Mark Zuckerberg just dropped a bombshell. Meta, the parent company of Facebook and Instagram, is abandoning its professional fact-checking program. Instead, they’re moving to a “community-driven” system, putting the onus on users to determine what’s true and what’s not.

Zuckerberg says it’s about fostering “free speech,” but it feels a lot like abdicating responsibility, saving money, bowing down to political pressure, and more.

Frankly, it’s the last straw. I’m done with Facebook.

I’ve been wrestling with this for a while now. The endless scroll, the monetization of my life, the performative outrage, the nagging feeling that I’m being manipulated by algorithms, the blatant and widely covered manipulation… it’s exhausting. But this latest move? It’s a dealbreaker.

Look, I get the appeal of crowdsourcing. The wisdom of the crowd, right? But when it comes to complex issues, “common sense” isn’t always enough. We need experts. We need evidence. We need nuanced analysis, not just knee-jerk reactions and confirmation bias.

Zuckerberg, in his infinite wisdom (read: with a healthy dose of self-preservation), has decided to throw his fact-checking partners under the bus. Possibly, those annoying truth-tellers were just too good at their jobs, exposing uncomfortable truths and generally making life difficult for the Facebook overlords.

According to Zuck, these fact-checkers were “too politically biased” and, get this, “destroyed more trust than they created.” It’s a classic case of blaming the Messenger, wouldn’t you say?

Of course, the fact-checking organizations themselves aren’t taking this lying down. They’ve fired back, pointing out the obvious: they simply flagged potentially false content. What Facebook chose to do with that information was entirely up to them.

It’s a bit like a chef blaming the health inspector for a dirty kitchen. “Oh, those inspectors are just too picky! They’re ruining my reputation!” Never mind the fact that the kitchen’s a mess and the menu is probably giving people food poisoning.

Take climate change, for example. The science is clear, yet misinformation runs rampant on social media. Do we really want the veracity of climate data determined by a popularity contest? Or how about public health? Anti-vaccine sentiment is already a serious problem, fueled by conspiracy theories and misleading claims. Letting those narratives go unchecked – or chosen to be true by coordinated consortiums of community members that have an agenda and a vote – could have devastating consequences.

This isn’t about censorship. It’s about accountability. Social media platforms have a responsibility to ensure the information they disseminate is accurate and trustworthy. They’ve become our primary source of news and information, and with that power comes a responsibility to combat the spread of harmful falsehoods.

So where do we go from here? I, for one, am turning to platforms and tools that prioritize expert verification and rigorous fact-checking. Solutions like Factiverse, for example, which leverages a network of over 350k human-performed fact-checks from more than 100 trusted outlets globally to analyze information and provide context.

Factiverse’s approach gives me hope and gives me the tools to see which sources back up and contest a statement, so I can be informed and balanced. It’s a reminder that truth still matters, and that there are people out there dedicated to upholding it. In a world where facts are increasingly contested, we need reliable sources of information more than ever.

Maybe Zuckerberg’s gamble will pay off. Maybe the “wisdom of the crowd” will prevail. But I’m not sticking around to find out. I’m logging off Facebook and investing my time in platforms that value truth and accuracy. Because in the end, facts matter. And we all deserve better than to be drowning in a sea of misinformation.

This article was originally published on Hackernoon and is republished with permission.

Throughout 2024, a disturbing trend has emerged as hackers exploit Facebook ads to distribute fake Chrome extensions masquerading as legitimate password managers like Bitwarden. This sophisticated malvertising campaign preys on users’ fears of cyber threats and deceives them into downloading malicious software.

Hackers exploit Facebook ads to distribute fake Chrome extensions

Bitdefender Labs has closely followed these campaigns, revealing that the latest operation was launched on November 3, 2024. Targeting users aged 18 to 65 across Europe, the attackers create a sense of urgency by claiming that users must install a critical security update. By impersonating a trusted brand, they effectively leverage Facebook’s advertising platform to gain users’ trust.

The deceptive process begins when users encounter a Facebook ad that warns them their passwords are at risk. Clicking the ad directs them to a fraudulent webpage designed to mimic the official Chrome Web Store. However, instead of a safe download, users are redirected to a Google Drive link hosting a ZIP file containing the harmful extension. To install it, users must follow a detailed process that involves enabling Developer Mode on their browser and sideloading the extension, a method that circumvents standard security protocols.

How the fake Bitwarden extension operates

Once the malicious extension is installed, it requests extensive permissions allowing it to intercept and manipulate user activity online. As outlined in the extension’s manifest file, it operates across all websites and can access storage, cookies, and network requests. This provides hackers full access to sensitive information. For instance, the permissions include:

• contextMenus
• storage
• cookies
• tabs
• declarativeNetRequest

The extension’s background script initiates a series of harmful activities as soon as it is installed. It routinely checks for Facebook cookies and retrieves vital user data, including personal identifiers and payment information associated with Facebook ad accounts. The sensitivity of the stolen data can lead to severe repercussions, including identity theft and unauthorized access to financial accounts.

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The use of legitimate platforms like Facebook and Google Drive obscures the malware’s true nature. Security experts recommend several strategies to mitigate risks associated with this threat:

• Verify extension updates through official browser stores rather than clicking on ads.
• Exercise caution with sponsored ads, especially those that prompt immediate updates for security tools.
• Review extension permissions critically before installation.
• Utilize security features, such as disabling Developer Mode when not in use.
• Promptly report suspicious ads to social media platforms.
• Implement a reliable security solution that detects and blocks phishing attempts and unauthorized extensions.

Bitdefender offers a tool called Scamio, which helps users identify malicious content online. It assesses links, messages, and other digital interactions to highlight potential scams, giving users an extra layer of defense.

Featured image credit: Soumil Kumar/Unsplash

How to turn off Meta AI is a question many users are asking as Meta (formerly Facebook) integrates artificial intelligence into its platforms. Whether you have concerns about privacy, prefer a more traditional user experience, or simply want to control your digital environment, understanding how to manage these AI features is key. This guide provides detailed instructions for each Meta platform, making it easy to adjust your settings to your liking.

While AI integration on Meta platforms offers potential benefits like personalized content recommendations and enhanced communication tools, it’s important to remember that not everyone embraces these changes. Some users may find AI-driven features intrusive or overwhelming. If you’re one of these users, rest assured that you have the option to disable or modify Meta AI’s presence in your digital interactions.

How to turn off Meta AI on Facebook

To adjust AI-powered features on Facebook, you can:

• Disabling AI-powered content suggestions:
  • Navigate to “Settings & Privacy,” then “Feed Preferences”.
  • Choose to prioritize posts from friends and family, reduce the visibility of suggested content, or even hide specific types of posts entirely.
• Turning off AI-powered chat features:
  • Go to “Settings” in Messenger, then “M Suggestions”.
  • Toggle the options for chat suggestions and “M” suggestions off.

How to turn off Meta AI on Instagram

For managing AI on Instagram:

• Disabling AI-powered recommendations:
  • Indicate your preferences on individual posts by tapping the three dots in the upper right corner and selecting “Not Interested”.
  • This trains the algorithm to show you more relevant content over time.
• Controlling AI-powered shopping features:
  • Adjust your ad preferences in “Settings” to limit personalized ads.

How to turn off Meta AI on WhatsApp

If you use WhatsApp:

• Disabling AI-powered chat features:
  • Go to “Settings,” then “Chats,” and toggle “Smart Replies” off to disable AI-generated responses.

Remember that Meta AI extends beyond the major platforms. Products like Oculus and Portal also have AI integration, and each offers its own settings to manage these features. Consult the individual platform’s settings or help resources to tailor your experience.

A personal choice

The decision to embrace or turn off Meta AI is ultimately a personal one. There’s no right or wrong answer. It’s about understanding how these technologies work and making informed choices that align with your preferences and priorities. If you decide to disable certain features, rest assured that you still have the option to re-enable them in the future if you change your mind.

Remember, technology should serve you, not the other way around. By taking the time to adjust your settings and understand how to turn off Meta AI, you can ensure that your digital experiences remain under your control.

