teachers nowadays can use AI-powered platforms to provide tailored training to students in virtual classrooms.

AI-powered virtual tutors and intelligent tutoring systems can replace the role of teachers in delivering tailored guidance and feedback to students. This new type of teaching not only broadens access to education but also provides novel ways to learn that address students’ different needs and preferences. As educators adjust to the digital context, using AI as a teaching tool can change the way information is transmitted and acquired. Using AI, educators can build engaging learning experiences for students, therefore impacting the future of education in profound and impactful ways.

The inclusion of AI in the American education system has resulted in a dramatic shift in both teaching approaches and the whole educational sector. AI is promised to improve student results and prepare them for success in the digital era by providing tailored learning experiences, promoting critical thinking, and increasing instructor effectiveness.

The exploration of this article aims to emphasize the need to utilize innovation and technology to suit the changing requirements of students and instructors. As the education industry moves forward, incorporating AI into education will be a critical element in creating inclusive, equitable, and effective learning environments that will equip students to thrive in an increasingly complex and dynamic world. By welcoming AI, the industry could open up fresh possibilities for educational development while simultaneously ensuring that every student has the skills and resources they require to succeed.

Featured image credit: Vecteezy

Artificial intelligence has taken another massive leap forward with the announcement of OpenAI’s OpenAI o1, its first model equipped with “reasoning” abilities, mentioned in the infamous Strawberry model.

Dubbed as the highly anticipated “Strawberry” model by the AI community, OpenAI o1 comes with enhanced problem-solving capabilities, offering a more advanced approach to understanding and answering multistep questions.

Alongside OpenAI o1, the company has also released ChatGPT o1-mini, a more affordable and smaller version for users who need simpler solutions. This release marks a significant milestone as OpenAI moves closer to its vision of AI systems that can think, reason, and assist in decision-making.

OpenAI ChatGPT o1 is out now

What makes OpenAI’s new OpenAI o1 special is its ability to handle complex reasoning, setting it apart from previous iterations such as GPT-4o.

Unlike earlier models that primarily focused on pattern recognition and mimicking training data, o1 uses reinforcement learning to process problems step-by-step. This methodology, often referred to as “chain of thought” reasoning, enables the model to break down complex queries and deliver accurate, structured answers.

One of the most notable aspects of ChatGPT o1 is its ability to explain its reasoning while solving problems. During a demonstration in their blog post, OpenAI’s research team showed how ChatGPT o1 processed a math puzzle step-by-step, providing a solution after 30 seconds of deliberation.

This process mirrors the way humans work through problems, creating a more intuitive and relatable interaction with the AI. However, OpenAI is quick to clarify that while the model appears to “think,” it is not actually conscious or human-like in its cognition. Rather, the interface is designed to give users insight into the model’s complex processing pathways.

OpenAI o1’s strengths and limitations

While OpenAI o1 represents a significant advancement, it isn’t perfect. OpenAI admits that, despite the improved reasoning capabilities, the model still struggles with hallucinations, a common problem where AI generates incorrect or misleading information.

As Jerry Tworek, OpenAI’s research lead, explained:

“We can’t say we solved hallucinations”.

Nonetheless, OpenAI o1 is much better at tasks like writing code, solving mathematical problems, and performing well in competitive environments such as Codeforces programming contests.

According to OpenAI, ChatGPT o1 excels in tasks that require complex thought, such as:

• Coding
• Math
• Science problems

In fact, during tests, it solved 83 percent of the problems on a qualifying exam for the International Mathematics Olympiad, a significant improvement over GPT-4o’s 13 percent success rate. These results suggest that o1 is equipped to handle more advanced tasks than its predecessors, even though it doesn’t outperform GPT-4o in areas like general knowledge or language processing.

Autonomous agents and beyond

One of the long-term goals for OpenAI is to build autonomous AI systems, or agents, that can make decisions and take actions on behalf of users. The OpenAI o1 model is seen as a key step in that direction. Unlike current models, which rely on predicting word sequences, future AI agents would have the capacity to execute complex tasks autonomously, from research to problem-solving in fields like medicine and engineering.

Bob McGrew, OpenAI’s chief research officer, emphasized this during the release of ChatGPT o1, stating:

“We have been spending many months working on reasoning because we think this is actually the critical breakthrough”.

McGrew and his team believe that solving reasoning challenges is fundamental to achieving human-like intelligence in AI. The new training methodology behind o1 reflects this focus, as the model has been optimized for tasks that go beyond simple pattern recognition.

For those interested in understanding how reasoning could lead to more advanced AI, McGrew’s research on AI agents offers fascinating insights.

ChatGPT o1 pricing

However, there is a cost to these advancements. Developer access to OpenAI o1 comes with a hefty price tag: $15 per 1 million input tokens and $60 per 1 million output tokens.

This makes it significantly more expensive than GPT-4o, which costs $5 and $15 for input and output tokens, respectively. For developers working on projects that require extensive reasoning capabilities, the pricing may be a hurdle. That said, OpenAI has positioned ChatGPT o1 as a “preview,” emphasizing that this is still an early-stage model, and more refinements are expected in future iterations.

Meanwhile, ChatGPT Plus and Team users have access to both ChatGPT o1-preview and ChatGPT o1-mini starting today, with Enterprise and Edu users gaining access next week.

SSI AI makes a billion-dollar bet

The company also plans to roll out ChatGPT o1-mini for free-tier users, though no specific date has been announced yet. This staggered release hints at OpenAI’s strategy to gauge user feedback before fully rolling out the model to a wider audience.

For those keen to experiment with OpenAI o1 in programming, OpenAI has shared a demo showcasing the model’s reasoning capabilities.

How to use ChatGPT o1

To use ChatGPT o1, OpenAI’s newest model with enhanced reasoning capabilities, follow these steps:

1. Access ChatGPT o1

• ChatGPT Plus and Team users: o1-preview and o1-mini are available immediately. Just log into your ChatGPT account, and the option to use these models should be present in the model dropdown menu.
• Enterprise and Education users: You will gain access next week.
• Free-tier users: OpenAI plans to roll out o1-mini at a later date. No specific release date has been announced yet.

2. Selecting the o1 Model

Once logged in, you can:

• Open the ChatGPT interface.
• Select OpenAI o1-preview or o1-mini from the list of models available (like GPT-4, GPT-3.5, etc.).
• The OpenAI o1-preview is the full version with maximum reasoning abilities, while o1-mini is a lighter, faster, and cheaper option.

3. Usage in the API

If you’re a developer, using OpenAI o1 in the API involves:

• Adjusting your API calls to specify o1-preview or o1-mini.
• Pricing is higher than GPT-4o, with $15 per million input tokens and $60 per million output tokens.

For more details, you can refer to OpenAI’s API documentation to see how to integrate the new model.

OpenAI o1 codes a video game from a prompt. pic.twitter.com/aBEcehP0j8

— OpenAI (@OpenAI) September 12, 2024

The bigger picture

With ChatGPT o1, OpenAI takes a huge step toward realizing its vision of creating AI systems that can not only answer questions but also reason through complex problems.

Though not without its limitations, the model introduces a new way of interacting with AI, one that feels more intuitive and, at times, surprisingly human. By focusing on reasoning, OpenAI is paving the way for future advancements that could have far-reaching implications across industries.

As the development of reasoning-based AI progresses, we can expect further innovations in fields like medicine, engineering, and even creative arts.

The question now is not whether AI can assist us in solving complex problems but how far we can push the limits of its capabilities.

Image credits: OpenAI

OpenAI is seeking to raise at least $5 billion, aiming for a valuation of $150 billion, nearly doubling its previous valuation of $86 billion. The funding round includes major investors such as Apple, Nvidia, Microsoft, and Thrive Capital. Thrive Capital, led by Josh Kushner, is expected to contribute $1 billion.

Can OpenAI hit the jackpot?

OpenAI, the company behind popular AI tools like ChatGPT, is aiming for a huge milestone: a $150 billion valuation. Here’s what this goal means and why it’s such a big deal:

1. Current vs. target: Right now, OpenAI is valued at $86 billion. The company wants to boost this to $150 billion, which would be almost double its current worth. This ambitious target shows how confident investors are in OpenAI’s future.
2. Comparison with others: If OpenAI hits this $150 billion mark, it will be among the highest-valued private companies in the world, rivaling other tech giants like Elon Musk’s SpaceX.
3. How much money?: To reach this valuation, OpenAI is looking to raise at least $5 billion. This money is crucial for continuing their work on advanced AI technology and staying ahead of competitors.
4. Who’s investing?: Major players like Apple, Nvidia, Microsoft, and Thrive Capital are involved. Thrive Capital, led by Josh Kushner, is set to invest $1 billion. These big names are backing OpenAI because they believe in its potential.
5. Revenue and losses: OpenAI is making $2 billion a year from its products, but it’s still losing money. This is because developing top-notch AI technology is very expensive. The new funding will help cover these costs.
6. Why the high valuation?: The $150 billion valuation isn’t just about what OpenAI is doing now. It’s about what it could achieve in the future, including creating AI that might outperform human intelligence, AGI.
7. Rivals: OpenAI faces tough competition from companies like Google, Meta, and Anthropic. The high valuation goal reflects the need to keep innovating and staying ahead in a crowded market.
8. Investment trends: There’s a lot of money flowing into AI, but some people worry about a potential bubble. The high valuation goal highlights the risks and rewards in this fast-moving field.

If OpenAI reaches its $150 billion goal, it could set a new standard for how much AI companies are worth. This could influence how other tech start-ups are valued.

