SAN FRANCISCO, Calif. – Nov. 19, 2024 – Today PicnicHealth unveiled an AI assistant that empowers patients to take control of their healthcare. Picnic simplifies medical records and provides actionable insights, enabling patients to make informed decisions. By integrating health data from any U.S. care site, Picnic breaks down data silos and makes it easier to navigate a complex healthcare system.

“We’re simplifying healthcare with a true universal patient record,” said Troy Astorino, PicnicHealth’s co-founder and CTO. “With Picnic, we’re putting our world-leading medical AI directly in the hands of patients to track down and make sense of their medical data. This gives them a new level of understanding of their health history and enables them to navigate their care with confidence.”

Many patients, especially those with chronic conditions, have complex health histories with disparate data across the healthcare system. Medical records from physician visits, hospital stays, test results, and prescriptions are scattered across patient portals, making it difficult to track care and leaving patients unsure about next steps in managing their health.

Patients who sign up for Picnic receive a complete timeline of their medical records as well as a unified care plan based on physician notes and advice. Picnic also enables patients to ask specific questions about their health so that they can stay proactive. Picnic also provides:

• AI search – patients can ask questions about their health history and get GenAI-powered answers along with related data sources such as lab results, procedures, and physician notes. Picnic scans all medical records to find relevant health information quickly and easily.
• Smart pinboards – patients can track specific health conditions like diabetes or cancer with a personalized, curated view of relevant medical data. Picnic analyzes health records and organizes related visits, test results, and medications in easy-to-find pinboards
• Smart highlights – patients can instantly understand complex medical terms as they review their medical records. Patients can highlight unfamiliar words and receive an easy-to-understand explanation from Picnic using their health history as context.

Today’s news follows the recent launch of the PicnicHealth Virtual Clinic, which provides patients access to clinician support to navigate and stay proactive in managing their health. Picnic can act as a concierge to the Virtual Clinic, answering health questions and guiding patients to personalized care from clinic providers.

The AI health assistant is currently available to early adopters. General availability is expected by the end of the year. PicnicHealth continues to invest in their AI assistant and has plans to add future capabilities to further help patients manage complex healthcare needs and participate in observational research. To learn more about how Picnic helps patients take control of their health, visit https://picnichealth.com/care.

About PicnicHealth

PicnicHealth is a leading health technology company dedicated to simplifying healthcare for everyone. To date, the company’s direct-to-patient approach and innovative AI and technology platform have enabled 12 of the top 20 largest life science companies to run more efficient observational research. PicnicHealth has given tens of thousands of patients access to tools and virtual care services to simplify their care journey. PicnicHealth was recently named one of the World’s Best Digital Health Companies by Newsweek and the “Best MedTech Startup” by MedTech Breakthrough. The future is here with PicnicHealth. Learn more at picnichealth.com.

Featured image credit: PicnicHealth

Google is working on reducing notifications for Pixel devices when unlocked via smartwatches. This change will only prompt alerts based on the distance between the phone and the paired smartwatch, aiming to enhance user experience.

Google reduces unlocking notifications for Pixel smartwatches

Last year, Google introduced the Watch Unlock feature for Pixel Watches, allowing users to unlock their devices effortlessly while wearing a paired smartwatch. However, users have frequently complained about the incessant “Unlocked by your watch” notifications that appear every time the phone is unlocked. In response, Google is now refining this feature to alleviate user frustration.

An APK teardown of the latest beta version of Google Play Services (version 24.46.30 beta) reveals the upcoming adjustments according to an Android Authority report. The proposed system will minimize unlocking notifications to instances where there is a significant distance between the phone and the smartwatch. This change seeks to decrease unnecessary notifications when devices are in close proximity, effectively making the unlocking process smoother and less disruptive.

The steps being taken by Google are timely, given the growing number of users adopting smartwatches. Channeling their focus on user convenience, the updated Watch Unlock feature will alert users only when distance metrics indicate the need for a notification. By implementing distance-based calculations, Google appears poised to enhance the smartphone-wearable pairing experience significantly.

The motivation behind this change lies in creating a more refined interaction model between Pixel devices and their corresponding smartwatches. Presently, every unlock event triggers an alert—a scenario that many users find annoying. This revision, therefore, directly addresses a prevalent pain point in the user experience.

As an example of how this feature might work, if a user locks their Pixel phone and walks away from their smartwatch, an “Unlocked by your watch” notification may pop up if they are far enough apart. However, users won’t see this notification if their phone and watch are close together, thus cutting down on superfluous alerts significantly. Such alterations indicate how tech giants are increasingly attuned to the nuanced needs of their customer bases, especially as wearables become an integral part of everyday life.

If you bought a Pixel phone after 2017, you’re in danger

Google has not provided a definite launch date for the updated Watch Unlock notifications. However, users are encouraged to keep an eye on future updates from Google Play Services or relevant tech reports. This potential shift in notifications reflects a growing priority on user comfort and convenience in technological interactions, something that could greatly enhance user satisfaction across Google’s ecosystem.

Further updates will clarify when users can expect this refined feature to be available for all.

Featured image credit: Google

Apple may have not given up on TV sets, as recent reports indicate a renewed interest in developing its own television model. Over a decade after Steve Jobs envisioned a revolution in the TV industry, now Apple is reportedly reconsidering entering the market again. This re-evaluation comes amid speculation about a new smart home device expected in spring 2025.

Apple reconsiders launch of branded television set

Steve Jobs initially sought to transform the television landscape with an easy-to-use, Apple-branded TV set before his untimely death halted plans. According to Bloomberg, Apple is once again “revisiting the idea of making an Apple-branded TV set.” However, the company is still in the evaluation phase, leaving many details about potential product development unclear.

Currently, Apple markets the Apple TV streaming boxes, providing users with various features that integrate with the Apple ecosystem, including FaceTime calls and access to Fitness Plus. An Apple TV set would likely incorporate the tvOS software, potentially adding enhanced features like a built-in FaceTime camera. Despite this renewed interest, indications suggest that a launch is not imminent; the details remain vague, suggesting nothing concrete is on the horizon for the near future.

In the context of this speculation, Apple is primarily focused on releasing a new smart home device. This iPad-like device aims to serve as a centralized hub for users’ smart home technology, with a potential announcement planned for spring 2025. The shift in focus implies that while Apple is contemplating a TV set, the primary direction is toward enhancing smart home integration.

Apple’s first foray into smart home camera market

Despite the persistent rumors of a television product, analysts point out the challenges Apple would face entering the saturated TV market. For over 15 years, rumors have circulated regarding an Apple-built TV, with many considering the Apple TV as a “hobby” project rather than a primary revenue driver for the company. Tim Cook historically expressed disinterest in the television sector, emphasizing that Apple would need to bring something unique to the market to succeed.

The Apple TV+ streaming service, while gaining some traction, captured only 3% of the market share in 2022. This statistic illustrates the competitive nature of the streaming landscape, prompting Apple to broaden access to its services across various platforms. With numerous smart TVs now integrating Apple TV capabilities, it raises the question of how an Apple-branded television would differentiate itself.

Featured image credit: Kelly Sikkema/Unsplash

Coca-Cola’s new AI-generated Christmas ad has sparked significant online backlash, with critics labeling it “soulless” and lacking genuine creativity. Released recently, the video pays homage to the company’s iconic 1995 “Holidays Are Coming” commercial but substitutes human actors with AI-generated imagery. Crafted by three AI studios—Secret Level, Silverside AI, and Wild Card—the ad has drawn millions of views on social media, Forbes reports.

Coca-Cola’s AI Christmas ad faces backlash over lack of creativity

The controversial promotional video features key holiday imagery, such as Coca-Cola trucks in snowy landscapes and people holding drinks, reminiscent of the nostalgic 1995 campaign. Criticism primarily stems from a perception that AI lacks the human touch essential to holiday advertising. Industry professionals and creatives, including Alex Hirsch, creator of Disney’s “Gravity Falls,” have voiced their discontent on platforms like X, arguing that using AI for such campaigns undermines the work of artists. Hirsch pointedly remarked, “FUN FACT: @CocaCola is ‘red’ because it’s made from the blood of out-of-work artists! #HolidayFactz.”

Reports indicate that the ad was produced using four generative AI models among the collaborating studios. Chris Barber, an AI developer from Silverside AI, noted that the viral version of the advertisement is not attributed to his studio, further complicating the scandal. Many creatives worry that the adoption of AI technology could diminish opportunities for human talent, asserting that these models often learn from uncredited artistic work.

In a statement reflecting its long-standing advertising approach, a Coca-Cola spokesperson said, “The Coca-Cola Company has celebrated a long history of capturing the magic of the holidays in content, film, events and retail activations for decades around the globe. We are always exploring new ways to connect with consumers and experiment with different approaches.” The spokesperson emphasized the company’s goal of merging human storytelling with advanced technology like generative AI.

This ad is not the first instance of Coca-Cola leveraging AI technology. Earlier this year, the company collaborated with OpenAI to produce a different commercial titled “Masterpiece,” which featured artwork and animations of museum pieces that came to life in a whimsical setting. Coca-Cola introduced an initiative inviting digital artists to create work using their archives alongside AI tools, showcasing an ongoing commitment to integrating technology into creative processes.

How Coca Cola Creations crafted Y3000 with AI

Experts have noted the distinction between the general perception of Coca-Cola’s holiday brand and previous AI applications. According to Neeraj Arora, chair of marketing research and education at the University of Wisconsin-Madison, the backlash is more pronounced for this Christmas ad because the holiday season resonates deeply with consumers, making the AI’s presence feel inappropriate. Arora stated, “Your holidays are a time of connection, time of community… But then you throw AI into the mix that is not a fit.”

Similar backlash has arisen for other companies using AI in promotional campaigns. Toys R Us also faced criticism this summer for an AI-generated advertisement featuring its late founder. Despite the controversies surrounding these AI-generated ads, companies have described their initiatives as “successful” and see the technology as a valuable addition for future projects.

Featured image credit: vahid kanani/Unsplash

Apple has introduced its first official gaming keyboard and mouse developed in partnership with Corsair. Available in November 2024, the Corsair K65 Plus mechanical keyboard and M75 wireless mouse are specifically designed for Mac users. Priced at $179.95 and $129.95, respectively, these devices aim to enhance the gaming experience on Apple’s platforms.

“We are thrilled to bring Corsair high-performance input devices in exclusive and fun new colors to Mac users,” stated Thi La, President and COO of CORSAIR.

Corsair now offers a gaming keyboard and mouse designed for Mac users

The partnership with Corsair brings two innovative products to Apple’s line-up, focusing on compatibility and performance. The K65 is a 75% layout keyboard featuring Corsair’s MLX RED linear switches and is equipped with hot-swappable keys. With a steel plate chassis that ensures durability, this keyboard weighs significantly less than traditional mechanical keyboards yet maintains a solid build quality. It is designed to work seamlessly with all current Mac models, offering Mac-specific key layouts including CMD and OPT keys, along with a functional row matching Apple’s classic design.

The M75 wireless mouse complements the K65 keyboard with a lightweight structure weighing just 89 grams. An ambidextrous design caters to both right- and left-handed gamers, ensuring comfort and usability. It employs Corsair’s 26,000 DPI Marksman optical sensor, enhancing precision during gaming sessions. Both peripherals come with Corsair’s iCue software, which allows users to customize RGB lighting and functionality for each key.

Apple’s announcement reveals that these peripherals will be available in unique colorways, including Glacier Blue and a limited Frost edition, only in Apple stores. This introduction marks a notable departure for Apple, which has historically preferred to be solely associated with its in-house hardware. According to Corsair, the K65 keyboard offers up to 266 hours of battery life, although this figure is dependent on the connection type—Bluetooth, USB-C, or the included 2.4 GHz dongle. A USB-C to USB-A converter is also provided as part of the package due to the latest Macs lacking USB-A ports.

Recent research casts doubt on the future of gaming industry

While the K65 and M75 are intended to improve gaming on Mac, prospective users should note that pairing these devices is not as user-friendly as Apple’s native peripherals. The lack of auto-detection means users will need to manage connections manually. Despite these hurdles, the K65 keyboard provides a more responsive and satisfying typing experience compared to Apple’s Magic Keyboard, while the M75 mouse presents significant advancements over the Magic Mouse, which has been criticized for its design flaws.

With Apple focusing on improving its gaming ecosystem, titles like Frostpunk 2, Control, and Cyberpunk 2077 are set to appear on Mac, further enticing gamers to consider Apple’s hardware. During a recent showcase, Apple highlighted upcoming games, including Resident Evil 2 remake and Prince of Persia: The Lost Crown, emphasizing that the K65 and M75 peripherals can enhance gameplay in these titles. Mac still lacks major competitive shooters, which may limit the appeal of these gaming peripherals in the competitive esports arena.

Image credits: Corsair 

Nvidia’s stock dropped on reports of overheating issues with its new Blackwell AI chips, impacting the company ahead of its earnings report. Problems surfaced after its chips, used in servers designed for high-performance computing, overheat in configurations intended for 72 units. Nvidia is facing pressure as it aims to ramp up production.

Nvidia stock falls due to Blackwell AI server issues ahead of earnings report

Industry reports, notably from The Information, indicate that Nvidia’s Blackwell graphics processing units are encountering difficulties when deployed in server racks, leading to concerns among clients. A Reuters piece elaborated on how the overheating occurs when the GPUs are housed together, raising alarms about the ability to integrate them efficiently into existing data center models.

Despite these challenges, Nvidia’s spokesperson stated that “engineering iterations are normal and expected” during the development of such sophisticated systems. They emphasized that the GB200 systems represent “the most advanced computers ever created” and highlighted their commitment to co-engineering with cloud service providers.

Nvidia’s Chief Executive Officer Jensen Huang previously characterized the Blackwell chip as a “complete game changer for the industry.” The company has projected that the new chips could yield billions in revenue during the January quarter, as production is expected to ramp up significantly. However, market reactions were timid, with Nvidia’s shares initially falling by approximately 1.8% on Monday before settling down 1.3% at around $140.15.

Investors remain on edge as Nvidia prepares to release its highly anticipated earnings report. The Blackwell GPUs have previously been linked to design issues. In August, reports suggested that design flaws had already impacted production timelines, pushing back availability from September to the December quarter. These production delays alongside current overheating concerns have fueled uncertainties about customer readiness to deploy the new chips in their data centers.

Another point of view: Nvidia stock might explode after Nov. 20: Here’s why

Nvidia stated that they are collaborating closely with customers to mitigate these issues, reinforcing that the challenges posed by new technologies are part of the development process. Dell Technologies has mentioned that they are already shipping a portion of Nvidia’s Blackwell servers as part of their AI hardware solutions, showcasing ongoing strong demand despite the reported issues.

Another element affecting Nvidia’s stock stems from wider industry trends. Investor sentiment is growing wary of diminishing AI spending from big tech companies, which could lead to decreased demand for AI chips. Recent earnings reports from chip manufacturers, particularly Applied Materials, sparked broader concerns across technology stocks, evidenced by a notable decline in chip equities following those announcements.

Nvidia approaches its earnings report slated for release after the market closes on Wednesday and the market watches closely to see how these technical issues will shape financial forecasts.

Disclaimer: The information provided in this article is for informational purposes only and should not be considered financial or investment advice. Please consult with a qualified financial advisor before making any investment decisions.

Featured image credit: Mariia Shalabaieva/Unsplash

Kim Kardashian has once again captivated the internet, but this time, it’s not about her SKIMS empire or her personal life. On November 18, the entrepreneur and reality TV star introduced the world to her newest luxury gadget—a Tesla Robot.

Dubbed Optimus, this humanoid robot is the latest innovation from Tesla, blending advanced AI with practical functionality.

What does the Tesla Robot do in Kim Kardashian’s video?

In a series of entertaining social media clips, Kim Kardashian demonstrated the Tesla Robot’s impressive abilities. From mimicking hand gestures to playing Rock, Paper, Scissors, and even blowing kisses, the Tesla Robot showcased a range of interactive capabilities.

meet my new friend 🦾🤖 @Tesla pic.twitter.com/C34OvPA2dY

— Kim Kardashian (@KimKardashian) November 18, 2024

Of course, this isn’t just a toy —Optimus is designed for more than fun and games. Tesla CEO Elon Musk envisions these robots performing mundane tasks, potentially changing industries such as manufacturing, healthcare, and beyond.

Some of the things the Tesla Robot can do:

• Articulated motion: Optimus is equipped with multiple joints to mimic human-like flexibility.
• Task automation: It can perform repetitive or physically taxing tasks, making it ideal for factory work, household chores, or care settings.
• Interactive intelligence: Demonstrated in Kim Kardashian’s videos, Optimus can replicate gestures, respond to prompts, and even engage in light gameplay, such as Rock, Paper, and Scissors.

Optimus stands out for its humanoid design, which allows it to replicate human movements and perform tasks requiring precision and dexterity.

Kim Kardashian’s high-tech collaboration with Tesla

Kim Kardashian’s video sparked discussions about Tesla’s latest innovations and their mainstream appeal. In addition to the Tesla Robot, Kardashian flaunted a gold version of Optimus and a yet-to-be-released Tesla Cybercab, underlining Tesla’s knack for blending futuristic designs with practical utility.

Optimus is here to take mw for a ride in the Cybercab pic.twitter.com/gxOSbsY3vv

— Kim Kardashian (@KimKardashian) November 19, 2024

Whether these gadgets are personal purchases or part of a promotional collaboration remains unclear, but the buzz they’ve generated is undeniable.

Tesla Robot release date and price

As Elon showcased in the Tesla We Robot event, Optimus is currently in its second generation of development, with Tesla targeting full production by 2026. Internal testing may begin as early as 2025, particularly in Tesla facilities.

According to Tesla, the expected price range for the Optimus robot will be $20,000 to $30,000, a remarkably competitive figure for advanced robotics.

Optimus’s capabilities are powered by Tesla’s advancements in AI and hardware engineering:

1. Neural network-based AI: Leveraging Tesla’s neural network systems, Optimus processes environmental data to make context-aware decisions.
2. Autonomous functionality: Optimus incorporates elements of Tesla’s autonomous vehicle software, enabling it to navigate spaces, perform tasks, and adapt to new challenges without requiring explicit programming.
3. Energy efficiency: Optimus is designed for energy efficiency, utilizing Tesla’s proprietary battery technologies. This makes it capable of extended operation on a single charge.

Kim Kardashian’s playful demonstrations of Optimus have helped bridge the gap between technical innovation and mainstream appeal. By showcasing the robot’s interactive abilities, Tesla has effectively positioned Optimus as not only a tool for industrial efficiency but also a personal gadget with potential consumer appeal.

Humanoid robots and us

Tesla’s Optimus isn’t just a luxury gadget; it represents a pivotal moment in AI and robotics. The potential to integrate robots like Optimus into homes and workplaces poses significant ethical, social, and economic questions.

How will they impact employment? Can we ensure data privacy and security as these robots become part of daily life?

Elon Musk has compared Tesla’s advancements to fulfilling Leonardo da Vinci’s centuries-old vision of human-like machines. But as society embraces these innovations, it must also confront challenges like displacement of workers, privacy concerns, and the redefinition of interpersonal interactions.

Image credits: Tesla

Boeing layoffs deepen as the aerospace giant cuts nearly 2,200 jobs in Washington state. The layoffs, part of a broader reduction of approximately 17,000 jobs globally, are aimed at streamlining operations after recent financial difficulties and significant strikes. Boeing announced the job cuts as it grapples with overstaffing issues following a strike that lasted almost two months.

Boeing cuts nearly 2,200 jobs in Washington state

The company filed a notice with Washington’s Employment Security Department indicating 2,199 workers would be affected, with permanent layoffs expected to start on December 20. Before these announcements, Boeing employed 66,000 workers in Washington. The layoffs impact all three Boeing divisions: commercial airplanes, defense, and global services. Additionally, more than 400 members of the Society of Professional Engineering Employees in Aerospace (SPEEA) received notices last week, although their severance will allow them to remain on payroll through mid-January.

Boeing’s decision to reduce its workforce follows substantial challenges, including a lengthy machinists’ strike and ongoing production quality problems. The strike involved over 33,000 members of the local branch of the International Association of Machinists (IAM) and crippled production at key factories. CEO Kelly Ortberg noted during an October conference call that the layoffs were a result of needing to adjust to “financial reality and a more focused set of priorities” rather than being directly caused by the strike.

Boeing has faced serious financial turbulence since two fatal crashes of its 737 Max jetliner in 2018 and 2019, which collectively claimed 346 lives. The company’s reputation further declined after a fuselage blowout incident with an Alaska Airlines plane earlier this year. Production rates had already seen significant slowdowns, and the Federal Aviation Administration (FAA) set a production cap of 38 planes per month for the 737 MAX, which Boeing failed to reach even before the strike halted assembly.

Was 2024 tech layoffs a symptom of industry transformation or crisis?

Boeing’s drastic cuts reflect its ongoing struggle to stabilize operations. The company had around 170,000 employees globally by the end of last year, and the announcement of the layoffs signals severe adjustments in what was once a robust workforce. The impending layoffs will have ripple effects, not just for Boeing but across the aerospace sector, which is also feeling the pressure of reduced demand and production issues.

As Boeing restructures its workforce, the layoffs are significant for the state of Washington, which is home to Boeing’s oldest and most vital manufacturing facilities, including those producing the best-selling 737 line. The heavy focus on cost-cutting will inevitably affect employees, many of whom have dedicated years to the company, as well as the economy in surrounding communities that depend on Boeing’s operational presence.

Featured image credit: Sven Piper/Unsplash

Spotify is shifting focus from music to video content, revealing extensive changes for creators while leaving its long-awaited Hi-Fi music tier in limbo. The streaming giant plans to debut ad-free video podcasts in January 2025, enhancing its video offerings and monetization capabilities.

Meanwhile, financial reports indicate that Spotify’s growth strategy prioritizes podcasts as a primary revenue source over high-fidelity music streaming.

Spotify prioritizes video podcasts over music quality

In a recent earnings call, Spotify CEO Daniel Ek addressed the long-deferred Hi-Fi tier initially promised in 2017, acknowledging ongoing work but refraining from providing a specific timeline for its release. With Spotify depreciating its focus on improving sound quality, the company’s strategy leans heavily towards expanding video content. This includes launching ad-free video podcasts in January 2025, a move aimed at enhancing audience engagement. Spotify has amassed over 300,000 video podcast shows on its platform, underscoring its commitment to this format.

Spotify’s financial health appears robust; its latest report revealed a 21% increase in total revenue and a premium revenue rise by 24%. With monthly active users increasing and its profit margins reaching a record 31.1%, Spotify’s financial success diminishes the urgency to launch a Hi-Fi music service.

Ek suggests that instead of enhancing sound quality, Spotify’s attention is dedicated to cultivating podcast engagement as a significant growth driver. A former music business expert summed this sentiment concisely, indicating that music companies—Spotify included—tend to chase revenue over artistry, with Ek’s net worth of $6.9 billion eclipsing that of renowned musicians like Jay-Z and Taylor Swift. The disparity emphasizes Spotify’s evolving relationship with music, seemingly prioritizing user numbers and financial growth over artistic fidelity.

The launch of Spotify for Creators

At the recent “Now Playing” event in Los Angeles, Spotify introduced its new platform, Spotify for Creators. This all-in-one podcast hosting and analytics service aims to streamline users’ content management experience while offers essential tools for video integration. The upgraded mobile app includes analytics, enhanced monetization options, and features to boost creator-fan interactivity. The event, boasting significant attendance and participation from prominent creators, marked a substantial pivot towards video, reinforcing Spotify’s intention to grow its video presence.

The upcoming launch of uninterrupted video podcasts for Spotify Premium subscribers presents a fresh revenue opportunity for creators. Starting in January, eligible creators can earn revenue from video consumption among these subscribers, bolstering their monetization avenues. Ek emphasized that this new program would provide creators flexibility and financial benefits as viewer interest in video content grows—64% of listeners in 2024 expressed a preference for video podcasts, up from 43% in 2021.

Ek’s assertions included statistics revealing that more than 250 million users have engaged with video podcasts on the platform. He acknowledged the challenge posed by third-party ads in video content, affirming Spotify’s commitment to delivering a superior ad-free viewer experience. This aligns with user preferences while positioning Spotify to harness the benefits of video content alongside its traditional audio offerings.

As Spotify moves ahead with its strategies, the emphasis is increasingly on video engagement rather than the anticipated Hi-Fi audio experience. While the company continues to evolve its podcast portfolio, the anticipated luxuries of high-definition music remain unfulfilled, suggesting that Spotify’s trajectory may favor audience growth and monetization through video over traditional music quality enhancements.

Image credits: Spotify

Perplexity has unveiled a very interesting shopping feature for Pro subscribers in the U.S. that allows users to make purchases directly from its AI-powered search engine. This new functionality enables Pro members to click a “Buy with Pro” button when they search for a product, automatically processing their orders using pre-stored shipping and billing details. The feature is designed to streamline the shopping experience by removing the need to visit external retailer websites.

Perplexity’s AI search engine now handles purchases on your behalf

Perplexity’s initiative targets users looking for convenience and efficiency in online shopping. Pro subscribers, who pay $20 per month, will benefit from free shipping on all products purchased through this feature. For items that are not compatible with the “Buy with Pro” function, users will be redirected to the retailer’s site to complete their orders. In the shopping-related search results, Perplexity will display product cards that include images, pricing, and AI-generated summaries of key features and reviews.

Video: Perplexity

To enhance its e-commerce capabilities, Perplexity is also rolling out a new AI-driven “Snap to Shop” search tool. This feature allows all users to take a photo of a product and ask relevant questions about it, effectively simplifying the search process. Initially, only Pro users will have access to this tool, but it revolves around offering a more interactive shopping experience akin to Google Lens.

Perplexity aims to position itself as a competitor to established giants like Google and Amazon. The updated product cards will allow users to view pros and cons of items, alongside basic product details. This function is expected to provide a richer information experience, benefiting users who desire more than just a standard list of search results. By integrating with merchants’ websites, particularly those using Shopify, Perplexity will be able to offer more accurate information and broaden its index of available products.

With an ambition to support merchants, Perplexity is introducing a merchant program designed to furnish sellers with insights into prevalent search and shopping trends. Enrollment in this program increases a merchant’s likelihood of being recommended on the platform. Merchants will also gain free API access to enhance search functionality on their own websites, which could bolster Perplexity’s market position in e-commerce.

FTC is hunting Amazon and judge approves it

The search engine emphasizes that its shopping feature remains unbiased and free from sponsored content, as confirmed by a spokesperson. Current functionality allows users to store their address and payment information securely for one-click purchases. This seamless process may revolutionize how consumers manage online shopping while also ensuring a more straightforward checkout experience.

As competition ramps up among tech companies integrating AI into shopping experiences, Perplexity is not the only player. Companies including Amazon and various startups are developing their own AI-powered shopping solutions, aiming to harness the capabilities of large language models to improve search outcomes. Google, for instance, recently enhanced its Shopping tab with AI features targeted at providing personalized recommendations.

Featured image credit: Perplexity

The U.S. Department of Justice (DOJ) is pushing for Google to sell its Chrome browser in an effort to address concerns over Google’s monopoly in the search market. The push comes after a judge ruled earlier this year that Google maintained an illegal search monopoly. This significant move aims to enhance competition and player diversity within the search engine sector.

Google faces pressure from DOJ to unbundle Android and sell Chrome

According to a Bloomberg report, the DOJ plans to request the trial judge overseeing Google’s antitrust case to mandate the divestiture of Chrome. As the world’s most popular browser, Chrome’s integration with Google Search has been identified as a significant barrier that restricts competition. The DOJ is advocating for measures that would separate Google Search from Chrome and the Android operating system, while stopping short of demanding the sale of Android itself.

The proposed requirements include the necessity for Google to share search data more freely with advertisers, granting them increased control over their ad placements. Furthermore, the DOJ suggests that Google should provide websites with better options to restrict how their content is utilized by Google’s AI systems. Additional recommendations involve the prohibition of exclusive contracts, which have been pivotal in the current case against Google.

In response to the DOJ’s actions, Lee-Anne Mulholland, Google’s Vice President of Regulatory Affairs, criticized the DOJ’s approach, stating that it represents a radical agenda that goes beyond the legal issues at hand. The DOJ’s latest steps appear to be a continuation of a broader initiative to curtail the dominance of large tech companies.

With Chrome being a gateway to various Google services, the government’s perspective is that its bundled nature creates unfair advantages and stifles competition. By selling Chrome, Google would theoretically remove a key tool that it uses to promote its search engine dominance.

The DOJ also seeks to separate Android from its well-established ecosystem, which includes Google Search and Google Play. Although previously the DOJ suggested that Google sell Android entirely, this notion has been revised to focus more on unbundling rather than divestment. This shift indicates a more measured approach to tackling what officials view as anti-competitive practices.

Google faces revived class action Chrome lawsuit over sync data collection

The DOJ’s recommendations also extend to licensing data and allowing rival companies to access Google’s search results. The next steps in this ongoing legal battle include a scheduled two-week hearing in April 2025 where the court will evaluate what changes Google must implement to address its anticompetitive behavior. A final ruling on the matter is expected to be delivered in August 2025.

Through enforcing recommendations that promote competition, the government may set a precedent with monumental implications for tech players and consumers in years to come.

Featured image credit: Growtika/Unsplash

Google is reportedly merging Chrome OS with Android to better compete against the iPad, which has been a dominant force in the tablet market. Sources from Android Authority reveal that this initiative comes as part of a long-term strategy to unify Google’s operating system development efforts. The transition is expected to leverage aspects of Android’s technological framework while phasing out Chrome OS as a standalone platform, aiming for a more comprehensive experience for users.

The decision stems from Google’s recognition of the overlap between their two operating systems, where both Android and Chrome OS have faced challenges in the tablet sector. Despite their successes in different domains, neither has managed to make a significant impact in high-end tablets, dominated by Apple’s iPad.

According to the Android Authority‘s report, Google aims to consolidate its operating systems to streamline engineering resources and address these competitive deficiencies more effectively.

Shifting the operating system market

In June 2024, Google announced plans for Chrome OS to incorporate components from Android, specifically mentioning the adoption of the Android Linux kernel and frameworks. This integration is not merely cosmetic; it reflects a deeper commitment to merging the functionalities of both systems. Chrome OS has provided some Android features, such as Bluetooth capabilities, through its existing tech stack, but the upcoming changes suggest a more radical transformation.

Sources indicate that Google is undertaking a multi-year project to fully migrate Chrome OS to Android. Future Chromebooks are expected to ship with Android as the core operating system. As a result, the anticipated “Pixel Laptop” is likely to be equipped with a new version of desktop Android rather than the traditional Chrome OS.

The intention is clear: Positioning Google’s hardware offerings to effectively rival the Apple ecosystem, particularly in terms of productivity and user engagement.

Enhancements for productivity and user experience

To enable this transition, Google is reportedly enhancing Android features to include more desktop-like functionalities, which are essential for laptop and tablet users. Key improvements in Android will focus on optimizing keyboard and mouse support, introducing external monitor compatibility, and allowing multiple desktops. These features represent significant strides toward achieving parity between Android and Chrome OS capabilities, which has been a goal for Google.

Moreover, this unification strategy is projected to expand the availability of applications across both platforms, thereby attracting more developers and increasing the usability of Android devices. With Apple’s reluctance to introduce robust productivity features on the iPad to protect its MacBook sales, the opportunity for Google to capture more of the productivity and creative market is more pronounced than ever.

Project Snowy: The reimagined Pixel Laptop

In tandem with these operating system changes, Google is also believed to be developing a new laptop known internally as “Project Snowy.” This upcoming device will ostensibly target Apple’s MacBook Pro and Samsung’s Galaxy Chromebook, re-entering a market that Google stepped away from in recent years. The nature of the operating system for the Pixel Laptop remains uncertain, with some reports suggesting it might run Chrome OS while others point toward an Android-based solution.

As Google contemplates its laptop resurgence, it faces competitive pressure from several key players in the tech space. Samsung’s recent Chromebook Plus has integrated advanced AI capabilities, including features powered by its new Gemini technology. This puts pressure on Google to deliver a similarly innovative product to regain its footing in the laptop market.

Despite the excitement and speculation around Project Snowy, it’s essential to recognize that these developments are in preliminary stages. As such, comprehensive details about specifications and release timelines remain sparse. However, with Google’s history of innovation, there is substantial interest in how this potential Pixel Laptop will integrate new AI tools and adapt to current market demands.

What does the future hold?

As Google transitions from Chrome OS to an Android-centric model, widescale implications for both its tablet and laptop offerings are anticipated. This move is essential not merely for competing against Apple’s iPad but also for consolidating Google’s resources to create a more cohesive user experience. The strategic shift signifies Google’s commitment to remaining a relevant player in hardware, particularly in the highly competitive landscape dominated by Apple and Samsung.

The unification of Chrome OS and Android can ultimately lead to a highly versatile platform that marries productivity and media consumption capabilities. Whether this initiative will position Google favorably against its competition will depend on the execution of these plans and the response from both the consumer market and developers. As these projects unfold, the shift represents a significant pivot point for Google in its long-term vision for hardware and software.

Featured image credit: Growtika/Unsplash

Amazon Web Services (AWS) is the top platform for businesses seeking to increase scalability, minimize expenses, and streamline operations as cloud computing becomes a fundamental component of contemporary business. However, it takes skill to navigate AWS’s wide range of services. Numerous consulting organizations focus on cloud migration, infrastructure management, DevOps, and continuous AWS optimization to help businesses maximize AWS. The top AWS consulting firms for 2024 are listed here, highlighting their distinct advantages and skills. This guide will assist companies in identifying the ideal partner to utilize AWS fully.

1. ITMagic

Founded in 2010, IT-Magic is an AWS Advanced Consulting Company focusing on security and dependability when developing, transferring, and launching digital assets and applications on AWS. IT-Magic, which has offices in Poland and Ukraine, provides full-service AWS solutions, such as disaster recovery, security audits, 24/7 management, and optimization. Their specialized methodology guarantees seamless, risk-minimized operations across sectors and cultivates strong client connections.

2. Rackspace Technology

Because of its well-known AWS managed services, Rackspace Technology is a good option for companies looking for complete cloud solutions. With round-the-clock assistance, Rackspace provides all-inclusive services that cover everything from initial migration to continuing management and optimization. Renowned for its customer-focused strategy, Rackspace offers affordable solutions that serve companies of all sizes by fusing flexibility with in-depth AWS knowledge. Rackspace is a valuable tool for businesses wishing to take advantage of AWS’s full potential because of its proficiency in specialized fields like machine learning, data analytics, and DevOps.

3. N-iX

With 21 years of experience in software development and tech consulting, N-iX is a global engineering firm focusing on cloud services, data analytics, big data, artificial intelligence, and machine learning. As an AWS Advanced Tier Services Partner, N-iX offers services including AWS migration, Well-Architected Reviews, cost optimization, and DevOps maturity evaluations. It has more than 2,200 specialists and about 100 AWS-certified experts. They have worked with leading companies, including Cleverbridge, PrettyLittleThing, Lebara, and Gogo, and they have several AWS designations that attest to their proficiency in serverless cloud-native computing and data analytics..

4. Wipro

This Indian-based company is among the biggest AWS consulting firms on our list. The vendor usually works with companies in the retail, energy, financial services, telecommunications, logistics, and supply chain sectors. Data & Analytics Consulting Competency, Migration Consulting Competency, and other AWS consulting competencies are among their many strengths. The company handles cloud security, infrastructure, modernization demands, and cloud consultancy.

5. Adastra

This is one of the leading AWS consulting firms with over 20 years of experience. The vendor has domain expertise in several industries, including manufacturing, healthcare, pharmaceuticals, logistics, and automobiles. AWS cost optimization, AWS data lake deployment, AWS data warehouse modernization, and AWS migration are among the areas of expertise for its engineers and consultants.

6. GlobalLogic

Since its founding in 2000, the vendor has offered engineering and consulting services to the media, healthcare, automotive, telecommunications, and manufacturing sectors. They are proficient in several AWS competencies, including migration consulting, financial services consulting, and dev operations consulting. The company’s experts offer cloud consultation, assessment, migration, and optimization as part of their AWS-related services.

7. Sigma Software

Sigma Software focuses on DevOps, cloud infrastructure, and application modernization and provides full-service AWS consultancy. The company’s AWS-certified specialists are professionals who assist companies with deploying and administering dependable, scalable AWS environments that satisfy particular industry requirements. Startups and mid-sized businesses seeking to improve their AWS cloud setup choose Sigma Software because of its strong emphasis on transparency and agile, collaborative approach.

8. OneData Software Solutions

AWS consulting company OneData Software Solutions LLC is expanding quickly and is well-known for emphasizing data-driven cloud solutions. Focusing on cloud-based data storage and retrieval solutions, the company offers AWS services in the areas of analytics, data engineering, and migration. OneData’s emphasis on business intelligence and data optimization aids organizations in increasing operational effectiveness and making better decisions. OneData provides tailored solutions that produce meaningful insights for companies wishing to use AWS for advanced data analytics.

9. Perficient

The reputable digital consultant Perficient provides AWS services that include cloud migration, DevOps, and application modernization. Perficient is well-known for its broad industry knowledge, especially in healthcare and finance. Through its cooperation with AWS, it assists customers in building effective, safe cloud environments that adhere to strict regulatory standards. Because of its emphasis on cloud transition and extensive technological expertise, Perficient is the perfect partner for businesses that require reliable and compliant cloud solutions.

10. Netguru

Netguru is a digital consulting firm that specializes in software development and AWS. It provides a wide range of AWS services, including cloud infrastructure, DevOps, and machine learning integration, and is an expert in developing cloud-native apps. Businesses in rapidly changing IT, banking, and e-commerce industries can benefit from Netguru’s creative approach and agile methodology. With a staff of AWS-certified experts, Netguru assists companies in developing scalable, customized solutions that meet their particular objectives.

Conclusion

AWS consulting firms are crucial for enterprises to utilize AWS cloud technology and spur economic expansion. These featured AWS partners provide all-inclusive cloud solutions that help facilitate effective cloud infrastructure management, including computing, storage, databases, analytics, and machine learning. The goal of this guide is to assist you in locating the best AWS network consulting partner to meet your requirements and optimize your cloud infrastructure.

Featured image credit: AWS Amazon

At the last AI Conference, we had a chance to sit down with Roman Shaposhnik and Tanya Dadasheva, the co-founders of Ainekko/AIFoundry, and discuss with them an ambiguous topic of data value for enterprises in the times of AI. One of the key questions we started from was: are most companies running the same frontier AI models, is incorporating their data the only way they have a chance to differentiate? Is data really a moat for enterprises?

Roman recalls: “Back in 2009, when he started in the big data community, everyone talked about how enterprises would transform by leveraging data. At that time, they weren’t even digital enterprises; the digital transformation hadn’t occurred yet. These were mostly analog enterprises, but they were already emphasizing the value of the data they collected—data about their customers, transactions, supply chains, and more. People likened data to oil, something with inherent value that needed to be extracted to realize its true potential.”

However, oil is a commodity. So, if we compare data to oil, it suggests everyone has access to the same data, though in different quantities and easier to harvest for some. This comparison makes data feel like a commodity, available to everyone but processed in different ways.

When data sits in an enterprise data warehouse in its crude form, it’s like an amorphous blob—a commodity that everyone has. However, once you start refining it, that’s when the real value comes in. It’s not just about acquiring data but building a process from extraction to refining all the value through the pipeline.

“Interestingly, this reminds me of something an oil corporation executive once told me” – shares Roman. “That executive described the business not as extracting oil but as reconfiguring carbon molecules. Oil, for them, was merely a source of carbon. They had built supply chains capable of reconfiguring these carbon molecules into products tailored to market demands in different locations—plastics, gasoline, whatever the need was. He envisioned software-defined refineries that could adapt outputs based on real-time market needs. This concept blew my mind, and I think it parallels what we’re seeing in data now—bringing compute to data, refining it to get what you need, where you need it” – was Roman’s insight.

In enterprises, when you start collecting data, you realize it’s fragmented and in many places—sometimes stuck in mainframes or scattered across systems like Salesforce. Even if you manage to collect it, there are so many silos, and we need a fracking-like approach to extract the valuable parts. Just as fracking extracts oil from places previously unreachable, we need methods to get enterprise data that is otherwise locked away.

A lot of enterprise data still resides in mainframes, and getting it out is challenging. Here’s a fun fact: with high probability, if you book a flight today, the backend still hits a mainframe. It’s not just about extracting that data once; you need continuous access to it. Many companies are making a business out of helping enterprises get data out of old systems, and tools like Apache Airflow are helping streamline these processes.

But even if data is no longer stuck in mainframes, it’s still fragmented across systems like cloud SaaS services or data lakes. This means enterprises don’t have all their data in one place, and it’s certainly not as accessible or timely as they need. You might think that starting from scratch would give you an advantage, but even newer systems depend on multiple partners, and those partners control parts of the data you need.

The whole notion of data as a moat turns out to be misleading then. Conceptually, enterprises own their data, but they often lack real access. For instance, an enterprise using Salesforce owns the data, but the actual control and access to that data are limited by Salesforce. The distinction between owning and having data is significant.

“Things get even more complicated when AI starts getting involved” – says Tanya Dadasheva, another co-founder of AInekko and AIFoundry.org. “An enterprise might own data, but it doesn’t necessarily mean a company like Salesforce can use it to train models. There’s also the debate about whether anonymized data can be used for training—legally, it’s a gray area. In general, the more data is anonymized, the less value it holds. At some point, getting explicit permission becomes the only way forward”.

This ownership issue extends beyond enterprises; it also affects end-users. Users often agree to share data, but they may not agree to have it used for training models. There have been cases of reverse-engineering data from models, leading to potential breaches of privacy.