Featured image credit: Meta

Updated: “Why is Facebook chirping when I scroll?” is today’s question for millions of users of Facebook, including me. Ever been scrolling through Facebook on your iPhone, only to hear a weird sound that seems to come out of nowhere? You’re not alone. Some iPhone users have noticed this odd “newsfeed sound” while browsing their social media updates.

facebook started to make weird sounds pic.twitter.com/5ZtXazlRUy

— Eray Eliaçık (@eliacikeray) February 12, 2024

Picture this: You’re casually swiping through your Facebook feed, catching up on posts from friends and family, when suddenly, you hear a faint noise coming from your phone. At first, you might think it’s a glitch or just your imagination. But as you keep scrolling, the sound keeps popping up, almost like background music for your scrolling session.

This strange sound has left many users scratching their heads. What could be causing it, and why does it happen only when scrolling through Facebook? Some people think it might be a recent update’s hidden feature or bug. Others wonder if Facebook is trying something new to grab our attention. So, let’s dig deeper and find out the reason behind it and how to stop it.

Why is Facebook chirping when I scroll?

The cause of the sound is described as “an unfortunate technical error” that Facebook is in the process of fixing. It seems to be an unexpected glitch within the Facebook platform rather than an intentional feature

Hearing a chirping noise while scrolling through your Facebook Feed on your iPhone? It’s not you, it’s us! And it’s an unfortunate technical error that we're in the process of fixing.

— Andy Stone (@andymstone) February 13, 2024

Of course, not everyone is thrilled with the Facebook chirping sound. Some users find it distracting or unnecessary, preferring a quieter browsing experience.

Fortunately, Facebook typically provides options to customize app settings, so users who prefer silence can likely disable the feature if they choose.

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How to stop the Facebook chirping sound?

Now we know why is Facebook chirping when I scroll, and it’s time to disable it! Stopping the Facebook chirping sound is simple. Here’s how you can do it:

• Open the Facebook app on your iPhone.
• Tap on the three horizontal lines in the bottom-right corner to open the menu.
• Scroll down and tap on “Settings & Privacy.”
• Select “Settings.”
• Scroll down and find “Media.”
• Under “Sounds,” toggle off the switch next to “In-App Sounds.”

By following these steps, you’ll disable the chirping sound in your Facebook app, allowing you to browse in peace without any unexpected noises.

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Featured image credit: Malte Helmhold/Unsplash

We finally have the Facebook settlement payout date 2024; despite not being exact, it is still something! This development heralds a pivotal moment for millions of Facebook users who have been closely following the case, anticipating a tangible acknowledgment of their privacy concerns.

This case, which has been in the courts for a while, ends in a significant way for users who felt their privacy was compromised. The payout is more than just money; it represents a change in how big tech companies manage user data. As we near the payout date, this situation is a key moment in the ongoing discussion about digital privacy. It sets an example of how large tech firms might be held responsible for their actions in the future. Here is what you need to know about the Facebook settlement payout data 2024!

Facebook settlement payout date 2024

So, when is the Facebook settlement payout data 2024? Good news for Facebook users! The Facebook settlement payout date 2024 is finally set for early this year. This is a significant update for millions who have been awaiting their share of the settlement. The announcement follows Judge Vince Chhabria’s decision, with payouts starting 90 days after the final settlement approval. For many, this means the wait is almost over.

Users who filed a claim by the August 25th deadline can expect to receive their payments through the method they selected – PayPal, Venmo, prepaid Mastercard, Zelle, or a check mailed directly. With around 17 million valid claims processed, the average payout is estimated at $30 per user, although this amount may vary based on the duration of your Facebook account usage during the eligibility period.

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Facebook settlement details

A California federal judge finalized Facebook’s $725 million privacy settlement in a major development. If you’re one of the users who filed a valid claim, you can expect your compensation by early 2024. A recent court hearing revealed that over 28 million claims were filed, with 17 million already approved. While the individual amounts might not seem large, the overall settlement remains substantial, even after legal fees and other costs.

Payments will be around $30 for most, but remember, this can vary. It depends on your Facebook usage between May 2007 and December 2022. More active and long-term users might receive a higher amount. That is why the Facebook settlement payout date 2024 concerns many people.

How to claim your share

Were you using Facebook in the U.S. between May 24, 2007, and December 22, 2022? If yes, you could be eligible for a part of the settlement. But you need to have completed a claim form to receive your payout. The payout method will be the one you chose in your application – options include Venmo, PayPal, a prepaid Mastercard, Zelle, or a paper check sent to your home.

Users had to visit the official settlement website to file a claim and provide essential details. This included verification of active Facebook use during specific dates, residency in the United States, and basic personal information like name, address, phone number, and email. Claimants also had to specify their preferred payment method.

The Facebook settlement payout date 2024 is a significant milestone for many users. It’s a culmination of a lengthy legal process, and for those who filed claims, it’s a chance to receive a part of the $725 million settlement. Payments are expected to start in early 2024, with an average estimated payout of about $30 per user. The actual amount varies depending on each individual’s Facebook usage history.

For those awaiting their share, it’s a matter of a few more months. Keep an eye on your chosen payment method – whether it’s a digital platform like PayPal or a traditional check in the mail. This settlement is a reminder of the importance of digital privacy and the impact it has on users worldwide.

Featured image credit: Deeksha Pahariya/Unsplash

The deceptive Facebook phishing campaign using the emotionally charged phrase “I can’t believe he is gone. I’m gonna miss him so much,” is currently exploiting users’ trust, leading them to a malicious website designed to hijack Facebook credentials. This ongoing scam has become widespread on Facebook, utilizing accounts compromised by threat actors to expand its reach and ensnare more victims.

This phishing attack, leveraging the guise of friend’s hacked accounts, presents an added layer of credibility, making the scam more effective. The familiarity of seeing such a post from a friend increases the likelihood of unsuspecting users falling prey to the scam.

The campaign, which began about a year ago, has proven challenging for Facebook to contain. Despite efforts to deactivate the redirect links in these posts once they are reported, the campaign persists, indicating the sophistication and persistence of the threat actors behind it. The phrase “I can’t believe he is gone” serves as a potent emotional trigger, drawing users into the scam’s web.

How does “I can’t believe he is gone” scam work?

The ongoing Facebook phishing campaign, marked by the poignant message “I can’t believe he is gone. I’m gonna miss him so much,” utilizes two distinct approaches to ensnare users. One variant of the scam includes a simple Facebook redirect link, while the other presents a seemingly authentic BBC News video of a car accident or a crime scene.

Investigations into these phishing posts revealed that the links lead to different sites based on the device used. For mobile Facebook app users, clicking the link redirects to a fabricated news site called ‘NewsAmericaVideos’. This site prompts users to enter their Facebook credentials under the guise of identity confirmation to view a blurred video, which is, in reality, just an image sourced from Discord.

Should users input their Facebook credentials, these are immediately captured by the threat actors, and the user is then redirected to Google. The exact purpose of collecting these credentials is unclear, but it’s evident they are used to perpetuate the scam by posting similar phishing messages through the hacked accounts.

On desktop computers, the phishing sites exhibit different behavior, often redirecting users to Google or leading them to other scams involving VPN apps, browser extensions, or affiliate sites.

This phishing scam’s reach is extensive, with daily creation of numerous deceptive posts by individuals whose accounts have been compromised by the same scheme. Given that this attack does not target two-factor authentication (2FA) tokens, Facebook users are strongly advised to enable 2FA.

This additional security layer requires a unique one-time passcode for login attempts from unrecognized locations. Thus, even if credentials are compromised, unauthorized access is significantly hindered, as the unique codes remain with the legitimate user.

What does phising mean?

Phishing is a prevalent form of cybercrime where attackers masquerade as trustworthy entities to deceive individuals into providing sensitive data. This malicious activity can take various forms, and understanding its nature is crucial for digital safety.

1. The basics of phishing

At its core, phishing involves tricking people into divulging personal information, such as login credentials, credit card numbers, or social security details. Attackers typically use email, social media, or text messages to lure victims with seemingly legitimate requests or alarming statements. For instance, the Facebook phishing scam using the emotionally charged phrase “I can’t believe he is gone. I’m gonna miss him so much” cleverly plays on human emotions to prompt action.