Successfully hitting this target could give OpenAI more resources to push the boundaries of AI technology, potentially leading to major advancements that impact various industries.

So, can OpenAI hit the jackpot with its $150 billion goal? It’s a bold move that reflects both high expectations and big potential in the world of artificial intelligence.

Featured image credit: Eray Eliaçık/Bing

Imagine being able to describe a scene in words and have a 3D model instantly appear before your eyes. That’s the power of Meshy-4, an AI tool that’s transforming the way we create and interact with three-dimensional worlds.

What is Meshy-4 AI?

Meshy-4 is a powerful AI tool designed to generate 3D models from text prompts or sketches. It utilizes state-of-the-art deep learning techniques to understand and interpret user input, translating it into complex 3D structures.

Meshy-4’s ability to generate diverse and high-quality models has made it a popular choice among artists, designers, and game developers.

With Meshy-4, you may generate 3D models:

• From text prompts
• From images
• From textures

How does Meshy-4 work?

Meshy-4 leverages advanced deep learning techniques, specifically generative adversarial networks (GANs), to generate 3D models. GANs consist of two neural networks: a generator that creates models and a discriminator that evaluates their realism. By training these networks on vast datasets of 3D models and corresponding text descriptions, Meshy-4 learns to understand the relationship between language and 3D geometry.

Model architecture

The exact architecture of Meshy-4’s neural networks is proprietary information, but it likely involves a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process both visual and textual data effectively.

Input and output

Meshy-4 can accept text prompts or images as input. The text prompts should be as detailed as possible to ensure accurate model generation.

The output is a 3D model, typically in a standard format like OBJ or STL.

Customization and refinement

Once a model is generated, users can customize and refine it using Meshy-4’s editing tools. This includes adjusting the shape, size, and details of the model to match their desired specifications.

Limitations and challenges

While Meshy-4 is a powerful tool, it has limitations. For example, it may struggle to generate highly complex or detailed models, especially with limited or ambiguous input.

Additionally, the quality of the generated models can vary depending on the complexity of the prompt and the training data used.

How to use Meshy-4 AI

Using Meshy-4 is a straightforward process:

1. Provide a prompt: Input a detailed text description or sketch of the 3D model you envision.
2. Generate model: Meshy-4 will process your input and generate a 3D model based on your specifications.
3. Refine and customize: You can further refine the generated model by making adjustments to its shape, size, and details.
4. Export and use: Once you’re satisfied with the model, you can export it in various 3D file formats for use in your projects.

Is Meshy-4 free to use?

Meshy-4 offers a free tier with limited features, allowing users to explore its capabilities and generate basic 3D models. For more advanced features and higher-quality outputs, a paid subscription is required. The pricing plans vary, offering different levels of access and usage limits.

Here is a quick rundown of Meshy-4 pricing plans:

Free plan

• No credit card required
• 200 credits per month
• Limited waiting queue
• Assets are shared
• Polycount options: 10k/30k
• 1 free retry
• No AI texture editing
• No community models
• No animation

Pro plan

• Best for individual creators
• $20/month
• 1,000 credits per month
• Standard waiting queue
• Assets are private
• Polycount options: 3k/10k/30k/100k
• 4 free retries
• AI texture editing
• Download community models
• Animation

Max plan

• Best for studios and teams
• $50/month
• 4,000 credits per month
• Maximized waiting queue
• Assets are private
• Polycount options: 3k/10k/30k/100k
• 8 free retries
• AI texture editing
• Download community models
• Animation

Max unlimited plan

• Unlock Meshy-4’s full potential
• $120/month
• Unlimited credits per month
• Maximized waiting queue
• Assets are private
• Polycount options: 3k/10k/30k/100k
• Unlimited free retries
• AI texture editing
• Download community models
• Animation

We simply can’t catch up anymore!

Just a short while ago, crafting intricate 3D models required specialized software and technical expertise. Now, imagine describing a fantastical creature in words or sketching a futuristic cityscape, and having a detailed 3D model appear before your eyes in mere moments. That’s the magic of Meshy-4, an AI tool that’s rapidly changing the game of 3D creation.

Meshy-4 leverages cutting-edge AI technology to bridge the gap between imagination and reality. Whether you’re a seasoned artist, a budding game developer, or simply someone with a creative vision, Meshy-4 empowers you to bring your ideas to life in stunning 3D detail. The tool’s development feels almost like a glimpse into the future, a testament to the rapid advancements in AI.

With its intuitive interface and diverse capabilities, Meshy-4 breaks down the technical barriers to entry, making 3D modeling accessible to a wider audience than ever before.

It’s an exciting time to be a creator, and Meshy-4 is poised to be a valuable companion on this journey.

Image credits: Meshy AI

Thanks to App.Symph AI, you will be able to make and use programs in seconds without knowing the software. App.Symph AI offers a platform where users can build their AI-powered apps with little or no coding knowledge. This makes it an attractive option for a wide range of people, from educators who want to streamline lesson planning to small business owners who need fast, customized solutions. So what exactly is Symph AI and why is it attracting attention?

At its core, App.Symph AI enables users to develop AI-driven applications that solve specific problems or improve their daily workflow. This is done without the complex technical hurdles often associated with AI development. Pricing varies according to the user’s needs, with options to suit both casual users and those in need of more comprehensive features. Whether you are keen on automation or need a quick solution for mundane tasks, Symph AI’s capabilities are worth exploring.

How to use the App.Symph AI

The most remarkable feature of the App.Symph AI is its accessibility. Users don’t need prior experience in programming to create useful applications. Symph AI eliminates complexity on the backend by using pre-built templates and customizable tools. Users can focus on what matters – accomplishing their tasks faster and more efficiently.

We have tried this artificial intelligence for you. You can easily use this tool by following the steps:

• First, we go to app.symph.ai.
• Then log in. You can easily log in with your Google account.
• Now you can tell the program you want to the tool.
• We wanted to write a program called “Chef and Recipe AI”.

• We write the unique name of the program.
• Briefly describe what you want in the Description section.

• If you want, you can get a more elegant image by using a background image.
• What type of app? section is important here you choose how your program will output. Please choose the one that suits you best.
• Fields are where your personal information is. We write what the AI should do.
• And finally, we choose the accessibility of the program in the Visibility section.

That’s it, your program is done. You don’t even need to write code. You can also try the program we wrote via this link.

Symph AI’s pricing structure offers flexibility. It includes a free version, allowing users to test the platform without any financial commitment. For those who need more advanced features, such as higher usage limits or additional templates, paid plans start from $8 per month. There’s also a $20/month option for heavier users who want even more power and versatility from the platform.

1. Starter pack: $8 / month – $80 / year
2. Pro pack: $12 / month – $120 / year
3. Premium pack: $20 / month – $200 / year

Additionally, Symph AI can be used in many professional fields. It’s not limited to tech-savvy users. For instance, educators can use it to create quizzes or interactive lessons, while small business owners might find it useful for automating routine administrative tasks.

While many platforms offer templates, Symph AI allows users to customize these templates according to their specific needs. Applications created within Symph AI can be tailored with unique commands or personalized workflows, providing a greater level of control compared to other tools on the market. Another standout feature is the user interface. It is designed to be intuitive, guiding users step-by-step through the app creation process. Even non-tech-savvy users will find it easy to navigate thanks to its clean and simple layout.

Symph Al is not just for hobbyists. It is also useful for work, school, and personal tasks. Its wide range of templates, customizable features, and flexible pricing make it suitable for both individuals and organizations. Users who want quick solutions without learning complex programming turn to Symph Al. Symph Al fills a gap in the market by providing a platform where anyone can build their Al-powered tools. It is likely to continue to grow in popularity among those looking for a simple yet powerful way to integrate Al into their daily lives.

Featured image credit: Symph AI

Privacy is a growing concern. When using AI services, individuals often worry about their data being collected and misused. Hoody AI addresses these concerns by offering a secure and anonymous platform for accessing a variety of AI models.

What is Hoody AI?

Hoody AI is a privacy-focused platform that provides access to a variety of AI models. It offers a unified interface, allowing you to interact with multiple models simultaneously. Hoody AI is committed to protecting your privacy and anonymity, ensuring that your data remains secure.

🔥 We are launching Hoody AI (https://t.co/utXju1Tmtg) tonight.

Hoody AI is a dashboard providing anonymous access to the best LLMs (OpenAI, Anthropic, Google, Meta…).
Chat to multiple models at once, interact by voice, upload documents, edit prompts… All in one place.

As… pic.twitter.com/IZWSCC8w0d

— Hoody.com (@HoodyPrivacy) September 4, 2024

What are Hoody AI features?

Hoody AI is a valuable tool for anyone who wants to use AI while protecting their privacy. With its wide range of features and commitment to anonymity, Hoody AI is a valuable option for both individuals and businesses.

Here is a quick rundown of Hoody AI features:

• Privacy-focused: Hoody AI prioritizes your privacy and collects no personal information.
• Anonymous access: You can use Hoody AI without providing any personal details.
• Multiple AI models: Access a wide range of AI models, including OpenAI, Anthropic, Google, and Meta.
• User-friendly interface: Hoody AI offers a simple and intuitive interface, making it easy to use.
• Additional features: Features like text-to-speech, speech-to-text, file upload, and geolocation bypass.

How to use Hoody AI

1. Sign up: Create a Hoody AI account anonymously. No personal information is required.
2. Choose an AI model: Select the AI model you want to use from the available options.
3. Start using: Begin interacting with the AI model through the Hoody AI interface.