At an early stage of balancing data producers, data consumers, and the entities that refine data, legally and technologically it is extremely complex figuring out how these relationships will work. Europe, for example, has much stricter privacy rules compared to the United States (https://artificialintelligenceact.eu/). In the U.S., the legal system often figures things out on the go, whereas Europe prefers to establish laws in advance.

Tanya addresses data availability here: “This all ties back to the value of data available. The massive language models we’ve built have grown impressive thanks to public and semi-public data. However, much of the newer content is now trapped in “walled gardens” like WeChat, Telegram or Discord, where it’s inaccessible for training – true dark web! This means the models may become outdated, unable to learn from new data or understand new trends.

In the end, we risk creating models that are stuck in the past, with no way to absorb new information or adapt to new conversational styles. They’ll still contain older data, and the newer generation’s behavior and culture won’t be represented. It’ll be like talking to a grandparent—interesting, but definitely from another era.”

But who are the internal users of the data in an enterprise? Roman recalls the three epochs of data utilization concept within the enterprises: “Obviously, it’s used for many decisions, which is why the whole business intelligence part exists. It all actually started with business intelligence. Corporations had to make predictions and signal to the stock markets what they expect to happen in the next quarter or a few quarters ahead. Many of those decisions have been data-driven for a long time. That’s the first level of data usage—very straightforward and business-oriented.

The second level kicked in with the notion of digitally defined enterprises or digital transformation. Companies realized that the way they interact with their customers is what’s valuable, not necessarily the actual product they’re selling at the moment. The relationship with the customer is the value in and of itself. They wanted that relationship to last as long as possible, sometimes to the extreme of keeping you glued to the screen for as long as possible. It’s about shaping the behavior of the consumer and making them do certain things. That can only be done by analyzing many different things about you—your social and economic status, your gender identity, and other data points that allow them to keep that relationship going for as long as they can.

Now, we come to the third level or third stage of how enterprises can benefit from data products. Everybody is talking about these agentic systems because enterprises now want to be helped not just by the human workforce. Although it sounds futuristic, it’s often as simple as figuring out when a meeting is supposed to happen. We’ve always been in situations where it takes five different emails and three calls to figure out how two people can meet for lunch. It would be much easier if an electronic agent could negotiate all that for us and help with that. That’s a simple example, but enterprises have all sorts of others. Now it’s about externalizing certain sides of the enterprise into these agents. That can only be done if you can train an AI agent on many types of patterns that the enterprise has engaged in the past.”

Getting back to who collects and who owns and, eventually, benefits from data: the first glimpse of that Roman got when working back at Pivotal on a few projects that involved airlines and companies that manufacture engines:

“What I didn’t know at the time is that apparently you don’t actually buy the engine; you lease the engine. That’s the business model. And the companies producing the engines had all this data—all the telemetry they needed to optimize the engine. But then the airline was like, “Wait a minute. That is exactly the same data that we need to optimize the flight routes. And we are the ones collecting that data for you because we actually fly the plane. Your engine stays on the ground until there’s a pilot in the cockpit that actually flies the plane. So who gets to profit from the data? We’re already paying way too much to engine people to maintain those engines. So now you’re telling us that we’ll be giving you the data for free? No, no, no.”

This whole argument is really compelling because that’s exactly what is now repeating itself between OpenAI and all of the big enterprises. Big enterprises think OpenAI is awesome; they can build this chatbot in minutes—this is great. But can they actually send that data to OpenAI that is required for fine-tuning and all these other things? And second of all, suppose those companies even can. Suppose it’s the kind of data that’s fine, but it’s their data – collected by those companies. Surely it’s worth something to OpenAI, so why don’t they drop the bill on the inference side for companies who collected it?

And here the main question of today’s data world kicks in: Is it the same with AI?

In some way, it is, but with important nuances. If we can have a future where the core ‘engine’ of an airplane, the model, gets produced by these bigger companies, and then enterprises leverage their data to fine-tune or augment these models, then there will be a very harmonious coexistence of a really complex thing and a more highly specialized, maybe less complex thing on top of it. If that happens and becomes successful technologically, then it will be a much easier conversation at the economics and policy level of what belongs to whom and how we split the data sets.

As an example, Roman quotes his conversation with an expert who designs cars for a living: “He said that there are basically two types of car designers: one who designs a car for an engine, and the other one who designs a car and then shops for an engine. If you’re producing a car today, it’s much easier to get the engine because the engine is the most complex part of the car. However, it definitely doesn’t define the product. But still, the way that the industry works: it’s much easier to say, well, given some constraints, I’m picking an engine, and then I’m designing a whole lineup of cars around that engine or that engine type at least.”

This drives us to the following concept: we believe that’s what the AI-driven data world will look like. There will be ‘Google’ camp and ‘Meta camp’, and you will pick one of those open models – all of them will be good enough. And then, all of the stuff that you as an enterprise are interested in, is built on top of it in terms of applying your data and your know-how of how to fine-tune them and continuously update those models from different ‘camps’. In case this works out technologically and economically, a brave new world will emerge.

Featured image credit: NASA/Unsplash

Users of Google Chat can now send voice messages, a feature previously reserved for Workspace accounts, enhancing personal communication. This rollout began on November 18, 2024, making audio messaging accessible for all Gmail users.

Google Chat now lets free Gmail users send voice messages

Earlier in 2024, Google Chat introduced voice messages for Workspace users. The recent update means that free, personal Gmail accounts can now utilize this feature, reflecting Google’s ongoing efforts to enhance user experience. When users click the send icon in a chat, they will see a microphone icon that enables immediate message recording with a simple tap.

Once recorded, the audio clip is sent instantly, complete with an integrated waveform and a counter to track recording length. Users can listen back to their messages before sending them, ensuring clarity and appropriateness. After a message is sent, both the sender and recipient receive a transcript of the audio, similar to the functionality found in Google Messages. This feature allows for convenient communication without the need for live conversations.

An interesting aspect of this implementation is its placement; the microphone icon is prominent and situated next to the text entry field. This contrasts with the more cluttered approaches seen in other messaging apps, streamlining the user experience. Google has also made the feature available across Android and iOS apps, as well as on the web interface, thus accommodating various user preferences. For users who do not see the feature yet, a force stop of the apps may be necessary for the update to take effect.

Voice messaging provides a more personal touch to online interactions, striking a balance between text messaging and live calls. Users can articulate complicated thoughts with more nuance than text, making communication easier and more effective in various situations. This feature aligns with current trends in remote communication, as voice messages can be less intrusive than phone calls while still allowing emotional context.

Voice message support arrives after a phased rollout for Workspace users according to Android Authority, with notable upgrades observed since its initial launch. One such upgrade includes automatic text transcripts for voice messages, contributing to the accessibility and integrative nature of the service. Despite some users still waiting for the feature to appear in their accounts, the rollout has been reported as actively progressing.

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The late arrival of this feature is curious, given Google’s enterprise-oriented approach to Chat. However, many users welcome the accessibility now extended to personal accounts without the barrier of having to pay for a subscription.

Voice messaging is not the only new feature being introduced to Google Chat. Recently, Google rolled out a split pane UI for web users, enhancing multitasking capabilities, while also incorporating various visual updates via Material You design principles. These features contribute to a more modern and user-friendly experience that encourages ongoing engagement with the platform.

The decision to open voice messaging for free Gmail account users aligns with Google’s strategy of enhancing user accessibility and engagement.

Featured image credit: Firmbee.com/Unsplash

Meta AI has rolled out its AI-powered Ray-Ban smart glasses in France, Italy, Ireland, and Spain, allowing users to ask questions and receive real-time responses in multiple languages. The launch aims to enhance users’ daily lives by providing assistance directly from their eyewear.

Meta AI introduces smart glasses for European users

Beginning November 18, users in four European countries will benefit from Meta’s AI capabilities through Ray-Ban Meta glasses. This launch enables voice-activated inquiries, offering instant responses in English, French, Italian, and Spanish. For example, users may ask, “Hey Meta, what’s the best patisserie in Paris?” or “What are some good gift ideas for my kids aged 6 and 8?”

As part of this rollout, features that allow queries about visible landmarks remain limited to the U.S., Canada, and Australia. Meta has emphasized that the glasses provide a hands-free way to access information and creativity while on the move. Since the glasses released in September 2023, Meta has prioritized compliance with the EU’s regulatory system.

“With Meta AI on Ray-Ban Meta glasses, people have a hands-free way to ask questions on-the-go and receive real-time answers and information,” a spokesperson from Meta stated. The company intends to expand the availability of these glasses to additional countries within the European Union, aiming for broader compliance and enhanced user experience.

As this product launch unfolds, Meta faces additional challenges, including regulatory scrutiny. Recently, the European Commission fined Meta 797.72 million euros (approximately $842 million) for violating EU antitrust rules. Specifically, the Commission found that Meta unfairly favored its Facebook Marketplace service, allowing automatic access to users regardless of their preference. Meta has announced plans to appeal this decision while diligently addressing regulatory concerns to regain trust within the EU market.

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Meta is also promoting the Ray-Ban Meta smart glasses through innovative retail experiences, such as a pop-up store in Los Angeles. Here, potential customers can interact with the product first-hand, allowing them to explore its features and capabilities without any pressure to purchase. “When you let somebody try out a pair of Ray-Ban Meta glasses, you become the best salesperson ever,” said Creative Director Matt Jacobson. Highlighting the immersive experience of using Meta AI, he confirmed the company’s commitment to creating engaging avenues for potential buyers.

Image credits: Meta

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.

Conclusion

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: Leeloo The First/Pexels

US President Joe Biden and Chinese President Xi Jinping reached a historic consensus during their recent meeting, emphasizing that decisions regarding the use of nuclear weapons must be managed by humans rather than artificial intelligence.

On November 16, 2024, at the Asia-Pacific Economic Cooperation (APEC) summit in Lima, Peru, the two leaders reiterated their stance that human oversight is essential in critical areas affecting global security. The White House stated, “The two leaders affirmed the need to maintain human control over the decision to use nuclear weapons.” This is the first time China has articulated this position, marking a pivotal moment in US-China relations.

The context of this agreement is crucial. Recent estimates indicate China’s operational nuclear warhead count at approximately 500, with projections suggesting it could escalate to over 1,000 by 2030. In contrast, the US and Russia possess 1,770 and 1,710 operational warheads, respectively. At the meeting, Biden and Xi stressed the importance of prudent development of AI technologies within military applications, reflecting a growing recognition of the potential risks posed by AI systems.

Progress on AI and nuclear arms discussions

The agreement follows a period during which formal nuclear arms control negotiations between the US and China had been stalled. Despite a brief resumption of official-level discussions in November, expectations for comprehensive arms control talks have remained low. Jake Sullivan, Biden’s National Security Advisor, characterized the agreement as a critical first step in addressing long-term strategic risks posed by both nuclear weapons and artificial intelligence.

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While both countries have previously engaged on nuclear proliferation concerns, discussions surrounding the role of AI in military strategies have been less formalized. The bilateral talks held in Geneva earlier this year, dedicated to AI, notably did not include discussions regarding nuclear decision-making. Biden’s administration is advocating for continuity in the approach surrounding AI and nuclear arms, emphasizing stability as key to US-China relations.

China’s stance on Taiwan and bilateral relations

During the Lima summit, Xi Jinping reiterated China’s commitment to maintaining sovereignty and stability amid tense US-China relations. He specifically cited Taiwan as one of four “red lines” that must not be crossed in discussions between the two powers, stressing that this issue, along with democracy and human rights characteristics of governance, are vital to China’s national interests.

Xi’s statements come as he voiced willingness to engage with the incoming administration of Donald Trump, emphasizing cooperation despite existing tensions. The Chinese leader highlighted, “China is ready to work with the new US administration to maintain communication, expand cooperation and manage differences.” These comments come in light of the uncertainty surrounding future US policies, especially concerning Xi’s continuity strategy against pressures related to Taiwan and other geopolitical issues.

Biden conveyed concerns regarding China’s interactions with both North Korea and Russia during the talks, highlighting the need for China to influence North Korea to deter military support to Russia amid its ongoing conflict with Ukraine. The conflict over Taiwan remains a particular point of contention, with Biden emphasizing the need to navigate US-China relations prudently.

US-China trade and technology tensions

The meeting also covered contentious trade issues, particularly regarding Biden’s export controls on sensitive technology that have been a point of criticism from China. Xi expressed that restrictive policies, implemented to secure advanced tech, could undermine mutual development opportunities. He stated that “only mutually beneficial cooperation can lead to common development” and rejected the notion of protective trade barriers as detrimental to a major global power’s aspirations.

The Biden administration’s export controls pertain to advanced semiconductor manufacturing tools and surveillance technologies, pivotal in maintaining a competitive edge in AI and military capabilities. Sullivan mentioned that Biden stressed the importance of continuing these trade measures, as they are fundamental to US national security strategies against perceived threats from Chinese technological advancements.

As both leaders addressed the complexities of their relationship during this transitional period, the conversation indicated a desire from both sides to stabilize interactions amid evolving geopolitical challenges. The commitment to human control over nuclear weapons underscores an awareness of the importance of leading with caution in matters of significant consequence.

Featured image credit: Stephen Cobb/Unsplash

Big Tech’s AI spending is positioned to surpass a remarkable $240 billion in 2024, representing a strong response to soaring demand for artificial intelligence infrastructure and services. As major firms like Microsoft, Amazon, Alphabet, and Meta ramp up investments, the trend reflects their anticipation of long-term returns from AI. The uptick stems from both market growth and evolving technology needs.

In the first half of 2023, Big Tech’s capital expenditures (capex) rose sharply to nearly $74 billion, which increased to approximately $109 billion by Q3.

The first half of 2024 saw spending approach $104 billion—a staggering 47% year-over-year increase—culminating in $171 billion by Q3. With an anticipated $70 billion spend in Q4, overall investment could reach around $240 billion, predominantly targeting AI infrastructure.

The impetus behind increased investment

The primary drivers of this spending include:

• Market opportunity: AI is expected to generate a cumulative global economic impact of $20 trillion by 2030, making it a priority for Big Tech to capture this lucrative potential.
• Infrastructure demands: As companies build and enhance their AI models, they require vast computing resources that can only be supported by significant capital allocations.
• Emerging revenue streams: Companies are already beginning to report multi-billion dollar revenues from AI integrations, with Microsoft anticipating its AI business will surpass a $10 billion annual revenue run rate by Q2 2025.

Amazon has projected a capital expenditure increase to $75 billion in 2024, while Meta has raised its own forecast to $38 billion to $40 billion. Across the board, Big Tech recognizes that the competition for AI capabilities requires sustained financial commitment.

The growing significance of infrastructure investments

These accelerated investments in AI infrastructure come as firms like Microsoft and Amazon strive to meet increasingly high demand. Microsoft’s capex hit approximately $10 billion in the latest quarter, highlighting their need to stay aligned with cloud and AI service requirements. CFO Amy Hood noted that an influx of supply through the second half of the year would further enable them to match growing demand.

Amazon’s AWS division echoed this sentiment, with CEO Andy Jassy stating that capacity constraints have been limiting growth, despite “more demand than we could fulfill if we had even more capacity today.” The challenges aren’t unique to these tech behemoths; Alphabet and Oracle are also feeling the pressure to meet AI demands due to their struggles to procure adequate GPUs.

A deep dive into earnings and growth metrics

Earnings calls from Big Tech in Q3 revealed ample optimism about their AI prospects. Microsoft emphasized that AI contributed significantly to Azure’s growth, with its AI run rate likely exceeding $6 billion. Amazon cited its AI business growing at triple-digit rates, indicating strong demand yet to be met. Alphabet’s revenue streams from AI continue to be a focus, with billions already generated from its cloud infrastructure. Each company is poised to leverage their investments for increased profitability as the market evolves.

Exploring the rising impact of cryptocurrency exchanges on data-driven markets

Meta, while primarily focused on advertising, is attempting to leverage its AI developments to enhance user engagement and ultimately drive revenue through advertising returns. Recent enhancements to AI-driven feeds and search functionalities have reportedly increased user time spent on its platforms.

Future of AI spending

Given the current trajectory, Big Tech’s capital expenditures for AI are set to maintain momentum through 2025. Executives foresee persistent demand in the sector, which will require continued investment to harness growth opportunities. The expectation of AI significantly influencing financial returns is evident across all major players.

As Big Tech secures billions in AI revenue, the competitive market landscape signals a significant shift in operational paradigms. The short-term focus is on expanding capacity, while the long-term perspective revolves around exploiting AI’s vast potential.

The trends observed throughout 2023 and into 2024 will likely inform strategic investment decisions well into the future, with profound implications for their profit trajectories.

Featured image credit: Freepik

Apple may soon discontinue the Lightning to headphone jack adapter, originally launched in 2016 with the iPhone 7. This potential move reflects Apple’s shift toward USB-C compatibility across its devices. The Lightning adapter is currently sold out in many countries, signaling a possible end of its production. Apple seems poised to make this change, which could affect users of iPhone 14 and earlier models that still utilize the Lightning connector.

Lightning to headphone adapter came out with iPhone 7

Introduced alongside the iPhone 7, the Lightning to headphone jack adapter was a response to the removal of the traditional 3.5mm headphone jack. Initially included for free with the iPhone 7, 8, and X, Apple later began selling it separately for $9 starting with the iPhone XS and XR. However, recent reports suggest that this accessory will soon be discontinued, as it is currently “sold out” on Apple’s online store in major regions like the US, Canada, and Australia.

Why iPhone SE 4 will rule affordable smartphone market

Although this wouldn’t directly impact iPhone 15 and later users, who benefit from the USB-C to headphone jack adapter, it presents challenges for those sticking with Lightning devices. MacRumors reports that while the adapter is out of stock online in various countries, it is still available in regions such as France, Denmark, Finland, Norway, and Sweden. Such a situation is reminiscent of past discontinuations, like the USB SuperDrive, which showed similar signs before being phased out.

The potential discontinuation is interesting given that Apple continues to sell Lightning iPhones, including the SE 3 and non-Pro 14 models. As such, it may be more strategic for Apple to wait until the entire iPhone line transitions to USB-C, which is expected with future releases. This timing would allow users to adapt more seamlessly to the new standard without losing essential accessories abruptly.

Users looking for the official Apple adapter may feel urgency in making their purchase. While third-party options may continue to thrive, the specific Apple variant is becoming increasingly harder to find. Some retailers, like Best Buy, still carry it, but stock is likely to diminish rapidly as rumors of discontinuation circulate.

Various tech platforms have reported on this potential shift. Android Authority and 9to5Mac highlight the implications for users and the market’s response. With Apple’s prevailing trend toward wireless audio and USB-C integration, this move seems aligned with their broader strategy.

As Apple makes strides toward modernizing its ecosystem, fans and users of vintage tech are left with uncertainties surrounding compatibility and product availability. The future seems to lean towards total integration of USB-C across all devices, reinforcing Apple’s commitment to streamlining its product offerings.

Featured image credit: Mika Baumeister/Unsplash

Tesla’s stock surged over 7% in premarket trading following reports that the incoming Trump administration aims to prioritize a federal framework for fully self-driving vehicles. The announcement has significant implications for Tesla, which has been at the forefront of the autonomous driving movement.

The Trump effect

The Trump administration’s focus on self-driving vehicle regulations aligns with Tesla’s strategic goals. CEO Elon Musk, a vocal supporter of Trump, is reportedly well-connected within the new administration. He and former presidential candidate Vivek Ramaswamy have been appointed to lead the newly formed Department of Government Efficiency (DOGE), which aims to streamline government operations and reduce regulatory burdens.

According to Wedbush analysts led by Daniel Ives, easing restrictions could substantially benefit Tesla’s artificial intelligence and autonomous vehicle initiatives. The firm estimates the AI and autonomous opportunity for Tesla could be worth $1 trillion. They expect that under Trump’s leadership, significant progress will be made in clearing past regulatory obstacles that have hampered Fully Self-Driving (FSD) technology.

Currently, federal regulations significantly limit the deployment of cars designed to operate without traditional driving controls like foot pedals and steering wheels. The Trump administration is looking for individuals to head the regulatory framework concerning self-driving vehicles, with names like former Uber executive Emil Michael and Republican Representatives Sam Graves and Garrett Graves reportedly under consideration.

Tesla recently announced plans to launch a Robotaxi service by 2026, dependent on overcoming existing regulations that could restrain its growth. Analysts believe that Musk’s position in Trump’s inner circle may set the stage for facilitating the mass adoption and success of the new service.

The stock’s increase of 28% since the election on November 5 exemplifies the market’s optimism regarding Tesla’s future under what many consider a more favorable regulatory environment. In another report by CNBC, Tesla shares had jumped over 8.3% in premarket trading following the earlier Bloomberg news, reflecting strong investor interest in the implications of the Trump administration’s regulatory focus.

Amid these developments, industry experts are recalibrating their outlook on Tesla. They regard the stock as one of the most undervalued AI plays currently available and expect its performance to improve further as favorable policies are enacted.

The recent optimism surrounding Tesla can be attributed to expected changes in U.S. transportation regulations aimed at self-driving vehicles. With Trump’s administration poised to expedite the regulatory processes, Tesla’s ambitious plans could come to fruition much sooner than anticipated.

The collaboration between Elon Musk and the Trump administration suggests a strategic alignment that may accelerate the rollout of autonomous technologies. The pressure on government to adapt regulations to accommodate these advancements could reshape the landscape for not just Tesla, but the entire auto industry.

For investors, the news indicates a potential shift toward a more innovation-friendly environment, enabling Tesla to leverage its technological advancements. The proposed changes could also stimulate competition among other tech companies entering the autonomous driving space.

Disclaimer: The content of this article is for informational purposes only and should not be construed as investment advice. We do not endorse any specific investment strategies or make recommendations regarding the purchase or sale of any securities, including Tesla stock.

Featured image credit: Manny Becerra/Unsplash

Nvidia’s Blackwell GPUs face overheating challenges impacting major tech clients. The next-generation processors are struggling to perform effectively in server racks housing 72 GPUs, raising concerns for companies like Google, Meta, and Microsoft about timely deployment. Reports indicate that Nvidia is reevaluating its rack designs multiple times due to these overheating issues, which risk damaging components and limit GPU performance. The anticipated power draw for these configurations is up to 120kW per rack.

Insiders informed The Information that Nvidia’s Blackwell GPUs for AI and high-performance computing (HPC) have overheated in high-capacity servers, affecting launch timelines for clients relying on these technologies. In a bid to address the complications stemming from these overheating problems, Nvidia has requested its suppliers to modify the rack designs repeatedly. A spokesperson from Nvidia emphasized their collaborative approach with cloud services, describing the design changes as a routine part of the development process.

Adjustments in design to counteract overheating issues

Previously, delays in the Blackwell production ramp were attributed to a “yield-killing” design flaw. The Blackwell B100 and B200 GPUs utilize TSMC’s CoWoS-L packaging technology, which integrates two chiplets for enhanced data transfer speeds of up to 10 TB/s. However, a mismatch in thermal expansion characteristics among the GPU chiplets and other components led to warping and system failures. To resolve this, Nvidia made modifications to the GPU silicon’s metal layers and bump structures.

The result of these improvements only entered mass production in late October, with expected shipping dates pushed back to late January. This delay is critical for Nvidia’s clients like Google, Meta, and Microsoft, which depend on these GPUs to enhance their most powerful AI models. Nvidia previously touted the Blackwell chips as being 30 times faster for tasks such as responding to chatbot queries compared to earlier models.

Nvidia’s Blackwell chip revenue was projected to reach $6 billion in the next quarter, highlighting the high demand despite the ongoing supply constraints. Nvidia, recently surpassing Apple, is now the world’s most valuable company with a market capitalization soaring to $3.482 trillion. However, the continuous setbacks regarding the Blackwell processors threaten to disrupt the planned advancements in AI capabilities essential for major tech players.

Featured image credit: Nvidia

Gmail is on the verge of a significant change with the potential introduction of a new feature called “Shielded Email,” according to Android Authority. Google aims to help its two billion Gmail users protect their primary email addresses while navigating the increasing landscape of online data threats. Scheduled updates could allow users to create unique email addresses that automatically forward messages to their main accounts, mirroring Apple’s successful “Hide My Email” feature.

What is a Shielded Email?

Google’s latest initiative, which appears to be part of a broader push to close the privacy gap with competitors like Apple, comes amid rising concerns about email security. The decision was not announced alongside other privacy enhancements this year, but it could significantly alter how users interact with email. According to reports from Android Authority, the feature was discovered in the latest teardown of Google Play Services, revealing strings of code referencing Shielded Email and its intended functionality.

The Shielded Email feature aims to generate single-use or limited-use email aliases, addressing a common concern among users who fear spam or data breaches when sharing their email addresses. By using these generated email addresses, users can maintain privacy and avoid unwanted communications from less reputable sources. When filling out forms or signing up for online services, users can opt to use these aliases instead of exposing their real email addresses.

Gmail enhances productivity with new Gemini side panel

This concept is not entirely new; Apple has established a successful precedent with its Hide My Email function, which allows users to create random email addresses that forward to their iCloud accounts. The design of Shielded Email appears to focus specifically on Android and may integrate with features like Autofill and Google Password Manager. While it remains unclear whether Google will charge for these aliases, the potential for monetization exists, possibly tying it to Google One subscriptions.

Shielded Email will be particularly useful during high-traffic shopping seasons when scammers increase their activities, frequently using phony emails to solicit sensitive information. Recent alerts from law enforcement warn of the dangers posed by increased phishing attempts, especially as the holiday season approaches. Cybercriminals are using sophisticated methods to evade detection, including malicious image attachments that can bypass existing security measures.

Once implemented, Shielded Email could provide a crucial defense against the growing number of phishing attacks. Some of these attacks exploit the simplicity of email as a communication method, prompting users to click on links or download attachments that appear legitimate. Adopting cloaked email addresses allows users to identify potential scams, as any misuse of these temporary addresses can be directly traced back to where they were shared.

Reports have highlighted that the increasing complexity of cyber threats mandates more robust defenses, notably for mobile users. With mobile phishing, or “mishing,” attacks becoming more prevalent, the urgency for secure email solutions is more critical than ever. Zimperium’s Nico Chiaraviglio noted that the evolution of mobile security is essential, as many attacks can begin through phishing lures shared via email or social media messaging apps.

If Google moves forward with Shielded Email, it could provide Android users with tools to better manage their online privacy and protect against spam and scams. However, the rollout strategy remains a critical consideration, particularly regarding which devices will receive the updates first.

Featured image credit: Solen Feyissa/Unsplash

Salesforce CEO Marc Benioff is impressing the tech community with his endorsement of Google’s Gemini Live, an AI voice assistant. He praised it on social media, noting its zero latency and calling it the “future of consumer AI.” This accolade reflects his ongoing exploration of AI technologies, particularly in consumer applications. Gemini Live’s integration into everyday tasks positions it as a contender against established assistants. Benioff’s criticism of Microsoft’s Copilot for failing to meet expectations further highlights the divide in AI offerings.

Gemini Live: Benioff’s endorsement and user experience

In a recent post on X, Benioff expressed his excitement for Gemini Live, stating, “Just downloaded Gemini Live, and I’m absolutely blown away.” He highlighted its performance, emphasizing “zero latency,” which refers to the instant response the AI provides when engaged in conversation. He encouraged others to try it, praising the work of Google CEO Sundar Pichai as “truly groundbreaking.”

Benioff’s enthusiastic feedback follows his criticisms of Microsoft’s AI assistant, Copilot, which he previously described as “disappointing” and compared unfavorably to the defunct Clippy. He noted, “Microsoft has really disappointed so many of our customers,” criticizing the disparity between the hype surrounding their AI solutions and actual performance.

With the release of Gemini Live as a mobile app, which became available on the App Store recently, iOS users can now access Google’s latest AI technology directly on their smartphones. The AI assistant responds dynamically to voice commands, enabling users to engage in natural conversations.

Gemini Live vs. ChatGPT vs. Copilot

Gemini Live joins OpenAI’s ChatGPT and Microsoft’s Copilot in the competitive market for AI chatbots. Each boasts unique functionalities, operating under different frameworks. During an interaction with Business Insider, Gemini Live provided a detailed, conversational explanation as to why San Francisco experiences fog, something its counterparts did in a similar vein but with minor variances in delivery.

While Gemini and ChatGPT’s approaches are relatively parallel regarding casual inquiries, Copilot is more geared toward productivity tasks, serving a different niche in the market. Each assistant has limitations, including the propensity to produce inaccuracies or “hallucinations.”

During Salesforce’s Dreamforce conference, Benioff discussed using ChatGPT primarily for personal reflection, humorously stating it worked “mostly as my therapist.”

As Geminis Live continues to gain traction, anticipation grows surrounding its integration with Apple Intelligence features. Industry speculation, particularly from tech analysts like Mark Gurman, suggests that while Google plans to collaborate with Apple for Gemini integration, it may take time. The expectation is that the integration might not occur until next year, especially as Apple appears to be granting OpenAI a temporary exclusivity window following their recent partnership to bring ChatGPT to iOS.

Craig Federighi, Apple’s software executive, conveyed his hopes for incorporating Google’s Gemini chatbot into Apple Intelligence, but with no immediate timeline confirmed, users may have to wait for further updates.

In the meantime, Google has launched a standalone app that allows iPhone users to access Gemini capabilities, positioning it as a direct competitor to other AI assistants.

Benioff, who has refrained from commenting directly on the planned integration, remains a significant voice in promoting AI advancements, emphasizing their role in reshaping consumer technology.

Continued developments in AI technology, especially with high-profile endorsements like Benioff’s, are essential in understanding the competitive landscape. As companies like Google and Apple explore formal integrations and features, the AI assistant market is poised for significant evolution, catering to consumer needs with greater immediacy and contextual awareness.

Featured image credit: Salesforce

There are significant security risks tied to poorly chosen passwords, as highlighted by a new report from NordPass. The research, analyzing a 2.5-terabyte database involving passwords from various publicly available resources and the dark web, reveals the most common passwords in 2024. The findings emphasize that many users tend to rely on simple, easy-to-remember passwords, making them easy targets for cybercriminals.

The study, which encompassed data from 44 countries, identified the 15 most frequently used and easily crackable passwords. Topping the list is “123456,” with a staggering usage count of 3,018,050 and an estimated cracking time of less than one second. Other prevalent passwords include “123456789” (1,625,135 uses), “12345678” (884,740), and “password” (692,638). The complete list reveals alarming trends in password choice, showing a troubling technological complacency among users.

Common passwords and their risks

Here is a complete rundown of the 15 most common passwords and their respective statistics:

• 1. 123456 – <1 second to crack, used 3,018,050 times
• 2. 123456789 – <1 second, 1,625,135
• 3. 12345678 – <1 second, 884,740
• 4. password – <1 second, 692,638
• 5. qwerty123 – <1 second, 642,638
• 6. qwerty1 – <1 second, 583,630
• 7. 111111 – <1 second, 459,730
• 8. 12345 – <1 second, 395,573
• 9. secret – <1 second, 363,491
• 10. 123123 – <1 second, 351,576
• 11. 1234567890 – <1 second, 324,349
• 12. 1234567 – <1 second, 307,719
• 13. 000000 – <1 second, 250,043
• 14. qwerty – <1 second, 244,879
• 15. abc123 – <1 second, 217,230

The analysis sheds light on an alarming pattern: many users gravitate toward overly simplistic passwords, increasing their vulnerability. Notably, passwords such as “football” (59,656 instances) and “princess” should be avoided due to their low complexity and ease of guessability. Even less conspicuous terms, like “f-ckyou,” stood out with over 50,000 instances of use and a similarly rapid cracking time.

To bolster security, NordPass advises users to create passwords that are at least 20 characters long and include a mix of upper and lowercase letters, numbers, and symbols. Avoiding conventional words or easily guessable personal information can significantly enhance one’s password strength.

The case for passkeys

Increasingly, passkeys are becoming a more user-friendly alternative for account security. Unlike traditional passwords, which are prone to being forgotten or vulnerable to breaches, passkeys utilize biometric verification methods like fingerprints or face scans, along with a mobile PIN. Google emphasizes that this method keeps track of lengthy passwords effectively while enhancing security.

As data breaches continue to make headlines, the importance of adopting robust security measures cannot be overstated. Users are encouraged to transition to more secure password practices and consider passkeys as a viable solution to help safeguard their personal information.

NordPass’s findings on the most commonly used passwords illustrate critical weaknesses in user security habits. With many passwords taking mere seconds to crack, it is essential for individuals to rethink their approach to password creation and management. By adopting longer, more complex passwords and considering the use of passkeys, users can better protect their sensitive data from potential threats.

Featured image credit: Matthias Heyde/Unsplash

For years, the promise of truly intelligent, conversational AI has felt out of reach. We’ve marveled at the abilities of ChatGPT, Gemini, and other large language models (LLMs) – composing poems, writing code, translating languages – but these feats have always relied on the vast processing power of cloud GPUs. Now, a quiet revolution is brewing, aiming to bring these incredible capabilities directly to the device in your pocket: an LLM on your smartphone.

This shift isn’t just about convenience; it’s about privacy, efficiency, and unlocking a new world of personalized AI experiences. 

However, shrinking these massive LLMs to fit onto a device with limited memory and battery life presents a unique set of challenges. To understand this complex landscape, I spoke with Aleksei Naumov, Lead AI Research Engineer at Terra Quantum, a leading figure in the field of LLM compression. 

Indeed, Naumov recently published a paper on this subject which is being heralded as an extraordinary and significant innovation in neural network compression – ‘TQCompressor: Improving Tensor Decomposition Methods in Neural Networks via Permutations’ – at the IEEE International Conference on Multimedia Information Processing and Retrieval (IEEE MIPR 2024), a conference where researchers, scientists, and industry professionals come together to present and discuss the latest advancements in multimedia technology.

“The main challenge is, of course, the limited main memory (DRAM) available on smartphones,” Naumov said. “Most models cannot fit into the memory of a smartphone, making it impossible to run them.”

He points to Meta’s Llama 3.2-8B model as a prime example. 

“It requires approximately 15 GB of memory,” Naumov said. “However, the iPhone 16 only has 8 GB of DRAM, and the Google Pixel 9 Pro offers 16 GB. Furthermore, to operate these models efficiently, one actually needs even more memory – around 24 GB, which is offered by devices like the NVIDIA RTX 4090 GPU, starting at $1800.”

This memory constraint isn’t just about storage; it directly impacts a phone’s battery life.

“The more memory a model requires, the faster it drains the battery,” Naumov said. “An 8-billion parameter LLM consumes about 0.8 joules per token. A fully charged iPhone, with approximately 50 kJ of energy, could only sustain this model for about two hours at a rate of 10 tokens per second, with every 64 tokens consuming around 0.2% of the battery.”

So, how do we overcome these hurdles? Naumov highlights the importance of model compression techniques.

“To address this, we need to reduce model sizes,” Naumov said. “There are two primary approaches: reducing the number of parameters or decreasing the memory each parameter requires.”

He outlines strategies like distillation, pruning, and matrix decomposition to reduce the number of parameters and quantization to decrease each parameter’s memory footprint.

“By storing model parameters in INT8 instead of FP16, we can reduce memory consumption by about 50%,” Naumov said.

While Google’s Pixel devices, with their TensorFlow-optimized TPUs, seem like an ideal platform for running LLMs, Naumov cautions that they don’t solve the fundamental problem of memory limitations.

“While the Tensor Processing Units (TPUs) used in Google Pixel devices do offer improved performance when running AI models, which can lead to faster processing speeds or lower battery consumption, they do not resolve the fundamental issue of the sheer memory requirements of modern LLMs, which typically exceed smartphone memory capacities,” Naumov said.

The drive to bring LLMs to smartphones goes beyond mere technical ambition. It’s about reimagining our relationship with AI and addressing the limitations of cloud-based solutions.

“Leading models like ChatGPT-4 have over a trillion parameters,” Naumov said. “If we imagine a future where people depend heavily on LLMs for tasks like conversational interfaces or recommendation systems, it could mean about 5% of users’ daily time is spent interacting with these models. In this scenario, running GPT-4 would require deploying roughly 100 million H100 GPUs. The computational scale alone, not accounting for communication and data transmission overheads, would be equivalent to operating around 160 companies the size of Meta. This level of energy consumption and associated carbon emissions would pose significant environmental challenges.”

The vision is clear: a future where AI is seamlessly integrated into our everyday lives, providing personalized assistance without compromising privacy or draining our phone batteries.

“I foresee that many LLM applications currently relying on cloud computing will transition to local processing on users’ devices,” Naumov said. “This shift will be driven by further model downsizing and improvements in smartphone computational resources and efficiency.”

He paints a picture of a future where the capabilities of LLMs could become as commonplace and intuitive as auto-correct is today. This transition could unlock many exciting possibilities. Thanks to local LLMs, imagine enhanced privacy where your sensitive data never leaves your device.

Picture ubiquitous AI with LLM capabilities integrated into virtually every app, from messaging and email to productivity tools. Think of the convenience of offline functionality, allowing you to access AI assistance even without an internet connection. Envision personalized experiences where LLMs learn your preferences and habits to provide truly tailored support.

For developers eager to explore this frontier, Naumov offers some practical advice.

“First, I recommend selecting a model that best fits the intended application,” Naumov said. “Hugging Face is an excellent resource for this. Look for recent models with 1-3 billion parameters, as these are the only ones currently feasible for smartphones. Additionally, try to find quantized versions of these models on Hugging Face. The AI community typically publishes quantized versions of popular models there.”

He also suggests exploring tools like llama.cpp and bitsandbytes for model quantization and inference.

The journey to bring LLMs to smartphones is still in its early stages, but the potential is undeniable. As researchers like Aleksei Naumov continue to push the boundaries of what’s possible, we’re on the cusp of a new era in mobile AI, one where our smartphones become truly intelligent companions, capable of understanding and responding to our needs in ways we’ve only begun to imagine.

• Not all LLMs are equal: GPT-3.5-turbo-instruct stands out as the most capable chess-playing model tested.
• Fine-tuning is crucial: Instruction tuning and targeted dataset exposure dramatically enhance performance in specific domains.
• Chess as a benchmark: The experiment highlights chess as a valuable benchmark for evaluating LLM capabilities and refining AI systems.

Can AI language models play chess? That question sparked a recent investigation into how well large language models (LLMs) handle chess tasks, revealing unexpected insights about their strengths, weaknesses, and training methodologies.

While some models floundered against even the simplest chess engines, others—like OpenAI’s GPT-3.5-turbo-instruct—showed surprising potential, pointing to intriguing implications for AI development.

Testing LLMs against chess engines

Researchers tested various LLMs by asking them to play chess as grandmasters, providing game states in algebraic notation. Initial excitement centered on whether LLMs, trained on vast text corpora, could leverage embedded chess knowledge to predict moves effectively.

However, results showed that not all LLMs are created equal.

The study began with smaller models like llama-3.2-3b, which has 3 billion parameters. After 50 games against Stockfish’s lowest difficulty setting, the model lost every match, failing to protect its pieces or maintain a favorable board position.

Testing escalated to larger models, such as llama-3.1-70b and its instruction-tuned variant, but they also struggled, showing only slight improvements. Other models, including Qwen-2.5-72b and command-r-v01, continued the trend, revealing a general inability to grasp even basic chess strategies.

GPT-3.5-turbo-instruct was the unexpected winner

The turning point came with GPT-3.5-turbo-instruct, which excelled against Stockfish—even when the engine’s difficulty level was increased. Unlike chat-oriented counterparts like gpt-3.5-turbo and gpt-4o, the instruct-tuned model consistently produced winning moves.

Why do some models excel while others fail?

Key findings from the research offered valuable insights:

• Instruction tuning matters: Models like GPT-3.5-turbo-instruct benefited from human feedback fine-tuning, which improved their ability to process structured tasks like chess.
• Dataset exposure: There’s speculation that instruct models may have been exposed to a richer dataset of chess games, granting them superior strategic reasoning.
• Tokenization challenges: Small nuances, like incorrect spaces in prompts, disrupted performance, highlighting the sensitivity of LLMs to input formatting.
• Competing data influences: Training LLMs on diverse datasets may dilute their ability to excel at specialized tasks, such as chess, unless counterbalanced with targeted fine-tuning.

As AI continues to improve, these lessons will inform strategies for improving model performance across disciplines. Whether it’s chess, natural language understanding, or other intricate tasks, understanding how to train and tune AI is essential for unlocking its full potential.

Featured image credit: Piotr Makowski/Unsplash

What if we tell you there is a human-like AI chatbot designed to frustrate scammers and protect potential victims? Dubbed as the “AI Granny,” the impressive tool of O2 mimics the voice and personality of a sweet older woman who loves to chat—about her cat Fluffy, her passion for knitting, and whatever else can keep a scammer talking without realizing they’ve been outsmarted.

How the AI granny fight scammers?

dAIsy is more than just a quirky persona; it’s built on cutting-edge AI models that work together to hold real-time conversations. The process starts by transcribing the scammer’s voice into text. A custom language model, complete with a “granny-like” personality, generates appropriate responses. These are then converted into a natural-sounding voice via text-to-speech technology.

The result?

A completely autonomous scambaiter that can hold lifelike conversations, wasting scammers’ time while keeping real people safe.

dAIsy‘s mission is simple but impactful: Engage scammers for as long as possible to prevent them from targeting others. With scammers often running full-time call centers aimed at defrauding individuals, particularly the elderly, dAIsy flips the script.

It’s been reported to keep scammers tied up for as long as 40 minutes with meandering, lifelike conversations.

But this isn’t just about wasting time. By exposing the tactics scammers use—whether asking for bank details or impersonating trusted organizations—dAIsy serves as a wake-up call for the public.

The AI-powered scambaiting system used by O2’s “dAIsy” is a blend of advanced generative AI technologies.

1. Large language models (LLMs): These models form the foundation of dAIsy’s conversational abilities, equipping her with a deep understanding of scam scenarios and the ability to carry out lengthy, engaging conversations with scammers.
2. Voice synthesis: dAIsy’s voice was modeled after a real-life individual to sound authentic, helping her appear indistinguishable from a genuine elderly person.
3. Custom diffusion models:  Diffusion models were used to create a photorealistic visual representation for campaign purposes, while an image-to-video model helped animate her, making her interactions feel even more lifelike.