2. Common phishing techniques

Phishing attacks come in several forms. Email phishing, the most common type, involves sending fraudulent emails that mimic real communications from trusted organizations. Spear phishing targets specific individuals or companies, while whaling focuses on high-profile targets like executives. The Facebook scam, a classic case of social media phishing, utilizes familiar platforms to spread deceitful messages.

Never lose your ID, especially in cyberspace

3. The dangers of phishing

Phishing poses significant risks as it can lead to financial loss, identity theft, and unauthorized access to sensitive systems. In cases like the “I can’t believe he is gone” phishing campaign on Facebook, personal accounts can be hijacked, leading to further spread of the scam and potential data breaches.

Featured image credit: Kerem Gülen/Midjourney

Facebook has long held its place as a beloved platform where people connect, share, and explore. However, recently, a pesky bug has been causing quite a stir among users—the Facebook back button not working issue. Imagine scrolling through your newsfeed, only to find that your trusted back button refuses to play along, leaving you stranded on a screen with no way to navigate back. Frustration sets in, and in some cases, users have resorted to drastic measures like device restarts and factory resets, all hoping for a quick fix.

But fret not, for in this article, we delve into the heart of the matter, exploring the causes of this predicament, offering workarounds, and providing you with the much-needed guidance to regain control over your Facebook experience. So, let’s embark on a journey to understand, troubleshoot, and conquer the Facebook back button bug, ensuring your social media escapades remain smooth and uninterrupted.

Why is the Facebook back button not working? Possible reasons

The Facebook back button not working issue can be a frustrating experience, and while the exact cause may vary in individual cases, there are several common reasons why this problem might occur:

• Compatibility issues: One of the primary reasons for the back button not working on Facebook is compatibility issues between the Facebook app and the Android or iOS operating system on your device. When the app and the system don’t sync correctly, it can disrupt the functionality of the back button.
• Recent updates: Often, such issues arise after a recent update to the Facebook app. Updates are intended to improve the app’s performance and introduce new features, but sometimes they inadvertently introduce bugs or incompatibilities.
• App conflicts: Other apps installed on your device can sometimes interfere with the Facebook app’s functionality, including the back button. These conflicts can result from apps running in the background and making unexpected demands on system resources.
• Device software problems: Device software issues, such as software bugs, corrupted files, or hardware problems, can also disrupt the proper functioning of the back button. These issues may not be related to the Facebook app but can still affect its performance.
• Facebook app glitches: Like any software, the Facebook app is not immune to glitches and bugs. Temporary issues within the app can disrupt the back button’s functionality, and these glitches are usually addressed in subsequent app updates.
• Facebook beta testing: If you’re participating in the Facebook beta testing program, you might encounter issues that haven’t been fully tested and optimized. Beta versions often contain experimental features or changes that can introduce unexpected problems.
• Server issues: In rare cases, the back button not working on Facebook might be caused by temporary glitches or outages on Facebook’s servers. Server issues can affect the app’s performance, including the back button’s responsiveness.

It’s important to note that the specific cause of the back button issue may vary from one device to another. Facebook developers continuously work to address these problems, and regular app updates are intended to resolve such issues. If you experience the Facebook back button not working problem, keep reading and find out the workarounds to potentially fix the issue.

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How to fix the Facebook back button not working issue

Here is what you can do to fix the Facebook back button not working issue:

• Press and hold the button
• Update the Facebook App
• Close other apps
• Restart your device
• Check for app conflicts
• Review device navigation settings
• Reach Facebook Support

Let’s take a closer look at the workarounds.

Press and hold the button

Some users have reported success by pressing and holding the back button instead of tapping on it. This workaround involves the following steps:

• Open Facebook: Launch the Facebook app on your device.
• Navigate to a page or screen: Go to the page or screen within the app where you want to use the back button.
• Press and hold the back button: Instead of tapping the back button, press and hold it for at least four seconds.
• Test the Facebook back button: Release the button and check if it navigates back to the previous page or screen. This method might help circumvent the back button issue.

Update the Facebook App

Ensuring that your Facebook app is up to date is crucial to resolve the back button issue. Outdated app versions may contain bugs or compatibility problems that can lead to the back button malfunction. Here’s how to update your Facebook app:

• Open your device’s app store: Locate and open the app store on your Android or iOS device. On Android, it’s the Google Play Store, while on iOS, it’s the App Store.
• Search for Facebook: In the app store’s search bar, type “Facebook” and locate the Facebook app.
• Check for updates: If an update is available, you will see an “Update” button. Click or tap on it to initiate the update.
• Wait for installation: The app will begin downloading and installing the update. Once completed, the new version should resolve the back button issue.

Close other apps

Running multiple apps simultaneously can sometimes interfere with the functionality of the Facebook app, including the back button. To resolve this issue, follow these steps:

• Navigate to your device’s home screen: Press the home button on your device to access the home screen.
• Close background apps: Check for any apps running in the background that you’re not actively using. On Android, tap the square or recent apps button to view and close open apps. On iOS, double-click the home button or swipe up from the bottom to access the app switcher and close apps by swiping them off the screen.
• Return to Facebook: After closing background apps, return to the Facebook app and test whether the back button is functioning correctly.

Restart your device

A simple device restart can often clear temporary glitches and refresh the system, potentially resolving the back button issue. Here’s how to restart your device:

• Power off your device: Press and hold the power button on your Android or iOS device until the power-off menu appears.
• Turn off the device: Select “Power Off” or “Restart” from the menu and confirm the action.
• Please wait a few seconds: Allow the device to power off completely, wait for a few seconds, and then power it back on.
• Test the back button: Once the device has restarted, open the Facebook app and test the back button to check if it’s working as expected.

Check for app conflicts

Sometimes, newly installed apps can conflict with existing apps, causing unexpected behavior, including issues with the back button. Here’s how to address potential app conflicts:

• Uninstall recently installed apps: If you installed a new app around the time the back button issue started, try uninstalling it temporarily.
• Test Facebook again: After uninstalling the new app, return to Facebook and check whether the back button is functioning correctly.

Review device navigation settings

If your device uses gesture navigation instead of traditional buttons, ensure that the back gesture is configured correctly. Follow these steps:

• Access device settings: Open your device’s settings menu.
• Navigate to system or display: Depending on your device, you’ll find navigation settings under “System” or “Display.”
• Check gesture navigation settings: Review the settings for gesture navigation to ensure the back gesture is working as intended. If necessary, adjust the gesture navigation settings to resolve the back button issue.

Reach Facebook Support

If none of the above solutions work, contact Facebook Support for assistance. They may have more specific solutions or insights into the problem.

You can contact Facebook Support through the Help Center or the support options provided by the app.

Conclusion

While the Facebook back button not working issue can be frustrating, these solutions should help you address the problem and restore normal functionality. Keeping your Facebook app updated, managing background apps, and ensuring proper device settings are all essential steps to resolve the back button not working issue. If all else fails, don’t hesitate to seek assistance from Facebook’s support team for further guidance. The additional workaround of pressing and holding the button can be particularly useful in resolving this issue.

Featured image credit: Roman Martyniuk /Unsplash

Do you know what the @Highlight Facebook feature is? If you want to boost your online presence, keep reading and exploring this hidden social media gem.

Your Facebook posts are a canvas for self-expression, but in the sea of updates, how can you ensure that your most essential moments and messages receive the attention they truly deserve? The answer lies in a powerful yet often overlooked feature: @Highlight. It’s the magic wand that can transform your ordinary Facebook posts into extraordinary showcases.

Join us on a journey to master the art of @Highlight Facebook feature and learn how to make your online presence shine brighter than ever before. Whether you’re promoting your business, cherishing life’s milestones, or want to ensure your voice is heard, @Highlight is your ticket to the Facebook spotlight. Let’s dive into the world of Facebook’s hidden gem and unlock its potential together.