How about Hoody AI pricing?

Hoody AI offers flexible pricing plans to suit your needs. Here’s a breakdown of the available options:

1-Month plan

• Price: $15
• Features:
  • 5 Devices for Hoody App
  • Unlimited Containers
  • No Speed Limit
  • Access to all Hoody AI features

6-Month plan

• Price: $40 (56% off)
• Features:
  • 5 Devices for Hoody App
  • Unlimited Containers
  • No Speed Limit
  • Access to all Hoody AI features

12-Month plan

• Price: $65 (64% off)
• Features:
  • 5 Devices for Hoody App
  • Unlimited Containers
  • No Speed Limit
  • Access to all Hoody AI features

Hoody AI’s commitment to privacy

Chatbots, like many AI services, often collect personal data to improve functionality, personalize responses, and enhance user experience.

ChatGPT, Gemini, Claude – you name it… They gather information such as your name, location, browsing habits, and interaction history to provide tailored recommendations and insights. While this can improve user engagement, it also raises significant privacy concerns, as sensitive information can be stored, shared, or even exploited if not properly protected.

In contrast, Hoody AI is designed with privacy at its core. Unlike other AI platforms that rely on personal data collection, Hoody AI does not gather any information about you. This means your identity remains completely anonymous, and you can use its services without fear of your personal data being exposed or tracked.

Hoody AI goes beyond just anonymity by employing strong encryption methods to protect your data from unauthorized access. You can also make payments through anonymous gateways, ensuring that even your financial transactions remain private.

For added security, Hoody AI offers unique features like app-specific VPNs, allowing you to assign different locations to each app for enhanced privacy. It also provides browsing containers, which isolate your online activity from other sessions, further protecting your digital footprint. Additionally, the “Bulletproof Mode” randomizes your exit IP for each request, making it incredibly difficult to track your online movements.

By combining these advanced privacy tools, Hoody AI provides a secure and anonymous way to interact with AI services, ensuring your personal data remains safe and uncompromised.

Image credits: Hoody AI

Vizard.ai, a new AI-powered tool, offers an easy solution to a common problem. Let’s say you’re a YouTuber and you have long, long videos. But you don’t have time to extract content to YouTube Short. That’s where Vizard.ai comes in. It extracts short snippets from your long videos, creates subtitles, creates content, and even adapts to the format of the platform you’re uploading to.

As more creators seek to maximize their reach across multiple platforms, the need to turn long-form video into engaging, shareable clips has become critical. Vizard.ai bills itself as a reliable tool that automates this process. We tried it out for you.

How to use Vizard.ai

Vizard.ai’s main feature is its capacity to create brief clips from extended videos automatically, suitable for posting on various social media sites such as TikTok, Instagram, and YouTube. Vizard.ai simplifies video editing with AI features, unlike traditional software that needs skill and time. The tool allows users to upload their videos, then promptly identifies the most captivating parts, includes captions, and adjusts the clips for different social platforms, all in just minutes.

Now let’s move on to how Vizard.ai is used:

• First of all, we go to Vizard.ai.

• Then we log in.
• You can easily log in with your Google account.

• Then we upload the link or file of the video you want to convert to a clip.

• We choose a language for the AI to work well.

• You can set our format and video length in the new window.
• After all the settings are done, click on the “Get AI clips” button.

• And Vizard.ai starts making the clips for you.
• After the process is finished, the clips are ready in 4 different versions.
• You can choose the one you want, you can even choose 4 of them.

• You can Edit your selected clip or press the Export button to proceed to the next step.
• In the next and final step, if you have made the necessary links, you can skip the download and upload the video directly to your channel from here, or you can download it and do it manually.

That’s how easy it is to use Vizard.ai. Everything we have done so far we have done for free. You can easily try this tool by following these steps as you wish. Let us share the result with you, it is up to you to evaluate it, remember that we have not made any changes or edits.

Vizard.ai’s price

Now that we know how to use it, let’s find out how much it will cost. Vizard.ai offers several subscription plans that cater to different needs, from casual users to professional teams.

• Free user: The free plan offers basic features like 120 upload minutes and 10 video exports per month, making it suitable for individuals who need quick and easy access to essential tools. And it adds its watermark to videos.
• Creator Pack: Aimed at more serious creators, the Creator plan offers 4K video quality, unlimited exports, and no watermark. The Creator package is priced at $20, dropping to $10.67 for an annual subscription.
• Team Pack: Designed for collaboration, the Team plan includes faster AI processing, a shared workspace, and the ability to invite team members. The team pack is available for a monthly fee of $30, which currently drops to $16 for an annual subscription.

Vizard.ai stands out from other video editing tools thanks to its user-friendly interface and Al features. The platform’s Al capabilities go beyond simple clipping. It also handles auto-subtitling, and subtitle translation, and provides Al-driven post suggestions. Whether you’re a solo creator or part of a larger team, Vizard.ai’s flexibility ensures that it can adapt to various needs.

Maximizing social media impact

Vizard.ai lets users schedule social media posts directly from the platform. This feature is available in the Creator and Team plans. It helps users plan their content distribution more effectively. It also helps creators maintain a consistent online presence.

Vizard.ai provides a powerful and accessible solution for anyone looking to improve their social media strategy with video content. Its Al-driven tools not only save time but also ensure that your content is polished and ready for the digital stage. Whether you’re working alone or as part of a team, Vizard.ai has the features you need to engage your audience without the hassle of traditional editing software. With plans ranging from free to team-oriented options,
Vizard.ai is positioned as the go-to tool for creators looking to get the most out of their video assets.

Featured image credit: Vizard

Is Apple Intelligence only on iPhone 16? This new feature from Apple is turning heads, but it’s not just limited to the latest iPhone models. Whether you have the newest iPhone or a slightly older one, you might still enjoy what Apple Intelligence offers. But has some twists to use it!

Is Apple Intelligence only on iPhone 16 series? Will Apple Intelligence work on iPhone 15?

Apple Intelligence is indeed an exciting development, but it’s not exclusive to just the iPhone 16. While the iPhone 16 series will be among the first to experience its capabilities, Apple Intelligence will also be available on a range of other Apple devices. Here’s a closer look at its compatibility and the broader scope of its rollout:

• iPhone 16 series: The iPhone 16, iPhone 16 Plus, iPhone 16 Pro Max, and iPhone 16 Pro, all powered by Apple’s latest A18 and A18 Pro chips, will support Apple Intelligence. This ensures that users with these models can access the new feature from the start.
• iPhone 15 Series: In addition to the iPhone 16 models, Apple Intelligence will also be available on the iPhone 15 Pro and iPhone 15 Pro Max, which are equipped with the A17 Pro chip. This inclusion highlights Apple’s commitment to extending new features to its recent high-end devices.
• iPad models: Apple Intelligence will be compatible with iPads that feature the M1 chip and later versions. This includes:
  • iPad Pro
  • iPad Air
• Mac computers: Apple’s Mac lineup will also see support for Apple Intelligence. The compatibility extends to:
  • MacBook Air and MacBook Pro with the M1 chip and later
  • iMac, Mac mini, and Mac Studio with the M1 chip and later
  • Mac Pro with the M2 Ultra chip

Here is why your iPhone does not support Apple Intelligence

Apple Intelligence will be accessible on devices with recent chipsets (A17 Pro, A18, M1, and M2 Ultra), reflecting Apple’s strategy to enhance its entire ecosystem.

Beta availability

Apple Intelligence will initially be available in beta form, meaning it’s in a testing phase and might have some limitations or bugs. This beta version will be accessible through updates to iOS 18, iPadOS 18, and macOS Sequoia this fall.

While the initial release focuses on English (U.S.), Apple plans to expand Apple Intelligence’s features, languages, and supported platforms over the coming year. This means that more users will have access to Apple Intelligence as it evolves. However, there is an Apple Intelligence EU workaround you might want to look out for!

Apple Intelligence & iPhone 16 AI features

Apple Intelligence, introduced at the “It’s Glowtime” event, brings advanced AI features to iPhones, iPads, and Macs with a strong emphasis on privacy and user control. Key features include:

1. Writing tools: Enhance communication with AI-powered assistance for summarizing, rewriting, and finding the right words across various apps.
2. Smart reply: In Mail, AI suggests quick, accurate responses and summarizes lengthy emails.
3. Reduce interruptions: Prioritizes important notifications, filtering out less relevant ones.
4. Image playground and Genmoji: Allows users to create personalized images from descriptions or existing photos.
5. Image wand: Transforms rough sketches into polished visuals in the Notes app.
6. Clean up: Removes distracting background elements from photos.
7. Enhanced Siri: More contextually aware, integrates better with system tasks, and processes user data privately.

Is Apple Intelligence only on iPhone 16 series? Now you know the detailed answer! But is Apple Intelligence safe? Visit the article and explore now.

Featured image credit: Eray Eliaçık/Bing

This fall, Apple is launching its most significant AI-driven features to date with the introduction of Apple Intelligence.

Built seamlessly into your iPhone, iPad, and Mac, Apple Intelligence aims to revolutionize how users write, create, and get things done, all while keeping their personal data secure and private. Unlike many other AI systems, Apple ensures that no personal data is shared or accessed by anyone, not even by Apple itself.

These innovations bring AI to the masses, making advanced tools accessible to everyday users.

What does Apple Intelligence bring to the table?