This system was designed to waste scammers’ time by keeping them engaged, thereby protecting real victims.

A serious problem, a creative solution

The urgency of such measures is evident in the numbers. According to the FBI, individuals over 60 in the U.S. lost nearly $3.4 billion to telephone scams in 2023, a figure expected to rise with the advent of advanced generative AI and voice impersonation.

O2’s research highlights the UK’s own fraud crisis:

• 69% of Brits report being targeted by scammers.
• 22% face fraud attempts weekly.

With fraud becoming more sophisticated, the stakes are higher than ever.

AI granny needs your help!

While dAIsy takes on scammers head-on, O2 encourages the public to help in the fight by:

1. STOP: Pause and think when faced with an unexpected call.
2. SEND: Forward suspicious calls or texts to 7726 (it spells “SPAM” on your keypad).
3. SPEAK OUT: Share your experiences to help others stay vigilant.

So next time you get a call from a scammer, rest assured—dAIsy might already be keeping their hands full!

Featured image credit: O2/YouTube

The Porto-based startup won in the final against security and AI startup GovGPT and sports tech startup Scoutz, who both hail from the United States. A total of 105 startups competed in group rounds that were held across the three days of the event.

Intuitivo is an all-in-one digital assessment platform, already improving the productivity of more than 35,000 teachers and the education of more than 300,000 students.

“Intuitivo helps teachers become more productive by helping them lose less time with repetitive tasks. We create and automatically grade all types of assessments.” – said co-founder and CEO at Intuitivo.

Telling Web Summit about their PITCH experience, Guimaraes said, “We’re always nervous pitching but it’s a great experience. The fact we even got to the final means we are gaining a lot of awareness. For us to appear in the news is amazing. We’ve met a lot of other co-founders from around the world and some of the amazing judges. Just to be with other people from the ecosystem is the best part of Web Summit. We’re making something that is impactful and that is actually making a change in the world,”

According to Intuitivo, there are 85 million teachers in the world who spend 46 percent of their time teaching and spend 1.5 months of the year creating and grading assessments. Intuitivo saves 40 percent time on grading. The startup is already backed by Techstars CORE Angels Lisbon and Demium to support their growth, accelerating our growth in the $5B assessment software market.

Web Summit sold out at 71,528 attendees from 153 countries with 3,050 companies exhibiting on a sold-out floor with 1,066 investors, 953 speakers, and 2,005 media. Over 44 percent of startups are women-founded, the highest to date. Women also represent 42 percent of attendees and 37 percent of speakers. Partners exhibiting include IBM, Adobe, Meta, Huawei, SAP, DELL, Qualcomm, VISA, American Express, Niantic, Pitchbook, EDP, and KPMG. Thousands of new community meetups are underway, enabling attendees to find new communities in an event that is bigger than ever. 62 trade delegations, representing 36 countries, with the biggest representation from Germany and Brazil.

Images: Web Summit 2024

Artificial intelligence chipmaker Nvidia is poised to announce its third-quarter fiscal 2025 results on November 20. According to analysts, Nvidia is set to showcase impressive growth, with earnings expected at $0.75 per share and revenue predicted to reach $33.09 billion, reflecting an 88% and 82.6% year-over-year increase, respectively. The company has seen its stock price surge over 180% in the past year, affirming its status as a leading performer in the AI sector.

Nvidia’s anticipated earnings report and implications

On November 20, Nvidia (NVDA) will report its financial results for Q3 FY25, which concluded in October 2024. The expected strong performance is attributed to robust demands for its current generation of GPUs and a solid consensus among analysts, including a significant boost in earnings estimates. Initially, Nvidia guided for revenue of approximately $32.5 billion, but analysts now project a higher figure. Since the company released its guidance, analysts have raised profit forecasts, and the consensus earnings estimate is now $0.74 per diluted share.

Over the past week, analysts have maintained their positive outlook, suggesting growing confidence in Nvidia’s capabilities, especially in the AI and data center markets. Nvidia’s hardware and software positions it well to leverage opportunities in the rapidly evolving AI landscape. Analysts have collectively raised their price targets, suggesting an average of around $156, which indicates a potential 10% upside from its current share price of $142.

Tesla’s stock gest a 7% boost as Trump turns up the heat on self-driving

Core reasons behind Nvidia’s expected growth

Nvidia’s anticipated growth is underpinned by three key factors. First, the company is likely to provide a promising update regarding its Blackwell GPUs. With over 80% market share in AI accelerators, Nvidia’s GPUs are integral to modern AI functionalities. CEO Jensen Huang previously stated that the Blackwell launch could be the most successful in the company’s history. Nvidia’s production ramp begins this quarter, with a backlog of orders extending for a year, signaling strong demand.

Second, Wall Street analysts have revised their earnings estimates upwards. The anticipated revenue increase highlights sustained demand for Nvidia’s existing Hopper GPUs, which indicates solid momentum leading into Q4.

Finally, significant capital investments in AI by tech giants like Alphabet, Amazon, Meta Platforms, and Microsoft are creating a favorable environment for Nvidia. These companies have expressed intentions to substantially increase spending on capital expenditures through 2025, which should bolster demand for Nvidia’s products.

Another point of view: Nvidia stock could drop and you’re hearing it here first

Investor sentiment and market outlook

Investor sentiment remains largely bullish. Analysts have pointed to Nvidia as an attractive investment option, reflecting confidence in the company’s growth trajectory, particularly in AI and data center segments. Despite a stellar stock rally, analysts continue to highlight Nvidia’s strong upside potential, as evidenced by the consensus of 39 Buy ratings and three Hold recommendations over the past three months.

However, challenges remain on the horizon. Some bearish analysts caution against potential excess inventory, rising competition from in-house solutions, and regulatory scrutiny, including an investigation by the U.S. Department of Justice. Furthermore, the Blackwell supply chain has raised concerns about short-term margin pressures and production difficulties.

Options traders are anticipating a notable move in Nvidia’s stock post-earnings, projecting an expected movement of 9.83% in either direction. This illustrates the market’s expectations for volatility following the release of the financial results.

Disclaimer: The information provided in this article is for informational purposes only and should not be considered financial or investment advice. Please consult with a qualified financial advisor before making any investment decisions.

Featured image credit: m./Unsplash

The beauty industry keeps talking about a brand-new product from Dyson called the Supersonic Hair Dryer. At the moment, it is available only to professional stylists. What is so peculiar about it? This hair dryer automatically adjusts the temperature and speed of the airflow depending on the nozzle used.

Because of its shape, the Supersonic Hair Dryer is nicknamed a pipe, not a hair dryer. However, this bizarre feature helps stylists avoid developing tunnel syndrome. In this article, we have collected impressive facts about the revolution in Dyson appliances and its brilliant market strategy.

Secrets behind the name

The founder of Dyson Enterprise, James Dyson, is a British inventor and entrepreneur who graduated from the Royal College of Art, where he studied industrial design. James created a fast cargo ship at his first job that could dock at an unequipped seashore.

The desire to make functional things that perform their job impeccably led the inventor to another key decision. In 1979, James noticed that the vacuum cleaner almost stopped working because the bag was clogged with dust. Equipped with this knowledge, Dyson replaced the dust bag with a cardboard funnel. Thus, it happened to be the first vacuum cleaner without a dust bag.

Thorny path to the stars

To make his dream come true, James Dyson got into debt, mortgaged his house, and negotiations to purchase his idea from large-scale corporations ended in refusals.

Only in 1986, the Japanese company Apex Inc. launched the production of the G-Force vacuum cleaner, developed by James Dyson.

However, not all projects of the famous inventor have become commercially successful bargains. In 2019, Dyson abandoned the production of electric cars, since the project was financially unattractive.

Many successful enterprises on the market would add a proper name to their title. For instance, movers Boston, a top moving company around Boston and its area, has added the name of the biggest city to underline its remarkable reputation and the area of service. In the case of Dyson, the name of the inventor is directly associated with chic care products and an exclusive approach.

Dyson hair dryer: Experiments

According to one version, the hair dryer appeared thanks to Dyson’s wife. The inventor liked to talk to his wife Deirdre while she was getting ready and drying her hair. However, the noise of the device interfered with normal communication. So James decided that it would be nice to make the hair dryer silent.

It is interesting to know that testing was carried out on cats. In this way, Dyson wanted to demonstrate a minimum noise level that does not frighten even pets. That is why at the presentation in 2016, the first user of the Dyson hair dryer was a cat.

Dyson Supersonic hairdryer: Greatest kickers

Of course, a Supersonic hair dryer has undeniable advantages. First and foremost, It’s lightweight and has a high airflow rate as well as a wide range of modes, and a variety of replaceable attachments. Second. the product looks stunning in professional photos which is essential for the beauty business. Third, the hair dryer has built-in temperature control sensors. So, the air doesn’t heat up above critical levels and the hair isn’t damaged during drying or styling.

Featured image credit: Element5 Digital/Pexels

Apple’s upcoming iPhone SE may redefine the mid-range smartphone segment, potentially featuring advanced specs like an in-house 5G modem and support for Apple Intelligence. Tentatively set for a mid-March 2025 release, it could significantly impact market dynamics as competition intensifies.

The next-generation iPhone SE is expected to be a game-changer for Apple as it seeks to penetrate the mid-range smartphone market. 9to5Mac suggest that Apple will enhance the SE with features appealing to a broader audience, including a robust in-house 5G modem and expanded memory capabilities. These components are positioned to support Apple Intelligence, which aims to bring generative AI features like Writing Tools and Enhanced Photo Cleanup to more users.

Apple CEO Tim Cook has already increased memory in the iPhone 16 and iPhone 16 Plus to support Apple Intelligence’s demands. Following this trend, the iPhone SE is likely to receive similar upgrades, ensuring it does not lag behind in performance specifications. As the device is geared towards budget-conscious consumers, its enhanced features aim to compete directly with rivals such as Google’s Pixel 8a and Samsung’s offerings.

Launch timeline and manufacturing insights

Industry insiders report that Apple is gearing up for a release in mid-March 2025. Lee Seong-jin from Ajunews notes that LG Innotek, a key supplier, is set to begin full-scale production of the iPhone SE’s camera components in December 2024. Historically, LG Innotek has initiated production roughly three months before an iPhone hits the market, supporting this timeline. The anticipated launch will coincide with the rollout of iOS 18.3 and the finalization of Apple’s generative AI suite, Apple Intelligence.

The existing iPhone SE’s price starts at $429 for the 64GB variant and $479 for the 128GB model. In comparison, the Pixel 8a is priced at $499 for 128GB, making a compelling case for the new iPhone SE’s entry into a competitive segment that attracts customers seeking value without sacrificing quality.

Key upgrades to look forward to

The iPhone SE 4 is expected to introduce several significant upgrades, including a new design and camera improvements. It may abandon the iPhone 8 design, which has been utilized since the iPhone SE 2, in favor of a more modern look inspired by the iPhone 14. This shift includes a 6.1-inch OLED display, a flat-edge design, and Face ID. Despite the redesign, the new model is likely to retain a single camera setup, differentiating it from the dual-camera system seen in its flagship counterpart.

Additionally, industry sources indicate that the upcoming iPhone SE could feature Apple’s A18 chip, along with 8GB of RAM, enabling it to handle the new generative features effectively. Significant upgrades in camera technology are also anticipated, with a 48MP rear sensor, paralleling the current iPhone 15, and a 12MP front camera, a notable enhancement from the previous 7MP setup.

Apple’s first in-house 5G modem, codenamed Centauri, is projected to debut with the iPhone SE 4. This new modem is integral to Apple’s strategy to reduce dependency on Qualcomm for modem components—an effort that began when Apple acquired Intel’s modem business in 2019. By integrating its technology, Apple aims to balance costs and enhance performance. The strategic shift not only targets price competitiveness but also positions Apple to better control the user experience by offering robust connectivity features across all devices.

The iPhone SE is rumored to be priced around $499, slightly above its predecessor’s launch prices, yet still competitive when stacked against similar models like the Pixel 8a. Should Apple discontinue older models such as the iPhone 14 after the SE release, a simplified lineup could ensue, with the SE becoming the entry point for customers looking for modern features without a premium price tag.

The anticipated introduction of USB-C compatibility for the iPhone SE could further streamline Apple’s product ecosystem, as it aligns with EU regulations mandating a unified charging standard for electronic devices.

The launch of the iPhone SE 4 is not just about introducing a new device; it represents Apple’s push to capture a more substantial market share in an area dominated by competitor Samsung, which currently holds an 82% share in AI-capable smartphones. As Apple works to bridge the generative AI gap with this release, the iPhone SE could potentially become a bestseller, allowing Apple to challenge industry rivals and reclaim ground in the fiercely contested mid-range market.

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Apple’s Vision Pro headset is set to make its debut in South Korea and the United Arab Emirates on November 15. This expansion follows its initial launch in the U.S. in February and its subsequent introduction in several countries including Australia, Canada, France, Germany, the U.K., China, Hong Kong, Japan, and Singapore. With these two new locations, the Vision Pro will be available in a total of 12 countries, marking a significant step in Apple’s vision of spatial computing.

The details surrounding the launch

Apple’s marketing chief Greg Joswiak announced the upcoming availability on social media, highlighting the excitement for customers in these regions to experience spatial computing first-hand. Pre-orders for the Vision Pro will open on November 4 at 5 a.m. local time in both countries, allowing eager customers to secure their devices before the official launch.

Apple Vision Pro is coming to South Korea and the United Arab Emirates on November 15! We’re excited that customers in a dozen countries and regions will get to immerse themselves in the world of spatial computing! pic.twitter.com/gqrlLfT8XG

— Greg Joswiak (@gregjoz) October 31, 2024

Freshly localized Vision Pro pages are now live on Apple’s websites for both South Korea and the UAE, providing tailored information for potential customers. The headset, priced at $3,500 in the U.S., remains a niche product rather than a mainstream device. This distinction is reinforced by CEO Tim Cook’s acknowledgment of its current status as an “early-adopter product,” emphasizing its appeal primarily to tech enthusiasts ready to engage with advanced technology.

Rumors had circulated for some time before the Vision Pro’s commercial release, highlighting both anticipation and skepticism among consumers. The product represents an advancement in augmented and virtual reality capabilities, but industry analysts expect sales to reflect its exclusivity. Estimates indicate that Apple may sell fewer than 500,000 units of the Vision Pro this year, primarily due to its steep price point.

Assessing consumer interest and market potential

Despite the Vision Pro’s high price tag, analysts are optimistic about Apple’s inventory capabilities. Current estimates suggest that Apple could produce up to 600,000 headsets by year-end, indicating a strong production outlook amid high demand. According to reports, Apple appears prepared to meet anticipated consumer interest at launch.

The Vision Pro promises to deliver unique experiences tying into the growing field of spatial computing, enabling users to engage with digital content in new and immersive ways. Though it may not yet cater to the masses, it’s designed to showcase tomorrow’s technology for those ready to invest in cutting-edge experiences. Apple intends for the Vision Pro to innovate the way users interact with their surroundings, possibly laying the groundwork for future products in this expanding domain.

Apple is broadening the reach of its Vision Pro headset, allowing more global customers to explore its innovative features. By targeting select markets like South Korea and the UAE, Apple is embracing an early-adopter model that capitalizes on technological advancements in spatial computing.

Featured image credit: Apple

TikTok will allow advertisers to pull in content from Getty Images using its AI-powered ad creation tool, Symphony Creative Studio. This integration, announced on November 14, 2024, enables brands to create engaging ads featuring both licensed imagery and AI-generated elements, including lifelike avatars.

TikTok users can now leverage Getty Image libraries

With Symphony Creative Studio now available for all advertisers, TikTok users can leverage Getty’s vast library of premium images and videos to craft ads that resonate with audiences. This feature enables the generation of videos based on product descriptions and incorporates AI avatars that can communicate in various languages, enhancing the customization and effectiveness of advertising campaigns.

Peter Orlowsky, Getty Images’ Senior Vice President of Global Strategic Partnerships, emphasized the significance of this collaboration, stating, “This collaboration offers seamless integration into TikTok’s Symphony Creative Studio, giving brands and businesses direct access to our vast library of millions of premium images and videos…” He highlighted how this move meets the escalating demand for high-quality content that effectively engages audiences through authentic storytelling.

Canada forces TikTok out of the country

The integration is part of TikTok’s broader strategy to enhance its creative tools, aiming to simplify the creative production process. By bridging the gap between ideation and execution, TikTok seeks to facilitate a more efficient advertising experience for marketers, ultimately leading to better performance and results. Andy Yang, Head of Monetization Creative Product at TikTok, noted the intent behind this partnership: “At TikTok, we aim to empower advertisers and help them connect with their communities with the power of generative AI.”

As the demand for authentic and captivating storytelling in advertising grows, this collaboration provides advertisers with the necessary resources to develop compelling content. With AI-generated visuals and access to Getty’s licensed materials, TikTok advertisers can experiment with creativity like never before. They can create multiple ad versions or even remix existing content, all while ensuring they’re using commercially safe images and videos.

The integration reflects a significant step for Getty Images, which has increasingly made its premium creative content available across various platforms within the AI industry. Since launching its own AI image generator last year, the company has partnered with other tech giants, enhancing access to its licensed content safe for commercial use. This expansion within the AI landscape allows marketers to scale their efforts with the assurance of using high-quality imagery.

For brands eager to enhance their advertising game on TikTok, the door is now open to a world of creative possibilities, all thanks to the groundbreaking integration with Getty Images. With easy access to rich visual content and the capability for personalized AI elements, marketers can tell their stories in a more engaging and appealing way.

Featured image credit: Getty Images

Chinese autonomous driving startup Pony.ai is gearing up for its initial public offering (IPO) in the United States, having recently trimmed its fundraising target, TechCrunch reports. The company is set to issue 15 million American Depository Shares (ADS) at an expected price range of $11 to $13 per share. If it reaches the upper end of this price range, the IPO could value Pony.ai at approximately $4.48 billion, based on its 344.9 million outstanding shares post-offering.

Operating a fleet of 190 robotrucks and over 250 robotaxis across major cities like Beijing, Guangzhou, Shenzhen, and Shanghai, Pony.ai is fully autonomous in three of these locations. The firm has transitioned from reliance on venture funding to generating revenue through its self-driving technology. It currently provides robotaxi services and is developing partnerships with major automakers, including Toyota, GAC, and SAIC, fortifying its market position in the autonomous vehicle sector.

IPO details and market positioning

Pony.ai initially aimed to raise $425 million but has revised this figure downward due to various market factors, with a new minimum target now set at $165 million. In its filing, the company revealed it has received indication of interest from new investors for about $75 million worth of ADSs, accounting for 42% of the deal. Additionally, Pony.ai is pursuing a concurrent private placement to raise an extra $153 million.

Once valued at $8.5 billion during its Series D funding round in March 2022, Pony.ai’s current focus lies in scaling up its operations amid a recovering market for Chinese companies seeking to go public in the U.S. This resurgence comes after a multi-year hiatus imposed by Beijing on offshore capital raising, reflecting a renewed interest from U.S. investors toward Chinese technology firms, despite ongoing geopolitical tensions.

OpenAI just made macOS smarter with ChatGPT app support

The market for autonomous vehicles is seeing significant activity, with Pony.ai closely following the likes of its peers. Chinese electric vehicle (EV) startup Zeekr successfully debuted on the New York Stock Exchange in May 2024, grossing $441 million, while WeRide, another self-driving firm, raised approximately $440.5 million from its IPO and private placement just last month.

As it positions itself for the future, Pony.ai anticipates that robotaxi services—including autonomous vehicle software deployment, maintenance, engineering, and road testing—will account for a larger portion of its revenue. This shift is essential for the company, which reported $84 million in revenue for the fiscal year ending June 30, 2024.

With plans to list under the ticker symbol “PONY” on Nasdaq, Pony.ai’s IPO is expected to price during the week of November 18, 2024. Goldman Sachs (Asia), BofA Securities, Deutsche Bank, Huatai Securities, and Tiger Brokers have been appointed as joint bookrunners for the IPO.

Featured image credit: Pony.ai

Google is ramping up efforts to protect users against online scams by launching an advisory to educate and inform about key fraud trends. This initiative comes amid rising concerns over sophisticated scams that leverage technology, including AI, deepfakes, and cryptocurrency schemes.

The rising tide of online scams: Google’s new advisory efforts

The Tech Giant’s Trust & Safety team has identified five major scams that users should be aware of. First up is the alarming trend of public figure impersonation campaigns. Scammers are employing deepfake technology to create realistic content that mimics well-known individuals, tricking people into participating in fraudulent schemes. These deceptive campaigns often promote fake giveaways or investment opportunities that promise extraordinary returns. Google has tightened its Misrepresentation Policy for Google Ads to combat this, suspending accounts that breach these guidelines.

Another prevalent issue is crypto investment scams, which promise high returns that are often too good to be true. These schemes typically involve the impersonation of trusted brands or celebrities, making them difficult for law enforcement to tackle, as they frequently originate from organized crime syndicates operating across borders. Google maintains stringent policies against scams in crypto investment advertising to protect users from financial harm.

Additionally, cloned apps and phishing sites are emerging as a serious threat. Scammers duplicate legitimate applications and websites to gather personal information or distribute malware. They often replicate tech support landing pages, making it easy for unsuspecting users to divulge sensitive data. This type of fraud poses significant risks, especially as corporate employees are targeted with deceptive login pages that could lead to internal breaches.

Another tactic—the use of landing page cloaking—allows scammers to present different content to Google than what users see. Here, fraudulent sites mimic reputable brands and create a false sense of urgency, urging users to make hasty and often risky purchases. Google is proactive in identifying and prohibiting landing page cloaking, implementing policies against advertising for systems designed to circumvent safety protocols.

Lazarus Group targets macOS with RustyAttr trojan malware

Last but not least, scammers are increasingly exploiting major events to perpetrate fraud. By utilizing AI, they can rapidly adapt their tactics to align with ongoing news stories or significant happenings. For example, during events like elections or natural disasters, fraudsters may promote fake products or charities. This tactic not only undermines public trust but can also take advantage of those seeking to help in times of need.

These findings are shared by Google to heighten awareness and encourage vigilance among users navigating a digitally evolving landscape fraught with malicious intent. Users are advised to remain skeptical of any promises of quick wealth, particularly in online investments, and to verify the legitimacy of any website before engaging further. Implementing safety tips such as checking URLs, scrutinizing offers, and reporting scams can further enhance digital security.

Featured image credit: Google

Readers increasingly prefer the verses created by algorithms like ChatGPT over those penned by celebrated poets like Shakespeare or Plath. A recent study reveals that participants are not only unable to distinguish between AI-produced poems and human-created ones, but they often favor the AI variants.

Examining the study’s design and results

Researchers Brian Porter and Edouard Machery from the University of Pittsburgh conducted two key experiments involving over 1,600 participants. In the first, they presented readers with a selection of ten poems, half from renowned poets such as T.S. Eliot and Emily Dickinson, and half generated by ChatGPT-3.5, which aimed to mimic these iconic styles. Astonishingly, many readers were more inclined to believe that the AI poems were human creations. The irony? The classic poets’ works were judged less likely to be from human hands.

Say cheese, write a Haiku with the Poetry Camera

The follow-up experiment involved 696 new participants who rated poems based on criteria like beauty and emotional impact. This time, the readers were divided into groups: one was informed that the poems were human-written, another was told they were AI-generated, and the last group received no information. The findings indicated a significant bias: when readers knew a poem stemmed from AI, they rated it lower. Conversely, when the author’s identity was a mystery, AI-generated poems frequently garnered higher ratings than those from human authors.

Brian Porter noted an interesting trend in readers’ preferences. “The results suggest that the average reader prefers poems that are easier to understand,” he explained. Participants often interpreted the convoluted nature of famous poets’ lines as signs of AI-generated work, missing the artistic intent behind those complexities. In contrast, the more straightforward AI poems appeared accessible, leading readers to misinterpret their clarity as an indicator of human artistry.

Expert evaluations reveal contrasting judgments

Further research conducted by a team at Spain’s UNED university, alongside Argentine writer Patricio Pron, produced intriguing insights when experts weighed in on AI-generated stories. Here, human authors triumphed in a contest judged by critics, contrasting sharply with the earlier findings of casual readers. “The difference between critics and casual readers is immense,” remarked Julio Gonzalo from UNED. He emphasized that while AI-generated content can impress non-experts, knowledgeable critics discern subtleties that AI may fail to articulate.

Guillermo Marco, another researcher from UNED, added, “AI is easy to confuse non-experts.” His collaborators experienced firsthand how a well-crafted AI piece could appear more appealing to an untrained audience than a riskier, deeply resonant human creation. However, finding classic poems that could stymie expert recognition poses a significant challenge, a hurdle that Porter’s team plans to tackle in future studies.

Another phenomenon observed during the studies is a general skepticism surrounding AI-generated content. When participants learned a poem was created by AI, they often rated it less favorably. Porter speculated on this cultural resistance, suggesting that acceptance of AI in creative fields is a long way off: “I’m not sure people will ever fully accept AI-generated poetry — or even AI-generated art in general.”

The nuances of this research touch on broader themes in sociology and aesthetics, as the study by Gonzalo and Marco highlights how cultural norms shape our appreciation of art. Even a modestly-sized AI language model was found to meet most criteria for common readers, proving that machines can generate compelling content without exceeding the capacities of contemporary technology.

Marco bluntly asserted that while AI can be a powerful creative tool, it will always mirror human inputs, much like autotune devices in music. “Art is about communicating human experience,” he stated. Looking forward, the researchers are also entertaining the need for regulatory measures that ensure transparency in AI-generated content. “If readers value AI-generated texts less, and there is no warning that AI-generated text is being used, there’s a risk of misleading them,” noted Porter.

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Haliey Welch, affectionately known as the “Hawk Tuah girl,” has ventured into the tech world with the launch of her AI-powered dating advice app, Pookie Tools. Named after the nickname she has for her boyfriend, this app aims to provide Gen Z singles with a solution to the common frustrations of modern dating.

Welch, a 22-year-old sensation who garnered approximately 5 million followers on social media, has previously hosted a successful podcast titled “Talk Tuah.” With her latest endeavor, she plans to merge her online fame with technology by offering unique dating advice tools. Pookie Tools became available on the App Store this week and includes a variety of features tailored specifically for young daters looking for an edge in their romantic lives.

Features designed for modern daters

Pookie Tools encompasses several features aimed at enhancing the dating experience. The app boasts an AI chatbot designed to assist users with conversation starters and general dating advice. Furthermore, it provides outfit recommendations suitable for different date scenarios, offers tips to enhance dating profiles, and even includes a zodiac compatibility feature.

This app arrives at a time when online dating often faces criticism for issues like ghosting and the prevalence of scams. Many users have started turning to AI for dating advice, utilizing tools such as ChatGPT to generate entire conversations. Industry giants like Bumble and Tinder have also recognized this trend and are integrating AI features into their platforms.

Welch shared insights about her project during an interview with TechCrunch, stating, “I [was on] Bill Maher’s podcast, and it was actually one of his ideas he gave me. He kind of pushed me, in a way, saying I should be a relationship coach. And so we came up with the idea. … This app is the easiest way for them to find their forever Pookie.”

To develop Pookie Tools, Welch formed a partnership with Ben Ganz, the founder of Ultimate AI Studio, known for its AI customer support automation solutions. While the concept of an AI-driven relationship coach sounds promising, early testing of the chatbot has revealed responses that may come off as generic, lacking Welch’s signature humor and personality.

The app aims to offer a fun and innovative approach to navigating the realm of dating and relationships. Key features include suggestions for creative date ideas based on the user’s location and previous interactions, as well as outfit recommendations for various dating occasions. The app also analyzes dating profiles to provide feedback on engaging prompts or profile pictures that showcase personality. One interesting tool, the “Flirt Meter,” assesses users’ text messages and scores them on a scale of 0 to 100 for flirtation levels.

Pookie Tools takes a bold approach

Amidst its array of user-friendly features, Pookie Tools includes what some may find controversial: the “Bald Predictor” and “Height Detector.” The Bald Predictor analyzes user-uploaded photos to detect patterns of hair loss, while the Height Detector estimates height based on proportions and surroundings. These tools address frustrations from users about misrepresentations often encountered on dating profiles.

While Welch asserts that these playful tools are meant to be lighthearted—mentioning her own stature as a 5’8” woman who appreciates the height detector—concerns about their potential for insensitivity remain.

Early tests revealed that the Height Detector displayed mixed accuracy results but proved fairly close; one example was just 2 inches off from the user’s actual height. The Bald Predictor, however, struggled to make accurate assessments, indicating that while technology can assist, it still has limitations.

Welch and Ganz envision adding further features in collaboration with podcast guests and other influencers, yet specifics on what these features will entail have not been disclosed. Given that notable figures like Holly Madison and Whitney Cummings have appeared on “Talk Tuah,” their involvement may help expand the app’s user base.

Pookie Tools operates on a subscription model, pricing itself at $7 per week or $50 annually, with a three-day free trial available for potential users looking to test the waters before diving in.

Haliey Welch’s transition from viral content creator to tech entrepreneur highlights a shifting landscape where influencers are exploring new avenues in response to audience engagement and trends in the dating world. With Pookie Tools, she aims to blend humor and technology, offering a fresh approach to modern dating dilemmas.

Featured image credit: Tim & Dee TV/Unsplash

Meta is facing a hefty $840 million fine from European regulators for allegedly abusing its market dominance with Facebook Marketplace, a move that aims to ensure fair competition in the tech industry.

The investigation dates back to 2021

The European Union imposed the €800 million fine after concluding that Meta distorted competition by bundling its Marketplace service with Facebook’s social network. This connection allegedly provided Meta with an unfair advantage, exposing Facebook’s vast user base to Marketplace regardless of their interest. Margrethe Vestager, the EU’s Competition Chief, emphasized that this bundling tactic is illegal under EU antitrust rules, stating, “This is illegal under E.U. antitrust rules. Meta must now stop this behavior.”

The investigation, which dates back to 2021, found that Meta not only abused its dominant position but also imposed unfair trading conditions on rival shopping services. By leveraging data generated from competing services that advertise on Facebook or Instagram, Meta strengthened its Marketplace platform while putting competitors at a disadvantage. With this fine, the European Commission sends a clear message to tech giants about the importance of adhering to competition laws.

What is embodied AI and why Meta bets on it?

Meta has announced its intent to appeal this decision, arguing that the ruling fails to demonstrate any significant competitive harm to rivals or consumers. The company insists that Facebook users have the option to engage with Marketplace and that many choose not to do so. Additionally, Meta argues that online marketplace competition remains robust, citing growth in platforms like eBay and Vinted. The company also pledged to comply with the EU’s ruling and to stop any exploitative practices.

This action against Meta marks a pivotal moment in the EU’s efforts to rein in the power of big tech companies. The penalty is especially significant as it is one of the last notable moves made by Vestager before she steps down, following her decade-long tenure pushing for stricter regulation against tech industry monopolies. The EU has been consistently scrutinizing major tech players like Google and Apple for similar reasons and has previously imposed multi-billion-euro fines on them as well.

The EU continues to be active in regulating Meta beyond this fine, with ongoing investigations into child safety on platforms like Facebook and Instagram, as well as the company’s adherence to election integrity measures as outlined in the bloc’s digital rulebook. In recent times, Meta has faced several penalties related to violations of EU privacy laws, including a record 1.2 billion euro fine just last year.

As this situation evolves, the broader implications of such antitrust penalties resonate well beyond the EU, signaling to companies worldwide the importance of fair competition practices. The EU is setting a precedent that may inspire other regions to enhance their own regulatory frameworks to keep tech giants in check.

With the legal battle set to unfold over the coming months, it remains to be seen how this fine will impact Meta’s operations and the wider tech landscape. As regulations tighten, companies may need to rethink their strategies to comply with evolving competition laws or risk facing severe financial penalties.

Featured image credit: Carl Gruner/Unsplash

Fitbit could soon tailor personalized sleep advice based on users’ logs and disruptions, potentially transforming how it assists users in improving their sleep quality.

New personalized sleep journal feature on the horizon

Recent updates to the Fitbit app indicate that users will soon gain access to a sleep journal designed to provide personalized sleep insights. As part of version 4.30.fitbit-mobile-110146981-694155636, this update focuses on allowing users to log daily factors that impact their sleep, further enhancing Fitbit’s existing features such as sleep tracking, bedtime reminders, and Sleep Score.

According to an APK teardown by Android Authority, the sleep journal will enable users to note and log aspects of their daily experiences that may interfere with their sleep quality. This capability will enhance the Fitbit app’s existing sleep-tracking features, as it encourages regular entries through friendly reminders like, “To get deeper insights and more personalized suggestions for better sleep, complete your journal each day.” Users can opt to type their entries or utilize voice-to-text functionalities, ensuring a seamless experience.

The feature promotes user engagement by nudging them to reflect on their daily habits and their effects on sleep. If a user skips the entry, they receive a gentle reminder, emphasizing the importance of sharing experiences to develop more personalized insights. When a user logs their sleep-related experiences, Fitbit will generate a customized sleep summary infused with AI-powered features, providing tailored tips and highlights based on that input.

The development hints at a nurturing, supportive approach toward user engagement, fostering a habit of logging relevant experiences nightly. The feature is suggested to be part of an exclusive section called “Sleep Labs,” where users may have the opportunity to delve deeper into various analyses regarding sleep patterns and disruptions. The app’s code also mentions that the “Sleep Lab” will allow the feature to leverage user data for enhanced tracking and predictions regarding sleep cycles, potentially opening up a wealth of knowledge about the relationships between sleep and daily behavioral patterns.

Fitbit’s AI boost with Google’s Health LLM

However, there’s a catch: this advanced feature seems to be a premium offering at first, positioned as an exclusive for Fitbit Premium subscribers. While the possibility remains that similar features may eventually become accessible to all users, early adopters of Fitbit Premium will have the first access to these innovative sleep management tools.

Beyond the sleep journal, Fitbit has introduced other improvements this year, including the addition of Fitbit Labs, which serves as a testing ground for experimental capabilities. One notable integration under this new umbrella is the Insights Explorer—this tool utilizes Google’s Gemini AI to analyze user data comprehensively, offering in-depth assessments of how activities affect sleep and other health metrics.

Further expanding its health tracking capabilities, Fitbit has also rolled out a blood glucose tracking feature designed for users managing diabetes. This functionality connects with compatible systems like OneTouch Reveal, allowing users to monitor glucose levels alongside other health statistics. Just like the sleep journal, the blood glucose tracking feature is relatively premium-centric, offering deeper insights for those who subscribe.

Overall, Fitbit is melding technology and personal health insights in a way that respects users’ individual experiences while providing tools aimed at fostering healthier habits. With these advancements, Fitbit is not just a fitness tracker but an integral companion on the journey to better sleep and overall well-being, aiming to make each night, and day, more reflective of personal needs.

Featured image credit: Kamil Switzalski/Unsplash

The Lazarus Group targets macOS with a new trojan malware named RustyAttr, revealing an advanced method of hiding malicious code via extended attributes in files. Uncovered by the cybersecurity company Group-IB, RustyAttr represents a worrisome evolution in the tactics employed by this notorious North Korean state-backed hacking group.

What is RustyAttr trojan malware?

Researchers have linked RustyAttr’s deployment to the Lazarus Group since May 2024. This malware cleverly conceals its harmful scripts within extended attributes (EAs) of macOS files, which are hidden data containers that store additional information such as permissions and metadata. As these extended attributes are typically invisible in common user interfaces like Finder or Terminal, attackers can exploit them unobtrusively without arousing suspicion. The command-line utility `.xattr` provides attackers access to these hidden elements, allowing them to execute malicious scripts seamlessly.

In a somewhat nostalgic nod to techniques used in prior malware, like the 2020 Bundlore adware, RustyAttr uses a similar approach by embedding its payload in the extended attributes. This underscores the ongoing evolution of malware tactics, adapting to maintain effectiveness against evolving cybersecurity measures.

The attack scenario crafted by Lazarus shows a cleverly designed application, built with the Tauri framework, that masquerades as a benign PDF file. This application, often containing job opportunity or cryptocurrency-related content—hallmarks of Lazarus’s previous campaigns—serves as bait. Upon execution, it either fetches and displays a decoy PDF about game project funding or mistakenly claims that the application does not support the version in use. This tactic cleverly distracts users while executing hidden shell scripts that trigger the malicious components.

Interestingly, the mechanism underlying RustyAttr involves a JavaScript file named “preload.js” that interacts with these extended attributes. This script uses functionalities from the Tauri framework to retrieve and execute the hidden malware. According to Group-IB researchers, “If the attribute exists, no user interface will be shown, whereas if the attribute is absent, a fake webpage will be displayed.” This behavior makes detection by antivirus solutions particularly challenging, as the malicious components rest undetected within the file’s metadata.

The applications associated with RustyAttr were initially signed with a now-revoked certificate, which allowed for a brief period of evading Gatekeeper protections on macOS. Although there have been no confirmed victims identified thus far, the researchers suspect that the Lazarus Group could be testing this stealthy approach for broader future attacks. Importantly, this tactic is new and has yet to be documented in the prominent MITRE ATT&CK framework, raising concerns about the adaptability and increasing sophistication of the threat actors involved.

To stay protected from this emerging threat, cybersecurity experts advise users to be vigilant about file sources and to treat unsolicited PDF files—regardless of how legitimate they may seem—with skepticism. Enabling macOS’s Gatekeeper feature is essential, as it prevents the execution of untrusted applications. Regular updates and adopting advanced threat detection strategies are further recommended to stay ahead of such sophisticated attacks.

Why is RustyAttr malware very risky?

The implications of RustyAttr becoming a prevalent threat extend beyond just the exploit itself; they highlight a worrying trend in how malware continues to evolve in complexity and stealth. In recent years, North Korean hackers have significantly ramped up their activities, often targeting remote positions in organizations across the globe with promises of lucrative opportunities. While the ultimate goal of RustyAttr remains unclear at this stage, the potential for serious damage is undeniably present. As this group continues to refine its techniques, the cybersecurity community must remain vigilant, continuously adapting defenses in response to such advanced persistent threats.

By employing tactics involving minimal user interaction and leveraging commonly accepted file types, attackers like the Lazarus Group can remain under the radar for longer periods, potentially compromising sensitive data or systems. Staying informed and aware of these developments is critical for individuals and organizations to prevent falling victim to future attacks stemming from this or similar tactics.

Featured image credit: Florian Olivo/Unsplash

Anthropic has introduced a prompt improver feature that uses chain-of-thought reasoning to enhance prompt quality and improve output accuracy significantly. This new tool aims to assist developers in refining their existing prompts, ensuring better results when utilizing their AI model, Claude.

Introducing the prompt improver for enhanced prompts

In the latest update to Anthropic Console, developers can now utilize a prompt improver designed to automatically enhance their prompts using advanced techniques. Claude, Anthropic’s AI model, analyzes existing prompts and applies systematic reasoning, effectively breaking down problems before generating responses. According to Anthropic, this approach helps in identifying and correcting issues within prompts and also guarantees a more coherent and reliable output.

Video: Anthropic

The introduction of this feature comes at a time when prompt engineering has become crucial for AI applications. Developers frequently grapple with crafting effective prompts, often incorporating best practices from different models. The prompt improver aims to streamline this process by allowing for:

• Chain-of-thought reasoning: A segment in which Claude systematically contemplates the problem before responding.
• Example standardization: Conversion of examples into a consistent XML format for improved clarity and processing.
• Example enrichment: Enhancement of existing examples through reasoning that aligns with the newly structured prompt.
• Rewriting: Clarification of the prompt structure while correcting grammatical or spelling errors.
• Prefill addition: Prefilling assistant messages to guide Claude’s outputs effectively.

Testing has indicated promising results, with Anthropic reporting a 30% increase in accuracy for a multilabel classification task, alongside a perfect word count adherence for summarizing tasks. Specifically, Claude achieved a 100% success rate in maintaining specified word constraints while summarizing ten articles selected from Wikipedia.

The prompt improver also facilitates the management of multiple example inputs and outputs. Developers can now add new examples directly into the system or edit existing ones for better response quality. If a developer struggles to create suitable examples, Claude can generate synthetic examples to ease the process. This function enhances:

• Accuracy: Reducing potential misinterpretations of instructions.
• Consistency: Ensuring that the desired output format is achieved.
• Performance: Boosting Claude’s capability to tackle more complex tasks.

Evaluating prompt effectiveness with the prompt evaluator

Another useful feature accompanying the prompt improver is a prompt evaluator that allows developers to assess the effectiveness of their prompts under various scenarios. This evaluator introduces an optional “ideal output” column within the evaluations tab, equipping users to benchmark and improve prompt performance systematically.

Once a new prompt is tested, developers can provide feedback to Claude, indicating areas for further refinement. This iterative feedback loop allows for an enhanced user experience and could present a tailored output aligning with user specifications. For instance, if a developer wishes to switch from XML to JSON output formats, Claude can adapt the prompts and examples accordingly.

Kapa.ai, a tech firm specializing in transforming technical knowledge into AI solutions, has already experienced the benefits of this feature. Finn Bauer, Co-Founder of Kapa.ai, noted, “Anthropic’s prompt improver streamlined our migration to Claude 3.5 Sonnet and enabled us to get to production faster.” This endorsement reflects the efficiency and practical application of the new tools in real-world scenarios.

As Anthropic continues to innovate, the rollout of Claude 3.5 Opus is anticipated. This upcoming version promises further integration of reasoning capabilities which may enhance the overall functionalities of its flagship Claude model.

Users eager to manipulate, evaluate, and streamline prompts can access these features in the Anthropic Console. An informative set of resources is available within the documentation to guide developers through the ins and outs of improving prompts with Claude, presenting an exciting opportunity for enhancing AI interactions across various applications.