Get noticed: @Highlight Facebook feature

The @Highlight Facebook feature is a simple yet effective tool that allows users to make a specific post more prominent and visible on their profile and in their followers’ news feeds. It is akin to “pinning” a post, ensuring that it remains one of the first things people see when they visit your profile or scroll through their feed. Here’s a more detailed explanation of how the @Highlight Facebook feature works:

• Increasing post visibility: When you use the @Highlight function on a post, it essentially enlarges the post and makes it more eye-catching. This increased size and prominence draw more attention to the content, making it stand out from regular posts in a user’s news feed.
• Pinned post effect: Think of the @Highlight Facebook feature as a way to pin a post to the top of your profile, just like you might pin a tweet on Twitter. This means that the highlighted post remains at the top of your profile, ensuring that anyone who visits your profile will see it immediately. It’s a great way to showcase important information or moments that you want to emphasize.
• Extended lifespan: In addition to making a post more visible, using @Highlight also extends the duration during which the post remains near the top of people’s news feeds. While a typical post may get pushed down in the feed as new content is added, a highlighted post retains its position for a more extended period, allowing more users to see it over time.
• Permanent position on profile: Highlighting a post also gives it a permanent spot near the top of your profile page. This means that anyone visiting your profile can quickly access and engage with the content you’ve chosen to highlight.
• Versatile usage: You have the flexibility to highlight a wide range of content. Whether it’s a special life event, a product promotion, an important announcement, or anything you want to draw attention to, the @Highlight feature allows you to ensure that it doesn’t go unnoticed.
• Use with caution: While @Highlight is a valuable tool for emphasizing key content, it’s essential to use it judiciously. Overusing this feature may lead to diminishing returns, as your audience may start to disregard all content you’ve highlighted if everything is treated as equally important. Reserve it for content that truly warrants extra attention.

In summary, the @Highlight feature on Facebook is a user-friendly way to give your posts a boost in visibility and ensure that crucial content remains visible on your profile and in your followers’ news feeds. By using this feature wisely, you can make sure that your most important posts get the attention they deserve and stand out among the sea of content on the platform.

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How to use @Highlight Facebook feature

Using the @Highlight Facebook feature is a straightforward process. Here’s a step-by-step guide on how to utilize this feature:

• Create your post: Start by creating the post that you want to highlight. This can be a status update, a photo, a video, a link, or any other type of content you typically share on Facebook. Make sure your post is ready to be shared with your audience.
• Post your content: After creating your post, click on the “Post” button to share it on your timeline or in a group, on a page, or wherever you wish to share it. Your post will now be visible to your friends or followers, depending on your privacy settings.
• Locate your post: Once your post is published, scroll through your timeline or go to the specific location where you shared the post.
• Use the @Highlight comment: To highlight your post, comment “@Highlight” under the post you want to emphasize. This comment should be added as a regular text comment, just like any other comment you might make on a post.

• Confirm the highlight: Your post will be enlarged, making it more visible and prominent. It will also be pinned to the top of your profile, ensuring it remains easily accessible to anyone who visits your profile. In addition, it will stay near the top of your followers’ news feeds for an extended period.
• Customize your highlighted post: If you want to make changes to your highlighted post, you can click on the three dots (more options) located at the top right corner of your post. From there, you can choose to unhighlight the post or make additional edits.
• Use with discretion: While the @Highlight Facebook feature can be a powerful way to draw attention to specific content, it’s essential to use it judiciously. Highlight only the posts that truly warrant extra visibility. Overusing this feature may lead to your audience ignoring the content you highlight if everything is treated as equally important.

That’s it! By following these steps, you can effectively use the @Highlight feature on Facebook to make your posts more visible, prominent, and easily accessible to your friends, followers, and anyone who visits your profile.

Featured image credit: Dawid Sokołowski/Unsplash

Meet Facebook Messenger AI stickers, the new Meta AI feature that’s sparking both laughter and eyebrow raises in equal measure. Imagine a tool that can conjure up stickers featuring anything from the most iconic pop culture figures to, well, let’s say, the most peculiar and improbable scenarios.

Welcome to the world of Facebook Messenger AI stickers, where creativity knows no bounds and mischief dances cheekily on the sidelines. The controversy surrounding Facebook Messenger AI stickers centers on their potential for generating inappropriate and offensive content, raising concerns about content moderation and responsible use of AI technology.

@geordiemcgrath

Replying to @stinko.malinko turns out the AI is more than happy to make stickers with guns. you just gotta say rifle. #facebook #aiart #ai #wtf

♬ Quirky Suspenseful Indie-Comedy(1115050) – Kenji Ueda

What are Facebook Messenger AI stickers?

Facebook Messenger AI stickers are a new and innovative feature that harnesses the power of artificial intelligence (AI) to generate unique and personalized stickers for use in Messenger conversations. These stickers are a playful and expressive way for users to enhance their messages and express themselves creatively.

Here’s how Facebook Messenger AI stickers work right now:

• AI-driven generation: The stickers are generated by Meta’s Llama 2 large language model, which is a sophisticated AI system. Users can input text-based prompts, and the AI then interprets these prompts to create stickers.
• Diverse content: The AI can produce a wide range of sticker content, from iconic pop culture figures to imaginative and whimsical scenarios. Users have “all the freedom” to let their imaginations run wild.

• High-quality graphics: The stickers generated by the AI are of high quality, making them visually appealing and suitable for sharing in chats and stories.
• Ease of use: Facebook Messenger AI stickers are designed to be user-friendly, allowing individuals to quickly create stickers without the need for advanced design skills or software.
• Integration: These stickers are seamlessly integrated into the Facebook Messenger platform, making it easy for users to access and share them with friends and contacts.
• Limited rollout: Initially, AI-generated stickers are introduced to select English language users, ensuring that Meta can address any potential misuse or issues before a broader release.

While Facebook Messenger AI stickers offer a fun and creative way to enhance conversations, they also come with certain challenges, such as the potential for misuse and the need for content moderation to adhere to community guidelines. Meta, the company behind Facebook, is actively working to refine and improve this feature to strike a balance between creative freedom and responsible use.

Facebook Messenger AI stickers are not perfect yet, and here is why

Facebook Messenger AI stickers, while a creative and fun addition to messaging, have faced several issues since their introduction. These issues highlight some of the challenges associated with AI-driven content generation and moderation:

• Inappropriate content generation: One of the primary issues with Facebook Messenger AI stickers is the potential for users to generate inappropriate or offensive content. Some users have exploited the AI to create stickers featuring sensitive and controversial subjects, including child soldiers, nudity, and violence. This raises concerns about the responsible use of AI technology.

found out that facebook messenger has ai generated stickers now and I don't think anyone involved has thought anything through pic.twitter.com/co987cRhyu

— grocery store battlepass (@Pioldes) October 3, 2023

• Content moderation: Moderating AI-generated content can be a complex task. While Meta has implemented word blocks and warnings based on community guidelines, users have found ways to work around these restrictions. This highlights the difficulty in ensuring that AI-generated content adheres to appropriate standards and guidelines.
• Algorithmic bias: AI algorithms, including those used to generate stickers, can sometimes exhibit biases present in the data they were trained on. This can lead to the inadvertent generation of content that may perpetuate stereotypes or biases, even if unintentional.
• Rollout challenges: Facebook Messenger AI stickers have been introduced gradually to select users to address potential misuse. However, this phased rollout can create disparities in user experiences and raise questions about fairness and access.
• Misuse of keywords: Some users have discovered that certain keywords or typos can be used to bypass content moderation restrictions. This highlights the need for continuous refinement and updates to the AI’s content generation and filtering mechanisms.
• Community standards: The challenge of defining and enforcing community standards for AI-generated content remains an ongoing concern. Striking the right balance between creative freedom and responsible use is a complex task for platform providers like Meta.

In response to these issues, Meta is actively working to refine the AI-driven sticker feature, enhance content moderation, and improve the user experience. They aim to ensure that the feature remains a safe and enjoyable addition to Facebook Messenger while also addressing the challenges associated with AI-generated content.

Why are Facebook Messenger AI stickers not working?