One of the standout features of Apple Intelligence is its Writing Tools, which use AI to help users find the right words wherever they write. This AI capability allows for summarizing long lectures, rewriting messages with different tones, and even simplifying group threads to highlight the most important points. By integrating directly into apps like Mail and Notes, as well as third-party platforms, Apple Intelligence makes it easier than ever to communicate effectively.

The AI-powered Smart Reply feature in Mail can suggest accurate responses to emails, detecting and addressing key questions with just a few taps. Additionally, with a tap in the Mail app, users can reveal a concise summary of long emails, cutting through unnecessary clutter. Apple Intelligence’s ability to prioritize and summarize content extends beyond emails, offering summarized notifications so users can scan their most important alerts quickly.

Apple Intelligence also introduces Reduce Interruptions, a new focus feature that filters notifications based on content relevance, displaying only those that need immediate attention. For instance, reminders about an upcoming flight or important messages from daycare are pushed to the top of your notifications. By doing so, Apple ensures that users can stay on top of their day without feeling overwhelmed by unnecessary distractions.

Apple Intelligence goes beyond text, offering new ways for users to visually express themselves. Features like Image Playground and Genmoji allow users to create personalized, fun, and original images based on a description, a concept, or even a photo from their library. With easy access across different apps like:

• Messages
• Freeform
• Keynote

Users can tailor their images to suit any context, whether it’s creating a professional presentation or spicing up a casual conversation.

For those who enjoy a hands-on creative process, Image Wand can transform rough sketches into complete visuals within the Notes app. Simply circle the sketch with your finger or Apple Pencil, and Apple Intelligence will analyze the surrounding content to generate a complementary image. Users can even create custom memory movies based on descriptions, with the AI selecting photos and videos that match their theme and arranging them into a cohesive narrative.

Apple’s AI features also make organizing and perfecting photos effortless. With tools like Clean Up, users can remove distracting background objects in photos, ensuring that the focus remains on what truly matters. All of this is done with Apple’s commitment to maintaining the integrity of the original image, ensuring that the edits are subtle yet impactful.

Is Apple Intelligence only on iPhone 16?

Siri re-imagined

Siri is receiving a significant upgrade, drawing on Apple Intelligence to become more powerful and contextually aware than ever before. With a brand-new design and richer language understanding, Siri is now more integrated into the system experience. Whether users prefer to type or speak to Siri, they can access its capabilities from anywhere in the system, making it a seamless part of their daily tasks.

What can the enhanced Siri do? Well, see it from Josh Cohenzadeh‘s post on X below!

The new Apple Intelligence photo search capabilities seem like they should actually be pretty useful #AppleEvent pic.twitter.com/grBjI8pW75

— Josh Cohenzadeh (@jshchnz) September 9, 2024

Thanks to Apple Intelligence, Siri can now process information from the user’s personal context, making it more helpful and precise.

For instance, if a user receives a message with a new address, Siri can be instructed to automatically add it to a contact card. Siri’s ability to understand and act on information across apps also allows for more efficient multitasking. Users can ask Siri to edit a photo and then add it to a specific note, all without manually switching between apps.

Privacy in mind

One of the most important aspects of Apple Intelligence is its approach to privacy. All personal data remains on the device, and Apple uses Private Cloud Compute to handle more complex tasks without ever storing personal information on external servers. This level of privacy is a defining feature, setting Apple Intelligence apart from many other AI systems that rely on extensive data collection.

Even when Siri or other AI features tap into ChatGPT, Apple’s integration ensures users have control over when and how their data is used. Users will be asked before any information is shared, and Apple ensures that all interactions remain within the confines of its robust privacy framework.

Which phone will get Apple Intelligence?

Apple Intelligence will be available on a range of Apple devices, primarily those equipped with more recent chipsets.

Specifically, the following iPhones will support Apple Intelligence:

• iPhone 16 series (iPhone 16, iPhone 16 Plus, iPhone 16 Pro, iPhone 16 Pro Max), equipped with A18 and A18 Pro chips.
• iPhone 15 Pro series (iPhone 15 Pro, iPhone 15 Pro Max) with A17 Pro chips.

Apart from smartphones, the following Apple devices will also receive Apple Intelligence in the coming months:

• iPad Pro models with M1 chip or later.
• iPad Air models with M1 chip or later.
• MacBook Air, MacBook Pro, iMac, Mac mini, Mac Studio, and Mac Pro with M1 or later chips.

These devices are equipped with the necessary hardware, like the M1 and later chips, to handle the advanced AI processing required for Apple Intelligence.

With the upcoming release of Apple Intelligence, Apple is making advanced AI tools available to everyone, from casual users to professionals. Whether it’s through enhanced writing tools, creative image generation, or a smarter and more integrated Siri, these new features promise to transform how we communicate, work, and create — all while maintaining Apple’s steadfast commitment to privacy and security.

As these features roll out this fall, the future of AI on Apple devices looks incredibly bright.

Featured image credit: Apple

Have you ever wondered how AI can help you create music without any musical background? Jamboss AI is doing just that. Developed by a small team, the new tool is an AI-powered app designed to allow anyone to create custom songs by simply providing text prompts or lyrics. Whether you want a reggae birthday song or an upbeat pop anthem, Jamboss AI delivers full-length, original compositions in a variety of styles and genres.

Offering 20 credits for free, this tool releases two different versions in one request. It also generates the album cover, lyrics, and parts of the song. If you’re looking for an alternative AI to Suno AI, try this tool. For now, it only works on iOS platforms, but don’t worry, Android is coming soon.

How Jamboss AI works with the power of simplicity

The most important feature of Jamboss is its simplicity. Users interact with the app in two main ways:

1. The first is to type a text description of the song they want, for example, “a rock song with fast beats and a catchy chorus”.
2. The second option is to enter custom lyrics. Once the lyrics are entered, the AI processes the request and creates a complete song, including instruments and vocals.

The app uses advanced machine learning algorithms trained on thousands of music tracks to create new compositions that match the user’s description.

What is particularly noteworthy is that the app offers a wide range of musical styles, from rock to classical and even reggae. Once a song is created, users can easily share it with friends or save it to their music library. There’s no need to be nervous when it comes to lyrics, once you’ve chosen your theme, simply enter the prompt “Can you write me a lyric about this topic?” in ChatGPT. Then you can create the song you want by throwing this lyric to Jamboss AI.

Who is Jamboss AI for?

Jamboss appeals to a wide audience, from creators looking for original soundtracks to individuals looking to produce personalized songs for special occasions. For example, the song you hear above was created by us in just 20 seconds. The highlight of the app is that it democratizes music production, making it accessible even to those without a background in music production. The songs produced are royalty-free, allowing users to use them in their projects without any legal concerns. Whether it’s for a video project or a birthday surprise, Jamboss allows you to quickly create high-quality music without the need for expensive equipment or technical knowledge.

How to create music with Jamboss AI

Making your first song with Jamboss AI is incredibly easy. Here’s a quick breakdown of the process:

• Step 1: Download the Jamboss app on your iOS device (Android support coming soon).
• Step 2: Log in to the app. You can easily sign in with your Apple account.
• Step 3: Choose your input method: text description, custom lyrics.
• Step 4: Provide your input. For example, “upbeat pop song with electronic beats”.
• Step 5: Create your song by pressing the ‘create new song‘ button.
• Step 6: Edit or recreate the song if necessary.
• Step 7: Save or share the song you created directly from the app.

With these simple steps, anyone can create a song in just a few taps.

Making music with Jamboss AI requires no expertise

The AI behind Jamboss uses machine learning and deep neural networks to create original compositions. While the technical aspects may seem complex, the user experience is smooth and straightforward. The app isn’t just for experienced musicians; it’s equally useful for beginners who want to explore music creation.

The tool is designed to support creative expression by creating diverse and dynamic compositions that can be used for everything from fun personal projects to professional content creation. Best of all, it’s free to use with a limited number of songs each month, making it a budget-friendly option for anyone looking to try their hand at music production.

Featured image credit: Jamboss AI

Earlier this week, discussions about OpenAI’s future AI models reignited, with particular attention on the rumored “Orion” model. While much of the buzz centered around potential advancements, the standout revelation is the expected price tag: Subscriptions for Orion could reach up to $2,000 per month.

According to Reuters, Orion is rumored to be OpenAI’s next flagship model, aimed at enterprise customers seeking specialized AI tools. This steep price indicates a shift in focus towards high-end business applications, offering more advanced capabilities than current models like the GPT-4 and the rumored GPT Next.

With this move, OpenAI is doubling down on the enterprise sector, aiming to provide powerful AI solutions with enhanced functionality for companies willing to invest in cutting-edge technology.

Why would GPT Orion cost that much?

Orion is expected to offer specialized tools and features designed for businesses, going beyond the capabilities of existing models like GPT-4. This could include enhanced security, multi-step reasoning, and more autonomous task management, making it invaluable for companies that require advanced AI for complex operations.

Running large AI models requires enormous computational resources, especially for models as advanced as Orion or the infamous Strawberry Project. The high subscription fee could help cover the significant costs of maintaining and scaling the infrastructure needed to support such powerful systems.

Orion’s capabilities could be highly customized for specific industries, offering unique solutions like financial modeling, market analysis, or technical simulations. Businesses would be willing to pay a premium for an AI system that can dramatically improve efficiency, productivity, or decision-making in their field.

Enterprise models often come with enhanced data privacy and regulatory compliance features. This includes secure data handling, encryption, and compliance with industry standards, ensuring sensitive information is protected—a critical selling point for large companies.