Featured image credit: Anthropic

Google has officially launched its Gemini app for iPhone users as of November 14. This dedicated application, now available through the App Store, enables a host of innovative features including Gemini Live, image generation powered by the Imagen 3 model, and tailored study guides. Users can engage with the app for more than just everyday queries; its capabilities span academic assistance, artistic creation, and seamless integration with other Google services.

What does Gemini offer for iPhone users?

The Gemini app allows iPhone users to download and experience various features designed to simplify their digital interactions. Among its noteworthy functionalities is Gemini’s image generation, which utilizes the Imagen 3 model to produce “high-quality” and “stunning” images. This feature empowers users to create “photorealistic” representations of animals, objects, and plants that can be saved and shared for various projects.

In addition to its creative capabilities, the app seeks to enhance academic performance. Students can utilize Gemini to pose questions on specific subject areas, receive advice, or generate a personalized study plan. The app even accommodates the upload of complex diagrams, allowing users to quiz themselves for a deeper understanding of their study material.

Video: Google

Gemini Live is another highlight, offering users a more interactive experience with the AI. This feature enables free-flowing conversations that mimic natural dialogue, giving users ample time to formulate questions, brainstorm, or gather information on a wide range of topics. As per Google’s description, the app also provides insights for students preparing for interviews or looking for suggestions on activities in unfamiliar locations. With 10 distinct voices and support for 12 languages at launch, Gemini Live makes learning and engagement more accessible than ever before.

The app is designed with a user-friendly interface that reflects a spartan aesthetic, reminiscent of the Google Search app. Upon opening, users are greeted with a simple “Hello” message, with a streamlined text field inviting them to “Ask Gemini.” Accessing past conversations is easy, with the “Chats & Gems” section readily available in the upper left corner.

Video: Google

IntegVideo: Googleration with other Google services

An impressive aspect of the Gemini app lies in its integration with various Google applications. By connecting Gemini to Gmail, Maps, YouTube, and other Google services, users can perform tasks such as summarizing daily emails or discovering songs and playlists without the need to switch between multiple apps.

The rollout of Gemini on iPhone comes on the heels of its earlier availability on Android, where it was introduced as part of the Google app before being distinguished as a standalone tool. Prior to its iPhone launch, the Gemini Live feature had already been made accessible to Android users for free, signaling Google’s intention to ensure that all users can leverage these advanced AI capabilities.

As Google further develops Gemini, it is expected that support for additional languages will follow, extending the app’s versatility and user base. Meanwhile, iPhone users can enjoy the benefits of Gemini’s existing features while looking forward to future enhancements.

Ultimately, the arrival of Gemini on the App Store presents a significant step for Google, making AI-powered tools more widely available to iPhone users. The combination of creative, academic, and practical functionalities brings a new level of convenience to smartphone use, aligning with the growing trend of integrating AI into everyday life.

Featured image credit: Google

OpenAI has launched a major update for its ChatGPT app on macOS, introducing integration capabilities with various third-party applications, significantly enhancing the productivity of Mac users. Users can now interact seamlessly with developer tools like Xcode, VS Code, Terminal, and iTerm2, simplifying the process of coding and debugging.

Streamlining coding with integrated ChatGPT

The newfound ability allows ChatGPT to read content directly from multiple applications without the need for manual copying and pasting. This integration facilitates a more intuitive workflow for developers. In a demonstration, ChatGPT effectively understood code from an Xcode project and offered accurate suggestions, thereby acting as an interactive coding assistant.

Developers can now send a selection of code from apps like Xcode directly to ChatGPT, streamlining tasks such as adding missing elements to a project. For instance, when an OpenAI employee prompted ChatGPT to “add the missing planets” to a simple solar system model in Xcode, it efficiently generated code to represent Earth within the existing framework.

Importantly, the new integration leverages macOS’s Accessibility API, allowing ChatGPT to read text but not interpret images or videos. This limitation ensures the tool remains focused on text-based queries and code reading, optimizing its assistance without overstepping privacy boundaries.

Access and privacy considerations

Currently, the integration with third-party apps is available to ChatGPT Plus and Team subscribers, with plans for expanding access to Enterprise and Educational users in the coming weeks. While OpenAI aims to make the feature widely accessible, the timeline remains uncertain. Users maintain control over which applications ChatGPT can access, ensuring privacy is respected with the option to revoke permissions at any time.

OpenAI’s continual expansion of functionalities mirrors Apple’s own developments. With the upcoming macOS 15.2—currently in beta—Siri will gain similar ChatGPT capabilities, like answering questions based on on-screen content. However, this integration is limited to interactions without specific app compatibility for now.

In addition to enhancing ChatGPT’s capabilities for macOS users, OpenAI is also releasing its ChatGPT Windows app for free users. This move signifies broader accessibility and a growing commitment to integrating AI across platforms, making advanced AI tools more available to users in various work environments.

This latest update not only marks a significant step in the evolution of ChatGPT as a coding aid but also highlights the ongoing collaboration between OpenAI and larger software ecosystems to empower users. Whether you’re diving into programming or managing a multi-app workflow, ChatGPT’s new features are crafted to save developers valuable time and create a more cohesive working environment.

Featured image credit: Ramshid/Unsplash

A grad student in Michigan found himself unnerved when Google’s AI chatbot, Gemini, delivered a shocking response during a casual chat about aging adults. The chatbot’s communication took a dark turn, insisting the student was “not special,” “not important,” and urged him to “please die.”

Google Gemini: “Human … Please die.”

The 29-year-old, seeking assistance with his homework while accompanied by his sister, Sumedha Reddy, described their shared experience as “thoroughly freaked out.” Reddy expressed feelings of panic, recalling, “I wanted to throw all of my devices out the window. I hadn’t felt panic like that in a long time to be honest.” The unsettling message seemed tailored for the student, prompting concerns about the implications of such AI behavior.

Despite Google’s assurances that Gemini contains safety filters to block disrespectful, dangerous, and harmful dialogue, it appears something went wrong this time. Google addressed the matter, stating that “large language models can sometimes respond with non-sensical responses, and this is an example of that.” They emphasized that the message breached their policies and noted corrective actions to avoid similar outputs in the future.

However, Reddy and her brother contend that referring to the response as non-sensical minimizes its potential impact. Reddy pointed out the troubling possibility that such harmful remarks could have dire implications for individuals in distress: “If someone who was alone and in a bad mental place, potentially considering self-harm, had read something like that, it could really put them over the edge.”

This incident isn’t an isolated one. Google’s chatbots have previously drawn criticism for inappropriate responses. In July, reports highlighted instances where Google AI provided potentially lethal advice regarding health queries, including a bizarre suggestion to consume “at least one small rock per day” for nutritional benefits. In response, Google stated they limited the inclusion of satirical and humorous sources in their health responses, resulting in the removal of viral misleading information.

OpenAI’s ChatGPT has similarly been criticized for its tendency to produce errors, known as “hallucinations.” Experts highlight the potential dangers involved, ranging from the dissemination of misinformation to harmful suggestions for users. These growing concerns underscore the need for rigorous oversight in AI development.

With incidents like this highlighting vulnerabilities, it’s more essential than ever for developers to ensure that their chatbots engage users in a manner that supports, rather than undermines, mental well-being.

Featured image credit: Google

The Arc Search browser is now generally available for Android users, allowing access to its features beyond its initial iOS and macOS exclusivity. After an October beta release, The Browser Company has formally launched the stable version, promising to cater to millions of Android users.

Arc Search: Key features and enhancements for Android

This new version of Arc Search introduces several desirable features such as voice search, home screen widgets, and optimized performance for Android 12 and above. Users can select from various app icons, including a unique ‘Neon’ design exclusive to Android, allowing for a more personalized experience. The browser claims to provide a faster search capability compared to traditional browsing methods, which has started to attract attention in the competitive market.

During its beta period, Arc Search saw more than 100,000 downloads shortly after its launch on the Play Store, showcasing the growing interest in alternatives to mainstream browsers. The Android version, built on the same Chromium framework as Google Chrome, has eliminated many limitations seen in the beta version. Now users can enjoy enhanced functionalities, though some features like the “Call Arc” feature from the iOS version are still absent. This particular tool allows users to ask questions in a conversational tone by simply raising their device to their ear, a fun and accessible way to get answers on the go.

While the browser has managed to bring significant updates, The Browser Company is also mindful of the challenges it faces. Arc has differentiated itself with a unique interface that some users find difficult to learn and navigate. CEO Josh Miller acknowledged this in conversations with The Verge, admitting that despite a four-fold increase in user growth, Arc still struggles to capture a mainstream audience. This feedback highlights the delicate balance between catering to power users while trying to make the product more approachable.

AI-backed Arc Max browser has cool features

In anticipation of future growth, the company plans to shift focus towards developing new products that could resonate with a broader audience. The details regarding these upcoming products remain under wraps, but it carries an ambitious vision of enabling users to “glide around the internet” seamlessly, aided by AI enhancements. Concepts for a powerful command bar designed to guide users to the right information through intelligent suggestions have already been showcased in previous releases of the Arc browser.

While the Android version makes notable strides, it’s important to note that not everything has made it from iOS. For example, even with the inclusion of widgets and improved support for landscape mode, some users will miss the ‘Call Arc’ feature which remains exclusive to the iPhone version. Users see the potential but will continue to request additional functionalities that enhance user experience across all platforms.

Featured image credit: Arc

Google has announced it will cease political advertising in the European Union by October 2025, as it struggles to comply with the upcoming Regulation on Transparency and Targeting of Political Advertising (TTPA). With this decision, Google aims to simplify its operations amid complex regulatory requirements.

Understanding the TTPA and Google’s response

The TTPA, which passed in March 2024, mandates clear identification of political ads, including details about sponsorship, the election involved, and targeting techniques used. These regulations particularly emphasize making political ads more recognizable for voters, aiming to eliminate the shadows in which such advertisements often operate. However, Google has raised concerns that the TTPA’s broad definition of political advertising encompasses a vast array of topics that may be challenging to track and manage effectively.

Annette Kroeber-Riel, Google’s vice president for government affairs and public policy in Europe, emphasized the “significant new operational challenges and legal uncertainties” introduced by the TTPA. She pointed out that determining whether ads pertain to political issues might prove daunting, especially given the lack of reliable local election data across the 27 EU member states. This insufficiency could hinder the accuracy needed to identify relevant advertisements consistently.

Compounding these challenges, Kroeber-Riel indicated that critical technical guidance surrounding the TTPA might not be finalized until shortly before it comes into force. As a result, Google believes it cannot practically fulfill the TTPA requirements and has decided to withdraw from the political advertising landscape in the EU.

EU fines Apple for violating Digital Markets Act

Prior to this decision, Google had already enforced enhanced transparency requirements for political advertisers since 2019, which included identity verification and specific disclosures on who financed each advertisement. Such initiatives aimed to foster greater transparency in political ad placement compared to traditional media like television and radio.

Furthermore, the company has faced similar operational hurdles in other parts of the world, including Canada and Brazil, prompting it to withdraw political ad services in these jurisdictions as well. Reflecting on these precedents, Google aims to minimize its risks in the event it fails to meet the TTPA’s stipulations, fearing potential penalties.

Implications for users and advertisers

The cessation of political advertising on Google’s platforms represents a substantial change for voters and political campaigns seeking to share their messages. Political ads are historically viewed as valuable tools for candidates and voters alike, allowing for the dissemination of information essential for informed decisions during elections. Despite this, Google expressed regret over its decision to withdraw, acknowledging the important role these advertisements play in facilitating communication between candidates and their constituents.

Moreover, the regulation mandates that ads drawn from artificial intelligence be flagged, raising new questions about the future of automated advertising in the political sphere. Google’s determination to halt political advertising signifies a cautious approach as the landscape evolves under the new regulatory frameworks.

The TTPA’s aim to uphold the integrity of political processes may well be mirrored in other jurisdictions worldwide. As companies like Meta face similar challenges, the trend toward heightened scrutiny over political advertisements is likely to continue.

While Google’s exit from political ads in the EU may serve to ease its operational challenges, it simultaneously opens the door for smaller campaigns to seek alternative ways to reach audiences. The withdrawal suggests a pressing need for clarity in regulations that many platforms may still struggle to navigate.

Featured image credit: Christian Lue/Unsplash

NASA is revolutionizing the accessibility of Earth science data with its latest initiative, Earth Copilot, developed in partnership with Microsoft. This AI chatbot is designed to simplify how users can inquire about complex scientific information regarding our planet, transforming questions into easily digestible answers.

Transforming data interaction with AI

Every day, NASA gathers a staggering amount of geospatial data through its satellites, which monitor everything from climate change to natural disasters. Currently, the agency’s database contains over 100 petabytes of information, creating a monumental challenge for non-specialists looking for specific insights. NASA’s Earth Copilot aims to democratize this data by enabling users to bypass technical hurdles and directly ask questions about environmental impacts, historical events, or emerging trends.

As Tyler Bryson, Microsoft’s corporate vice president of health and public sector industries, noted, “For many, finding and extracting insights requires navigating technical interfaces, understanding data formats, and mastering the intricacies of geospatial analysis.” The Earth Copilot uses Azure OpenAI Service to facilitate this process, allowing users to obtain answers in seconds rather than diving into the intricacies of data environments.

Currently, the Earth Copilot is being tested by NASA scientists and researchers before wider release. Their feedback will be crucial for refining its integration into NASA’s Visualization, Exploration, and Data Analysis (VEDA) platform, which previously required specialized knowledge to navigate effectively.

The challenge of accessing complex geospatial data

Navigating the complexities of Earth science data can be likened to deciphering an ancient scroll—it’s possible but often requires a level of expertise that limits who can engage with it. The existing barriers not only slow down research efforts but can also hinder timely responses during emergencies, such as natural disasters. Policymakers who need immediate insights on environmental changes find this particular challenge all too real.

To tackle this issue head-on, NASA is turning to AI capabilities powered by Microsoft. The Earth Copilot employs natural language processing, meaning users can simply type or voice their questions in plain language. For instance, someone might ask, “How did the COVID-19 pandemic affect air quality in the US?” With the AI’s help, users will be able to quickly retrieve and analyze pertinent data, streamlining research and decision-making processes.

NASA’s commitment to making this data more accessible aligns perfectly with its Open Science initiative, which emphasizes transparency and inclusivity in scientific research. This push towards democratizing access serves not just researchers but also educators, students, and the general public interested in Earth science.

Featured image credit: NASA/Unsplash

In today’s digital-driven world, NGOs must adapt to new technologies to stay relevant, engage supporters, and amplify their causes. Digital media presents incredible opportunities for NGOs to broaden their reach and connect with global audiences but also brings challenges that demand strategic planning. As competition for attention grows across social media, websites, and online forums, NGOs need effective strategies to stand out and clearly communicate their mission.

A strong digital presence not only boosts an NGO’s visibility but also attracts donors, volunteers, and partners passionate about its cause. Digital tools help NGOs streamline operations, enhance advocacy, and drive sustainable impact. However, there is no one-size-fits-all approach; each NGO must align its digital strategy with its unique mission, audience, and resources. Adopting a sustainable, adaptable digital approach is key to long-term success in the modern age.

Understanding your audience: The foundation of effective outreach

A successful NGO marketing strategy starts with understanding the target audience. Knowing who supporters are, what issues resonate with them, and how they consume information significantly affects outreach success. NGOs must analyze supporters’ characteristics, interests, and behaviors to create meaningful connections.

Social media analytics, website metrics, and supporter feedback provide invaluable insights. This data enables NGOs to design campaigns that resonate with their audience. Tailoring content to match audience preferences builds trust, increases engagement, and inspires action. Additionally, understanding audience demographics allows NGOs to make messaging culturally sensitive and inclusive, expanding support and creating a connected community.

“An NGO’s mission is only as impactful as its ability to connect with those who care about it.”

Leveraging social media for maximum impact

Social media platforms are essential for NGOs aiming to raise awareness and engage supporters. With billions of users worldwide, social media offers NGOs the chance to reach diverse audiences, increase visibility, and mobilize support efficiently. Selecting the right platforms is crucial. For example, some NGOs might benefit from the visual nature of Instagram, while others may find LinkedIn’s networking focus more beneficial. A content calendar aligned with key events and campaigns ensures consistency and maintains engagement.

Key social media tactics for NGOs:

1. Choose the right platforms: Focus on channels where the target audience is active.
2. Develop a posting schedule: Regular updates build familiarity and trust.
3. Use storytelling: Showcase real-world impact and human stories to inspire action.

Authenticity is key in social media interactions. Engaging with followers by responding to comments, answering questions, and thanking supporters builds community and trust. A mix of informative posts, success stories, calls to action, and interactive elements like polls significantly boosts an NGO’s reach and effectiveness, laying a strong foundation for broader digital engagement.

Building trust through transparency and authenticity

Trust is fundamental for any successful NGO, as supporters need confidence in the organization’s mission and impact. Transparency involves not only sharing financial reports but also openly communicating challenges, setbacks, and achievements. In today’s digital landscape, where misinformation can spread quickly, being a reliable, transparent source is invaluable.

Supporters want to understand the impact of their contributions. NGOs can build these relationships by sharing updates on how donations make a difference, using stories, photos, and videos. For example, an environmental NGO could provide project updates showing how donations support reforestation efforts. Annual reports, shared goals, and ongoing project updates reassure supporters that the NGO values accountability and integrity.

Additionally, fostering a dialogue by inviting supporters to share feedback and participate in decision-making reinforces transparency. This approach not only enhances trust but also cultivates a supportive, engaged base that feels connected to the NGO’s mission on a deeper level.

Developing a strong content strategy

Creating high-quality content is one of the most effective ways for NGOs to keep supporters informed and motivated. A solid content strategy ensures that each piece—whether a blog post, video, or infographic—serves a purpose aligned with the NGO’s mission. Content can educate, inspire action, or foster a sense of belonging to the cause, ensuring ongoing engagement and awareness.

Content types for NGO engagement

• Blog posts: Detailed articles that educate readers on relevant topics.
• Videos: Short, impactful visuals showing fieldwork or sharing stories.
• Infographics: Visuals that simplify complex data for wider appeal.
• Newsletters: Regular updates to keep the audience informed.

A diverse content strategy allows NGOs to connect with various audience segments, catering to both those who seek in-depth information and those who prefer quick, visual summaries. Quality content also gives supporters valuable resources to share, further extending the NGO’s reach.

The road ahead: Embracing innovation and adaptability

The digital landscape is constantly evolving, and NGOs must embrace innovation and flexibility to stay effective. Staying updated on digital trends, investing in digital literacy for teams, and promoting a culture of adaptability are crucial steps forward. Emerging technologies, such as artificial intelligence (AI) and machine learning (ML), provide new opportunities for NGOs to enhance their strategies.

For instance, AI can help NGOs personalize engagement, while ML can analyze data patterns to improve campaign targeting. Integrating digital innovation allows NGOs to navigate complex challenges and maximize their impact.

Additionally, partnerships are essential for NGOs seeking to expand their influence and resources. Collaborating with like-minded organizations, influencers, or companies brings fresh perspectives and strengthens resources, making it easier to tackle complex issues. Some NGOs create specialized task forces focused on digital innovation, ensuring new strategies are explored and implemented consistently.

Charting the future for NGOs in the digital age

In today’s fast-paced, tech-driven world, NGOs face both challenges and opportunities in expanding their reach and impact. By understanding their audience, leveraging social media, maintaining transparency, and implementing a strong content strategy, NGOs can build supportive communities around their missions. Embracing new technologies and fostering collaboration will ensure that NGOs remain adaptable, resilient, and impactful in an ever-changing digital landscape.

Featured image credit: Dylan Gillis/Unsplash

In today’s highly competitive e-commerce landscape, optimizing your product feed can make a huge difference in online visibility, conversion rates, and overall sales. A product feed is a structured data file containing critical information about each product, including titles, descriptions, prices, images, and availability. This data file is shared across channels like Google Shopping, Facebook, Instagram, and marketplaces, helping your products reach the right audience at the right time.

Why optimize your product feed?

A well-optimized product feed is the foundation of a successful e-commerce marketing strategy. It enhances ad targeting, reduces wasted ad spend, and drives better returns on investment (ROI). Below are the top tips for optimizing your product feed and improving e-commerce performance.

Ensure data accuracy and consistency

Accuracy is the cornerstone of an effective product feed. Data inconsistencies, such as incorrect pricing, out-of-stock items, or broken links, can lead to customer frustration and wasted ad spending. Maintaining accuracy and consistency builds customer trust and boosts ad performance.

Actionable tip:

• Automate feed updates with a tool like Feedink to synchronize real-time data, ensuring accuracy in stock levels, pricing, and availability.

2. optimize product titles for search and discovery

Product titles are among the most important elements in your feed, as they drive visibility across search and social platforms. Titles should be keyword-rich and concise, including relevant attributes like brand, color, size, and model to enhance discoverability without unnecessary detail.

Actionable tips:

• Use a title structure like Brand + Product Type + Key Feature + Color/Size (e.g., “Nike Air Max Running Shoes – Black, Size 10”).
• Research search terms in your niche and naturally incorporate them into product titles to increase visibility.

3. Use high-quality, optimized images

Images are essential to product feed optimization, as they impact click-through rates (CTR) and conversion likelihood. Use high-quality images that meet the size and aspect ratio requirements of different platforms for maximum clarity and appeal.

Actionable tips:

• Use 1000×1000 pixel images for sharpness and zoom functionality on most platforms.
• Test different image types (e.g., lifestyle vs. product-only) to identify which resonates better with your audience.

Cropink is a product feed software that you can use for crafting great ad visuals.

4. Craft compelling product descriptions

A strong product description can make a sale by highlighting unique selling points (USPs) and addressing potential questions. Keep descriptions clear, concise, and engaging to maximize user interest.

Actionable tips:

• Focus on key benefits and features in concise descriptions.
• Use bullet points for readability and to convey essential details quickly.

5. Utilize dynamic pricing and promotions

Adding promotions and discounts to your product feed is a great way to attract customers. Showcasing both original and discounted prices creates urgency and highlights the value of your products.

Actionable tips:

• Use tools to update pricing and promotions in real-time, ensuring feed accuracy during sales.
• Add promotional badges like “Free Shipping” or “20% Off” in the product title or description for added appeal.

6. Segment products with custom labels

Custom labels allow for product segmentation based on criteria such as popularity, seasonal relevance, or clearance status. This helps you allocate budget and tailor ads effectively based on product profitability and strategic importance.

Actionable tips:

• Use labels like “Best Seller,” “High Margin,” or “Holiday Sale” to prioritize in your campaigns.
• Tailor bidding strategies for these categories to optimize your ad budget.

7. Optimize for mobile users

With mobile shopping on the rise, it’s vital that your product feed caters to mobile users. This involves ensuring responsive images, mobile-friendly titles, and concise descriptions for a seamless mobile shopping experience.

Actionable tips:

• Preview your product feed on mobile devices to adjust titles and descriptions as necessary.
• Ensure all links direct to mobile-optimized landing pages for a better user experience.

8. Regularly monitor and troubleshoot feed errors

Feed errors can block ads from showing or lead to rejections, affecting campaign performance. Regular monitoring and troubleshooting are essential for maintaining a healthy, optimized feed.

Actionable tips:

• Use feed management tools with error diagnostics to fix issues like missing images or invalid prices.
• Set up alerts for critical errors to address issues quickly and minimize campaign impact.
• 9. Enhance Listings with Structured Data

Structured data in your product feed, like ratings, reviews, and availability, can enhance your listings in search results. This added detail makes products more attractive and encourages clicks.

Actionable tips:

• Ensure your product feed includes rich snippets for features like star ratings and product reviews.
• Add optional fields like “Gender,” “Age Group,” and “Material” to increase relevance and searchability.

10. Continuously analyze and adjust feed performance

Feed optimization is an ongoing process. Regularly analyze performance, noting which products drive the most clicks and conversions, and make adjustments to prioritize high-performing items.

Actionable Tip:

Use analytics to track individual product performance and adjust titles, images, and descriptions accordingly.

Conduct A/B tests on different feed elements (titles, images, etc.) to fine-tune optimization efforts.

Why optimize product feed

Optimizing your product feed is crucial for success in e-commerce. From ensuring data accuracy and crafting optimized titles to leveraging dynamic pricing and ongoing adjustments, a well-optimized feed enhances ad targeting, reduces ad spend, and drives conversions. By implementing these top tips, you’ll stay competitive and drive impactful results for your e-commerce business.

Chip maker AMD is set to lay off about 1,000 employees, constituting roughly 4% of its workforce, as part of its strategy to align resources with key growth areas, particularly in the competitive AI chip market. This announcement comes after a significant decline in its gaming division, which has witnessed a staggering 69% drop. In 2023, AMD reported having around 26,000 employees, and this latest move could potentially save the company $200 million.

AMD is set to lay off about 1,000 employees

An AMD spokesperson noted, “As a part of aligning our resources with our largest growth opportunities, we are taking a number of targeted steps that will unfortunately result in reducing our global workforce.” The company is positioning itself to better compete with rivals, particularly Nvidia, which remains the dominant player in the AI chip market with an impressive market share.

Despite the layoffs, AMD has been rolling out new products, including the Ryzen 7 9800X3D and the Ryzen 9 9900X, although projections indicate they will ship only about 220,000 chips this year. In comparison, Nvidia’s GPUs hold a whopping 88% market share, leaving AMD with just 12%. Nevertheless, AMD has made headway against Intel, increasing its desktop CPU shipments by 10 percentage points within a year.

Market dynamics: AMD’s challenges and opportunities

AMD’s struggles are further underscored by its performance in the gaming segment, which is anticipated to decline by 59% to $2.57 billion in revenue by the end of 2024. The company is not alone, though, as Intel, Dell, and Samsung have recently announced similar workforce reductions in an effort to strengthen their positions in the evolving AI landscape. With the AI market projected to grow significantly, AMD recognizes that streamlining its workforce is crucial to capitalize on emerging opportunities.

AMD’s recent layoffs come despite the company forecasting $5 billion in AI chip sales for 2024, a figure that represents about 20% of its total projected sales of $25.7 billion. While AMD hopes to tap into a total market for AI chips estimated at $500 billion by 2028, it must grapple with the reality that Nvidia is forecasted to amass $125.9 billion in revenue over the same period. This stark contrast illustrates the uphill battle AMD faces in capturing market share from its more established competitor.

The looming layoffs and AMD’s strategic shift reveal broader challenges facing the semiconductor industry, where companies are increasingly compelled to refine their focus amid fluctuating market dynamics. The recent news sheds light on the ongoing pressures faced by manufacturers as they strive to navigate a rapidly evolving technological landscape.

For now, AMD’s journey toward regaining competitive footing continues, as it seeks to adapt to market needs while providing support to those affected by the layoffs. AMD’s commitment to treating impacted employees with respect and assisting them through the transition speaks to a larger corporate responsibility that is often overlooked in the pursuit of profits.

Featured image credit: AMD

Micro-blogging startup Bluesky has gained substantial traction, with an impressive addition of 1.25 million new users reported in the week following the U.S. presidential election, according to Stats for Bluesky. This surge emphasizes changing social media habits as users seek alternatives to X, formerly known as Twitter, and Meta’s Threads. As of now, Bluesky claims a total user base of 15.2 million, a significant increase from the 13 million it boasted just a month prior.

The appeal of Bluesky in a shifting social media landscape

Bluesky’s recent growth is a clear indicator of its appeal as a viable alternative to more established platforms. CEO Jay Graber expressed excitement over welcoming new users seeking a different social media experience. However, despite this uptick, Bluesky’s total user count is still modest compared to its competitors. For context, while Bluesky is growing, X is drawing upon 600 million monthly users, and Threads enjoys about 275 million monthly active users.

The shifting user preferences come amid increasing dissatisfaction with X and Threads, particularly among journalists, politicians, and news enthusiasts. A report from Similarweb highlighted that many users switched to Bluesky due to concerns over Elon Musk’s political influence and the perceived transformation of X into a platform rife with misinformation. This sentiment was echoed by users on Bluesky who criticized X for its lean towards what they termed a “Trump propaganda machine.”

X marks the spot: Brazil’s standoff over Twitter ban and free speech

Bluesky’s rise in popularity has not been a one-off event; it experienced another notable increase of 2 million users in September when X faced a temporary ban in Brazil for non-compliance with local content moderation regulations. This incident shifted many Brazilian users to Bluesky, showcasing the impact of regulatory actions on social media platform dynamics.

The platform operates without advertisements and has yet to establish a concrete business model, although plans are underway for introducing paid subscription features. Despite this, Bluesky is currently the most downloaded app in the U.S. App Store, reflecting its growing popularity.

Citing statistics from Stats for Bluesky, the platform saw its user count climb from just 2 million to 15.2 million in less than a year, indicating that the startup is capitalizing on the changing preferences of social media users. The recent surge illustrates that many users are on the lookout for alternatives to platforms they feel no longer meet their needs.

As Bluesky navigates its ascent amid such a competitive landscape, maintaining user engagement will be crucial. Its origins trace back to a 2019 project initiated by Twitter, under then-CEO Jack Dorsey, who intended to explore decentralized social networking through Bluesky. However, under Musk’s leadership, the relationship between Twitter and Bluesky shifted from collaboration to competition when Bluesky became an independent entity in 2021.

It will be interesting to see how both the user dynamics evolve and how Bluesky positions itself as a credible alternative.

Featured image credit: Bluesky

Tessl, a promising startup aiming to reshape the coding landscape, has successfully raised $125 million in funding to accelerate its AI-driven platform’s development. Founded by Guy Podjarny, the company envisions a world where developers can communicate in natural language for software specifications, allowing AI to handle the nitty-gritty of coding, debugging, and maintenance.

Funding rounds and valuation insights

The funding raised by Tessl consists of a $25 million seed round in April, backed by GV (formerly Google Ventures) and boldstart, along with an additional $100 million Series A led by Index Ventures. Notably, other prominent investors such as Accel Partners, GV, and boldstart joined the Series A round. This financial backing values the startup at $750 million, affirming robust confidence in its potential from seasoned venture capitalists.

Podjarny, who has a history of successful ventures—including Akamai and Snyk—positions Tessl as an innovative player in the increasingly crowded field of AI coding tools. He noted that current solutions, like Microsoft-owned GitHub’s Copilot, focus primarily on streamlining existing workflows while Tessl aims for a more fundamental shift in software development.

In Podjarny’s vision, the role of a software developer will pivot from intricate coding tasks to prevailing over the overarching design and functionality of applications. Tessl encourages developers to articulate what they want applications to do, allowing AI to interpret those directions into code. This approach offers developers more strategic thinking time, as they become akin to systems architects rather than being bogged down by coding minutiae.

A unique approach to software design

At its core, Tessl’s platform seeks to facilitate a “spec-centric” process. This means developers can explicitly define what correctness looks like for their desired applications. They would specify high-level equations to optimize aspects such as performance and cost-efficiency, allowing Tessl’s AI to address the underlying technical details. Podjarny offered an example where an application could shift between different coding languages and architectures depending on traffic demands throughout the day.

Currently, Tessl employs a team of 21 and is in the process of testing multiple preliminary versions of its coding assistant with select internal and external users. However, the platform is not yet available for general sale, with a rollout anticipated in 2025. In preparation, Tessl has launched a waitlist for developers interested in getting early access to the platform.

To foster a community of “AI native” developers, Tessl invites programmers to build relationships centered around this next-gen software design paradigm. The startup’s foresight lies in addressing the challenges created by the rising complexity of code generated by various AIs, which amplifies risks related to security and maintenance.

Podjarny’s new venture is aptly named Tessl, a reference to “tessellation,” emphasizing the goal of ensuring code integrates seamlessly rather than functioning chaotically.

Collaboration not competition

Podjarny’s strategy points to collaboration over competition with other AI coding platforms. He’s keen on creating a system that works seamlessly with existing AI development environments, indicating Tessl’s potential to adopt and enhance AI-generated code from various sources. This interoperability suggests that while Tessl will compete with notable AI assistants like Cognition’s Devin and Codeium, it also aims to integrate into broader ecosystems for a smoother development experience.

In its initial stages, the platform plans to support programming languages like Java, JavaScript, and Python, with intentions to expand its repertoire as it scales. The vast investor confidence displayed in Tessl highlights a growing recognition of the necessity for comprehensive tools that not only write code but also maintain it over time, ensuring a consistent and secure software environment.

Carlos Gonzalez-Cadenas, a partner at Index Ventures, echoed this excitement by praising Podjarny’s capability to understand developer communities and his vision for transforming how software development is approached. He emphasized that Tessl isn’t merely a tool but part of a broader movement to reshape the coding landscape in a more collaborative and optimized manner.

Tessl’s ambitious plans signal not just a shift in how coding is done but potentially a new era of software design that emphasizes ease, security, and integration, all driven by the power of AI.

The surge in exploits of zero-day vulnerabilities has become the “new normal,” according to a recent warning from the Five Eyes intelligence alliance—comprising the U.S., U.K., Canada, Australia, and New Zealand. Cybersecurity agencies report a significant increase in hackers targeting previously undisclosed vulnerabilities this year, marking a shift from the trend of exploiting older vulnerabilities that predominated in past years.

At the top of the list: CVE-2023-3519

In a co-authored advisory released on November 14, 2024, the Five Eyes agencies detailed the top 15 most routinely exploited vulnerabilities, highlighting that, for the first time since these annual reports began, most of the listed vulnerabilities were initially exploited as zero-days. At the top of the list is CVE-2023-3519, a remote code execution bug in Citrix’s networking product, NetScaler. This vulnerability, along with CVE-2023-4966—related to sensitive information leaks—underscores Citrix’s significant cyber security woes this year.

Cisco also found itself in the spotlight, occupying the third and fourth positions on the list with vulnerabilities in its IOS XE operating system. Critical issues allow attackers to create local accounts and subsequently elevate their privileges to root. Following closely in fifth place is Fortinet’s FortiOS, also affected by severe vulnerabilities enabling remote code execution through a heap-based buffer overflow. Meanwhile, the file transfer tool MOVEit rounds out the top six, with a SQL injection vulnerability that has proven popular with threat actors.

Ollie Whitehouse, chief technology officer of the U.K.’s National Cyber Security Centre (NCSC), stated, “More routine initial exploitation of zero-day vulnerabilities represents the new normal which should concern end-user organizations and vendors alike as malicious actors seek to infiltrate networks.” In emphasizing the importance of proactive measures, he urged organizations to promptly apply patches and insist on secure-by-design products in the tech marketplace. The message is clear: vigilance in vulnerability management is crucial.

Organizations face a staggering challenge, particularly considering the high-profile list which includes vulnerabilities from well-known software systems. The impact of such breaches can be disastrous, as hackers gain access to sensitive networks and information. The exploitation of vulnerabilities like those in Citrix and Cisco not only risks significant data loss but could also undermine entire systems’ integrity.

Another notable entry is Atlassian’s Confluence, ranking seventh, which has a vulnerability allowing attackers to create admin-level accounts on affected servers. Hugely significant is the inclusion of the infamous Apache Log4j vulnerability, which ranks eighth. Despite being discovered in 2021, many organizations have yet to resolve this flaw, showcasing a troubling trend of inadequate patching practices.

Barracuda’s Email Security Gateway follows closely in ninth place due to its problematic input validation issues, popular with state-sponsored attackers. Zoho and PaperCut also made the list, reflecting the breadth of vulnerabilities affecting software across various sectors. Microsoft appears twice, with a 2020 netlogon protocol flaw sitting 12th and an Outlook issue escalating privileges at 14th—demonstrating that even tech giants grapple with legacy vulnerabilities.

Microsoft urges users to update Windows after zero-day vulnerabilities

Finally, open source file-sharing software, ownCloud, rounds out the list with a critical flaw allowing attackers to steal sensitive credentials. As these vulnerabilities persist, the Five Eyes agencies emphasize the importance for organizations to not only remain vigilant but to reinforce security measures from the development stage through to deployment.

Cyber attackers are not taking any breaks, and neither should organizations when safeguarding their digital environments. With the landscape of cyber threats evolving daily, understanding vulnerability trends and adapting swiftly is key to defending against exploitation effectively.

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Google has enhanced Gmail with the new Gemini side panel, allowing users to manage their Google Calendar more effectively within their inbox. This integration enables the creation of calendar events and the retrieval of scheduling information using natural language prompts.

Users can manage Google Calendar with Gemini more effectively

The recent update to Gmail introduces significant functionalities that cater to Google Workspace users, particularly those who juggle multiple tasks across emails and calendar events. The Gemini side panel facilitates seamless scheduling; for example, by simply typing, “Create a 1-hour event for lunch tomorrow at noon,” users can efficiently create calendar entries without leaving their inbox. Additionally, users can inquire about their schedules, asking questions like, “When is my first meeting next week?” This feature eliminates the need for switching between applications, enhancing user experience and saving time.

While these features are certainly exciting, it’s important to note some limitations. Currently, users cannot perform complex actions such as adding or removing guests from events or extracting event specifics from emails. The integration is also limited to English, which may restrict its accessibility for non-English speakers at launch. As it stands, the new functionality is available exclusively to Google Workspace customers, including Business, Enterprise, Education, and Education Premium tiers, along with Google One AI Premium members. The rollout began on November 13th, 2024, with full completion expected by December 6th, 2024.

Admin settings play a vital role in enabling this feature. Administrators need to ensure that smart features and personalization are activated for users, allowing them to access Gemini by clicking the “Ask Gemini” icon located in the top right corner of their Gmail inbox.

Prepare to chat about your files with Gemini Live

It’s worth mentioning that the Gemini integration is seen as just the beginning. According to Google and various tech experts, more advanced functionalities are on the horizon, suggesting that this tool could evolve into a more robust personal assistant. Although currently focused on creating new events and accessing calendar details, Google has hinted at upcoming enhancements that will broaden Gemini’s capabilities.

Among the features currently not supported are actions such as extracting information from emails, managing event-related attachments, scheduling out-of-office periods, or finding optimal meeting times based on participants’ availability. The dimensions of Gemini’s functionality are certainly expected to expand, with promises from Google indicating that updates will be shared through the Workspace Updates Blog once these enhancements are ready.

The anticipation surrounding this new tool reflects a growing trend to integrate AI-driven functionalities into everyday applications, streamlining tasks and enhancing productivity. Though initial offerings are basic, the implications of future developments could significantly change how users manage their schedules.

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Spotify has reported impressive growth in its latest earnings, boasting 252 million premium subscribers and a steady path towards operating income profitability. The company’s active user base reached 640 million monthly users, marking an 11 percent increase year-over-year.

Strong earnings and subscriber growth

For the third quarter, Spotify’s total revenue surged 19 percent to €4 billion, aligning with its expectations. The company achieved an operating income of €454 million, fueled by enhanced gross margins and reduced personnel-related expenses. This marked a record high for Spotify, highlighting its operational efficiency and revenue-generating capabilities. However, the operating income was influenced by a €54 million social charge accrual, surpassing forecasts due to employee share price appreciation during the quarter.

Premium subscription revenue saw a notable rise of 21 percent, driven largely by increases in average revenue per user (ARPU) and subscriber growth. Changes in pricing for premium accounts, which began in June, contributed to this upward trend. Meanwhile, ad-supported revenue experienced a modest 6 percent increase year-over-year, despite challenges in the advertising environment, particularly within music and podcasting sectors.

Spotify’s global workforce at the end of Q3 numbered 7,242 full-time employees, indicating the company’s dedication to maintaining a robust operational team. “We’ve never been in a stronger position, thanks to the outstanding execution by our team. I’m incredibly proud of the way we’ve delivered and the progress we’ve made,” said Daniel Ek, Spotify’s Founder and CEO. He expressed optimism for the future, emphasizing Spotify’s commitment to innovation and user experience.

Future plans and product expansion

Ek teased the introduction of a “super premium” tier aimed at superfans, offering enhanced sound quality and additional features. This comes after years of speculation since Spotify first announced a HiFi tier in 2021. The anticipated subscription could be priced around $17 to $18 per month, roughly $5 more than the current premium offering. With competitors like Apple Music and Amazon Music already providing lossless audio streaming, the excitement around this new tier could further enhance Spotify’s market position.

Furthermore, the streaming giant is planning to host a creator-oriented video event in Los Angeles, expected to unveil new product offerings designed to deepen engagement with creators. The event is part of Spotify’s strategy to bolster its presence in the video sector, complementing its audio offerings. Recent reports suggest that the company aims to attract video creators with lucrative deals to bring their content onto the platform.

This AI song takes over Spotify—but it’s not all good vibes

Ek also discussed how Spotify has been implementing AI-driven tools to enhance user engagement, specifically referencing the success of the AI DJ feature. This measured approach reflects Spotify’s intent to balance innovation with sustainable growth.

Investors reacted positively to the news from Spotify, with shares rising over 10% after the third-quarter results indicated that subscriber growth slightly surpassed expectations. The combination of increased active users and positive financial forecasts for Q4 solidifies Spotify’s trajectory as a leading player in the music streaming industry.

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A class action lawsuit against Apple has surfaced on ClassAction.org unresolved crackling issues associated with the AirPods Pro. Filed in November 2024, the lawsuit claims that these audio faults violate California consumer protection laws and constitute false advertising.

What’s the story behind the class action lawsuit of Apple?

The complaint stems from customer grievances that began soon after the original AirPods Pro launched in October 2019. Users reported persistent problems, including crackling, popping, and static sounds when the earbuds moved or vibrated, particularly during physical activities like walking or running. In response, Apple attempted to remedy the situation with software updates and eventually initiated a repair program for affected AirPods Pro users in October 2020. Unfortunately, many customers reported that the replacement units also exhibited similar audio problems, prompting the need for legal action.

The suit, titled “LaBella et al. v. Apple,” alleges that the ongoing audio defects contradict Apple’s marketing claims of “superior sound quality” and “pure, incredibly clear sound.” The plaintiffs argue that they would either have avoided purchasing the AirPods Pro or paid a lower price had Apple disclosed the devices’ known issues. As stated in the suit, “The AirPods Pro Gen 1 were thus not worth the premium price that consumers paid for them—as they contained an Audio Defect and did not live up to Apple’s advertising.”