As we mentioned earlier, Facebook Messenger AI stickers may not be working for some users due to the following reasons:

• Limited rollout: The feature is in a testing phase and has been rolled out gradually to a select group of users who communicate in English. This limited release allows Meta (formerly Facebook) to assess its performance and address any issues before a broader launch.
• Geographical restrictions: Even among English-speaking users, availability may vary by location. Meta may prioritize certain regions for testing before expanding access globally.
• Device compatibility: Compatibility issues can also affect whether the feature works for a user. Some older devices or outdated Messenger app versions may not support AI-generated stickers.
• Server-side changes: Updates to the AI sticker feature are often implemented server-side. Users may need to ensure they have the latest version of the Messenger app, but Meta’s servers control the feature’s availability.

If you’re experiencing issues with Facebook Messenger AI stickers, it’s advisable to ensure that your app is up-to-date and that you are using an English language setting. Additionally, patience may be required as Meta gradually expands access to this feature based on their testing and refinement process.

Oh, are you new to AI, and everything seems too complicated? Keep reading…

AI 101

You can still get on the AI train! We have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of AI. Feel free to use them. Learning how to use AI is a game changer! AI models will change the world.

In the next part, you can find the best AI tools to use to create AI-generated content and more.

AI tools we have reviewed

Almost every day, a new tool, model, or feature pops up and changes our lives, and we have already reviewed some of the best ones:

• Text-to-text AI tools
  • Google Bard AI 
    • Google Bard Extensions
  • Chinchilla
  • Notion AI
  • Chai
  • NovelAI
  • Caktus AI
  • AI Dungeon
  • ChatGPT
  • Snapchat My AI
  • DuckAssist 
  • GrammarlyGO
  • Jenni AI
  • Microsoft 365 Copilot
  • Tongyi Qianwen
  • AutoGPT
  • Janitor AI
    • How to fix Janitor AI not working
    • How to use Janitor AI API
    • Janitor AI alternatives
  • Character AI
    • Character AI Rooms
    • Character AI App
    • Character AI alternatives
    • Character AI no filter
  • WordAi
  • Venus Chub AI
  • Crushon AI
  • FreedomGPT
  • Charstar AI
  • Jasper AI
  • WormGPT
    • How to use WormGPT AI
    • WormGPT download, here are the dangers waiting for you
  • Llama 2
  • Kajiwoto AI
  • Harpy AI Chat
  • RizzGPT
  • GigaChat
  • Easyerp.ai 
  • Jesus AI
  • Gemini AI 
  • Answer AI
  • Mistral 7B
  • Talkie: Soulful AI
  • Meta AI
  • Candy.ai
  • Dopple AI 

See this before login ChatGPT; you will need it. Do you want to learn how to use ChatGPT effectively? We have some tips and tricks for you without switching to ChatGPT Plus, like how to upload PDF to ChatGPT! However, When you want to use the AI tool, you can get errors like “ChatGPT is at capacity right now” and “too many requests in 1-hour try again later”. Yes, they are really annoying errors, but don’t worry; we know how to fix them. Is ChatGPT plagiarism free? It is a hard question to find a single answer. Is ChatGPT Plus worth it? Keep reading and find out!

• Text-to-image AI tools
  • MyHeritage AI Time Machine
  • Reface app
  • Dawn AI
  • Lensa AI
  • Meitu AI Art
  • Stable Diffusion
    • Img2Img: Stable Diffusion image-to-image
    • Stable Diffusion models
  • DALL-E 2
  • Google Muse AI
  • Artbreeder AI
  • Midjourney
    • How to fix Midjourney invalid link
    • Midjourney alternatives
    • Midjourney AI tips
    • Midjourney V5.2
    • Midjourney video generation guide
    • Where to look for the best Midjourney images?
    • DALL-E 3 vs Midjourney
  • DreamBooth AI
  • Wombo Dream
  • NightCafe AI
  • QQ Different Dimension Me
  • Random face generators
  • Visual ChatGPT
  • Adobe Firefly AI
  • Leonardo AI
  • Hotpot AI
  • DragGAN AI photo editor
  • Freepik AI
  • 3DFY.ai
  • Photoleap
  • Artguru
  • Luma AI
  • BlueWillow AI
  • Scribble Diffusion
  • Clipdrop AI
  • Artguru AI
  • Stable Doodle
  • Pica AI 

While there are still some debates about artificial intelligence-generated images, people are still looking for the best AI art generators. Will AI replace designers? Keep reading and find out.

• AI video tools
  • Runway AI Gen-2
  • Make-A-Video
  • MOVIO AI
  • Nvidia Eye Contact AI
  • Kreado AI
• AI presentation tools
  • Tome AI
  • Beautiful.ai
• AI search engines
  • Consensus AI
  • Google Bard
  • Komo AI
  • You.com
  • Bing AI
    • Bing Chat Enterprise
• AI interior design tools
  • Reimagine Home AI
  • Interior AI
  • Remodeled.ai
• Other AI tools
  • Poised AI
  • Uberduck AI
  • Spotify AI DJ
  • Pimeyes
  • Microsoft Security Copilot
  • OpenAI ChatGPT plugins
  • Otter.ai
  • Adobe Podcast AI
  • Kaiber AI 
  • CarynAI
  • Paragraphica
  • Silly Tavern AI
  • Meta Voicebox
  • Network AI
  • FraudGPT
  • Murf AI
  • AudioCraft
  • Fliki AI
  • Stable Audio
  • Conker AI
  • Alexa AI
  • Beatoven.ai 
  • Andisearch AI

Do you want to explore more tools? Check out the bests of:

Featured image credit: podesbiens.bsky.social/X

The $725 million Facebook privacy settlement, potentially the largest U.S. class-action lawsuit, faces deductions for legal fees and plaintiff compensation. The settlement, stemming from allegations of data sharing with third parties like Cambridge Analytica, awaits final approval but may encounter delays due to possible appeals.

A record-breaking number of claims for the Facebook settlement

Lawyers involved in the case recently revealed that over 28 million applications for compensation have been submitted by affected users. Lesley Weaver, co-lead counsel for the plaintiffs, stated, “As far as we can tell, that’s the largest number of claims ever filed in a class action in the United States.”  Out of the 28 million claims, approximately 17 million have already been preliminarily validated, signaling that a substantial portion of users will receive compensation pending final approval.

However, this validation process has not been without its challenges, with 2 million duplicate claims and 8 million potentially fraudulent ones weeded out. Approximately 1 million claims remain under review.

Unpacking the Facebook settlement fund

Once the total number of eligible claimants is confirmed, the $725 million Facebook settlement will be distributed, but not without deductions. The legal team handling the case is requesting about $180 million in attorneys’ fees, reducing the settlement fund to $545 million. Administrative fees for overseeing the claims process will further diminish the fund, but the exact amount remains undisclosed.

Additionally, each of the eight plaintiffs representing all Facebook users in the case is entitled to $15,000, which will further reduce the available funds. These necessary deductions are crucial in ensuring that the remaining compensation reaches those who were affected by the privacy breaches.

Fair compensation, uneven distribution

While the final amount each claimant will receive depends on various factors, it’s certain that those with a longer history on Facebook will receive larger sums. Predicting an exact amount in advance is challenging, but class counsel estimates a median payment size of around $30.

Judge Vince Chhabria has granted the plaintiffs’ lawyers an additional week to file necessary documents with the court. Once the Facebook settlement receives final approval, the distribution of payments will move one step closer. However, potential appeals could introduce delays in the process.

Do you remember Cambridge Analytica?

The settlement stems from allegations that Meta, Facebook’s parent company, allowed users’ personal data to be shared with third parties, most notably Cambridge Analytica, which played a significant role in supporting Donald Trump’s 2016 presidential campaign. Meta, while agreeing to the payout, denies wrongdoing, highlighting the ongoing battle for online privacy and accountability.

In conclusion, the Facebook settlement represents a monumental legal battle with an unprecedented number of claimants seeking justice for data breaches. This case underscores the importance of protecting online privacy and holding tech giants accountable for their actions. As the legal process unfolds, it serves as a stark reminder of the ongoing struggle to safeguard our digital lives.

Attention, Facebook users! You have a chance to claim your share of a $725 million settlement in a class-action lawsuit against the social media giant. Today, we will show you how to sign up for Facebook settlement and get your share before it is too late.