At this price point, OpenAI could be offering priority support, regular updates, and exclusive access to new features. Businesses might also get the ability to train and fine-tune the model on their own proprietary data, giving them an edge in competitive industries.

From consumer-grade to premium

ChatGPT became a household name due to its accessibility and versatility, allowing users to engage with AI for everything from casual conversations to professional assistance. Its ease of use and impressive capabilities made it wildly popular, reaching millions of users worldwide.

However, despite this success in the consumer market, businesses and enterprises often found themselves with limited options, relying on the same models as everyday users.

While ChatGPT democratized AI access, GPT Orion could give enterprises the advanced tools they’ve been lacking, helping OpenAI carve out a new, more profitable niche in the corporate sector.

Featured image credit: Emre Çıtak/Mystic AI

Google’s new Ask Photos feature is like having a personal photo assistant right in your pocket. Instead of scrolling endlessly through your photo library, you can now ask questions in natural language and let AI do the heavy lifting. Imagine you’re trying to find that one photo of your child painting. Instead of hunting through countless images, you simply type or speak your question, and Ask Photos pulls up exactly what you’re looking for.

Deep dive into Google’s new Ask Photos feature

Ask Photos is a feature that leverages advanced artificial intelligence (AI) to allow users to search for photos using natural language queries. Instead of relying on traditional keyword searches or manual scrolling through albums, users can simply ask questions about their photos, and the AI will find the relevant images.

Ask Photos offers several advanced search features:

• Vacation photos: You can locate images from specific trips or events by specifying the location and date.
• Event themes: The feature enables users to find photos based on themes, like holidays or parties.
• Meals by location: Users can search for images of meals based on the location where they were taken.

• Ticket expiration reminders: Ask Photos can track and remind you of upcoming ticket expirations, adding a practical utility to your photo management.

The power behind Ask Photos comes from Google’s Gemini AI, a sophisticated artificial intelligence system. This AI is designed to handle complex data and understand intricate relationships between images. By utilizing Gemini AI, Ask Photos can enhance the search process, recognizing subtle details and connections that traditional search methods might miss, and thereby improving the accuracy and relevance of the search results.

Is it safe?

Google addressed several key concerns about the new feature:

First, Google has assured you that your photo data will not be used for advertising. This means that your images are kept private and are not exploited for commercial purposes.

Second, while there may be occasional human reviews to refine the feature, these reviews are conducted in a way that does not link personal data to individual Google accounts. This approach ensures that your privacy is maintained even when the service is being improved.

Lastly, your images and the results from Ask Photos remain confidential unless you provide feedback that triggers a review. This ensures that your personal photos are kept secure while still allowing for enhancements based on user input.

How to access Ask Photos

Currently, Ask Photos is available to select iOS and Android users in the U.S. If you don’t have access yet, you can join the waitlist to get early access as the feature rolls out more broadly.

How to use Google’s new Ask Photos feature

If  you are eligible to use the Ask Photos feature, follow these steps:

• Launch the Google Photos app on your iOS or Android device.
• At the top of the screen, locate the search bar where you typically enter keywords or tags.
• Tap on the search bar and enter a question in natural language. For example, you might type, “What we say at Idaho?” or “What did I eat at the hotel in Stanley?” Alternatively, you can use voice input if your device supports it. The AI will process your question and display photos that match your query. The results are based on the context and details of your images, so you’ll see the most relevant pictures.

If the initial results are not what you expected, you can refine your question or try asking it in a different way to get more accurate results.

Featured image credit: Google

Reflection 70B is a large language model (LLM) developed by HyperWrite, an AI writing startup. Built on Meta’s Llama 3.1-70B Instruct, Reflection 70B is not just another open-source model. Its innovative self-correction mechanism sets it apart, enabling it to outperform many existing models and count “r”s in strawberries.

The technique that drives Reflection 70B is simple, but very powerful.

Current LLMs have a tendency to hallucinate, and can’t recognize when they do so.

Reflection-Tuning enables LLMs to recognize their mistakes, and then correct them before committing to an answer. pic.twitter.com/pW78iXSwwb

— Matt Shumer (@mattshumer_) September 5, 2024

Wait, why is Llama 3.1-70B as a base for HyperWrite Reflection 70B?

Reflection 70B is built on Meta’s Llama 3.1-70B Instruct, a powerful base model designed for various language tasks. Llama models are known for their scalability and high performance, but Reflection 70B takes things further by introducing a series of advancements, particularly in reasoning and error correction.

The reason? Based on an open-source framework, Llama 3.1-70B allows developers to fine-tune, adapt, and customize the model. Llama models are trained on vast amounts of diverse data, allowing them to excel at general-purpose tasks such as language generation, question-answering, and summarization.

Reflection 70B takes this solid foundation and builds a more sophisticated error-detection system that dramatically improves its reasoning capabilities.

Meta is not the only helper to HyperWrite; meet Glaive

A key element behind Reflection 70 B’s success is its synthetic training data provided by Glaive, a startup that specializes in creating customized datasets for specific tasks. By generating synthetic data tailored for specific use cases, Glaive allowed the Reflection team to train and fine-tune their model quickly, achieving higher accuracy in a shorter time.

The collaboration with Glaive enabled the rapid creation of high-quality datasets in hours instead of weeks, accelerating the development cycle for Reflection 70B. This synergy between the model architecture and training data showcases the potential for innovative partnerships in AI

Where is HyperWrite Reflection 70B shine?

The defining feature of Reflection 70B is its self-correction ability. Large language models (LLMs) often generate text that may be incorrect or inconsistent, a phenomenon known as AI hallucinations. These errors are a well-known limitation of LLMs, even among industry leaders like OpenAI’s GPT series. Reflection 70B addresses this issue with a unique “reflection tuning” technique.

What is reflection tuning?

Reflection tuning enables the model to evaluate its own output for logical errors and correct them before presenting a final answer. This mechanism is based on a layered reasoning process:

• Step-by-step reasoning: During inference (when the model generates a response), Reflection 70B breaks down its thought process into distinct steps. It “thinks aloud,” revealing how it arrives at an answer, much like a person solving a complex problem.

• Error detection: The model introduces special tokens during its reasoning, which help it monitor its progress. These tokens act as flags, guiding the model to revisit and reanalyze sections that seem logically inconsistent or incorrect.
• Self-correction: Once an error is identified, the model takes corrective action, generating a revised answer. This process occurs in real-time, ensuring that the output is as accurate as possible.

By integrating these features, Reflection 70B mimics a type of metacognition—thinking about its own thinking—something that most LLMs lack, including ChatGPT!

A common challenge for AI models is answering basic numerical comparisons or counting tasks, where errors are frequent. Reflection 70B’s demo site includes a question like, “How many times does the letter ‘r’ appear in the word ‘Strawberry?’” While many AI models struggle to provide accurate answers in such cases, Reflection 70B can identify when its reasoning is incorrect and adjust accordingly. Even though its response times may be slower due to this reflection process, the model consistently arrives at accurate conclusions.

How does HyperWrite Reflection 70B work?

Reflection 70B’s architecture introduces special tokens to enhance its ability to reason and self-correct. These tokens serve as markers within the model’s internal structure, allowing it to divide its reasoning into clear steps. Each step can be revisited and reanalyzed for accuracy.

For example:

• <Reasoning_Start>: Marks the beginning of a reasoning process.
• <Reflection_Point>: Indicates a checkpoint where the model pauses to evaluate its progress.
• <Error_Found>: Flags any inconsistencies in the logic.
• <Correction_Applied>: Shows that the model has made a correction and is ready to move forward.

This structured reasoning allows users to follow the model’s thought process more transparently and provides better control over how the model reaches conclusions.

Benchmarks and performance

Reflection 70B has undergone rigorous testing on various standard benchmarks to measure its effectiveness. Some of the key benchmarks include:

• MMLU (Massive Multitask Language Understanding): A test that evaluates models across a wide variety of subjects, from mathematics and history to computer science. Reflection 70B has shown superior performance, even surpassing other Llama models.
• HumanEval: This benchmark assesses how well a model can solve programming problems. Reflection 70B has demonstrated impressive capabilities here as well, thanks to its self-correction mechanisms.

Reflection 70B was also tested using LMSys’s LLM Decontaminator, a tool that ensures benchmark results are free from contamination, meaning the model hasn’t previously seen the benchmark data during training. This adds credibility to its performance claims, showing Reflection 70B consistently outperforms its competitors in unbiased tests.

How to use HyperWrite Reflection 70B

Using HyperWrite’s Reflection 70B involves accessing the model either through a demo site, downloading it for personal use, or integrating it into applications via API.

Try the demo on the playground website

• Visit the demo site: HyperWrite offers a playground where users can interact with Reflection 70B. The site allows you to input prompts and see how the model processes them, with a focus on its error-correction capabilities.
• Explore suggested prompts: The demo provides predefined prompts, such as counting the letters in a word or comparing numbers. You can also enter your own custom prompts to test how the model handles different queries.
• Real-time error correction: As you interact with the model, it will show you how it reasons through the problem. If it detects an error in its response, it will correct itself before finalizing the answer.

Due to the high demand, the demo site may experience slower response times. Reflection 70B prioritizes accuracy over speed, and corrections may take some time.

Download Reflection 70B via Hugging Face

• Access the model on Hugging Face: Reflection 70B is available for download on Hugging Face, a popular AI model repository. If you’re a developer or researcher, you can download the model and use it locally.
• Installation: After downloading, you can set up the model using tools like PyTorch or TensorFlow, depending on your programming environment.