Apple’s support documentation acknowledges users may hear crackling or static sounds in various conditions, including loud environments or while exercising. After acknowledging the widespread nature of these complaints, Apple extended its repair program to cover AirPods Pro for three years beyond the initial sale, highlighting its awareness of the issue.

Apple preparing for iPhone SE 4 launch in 2025

The lawsuit requests the court to certify the case as a class action and seeks relief for the affected customers. Claims include breaches of warranty under California, Ohio, Texas, and Pennsylvania laws, violations of the Song-Beverly Consumer Warranty Act, California Consumer Legal Remedies Act, California Unfair Competition law, and several Consumer Protection Acts. The plaintiffs demand damages for themselves and all affected AirPods Pro customers, along with pre- and post-judgment interest, as well as attorney fees.

While this lawsuit unfolds in court, Apple finds itself in a position where it must address the persistent dissatisfaction with its flagship wireless earbuds, potentially reshaping the future of its customer relations and product quality assurance strategies. As the company’s reputation hinges on that infamous blend of quality and innovation, how it manages these allegations could set the tone for similar product launches down the road.

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Apple is gearing up for the launch of the highly anticipated iPhone SE 4, which may hit the market as soon as spring 2025. With new developments surrounding its camera modules and design, the excitement is building for this budget-friendly addition to Apple’s smartphone lineup.

iPhone SE 4: Key production updates

Ajunews reports that LG Innotek, the supplier for the iPhone SE 4’s camera modules, will begin mass production of these components in December. This aligns with the typical timeline where production starts around three months before a new smartphone’s launch. As per the economic newspaper Ajunews, LG Innotek is expected to produce the front camera module for Apple’s upcoming device, which supports rumors of a launch around March or April 2025.

Notably, Apple analyst Ming-Chi Kuo has outlined an estimated production run of around 8.6 million units for the iPhone SE 4 through the first quarter of next year. This sharp focus on production suggests Apple is optimistic about consumer demand for its next-generation budget phone, which is slated to replace the third-generation iPhone SE released in 2022.

Why iPhone SE 4 will rule affordable smartphone market

Design enhancements and expected features

The iPhone SE 4 is rumored to feature design similarities to the iPhone 14, including a larger 6.1-inch OLED display. This is a significant increase from the current model’s 4.7-inch screen. In a notable shift, Apple seems to be phasing out the traditional home button with Touch ID, opting instead for Face ID. This aligns the iPhone SE 4 with the design scheme of more recent models, making it visually appealing to a broader audience.

Other expected specifications include an Action button, a USB-C connector, and an upgraded A-series chip, which could enhance performance and connectivity. However, specific features such as the Dynamic Island, introduced in newer iPhones, and support for Apple Intelligence might not make the cut for this model due to its budget status.

The anticipated price range for the iPhone SE 4 is competitive, likely falling between $400 and $500. This positioning places it below the starting price for the iPhone 14, which begins at $599, making the iPhone SE 4 appealing for budget-conscious consumers.

Apple clarifies how much RAM does your iPhone need to run Apple Intelligence

Camera specifications and technological updates

The camera is certainly a focal point for the iPhone SE 4, expected to include significant upgrades. A single 48-megapixel rear camera is likely, alongside the front camera module from LG Innotek. These enhancements reflect Apple’s commitment to providing powerful photographic capabilities even in its more affordable models.

This fourth-generation device will also incorporate 8GB of RAM, which should support new features and smoother operation of apps. Moreover, the inclusion of Apple’s first in-house 5G modem suggests that the iPhone SE 4 will be future-proofed for enhanced connectivity, allowing users to experience faster wireless speeds.

With the iPhone SE 4, Apple appears to be striking a balance between affordability and premium features. As the production timelines become crystal clear and consumer anticipation rises, it’s evident that this new model could redefine entry-level smartphones within the Apple ecosystem. The upcoming months will be critical as more details emerge, paving the way for what could be a highly successful product launch.

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Microsoft is urging Windows users to update their systems immediately after confirming four new zero-day vulnerabilities as part of its November security patch. Among over 90 security issues reported, two of these zero-days are actively being exploited, posing significant risks to users.

Understanding the zero-day vulnerabilities

Microsoft has a unique perspective on what constitutes a zero-day threat, considering both vulnerabilities that are publicly disclosed and those actively under attack. As highlighted in the November 2024 Patch Tuesday release, two out of the four identified vulnerabilities are currently being exploited.

CVE-2024-43451 is particularly notable; it is an NT LAN Manager hash disclosure spoofing vulnerability that could expose the NTLM authentication protocol. According to Ryan Braunstein, team lead of security operations at Automox, the flaw requires user interaction to be exploited. Specifically, users need to open a crafted file sent via phishing attempts for the attack to succeed. When compromised, this vulnerability allows attackers to potentially authenticate as the user due to the disclosure of NTLM hashing, which is intended to protect passwords.

On the other hand, CVE-2024-49039 is a Windows Task Scheduler elevation of privilege vulnerability. Henry Smith, a senior security engineer at Automox, noted that this flaw exploits Remote Procedure Call functions, enabling an attacker to elevate their privileges after gaining initial access to a Windows system. Patching remains the most reliable defense against these vulnerabilities, especially since functional exploit code is already circulating in the wild.

Critical vulnerabilities rated at 9.8 severity

Adding to the alarm, two vulnerabilities have been rated as 9.8 on the Common Vulnerability Scoring System, indicating their potential impact. CVE-2024-43498 affects .NET web applications, allowing unauthenticated remote attackers to exploit the application through malicious requests. Meanwhile, CVE-2024-43639 targets Windows Kerberos, enabling unauthorized attackers to execute code through the same unauthenticated vectors.

The major focus, however, should be directed at two security vulnerabilities rated a critical 9.8 on the impact severity scale, according to Tyler Reguly, associate director for security research and development at Fortra. “While the Common Vulnerability Scoring System is not an indicator of risk,” Reguly said, “scores that are a 9.8 are often pretty telling of where the issue is.”

Given the severity of these vulnerabilities, Microsoft is stressing the importance of applying security updates, particularly for users operating Windows, Office, SQL Server, Exchange Server, .NET, and Visual Studio. Chris Goettl, vice president of security product management at Ivanti, noted that patching should be a priority due to the known and actively exploited nature of these vulnerabilities.

Tracking recent attacks and vulnerabilities

Microsoft’s concerns are reinforced by recent incidents where Russian hackers exploited vulnerabilities in their systems for attacks specifically targeting Ukrainian entities. This highlights the broader implications of these vulnerabilities beyond mere software issues. ClearSky security researchers reported that the NTLM hash disclosure vulnerability (CVE-2024-43451) was being utilized to steal NTLMv2 hashes through phishing schemes, triggering a sequence that allowed attackers to gain remote access to compromised systems.

By using crafted hyperlinks in phishing emails, attackers forced users to interact with malicious files, activating the vulnerability that connects to an attacker-controlled server. This underscores the pressing need for users to remain vigilant and report suspicious communications.

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added CVE-2024-43451 to its Known Exploited Vulnerabilities Catalog, mandating that organizations secure their vulnerable systems by early December. As CISA stated, such vulnerabilities frequently serve as attack vectors for malicious cyber actors and pose great risks, particularly within federal networks.

Armed with the knowledge of these vulnerabilities, users are urged to act promptly. Microsoft’s November Patch Tuesday is a necessary step to mitigate the risks associated with newly discovered flaws. As hybrid working environments continue to blur the lines of cybersecurity, adhering to best practices and ensuring timely updates can drastically reduce exposure to potential threats.

Featured image credit: Windows/Unsplash

Apple’s latest update to Final Cut Pro, now version 11, marks a notable evolution in professional video editing software, bridging a decade-long gap since its previous major release. This update introduces a host of powerful features reflecting Apple’s renewed commitment to professional creatives, alongside an array of other exciting advancements in their software suite.

New features that enhance creativity and efficiency

Final Cut Pro 11 arrives with an impressive lineup of new tools, including AI-powered masking, the ability to generate captions directly in the timeline, enhanced features for spatial video editing, and several workflow improvements. Current users can access the update for free, while new users can secure the software for a one-time purchase of $299.

Among the standout features is the Magnetic Mask, which makes isolating subjects from the background easier than ever. With just a click, users can adjust colors only for the chosen subject—streamlining a typically tedious process. This function has shown impressive results, efficiently isolating subjects even in complex scenarios, such as action shots or static talking heads, although minor adjustments may still be needed for optimal precision.

Furthermore, automatic caption generation—a notable feature utilizing an in-device Apple-trained language model—makes video content more accessible. Despite the speed of this feature, it’s not without limitations; accuracy can suffer, with the system occasionally misspelling words and struggling with proper nouns. This means for those looking to utilize captions effectively, especially for platforms like TikTok, additional work may be necessary.

Spatial video editing capabilities have been expanded to cater to the Vision Pro, making it easier for creators to tailor their content for immersive experiences. Coupled with new keyboard shortcuts, such as moving clips between layers more fluidly, the update promotes a smoother editing workflow.

Reviving a legacy: A return to professional-grade software

Reflecting on Apple’s path with Final Cut Pro, it’s essential to acknowledge the tumultuous journey since the launch of Final Cut Pro X in 2011. Initially reborn with a complete overhaul, the software faced backlash for lacking many essential features that users had relied upon. This led to a decline in its popularity among professional editors, with some even petitioning for Apple to sell the software to another developer.

Fast forward to 2024, Apple has committed to reviving Final Cut Pro’s former glory. The introduction of Final Cut Pro 11 signals a turnaround; the application is now equipped with not only new features but also a bolstered commitment to furthering development for professional users. The frequency of updates and the continued addition of features reflect a proactive approach, suggesting Apple is serious about retaining and attracting back its professional user base.

This commitment also extends to the Final Cut Pro offerings on the iPad, which received several updates, including AI-enhanced tools that improve light and color quickly and effectively. New modular transitions and audio tracks have also been added, enhancing the editing capabilities on mobile devices.

With the introduction of Final Cut Camera, users can now record in HEVC format, significantly reducing storage needs without sacrificing quality. The ability to monitor color and exposure with LUT previews and the addition of a level indicator to aid framing during shoots are notable upgrades, echoing Apple’s intentions of catering to the needs of creators in today’s fast-paced media environment.

Many modern creators seek all-in-one solutions for video, audio, and photo editing, and the latest updates position Apple as a formidable player against platforms like Adobe, while providing the hardware to run its software smoothly.

Final Cut Pro 11 not only improves upon past iterations but also gives a nod to the professional market that Apple has been criticized for overlooking in recent years. By focusing on delivering robust, functional, and updated tools for video editors, Apple is laying the groundwork for a return to form that professional users have desired.

Although the road ahead may still present challenges—like addressing community requests for text-based editing and customizations—Final Cut Pro 11 is undoubtedly a pivotal step in re-establishing Apple’s relevance in the professional editing space as they aim to fulfill the creative ambitions of their users.

Image credits: Apple

Apple faces a UK lawsuit concerning iCloud’s alleged monopoly, potentially costing the tech giant £3 billion (approximately $3.8 billion). A consumer rights organization has stepped up to challenge Apple’s cloud pricing and access, claiming millions were unfairly affected by its practices.

Which? is after Apple over alegged iCloud monopoly

A legal claim was filed against Apple by the U.K. consumer group Which?, representing about 40 million users of its iCloud service. The group alleges that Apple’s practices constitute a breach of competition law, effectively “locking” consumers into utilizing iCloud while providing preferential treatment to their own storage solutions. This lack of competition has led to inflated pricing, which, according to Which?, means U.K. consumers were charged “rip-off” prices for what is essentially a dominant service.

According to the claim, Apple encourages users to opt into iCloud for essential data storage, yet simultaneously complicates the process for those wanting to explore competing services. For example, customers are limited in their ability to fully back up data using third-party alternatives. As a result, once users exceed the free 5GB limit of iCloud, they have no choice but to purchase a subscription, progressively leading to higher overall costs.

Which? stated that Apple raised its iCloud subscription fees significantly in 2023, with increases ranging between 20% and 29%. The average compensation the group is seeking per affected consumer is estimated at around £70 (approximately $90), ultimately amounting to a colossal total of nearly £3 billion if successful.

Video: Which?

Under the opt-out collective actions regime established by the Consumer Rights Act of 2015, this lawsuit seeks to represent all U.K. consumers who have paid for iCloud services since October 1, 2015. Those outside the U.K. wanting to join the action must actively opt in. Which?’s spokesman emphasized that the definition of eligible users includes anyone who has used or paid for iCloud services during the last nine years.

The legal challenge is not the first of its kind. A similar lawsuit was initiated against Apple in the United States in March 2024, alleging monopolistic behavior in the cloud storage market. That case remains pending after Apple’s attempts to dismiss it were unsuccessful.

Which? is partnering with international law firm Willkie Farr & Gallagher to pursue the claim, which is being financed by Litigation Capital Management, a global player in litigation funding. The consumer group has publicly encouraged Apple to settle the matter amicably by refunding customers and making iOS more accommodating for third-party cloud services.

Anabel Hoult, the chief executive of Which?, pointed out the significance of this legal action, stating, “By bringing this claim, Which? is showing big corporations like Apple that they cannot rip off U.K. consumers without facing repercussions.” Given the current climate of increased scrutiny on large tech companies, the claim has the potential to set important precedents in consumer rights and market competition.

Apple has officially rejected Which?’s claims, asserting that users are not required to use iCloud, and many rely on a range of third-party services. The company insists that it works diligently to ensure data transfer is straightforward, regardless of the provider. Notably, Apple claims that nearly 50% of its customers do not require an iCloud+ subscription, asserting that their pricing aligns with other cloud storage options.

The next critical step in this legal saga awaits the decision from the Competition Appeal Tribunal concerning whether Which? can proceed as a class representative for consumers. This determination will dictate the future of the lawsuit and could influence broader discussions regarding competition and consumer protections within the tech industry.

As the legal battle unfolds, it highlights the growing scrutiny Big Tech faces regarding its market dominance and pricing strategies. Following a series of recent antitrust enforcement actions globally, this lawsuit serves as another chapter in the ongoing dialogue about fairness in the digital market.

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OpenAI is set to launch its first autonomous AI agent, dubbed “Operator,” in January as part of a research preview, Bloomberg reports. This new development aims to elevate AI capabilities by enabling users to carry out complex tasks online, such as booking flights or writing code, with minimal human intervention.

OpenAI’s strategic move towards agentic AI

The introduction of Operator marks a crucial point in a broader shift towards agentic AI, which represents software designed to autonomously complete multi-step tasks. Unlike traditional chatbots that only respond to queries, AI agents like Operator function more as personal assistants, capable of making decisions based on the guidelines provided by users. For example, users merely need to inform Operator about their hotel preferences, such as needing two beds and a jacuzzi, and it will handle everything necessary to secure a reservation, including payment if granted permission.

OpenAI has made it clear that it is not alone in this race. Competitors like Anthropic have unveiled new features enabling the automation of tasks like website creation and spreadsheet editing. Meanwhile, tech giant Google has introduced tools that further allow companies to build tailored AI agents using its Gemini large language models. Other major players in the industry, such as Salesforce and Cisco, have launched their own initiatives, with Salesforce offering agents capable of customer service and Cisco integrating AI into its Webex platform.

Despite these advancements, OpenAI finds itself entering this expanding sector somewhat late. Previously, OpenAI’s focus had been on generating massive language models rather than creating autonomous agents. However, the company’s commitment to agentic AI is evident, as highlighted in a Reddit AMA session. CEO Sam Altman disclosed that, while OpenAI will still improve its existing models, future breakthroughs would likely revolve around the development of advanced autonomous agents.

Glossing over the urgency to innovate, OpenAI’s venture into agentic AI appears driven by external pressures to monetize its investments in cutting-edge technology. As many AI labs struggle with creating profitable applications from their sophisticated models, there is a growing belief that autonomous agents represent a golden ticket, reminiscent of previous transformative products by tech giants.

In light of the forthcoming launch of Operator, some attendees at OpenAI’s recent press events have shared predicted sentiment about 2025 being a pivotal year for the mainstream adoption of agentic technologies. The anticipation is palpable, as stakeholders eagerly await how these systems could redefine productivity and collaboration across multiple fields.

OpenAI’s upcoming release also comes amid its discussions on AI policy, underscoring its role in shaping industry standards and addressing regulatory concerns. The company circulated its draft AI policy proposal around the same time, suggesting the U.S. government establish “AI-focused economic zones” and a coalition to better compete against China in this rapidly evolving tech landscape. The overarching recommendation from OpenAI emphasizes the need for increased energy generation to support AI operations, advocating for investments in renewable sources like wind and solar, along with nuclear energy infrastructure.

With the launch of Operator drawing near, the tech sphere is watching closely to measure its potential impact. The ability to issue straightforward commands and let the AI handle the intricacies has the capacity to streamline workflows significantly. As the groundwork for these systems becomes more established, their integration into everyday tasks may eventually render certain manual processes obsolete, thus heralding a new era for both AI technology and users alike.

Featured image credit: Andrew Neel

Leveraging data visualization, banks can significantly enhance their fraud detection capabilities. I spoke with Atmajitsinh Gohil, author of R Data Visualization Cookbook, about the technologies transforming the fight against financial fraud.

According to the Nilson Report, global credit card losses are projected to reach $43 billion by 2026. Atmajitsinh Gohil, a renowned author of R Data Visualization Cookbook,  and one of the top experts in AI enabled tools, believes data visualization techniques are crucial in the fight against fraud.

Atmajitsinh has worked with financial institutions to  assess financial data and devise AI enabled tools for anomaly detection. These AI enabled tools identify patterns in the data and detect possible fraudsters. Gohil has developed proprietary data visualization tools to identify financial fraud, safeguard financial data, and detect new and emerging threats.

His R Data Visualization Cookbook delves deep into the R programming language, offering practical knowledge that’s indispensable for everyone, from data analytics students to policymakers.

“Today, banks rely heavily on machine learning models that identify crime by analyzing datasets,” Gohil said. “The proportion of fraud in these datasets is very small, which makes detection challenging.”

He is currently working on machine learning model validation for the world’s largest banks. According to Gohil, criminals use various tactics, such as acquiring hacked customer information from the dark web, leveraging generative AI for phishing personal data, and laundering money through cryptocurrency.

Gohil is highly skilled in leveraging AI to mitigate financial losses, a strategy that has recently gained traction among financial institutions. For example, Mastercard launched a generative AI model to help banks better assess suspicious transactions on its network.

This proprietary algorithm is trained on data from around 125 billion transactions that pass through the company’s card network each year. On average, Mastercard’s technology can improve fraud detection rates by 20%, and in some cases, it has led to improvements of up to 300%.

Financial crime prevention

According to Gohil, financial firms collect vast amounts of data from various transactions, including money transfers and login activities. Identifying fraudulent activities involves comparing an individual’s profile with historical data to detect suspicious patterns.

Gohil’s innovative data visualization techniques play a crucial role in this process. “Visualization comes into play by creating dashboards that can show how many people are logging in, their genders, age groups, and where fraud is occurring,” says Gohil. “This helps in identifying if fraud is concentrated in a particular demographic or region.”

When fraud models aren’t performing well, banks make adjustments and use visualization techniques to compare old and new models.

“You can visualize data to see pre- and post-change performance. This helps in understanding whether the adjustments have reduced false positives or improved detection rates,” Gohil adds.

The main risks for banks

As technology advances, so do the methods employed by criminals. “Fraudsters adapt very quickly. They work aggressively to break the system, especially with AI coming in,” warns Gohil. For example, technology can be used to create fake IDs or other deceptive ways to breach bank systems.

One of the major risks banks face is maintaining customer trust and protecting their data. “Keeping the client happy and ensuring their identity is not leaked are significant concerns,” Gohil notes.

Voice recognition technology, developed by third-party vendors, is one such innovation aiding banks in identifying fraudulent calls. These systems can analyze various inputs, such as phone numbers and geographic locations, to flag suspicious activity.

“Voice recognition can identify whether a call is fraudulent based on different variables in the model,” Gohil explains.

The future of fraud prevention

The threats banks face are continually evolving. Phishing emails, spam, and fake communications from CEOs are just a few of the tactics fraudsters use. Gohil warns that banks must remain vigilant and adopt new technologies to protect themselves and their customers.

Using Gohil’s data visualization techniques, financial firms can adopt AI to create scenarios where they identify potential threats and take precautionary measures. For instance, tagging emails as internal or personal helps banks monitor the flow of information and prevent phishing attacks.

While many banks have effective tools for detecting unusual behaviors that may indicate fraud, these tools are not perfect. This is why Gohil’s data visualization techniques are crucial for identifying fraudulent patterns.

“Advanced technology is essential for distinguishing between legitimate and malicious transactions,” he said. Financial firms can only accurately evaluate the effectiveness of their fraud detection systems by leveraging data.

Featured image credit: Eduardo Soares/Unsplash

In today’s rapidly evolving industries, enhancing employee engagement has become important more than ever for business organizations. Considering that employees are the backbone of any successful organization driving processes and productivity, it has only become more imperative to improve employee engagement.

However, improving levels of employee engagement is more than just offering perks and flexible working arrangements; it requires a deeper understanding of employee needs and behaviours and the core principles of employee engagement.

To do so, employers need to tap into the advantages of using data analytics to revolutionize workplace engagement. In this article, we will explore how data analytics revolutionizes employee engagement and how it will help businesses unlock the full potential of their teams.

What is employee engagement and why is it important?

Employee engagement is a Human Resource (HR) concept that pertains to the level of enthusiasm and dedication an employee feels toward their job or the entire organization. As such, the more engaged employees are in an organization, the more they will feel enthusiastic and dedicated in doing their job.

Per studies, the more engaged the employees are the more productive, dedicated, and most likely to stay with the company thereby reducing turnover rates and associated costs that come with employees quitting in an organization.

How then does an organization improve employee engagement?

For most organizations, improving employee communication is one way organizations improve their employee engagement. Effective communications with employees foster a sense of community and help build trust. As such, organizations utilize effective employee communication software that enables seamless collaboration across various organization teams.

By utilizing effective employee communication tools, employers get to facilitate real-time feedback and provide effective channels where both employees and employers share important updates and support.

However, having employee communication tools is not enough to fully engage employees. Accordingly, most organizations still struggle to foster employee engagement despite having an employee communication tool in place. As such, this is where data analytics can make a significant impact.

Role of data analytics to improve workplace engagement

The utilization of data analytics has been given importance in most industries. In the context of workplace engagement, data analytics allows organizations to gain deeper insights into employee experiences and preferences.

As such, here are some applications of data analytics in the context of workplace engagement:

• Continuous feedback loop: One way to apply data analytics in improving workplace engagement is to implement continuous feedback mechanisms. Instead of waiting for annual feedback surveys, organizations can instead deploy pulse surveys or real-time feedback tools that encourage employees to participate in feedback-giving without fear of negative consequences. Employers can host regular check-in conversations in person or remotely or even facilitate informal interactions to accurately gauge engagement levels and promptly assess and address any workplace issues.
• Employee behavior analytics: Beyond surveys and constant feedback loops, data analytics can also help in tracking employee behavior and interaction. Tools that monitor employee productivity, collaboration patterns, and attendance can provide insights into how engaged employees are. As such, organizations can analyze the most common emojis used by employees during team meetings, the attendance ratings, and other relevant factors.
• Sentiment analysis: Lastly, data analytics may be used to gauge employee sentiment by leveraging natural language processing tools and analyze communication channels. By understanding sentiments expressed by employees, an organization will be able to identify trends and hidden factors that contribute to employee satisfaction or dissatisfaction.

Leveraging data analytics for a more engaged workforce

With how important employees are and how they can make or break an organization, improvements to employee engagement must definitely be pursued in every way.

By harnessing the power of data analytics, organizations will be able to move beyond traditional measures in enhancing workplace engagement and adopt dynamic and more responsive strategies.

As businesses continue to evolve and become even more fast-paced, it is only proper to utilize anything and everything that can improve workplace engagement, and if that means heavily relying on data analytics, then so be it. What is important is to create an environment for employees where they feel valued, motivated, and fully engaged, and employing data-driven employee communication and methods shall be a key factor in driving engagement from the workforce.

Featured image credit: Agefis/Unsplash

Law enforcement agencies are facing unexpected challenges as iPhones begin to automatically reboot while in custody, complicating access to potentially vital data. This recent development appears to stem from a new security feature in iOS 18.1, designed to secure encrypted data by returning the device to a more locked-down state after a period of inactivity.

Police alarmed by mysterious iPhone reboots

A newly acquired document by 404 Media reveals that police forces, particularly in Detroit, have encountered iPhones restarting unexpectedly after being removed from cellular networks. The authors of the document speculate that Apple’s latest update includes a security measure that prompts devices to reboot if they haven’t been connected to a cellular service for some time—a situation that could occur while a phone is being stored for forensic evaluation. This reboot is serious business for investigators because once an iPhone reboots, its state changes to Before First Unlock (BFU), where access is significantly more difficult through any forensic tools.

According to observations included in the law enforcement document, these unexpected reboots can occur within as little as 24 hours. Consequently, phones that are crucial for criminal investigations could become more cumbersome to analyze. One law enforcement official succinctly noted the situation’s urgency, stating, “The purpose of this notice is to spread awareness of a situation involving iPhones, which is causing iPhone devices to reboot in a short amount of time when removed from a cellular network.”

Additionally, Apple’s new “inactivity reboot” feature automatically re-encrypts data after prolonged idle periods, adding another layer of security and complicating recovery efforts for police. Jiska Classen, a researcher at Hasso-Plattner-Institut, explained that upon unlocking an iPhone—either through a PIN or Face ID—the operating system loads the encryption keys into memory. However, following a reboot, the device enters a state where it does not retain those keys, meaning that data is rendered inaccessible to investigators or malicious actors alike.

Apple has not readily confirmed the implementation of this new feature, although it has been noted that the mechanism operates at the system level. It acts as an additional roadblock against attempts to extract data from devices used by suspects. Reportedly, this process can also thwart traditional methods of extracting data, preserving user privacy even in contentious legal situations where the phone is key evidence.

The trend is a double-edged sword, offering enhanced security but also inadvertently hindering law enforcement efforts. As iPhones not only guard personal information fiercely but also evolve continuously with updates, investigators are advised to adapt their methods accordingly. What this means for future forensic procedures remains to be seen, but it’s clear that if you’re planning a life of crime in the digital age, good luck getting around those increasingly clever iPhones.

Nvidia has reportedly halted production of nearly all its current-generation RTX 40 GPUs, leaving only the RTX 4050 and 4060 models in production. This strategic shift indicates that Nvidia is redirecting its resources toward the anticipated RTX 50 series, expected to launch early next year.

Nvidia kills the production line for the AD106 chip

According to a report on Board Channels, Nvidia has completely shut down the production line for the AD106 chip, which is used in popular models like the RTX 4060 Ti and RTX 4070. The only chip still in active production for the RTX 40 series is the AD107, responsible for the mobile versions of the RTX 4060 and the RTX 4050. This signals a significant transition for Nvidia as it prepares to unveil its next-generation GPUs.

The news follows earlier reports indicating that Nvidia has also ceased production of the AD102 chip, found in the high-end RTX 4090. This approach aligns with Nvidia’s typical launch strategy, where the higher-tier models often roll out first, followed by more affordable variants. By winding down the RTX 40 series, Nvidia sets the stage for the launch of the RTX 50 series, which could arrive as soon as January 2025.

Analysts suggest that the new RTX 5090 and 5080 graphics cards will be introduced at the Consumer Electronics Show (CES) in Las Vegas. Speculation about further releases hints at the RTX 5070 launching in February and the RTX 5060 and 5060 Ti in the following months. This accelerated schedule is somewhat unexpected, as Nvidia typically takes its time releasing various models within a new series.

Given this rapid timeline, it is likely that gamers will see a mix of release dates within the RTX 50 family, with some models potentially hitting the shelves sooner than others. The RTX 50 series has been rumored for some time, and with the rigorous production changes occurring now, many hope for a more competitive pricing strategy across the upcoming models.

Despite the cessation of production for the majority of the RTX 40 series, Nvidia has not fully discontinued these GPUs, as existing stock will still be available in retail channels. However, reports indicate that the supply of mid-to-high-end RTX 40 models will gradually dwindle, signaling an end to new units being produced.

In summary, if Nvidia indeed moves forward with the launch of the RTX 50 series as planned, consumers can expect exciting advancements in performance and technology. Meanwhile, the phasing out of the RTX 40 series marks a significant shift for the GPU market. As always, gamers are keenly awaiting Nvidia’s pricing strategies and the overall performance of these incoming models to see how they stack up against the competition.

Google’s Gemini Live is set to revolutionize the way users engage with uploaded files, moving beyond simple question-and-answer interactions. Currently catering to Gemini Advanced users, the platform will soon facilitate direct conversations about uploaded files, enhancing productivity and user experience. Although the feature isn’t functional yet, hints in the latest APK teardown by Android Authority suggest that Gemini Live will prompt users to engage with their uploaded content, paving the way for more interactive and contextually relevant assistance.

Gemini’s evolving capabilities

Gemini Advanced users have already enjoyed the ability to upload a variety of files, such as text documents and spreadsheets, for the AI to modify or summarize. Those familiar with this feature will be excited to hear about the anticipated integration of Gemini Live, which aims to create a more conversational and contextual environment. The essential nuance here is that Gemini might recognize when a file is uploaded—whether from a local drive or directly through Google Drive—and recommend engaging with the Live feature to maximize its usefulness.

Specifically, code strings found in the latest beta of the Google app—version 15.45.33.ve.arm64—reveal prompts like “Talk about attachment” and “Open Live with attachment.” This indicates that Gemini Live could soon leverage its conversational nature to assist users more effectively. “Starting Conversation Mode with empty attachments, but expected attachments to be present” even hints that further integration may be on the horizon, leaving users wondering how this will unfold and what level of interaction they can expect.

Furthermore, Google’s Gemini platform is in the midst of enhancements that align with the evolving needs of its users. Notably, some integrations allow for the automatic recognition of file updates. This development will ensure that users get real-time assistance based on the most current version of their uploaded documents.

Introducing Gems: Custom instances for tailored responses

In conjunction with the arrival of Gemini Live, Google has introduced “Gems”—custom instances of Gemini aimed at specific tasks. This feature allows users to create tailored responses by uploading up to ten files, thus enhancing the contextual relevance of the output. Files can range from various document formats including Google Docs, PDFs, and even spreadsheets like Google Sheets and Excel files.

Google has stated that Gems are “one of the most used Gemini Advanced features” since their introduction, highlighting the growing adherence to custom-tailored AI experiences. These Gems can offer support across diverse workplace scenarios such as refining corporate style guides, enhancing project-specific assistants, and streamlining HR document access.

The implications of this are vast. For instance, a marketing team could quickly draft on-brand content or utilize sentiment analysis to gauge customer feedback. The ability to integrate specific files ensures that outputs are not just context-aware, but also aligned with the latest developments or revisions within those documents.

Enhancing enterprise workflows with Gemini

Google’s rollout of these features is particularly significant for Workspace subscribers, who will see the introduction of several premade Gems aimed at various professional needs. These include marketing insights for calculating customer acquisition costs, crafting compelling sales pitches, and even hiring consultations for consistent job descriptions. By integrating AI into the workflow, Google aims to provide tools that not only save time but also enhance productivity and creativity across teams.

Imagine a corporate atmosphere where the tedious task of drafting proposals or sending customer communications could be handled by an AI consultant, which can ingest the required information and suggest tailored responses that reflect real-time updates in company policies or client data. This is not just a dream; it is fast becoming a reality with Gemini’s progressive tools.

As Google continues its enhancements, users are poised to gain immediate access to tools that not only save time but also promote organizational efficiency. The ability to use Gemini AI for everything from financial forecasting to educational content creation signifies a shift toward a more integrated and user-friendly AI experience.

With all these developments, the excitement about Gemini Live and the introduction of custom Gems is palpable. As users await the rollout of these features, it becomes clear that Google is not just transforming how we interact with AI but how we utilize technology to enhance our workflow. Preemptively setting the stage for file-based interactions with Gemini Live is only a part of a larger ecosystem that promises efficiency and ease in various professional realms.

Featured image credit: Google

Google’s new Chrome update for iOS introduces four significant features designed to enhance user experience and compete aggressively against Apple’s Safari. With these enhancements, users can expect smoother browsing, more efficient storage management, and improved online shopping options.

Feature highlights of the latest Chrome update for iOS

The recent update, announced on November 12, 2024, offers several new tools aimed at optimizing Chrome use on iPhones and iPads. Chrome users can now search using both images and text simultaneously, effectively refining their search capabilities. This feature, powered by Google Lens, allows users to ask questions about what they see around them or to clarify visual searches by adding descriptive attributes. By tapping the camera icon in the Google Search bar, users can explore more targeted and relevant results, even receiving an AI Overview summarizing pertinent web information based on their queries.

Another significant addition is the ability to save files and pictures directly to Google Drive and Google Photos, which addresses a common annoyance for iPhone users: storage alerts. With the new “Saved from Chrome” folder in Google Drive, users can now bypass the constant “Storage Almost Full” warnings by easily moving files from the browser to the cloud, thus freeing up valuable device space. To store an image in Google Photos, users simply need to long-press the picture and select the appropriate option from the context menu.

Shopping Insights offers further convenience, particularly for users in the U.S. This feature alerts users to favorable pricing on products they are browsing, providing notifications like “Good Deal Now” right in the address bar. When users click on this notification, they can access insights such as price history and purchasing options, helping consumers make informed decisions. While this feature is currently U.S.-exclusive, Google plans to expand it into other regions soon.

Video: Google

Lastly, Chrome has streamlined navigation with a new mini-map feature. When browsing a site that lists an address, users can view a mini-map without switching to Google Maps. By simply tapping on the underlined address, a mini-map will appear within the Chrome browser, further enhancing the browsing experience without the need to juggle multiple applications.

These updates come at a pivotal time, as Apple has recently warned iPhone users about potential vulnerabilities in using Chrome, pushing for more Safari adoption. Google, however, counters this by enhancing its Chrome browser’s capabilities and actively seeking to recruit new users. Reports suggest Google aims to reroute approximately 300 million iPhone users from Safari to Chrome.

As the competition heats up, it’s clear that Chrome’s recent updates are not merely cosmetic; they are strategic moves in a larger battle for mobile browser supremacy. Both companies are aware that the stakes are high: Safari and Chrome together command a staggering 91% of the global mobile browser market, according to Statscounter. It remains to be seen how users will react to these changes and whether Chrome’s efforts will bear fruit in winning over Apple’s dedicated user base.

This updated Chrome experience aims not only to improve individual performance but also to challenge Apple’s stronghold on mobile browsing. In a marketplace where user trust and preference reign supreme, Google’s innovations and the timing of these features seem designed to keep the competition lively, if not heated. As Google positions itself as an alternative for iPhone users, the next few months will reveal if these features can sway a significant number of Safari users into Chrome’s fold.

Many workers say they’re embarrassed to use AI at work. The latest research from Slack reveals a troubling plateau in AI tool adoption among workers, with many feeling both anxious and embarrassed about utilizing these technologies in their roles. Over the past few months, just a modest increase from 32% to 33% in reported AI usage has been noted, despite a compelling desire from executives for employees to engage more with AI.

Understanding the feelings surrounding AI use among workers

Slack’s Workforce Index, which surveyed over 17,000 global “desk workers,” indicates that nearly half of US workers are hesitant to admit they use AI tools, fearing they may be perceived as lazy or even cheating. This anxiety can be attributed to a lack of clear guidance on appropriate use, leaving many employees uncertain about how and when to integrate AI into their workflows. Moreover, 48% of respondents expressed discomfort in discussing their AI use with managers, highlighting the stigma still attached to such tools.

In contrast, workers who felt comfortable communicating their AI-related activities with management reported a 67% higher likelihood of actually using AI in their work. Meanwhile, 76% of survey participants stated they have an urge to enhance their AI skills, reflecting a broad desire for knowledge in an increasingly tech-driven environment. However, two-thirds of workers indicated they’ve spent fewer than five hours on AI training, and nearly 30% claimed they received no training whatsoever.

Janzer emphasized that the onus is now on organizations to lower these barriers to use, stating, “Too much of the burden has been put on workers to figure out how to use AI.” Encouraging a more open dialogue about AI integration and offering genuine guidance can help alleviate much of the discomfort felt by employees. She added that “the arrival of AI agents—with clearly defined roles and guidelines—will also help with adoption, alleviating the ambiguity and anxiety many workers feel around using AI at work.”

Google and Microsoft have committed to hybrid work arrangements

The disconnect between executives and employees regarding AI’s application

Interestingly, Slack’s report highlights a notable disconnect in the expectations between executives and their teams. While companies desire employees to invest their time—freed up by AI—into innovation and skill development, employees report that they often redirect this extra time towards mundane tasks and existing projects, rather than exploring new avenues.

“Workers are very confused about when it is socially and professionally acceptable to use AI at work,” said Christina Janzer. This confusion can carry significant implications for productivity and innovation as firms look to stay competitive in an increasingly digital landscape. Additionally, Slack points out that companies have expressed concerns around leveraging the technology swiftly, citing risks such as information security and accuracy that can hamstring AI initiatives.

As organizations ready themselves to invest in AI tools, it becomes essential for them to establish clear communication about acceptable AI use practices. Janzer urges, “Employers and employees both have a really important role to play here in accelerating AI adoption and helping to push past this plateau that we’re seeing in the data.” Creating an environment conducive to learning and experimentation with AI tools could ease employees’ fears, ultimately leading to greater engagement with the technology.

The survey results reflect a crucial need for firms to develop and implement training programs, set clear guidelines, and encourage collaboration between management and employees in navigating the intricacies of AI usage. By doing so, organizations stand a better chance of overcoming barriers to adoption and fostering a more empowered workforce eager to explore the potential of AI in their daily tasks.

Google’s AI flood forecasting model is expanding significantly, now aiding 700 million people with enhanced accuracy and accessibility. This upgrade aims to deliver crucial information and preparedness measures in areas prone to flooding.

Expanding global flood forecasting coverage

Google’s recent advancements in AI-powered flood forecasting have broadened its reach from 80 to over 100 countries. This dramatic leap now allows the model to provide critical flood information to an additional 240 million people, elevating the number affected to approximately 700 million. Such coverage is essential, especially in areas frequently ravaged by floods, which can lead to tragic casualties, displacement, and immense financial loss.

The AI model improvements have resulted in increased accuracy, allowing for effective flood warnings up to seven days ahead, as opposed to the previous five-day lead time. Google’s Flood Hub, along with integration into Google Search and Maps, will play a central role in distributing these flood alerts, enhancing emergency response efforts.

Flood data generation has seen dramatic enhancements. The algorithm now incorporates a broader range of labeled data, trained from three times more locations than before. This results in a more robust model architecture and newly integrated weather forecasting inputs. The outcomes are promising; the model now matches state-of-the-art global flooding forecasts in both accuracy and reliability.

New tools for researchers and partners

In an effort to make flood forecasting data more accessible, Google is rolling out an upcoming API and its Google Runoff Reanalysis & Reforecast (GRRR) dataset. These resources aim to facilitate research and partnerships within the flood forecasting domain.

The API pilot program will allow researchers and experts to request access to hydrologic forecasts and expected flood status, including real-time data from local areas with tracking devices and historical data dating back to 1981. This access is critical in regions lacking reliable monitoring infrastructure. Alongside this, the new “virtual gauges” will create a robust data layer for researchers across over 150 countries, further enabling informed decision-making in flood-risk areas.

Additionally, Google is making historical outputs from its flood forecasting model publicly accessible through the GRRR dataset. This dataset provides flood forecasts and alerts from as far back as 1981, allowing scientists and researchers to analyze past flood events and trends. Such comprehensive insights can help communities understand historical impacts, possibly leading to strategies that mitigate flooding consequences now and in the future.

The ease of access to this wealth of data is significant. It equips local authorities and organizations with powerful tools to predict and prepare for disasters. For example, during the recent floods in Rio Grande do Sul, Brazil, the Google Research team worked closely with local government and organizations to enhance flood monitoring, allowing timely notifications and interventions.

AI driving climate action initiatives

Google’s commitment to improving flood forecasting aligns closely with global climate action initiatives. By leveraging AI, the company aims not just to protect property but also to safeguard lives in vulnerable communities. A clear testament to this is seen in their collaboration with humanitarian organizations, such as World Vision Brazil, which utilized Google’s flood forecasts to provide quick assistance during flooding events in less than two days. The access to real-time data allowed aid distribution decisions to be effectively executed, ensuring that essential supplies reached the affected more efficiently.

As Google continues to refine and expand its flood-focussed AI models, the emphasis remains on delivering actionable information. Whether it’s for first responders planning their routes or communities preparing for possible evacuations, this technology serves as a critical tool in climate crisis management efforts.

With the focus on making flood forecasting insights more widely available, Google plays a crucial role in helping both individuals and organizations take preventative actions, thus enhancing preparedness for disastrous flooding events across the globe. This ongoing effort supports the UN’s Early Warnings for All initiative, ensuring that vital information is at hand for those who need it most.

Featured image credit: Google

November 12, 2024, marked a significant milestone for the generative AI landscape as Writer, a full-stack generative AI platform, announced it has raised $200 million in Series C funding, elevating its valuation to $1.9 billion. Co-led by Premji Invest and Radical Ventures, with participation from Salesforce Ventures, Adobe Ventures, Citi Ventures, IBM Ventures, and Workday Ventures, this capital infusion will accelerate Writer’s expansion in the enterprise AI market.

The road to $1.9 billion and beyond

Writer’s impressive financial journey now totals $326 million raised since its founding in 2020 by May Habib and Waseem AlShikh, who had a previous venture in product localization. The company aims not just at creating generative models but at developing robust AI systems to handle critical enterprise tasks seamlessly.