Meta, Facebook’s parent company, has agreed to pay out this substantial sum to settle allegations of sharing private information with third-party companies. Among the notable entities involved was Cambridge Analytica, a former political consulting firm. Despite the settlement, Meta denies any wrongdoing.

How to sign up for Facebook settlement: Get your share

This guide will show you how to sign up for Facebook settlement. Even if you’re Canadian and live in the U.S., you’re eligible to get your piece of the pie. The process is quick and simple; you have until August 25, 2023, to make your claim.

We have you covered if you don’t know whether you can get a share. Below you will find all the information you need to know about the case, and here is how to sign up for Facebook settlement!

Who Qualifies for Payouts?

If you were a Facebook user residing in the United States from May 24, 2007, to December 22, 2022, you’re automatically eligible for a payout, even if you’re a Canadian resident. However, you must complete a simple form to secure your share. The online claim deadline is August 25, 2023, at 11:59 p.m., or you can mail in your claim, ensuring it’s postmarked by the same date.

For users who had accounts during the eligible period but have since passed away, their claims can still be filed under the deceased person’s name.

Get your money ASAP, here is how to file Facebook class action lawsuit

You also have the option to exclude yourself from the settlement if you wish to pursue legal claims separately. The opt-out deadline is July 26, 2023, and you can access the required form for this purpose.

How Much Can You Expect?

The amount you’ll receive depends on several factors:

1. The total settlement will be divided among all claimants, so more claims mean a smaller share for each person.
2. Users who held accounts for the longest duration during the eligibility period will receive more money.
3. If you deleted your account within the settlement period but created new ones within the same timeframe, include this information to boost your payout potentially.

File your Facebook settlement claim, 24 days left

The distribution of payments is expected after final approval, currently scheduled for September 7, 2023, although this date could change.

Claiming Your Share: Online or by Mail

If you qualify for a settlement, there are two ways to file your claim:

1. Online: Visit the provided website and provide the necessary details, including Facebook usernames associated with your account.
2. By Mail: Download the claim form or contact the settlement administrator for a paper copy. You can reach the administrator by phone, email, or mail.

Download the claim form or ask the settlement administrator for a paper copy if you want to send your claim. The administrator can be reached by:

• Call: 855-556-2233
• Email: info@FacebookUserPrivacySettlement.com
• Send a letter: 1650 Arch Street, Suite 2210, Philadelphia, PA, 19103

When you apply for your settlement, indicate your preferred method of payment: PayPal, prepaid Mastercard, or a physical check mailed to your address.

Don’t miss out on this opportunity to claim your part of the settlement before the August 25th deadline! If you meet all the requirements, follow the steps on this guide and claim your share!

Featured image credit: Dima Solomin/Unsplash

People on the internet are looking to get compensated by Facebook after the Cambridge Analytica incident but don’t know how to. Today, we will show you how to file Facebook class action lawsuit easily.

Before we show you how to file Facebook class action lawsuit, let’s take a look at the information you need to provide. You need to get these ready to fill out the form. Here is all of the information you need to provide:

• Your name
• Your postal address
• Your email address
• Your mobile phone
• Your Facebook handle
• If you resided in the United States between May 24, 2007, and December 22, 2022
• If you used Facebook between May 24, 2007, and December 22, 2022
• If you cancel your account during that time frame, the date range during which you were a Facebook user
• Your preferred method of payment

How to file Facebook class action lawsuit

Individuals who want to be paid as part of the class-action settlement can make a claim here at any time until Aug. 25, 2023. Here is how to file Facebook class action lawsuit:

1. Go to the Facebook Settlement Website.
2. Select “Submit Claim.”
3. To update your claim, go to the top of the page and click the link (“Filed A Claim? Click Here to Edit Your Claim”).
4. To access and update your claim, use the Notice ID and Confirmation Code found at the top of this notice.
5. Proceed to the third question in the form’s “Details” section (“Are you filing a claim for a current account, a deleted account, or a combination of both?“).
6. Choose “Current Account(s),” “Deleted Account(s),” or “Both Current and Deleted Accounts.”
7. Fill out the information provided about your account(s), if relevant.

File your Facebook settlement claim, 24 days left

Only a few weeks remain for eligible Facebook users to make a claim in a new countrywide settlement involving the social media giant. The filing date for the $725 million class-action settlement is August 25, 2023.

Following a lawsuit alleging that Facebook made users’ data available to third parties without their consent and that the platform did not monitor or enforce third-party access to the data they obtained, the settlement was struck with Facebook’s parent company, Meta Platforms Inc. This includes data collected by the now-defunct political consulting firm Cambridge Analytica and utilized for political advertising on the site.

What is Facebook class action lawsuit?

The settlement is the result of many lawsuits filed against Facebook by individuals who alleged that the business inappropriately shared personal information with third-party sources such as marketers and data brokers. The lawsuit began when Facebook was caught in a privacy crisis with Cambridge Analytica in 2018, which collected user data from the platform in order to profile voters.

According to a newly constructed class-action website set up to pay out money to the social network’s users, Meta denied any obligation or wrongdoing under the settlement.

If you had an active Facebook account in the United States between May 2007 and December 2022, you have until August 25 to file a claim. The settlement’s individual compensation has not yet been established because it is dependent on the number of claims and the length of time each user had a Facebook account.

According to Keller Rohrback, the law firm that filed the claim, the payment was confirmed in December 2022, making it the largest class-action settlement of its kind. This ended years of legal wrangling over Facebook’s role in improperly sharing data with a consulting company hired by Donald Trump’s 2016 presidential campaign.

Meta, Facebook’s parent corporation, lost over $5.9 billion as a result of the Cambridge Analytica incident. This massive sum comprises the $725 million settlement, a record $5 billion settlement paid to the Federal Trade Commission, and an additional $100 million paid to the Securities and Exchange Commission.

We hop you enyoed reading our guide on how to file Facebook class action lawsuit and get your money. Remember, you need around two weeks before the agreement time runs out!

Featured image credit: Solen Feyissa/Unsplash

If you’re a Facebook user, remember that you have less than a month to submit and be part of the $725 million Facebook settlement claim. This settlement is part of the aftermath of the Cambridge Analytica controversy that shook both the U.S. elections and the tech industry of Silicon Valley.

In December 2022, the settlement was confirmed, marking it as the biggest class-action settlement of this type according to Keller Rohrback, the legal firm that initiated the lawsuit. This concluded years of legal disputes surrounding Facebook’s part in inappropriately sharing data with a consulting firm engaged by Donald Trump’s 2016 campaign for presidency.

The scandal involving Cambridge Analytica caused Meta, the parent company of Facebook, to lose almost $5.9 billion. This huge amount includes the $725 million settlement, along with a record $5 billion settlement paid to the Federal Trade Commission, and another $100 million to the Securities and Exchange Commission.

Here’s what you need to know to claim: if you had an active U.S. Facebook account between May 2007 and December 2022, you have until Aug. 25 to submit a claim. The individual payout from the settlement hasn’t been determined yet, as it depends on the number of claims and how long each user had a Facebook account.

To make a claim, Facebook users need to visit Facebookuserprivacysettlement.com.

There, they need to provide their name, address, and email, and confirm they were residing in the U.S. and actively using Facebook within the given dates.

But don’t worry, we are going to guide you through step by step below.

Meta explains everything about its evolving feed algorithms and its AI intentions

Facebook, which changed its name to Meta in 2021, managed to settle the class action lawsuit by 2022. Since the Cambridge Analytica controversy, the company has significantly evolved. It is further exploring the metaverse with new products like Quest 3, which will be available this fall. The company has also introduced its large language artificial intelligence model called Llama 2; Reels, to rival TikTok; and most recently, Threads, to compete with Twitter.

The privacy breach led Mark Zuckerberg, the founder of Facebook, to appear before Congress and publish full-page advertisements expressing his apologies for the mishap. “I’m sorry we didn’t do more at the time. We’re now taking steps to ensure this doesn’t happen again,” stated Zuckerberg.

It should be noted that the $725 million settlement does not signify an admission of any wrongdoing on Facebook’s part.