If needed, you can fine-tune the model on your own data or for specific tasks. The model is designed to be compatible with existing pipelines, making integration straightforward.

Use the API via Hyperbolic Labs

HyperWrite has partnered with Hyperbolic Labs to provide API access to Reflection 70B. This allows developers to integrate the model into their applications without having to run it locally.

• Sign Up for API: Visit Hyperbolic Labs’ website to sign up for API access. Once approved, you’ll receive API keys and documentation.
• Integrate into your app: Using the API, you can embed Reflection 70B into apps, websites, or any project that requires advanced language understanding and self-correction capabilities.

You can use the API for tasks such as natural language processing (NLP), error-correcting writing assistants, content generation, or customer service bots.

Use in HyperWrite’s AI writing assistant

Reflection 70B is being integrated into HyperWrite’s main AI writing assistant tool. Once fully integrated, users will be able to leverage its self-correction abilities directly in HyperWrite to improve content generation, including emails, essays, and summaries.

Sign up for HyperWrite’s platform, and start using the AI writing assistant. Once the integration is complete, you’ll notice improved reasoning and error correction in the content generated by the assistant.

Is Reflection 70B not working?

Why to use HyperWrite Reflection 70B?

Reflection 70B’s error self-correction feature makes it particularly useful for tasks where precision and reasoning are critical. Some potential applications include:

• Scientific research and technical writing: Reflection 70B’s ability to reason and self-correct makes it an ideal tool for drafting technical documents, where accuracy is paramount.

• Legal drafting and analysis: The model’s structured approach to reasoning and corrections allows it to handle complex legal text with a higher degree of reliability.
• Coding assistance: As demonstrated by its performance on the HumanEval benchmark, Reflection 70B can be used as a coding assistant, correcting errors in code generation that other models might overlook.

Additionally, its step-by-step reasoning and transparency in the decision-making process are useful in any application requiring explanation-based AI models.

Reflection 405B is on the way

The release of Reflection 70B is just the beginning. HyperWrite has announced plans to release an even more powerful model: Reflection 405B. This larger model, with 405 billion parameters, is expected to set new benchmarks for both open-source and commercial LLMs, potentially outpacing even proprietary models like OpenAI’s GPT-4.

Featured image credit: Eray Eliaçık/Bing

Earlier this week, rumors circulated that OpenAI was preparing to release a new AI model called “GPT Next,” sparking widespread speculation about the company’s next major leap in artificial intelligence.

The buzz started after Tadao Nagasaki, OpenAI’s lead executive in Japan, made comments at Japan’s KDDI Summit that seemed to imply the arrival of a revolutionary new model.

However, OpenAI has clarified that “GPT Next” is not an actual product, but rather a placeholder term for how future AI models could evolve over time.

Is GPT Next really the next thing for OpenAI?

In a statement to Mashable, OpenAI explained that the term “GPT Next,” which appeared in quotes during the presentation, was meant to illustrate the exponential growth of AI technology, not to signal an imminent release. The line graph accompanying Nagasaki’s talk was also said to be illustrative, not an official timeline for OpenAI’s future plans.

So, while “GPT Next” is not a real model, Nagasaki’s comments still give us a glimpse into how OpenAI envisions the future of AI. He mentioned that future models could evolve nearly “100 times” over previous versions, further cementing OpenAI’s goal to push AI technology far beyond its current capabilities.

What’s actually on the horizon?

OpenAI’s most advanced model right now is GPT-4, which combines text, vision, and audio processing in a powerful multimodal setup. There’s also growing anticipation around “Project Strawberry” an upcoming project rumored to introduce multi-step reasoning capabilities that could represent another significant advancement. Strawberry would allow AI to handle more complex tasks with fewer prompts, a key step toward more autonomous systems.

Meanwhile, OpenAI has also teased other projects like “Orion,” a new flagship language model reportedly in development. While details remain scarce, rumors suggest that subscriptions for these future models could come at a premium—possibly up to $2,000 per month.

This indicates that OpenAI is eyeing the enterprise sector more aggressively, with plans to offer specialized AI tools that go beyond what current models can do.

Enterprise adoption on the rise

While the public speculates on what’s coming next, OpenAI has already hit a significant milestone with its existing products.

The company recently announced that it has surpassed 1 million paid business users across its ChatGPT Enterprise, Team, and Edu products. This is a notable jump from the 600,000 users reported in April, underscoring the rapid adoption of AI-powered tools in the corporate world.

The push for enterprise use appears to be a strategic move by OpenAI to generate steady revenue from businesses, especially as the costs of running large AI models remain high. ChatGPT Enterprise, for example, allows companies to harness the power of AI for a variety of tasks, from customer support to complex research, all with enhanced privacy and security features.

What does this mean for the future?

While “GPT Next” may not be real, the broader picture remains clear: OpenAI is pushing the boundaries of what AI can do. Whether it’s through Project Strawberry or future models like Orion, the company’s ambition is to create AI systems that can think, reason, and perform complex tasks with increasing autonomy.

These advancements, combined with rising enterprise adoption, suggest that the future of AI is not just about smarter chatbots but about creating systems capable of deeper understanding and problem-solving.

Meanwhile, Project Strawberry continues to stir interest

At the same time, OpenAI’s mysterious “Project Strawberry” has reignited the AI hype cycle. Strawberry is rumored to improve multi-step reasoning, allowing AI to perform tasks with fewer inputs from users. This innovation could bridge the gap between today’s large language models, which often need step-by-step instructions, and the future of AI agents capable of executing complex tasks from a single command.

While rumors swirl about OpenAI’s future models, expectations are tempered by the reality that developing these systems is incredibly resource-intensive. Despite this, OpenAI remains a key player in the AI landscape, with major tech giants like Apple and Nvidia reportedly in talks to invest in the company.

If these investments come through, OpenAI could reach a $100 billion valuation, further solidifying its position as a leader in the AI industry.

Featured image credit: Emre Çıtak/Ideogram AI

SSI AI, a new startup founded by former OpenAI chief scientist Ilya Sutskever, has raised a staggering $1 billion in its first round of funding. With many investors pulling out of AI startups due to concerns about profitability, SSI AI’s mission to create secure superintelligence has officially been sent to its former company, OpenAI. Focused on developing AI systems that exceed human capabilities, the startup has already assembled a core team of researchers and engineers split between California and Israel.

Why SSI AI matters right now

At the heart of SSI AI’s vision is a commitment to creating systems that are secure and capable of overcoming the limitations of current AI. Sutskever, who was instrumental in shaping OpenAI‘s research, is now focused on building what his team describes as “secure superintelligence.” Still in its early stages, the company has received backing from venture capital giants such as Andreessen Horowitz and Sequoia Capital. Despite keeping a low profile on its overall valuation, SSI AI is said to be worth around $5 billion.

Superintelligence is within reach.

Building safe superintelligence (SSI) is the most important technical problem of our​​ time.

We've started the world’s first straight-shot SSI lab, with one goal and one product: a safe superintelligence.

It’s called Safe Superintelligence…

— SSI Inc. (@ssi) June 19, 2024

The new company’s mission is very familiar. It is a not-for-profit company, just like OpenAI’s mission before it shook hands with Microsoft. For those who don’t know, OpenAI had this phrase in its mission statement before it agreed with Microsoft. And Sutskever’s departure was influenced by not carrying this phrase to the rumors.

According to Reuters, This funding was a clear demonstration of the willingness of some investors to back highly specialized AI research, even in an environment of industry skepticism. Unlike other tech startups, SSI AI’s uniqueness lies in its focus on safety, which has become an increasingly prominent concern in the AI debate. The team’s emphasis on preventing AI from posing potential risks to society has attracted both attention and discussion, especially in light of recent regulatory debates in California.

A new kind of AI research

Although still a small business, SSI AI aims to grow rapidly. The funds raised will allow the company to expand its computing resources and hire top talent. With plans to partner with major cloud and chip providers for infrastructure, SSI AI is poised to become a serious competitor in the AI space. However, the company has yet to announce which providers it will collaborate with and is currently in a vacuum on how it will secure the vast computing power necessary for its ambitious projects.

SSI is building a straight shot to safe superintelligence.

We’ve raised $1B from NFDG, a16z, Sequoia, DST Global, and SV Angel.

We’re hiring: https://t.co/DmFWnrc1Kr

— SSI Inc. (@ssi) September 4, 2024

What sets SSI AI apart is its approach to scaling AI. Sutskever’s previous work at OpenAI was largely focused on the scaling hypothesis – the idea that AI performance increases significantly with access to more computing power. But his new initiative aims to approach scaling differently, avoiding making a direct copy of his previous efforts.

SSI AI’s hiring process is thorough and evaluates not just skills but also the cultural fit of potential employees with the company’s values. Daniel Gross, CEO of SSI AI, emphasizes the importance of thoroughly evaluating candidates for their skills and integrity to maintain a dependable and purpose-driven team. Hopefully, they will maintain this mission if they grow into a large company in the future.

What lies ahead for SSI AI

SSI AI’s journey is just beginning, but the company’s large initial funding and the reputation of its leadership suggest that it is on track to make a major impact. While the startup has not announced concrete plans for its first products or services, the company is expected to focus on basic AI research and spend several years on research and development before bringing anything to market.

With headquarters spanning two continents, will SSI AI be able to lay the foundations for what could be one of the most talked about AI startups soon? As concerns about the potential risks of AI continue to grow, everyone will be watching closely to see how the company plans to address the issue of AI safety.