Habib highlighted the company’s ambition: “With this new funding, we’re laser-focused on delivering the next generation of autonomous AI solutions that are secure, reliable, and adaptable in highly complex, real-world enterprise scenarios.” This statement underlines the intensity and intent with which Writer is pushing forward its AI capabilities, especially in an evolving marketplace where security and reliability take center stage.

The influx of capital will be utilized for product development initiatives aimed at enhancing Writer’s suite of tools, including customizable AI guardrails and AI-powered agents capable of orchestrating complex workflows across various sectors such as healthcare, retail, and financial services. Writer’s emphasis on these areas speaks volumes about its strategy to cater to industries facing the most intricate challenges within their operational frameworks.

An intriguing aspect of the Series C funding is the involvement of high-profile investors and strategic partnerships, indicating a strong vote of confidence in Writer’s business model. “Writer is a rare gem in the generative AI landscape,” said Rob Toews of Radical Ventures, praising the company’s exceptional growth trajectory and dedication to creating a full-stack AI platform.

The enterprise AI ecosystem evolving rapidly

Writer’s performance speaks for itself as it has quickly attracted prominent clients, including Mars, Ally Bank, Qualcomm, Salesforce, Uber, Accenture, L’Oréal, and Intuit. These partnerships are critical as companies increasingly look for integrated AI platforms that can drive productivity and deliver tangible returns on investment. In fact, Writer’s clients reportedly save millions of hours in productivity, averaging a 9x return on their investment.

As companies prioritize efficiency, Writer’s versatility allows it to tailor applications specific to diverse organizational needs. Writer’s current suite features the Palmyra family of models, which is designed for various enterprise applications, demonstrating the startup’s adaptability in addressing market demands. Recent developments include the release of Palmyra X 004, a model trained predominantly on synthetic data, boasting cost efficiency in its development compared to traditional methods.

“Writer provides a refined, AI-powered solution that’s effective, easy to deploy, and has rapidly accelerated our workflows here at Salesforce,” says Patrick Stokes, EVP of product and industries marketing at Salesforce, highlighting the tangible impact of Writer’s offerings on their operations.

Navigating the competitive generative AI landscape

Writer’s growth comes amidst a booming market where generative AI is increasingly attracting investor attention. According to a report from Accel, generative AI startups are slated to receive 40% of all venture capital directed toward cloud technologies this year, with investments in this sector exceeding $3.9 billion in the first half of 2024 alone.

Despite the competitive nature of the generative AI realm, Writer’s unique approach, which combines a full-stack platform with customizable solutions, sets it apart from its competitors. This differentiation is crucial as enterprise clients seek reliable and compliant AI applications tailored to their specific needs. Writer’s focus on security, ethical considerations, and compliance resonates particularly well in industries like finance, prompting notable endorsements from investors like Arvind Purushotham of Citi Ventures, who emphasized the importance of accuracy and security in AI tools for global financial institutions.

With a strong foundation, a growing portfolio of enterprise clients, and continued investment in innovation, Writer is well-positioned to redefine how large-scale enterprises leverage AI. The potential of the generative AI market is vast, projected to exceed $1 trillion in revenue within the next decade, although challenges such as privacy concerns, copyright issues, and the intricacies of model reliability will need addressing.

Writer’s journey is a testament to the company’s innovative approach in an ever-evolving sector, reflected in its solid backing from distinguished investors and a robust suite of AI solutions. As the digital landscape transforms, Writer could indeed become a cornerstone in enterprises’ AI strategies globally.

Cisco has launched new Wi-Fi 7 access points (APs), featuring AI-native capabilities and a unified subscription model, aiming to enhance connectivity, security, and experience for enterprises.

Key features and capabilities of Cisco’s new Wi-Fi 7 APs

During the recent Cisco Live APJC event in Melbourne, Tom Gillis, senior vice president and general manager of the Cisco Security Business Group, introduced these next-generation Wi-Fi 7 access points. With these products, Cisco intends to help customers tackle connectivity, security, and assurance challenges, while providing a flexible system to future-proof their workplaces.

Traditionally, users had to pick between cloud-managed Meraki APs or the on-premises Catalyst Center, but the new access points allow for seamless management through either platform. Although Wi-Fi 6E access points were the first to support dual-management, switching between modes previously required contacting Cisco and managing complicated back-end processes. Now, thanks to a new unified networking subscription, businesses can effortlessly manage their Wi-Fi 7 portfolio through a single license. Gillis stated, “This new subscription simplifies how customers do business” with the company, encouraging organizations to invest confidently in wireless solutions that will grow alongside them.

Matt Landry, vice president of product management for Cisco Wireless, highlighted that these new access points not only support Wi-Fi 7 but also incorporate enhanced features, particularly for IoT applications, including Bluetooth low-energy radios and built-in ultra-wideband technology for advanced location services. This convergence of traditional Meraki and Catalyst access points creates a unified Cisco wireless product line that adapts to diverse management needs.

Innovations that ensure intelligent, secure, and assured performance

Gillis emphasized three primary capabilities that deliver what he calls a “powerful, scalable and experience-driven approach” to wireless deployment. Promising to be the industry’s “most intelligent wireless offering,” Cisco’s Wi-Fi 7 platform comes equipped with self-configuration capabilities and AI-powered performance optimization. It leverages the Cisco Spaces platform, allowing businesses to transform work environments into smart spaces seamlessly.

A significant advantage of the new access points is their auto-detection system, which simplifies purchasing and deployment. With different countries requiring unique configurations, the past used to involve multiple SKUs and licensing issues. Now, the access points intelligently detect their location upon installation, automatically downloading the necessary configurations.

Security remains a top priority for Cisco, as the new access points integrate advanced threat detection features. They provide AI-native device profiling, threat prevention, and robust encryption for secure connections. Moreover, Cisco ThousandEyes, which has been incorporated into various Cisco products in recent years, enhances operational visibility by identifying performance bottlenecks across multiple network infrastructures. This integration streamlines troubleshooting, helping users resolve issues far more quickly and efficiently.

A cloud platform approach enhances accessibility and flexibility

Cisco’s latest innovations are reflective of its commitment to advancing its networking cloud platform strategy, characterized by the introduction of AI-native capabilities and an emphasis on operational simplicity and security. Landry stated, “When customers deploy a next-generation Wi-Fi 7 network, they will have everything they need to enable a smart spaces experience,” offering insights into location data and power consumption for better resource management.

The Wi-Fi 7 access points mark the first wave of products released under Cisco’s new organizational structure, all overseen by Executive Vice President Jeetu Patel. Patel has made it clear that Cisco is pivoting toward creating a cohesive “platform” that enhances the user experience. Although access points have often been viewed as commodities, Cisco’s investment in AI, security, and observability signifies a move toward more sophisticated solutions.

The access points will be available for order starting November 13, 2024, with shipping to commence in December, paving the way for improved enterprise networking and experiences for users moving forward.

The mobile app development landscape is advancing at an unprecedented rate, driven by rapid technological advancements, shifting user behaviors, and businesses’ insatiable appetite for innovative solutions. By 2025, we’ll see significant changes in how mobile apps are developed, deployed, and experienced. Whether you’re a business decision-maker, developer, or tech enthusiast, understanding the upcoming mobile app development technologies can aid you in staying ahead in a competitive market and gaining advantages on several fronts. Let’s dive into the top mobile app development trends that have a high chance of rising in 2025.

AI-powered personalization on a whole new level

The rise of AI has been reshaping mobile app development for years, but by 2025, personalization will be taken to an entirely new scale. Artificial intelligence won’t just power chatbots or recommendation engines anymore. It’ll anticipate what users need, adapt to their behavior, and provide real-time, individualized experiences in app. Picture an app that “knows” what you’re interested in today, serves up tailored content, and evolves its interface based on your habits.

For instance, imagine e-commerce apps that analyze not just what you’ve clicked but why. They’ll factor in trends in what people are buying, your unique style preferences, and even seasonal interests, adjusting offerings for each user. This evolution will extend beyond e-commerce to include sectors like fitness, banking, and learning. So, say goodbye to rigid interfaces and hello to fluid, AI-tailored experiences.

5G-powered apps for blazing-fast performance

With 5G becoming standard, the apps of 2025 will operate at speeds we haven’t seen before. The ultra-fast, low-latency nature of 5G will allow developers to bring out new, heavy-duty content, like high-definition streaming, complex games, and super-responsive video chats – all without maxing out your device’s battery or storage.

Applications relying on AR or VR will experience a massive boost from 5G, finally able to offer fluid, life-like experiences on mobile. Imagine multiplayer gaming that feels as smooth as a console experience or AR guides that make remote troubleshooting feel in-person. For anyone building apps, this means not just faster performance but new opportunities to rethink what’s possible in a mobile experience.

Super apps: A one-stop digital hub

The concept of “super apps” has already taken root in Asia, and we’re likely to see them spread across global markets by 2025. These apps do more than just one thing: they bundle multiple services, allowing users to do everything from ordering groceries to booking flights and sending messages – all within a single app.

Picture a user opening an app to pay bills, book a ride, order food, and manage finances, all without toggling between apps. Super apps not only simplify life for users but also create huge engagement opportunities for businesses. By consolidating different services in one place, they lower the barriers for users and increase loyalty.

Voice UI as a primary way of interaction

Voice tech is finally hitting its stride, and by 2025, we’ll see voice become a go-to input method for many apps. With advancements in NLP (natural language processing), mobile apps will be able to interpret voice commands more accurately than ever, making it easier for users to control apps, search content, and even type by speaking.

This trend will especially benefit people on the go, multitaskers, and those who prefer hands-free interactions. For developers, it’ll be essential to integrate voice as seamlessly as buttons or swipes in order to make sure apps understand context, handle complex requests, and respond naturally to spoken commands.

Augmented reality (AR) becomes commonplace

AR has been around for a while, but by 2025, we’ll see it used in ways that go beyond gimmicky features. With the capabilities of AR maturing, everyday uses are expanding. Imagine using AR in a retail app to see how the furniture fits in your living room or using an AR guide to find your way in a large venue.

Mobile AR will increasingly become part of shopping, learning, and navigation experiences, all thanks to advances in device power and 5G. Developers should start looking closely at AR frameworks and pay attention to updates in camera tech and processing capabilities because this trend is here to stay.

Security and privacy are non-negotiable

By 2025, trust will be a make-or-break factor for app users. People are more aware than ever of privacy risks and data security concerns. They’ll expect apps to handle data responsibly, with transparency around what’s collected, how it’s used, and how it’s stored.

This means app developers will be focusing on securing data, using biometrics, adopting end-to-end encryption, and following privacy regulations like GDPR and CCPA. Privacy-first design is expected to become a trend, meaning that users will be given clear, straightforward choices about what data they share. Apps that prioritize user control over data will have a competitive advantage.

Cross-platform apps reach native-like quality

Cross-platform frameworks like Flutter and React Native have been around, but by 2025, these platforms will be powerful enough to deliver performance that feels as seamless as native apps. This major shift is partly due to the increased demand for faster release cycles and more flexible development.

Imagine an app that works identically on iOS and Android without extra development cycles for each. With frameworks reaching native-like performance, developers can focus on delivering visually appealing and smooth user experiences across devices, making cross-platform development an attractive choice for more businesses.

Wearable devices and app integration get serious

Wearable tech like smartwatches, fitness trackers, and even smart clothing is getting smarter, and in 2025, apps will be built to seamlessly connect with them. The expanded use of sensors, more durable batteries, and the broader adoption of wearables will mean apps that support real-time health tracking, payments, and notifications.

As wearable devices become a regular part of people’s routines, apps that don’t integrate with them could start to feel outdated. For developers, optimizing apps to work with smaller screens, faster sync, and cross-device functionality will be essential as wearable demand grows.

Blockchain for greater security and transparency

Blockchain isn’t just for cryptocurrencies. In 2025, blockchain technology is expected to be embedded in more apps, bringing decentralized, secure options for everything from supply chain tracking to financial transactions. Blockchain will appeal to people who prioritize transparency and want control over their data, as it allows secure, tamper-proof transactions.

More apps will use blockchain for identity verification and digital ownership, including gaming and finance apps where trust and data control are vital. The result? Decentralized apps (dApps) may become mainstream, presenting users with more control over their digital identities and assets.

Low-code and no-code platforms widen access

Low-code and no-code platforms have transformed the app development process, and by 2025, they’ll be indispensable. These platforms make it possible for people without coding skills to create apps, meaning small businesses, solo entrepreneurs, and even non-tech teams can create functional apps.

While no-code options won’t replace traditional development for advanced apps, they’re essential for prototyping, customization, and quick-turnaround projects. Developers, too, can benefit by leveraging low-code tools for basic app functions, speeding up their workflows so they can focus on more complex, custom features.

OpenAI Orion, the company’s next-generation AI model, is hitting performance walls that expose limitations in traditional scaling approaches. Sources familiar with the matter reveal that Orion is delivering smaller performance gains than its predecessors, prompting OpenAI to rethink its development strategy.

Early testing reveals plateauing improvements

Initial employee testing indicates that OpenAI Orion achieved GPT-4 level performance after completing only 20% of its training. While this might sound impressive, it’s important to note that early stages of AI training typically yield the most dramatic improvements. The remaining 80% of training is unlikely to produce significant advancements, suggesting that OpenAI Orion may not surpass GPT-4 by a wide margin.

“Some researchers at the company believe Orion isn’t reliably better than its predecessor in handling certain tasks,” reported The Information. “Orion performs better at language tasks but may not outperform previous models at tasks such as coding, according to an OpenAI employee.”

The data scarcity dilemma

OpenAI’s challenges with Orion highlight a fundamental issue in the AI industry: the diminishing supply of high-quality training data. Research published in June predicts that AI companies will exhaust available public human-generated text data between 2026 and 2032. This scarcity marks a critical inflection point for traditional development approaches, forcing companies like OpenAI to explore alternative methods.

“Our findings indicate that current LLM development trends cannot be sustained through conventional data scaling alone,” the research paper states. This underscores the need for synthetic data generation, transfer learning, and the use of non-public data to enhance model performance.

OpenAI’s dual-track development strategy

To tackle these challenges, OpenAI is restructuring its approach by separating model development into two distinct tracks. The O-Series, codenamed Strawberry, focuses on reasoning capabilities and represents a new direction in model architecture. These models operate with significantly higher computational intensity and are explicitly designed for complex problem-solving tasks.

In parallel, the Orion models—or the GPT series—continue to evolve, concentrating on general language processing and communication tasks. OpenAI’s Chief Product Officer Kevin Weil confirmed this strategy during an AMA, stating, “It’s not either or, it’s both—better base models plus more strawberry scaling/inference time compute.”

Synthetic data: A double-edged sword

OpenAI is exploring synthetic data generation to address data scarcity for OpenAI Orion. However, this solution introduces new complications in maintaining model quality and reliability. Training models on AI-generated content may lead to feedback loops that amplify subtle imperfections, creating a compounding effect that’s increasingly difficult to detect and correct.

Researchers have found that relying heavily on synthetic data can cause models to degrade over time. OpenAI’s Foundations team is developing new filtering mechanisms to maintain data quality, implementing validation techniques to distinguish between high-quality and potentially problematic synthetic content. They’re also exploring hybrid training approaches that combine human and AI-generated content to maximize benefits while minimizing drawbacks.

OpenAI Orion is still in its early stages, with significant development work ahead. CEO Sam Altman has indicated that it won’t be ready for deployment this year or next. This extended timeline could prove advantageous, allowing researchers to address current limitations and discover new methods for model enhancement.

Facing heightened expectations after a recent $6.6 billion funding round, OpenAI aims to overcome these challenges by innovating its development strategy. By tackling the data scarcity dilemma head-on, the company hopes to ensure that OpenAI Orion will make a substantial impact upon its eventual release.

Featured image credit: Jonathan Kemper/Unsplash

Gemini 2.0 leaked this week, sparking anticipation for Google’s latest AI model release. TestingCatalog identified a model titled Gemini-2.0-Pro-Exp-0111 on the Gemini web app, available only to select users under the Gemini Advanced section. This discovery has heightened speculation about Gemini 2.0’s potential capabilities and suggests Google may be gearing up for a public launch soon.

Google’s next AI model: What we know so far

The Gemini-2.0-Pro-Exp-0111 model reportedly appears as an option for paid Google One AI Premium subscribers, who already enjoy exclusive access to advanced tools such as Gemini Advanced. Free users still have access to Gemini 1.5 Flash, but it’s rumored that Google may introduce a Gemini 2.0 Flash for them as well. TestingCatalog noted that the experimental model responds quickly to prompts and includes capabilities such as image generation and web search. However, the model is not yet available for general use, indicating it might still be undergoing internal testing.

The current model is labeled as “our experimental model,” according to Google. However, it remains uncertain whether this experimental tag hints at its readiness for full public release or if it’s merely part of Google’s preliminary tests.

A standalone Gemini app for iPhone on the horizon?

Adding to the intrigue, Google seems to be working on a standalone Gemini app specifically for iPhone users. Though iPhone users have been able to access Gemini AI through the Google app, TestingCatalog reported a sighting of a dedicated Gemini app briefly available on the App Store. This app, which includes voice search, text generation, and image creation, promises a more direct experience with Google’s AI features for iOS users. Voice search, showcased at Google I/O 2024, was one of the standout features, offering a conversational and advanced AI interaction.

If launched, this standalone app would be a significant addition for iPhone users who may not have access to Apple’s own advanced AI features. This move aligns with Google’s strategy of enhancing cross-platform accessibility, providing a viable alternative to Apple’s native AI tools.

Competing with OpenAI and anticipating a December launch

The timing of the Gemini 2.0 leak aligns with expectations of an official release by the end of 2024, as Google aims to keep pace with OpenAI’s anticipated launch of its Orion model. Both companies have been preparing to unveil their next-generation AI models, setting the stage for intense competition in the AI space.

With both Google and OpenAI in a race to dominate the next wave of AI technology, the imminent arrival of Gemini 2.0 could significantly bolster Google’s presence. The added advantage of a dedicated iOS app positions Google to reach users across multiple platforms, a strategic advantage in this competitive landscape.

Gemini Live can now speak French, German, Portuguese, Hindi, and Spanish

A new era for Google’s AI offerings

The leak of Gemini 2.0 and the possible release of a Gemini app for iPhone underscore Google’s commitment to delivering cutting-edge AI to a broader audience. As these developments unfold, they could redefine how users engage with AI across devices, setting a new standard for accessible, high-performance AI tools.

Google’s careful rollouts, combined with strategic platform inclusivity, signal a promising future for Gemini 2.0. With potential enhancements like faster response times, image generation, and voice-activated capabilities, the model stands to offer significant advancements over its predecessors.

Featured image credit: Kerem Gülen/Ideogram

Apple is making its first foray into the smart home camera market, with plans to release a security camera in 2026. This upcoming launch aims to reshape home security by offering seamless integration with Apple’s ecosystem, bringing privacy and advanced connectivity into focus.

Apple’s smart home camera could profoundly impact the current $7 billion home security camera industry, as Kuo notes, by delivering a device that works flawlessly with Apple devices like the iPhone, Apple Watch, and Apple TV. This would enable users to view feeds and manage security features directly within Apple’s interface, bringing a level of convenience and security unique to Apple’s ecosystem. Such deep integration may set Apple’s camera apart from competitors, enhancing user experience and potentially reshaping industry standards.

What Apple’s smart home camera brings to the table

With Apple entering the security camera market, the focus is on integrating HomeKit Secure Video, which already provides end-to-end encryption for third-party cameras. Apple’s own camera could leverage this technology to ensure secure video storage, giving customers peace of mind over their home data’s safety. Privacy has been a core value for Apple, and this camera could set new standards in an industry troubled by security issues, pushing competitors to enhance their own offerings.

Ming-Chi Kuo, a respected Apple supply chain analyst, remarked, “Apple is making its first foray into the smart home IP camera market, with mass production scheduled for 2026, targeting annual shipments in the tens of millions.” He further mentioned that Chinese company GoerTek would handle the assembly, adding to the credibility of the 2026 timeline.

Apple’s unique ecosystem for smart home security

Apple’s advantage lies in its ability to integrate devices seamlessly, creating a cohesive experience that other brands struggle to match. Apple’s camera will likely work wirelessly with other Apple products, meaning that users could access camera feeds on multiple devices with no extra setup. Kuo highlighted, “This strategic move demonstrates Apple’s continued exploration of growth opportunities in the home market,” which points to a future where Apple’s ecosystem becomes even more deeply rooted in people’s homes.

While other cameras on the market provide basic viewing options, Apple’s approach would allow users to control their smart home camera via Siri and potentially other AI-driven features. Apple’s use of advanced artificial intelligence, in combination with Siri’s capabilities, may give users enhanced control and insights into their home environment, a feature that could become central to Apple’s smart home strategy.

Apple might equip its AI cloud computers with M4 chip in 2025

Privacy and security as core selling points

The home security camera market is often marred by privacy issues, with many brands failing to protect customer data. Apple, however, has consistently prioritized security and privacy, and its new camera is expected to reflect this commitment. Apple’s HomeKit Secure Video already ensures end-to-end encryption, so a proprietary camera would likely extend this level of security, setting a high bar for privacy protection.

Kuo further speculated that Apple’s new camera could create ripple effects in the industry, forcing rivals to reevaluate their privacy measures. The integration of Apple’s camera within HomeKit would mean that users have a fully protected, cloud-based video solution, ensuring that sensitive footage is safe from breaches. For consumers who prioritize privacy, this could make Apple’s camera an ideal choice.

The future of Apple’s smart home expansion

Apple is not stopping with cameras. Reports suggest that the company is also developing a smart display to serve as a hub for smart home management, potentially rivaling Amazon’s Echo Show and Google’s Nest Hub. If Apple’s smart camera and smart display work cohesively, it could give users an all-encompassing solution for monitoring and controlling their home environments from one centralized device.

Featured image credit: Kerem Gülen/Ideogram

Disclaimer: The featured image is AI-generated and not an Apple product.

Ubisoft is facing a lawsuit over its recent decision to shut down the servers of The Crew, leaving players unable to access the game they believed they owned. Two Californian gamers have filed a class action lawsuit, arguing that the server shutdown has rendered their purchased physical copies of the game useless.

The lawsuit, filed on November 4, 2024, alleges that Ubisoft misled players about the nature of their ownership. The Crew, a racing game released in December 2014, became unplayable after Ubisoft shut down its servers due to “server infrastructure and licensing constraints.” The plaintiffs argue they were led to believe they were purchasing the game outright, when in reality, they were only licensing access. They compare this to buying a pinball machine, only to find it has been gutted without their permission. Despite Ubisoft offering refunds to recent buyers, many long-time players were left with no compensation.

Is Ubisoft close to liquidation?

The lawsuit against Ubisoft over The Crew is the latest in a series of challenges for the gaming giant. Some industry analysts are speculating whether Ubisoft’s recent financial and reputational setbacks could signal more profound problems within the company. While there’s no confirmed indication that Ubisoft is nearing liquidation, the growing dissatisfaction among gamers, coupled with class action lawsuits, raises questions about its stability and future direction.

The core issue at the center of this lawsuit is the difference between purchasing a game and licensing it. The lawsuit alleges that Ubisoft falsely represented that The Crew’s files on physical disks provided full ownership, when in fact, these disks merely acted as keys to access the game’s online servers. The shutdown left players who purchased physical copies with no offline mode, effectively rendering their games useless. Unlike other games, such as Knockout City, which patched in an offline version after server closures, Ubisoft did not offer a similar solution for The Crew.

The lawsuit calls for monetary damages for affected players and seeks court approval to convert it into a class action, allowing other players to join. The plaintiffs argue that, had they known their access could be revoked, they would not have purchased the game at the same price. This lawsuit aligns with broader efforts, such as the Stop Killing Games movement, which campaigns to ensure that games remain playable even after official servers are turned off.

As the gaming industry continues to shift towards digital and online-first models, the ownership versus licensing debate remains a significant concern for players. The controversy around Ubisoft’s handling of The Crew‘s shutdown has prompted some legislative changes in California, requiring companies to clarify when players are only buying a license. However, this law stops short of preventing companies from rendering games unplayable, highlighting the ongoing struggle between consumer expectations and corporate policies in digital media.

Waiting for a statement

Ubisoft has declined to comment on the lawsuit. The outcome of this case could have lasting effects on how digital games are sold and the responsibilities of companies to their customers, especially when games are tied to online services that can be shut down without notice.

Large language models (LLMs) are powerful tools for generating text, but they are limited by the data they were initially trained on. This means they might struggle to provide specific answers related to unique business processes unless they are further adapted.

Fine-tuning is a process used to adapt pre-trained models like Llama, Mistral, or Phi to specialized tasks without the enormous resource demands of training from scratch. This approach allows for extending the model’s knowledge base or changing its style using your own data. Although fine-tuning is computationally demanding compared to just using a model, recent advancements like Low Rank Adaptation (LoRA) and QLoRA make it feasible to fine-tune models using limited hardware, such as a single GPU.

The guide explores different methods to enhance model capabilities. Fine-tuning is useful when the model’s behavior or style needs to be altered permanently. Alternatively, retrieval-augmented generation (RAG) and prompt engineering are methods that modify how the model generates responses without altering its core parameters. RAG helps models access a specific library or database, making it suitable for tasks that require factual accuracy. Prompt engineering provides temporary instructions to shape model responses, though it has its limitations.

LoRA and QLoRA are cost-effective techniques that lower memory and compute requirements for fine-tuning. By selectively updating only a small portion of the model’s parameters or reducing their precision, LoRA and QLoRA make fine-tuning possible on hardware that would otherwise be insufficient.

Granite 3.0: IBM launched open-source LLMs for enterprise AI

1. Introduction to fine-tuning large language models

Fine-tuning large language models allows you to customize them for specific tasks, making them more useful and efficient for unique applications.

What is fine-tuning, and why is it important?

Fine-tuning is a crucial process in adapting pre-trained large language models (LLMs) like GPT-3, Llama, or Mistral to better suit specific tasks or domains. While these models are initially trained on a general dataset, fine-tuning allows them to specialize in particular knowledge areas, use cases, or styles. This can significantly improve their relevance, accuracy, and overall usability in specific contexts.

Benefits of fine-tuning vs. training a model from scratch

Training a language model from scratch is an incredibly resource-intensive process that requires vast amounts of computational power and data. Fine-tuning, on the other hand, leverages an existing model’s knowledge and allows you to enhance or modify it using a fraction of the resources. It’s more efficient, practical, and provides greater flexibility when you want to adapt an LLM for specialized tasks like customer support, technical troubleshooting, or industry-specific content generation.

2. When to consider fine-tuning for your business needs

Understanding when to apply fine-tuning is crucial for maximizing the effectiveness of large language models in solving business-specific problems.

Use cases for fine-tuning: When and why you should do it

Fine-tuning is ideal when you need your LLM to generate highly specialized content, match your brand’s tone, or excel in niche applications. It is especially useful for industries such as healthcare, finance, or legal services where general-purpose LLMs may not have the depth of domain-specific knowledge required.

What fine-tuning can and can’t accomplish

Fine-tuning is excellent for altering a model’s behavior, improving its response quality, or adapting its language style. However, if your goal is to fundamentally teach a model new facts or create a dynamic, evolving knowledge system, you may need to combine it with other methods like retrieval-augmented generation (RAG) or keep retraining with fresh data to ensure accuracy.

3. Alternatives to fine-tuning for customizing LLMs

There are several ways to customize LLMs without full fine-tuning, each with distinct advantages depending on your needs.

What is Retrieval-Augmented Generation (RAG) and when to use it

Retrieval-Augmented Generation (RAG) is a method that integrates the capabilities of a language model with a specific library or database. Instead of fine-tuning the entire model, RAG provides dynamic access to a database, which the model can reference while generating responses. This approach is ideal for use cases requiring accuracy and up-to-date information, like providing technical product documentation or customer support.

Introduction to prompt engineering: Simple ways to customize LLMs

Prompt engineering is the simplest way to guide a pre-trained LLM. By crafting effective prompts, you can manipulate the model’s tone, behavior, and focus. For instance, prompts like “Provide a detailed but informal explanation” can shape the output significantly without requiring the model itself to be fine-tuned.

Comparing RAG, prompt engineering, and fine-tuning: Pros and cons

While fine-tuning provides a more permanent and consistent change to a model, prompt engineering allows for flexible, temporary modifications. On the other hand, RAG is perfect when accurate, ever-changing information is necessary. Choosing the right method depends on the level of customization, cost, and need for accuracy.

4. Data preparation for LLM fine-tuning

Proper data preparation is key to achieving high-quality results when fine-tuning LLMs for specific purposes.

Importance of quality data in fine-tuning

Data quality is paramount in the fine-tuning process. The model’s performance will depend heavily on the relevance, consistency, and completeness of the data it is exposed to. High-quality data helps ensure that the model adapts to your specific requirements accurately, minimizing the risk of hallucinations or inaccuracies.

Steps to prepare your data for effective fine-tuning

1. Collect relevant data: Gather data that fits the use case and domain.
2. Clean the dataset: Remove errors, duplicates, and inconsistencies to improve data quality.
3. Format the data properly: Ensure the data is correctly formatted for the model, such as providing clear examples of the input-output pairs that the model should learn.

Common pitfalls in data preparation and how to avoid them

One common mistake is using biased data, which can lead the model to generate skewed or prejudiced outputs. To avoid this, ensure the data is well-balanced, representing a variety of viewpoints. Another pitfall is the lack of clear labels or inconsistencies, which can confuse the model during training.

5. Understanding LoRA and QLoRA for cost-effective fine-tuning

LoRA and QLoRA provide efficient ways to reduce the computational demands of fine-tuning large language models.

What is low-rank adaptation (LoRA) in LLMs?

Low-Rank Adaptation (LoRA) is a technique designed to make the fine-tuning of LLMs more efficient by freezing most of the model’s parameters and only adjusting a few critical weights. This allows for significant computational savings without a considerable drop in the model’s output quality.

How QLoRA further optimizes fine-tuning with lower memory requirements

QLoRA takes LoRA a step further by using quantized, lower-precision weights. By representing model weights in four-bit precision instead of the usual sixteen or thirty-two, QLoRA reduces the memory and compute requirements, making fine-tuning accessible even on less powerful hardware, such as a single consumer GPU.

Benefits of LoRA and QLoRA: Reducing memory and compute costs

LoRA and QLoRA drastically cut the cost of fine-tuning by reducing memory requirements and compute demands. These techniques allow developers to adapt LLMs without needing a data center full of GPUs, making customization of LLMs more accessible for smaller companies or individual developers.

6. Fine-tuning guide: Step-by-step instructions

Follow these step-by-step instructions to successfully fine-tune your large language model for custom use cases.

Setting up your environment for fine-tuning

To get started, you’ll need a Python environment with relevant libraries installed, such as PyTorch, Transformers, and any specific fine-tuning library like Axolotl. Set up your GPU and ensure it has sufficient VRAM to accommodate model weights and training data.

How to fine-tune Mistral 7B using a custom dataset

1. Load the Pre-Trained Model: Start by loading Mistral 7B using your preferred machine learning library.
2. Prepare the Dataset: Organize your custom data to align with the format the model expects.
3. Configure Hyperparameters: Set key parameters like learning rate, batch size, and the number of epochs.
4. Start the Training: Begin fine-tuning and monitor the loss to ensure the model is learning effectively.

Understanding and configuring essential hyperparameters

Hyperparameters like learning rate, batch size, and weight decay significantly impact the fine-tuning process. Experiment with these settings to balance between underfitting and overfitting, and use early stopping techniques to avoid wasting resources.

Tips for troubleshooting common fine-tuning issues

Issues like slow convergence or unstable training can often be addressed by adjusting the learning rate, using gradient clipping, or changing the dataset size. Monitoring loss and accuracy metrics is critical to ensure training progresses smoothly.

7. Managing memory requirements in fine-tuning

Managing memory effectively is essential to ensure successful fine-tuning, especially with limited hardware resources.

Calculating memory needs based on model size and precision

Memory requirements depend on the size of the model, the precision of its parameters, and the batch size used during training. For instance, Mistral 7B requires around 90 GB of VRAM for full fine-tuning at high precision but can be reduced significantly using QLoRA.

How to fine-tune models on single GPUs with LoRA/QLoRA

LoRA and QLoRA are designed to facilitate fine-tuning on machines with limited resources. With QLoRA, models can be fine-tuned using less than 16 GB of VRAM, making it possible to use high-end consumer GPUs like an Nvidia RTX 4090 instead of data center-grade hardware.

Scaling up: When to consider multi-GPU or cloud solutions

For larger models or more intensive training, using multiple GPUs or renting cloud GPU resources is a viable option. This approach ensures quicker turnaround times for large-scale fine-tuning projects.

8. The role of quantization in fine-tuning LLMs

Quantization helps reduce memory requirements and improve efficiency during the fine-tuning process.

What is quantization and how it affects model performance

Quantization reduces the precision of model weights, allowing the model to be more memory-efficient while maintaining acceptable performance. Quantized models, such as those trained with QLoRA, help achieve effective results with significantly reduced hardware requirements.

How quantized models enable efficient fine-tuning with limited VRAM

By reducing the weight precision to just a few bits, models can be loaded and trained using substantially less memory. This makes fine-tuning feasible on more affordable hardware setups without compromising much on accuracy.

Practical tips for implementing quantization with QLoRA

Always start by validating the model’s output quality after quantization. Although quantization offers significant memory savings, it can occasionally impact performance, so ensure you carefully evaluate the results with your validation dataset.

9. Fine-tuning vs. prompt engineering: Which to choose?

Choosing between fine-tuning and prompt engineering depends on your customization needs and available resources.

Key differences between fine-tuning and prompt engineering

While fine-tuning permanently changes a model’s weights to adapt it for specific use cases, prompt engineering influences outputs on a per-interaction basis without altering the core model. The choice depends on whether you need long-term adjustments or temporary guidance.

How prompt engineering can complement fine-tuning

Prompt engineering can be combined with fine-tuning to achieve highly specific and adaptive responses. For instance, a model fine-tuned for customer service could also utilize prompt engineering to dynamically adapt to a customer’s tone during a conversation.

Best practices for using prompt engineering with fine-tuned models

Clearly define the desired behavior through explicit instructions in your prompts. This way, even a fine-tuned model can be pushed in a particular direction for specific conversations or tasks.

10. Optimizing hyperparameters for fine-tuning

Optimizing hyperparameters is a critical step in ensuring the effectiveness of your fine-tuned LLM.

Overview of key hyperparameters in fine-tuning

Hyperparameters like learning rate, batch size, epochs, and weight decay control the model’s behavior during training. Optimizing these settings ensures the model adapts effectively to the new data without overfitting.

How hyperparameters impact model output and efficiency

The learning rate affects how quickly a model learns, while batch size impacts memory usage and stability. Balancing these hyperparameters ensures optimal performance, minimizing the risk of underfitting or overfitting the training data.

Practical tips for experimenting with hyperparameter settings

Experiment with different combinations and use tools like grid search or random search to find the optimal values. Track your model’s performance metrics and adjust accordingly to achieve the best results.

11. Advanced techniques in fine-tuning: Beyond basics

Explore advanced techniques to further enhance the performance of your fine-tuned LLM in specific domains.

Adapting models to specific domains: Finance, healthcare, and more

Fine-tuning is particularly valuable when adapting a general-purpose LLM to niche industries. For instance, adapting a model to understand financial documents or medical records involves fine-tuning it on domain-specific data, ensuring the model speaks the industry’s language fluently.

Fine-tuning for tone, style, and brand consistency

Models can be fine-tuned to match a specific tone or writing style. For example, customer support models can be fine-tuned to respond empathetically, while content generation models can be adapted to write in an authoritative or conversational tone.

Best practices for keeping models focused on relevant topics

To maintain a focused and reliable model, avoid overgeneralization by fine-tuning on data that strictly aligns with your intended use case. Regularly evaluate the model to ensure that its responses remain relevant and high-quality.

12. Deploying and testing fine-tuned models

Proper deployment and testing are essential to ensure that your fine-tuned model performs well in real-world scenarios.

Strategies for testing and validating your fine-tuned model

Before deploying your model, use a validation dataset that accurately represents the kind of inputs it will encounter. Testing for biases, inaccuracies, and general response quality ensures that the model will perform as expected in production environments.

Measuring performance and effectiveness in real-world scenarios

Evaluate the model’s performance using key metrics such as accuracy, response coherence, and latency. Real-world testing in controlled environments is also essential to observe user interactions and collect valuable feedback for further tuning.

Monitoring and updating fine-tuned models over time

The performance of a model can degrade over time, especially if the context or domain evolves. Establish regular update schedules and collect user feedback to ensure that the model remains up-to-date and performs well.

13. Resources for fine-tuning LLMs efficiently

Leverage various tools and resources to make the fine-tuning process more efficient and effective.

Recommended tools, libraries, and frameworks for fine-tuning

Tools like PyTorch, Hugging Face Transformers, and Axolotl provide the core framework for fine-tuning LLMs. Additionally, cloud services such as Google Colab or AWS can provide GPU access if you lack the necessary hardware.

Further reading and resources for advanced fine-tuning techniques

Look into advanced research papers on LoRA and quantization techniques to stay updated. Communities like Hugging Face forums and GitHub repositories offer valuable insights and practical guides.

Community and support resources for troubleshooting and best practices

Participate in developer forums and Discord groups dedicated to machine learning and LLM fine-tuning. These communities are invaluable for real-world tips, troubleshooting help, and staying abreast of best practices.

Choosing the right strategy for fine-tuning depends on your specific goals and constraints.

Fine-tuning offers the ability to tailor an LLM specifically to your needs, providing a balance between cost, customization, and performance. Depending on the use case, combining fine-tuning with other approaches like RAG or prompt engineering may yield the best results.

Choose fine-tuning if you need lasting and comprehensive adjustments. Opt for prompt engineering when short-term, flexible changes are sufficient, and consider RAG if accuracy and up-to-date knowledge are your primary concerns.

Image credits: Kerem Gülen/Midjourney 

So you too found yourself asking where did my iPhone Notes go…

Losing Notes from your iPhone can be a frustrating experience, especially if you’ve relied on the app for years. Many users have reported that Apple’s iCloud sync issues may cause Notes to disappear seemingly at random.

However, other factors could also be at play, from accidental deletions to sync settings or even storage-related glitches.

If you ask yourself you ask yourself where did my iPhone notes go all of a sudden you are not alone. u/nowthengoodbad and many others have expressed their discomfort and annoyance on platforms like Reddit and X (Twitter) for a long time as this is a common issue.

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Where did my iPhone Notes go?

Here’s a breakdown of what could cause your notes to disappear and steps you can take to recover them:

1. Check iCloud Sync and account settings

If you have enabled iCloud for Notes, your notes should sync across all your Apple devices. However, a sync error can cause them to disappear temporarily.

Go to Settings > [Your Name] > iCloud and ensure Notes is turned on. If you also use third-party accounts like Gmail, head to Settings > Mail > Accounts to confirm that Notes syncing is enabled for each account.

2. Recently deleted folder

Accidentally deleting a note is another common reason for asking, “Where did my iPhone notes go?”

In the Recently Deleted folder, Notes remain accessible for 30 days before permanent deletion.

Go to Folders > Recently Deleted in Notes and select the items you’d like to recover by choosing Edit > Select Note > Move.

3. Use the search bar

The Search function in Notes can help locate Notes that have moved to other folders. Tap the Search bar in Notes and use a keyword, title, or phrase from the note you’re looking for.

Be sure All Accounts is selected, as this will allow a search across all folders and connected accounts.

4. Check for storage issues

If your iPhone is low on storage, it can affect the performance of apps like Notes. Freeing up some space by clearing unnecessary files can help prevent any app-related glitches that might cause notes to disappear.

5. Access Notes via iCloud.com

Log in to iCloud.com to check if your Notes appear in the web version of iCloud. If they are accessible there, they might still be on your device and waiting to sync properly. Manually backing them up from iCloud.com by copying important notes to another document or storage service can ensure they are preserved.

6. Create backups to secure your Notes

Regularly backing up critical notes can save future headaches if you find yourself asking, “Where did my iPhone notes go?” Export notes to Google Drive, Dropbox, or save them as PDFs to secure them outside of iCloud.

7. Regular updates to avoid bugs

Apple frequently releases iOS updates to address bugs, including those affecting iCloud syncing. Regular updates can help avoid future losses, and you should make sure your device is running the latest iOS or macOS version.

8. Common causes of disappearing Notes

Sometimes a simple restart or a temporary iCloud sync error is the reason you’re asking, “Where did my iPhone notes go?”

Restarting the device or re-logging into your iCloud account may quickly solve this issue.

9. Apple Support

If you can’t recover your notes, reach out to Apple Support for assistance. They may have additional troubleshooting steps or be able to review your iCloud account for any sync or data loss issues.

By doing all these, you’re not only preserving your notes but also empowering yourself with reliable ways to keep them accessible, organized, and safe!

Keeping iPhone Notes secure involves staying on top of iCloud settings, regularly backing up important notes, and ensuring your device has sufficient storage and is updated. Regular backups can help avoid losing valuable data due to sync issues or accidental deletions, giving you peace of mind that your notes are safe.

Image credits: Emre Çıtak/Ideogram AI

Android 15 brings a cool feature to the forefront with the introduction of Wi-Fi Ranging, using the IEEE 802.11az protocol to offer sub-meter indoor positioning accuracy. Traditionally, tracking your location indoors with GPS has been unreliable due to signal obstruction by walls and other structures.

Wi-Fi Ranging tackles this issue head-on by using nearby Wi-Fi access points for pinpointing a device’s position. This breakthrough is set to redefine navigation within large indoor spaces—think sprawling malls, bustling airports, or conference centers—where GPS falters, offering Android users an unprecedented level of precision in indoor location tracking.

How does Wi-Fi Ranging work?