Am I eligible to claim a payout from the Facebook settlement?

If you had an active Facebook account while living in the U.S. between May 24, 2007, and Dec. 22, 2022, you are automatically qualified to get a part of the settlement. But, remember, you need to fill out a form to get this money. The last date to submit your claim online is till 11:59 p.m. on Aug. 25, 2023. If you’re sending your claim through mail, make sure it is postmarked by this date.

For those Facebook users who had active accounts during the eligible period but are no longer alive, the claim should be made under the deceased person’s name. Their account information should be provided in the “Your Facebook Account” section of the claim form.

How much will I get?

The amount of money each person can get from the Facebook settlement claim will vary and depends on certain factors. The whole settlement will be divided among all Facebook users who make a claim. Therefore, if more people file claims, each person will get less money.

Users who maintained their Facebook accounts for the longest duration during the eligibility period will get the most money. So, even if you deactivated or deleted your account during the settlement period, you could still get some money, but it will be less than what people who kept their accounts active for the entire period will get.

If you deleted your account and made a new one during the given time frame, remember to mention this in your claim as it might increase the amount of money you could receive.

Getting your share of the settlement might take a while. It is expected that payments will be sent out after the final approval, currently set for September 7. However, this date could be moved to a later time.

How can I claim a payout from the Facebook settlement?

If you’re eligible for the Facebook settlement money, you can submit your claim in two ways: either online or via mail. You’ll need to confirm some details, such as your address, phone number, and all the Facebook usernames you’ve used for your account between May 24, 2007 and Dec. 22, 2022.

If you can’t remember your username, you can find it on Facebook in two ways.

First, go to your homepage or timeline, and your username will be in the address bar, like this: https://www.facebook.com/username. Another way to find it is by logging into Facebook and going to “Account” > “Settings and Privacy” > “Settings” > “username.”

Alongside your personal details, you’ll also need to respond to the following questions:

• Did you reside in the United States at any time between May 24, 2007, and Dec. 22, 2022, inclusive?
• Were you a Facebook user at any time between May 24, 2007, and Dec. 22, 2022?
• Are you filing a claim for a current account, a deleted account or a combination of both?

Lastly, you need to confirm under oath that all the information you have provided for the Facebook settlement claim is correct.

How to file your Facebook settlement claim online?

Filing your claim online is likely the quickest and easiest method. It should take just a few minutes of your time.

How to file your claim by mail?

If you prefer to submit your claim by mail, you can download the claim form or request a paper copy from the settlement administrator. To get in touch with the administrator, you can:

• Call them at 855-556-2233.
• Send an email to info@FacebookUserPrivacySettlement.com.
• Write a letter to Facebook Consumer Privacy User Profile Litigation, c/o Settlement Administrator, 1650 Arch Street, Suite 2210, Philadelphia, PA, 19103.

Receiving your settlement payment

When you apply for your share of the settlement, you can select how you want to receive your funds. You can choose Venmo, PayPal, Zelle, a prepaid Mastercard, or a check that will be mailed to your provided address.

Featured image credit: Kerem Gülen/Midjourney

“How to get blue tick on Instagram and Facebook via Meta Verified” is a trendy question after Mark Zuckerberg decided to sell verification badges like Elon Musk. Is it killing the power of verification on social media? It is still unknown, but one thing is sure, it is highly profitable. Musk’s Twitter Blue currently has over 4 million paying subscribers, which means 32 million dollars in revenue monthly! In addition, considering that the number of subscribers is increasing every second, we are not surprised that Zuckerberg made this decision. On the other hand, we also love these blue ticks and understand why you want them too!

Have you ever wondered how to get blue tick on Instagram? The blue ticks, checkmark, or verification emblem, is a marker of genuineness and trustworthiness on several social media sites. They’re visible cues indicating a user’s account or profile has been validated by the service. While the significance of blue ticks may shift slightly from platform to platform, they often represent the following:

• Authenticity: Blue ticks provide assurance to users that an account is legitimate and represents a genuine public figure, celebrity, brand, or organization. This verification helps protect users from impersonators or fake accounts trying to deceive or mislead others.
• Credibility: Verification badges enhance the credibility of the account holder. They indicate that the account has been deemed noteworthy or influential by the social media platform, which can positively impact the perceived trustworthiness and reputation of the account.
• Differentiation: Blue ticks allow verified accounts to stand out from the vast sea of unverified profiles. This differentiation is especially valuable in platforms with a large user base where individuals or brands are seeking visibility, recognition, and increased engagement.
• Enhanced reach: Some social media platforms offer additional features or perks to verified accounts, such as access to analytics, advanced settings, or promotional opportunities. These privileges can help verified users maximize their social media presence and reach a wider audience.
• Branding and marketing: For businesses and brands, blue ticks can serve as a valuable branding tool. They provide an official seal of approval, indicating that the account represents the legitimate presence of the brand. This verification can instill confidence in customers and help establish a brand’s authenticity and professionalism.
• Reduced confusion: Verification badges minimize confusion or ambiguity caused by numerous accounts claiming to represent the same public figure, brand, or organization. Users can easily identify and follow the official account, ensuring they receive accurate and up-to-date information from the verified source.

Do you want these privileges and more on Instagram and Facebook for $14.99/month per app or $11.99/month for Facebook web only? If so, keep reading because you are about the get your beloved blue tick finally!

What is Meta Verified?

If you want to learn how to get blue tick on Instagram and Facebook, you first need to understand Meta Verified. Meta Verified is a subscription service that allows you to get a blue tick on Instagram and Facebook. In addition to getting the blue tick, there are other advantages to subscribing to Meta Verified:

• Account verification (including the blue check badge)
• Account impersonation protection
• Exclusive stickers
• Expanded reach
• Access to human support agents

Are you interested in it? Before becoming Meta Verified and getting your blue tick, check the following criteria you must meet:

• Your account must represent a real individual person, not a company or brand.
• You must be 18 years or older.
• You must live in the United States, Canada, United Kingdom, Australia, New Zealand, and India. You can join the waitlist to get a notification when Meta Verified becomes available in your region.
• You must not violate the Terms of Use or Community Guidelines on Instagram or the Terms of Service or Community Standards on Facebook.
• Your profile must use your full real name, which must match the name on your government-issued ID.
• Your profile photo must show your face, which must match the photo on your government-issued ID.
• You must enable two-factor authentication.
• You must have a prior posting history (i.e., you cannot apply for a brand-new account).

Are you eligible? Then keep reading and be verified!

How to get blue tick on Instagram and Facebook

Here are the steps on how to get Meta Verified and get blue tick on Instagram:

• Open the Instagram app and go to your profile.
• Tap on the Settings icon in the top right corner.
• Tap on Accounts Center.
• Tap on Meta Verified.
• If Meta Verified is available for your account, you will see “Meta Verified available” under your name and the option to Subscribe.

• Tap on the profile you want to verify.
• Set up payment
• Verify your identity via a government-issued photo ID and a selfie video to confirm your identity before being approved for the subscription.

Once you have subscribed to Meta Verified, you will be able to see the blue tick on your Instagram and Facebook profiles.

Check out the new YouTube monetization requirements 

Meta verified waitlist

The United States, Canada, the United Kingdom, Australia, New Zealand, and India all have access to Meta Verified right now. Join the Meta Verified waitlist on Instagram or Facebook to get updates if you are not located in an eligible country.

When you’re eligible, Meta will let you know when the Verified service rolls out to your area.

Cost of blue tick on Instagram and Facebook: Meta Verified pricing

Meta Verified pricing plans are $14.99/month per app or $11.99/month for Facebook web only.

Is Meta Verified worth it?

Whether or whether you should invest in Meta Verified is something only you can decide. The advantages of the subscription may be worthwhile if you are an influential individual or if you have a sizable Instagram following. However, you may not require the subscription plan if you are not a public celebrity with a sizable fan base.

To learn more about the subscription, click here.

Meta’s new Segment Anything Model was revealed. The SAM model is a new way to create high-quality masks for image segmentation.