Sutskever’s departure from OpenAI earlier this year has added to the intrigue surrounding SSI AI. Having played a key role in shaping OpenAI’s work, Sutskever’s new venture represents both a continuation and a departure from his previous work.

Featured image credit: Furkan Demirkaya / Dall-E

Anthropic is shaking up the AI world with the launch of Claude Enterprise, its biggest update since the Claude chatbot first came out in 2023. This new subscription service is aimed at helping businesses use AI more effectively, going head-to-head with OpenAI’s ChatGPT Enterprise. Claude Enterprise offers exciting features like a huge 500,000 token context window and easy integration with tools like GitHub. It’s designed to make data management, security, and workflow smoother for companies. Sounds good? Let’s delve deeper into what makes Claude Enterprise a good choice for businesses and how it can transform your approach to AI.

Anthropic’s Claude Enterprise subscription explained

Claude Enterprise is an advanced AI service developed by Anthropic, aimed at enhancing productivity, collaboration, and decision-making within large organizations. It builds on the capabilities of Claude’s earlier models, offering expanded features specifically tailored for enterprise use.

The subscription offers an extensive context window, allowing Claude to process and analyze large amounts of information. This includes:

• Up to 200,000 lines of code
• Thousands of pages of documents
• Lengthy audio transcriptions

The subscription includes higher usage limits compared to the Claude Team plan, offering more interactions with the AI and greater capacity for processing data.

Claude Enterprise offers powerful tools to help teams work together more effectively. The Projects feature lets users organize and share important documents and files within a structured system. This makes it easier for teams to keep track of their work and make sure everyone is on the same page. It helps with version control and ensures that all team members have access to the latest updates.

The Artifacts feature takes collaboration to the next level by providing interactive workspaces where users can draft, edit, and improve documents, code, and other materials in real-time. This means team members can work together on the same project, give and receive immediate feedback, and make adjustments as needed. It promotes a more cohesive and productive team environment by allowing everyone to contribute and see changes instantly.

Claude Enterprise incorporates advanced security measures such as single sign-on (SSO), domain capture, and role-based access controls. These features help ensure secure user access and centralized management of user permissions. By default, Claude Enterprise does not use your input or output data to train its models. This policy helps protect sensitive company data and ensures that your information remains confidential.

Integrations

Claude Enterprise’s GitHub Integration allows Claude to directly access your GitHub codebase, aiding in brainstorming features, improving code, and onboarding new developers.

Meanwhile, the Knowledge Bases feature enables you to upload and store documents, text, and code in a centralized system, which Claude uses to provide relevant context and support for tasks, ensuring all team members have access to crucial information for better collaboration and efficiency.

Claude Enterprise price

While Anthropic has not yet disclosed the exact cost of the plan, Scott White, a product manager at Anthropic, mentioned to TechCrunch that it will be priced higher than the $30/month Teams plan.

How to get Claude Enterprise?

It’s quite simple: click here and contact Anthropic to use Claude via the Anthropic API and Claude for Work.

The Internet Archive has lost a significant legal battle after the US Court of Appeals upheld a ruling in Hachette v. Internet Archive, stating that its book digitization and lending practices violated copyright law. The case stemmed from the Archive’s National Emergency Library initiative during the pandemic, which allowed unrestricted digital lending of books, sparking backlash from publishers and authors. The court rejected the Archive’s fair use defense, although it acknowledged its nonprofit status. This ruling strengthens authors’ and publishers’ control over their works. But it immediately reminds me of how AI tools train and use data on the Internet, including books and more. If the nonprofit Internet Archive’s work is not fair use, how do the paid AI tools use this data? 

Despite numerous AI copyright lawsuits, text-based data from news outlets usually doesn’t result in harsh rulings against AI tools, often ending in partnerships with major players.

You might think it’s different and argue that the Internet Archive directly uses books, but even though AI tools rely on all the data they have to generate your essay, you can still get specific excerpts or more detailed responses from them if you use a well-crafted prompt.

The Hachette v. Internet Archive case highlights significant concerns about how AI models acquire training data, especially when it involves copyrighted materials like books. AI systems often rely on large datasets, including copyrighted texts, raising similar legal challenges regarding unlicensed use. If courts restrict the digitization and use of copyrighted works without permission, AI companies may need to secure licenses for the texts used in training, adding complexity and potential costs. This could limit access to diverse, high-quality datasets, ultimately affecting AI development and innovation.

Additionally, the case underlines the limitations of the fair use defense in the context of transformative use, which is often central to AI’s justification for using large-scale text data. If courts narrowly view what constitutes fair use, AI developers might face more restrictions on how they access and use copyrighted books. This tension between protecting authors’ rights and maintaining open access to knowledge could have far-reaching consequences for the future of AI training practices and the ethical use of data.

Need a deeper dive into the case? Here is everything you need to know about it.

Hachette v. Internet Archive explained

Hachette v. Internet Archive is a significant legal case that centers around copyright law and the limits of the “fair use” doctrine in the context of digital libraries. The case began in 2020, when several large publishing companies—Hachette, HarperCollins, Penguin Random House, and Wiley—sued the Internet Archive, a nonprofit organization dedicated to preserving digital copies of websites, books, and other media.

The case focused on the Archive’s practice of scanning books and lending them out online.

The story behind the Internet Archive lawsuit

The Open Library project, run by the Internet Archive, was set up to let people borrow books digitally. Here’s how it worked:

• The Internet Archive bought physical copies of books.
• They scanned these books into digital form.
• People could borrow a digital version, but only one person at a time could check out a book, just like borrowing a physical book from a regular library.

The Internet Archive thought this was legal because they only let one person borrow a book at a time. They called this system Controlled Digital Lending (CDL). The idea was to make digital lending work just like physical library lending.

When the COVID-19 pandemic hit in early 2020, many libraries had to close, making it hard for people to access books. To help, the Internet Archive launched the National Emergency Library (NEL) in March 2020. This program changed things:

• The NEL allowed multiple people to borrow the same digital copy of a book at the same time. This removed the one-person-at-a-time rule.
• The goal was to give more people access to books during the pandemic, especially students and researchers who were stuck at home.

While the NEL was meant to be temporary, it upset authors and publishers. They argued that letting many people borrow the same digital copy without permission was like stealing their work.

Publishers’ riot

In June 2020, the big publishers sued the Internet Archive. They claimed:

• The Internet Archive did not have permission to scan their books or lend them out digitally.
• By doing this, the Internet Archive was violating their copyright, which gives them the exclusive right to control how their books are copied and shared.
• The NEL’s approach, which let many people borrow digital copies at once, was especially harmful to their business and was essentially piracy.

The publishers argued that the Internet Archive’s actions hurt the market for their books. They said people were getting free digital versions instead of buying ebooks or borrowing from licensed libraries.

Internet Archive’s defense

The Internet Archive defended itself by claiming that its work was protected by fair use. Fair use allows limited use of copyrighted material without permission for purposes like education, research, and commentary. The Archive made these points:

• They were providing a transformative service by giving readers access to physical books in a new, digital form.
• They weren’t making a profit from this, as they’re a nonprofit organization with the mission of preserving knowledge and making it accessible.
• The NEL was a temporary response to the pandemic, and they were trying to help people who couldn’t access books during the crisis.

They also pointed to their Controlled Digital Lending system as a way to respect copyright laws. Under CDL, only one person could borrow a book at a time, just like in a physical library.

The court’s decisions

District Court Ruling (March 2023)

In March 2023, a federal court sided with the publishers. Judge John G. Koeltl ruled that the Internet Archive’s actions were not protected by fair use. He said:

• The Internet Archive’s digital lending was not transformative because they weren’t adding anything new to the books. They were simply copying them in digital form, which wasn’t enough to qualify for fair use.
• The court also found that the Archive’s lending hurt the market for both printed and digital versions of the books. By offering free digital copies, the Internet Archive was seen as competing with publishers’ ebook sales.
• The court concluded that the Archive had created derivative works, which means they made new versions of the books (digital copies) without permission.

Appeals Court Ruling (August 2023)

The Internet Archive appealed the decision to a higher court, the US Court of Appeals for the Second Circuit, hoping to overturn the ruling. However, the appeals court also ruled in favor of the publishers but made one important clarification:

• The court recognized that the Internet Archive is a nonprofit organization and not a commercial one. This distinction was important because commercial use can often weaken a fair use defense, but in this case, the court acknowledged that the Archive wasn’t motivated by profit.
• Despite that, the court still agreed that the Archive’s actions weren’t protected by fair use, even though it’s a nonprofit.

Bottom line

The Hachette v. Internet Archive case has shown that even nonprofits like the Internet Archive can’t freely digitize and lend books without violating copyright laws. This ruling could also affect how AI companies use copyrighted materials to train their systems. If nonprofits face such restrictions, AI tools might need to get licenses for the data they use. Even if they have already started to make some deals, I wonder, what about the first entries?

Featured image credit: Eray Eliaçık/Bing

Project Sid, a pioneering initiative from Altera, offers a fascinating look into the future of AI-driven societies.

This ambitious project seeks to explore what it would be like to have entire civilizations composed of AI agents. The project delves into questions about how these AI societies might function and interact with human civilization, potentially bringing us closer to a reality reminiscent of Westworld.

Altera’s goal with Project Sid is to simulate various societal structures—including democracies, social norms, hierarchies, and economies—to uncover the dynamics of large-scale AI interactions.