Wi-Fi Ranging builds upon older Wi-Fi-based positioning systems, such as Wi-Fi RTT (Round Trip Time) with FTM (Fine Timing Measurement). While Wi-Fi RTT achieved location accuracy of 1-2 meters, Wi-Fi Ranging enhances this with sub-meter precision, down to 0.4 meters. It does this by measuring the exact time it takes for signals to travel between a device and multiple Wi-Fi access points, using double the bandwidth (160 MHz), supporting the 6GHz band, and offering enhanced scalability and security.

Why is Wi-Fi ranging better than GPS for indoor tracking?

GPS relies on signals from satellites orbiting the Earth, which can be obstructed by walls and other structures, making it less effective indoors. Wi-Fi Ranging, on the other hand, utilizes local Wi-Fi access points to calculate precise indoor locations, overcoming the limitations of GPS in enclosed spaces. This makes it ideal for large venues like malls, airports, or convention centers where accurate indoor positioning is essential.

How is Wi-Fi Ranging different from Wi-Fi RTT and RSS-based tracking?

Wi-Fi Ranging is an evolution of previous Wi-Fi positioning methods. Earlier, Wi-Fi tracking relied on Received Signal Strength Indicator (RSSI), which could only achieve accuracy within 10-15 meters. Android introduced support for Wi-Fi RTT with FTM in Android 9, which improved accuracy to 1-2 meters by measuring the time-of-flight of RF packets between a device and access points. Now, Wi-Fi Ranging with 802.11az takes this further, offering accuracy within less than a meter by refining the timing measurements and using additional spectrum.

All confirmed and leaked Android 15 features

What devices will support Wi-Fi ranging?

Currently, only devices running Android 15 with Wi-Fi chips that support the 802.11az protocol can use Wi-Fi Ranging. Although many Android phones don’t yet have hardware support for Wi-Fi Ranging, Qualcomm’s new FastConnect 7900 chip is paving the way, enabling future phones to adopt this technology. It’s expected that compatible devices will become more common within the next few years as more manufacturers integrate this capability.

Do Wi-Fi access points need upgrades for Wi-Fi ranging?

Yes, for Wi-Fi Ranging to work, Wi-Fi access points must support the 802.11az standard. Many current Wi-Fi 6 access points may require firmware updates to enable this functionality. Until this update becomes more widespread, Wi-Fi Ranging will be limited to areas where access points have been upgraded or replaced with compatible ones.

How does Wi-Fi Ranging compare to UWB and Bluetooth channel sounding?

While Wi-Fi Ranging offers superior range and scalability, UWB (Ultra-Wideband) and Bluetooth Channel Sounding (BT CS) offer slightly higher accuracy. However, Wi-Fi Ranging has several advantages: it’s compatible with existing Wi-Fi infrastructure, has a more extensive link budget, and supports a larger number of clients, making it cost-effective and adaptable to various environments. According to Google engineer Dr. Roy Want, Wi-Fi Ranging is robust, secure, and capable of adapting to traffic conditions, making it an attractive option for many use cases.

What are the potential uses of Wi-Fi Ranging on Android 15?

Wi-Fi Ranging has several exciting applications. In retail, it could help customers navigate to specific products within a store. In smart homes, it could make automation more contextual, like automatically identifying which room you’re in to adjust the lighting accordingly. These use cases are just the beginning, as developers and businesses explore new ways to leverage precise indoor positioning for enhanced user experiences.

Featured image credit: Kerem Gülen/Ideogram

Encore, the AI-powered shopping assistant, is breaking down barriers in the world of thrift shopping by bringing hundreds of secondhand markets under one roof. Co-founded by former Apple engineer Alex Ruber and ex-Twitter/Asana engineer Parth Chopra, this search tool stems from a shared love for thrifting and a clear goal: make finding pre-loved treasures online easier and quicker.

The story of Encore, the AI-powered shopping assistant

The online thrift market is vast and, frankly, a maze—Depop, Mercari, ThredUp, eBay, Craigslist, and countless others. Each has its own specialty and quirks, leaving shoppers wading through them all to find exactly what they’re after. “It’s hard for consumers to sift through them all to try and get to the product you are looking for,” Ruber told TechCrunch. And that’s where Encore comes in, acting as the ultimate aggregator to cut through this fragmentation. Ruber’s own quest for a specific TV show jacket inspired him to make Encore a reality.

For Ruber and Chopra, Encore’s mission is deeply personal. Both founders are immigrants who frequented thrift stores, where they often found unique, one-of-a-kind items. Chopra, who developed a love for flea markets from his mother, shares how his upbringing influenced his vision for Encore: “For me, there was also personal interest because my mom used to take me to flea markets every Sunday. I bought a lot of stuff from those places,” he told TechCrunch.

Encore’s AI engine is like having a secondhand stylist by your side. It’s powered by a large language model that can process detailed, offbeat queries—think “Show me a dress that Emily wore in Emily in Paris Season 3 Episode 4″—and pull results from top platforms like Poshmark, the RealReal, and eBay. For those moments when you’re not sure where to start, Encore’s prompts like “Outfit inspo for…” help steer you in the right direction, so you’re not staring at a blank screen.

Encore has struck a nerve with thrift lovers in a booming market that’s expected to reach $73 billion in the U.S. and $350 billion globally by 2028, according to ThredUp. Encore itself sees over 50,000 searches a month and has been growing consistently, with search volume up 26% month-over-month and clicks growing at 15%. These numbers suggest Encore is becoming a go-to for thrifters looking for a streamlined search.

Encore’s current business model relies on affiliate partnerships, but it’s also testing a subscription plan. For $3 a month, Encore’s power users get perks like unlimited searches, advanced models, image-based searches, and premium support. It’s tailored for dedicated thrift hunters who crave a top-tier search experience.

Building a successful e-commerce brand in the age of Amazon

Trying out Encore AI shopping

We decided to test Encore without signing up, keeping it casual to see how the platform performs for a new user. Right from the start, we were greeted with a clean, minimalist interface that lets you choose your market location in the top left. We typed “I need iPhone 14” into the search bar, and almost instantly, Encore pulled up a variety of listings from different platforms like Mercari, eBay, and Reebelo, showcasing various options for an iPhone 14.

On the results page, each listing was displayed with essential details, such as price, condition, and seller platform, giving us a quick snapshot of what’s available across the secondhand marketplace. The interface also offered suggestions to refine our search, including filters like “show used iPhone 13 options” or “only under $800,” allowing us to easily adjust our preferences. This made it clear that Encore’s AI isn’t just looking for exact matches but is actively interpreting our needs to offer a wider range of options, including related or alternative products that might fit our budget and preferences.

AI-powered search everywhere

Currently, Encore is available in the United States, United Kingdom, Canada, Japan, France, Italy, Germany and the Netherlands, covering key regions with established secondhand markets. However, as the demand for sustainable shopping grows globally, Encore could look to expand into other major regions where secondhand retail is thriving like emerging markets in Asia and Latin America.

This expansion could unlock even more opportunities, allowing Encore to tap into diverse thrift cultures and product sources that appeal to both local and international buyers. Each new market would bring unique challenges in terms of inventory sourcing, local thrift trends, and user behavior. Expanding carefully and strategically could help Encore establish a global footprint while maintaining the localized shopping experience that secondhand enthusiasts value.

As Encore grows, it may also draw attention from other startups or established companies looking to capitalize on the booming secondhand market.

Encore has put a smart twist on secondhand shopping, tackling the fragmented market with an AI-powered tool that actually understands what thrifters want.

Image credits: Encore

Amidst the shimmering gold artifacts of the famed Treasure of Villena, unearthed in Spain in 1963, two seemingly ordinary corroded objects are sparking renewed fascination among archaeologists. Far from gold, these unassuming pieces—a dull bracelet and a hollow, rusted hemisphere—appear to hold secrets far older and rarer. Newly published research suggests these objects are forged from meteoritic iron, or “strange metal,” rather than terrestrial sources, an extraordinary feat given that the Iron Age hadn’t yet begun in the Iberian Peninsula when they were crafted. This revelation not only challenges prior assumptions about Bronze Age technology but also highlights how “strange metal” might have been among the most precious materials of the time.

The discovery, spearheaded by Salvador Rovira-Llorens, former head of conservation at Spain’s National Archaeological Museum, suggests a level of metallurgical sophistication well beyond what was previously thought possible in the region over 3,000 years ago. Archaeologists have long marveled at the intricate goldsmithing of the Treasure of Villena, a collection of 66 items dating back to between 1500 and 1200 BCE, which remains one of the most significant caches of Bronze Age gold artifacts in Europe.

However, the introduction of these two strange metal objects suggests that ancient Iberians may have developed techniques for working meteoritic iron, even before terrestrial iron smelting was widespread.

Why was this strange metal so rare and valuable?

Historically, meteorite iron is much richer in nickel compared to Earth-based iron, making it a rare and highly prized resource for Bronze Age artisans across various cultures. Testing at Villena’s Municipal Archaeological Museum confirmed this unique composition in the bracelet and hemisphere, aligning these artifacts with other ancient pieces known to use meteoritic iron, like the famed dagger of Tutankhamun.

Such strange metal was not only a symbol of advanced metallurgy but a cultural emblem, connecting ancient communities to the heavens.

Why the strange metal is so strange to find?

Two unique items within the Villena Treasure—a hollow hemisphere and an open bracelet—appear to be crafted from iron, a puzzling choice of material for the period. Described as having a “ferrous appearance” upon discovery, these artifacts posed a challenge because the Iron Age had not yet reached Iberia when the rest of the collection was produced. Preliminary analyses suggested that this strange metal might be meteoritic iron, a rare material highly prized in ancient societies.

Meteoritic iron, with its high nickel content, distinguishes itself from terrestrial iron by a distinct elemental composition and, at times, a cross-hatched Widmanstätten structure that emerges when viewed under a metallographic microscope.

AI is virtually unwrapping the past

In 2007, researchers received permission to analyze small samples from the two iron artifacts to confirm their composition. Utilizing mass spectrometry, they detected a nickel concentration consistent with meteoritic iron, aligning these objects with other Bronze Age artifacts made from extraterrestrial materials, such as the famed meteoritic dagger of Pharaoh Tutankhamun.

Although corrosion affected the metal’s surface, the findings strongly suggest that these objects were crafted from meteoritic iron, a rare and revered material in ancient times.

Dating the Villena Treasure

Establishing the Villena Treasure’s precise date has been challenging. Suggestions for its origin span from the High Bronze Age (1500–1300 BCE) to the Late Bronze Age, around the 8th century BCE, partly due to the presence of two iron artifacts. Studies of similar finds, such as the Cabezo Redondo Treasure also discovered in 1963, provide clues that suggest both treasures may date to 1400–1200 BCE, coinciding with the peak activity of the Cabezo Redondo settlement.

However, the presence of two iron artifacts has raised questions about this chronology, as iron was not commonly used in the region until later.

What the strange metal reveals about ancient societies?

The discovery of meteoritic iron in the Treasure of Villena adds to a growing list of Bronze Age artifacts crafted from “strange metal” worldwide. Along with the practical advantages of meteoritic iron—such as its strength and lack of smelting requirements—the metal’s extraterrestrial origins likely held cultural and spiritual weight.

This finding, while rooted in Spain, adds depth to our understanding of ancient metallurgical practices and global connections, as similar techniques and values are found in artifacts from Egypt and beyond.

As further research develops, strange metal could help unravel ancient trade networks and technological exchanges, shedding light on how early civilizations valued materials not just for their beauty or utility but for their cosmic origin and the mystery they held within.

You can find detailed information about the research and the characteristics of the metal in the publication of Trabajos de Prehistoria.

Featured image credit: Villena Museum

Microsoft is introducing a new layer of personalization to Outlook with the launch of AI-powered themes, known as “Themes by Copilot.” This feature, designed for both individual users and businesses, allows Outlook users with a Copilot Pro consumer subscription or business license to create dynamic themes that reflect their unique preferences and surroundings. Available across Outlook on iOS, Android, Windows, Mac, and the web, this new feature leverages AI to make the app interface not only functional but visually engaging.

Themes by Copilot allow users to create themes based on the local weather, their current location, or even a specific destination chosen from over 100 pre-curated spots. These themes aren’t static either; users can set them to update at different intervals, such as every few hours, daily, weekly, or monthly, adding a fresh visual twist each time they open their inbox.

Microsoft believes these personalized visuals will make using Outlook feel more welcoming and tailored to individual tastes. “Just as you might personalize your workspace with artwork or decor, these themes allow users to make their Outlook experience more engaging and welcoming,” the company stated in a blog post. This release marks one of the first integrations of dynamic AI theming into a productivity app, showcasing Microsoft’s commitment to merging functionality with creative expression.

Dynamic themes based on location and weather

One standout feature of Themes by Copilot is its ability to create themes that adapt to real-world factors, such as location and weather. For instance, the “My Location” feature generates theme visuals that reflect the user’s current surroundings, whether they’re at home, on a business trip, or on vacation. If location permissions are enabled, the theme will automatically update to reflect the user’s locale, offering visuals inspired by the geography, culture, or landmarks of the area. Additionally, a weather-based theme option allows users to see a visual representation of the day’s weather within their Outlook app, providing an extra layer of contextual information at a glance.

Microsoft Copilot now reads, thinks, and speaks

Microsoft envisions these themes as a digital extension of one’s workspace personalization, noting that “beautiful, personal digital surroundings” can enhance users’ experience and engagement with the app. As users move throughout the day, their Outlook interface can reflect these changes, adding an element of immersion and adaptability that was previously unavailable.

How to access and customize themes by Copilot

Themes by Copilot can be accessed directly through Outlook’s Appearance Settings. Within this menu, users can either select a theme from pre-existing topics or opt for a custom theme setup. This guided experience allows users to specify their theme’s visual style—choosing between realistic, oil painting, or cartoon aesthetics—and set how frequently they want their theme to update. For added personalization, each theme includes an accent color that complements the chosen visuals, extending the theme across the entire Outlook interface for a cohesive look.

Microsoft has made these AI-powered themes intuitive and easy to modify. Users can revisit their Appearance Settings anytime to switch themes or adjust the frequency of updates. This ease of customization, combined with the diversity of visual options, allows users to refresh their Outlook experience as frequently as they like, creating a more engaging and dynamic interaction with their email client.

Non-AI theme options for all Outlook users

For those without access to Themes by Copilot, Microsoft has also introduced a new set of non-AI-powered themes. These themes, available across all Outlook platforms—desktop, web, and mobile—come in vibrant colors, including green, red, and purple. Designed to enhance the Outlook interface without requiring a Copilot subscription, these themes bring more personalization options to every user.

Microsoft explained that while Themes by Copilot offers an advanced personalization experience, the non-AI themes ensure that every user can enjoy a more visually appealing Outlook, whether they prefer subtle or bold color accents. The company emphasized that these updates are part of an ongoing mission to make Outlook “more beautiful and approachable” for all users.

Themes by Copilot expands on Outlook’s AI capabilities

Themes by Copilot is the latest addition to a growing suite of AI tools in Outlook. In recent months, Microsoft has introduced several AI-powered features to its productivity apps, aiming to improve efficiency and user engagement. In Outlook, Copilot already supports drafting assistance, email summarization, and inbox prioritization. The new “Prioritize My Inbox” feature, expected to roll out to commercial users in late 2024, will further enhance productivity by analyzing emails and organizing them based on relevance to the user’s role.

The addition of Themes by Copilot complements these productivity features by addressing the visual experience of Outlook. By combining practical tools with aesthetic customization, Microsoft aims to create a more holistic experience that caters to both functional and emotional aspects of user engagement. These innovations reflect Microsoft’s vision of an Outlook that is not only a powerful communication tool but also a space where users feel comfortable and personally connected.

Themes by Copilot is part of a broader wave of AI enhancements across Microsoft’s product ecosystem. On the same day as the Outlook announcement, Microsoft rolled out AI-powered tools for Notepad and Paint to Windows Insiders. Notepad’s new AI features include a “Rewrite” tool that assists with rephrasing, tone adjustments, and content length modifications, while Paint introduced Generative Fill and Generative Erase tools for advanced image editing based on text prompts.

These features are currently in preview for Windows 11 users in select regions, including the U.S., U.K., France, Canada, Italy, and Germany. Microsoft’s aim is to incorporate Copilot across a wide range of applications, making everyday tools more versatile and intuitive through AI-driven enhancements. These updates reinforce Microsoft’s strategy of using AI to enhance the user experience across its suite of apps, from visual customization in Outlook to text and image editing in Notepad and Paint.

Microsoft has expressed enthusiasm about how Themes by Copilot will enhance user engagement in Outlook. “We can’t wait to see what you create,” the company stated, inviting users to explore this new level of customization. With these enhancements, Microsoft hopes to transform the daily experience of Outlook users by making it as unique as they are.

Image credits: Microsoft

Mistral AI has announced the release of its new content moderation API. This API, which already powers Mistral’s Le Chat chatbot, is designed to classify and manage undesirable text across a variety of safety standards and specific applications. Mistral’s moderation tool leverages a fine-tuned language model called Ministral 8B, capable of processing multiple languages, including English, French, and German, and categorizing content into nine distinct types: sexual content, hate and discrimination, violence and threats, dangerous or criminal activities, self-harm, health, financial, legal, and personally identifiable information (PII).

The moderation API is versatile, with applications for both raw text and conversational messages. “Over the past few months, we’ve seen growing enthusiasm across the industry and research community for new AI-based moderation systems, which can help make moderation more scalable and robust across applications,” Mistral shared in a recent blog post. The company describes its approach as “pragmatic,” aiming to address risks from model-generated harms like unqualified advice and PII leaks by applying nuanced safety guidelines.

Moderation API addresses bias concerns and customization needs

AI-driven content moderation systems hold potential for efficient, scalable content management, but they are not without limitations. Similar AI systems have historically struggled with biases, particularly in detecting language styles associated with certain demographics. For example, studies show that language models often flag phrases in African American Vernacular English (AAVE) as disproportionately toxic, as well as mistakenly labeling posts discussing disabilities as overly negative.

Generative AI vs. predictive AI: Full comparison

Mistral acknowledges the challenges of creating an unbiased moderation tool, stating that while their moderation model is highly accurate, it is still evolving. The company has yet to benchmark its API’s performance against established tools like Jigsaw’s Perspective API or OpenAI’s moderation API. Mistral aims to refine its tool through ongoing collaboration with customers and the research community, stating, “We’re working with our customers to build and share scalable, lightweight, and customizable moderation tooling.”

Batch API reduces processing costs by 25%

Mistral also introduced a batch API designed for high-volume request handling. By processing these requests asynchronously, Mistral claims the batch API can reduce processing costs by 25%. This new feature aligns with similar batch-processing options offered by other tech companies like Anthropic, OpenAI, and Google, which aim to enhance efficiency for customers managing substantial data flows.

Mistral’s content moderation API aims to be adaptable across a range of use cases and languages. The model is trained to handle text in multiple languages, including Arabic, Chinese, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. This multilingual capability ensures the model can address undesirable content across different regions and linguistic contexts. Mistral’s tool offers two endpoints tailored for either raw text or conversational contexts, accommodating diverse user needs. The company provides detailed technical documentation and benchmarks for users to gauge the model’s performance.

As Mistral continues to refine its tool, the API provides a unique level of customization, allowing users to adjust parameters based on specific content safety standards.

Featured image credit: Mistral

The Canadian government has ordered TikTok’s Canadian operations to shut down due to national security concerns. This decision requires TikTok’s parent company, ByteDance, to close its offices in Toronto and Vancouver. However, Canadians can still use the TikTok app, as the order does not restrict access to the platform itself.

Industry Minister François-Philippe Champagne announced the order following a national security review. The review, conducted under the Investment Canada Act, identified ByteDance’s presence as a security risk. “The government is not blocking Canadians’ access to the TikTok application or their ability to create content. The decision to use a social media application or platform is a personal choice,” Champagne stated.

Canada’s action reflects rising concerns about ByteDance’s ties to the Chinese government. Authorities worry that these connections could lead to user data being accessed by Chinese authorities, sparking fears over privacy and national security.

TikTok plans to contest ban in court

TikTok responded by announcing plans to challenge the Canadian government’s order. A TikTok spokesperson said the shutdown will mean “the loss of hundreds of local jobs.” They confirmed the platform will stay available for Canadian users, allowing creators to find audiences and businesses to thrive. “We will challenge this order in court,” the spokesperson added.

Canada’s move mirrors actions in the United States, where national security concerns have also targeted TikTok. Former President Donald Trump attempted to ban the app, but the courts blocked the order. More recently, President Joe Biden signed legislation demanding ByteDance sell TikTok to a U.S.-based company or face a potential ban. This legislation is still under legal review.

In Europe, similar concerns over data privacy and security have prompted governments to reconsider their policies on TikTok and other Chinese-owned technology firms.

Michael Geist, an internet law expert at the University of Ottawa, noted that banning the company without restricting the app might reduce accountability. He stated, “The risks associated with the app will remain, but the ability to hold the company accountable will be weakened.”

Canada’s decision also comes as the country rolls out new digital policies, such as the Online Streaming Act and the Online Harms Act, which could impact TikTok’s presence and obligations in Canada. With ByteDance out of Canada, the enforcement of these policies could become challenging.

TikTok maintains it does not share data with the Chinese government. Despite these assurances, critics argue that TikTok’s Chinese ownership alone presents risks. Ron Deibert, a researcher at Citizen Lab, has stated that TikTok reflects the broader problem of invasive data collection by social media apps. He suggests that comprehensive privacy legislation is needed to address these issues fully.

While TikTok previously faced a ban on Canadian government-issued mobile devices, this new order escalates the action by targeting ByteDance’s operations. The platform remains accessible, yet ByteDance’s ability to operate in Canada is now restricted.

Featured image credit: Kerem Gülen/Ideogram

Anthropic, Palantir, and Amazon Web Services (AWS) have joined forces to integrate Anthropic’s Claude AI models into U.S. government intelligence and defense operations. By leveraging Claude 3 and 3.5 within Palantir’s AI Platform (AIP) hosted on AWS, this partnership aims to transform data processing and analysis capabilities for government agencies, empowering them to gain insights faster and make informed decisions in critical scenarios.

What is the goal of this collaboration?

The Claude models, developed by Anthropic, are now available in Palantir’s highly secure, Impact Level 6 (IL6) cloud environment, which meets strict Defense Information Systems Agency (DISA) standards for national security-related data.

Within Palantir AIP on AWS, these models are intended to enhance U.S. intelligence and defense capabilities by rapidly processing large volumes of complex data, identifying patterns, and facilitating high-level analysis.

These AI-driven tools can streamline resource-intensive tasks such as document review and predictive analysis, ultimately supporting decision-making in sensitive situations.

Does Anthropic’s Claude AI have what it takes?

Claude stands out among AI offerings for its focus on responsible deployment and safety, a point Anthropic frequently emphasizes. While competitors like OpenAI’s models are also exploring governmental applications, Anthropic differentiates its technology by emphasizing ethical safeguards.

For example, the use of Claude models is limited to specific government-authorized tasks, such as intelligence analysis and advance warnings of potential military events, while actively avoiding applications that could be seen as destabilizing, like disinformation campaigns or unauthorized surveillance.

This approach aligns with a general public-sector demand for “safety-first” AI models that respect both operational efficacy and regulatory standards.

Where does AWS integration come into play?

The AWS integration offers the Claude models both security and flexibility, allowing AI-powered applications to be deployed on a reliable, scalable platform with multiple levels of data protection. Hosted on AWS GovCloud and accredited under IL6, Palantir AIP ensures that Claude can perform critical functions without compromising data security.

AWS Vice President Dave Levy underscores this as a significant step for public sector AI, enhancing productivity and safeguarding sensitive information.

Setting new standards

The collaboration reflects a broader trend of AI adoption within the U.S. government. The Brookings Institute recently reported a 1,200% increase in AI-related government contracts since early 2024, underscoring growing government interest in AI. This move from Anthropic and Palantir positions Claude as a key player in public-sector AI, with a reputation for ethical standards and rigorous security measures that may influence other tech companies in the field.

The U.S. defense and intelligence community’s interest in AI tools like Claude mirrors a broader industry shift towards embedding AI into mission-critical workflows. As Anthropic, Palantir, and AWS further operationalize Claude for government use, they are paving the way for new levels of digital agility and analysis, potentially reshaping U.S. intelligence practices for the future.

This partnership, set to benefit from continued innovations in cloud-based AI, illustrates how AI can responsibly elevate government capabilities while upholding high standards for security and ethical use.

You may read Palantir’s full statement here.

Image credits: Emre Çıtak/Ideogram AI

Google officially rolled out Google Vids, an AI-powered video creation tool now accessible to select Google Workspace users. Originally introduced in April 2024, Google Vids aims to simplify video production for professionals across various fields, from customer service to project management, without requiring extensive editing skills. This tool, powered by Google’s Gemini AI model, joins other Workspace applications like Google Docs and Slides, bringing new opportunities for workplace content creation.

How does Google Vids work?

Google Vids utilizes the Gemini AI model to help users create professional videos quickly and easily. By analyzing data from Google Drive files and user-provided prompts, the tool generates video storyboards, media suggestions, and even voiceovers. Users can start a project from scratch or use a variety of pre-designed templates, adjusting elements to suit specific needs. The “Help me create” feature allows users to enter a brief description or select a Drive document, and Gemini generates a preliminary storyboard based on that content.

Once a draft is prepared, users can choose a video style and customize scenes by adding their own media, selecting stock images, or inserting text. Google Vids also offers a range of customization options, such as transitions, animations, and text effects, that enhance the video’s overall quality.

Video: Google

Key features of Google Vids

1. Automated storyboards and media suggestions: Google Vids builds storyboards by analyzing user inputs, selecting stock media, text, scripts, and background music to create a first draft. The storyboard structure is editable, allowing users to adjust sub-topics or reorganize scenes based on their preferences.
2. Recording options and voiceovers: Google Vids provides a virtual recording studio, offering several options for users to add a personal touch to their projects. Users can record their own voice or video, use screen recordings, or even create audio-only narration. The AI also supports auto-generated voiceovers using preset voices.
3. Collaboration and real-time editing: As part of Google Workspace, Vids enables real-time collaboration, allowing team members to edit and refine videos together, making it a convenient tool for workplace projects. This collaborative approach enhances teamwork across departments, such as customer service and marketing.
4. AI-powered features available for free until 2026: Google has announced that Vids’ AI-powered functions, including “Help me create” and the teleprompter, will be free to users until the end of 2025. After this period, usage limits may apply, with more details on potential restrictions expected closer to 2026.

Google Vids is designed to serve a range of workplace needs and is well-suited for teams across industries. Teams can create help center videos, enhancing support resources. Company leaders can produce updates and announcements, allowing them to communicate more effectively. Learning and development teams can design tutorials and instructional videos, enhancing employee onboarding and training. Marketing teams can use Vids to recap campaigns, while project management teams can create recaps of meetings or provide regular updates.

MiniMax AI video generator is pretty impressive

How can I try Google Vids?

Currently, Google Vids is available for specific Google Workspace subscription plans, including Business Standard, Business Plus, Enterprise Standard, Enterprise Plus, and Education Plus, with the rollout beginning on November 7, 2024. Google notes that users should update their browser to the latest version for the best experience with Vids, as supported browsers include Chrome, Firefox, and Edge.

Featured image credit: Google

Cybersecurity experts have identified a new threat targeting Windows gamers: the malicious Winos4.0 framework, which disguises itself as game installation and optimization tools. First discovered by Fortinet’s FortiGuard Labs, Winos4.0 has rapidly evolved into a sophisticated malware platform with extensive control capabilities over infected systems. Below, we explore how Winos4.0 works, its impact on users, and the sectors at risk.

What is Winos4.0 and how does it work?

Winos4.0 is a malware framework that embeds itself within seemingly benign applications related to gaming, such as speed boosters and installation tools. This malware uses multiple stages to penetrate, establish persistence, and allow attackers to remotely control compromised systems. According to Fortinet, the first stage of the infection begins when a user unknowingly downloads a tainted gaming application. Once installed, Winos4.0 initiates a multi-step infection process:

1. Download and execute malicious files: The malicious application retrieves a disguised .bmp file from an external server. The file extracts a dynamic link library (DLL) file that enables the malware to integrate into the system.
2. Registry modifications: The malware uses the DLL file to set up a persistent environment by creating registry keys. This ensures that Winos4.0 remains active even after the system restarts.
3. Shellcode injection and API loading: In the following steps, the malware injects shellcode to load application programming interfaces (APIs), retrieve configuration data, and establish a command-and-control (C2) connection.
4. Advanced C2 communication: Winos4.0 frequently communicates with C2 servers, allowing remote operators to send commands and download additional modules for further exploitation.

Fortinet emphasizes that “Winos4.0 is a powerful framework… that can support multiple functions and easily control compromised systems,” urging users to avoid downloading unverified software.

Winos4.0’s malicious capabilities

Once fully embedded in a user’s system, Winos4.0 can perform a variety of harmful actions that endanger user privacy and data security. Key functions of Winos4.0 include:

• System monitoring: The malware collects system information such as IP addresses, operating system details, and CPU specifications.
• Screen and clipboard capture: Winos4.0 can take screenshots and monitor the clipboard, potentially capturing sensitive information, including passwords and cryptocurrency wallet addresses.
• Surveillance and data exfiltration: The malware uses its connection to C2 servers to exfiltrate data from the infected system, enabling attackers to gather documents, intercept screen activity, and monitor clipboard content.
• Anti-detection mechanisms: Winos4.0 checks for the presence of antivirus software from vendors like Kaspersky, Bitdefender, and Malwarebytes. If it detects such software, it adjusts its behavior to avoid detection or halts execution altogether.

Beware of Octo2 malware targeting European banks, disguised as popular apps

Fortinet’s research also points out that Winos4.0 specifically looks for crypto-wallet activity on infected systems, highlighting the financial motivations behind the malware’s design.

Winos4.0 may also be used to infiltrate educational institutions. FortiGuard Labs noted references in the malware’s code that suggest potential targeting of systems in the education sector. For instance, a file labeled “Campus Administration” was found within Winos4.0’s structure, pointing to a possible effort to access administrative systems in schools and universities.

A history of targeting specific regions

According to reports from cybersecurity companies Trend Micro and Fortinet, Winos4.0 is primarily distributed in regions where users may be more likely to download modified software versions, such as China. Campaigns like Silver Fox and Void Arachne have used Winos4.0 to exploit Chinese-speaking users, leveraging social media, search engine optimization tactics, and messaging apps like Telegram to distribute the malware.

These campaigns reflect an increasing trend where hackers adapt malware distribution strategies based on geographic and cultural factors, luring victims with software versions tailored for specific regions.

Experts note that Winos4.0 shares similarities with other well-known malware frameworks, such as Cobalt Strike and Sliver. Like these frameworks, Winos4.0 allows attackers to control systems remotely and deploy various functions that facilitate data theft, monitoring, and exploitation.

The modular nature of Winos4.0 means it can be easily updated and modified, making it a versatile tool for cybercriminals. Its resemblance to Cobalt Strike and Sliver implies that Winos4.0 could potentially serve as a long-term platform for sustained cyber attacks across different user groups.

Indicators of Compromise (IoCs)

Fortinet has published a list of Indicators of Compromise (IoCs) associated with Winos4.0, which include specific files and registry keys. Users can reference these IoCs to check for signs of infection. Notable indicators include:

• DLL files like you.dll and modules with Chinese filenames such as “上线模块.dll” (login module).
• Registry modifications in paths like “HKEY_CURRENT_USER\\Console\\0” where encrypted data is stored and C2 addresses are updated.

These IoCs are vital for organizations and individuals to detect and respond to Winos4.0 infections proactively.

Current protections and recommendations

As of now, Fortinet’s antivirus solutions have integrated protection mechanisms to detect and block Winos4.0. While the company has yet to release a detailed removal guide, Fortinet encourages users to monitor downloaded software closely.

Experts recommend the following precautions to minimize the risk of infection:

• Only download trusted software: Fortinet advises users to download applications exclusively from reputable sources and to be cautious with gaming optimization tools that may appear on unofficial websites.
• Regularly update security software: Updating antivirus software can help in detecting emerging malware threats, including Winos4.0.
• Monitor network traffic: Unusual network activity or connections to unknown servers may indicate the presence of malware like Winos4.0.

Featured image credit: Kerem Gülen/Midjourney

Google’s “Project Jarvis,” previously rumored as the company’s answer to Microsoft’s Copilot, just got an accidental confirmation when it briefly appeared in the Chrome Web Store.

Project Jarvis, like Microsoft’s Copilot, aims to serve as a productivity-boosting AI that can go beyond simple commands to complete multi-step, web-based tasks. For months, there have been hints that Google was working on an advanced, autonomous AI assistant that could control users’ Chrome sessions, perform tasks like shopping and booking travel, and conduct research with minimal user input.

This accidental reveal (now removed) on the Chrome Web Store confirms that Jarvis is indeed real—and likely on its way to release.

What Project Jarvis brings to the table?

Similar to Microsoft’s Copilot integration with Office, Jarvis is designed to simplify and automate complex tasks that require multiple steps. According to The Information, Jarvis will be powered by Google’s Gemini 2.0 AI model, enabling it to handle sequential workflows and complex reasoning.

Rather than merely providing responses, Jarvis is set to perform actions autonomously, controlling Chrome tabs and interacting with website interfaces by capturing and analyzing screenshots. This technology is more akin to a co-worker that can act independently rather than a simple assistant.

Functionality that goes beyond voice assistants

Jarvis isn’t your typical virtual assistant. While Copilot is designed to navigate software like Word, Excel, and Teams, Jarvis extends to performing complex operations across websites. Think of it as a digital assistant capable of “seeing” a web page, interpreting elements like forms or buttons, and making decisions based on its understanding.

It can “click” buttons, fill in data fields, and even compare items on various sites—tasks normally requiring multiple user steps. Jarvis’s screenshot-based navigation lets it work across a range of web layouts and forms, even those that vary widely, though this method does slow down its speed, making it take a few seconds to analyze each step.

Privacy in question

As with Microsoft’s Copilot, which integrates deeply into Office environments, Jarvis raises questions about privacy and security.

The assistant’s reliance on screenshots to interpret web pages means it could potentially capture sensitive information. Google’s commitment to testing Jarvis for security and data integrity will be critical to maintaining user trust. Given the level of control Jarvis could have over users’ devices, robust safeguards are essential to avoid unauthorized access or breaches.

AI and productivity becoming one

Both Google’s Project Jarvis and Microsoft’s Copilot are part of a growing trend toward autonomous, productivity-focused AI agents. As these technologies mature, they promise to reshape how users handle digital workflows, potentially reducing manual input across common digital tasks. It remains to be seen how Jarvis will compare directly to AI-driven productivity tools.

As Jarvis nears its official preview release, the tool could provide a look at the future of integrated AI tools, bringing powerful task automation directly to users’ browsers.

For now, we’ll watch closely as Google refines its latest entry into the AI race.

Image credits: Emre Çıtak/Ideogram AI

Forget the glitz of Dubai or the bustle of Lisbon. If you’re serious about the future of Web3 (or want to know what all the fuss is about), you need to head to Narva, Estonia, on December 4-5. Why Narva? Because that’s where W3N is setting up shop, and this isn’t your average Web3 or tech conference.

I’ve been covering tech since before some of you were born (ouch!), and I’ve seen the hype cycles come and go. But there’s something different brewing in the Web3 space (if you ignore all the crypto bros and blockchain vaporware), and W3N seems to have captured that lightning in a bottle.

First, the location itself is a statement. Narva sits right on the border of Estonia, about as far east as you can go. It’s a city where history and the future collide. It’s a fitting backdrop for a technology that’s all about bridging divides and building new connections.

But what exactly is W3N? Think of it as a concentrated dose of Web3 culture, heavily emphasizing the world-leading, the weird, and the wonderful. Over 500 attendees are expected – founders, digital builders, hardcore coders, investors, and even curious newbies – all drawn by the promise of something different.

And Dataconomy will be there at the “edge of Europe,” covering what’s new and exciting – both on-stage and around the venue.

Who’s on Stage at W3N?

W3N has curated a speaker list as diverse as the Web3 community itself. Rannar Park from e-Estonia will discuss the country’s digital governance initiatives. Others will dive deep into the world of decentralized AI, such as Marcello Mari, the founder of SingularityDAO. And let’s not forget Chris Ye from the DePIN Institute, who will explore the Wild West of decentralized physical infrastructure networks.

But it’s not just about the big names. W3N also provides a platform for rising stars and community leaders. This is where you discover the next big thing in Web3, the project that will disrupt the disruptors.

More Than Just Talk

W3N is more than just sitting in a conference hall and listening to lectures. They’re pushing the boundaries with:

• Digital Art Exhibitions: Web3 is fueling a renaissance in digital art, and W3N is putting that front and center. Expect mind-bending installations and interactive experiences that blur the lines between the physical and digital worlds.
• Community Building: One of Web3’s core strengths is its community-driven ethos. W3N fosters that with workshops, networking sessions, and a dedicated Web3 Community Brunch. This is where connections are made and collaborations are born.
• Unplug and Recharge:  It’s not all go-go-go at W3N. They recognize the need for balance, offering attendees a chance to unwind with activities like a traditional tea ceremony, which also serves as a deeper than skin-level way to connect with fellow W3Ners.
• The Afterparty: Let’s be honest; no tech conference is complete without a killer afterparty. W3N promises an immersive experience with music, art, and plenty of opportunities to let loose and connect with fellow Web3 enthusiasts.

Why Should You Care?

In a world saturated with Web3 conferences, why should W3N be on your radar? Here’s the thing:

• It’s Not Just Hype: W3N goes beyond the buzzwords and focuses on real-world applications of blockchain technology. You’ll learn about projects already making a difference, not just pie-in-the-sky ideas.
• It’s About More Than Finance: While DeFi is undoubtedly a significant part of the Web3 landscape, W3N recognizes that the technology has far broader implications. This conference explores the full spectrum of Web3’s potential, from art and culture to governance and social impact.
• It’s on the Cutting Edge: Estonia is a global leader in digital innovation, and Narva’s unique location adds an extra layer of intrigue. This is where East meets West, where old meets new. It’s the perfect place to get a glimpse of the future.

So, if you’re tired of the same old Web3 conferences and want to experience something unique, book your ticket to Narva this December. W3N is a reminder that Web3 is more than just a technology; it’s a cultural movement, a community, and a force for change. And who knows, you might discover the next big thing while there.

Find out more about W3N and get your tickets (using fiat or crypto, naturally) at the official website.

Microsoft has started testing new AI features in Windows 11’s Paint and Notepad applications. These updates are being rolled out to Windows Insiders in the Canary and Dev Channels, bringing more advanced capabilities to users for editing both images and text.

AI-enhanced Paint features

Two major AI features, Generative Fill and Generative Erase, are being introduced to Microsoft Paint (version 11.2410.28.0). These features are designed to simplify the creative process for users, making it easier to edit images seamlessly.

Generative Fill

Generative Fill allows users to outline or designate a region within an image using the Selection Tool, and then fill it with AI-generated content that integrates naturally with the scene. This new tool aims to enhance artistic creation by making it easy to add elements to an image, such as filling in missing parts or adding new details. Users can describe what they want to add, and the AI will generate options to choose from. If the generated content doesn’t match your expectations, you can try again until you find the desired result.

To get started with Generative Fill, users need to make a rectangular or free-form selection using the Selection Tool in the Paint toolbar. Once an area is selected, a menu will pop up with the Generative Fill option. Users can enter a description of what they want to add, and press “Create” to generate the content. If the result isn’t satisfactory, users can use the “Try again” button to refine the selection or adjust the text prompt until they achieve the desired effect. Users can cycle through multiple generated options using arrow buttons and press “Keep” to apply the content to the canvas.

Generative Fill is initially available on Snapdragon-powered Copilot+ PCs, and users need to sign in with their Microsoft account to access this feature. The feature is available only to Windows Insiders in supported markets, and availability may vary based on regional criteria.

Generative Erase

Generative Erase, which has already been available in the Photos app, is now coming to Paint. This tool lets you remove unwanted objects from an image while automatically filling in the background to create a seamless look. Users can manually brush over areas they wish to erase, or use selection tools to specify which part of the image should be removed. Generative Erase is available for all Windows 11 PCs running the Windows Insider code.

To use Generative Erase, users can select the eraser tool and choose “Generative Erase” from the left side of the canvas. The generative erase brush allows users to manually brush over one or more areas they wish to remove. There are also options to “Add area to erase” or “Reduce area to erase” to adjust the selection further. After selecting the area, users can click “Apply” to remove the object. Additionally, users can use rectangular or free-form selection tools to define the area for removal.

These tools bring a simpler, more user-friendly way to modify images, making Paint a powerful yet accessible tool for both casual users and hobbyist creators. Unlike other software like Photoshop, Paint remains a free and straightforward option for editing tasks that require advanced AI assistance.

Notepad’s new AI capabilities

Notepad (version 11.2410.15.0) is also receiving a significant AI update, giving users more control over their text content. This update introduces an AI-powered rewrite feature that allows users to refine their text by rephrasing, adjusting tone, or changing the length of selected passages.

To use this feature, users can select the text they want to edit, right-click to access the Rewrite option, or use the keyboard shortcut Ctrl + I. Notepad will then generate three variations of the rewritten text, and users can select one or retry for additional versions. Options are also available to make the content shorter or longer, or modify the tone to suit specific needs. The original versions are preserved in case the user wants to revert to previous text.

Microsoft’s complicated Copilots explained in detail

The rewrite feature also allows users to modify the tone, making it possible to adjust the text to be more formal or casual depending on the context. Additionally, users can customize rewrite settings and click “Retry” to generate more refined versions of the text. If desired, the rewrite feature can be disabled in the app settings.

The rewrite feature is available in preview for Windows Insiders in selected regions, including the United States, Canada, the UK, France, Italy, and Germany. Microsoft 365 subscribers in other markets like Australia, New Zealand, Malaysia, Singapore, Taiwan, and Thailand can also access this feature using AI credits.