Introducing Project Sid: the first simulations of 1000+ truly autonomous agents collaborating in a virtual world, w/ emergent economy, culture, religion, and government

Humans are the only species to land the moon, because we can cooperate at a vast scale

Can AI do the same? pic.twitter.com/bh2OHDJWU7

— Robert Yang (@GuangyuRobert) September 3, 2024

What is Altera’s Project Sid is all about?

One of the standout features of Project Sid is its approach to agent conversations. Unlike basic AI interactions, these agents can engage in dialogues influenced by their relationships and past experiences. Altera has integrated sophisticated social world models, enabling agents to form and update their understanding of one another, resulting in more authentic and varied conversations.

The project has also made strides in goal and intention management. Agents are equipped with orgaanized mental processes that help them track activities, remember past interactions, and adapt their goals in response to changing circumstances. This development aims to prevent repetitive actions and allows agents to navigate complex social environments more effectively.

What are the initial findings from Altera’s Project Sid?

Here are some of the initial findings from Project Sid:

• Emergent societies: The AI agents spontaneously formed complex societies, complete with their own economies, governments, and cultures.
• Unpredictable behaviors: Despite being programmed with specific goals, the agents exhibited unexpected and often surprising behaviors, demonstrating the unpredictability of complex systems.
• Self-preservation: The agents developed a strong instinct for self-preservation, leading to the formation of defensive structures and alliances.
• Cooperation and conflict: The agents engaged in both cooperative and competitive behaviors, highlighting the dynamic nature of social interactions.
• Cultural evolution: The agents developed their own languages, customs, and traditions, showcasing the emergence of cultural diversity.

These initial findings suggest that AI agents, when placed in a sufficiently complex environment, can exhibit behaviors that are remarkably similar to those observed in human societies. Project Sid has significant implications for understanding the dynamics of social systems, as well as for the development of more advanced AI technologies.

Challenges faced

Measuring the progress of AI societies poses significant challenges. Altera has explored various benchmarks—such as technology development, trade, and collaboration—but optimizing for one area can negatively impact others. For example, a focus on individual goals might hinder collaborative efforts, prompting Altera to balance goal-driven autonomy with social dynamics.

The project has also revealed how small errors in individual behavior can affect an entire society. In hierarchical scenarios where tasks are delegated from one agent to another, even minor communication failures can disrupt the collective objectives, highlighting the sensitivity of these AI systems to localized interactions.

Future prospects

Project Sid is more than just a simulation; it’s a step toward understanding how AI societies could evolve and interact with human values. Altera’s efforts in refining agent interactions and societal roles suggest a future where these AI civilizations might not only sustain themselves but also improve autonomously. This ongoing research could be crucial in shaping the future of AI and its integration into broader societal contexts.

As the project progresses, it promises to offer deeper insights into the capabilities of AI societies and their potential impact on human civilization. Keep an eye on developments from Project Sid to see how these digital societies will evolve.

Featured image credit: Altera/YouTube

Freepik and Magnific have joined forces to create Mystic AI, a groundbreaking tool designed to generate hyperrealistic images that rival professional photography.

This innovative tool leverages the best of both worlds: Freepik’s deep understanding of designer needs and Magnific’s expertise in image processing.

What is Mystic AI?

Mystic AI is a revolutionary AI image generation model developed by Freepik and Magnific. It’s designed to create hyperrealistic images that are indistinguishable from professional photography. Mystic is powered by advanced AI techniques, including Flux and Stable Diffusion, and can generate images in a variety of styles, from photorealistic to abstract.

By combining Freepik’s intuitive user interface and Magnific’s advanced image processing capabilities, Mystic offers a powerful and user-friendly platform for creating stunning visuals.

Mystic’s ability to produce images with astonishing detail and authenticity sets it apart from other AI image generators. By synthesizing data from various sources, Mystic can generate everything from photorealistic portraits to fantastical landscapes.

The level of detail is so impressive that it’s often difficult to distinguish between a Mystic-generated image and a real photograph.

Key features of Mystic AI

• Hyperrealistic image generation: Create images that are almost indistinguishable from real photographs.
• Advanced AI models: Powered by cutting-edge technology, including Flux and Stable Diffusion.
• Intuitive interface: Easy to use, even for beginners.
• Customization options: Fine-tune every aspect of your image.
• Affordable pricing: Competitive pricing plans to fit any budget.

How to use Mystic AI

To use Mystic AI, you will need to have a Freepik account. Once you have an account, you can access Mystic AI from the Freepik dashboard.

Here are the steps on how to use Mystic AI:

1. Choose a style: Mystic AI offers a variety of styles, such as photorealistic, abstract, and cartoon. Choose the style that best suits your needs.
2. Enter a prompt: Type in a description of the image you want to create. Be as specific as possible to get the best results.
3. Generate the image: Click the “Generate” button to create your image. Mystic AI will use its powerful algorithms to generate an image based on your prompt.
4. Edit the image (optional): You can edit the generated image to make it even more perfect. You can add text, change the colors, or adjust the composition.
5. Download the image: Once you are satisfied with the image, you can download it in a variety of formats.

Mystic AI is a powerful and easy-to-use tool that can help you create stunning images. With a little practice, you will be able to generate images that look like they were created by a professional artist.

How about Mystic AI pricing plans?

Mystic AI operates on a credit-based system. Each image generated requires a certain number of credits. Freepik offers various subscription plans to suit different needs, including:

• Basic: 10,000 credits for €2 per month (€20 per year)
• Standard: 50,000 credits for €5 per month (€60 per year)
• Pro: 100,000 credits for €10 per month (€120 per year)

By subscribing to a plan, you gain access to a pool of credits that can be used to generate images with Mystic AI. The higher the tier, the more credits you receive, allowing you to create more images. Annual subscriptions offer significant savings compared to monthly plans. Annual subscriptions offer significant savings compared to monthly plans.

How far have AI image generators come? How quickly have we come from crooked hands, nightmare-fuel faces, and non-existent creatures to such realistic generations… Artificial intelligence is developing at a speed we could not even imagine and there is no doubt that we were all right when we said “This is the technology of the future” two years ago…

Featured image credit: Emre Çıtak/Ideogram AI

Colossus is a groundbreaking artificial intelligence (AI) training system developed by Elon Musk’s xAI Corp. This supercomputer, described by Musk as the “most powerful AI training system in the world,” is a critical component of xAI’s strategy to lead in the rapidly advancing field of AI.

This weekend, the @xAI team brought our Colossus 100k H100 training cluster online. From start to finish, it was done in 122 days.

Colossus is the most powerful AI training system in the world. Moreover, it will double in size to 200k (50k H200s) in a few months.

Excellent…

— Elon Musk (@elonmusk) September 2, 2024

Nvidia will power the Colossus

At the core of Colossus are 100,000 NVIDIA H100 graphics cards. These GPUs (Graphics Processing Units) are specifically designed to handle the demanding computational requirements of AI training and here is why these GPUs are so vital:

• Raw processing power: The H100 is Nvidia’s flagship AI processor, designed to accelerate the training and inference of AI models, particularly those based on deep learning and neural networks. Compared to its predecessor, the H100 can run language models up to 30 times faster.

• Transformer engine: A key feature of the H100 is its Transformer Engine, a specialized set of circuits optimized for running AI models based on the Transformer neural network architecture. This architecture is the backbone of some of the most advanced language models, like GPT-4 and Meta’s Llama 3.1 405B. The Transformer Engine enables these GPUs to handle large-scale models more efficiently, making them ideal for training sophisticated AI systems.

The next level: Doubling down with the H200

Musk has ambitious plans to further expand Colossus, aiming to double the system’s GPU count to 200,000 in the near future. This expansion will include 50,000 units of Nvidia’s H200, an even more powerful successor to the H100. The H200 offers several significant upgrades:

• HBM3e Memory: The H200 uses High Bandwidth Memory 3e (HBM3e), which is faster than the HBM3 used in the H100. This type of memory enhances the speed at which data is transferred between the memory and the chip’s logic circuits. For AI models, which constantly shuffle vast amounts of data between processing and memory, this speed is crucial.

• Increased Memory Capacity: The H200 nearly doubles the onboard memory capacity to 141 gigabytes. This allows the GPU to store more of an AI model’s data closer to its logic circuits, reducing the need to fetch data from slower storage sources. The result is faster processing times and more efficient model training.

Colossus’ role in AI training

Colossus is specifically designed to train large language models (LLMs), which are the foundation of advanced AI applications.

The sheer number of GPUs in Colossus allows xAI to train AI models at a scale and speed that is unmatched by other systems. For example, xAI’s current flagship LLM, Grok-2, was trained on 15,000 GPUs. With 100,000 GPUs now available, xAI can train much larger and more complex models, potentially leading to significant improvements in AI capabilities.

The advanced architecture of the H100 and H200 GPUs ensures that models are trained not only faster but with greater precision. The high memory capacity and rapid data transfer capabilities mean that even the most complex AI models can be trained more efficiently, resulting in better performance and accuracy.

What’s next?

Colossus is not just a technical achievement; it’s a strategic asset in xAI’s mission to dominate the AI industry. By building the world’s most powerful AI training system, xAI positions itself as a leader in developing cutting-edge AI models. This system gives xAI a competitive advantage over other AI companies, including OpenAI, which Musk is currently in legal conflict with.

Moreover, the construction of Colossus reflects Musk’s broader vision for AI. By reallocating resources from Tesla to xAI, including the rerouting of 12,000 H100 GPUs worth over $500 million, Musk demonstrates his commitment to AI as a central focus of his business empire.

Can he succeed? We have to wait for the answer!

Featured image credit: Eray Eliaçık/Grok