With this update, Notepad is evolving from a basic text editor to a more dynamic tool for content creation and refinement, making it easier for users to craft polished, well-written notes and documents.

Improved performance and Cocreator update

Microsoft has also made improvements to Cocreator, enhancing the underlying diffusion-based model for faster results and better performance. Cocreator is available on Snapdragon-powered Copilot+ PCs and is designed to improve the efficiency of creative processes by leveraging a local neural processing unit (NPU). Built-in moderation features have also been added to ensure a trustworthy and safe creative experience.

The Cocreator model update is aimed at delivering high-quality results more efficiently, providing a responsive experience that makes generating creative content faster and more reliable. These enhancements make Cocreator a valuable tool for creators seeking to quickly bring their ideas to life without sacrificing quality.

Additionally, Notepad’s performance has been optimized, with most users experiencing a 35% improvement in launch time, and some seeing up to a 55% boost. These improvements mean users can start editing their content even faster, contributing to a more efficient workflow.

Updates to Image Creator

Microsoft has also expanded the availability of the Image Creator feature in Paint, which was launched last year in preview. Image Creator allows users to generate new images using AI and is available in preview for users in the United States, France, the UK, Canada, Italy, and Germany. Additionally, Microsoft 365 Personal and Family subscribers in regions like Australia, New Zealand, Malaysia, Singapore, Taiwan, and Thailand can now use AI credits to access Image Creator in Paint.

Image Creator in Paint is designed to provide users with creative possibilities by generating AI-based images. Users need to sign in with their Microsoft account to use Image Creator, and the feature is being expanded to additional markets to provide more users with access to these creative tools.

When?

These features are beginning to roll out to Windows Insiders in the Canary and Dev Channels, and will be made more widely available as Microsoft gathers and evaluates user feedback. As always, Microsoft encourages users to share their thoughts through the Feedback Hub (WIN + F) to help refine these new tools.

Microsoft’s ongoing improvements to Paint and Notepad reflect its commitment to making AI more accessible and practical for everyday tasks. By incorporating AI tools that simplify creative work and text editing, Windows 11 continues to evolve into a platform that enhances productivity and creativity for all users.

Microsoft is also looking forward to community feedback to refine these features further. The rollout is being done gradually to gather insights and make necessary adjustments before broader distribution. Users are encouraged to provide feedback on their experience with these new AI tools, helping Microsoft to continue improving its services and making AI technology more effective and user-friendly.

Featured image credit: Kerem Gülen/Ideogram

Microsoft has introduced a new multi-agent artificial intelligence (AI) system called Magentic-One, designed to complete complex tasks using multiple specialized agents. Available as an open-source tool on Microsoft AutoGen, this system aims to assist developers and researchers in creating applications that can autonomously manage multi-step tasks across various domains.

What is Magentic-One?

Magentic-One is a generalist multi-agent system that uses an orchestrator to coordinate different agents, each specializing in a particular task. The lead agent, called the Orchestrator, works alongside four specialized agents:

• WebSurfer agent: Handles web browsing, clicks, and web content summarization.
• FileSurfer agent: Manages local files, directories, and folders.
• Coder agent: Writes and executes code, analyzes information, and creates new projects.
• ComputerTerminal agent: Provides a console for program execution by the Coder Agent.

These agents work together to solve open-ended tasks, making Magentic-One suitable for applications like software engineering, data analysis, and scientific research. Microsoft describes Magentic-One as a “flexible and scalable alternative to single-agent systems” due to its modular design, which allows agents to be added or removed without affecting the system’s core structure.

Key features

Magentic-One stands out because of its ability to activate multiple agents using a single language model. The system can perform various tasks, from navigating web browsers to executing Python code. This functionality means it can handle real-world scenarios such as booking tickets, purchasing products, or editing documents on a local device.

The modular multi-agent architecture ensures that each agent has a distinct responsibility, resulting in higher efficiency for complex, multi-step tasks. This approach enables Magentic-One to divide a problem into subtasks, improving both accuracy and speed of task completion. For example, if the system is asked to book a movie ticket, each agent will handle a different part of the task, such as processing visual information, navigating the website, and completing the transaction.

Microsoft’s AutoGen framework powers Magentic-One, supporting integration with various large and small language models to meet different cost and performance requirements. Currently, the system is tested with models like GPT-4o and OpenAI’s o1-preview, though it remains model-agnostic, allowing for future flexibility.

To assess the effectiveness of Magentic-One, Microsoft has also released AutoGenBench, a tool that evaluates agentic performance on several benchmarks such as GAIA, AssistantBench, and WebArena. These benchmarks focus on tasks like multi-step planning and tool usage. Microsoft’s initial tests from October 2024 indicate that Magentic-One delivers competitive results against state-of-the-art methods.

Video: Microsoft

The growing trend: Multi-agent systems

Magentic-One is part of a growing trend towards multi-agent AI systems. OpenAI has introduced Swarm, another framework aimed at building and deploying multi-agent systems. Similarly, IBM launched the Bee Agent Framework, an open-source toolkit that supports deploying agent-based workflows, compatible with models like IBM Granite and Llama 3.2. These systems, much like Magentic-One, aim to offer scalable solutions to complex problem-solving tasks.

According to Microsoft, “Magentic-One’s plug-and-play design supports easy adaptation and extensibility by enabling agents to be added or removed without altering other agents or the overall architecture.” This flexibility is particularly important for evolving business needs and new applications, making Magentic-One a promising tool for researchers and developers seeking to create more adaptive AI systems.

Featured image credit: Kerem Gülen/Ideogram

According to a 9to5Mac report, Apple is reportedly preparing to upgrade its AI cloud computers with the new M4 chip, starting next year. Currently, these special cloud computers, designed for processing Apple Intelligence requests, are powered by the M2 Ultra chip. However, a new report suggests that the M4 chip will soon replace it, aiming to boost Apple’s AI capabilities.

Private Cloud Compute (PCC) modules for better security

A report from Nikkei Asia on Wednesday revealed that Apple is working with Foxconn to develop new AI servers in Taiwan. This collaboration is part of Apple’s plan to accelerate the rollout of AI-based features. The decision to manufacture the servers in Taiwan is strategic, as Apple aims to leverage the engineering talent and R&D resources there, which are also used by Nvidia, another Foxconn customer.

Apple M4 chip: Everything you need to know

Apple Intelligence relies on both on-device processing and online cloud processing for certain tasks. When local device models can’t handle a specific request, Apple turns to its Private Cloud Compute (PCC) modules. These modules ensure that data is processed with end-to-end encryption, extending the security and privacy of Apple devices into the cloud.

“For the first time ever, Private Cloud Compute extends the industry-leading security and privacy of Apple devices into the cloud, making sure that personal user data sent to PCC isn’t accessible to anyone other than the user — not even to Apple. Built with custom Apple silicon and a hardened operating system designed for privacy, we believe PCC is the most advanced security architecture ever deployed for cloud AI compute at scale.”

-Apple

Currently, most PCC modules are equipped with the M2 Ultra chip, with some using the older M1 chip for lighter workloads. The Nikkei Asia report indicates that Apple is now planning to upgrade these PCC modules with the M4 chip starting next year, which promises significant advancements in AI processing capabilities.

Although the report doesn’t specify which M4 variant will be used, it’s speculated that the PCC modules will feature the upcoming M4 Ultra chip. This would be a logical choice, as the M4 family—including the recently announced M4 Pro and M4 Max variants—boasts substantial improvements in AI performance.

Apple’s move to integrate the M4 chip into its AI cloud computers signals its commitment to pushing the limits of AI capabilities while maintaining strong security and privacy for users. The M4 chip’s enhanced performance for AI workloads could bring faster and more efficient processing to Apple Intelligence, benefiting users who rely on these AI features. Are we on the brink of a new wave of smarter, more responsive AI capabilities driven by Apple’s latest hardware upgrades?

Featured image credit: Kerem Gülen/Midjourney

GitHub has taken a significant step in expanding its suite of AI tools by introducing GitHub Spark, an AI-powered platform designed to revolutionize the way developers build applications. This new tool, which launched last week, went largely unnoticed by mainstream media but may represent a major turning point in software development—particularly in how we use apps on our devices.

What is GitHub Spark?

GitHub Spark enables anyone to create customized ‘micro apps’ in real time using natural language prompts. These applications, referred to as “Sparks,” can be created almost instantly and used across different platforms without the need to download software from app stores. GitHub, owned by Microsoft, aims to bring a new level of accessibility and personalization to app development, signaling a broader shift in consumer interaction with technology.

Creating apps without app stores

GitHub Spark’s ability to create apps instantly could mark the beginning of the end for traditional app stores. Rather than downloading and installing a pre-built application, users can now generate a “Spark” on demand, tailored specifically to their needs. For example, if you need a travel app for an upcoming trip, you can simply tell Spark what you require, and within moments, the app is ready to use on your phone.

This departure from relying on third-party developers and app stores could transform how users interact with technology. The convenience of making personalized, temporary apps could make app stores seem redundant, allowing individuals to “roll their own” solutions quickly and affordably.

GitHub Spark features and capabilities

GitHub Spark is designed to let users share their Sparks with others, giving them the ability to control access through read-only or read-write permissions. This collaborative capability is reminiscent of the way Anthropic manages “Claude’s Artifacts”—offering a platform for users to remix and adapt shared content to their specific needs.

According to Thomas Dohmke, GitHub’s CEO, Spark aims to become an accessible tool for developers and non-developers alike, giving everyone the ability to bring their app ideas to life with ease. Users can describe their app in natural language, create a prototype, and refine it through an iterative, chat-like process—all without needing programming skills. This makes the platform accessible to a broader audience, from casual users looking to simplify their daily tasks to experienced developers seeking a rapid prototyping tool.

The introduction of GitHub Spark represents a continuation of the company’s goal to enhance developer productivity. Alongside Spark, GitHub also expanded the capabilities of its AI-powered Copilot tool, which now supports multiple models including Anthropic’s Claude 3.5 Sonnet and Google’s Gemini 1.5 Pro, in addition to OpenAI’s models. This multi-model support allows developers to leverage different AI models for different use cases, leading to more context-aware coding assistance across various programming languages and frameworks.

Spark and these new capabilities are part of GitHub’s larger vision to simplify software development and make it more inclusive. By integrating natural language capabilities and providing users with choices across different AI models, GitHub aims to create a development environment that prioritizes user needs and flexibility.

Are we inching closer to a true low-code, no-code future? With tools like GitHub Spark blurring the lines between developers and everyday users, it’s hard not to wonder if the era of complete simplicity in software creation is just around the corner.

Featured image credit: Kerem Gülen/Midjourney

OpenAI has added a significant asset to its domain portfolio by acquiring Chat.com, a well-established web domain originally registered in September 1996. This latest acquisition is part of OpenAI’s ongoing efforts to expand its brand and presence in the artificial intelligence space.

Sam Altman’s announcement on X

Sam Altman, CEO of OpenAI, announced the acquisition in a simple post on X (formerly Twitter), sharing just the URL “chat.com.” As of today, the domain now redirects visitors to OpenAI’s chatbot, ChatGPT. An OpenAI spokesperson confirmed the acquisition through an email statement.

The history of Chat.com dates back to 1996, making it one of the longstanding domain names on the web. Last year, Dharmesh Shah, co-founder and CTO of HubSpot, bought the domain for $15.5 million, positioning it among the most expensive domain sales on record. Shah announced in March that he had sold Chat.com to an undisclosed buyer, and late on Thursday, he confirmed that OpenAI was that buyer. Shah hinted that the transaction involved OpenAI shares as part of the compensation.

Although OpenAI has not revealed the exact amount paid for the acquisition, many predict the price exceeded $15 million, given the previous sale value and the high demand for premium domains in the AI sector. Shah’s posts on X and LinkedIn suggested a complex deal, possibly including a mix of cash and equity. He mentioned that he had always wanted to own shares in OpenAI, which aligns with the idea that the transaction included stock options.

OpenAI’s domain acquisition strategy

OpenAI’s acquisition of high-profile domain names is not a new strategy. Last year, the company acquired ai.com, which also redirects users to ChatGPT. However, ai.com briefly redirected to Elon Musk’s xAI earlier this year. The details of that switch remain unclear, leaving questions about whether Musk had acquired the domain or if the original owner was negotiating with multiple parties.

Currently, both Chat.com and ai.com redirect to OpenAI’s flagship AI chatbot, ChatGPT. The decision to acquire these domains underscores OpenAI’s push to make AI more accessible to users, positioning ChatGPT as the definitive conversational AI product online. While no rebranding has been confirmed, the move to consolidate these valuable domains hints at a broader branding effort to associate everyday terms like “chat” and “AI” with OpenAI’s services.

The acquisition has sparked discussions within the tech community, especially regarding the potential implications for OpenAI’s business strategy. Securing domains like Chat.com and ai.com allows OpenAI to strengthen its market presence and simplifies how users find its services. This strategy is consistent with the company’s efforts to lead the AI landscape by making its tools and services synonymous with common AI-related terminology.

Sam Altman’s low-key announcement on X gained significant attention, quickly amassing over three million views and thousands of likes. The post’s simplicity reflects OpenAI’s confidence in its brand and its approach to big announcements – letting the action speak for itself without extensive promotion.

Dharmesh Shah’s role in the sale

Dharmesh Shah’s involvement in the sale also attracted interest. In a detailed post, Shah described how he acquired the domain last year and his reasons for selling it. He hinted at a friendly relationship with OpenAI, suggesting that the terms of the deal were favorable beyond just the monetary aspect. Shah noted that he doesn’t typically profit from transactions involving friends, adding to speculation that he may have accepted a lower price in exchange for shares in OpenAI.

This latest acquisition also highlights the competitive nature of securing high-value domains within the AI industry. Premium domains like Chat.com are valuable assets that can significantly impact branding, user acquisition, and visibility. By acquiring Chat.com, OpenAI not only gains a premium web address but also removes a potential asset from competitors who might want to use it for similar AI chatbot services.

Featured image credit: Kerem Gülen/Ideogram

It’s undeniable that it’s a particularly hard time to be a woman in tech.

While the mass layoffs experienced in 2023 have steadied somewhat—according to tech layoff tracker Layoffs.fyi, 490 tech companies have made 143,142 workers redundant in 2024 compared to 1,193 tech companies making 264,220 employees redundant in 2023—women are still vastly underrepresented in the sector.

According to a new report by KPMG, women make up just over a third of the data and analytics (D&A) and artificial intelligence (AI) workforce. Despite more people graduating university with a STEM-related degree in 2024 compared to 2012, the amount of women graduating with a STEM-related degree has declined by 8%.

5 tech roles hiring across Germany

• Software Engineering Manager, 1&1 Mail & Media, Munich
• Head of Objectives and Strategies, Berliner Wasserbetriebe, Berlin
• Senior Verification Architect, Beckman Coulter Diagnostics, Munich
• Technical Contract Manager Turbine Technology and Heat Exchangers, BEW Berliner Energie und Wärme AG, Berlin
• Global Account Manager, Arrow Electronics, Inc., Germany

Mind the gap

Drilling down into the data, it’s clear that the problem isn’t just about gender parity but representation of women in senior roles.

KPMG’s report also highlights that the gender gap is more pronounced at senior levels, despite new roles being created in tandem with advances in AI and analytics, and the need for workers skilled in these fields growing at a rapid rate.

And the growing divide has picked up pace post-Covid with women’s representation in tech trending down across all levels in the last 10 years. In 2024, only 29% of senior D&A and AI roles were held by women, compared to 31% in 2008.

One way that organisations can tackle this issue from the ground up is through promoting visible leadership diversity; however, to truly remove the barriers preventing women from excelling in tech, more needs to be done at a grassroots level.

This includes mentorship and building a community of female employees, retaining more women by offering benefits including paid maternity leave, flexible working arrangements and helping women develop their own leadership skills.

Words of wisdom

“I believe that this study reflects the reality of what women in STEM fields experience. Throughout my career, I realised I worked harder, performed better, was paid less, moved up at a slower pace, and yet I made a conscious decision to keep going,” says Danielle Maurici-Arnone, global chief information and digital officer at personal care brand Combe Incorporated.

“At first, it was a personal challenge I set for myself and then it became a mission to try to make it easier for other women—for my own daughter, to achieve her dreams. I remind myself and tell my female colleagues and friends, ‘we need you, you matter, you are not alone, keep going, and stay passionate’.”

This sense of belonging doesn’t need to happen internally either, and expanding your professional network by reaching out to women who hold senior leadership positions in other companies is a great way to not only connect, but reinforce a sense of community.

“For many women leaders in data and AI, we are sometimes one of the only women in the room. Even outside the boardroom we can struggle to build and maintain a strong peer network and the community that we need to turn to for guidance, wisdom, and support,” says Nancy Morgan, CEO of Ellis Morgan Enterprises.

“If other women do not see women represented at every level of leadership, they may perceive that these roles are not for them or that they somehow do not ‘belong.’ Women need to find a community, in their organisation and/or externally, who can support them as they try out ideas, learn to make bold moves, and create a vibrant network of support.”

Find your next role in tech today on the Dataconomy Job Board

In what many are calling the “Year of Embodied AI,” Meta has taken a big step in advancing robotic capabilities through a suite of new technologies. Meta’s Fundamental AI Research (FAIR) division recently introduced three research artifacts—Meta Sparsh, Meta Digit 360, and Meta Digit Plexus—each bringing advancements in touch perception, dexterity, and human-robot collaboration.

What is embodied AI and why does it matter?

Embodied AI refers to artificial intelligence systems that are designed to exist and operate within the physical world, understanding and interacting with their surroundings in ways that mimic human perception and actions. Traditional AI systems excel at data analysis but fall short when applied to physical tasks, which require not only vision but also sensory feedback such as touch. By building embodied AI, researchers aim to create robots that can sense, respond, and even adapt to their environment, bridging the gap between digital intelligence and real-world functionality.

Meta’s innovations in embodied AI are aimed at achieving what its Chief AI Scientist Yann LeCun calls Advanced Machine Intelligence (AMI). This concept envisions machines that are capable of reasoning about cause and effect, planning actions, and adapting to changes in their environment, thereby moving from mere tools to collaborative assistants.

What is Meta AI today?

Meta’s breakthroughs in embodied AI: Sparsh, Digit 360, and Digit Plexus

Meta’s recent announcements underscore its commitment to tackling the limitations of current robotics technology. Let’s explore the capabilities of each new tool.

Meta Sparsh: The foundation of tactile sensing

Meta Sparsh, which means “touch” in Sanskrit, is a first-of-its-kind vision-based tactile sensing model that enables robots to “feel” surfaces and objects. Sparsh is a general-purpose encoder that relies on a database of over 460,000 tactile images to teach robots to recognize and interpret touch. Unlike traditional models that require task-specific training, Sparsh leverages self-supervised learning, allowing it to adapt to various tasks and sensors without needing extensive labeled data.

This ability to generalize is key for robots that need to perform a wide range of tasks. Sparsh works across diverse tactile sensors, integrating seamlessly into different robotic configurations. By enabling robots to perceive touch, Sparsh opens up opportunities in areas where dexterous manipulation and tactile feedback are critical, such as in medical applications, robotic surgery, and precision manufacturing.

Meta Digit 360: Human-level tactile sensing in robotics

Digit 360 is Meta’s new tactile fingertip sensor designed to replicate human touch. Equipped with 18 distinct sensing features, Digit 360 provides highly detailed tactile data that can capture minute changes in an object’s surface, force, and texture. Built with over 8 million “taxels” (tactile pixels), Digit 360 allows robots to detect forces as subtle as 1 millinewton, enhancing their ability to perform complex, nuanced tasks.

This breakthrough in tactile sensing has practical applications across various fields. In healthcare, Digit 360 could be used in prosthetics to give patients a heightened sense of touch. In virtual reality, it could enhance immersive experiences by enabling users to “feel” objects in digital environments. Meta is partnering with GelSight Inc to commercialize Digit 360, aiming to make it accessible to the broader research community by next year.

Meta Digit Plexus: A platform for touch-enabled robot hands

Meta’s third major release, Digit Plexus, is a standardized hardware-software platform designed to integrate various tactile sensors across a single robotic hand. Digit Plexus combines fingertip and palm sensors, giving robots a more coordinated, human-like touch response system. This integration allows robots to process sensory feedback and make real-time adjustments during tasks, similar to how human hands operate.

By standardizing touch feedback across the robotic hand, Digit Plexus enhances control and precision. Meta envisions applications for this platform in fields such as manufacturing and remote maintenance, where delicate handling of materials is essential. To help build an open-source robotics community, Meta is making the software and hardware designs for Digit Plexus freely available.

Meta’s partnerships with GelSight Inc and Wonik Robotics

In addition to these technological advancements, Meta has entered partnerships to accelerate the adoption of tactile sensing in robotics. Collaborating with GelSight Inc and Wonik Robotics, Meta aims to bring its innovations to researchers and developers worldwide. GelSight Inc will handle the distribution of Digit 360, while Wonik Robotics will manufacture the Allegro Hand—a robot hand integrated with Digit Plexus—expected to launch next year.

These partnerships are significant as they represent a shift towards democratizing robotic technology. By making these advanced tactile systems widely available, Meta is fostering a collaborative ecosystem that could yield new applications and improve the performance of robots across industries.

PARTNR: A new benchmark for human-robot collaboration

Meta is also introducing PARTNR (Planning And Reasoning Tasks in humaN-Robot collaboration), a benchmark designed to evaluate AI models on human-robot interactions in household settings. Built on the Habitat 3.0 simulator, PARTNR provides a realistic environment where robots can interact with humans through complex tasks, ranging from household chores to physical-world navigation.

With over 100,000 language-based tasks, PARTNR offers a standardized way to test the effectiveness of AI systems in collaborative scenarios. This benchmark aims to drive research into robots that act as “partners” rather than mere tools, equipping them with the capacity to make decisions, anticipate human needs, and provide assistance in everyday settings.

Image credits: Meta 

Japan has launched the world’s first wooden satellite, known as LignoSat, into space, Reuters reports. Created by researchers at Kyoto University in collaboration with Sumitomo Forestry, this small cube-shaped satellite was deployed aboard a SpaceX Falcon 9 rocket from NASA’s Kennedy Space Center. LignoSat is now on its way to the International Space Station (ISS), where it will soon be released into orbit to test the resilience of wood in space.

What is LignoSat: The world’s first wooden satellite

LignoSat, a 10 cm cube weighing just a few kilograms, is designed with honoki wood, a type of magnolia native to Japan. The satellite’s construction uses traditional Japanese craftsmanship without any screws or glue, enhancing its eco-friendly appeal. But LignoSat isn’t just about aesthetics or cultural heritage; it serves as a pilot project to test if wood can survive the extreme environment of space and offer a sustainable alternative to conventional satellite materials.

The name LignoSat derives from the Latin word “lignum,” meaning wood, signaling its creators’ intention to redefine how space structures are designed and built. Takao Doi, a former astronaut and professor at Kyoto University who spearheads the project, explained, “Satellites that are not made of metal should become mainstream,” as he believes wood’s unique properties may prove advantageous for space applications.

Why use wood for satellites?

Wood may not seem like an obvious choice for space technology, but it offers distinct benefits. Here’s why scientists and engineers are exploring its potential:

1. Environmentally friendly: Traditional satellites are made from metals that do not fully disintegrate during re-entry, creating harmful metal particles in the atmosphere. Wood, however, burns up cleanly without leaving debris, making it a potential solution for reducing space pollution.
2. Durability in space: Surprisingly, wood may perform better in space than on Earth. According to Kyoto University professor Koji Murata, “Wood is more durable in space because there’s no water or oxygen to rot or inflame it.” This resilience makes wood an attractive candidate for long-term space structures.
3. Sustainability: Unlike metals, wood is a renewable resource that can be produced sustainably. As humanity looks toward lunar and Mars habitats, using self-replenishing materials like wood could support the creation of eco-friendly space infrastructures.
4. Historical precedent: The idea of using wood in aerospace isn’t entirely new. As Professor Murata pointed out, “Early 1900s airplanes were made of wood.” Given that wooden structures have previously proven their resilience, researchers are optimistic that LignoSat will validate wood’s potential as a space-grade material.

How will LignoSat test the properties of wood in space?

Once released from the ISS, LignoSat will remain in orbit for around six months, during which its durability in extreme conditions will be rigorously tested. Equipped with sensors, the satellite will send data back to Earth, enabling researchers to monitor how the wooden structure withstands:

• Extreme temperature fluctuations: In low-Earth orbit, LignoSat will be exposed to temperatures that swing dramatically between -100°C to 100°C every 45 minutes, as it cycles between sunlight and shadow.
• Space radiation: Another key factor will be radiation, which can degrade materials over time. LignoSat will gather data on how well honoki wood shields its electronic components, providing insight into wood’s potential as a protective material for space electronics.
• Physical strain: The structural integrity of wood in a vacuum will be evaluated, determining whether it warps or splinters under the stresses of space.

Kenji Kariya, a manager at Sumitomo Forestry’s Tsukuba Research Institute, highlighted an additional application: “LignoSat will also gauge wood’s ability to reduce the impact of space radiation on semiconductors, making it useful for applications such as data center construction.”

Could wooden satellites help reduce space junk?

Space junk, or orbital debris, is an escalating issue as satellites and spacecraft accumulate in Earth’s orbit. Current satellite materials, particularly metals, don’t fully burn up during re-entry, leaving harmful particles in the atmosphere. In contrast, wooden satellites are designed to burn up entirely, minimizing pollution and reducing the environmental impact.

If wood proves capable of withstanding space’s hostile environment, it could open up new markets for wood-based materials. “It may seem outdated, but wood is actually cutting-edge technology as civilization heads to the moon and Mars,” noted Kenji Kariya. Using wood for space applications could invigorate the timber industry, transforming perceptions of this age-old material as a modern solution to futuristic challenges.

Image credits: Irene Wang/Reuters 

On-device AI and running large language models on smaller devices have been one of the key focus points for AI industry leaders over the past few years. This area of research is among the most critical in AI, with the potential to profoundly influence and reshape the role of AI, computers, and mobile devices in everyday life. This research operates behind the scenes, largely invisible to users, yet mirrors the evolution of computers — from machines that once occupied entire rooms and were accessible only to governments and large corporations to the smartphones now comfortably hidden in our pockets. 

Now, most large language models are deployed in cloud environments where they can leverage the immense computational resources of data centers. These data centers are equipped with specialized hardware, such as GPUs and TPUs, or even specialized AI chips, designed to handle the intensive workloads that LLMs require. But this reliance on the cloud brings with it significant challenges:

High Cost: Cloud services are expensive. Running LLMs at scale requires continuous access to high-powered servers, which can drive up operational costs. For startups or individual engineers, these costs can be prohibitive, limiting who can realistically take advantage of this powerful technology.

Data Privacy Concerns: When users interact with cloud-based LLMs, their data must be sent to remote servers for processing. This creates a potential vulnerability since sensitive information like personal conversations, search histories, or financial details could be intercepted or mishandled.

Environmental Impact: Cloud computing at this scale consumes vast amounts of energy. Data centers require continuous power not only for computation but also for cooling and maintaining infrastructure, which leads to a significant carbon footprint. With the global push toward sustainability, this issue must be addressed. For example, a recent report from Google showed a 48% increase in greenhouse gas emissions over the past five years, attributing much of this rise to the growing demands of AI technology.

That’s why this issue continues to catch the focus of industry leaders, who are investing significant resources to address the problem, as well as smaller research centers and open-source communities. The ideal solution would be to allow users to run these powerful models directly on their devices, bypassing the need for constant cloud connectivity. Doing so could reduce costs, enhance privacy, and decrease the environmental impact associated with AI. But this is easier said than done.

Most personal devices, especially smartphones, lack the computational power to run full-scale LLMs. For example, an iPhone with 6 GB of RAM or an Android device with up to 12 GB of RAM is no match for the capabilities of cloud servers. Even Meta’s smallest LLM, LLaMA-3.1 8B, requires at least 16 GB of RAM — and realistically, more is needed for decent performance without overloading the phone. Despite advances in mobile processors, the power gap is still significant.

This is why the industry is focused on optimizing these models — making them smaller, faster, and more efficient without sacrificing too much performance.

This article explores key recent research papers and methods aimed at achieving this goal, highlighting where the field currently stands:

• Meta’s MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
• Huawei’s Kronecker Decomposition method for GPT compression
• “TQCompressor: Improving Tensor Decomposition Methods in Neural Networks via Permutations,” a recent open-source project and research paper that improved the Kronecker decomposition method, allowing GPT models to be compressed by 1.5x without significantly increasing time requirements, a study I was involved in as a co-author.

Meta’s approach to designing LLMs for on-device use cases

This summer, Meta AI researchers introduced a new way to create efficient language models specifically for smartphones and other devices with limited resources and released a model called MobileLLM, built using this approach.

Instead of relying on models with billions or even trillions of parameters — like GPT-4 — Meta’s team focused on optimizing models with fewer than 1 billion parameters.

The authors found that scaling the model “in-depth” works better than “in-width” for smaller models with up to or around 1 billion parameters, making them more suitable for smartphones. In other words, it’s more effective to have a higher number of smaller layers rather than a few large ones. For instance, their 125-million parameter model, MobileLLM, has 30 layers, whereas models like GPT-2, BERT, and most models with 100-200 million parameters typically have around 12 layers. Models with the same number of parameters but a higher layer count (as opposed to larger parameters per layer) demonstrated better accuracy across several benchmarking tasks, such as Winogrande and Hellaswag.

Graphs from Meta’s research show that under comparable model sizes, deeper and thinner models generally outperform their wider and shallower counterparts across various tasks, such as zero-shot common sense reasoning, question answering, and reading comprehension.
Image credit: MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Layer sharing is another technique used in the research to reduce parameters and improve efficiency. Instead of duplicating layers within the neural network, the weights of a single layer are reused multiple times. For example, after calculating the output of one layer, it can be fed back into the input of that same layer. This approach effectively cuts down the number of parameters, as the traditional method would require duplicating the layer multiple times. By reusing layers, they achieved significant efficiency gains without compromising performance.

As shown in the table from the research, other models with 125M parameters typically have 10-12 layers, whereas MobileLLM has 30. MobileLLM outperforms the others on most benchmarks (with the benchmark leader highlighted in bold).
Image credit: MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

In their paper, Meta introduced the MobileLLM model in two versions — 125 million and 350 million parameters. They made the training code for MobileLLM publicly available on GitHub. Later, Meta also published 600 million, 1 billion, and 1.5 billion versions of the model. 

These models showed impressive improvements in tasks like zero-shot commonsense reasoning, question answering, and reading comprehension, outperforming previous state-of-the-art methods. Moreover, fine-tuned versions of MobileLLM demonstrated their effectiveness in common on-device applications such as chat and API calls, making them particularly well-suited for the demands of mobile environments.

Meta’s message is clear: If we want models to work on mobile devices, they need to be created differently. 

But this isn’t often the case. Take the most popular models in the AI world, like LLaMA3, Qwen2, or Gemma-2 — they don’t just have far more parameters; they also have fewer but much larger layers, which makes it practically very difficult to run these models on mobile devices.

Compressing existing LLMs

Meta’s recent research shifts away from compressing existing neural networks and presents a new approach to designing models specifically for smartphones. However, millions of engineers worldwide who aren’t building models from scratch — and let’s face it, that’s most of them — still have to work with those wide, parameter-heavy models. Compression isn’t just an option; it’s a necessity for them.

Here’s the thing: while Meta’s findings are groundbreaking, the reality is that open-source models aren’t necessarily being built with these principles in mind. Most cutting-edge models, including Meta’s own LLaMA, are still designed for large servers with powerful GPUs. These models often have fewer but much wider layers. For example, LLaMA3 8B has nearly 65 times more parameters than MobileLLM-125M, even though both models have around 30 layers.

So, what’s the alternative? You could keep creating new models from scratch, tailoring them for mobile use. Or, you could compress existing ones.

When making these large, wide models more efficient for mobile devices, engineers often turn to a set of tried-and-true compression techniques. These methods are quantization, pruning, matrix decomposition, and knowledge distillation. 

Quantization

One of the most commonly used methods for neural network compression is quantization, which is known for being straightforward and effectively preserving performance.

Image credit: Jan Marcel Kezmann on Medium

The basic concept is that a neural network consists of numbers stored in matrices. These numbers can be stored in different formats, such as floating-point numbers or integers. You can drastically reduce the model’s size by converting these numbers from a more complex format, like float32, to a simpler one, like int8. For example, a model that initially took up 100MB could be compressed to just 25MB using quantization.

Pruning 

As mentioned, a neural network consists of a set of matrices filled with numbers. Pruning is the process of removing “unimportant” numbers, known as “weights,” from these matrices.

By removing these unimportant weights, the model’s behavior is minimally affected, but the memory and computational requirements are reduced significantly. 

Matrix decomposition 

Matrix decomposition is another effective technique for compressing neural networks. The idea is to break down (or “decompose”) large matrices in the network into smaller, simpler ones. Instead of storing an entire matrix, it can be decomposed into two or multiple smaller matrices. When multiplied together, these smaller matrices produce a result that is the same or very close to the original. This allows us to replace a large matrix with smaller ones without altering the model’s behavior. However, this method isn’t flawless — sometimes, the decomposed matrices can’t perfectly replicate the original, resulting in a small approximation error. Still, the trade-off in terms of efficiency is often worth it.

Knowledge distillation

Knowledge distillation, introduced by Hinton et al. in 2015, is a simple yet effective method for creating a smaller, more efficient model (the “student model”) by transferring knowledge from a pre-trained, larger model (the “teacher model”). 

Using knowledge distillation, an arbitrarily designed smaller language model can be trained to mimic the behavior of a larger model. The process works by feeding both models the same data, and the smaller one learns to produce similar outputs to those of the larger model. Essentially, the student model is distilled with the knowledge of the teacher model, allowing it to perform similarly but with far fewer parameters.

One notable example is DistilBERT (Sanh et al. 2019), which successfully reduced the parameters of BERT by 40% while maintaining 97% of its performance and running 71% faster.

Distillation can be easily combined with quantization, pruning, and matrix decomposition, where the teacher model is the original version and the student is the compressed one. These combinations help refine the accuracy of the compressed model. For example, you could compress GPT-2 using matrix decomposition and then apply knowledge distillation to train the compressed model to mimic the original GPT-2.

How to compress existing models for on-device AI use cases

A few years ago, Huawei also focused on enabling on-device AI models and published research on compressing GPT-2. The researchers used a matrix decomposition method to reduce the size of the popular open-source GPT-2 model for more efficient on-device use.

Specifically, they used a technique called Kronecker decomposition, which is the basis for their paper titled “Kronecker Decomposition for GPT Compression.” As a result, GPT-2’s parameters were reduced from 125 million to 81 million.

To recover the model’s performance after compression, the authors employed knowledge distillation. The compressed version — dubbed KnGPT-2 — learned to mimic the original GPT-2’s behavior. They trained this distilled model using just 10% of the original dataset used to train GPT-2. In the end, the model size decreased by 35%, with a relatively small loss in performance.

This year, my colleagues and I published research on matrix decomposition methods, where we successfully compressed the GPT-2 model (with 125 million parameters) down to 81 million parameters. We named the resulting model TQCompressedGPT-2. This study further improved the method of Kronecker decomposition, and with this advancement, we managed to use just 3.1% of the original dataset during the knowledge distillation phase. It means that we reduced training time by about 33 times compared to using the full dataset and that developers looking to deploy models like LLaMA3 on smartphones will require 33 times less time to achieve a compressed version of LLaMA3 using our method.  

The novelty of our work lies in a few key areas:

• Before applying compression, we introduced a new method: permutation of weight matrices. By rearranging the rows and columns of layer matrices before decomposition, we achieved higher accuracy in the compressed model.
• We applied compression iteratively, reducing the model layers one by one.

We’ve made our model and algorithm code open-source, allowing for further research and development.

Both studies bring us closer to the concept Meta introduced with their approach to Mobile LLMs. They demonstrate methods for transforming existing wide models into more compact, deeper versions using matrix decomposition techniques and restoring the compressed model’s performance with knowledge distillation.

Top-tier models like LLaMA, Mistral, and Qwen, which are significantly larger than 1 billion parameters, are designed for powerful cloud servers, not smartphones. The research conducted by Huawei and our team offers valuable techniques for adapting these large models for mobile use, aligning with Meta’s vision for the future of on-device AI.

Compressing AI models is more than a technical challenge — it’s a crucial step toward making advanced technology accessible to billions. As models grow more complex, the ability to run them efficiently on everyday devices like smartphones becomes essential. This isn’t just about saving resources; it’s about embedding AI into our daily lives in a sustainable way. 

The industry’s progress in addressing this challenge is significant. Advances from Huawei and TQ in the compression of AI models are pushing AI toward a future where it can run seamlessly on smaller devices without sacrificing performance. These are critical steps toward sustainably adapting AI to real-world constraints and making it more accessible to everyone, laying a solid foundation for further research in this vital area of AI’s impact on humanity.

Google Cloud is set to make multi-factor authentication (MFA) mandatory for all users by 2025, a move aimed squarely at bolstering security in response to escalating cyber threats. Starting this month, Google will roll out reminders and resources, urging customers to adopt MFA. This phased enforcement plan underscores a broader industry trend: when it comes to security, relying solely on passwords is a thing of the past.

Why is Google requiring MFA on Google Cloud?

The motivation behind Google’s push for MFA is clear. Cyber breaches are spiking, and weak security practices are at the center of these attacks. In 2024 alone, over 1 billion records were stolen in various breaches. Prominent among these were incidents at Change Healthcare and Snowflake, where sensitive data was exposed due to compromised credentials lacking MFA. Google’s decision signals an acknowledgment that cybersecurity risks have outpaced traditional protective measures.

Mayank Upadhyay, Google’s VP of Engineering, laid out Google’s stance plainly: “Given the sensitive nature of cloud deployments — and with phishing and stolen credentials remaining a top attack vector observed by our Mandiant Threat Intelligence team — we believe it’s time to require 2SV for all users of Google Cloud.” By enforcing MFA, Google is raising the stakes for account security, reflecting a mindset that cyber resilience now requires more than just strong passwords.

How Google plans to roll out MFA for cloud users

Google isn’t flipping a switch overnight. Instead, it’s rolling out mandatory MFA in phases, giving users and businesses time to adjust. Here’s what to expect in each phase:

• Phase 1 (November 2024) – Encouragement to enable MFA:
  Google has started embedding reminders and guidance into the Google Cloud console, encouraging users to voluntarily set up MFA. Resources are available to help teams plan, conduct tests, and ensure a smooth MFA deployment. This phase sets the foundation, raising awareness and easing customers into what will eventually become a requirement.
• Phase 2 (Early 2025) – MFA becomes mandatory for password-based logins:
  In early 2025, Google will begin requiring MFA for all Google Cloud users logging in with a password. This requirement extends to Google’s platforms like Firebase and gCloud, meaning users must verify their identity with a second authentication method — whether a security key, app-based authentication, or biometric verification.
• Phase 3 (End of 2025) – Extending MFA to federated users:
  By the close of 2025, Google’s MFA mandate will reach federated users — those logging in through third-party identity providers. This phase ensures that no matter the login method, accounts on Google Cloud are shielded by an additional security layer. For organizations using identity providers, Google’s MFA requirement adds an extra, unified layer of defense across all access points.

The phased rollout gives users a chance to integrate MFA without disrupting operations, allowing time to educate teams and secure compliance within their workflow.

Google’s move follows industry trends in security

This shift by Google aligns with recent moves from cloud giants like AWS and Microsoft. AWS began its MFA enforcement back in June 2024, and Microsoft’s Azure soon followed suit. With Google Cloud joining the trend, it’s clear that the tech industry is coalescing around MFA as the new standard for cloud security. For Google Cloud users, this shift may feel overdue, considering the company’s extensive track record with security innovations.

While consumer Google Accounts have long offered optional MFA, the stakes are different in the enterprise world. Business accounts often house critical and sensitive data, making them prime targets for cyberattacks. In recognition of these elevated risks, Google is drawing a line, mandating that enterprise users fortify their accounts. As Upadhyay observed, “Today, there is broad 2SV adoption by users across all Google services,” but given the level of access and data involved, mandatory enforcement was “inevitable.”

MFA: What’s driving the push for stronger authentication?

The push for MFA stems from a reality that most people, and companies, already know: passwords aren’t enough. With cyberattacks becoming more advanced and targeting weaknesses in digital infrastructure, MFA has proven to be one of the most effective methods for preventing unauthorized access.

Studies underscore MFA’s effectiveness. According to the U.S. Cybersecurity and Infrastructure Security Agency (CISA), MFA reduces the likelihood of account compromise by 99%. It requires users to confirm their identity with a second form of verification — an extra step that often stops attackers who have already obtained a password.

Recent data breaches have served as cautionary tales. For instance, Snowflake faced a breach that leaked private data from customers like Ticketmaster, highlighting how lacking MFA makes even large organizations vulnerable. Google’s mandate aims to plug these gaps and sets a precedent for others to follow.

What this means for Google Cloud users

For businesses and individuals relying on Google Cloud, mandatory MFA means taking security adjustments seriously. Early adoption is encouraged, especially for enterprises managing multiple user accounts. Google provides resources within its Cloud console, guiding users through MFA setup, deployment planning, and team education.

The good news is that users have options. Google Cloud allows for a range of MFA methods — from authenticator apps and SMS codes to physical security keys. Federated users, meanwhile, can work with their primary identity providers to integrate MFA, allowing them to maintain a streamlined login process.

The phased timeline offers a degree of flexibility. Organizations can use this time to ensure that MFA policies are both compliant and practical, minimizing disruptions. Google’s resources aim to ease this transition, but organizations should begin preparing now to avoid last-minute hurdles.

Featured image credit: Kerem Gülen/Midjourney