Reminder: Image segmentation is a fundamental task in computer vision that aims to partition an image into regions that correspond to different objects or semantic categories and has many applications, such as object detection, scene understanding, image editing, and video analysis.

However, image segmentation is also a challenging problem, especially when dealing with complex scenes that contain multiple objects with varying shapes, sizes, and appearances. Moreover, most existing image segmentation methods require large amounts of annotated data for training, which can be costly and time-consuming to obtain. Meta wants to solve this issue with the SAM model.

SAM model: What is Meta’s new Segment Anything Model?

Segment Anything Model (SAM) is a new and powerful artificial intelligence model that can segment any object in an image or video with high quality and efficiency. Segmentation is the process of separating an object from its background or other objects and creating a mask that outlines its shape and boundaries. With the SAM model, your editing, compositing, tracking, recognition, and analysis tasks will get easier.

SAM is different from other segmentation models in several ways, such as:

• SAM is promptable, which means it can take various input prompts, such as points or boxes, to specify what object to segment. For example, you can draw a box around a person’s face, and the Segment Anything Model will generate a mask for the face. You can also give multiple prompts to segment multiple objects at once. The SAM model can handle complex scenes with occlusions, reflections, and shadows.
• SAM is trained on a massive dataset of 11 million images and 1.1 billion masks, which is the largest segmentation dataset to date. This dataset covers a wide range of objects and categories, such as animals, plants, vehicles, furniture, food, and more. SAM can segment objects that it has never seen before, thanks to its generalization ability and data diversity.
• SAM has strong zero-shot performance on a variety of segmentation tasks. Zero-shot means that SAM can segment objects without any additional training or fine-tuning on a specific task or domain. For example, SAM can segment faces, hands, hair, clothes, and accessories without any prior knowledge or supervision. SAM can also segment objects in different modalities, such as infrared images or depth maps.

The SAM model achieves impressive results on various image segmentation benchmarks, such as COCO. SAM also outperforms or matches prior fully supervised methods on several zero-shot segmentation tasks, such as segmenting logos, text, faces, or sketches. It demonstrates its versatility and robustness across different domains and scenarios.

In the future: The Segment Anything Model (SAM model) project is still in its early days. According to Meta, these are some of the future applications of the Segment Anything Model:

• Future AR glasses may employ SAM to recognize commonplace objects and provide helpful reminders and instructions.

• SAM has the ability to affect many other fields, such as agriculture and biology. One day, it might even benefit farmers and scientists.

The SAM model can be a breakthrough in computer vision and artificial intelligence research. It demonstrates the potential of foundation models for vision, which are models that can learn from large-scale data and transfer to new tasks and domains.

Segment Anything Model (SAM model) features

Here are some of the SAM model’s capabilities:

• Using the SAM model, users may quickly and easily segment objects by selecting individual points to include or omit from the segmentation. A boundary box can also be used as a cue for the model.
• When uncertainty exists regarding the item being segmented, the SAM model can produce many valid masks, a crucial and critical skill for solving segmentation in the real world.
• Automatic object detection and masking are now simple with the Segment Anything Model.
• After precomputing the image embedding, the Segment Anything Model can provide a segmentation mask for any prompt instantly, enabling real-time interaction with the model.

Impressive, isn’t it? So what is the technology behind it?

How does the SAM model work?

One of the most intriguing discoveries in NLP and, more recently, in computer vision is the use of “prompting” approaches to enable zero-shot and few-shot learning on novel datasets and tasks using foundation models. Meta found motivation in this field.

If given foreground/background points, a rough box or mask, freeform text, or any other input indicating what to segment in an image, the Meta AI team taught the Segment Anything Model to generate a proper segmentation mask. The need for a proper mask merely implies that the output should be an appropriate mask for one of the things that the prompt might refer to (for example, a point on a shirt could represent either the shirt or the person wearing it). This task is used for model pre-training and to guide the solution of generic downstream segmentation problems.

Meta noticed that the pretraining task and interactive data collecting imposed certain limitations on the model construction. In particular, their annotators need to be able to utilize the Segment Anything Model in a browser, interactively, in real-time, on a CPU for it to be effective. Despite the fact that there must be some compromise between quality and speed to meet the runtime requirement, they discover that a straightforward approach produces satisfactory results.

On the back end, an image encoder creates a unique embedding for the image, while a lightweight encoder can instantly transform any query into an embedding vector. A lightweight decoder is then used to merge these two data sources in order to anticipate segmentation masks. After the image embedding has been calculated, SAM can respond to every query in a web browser with a segment in about 50 ms.

SAM is a useful tool for creative professionals and enthusiasts who want to edit images and videos with ease and flexibility. But first, you need to learn how to access and use it.

How to use the Segment Anything Model (SAM model)?

SAM is developed by Meta AI Research (formerly Facebook AI Research), and it is publicly available on GitHub. You can also try SAM online with a demo or download the dataset (SA-1B) of 1 billion masks and 11 million images. The model is quite easy to use; just follow these steps:

• Download the demo or go to the Segment Anything Model demo.
• Upload an image or choose one in the gallery.
• Add and subject areas
  • Mask areas by adding points. Select Add Area, then select the object. Refine the mask by selecting Remove Area, then select the area.

Then complete your task as you want!

For more information, click here.

Image courtesy: Meta

AI 101

Are you new to AI? You can still get on the AI train! We have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of AI. Feel free the use them. Learning how to use AI is a game changer! AI models will change the world.

AI tools we have reviewed

Almost every day, a new tool, model, or feature pops up and changes our lives, like the new OpenAI ChatGPT plugins, and we have already reviewed some of the best ones:

• Text-to-text AI tools
  • Google Bard AI 
  • Chinchilla
  • Notion AI
  • Chai
  • NovelAI
  • Caktus AI
  • AI Dungeon
  • ChatGPT
  • Snapchat My AI
  • DuckAssist 
  • GrammarlyGO
  • Komo AI

Do you want to learn how to use ChatGPT effectively? We have some tips and tricks for you without switching to ChatGPT Plus! When you want to use the AI tool, you can get errors like “ChatGPT is at capacity right now” and “too many requests in 1-hour try again later”. Yes, they are really annoying errors, but don’t worry; we know how to fix them. Is ChatGPT plagiarism free? It is a hard question to find a single answer. If you are afraid of plagiarism, feel free to use AI plagiarism checkers. Also, you can check other AI chatbots and AI essay writers for better results.

• Text-to-image AI tools
  • MyHeritage AI Time Machine
  • Reface app
  • Dawn AI
  • Lensa AI
  • Meitu AI Art
  • Stable Diffusion
  • DALL-E 2
  • Google Muse AI
  • Artbreeder AI
  • Midjourney
  • DreamBooth AI
  • Wombo Dream
  • Tome AI
  • Interior AI
  • NightCafe AI
  • QQ Different Dimension Me
  • Random face generators
  • Visual ChatGPT
  • Adobe Firefly AI

While there are still some debates about artificial intelligence-generated images, people are still looking for the best AI art generators. Will AI replace designers? Keep reading and find out.

• Other AI tools
  • Poised AI
  • Runway AI Gen-2
  • Make-A-Video
  • Uberduck AI
  • MOVIO AI
  • Nvidia Eye Contact AI
  • Tome AI
  • Spotify AI DJ
  • Pimeyes
  • Reimagine Home AI
  • Microsoft 365 Copilot
  • Consensus AI
  • Microsoft Security Copilot
  • Beautiful.ai

Facebook layoffs 2022 will start this week. Facebook’s parent company Meta is preparing to notify staff members of significant layoffs. Mass Meta layoffs are anticipated to affect thousands of employees. This year, the social media giant has seen a more than 70% decline in the price of its shares.

After Twitter, Lyft, Microsoft, and Stripe, tech layoffs continue with Meta. The changes, which will be disclosed on Wednesday, are anticipated to impact thousands of Meta’s 87,000 employees worldwide, according to the Wall Street Journal.

Facebook layoffs 2022: Meta layoffs explained

This week, massive Facebook layoffs will start at Meta, the company that owns Facebook and Instagram. Last month, Meta was valued at $270 billion, down from roughly $1 trillion a year earlier. The company representatives have already instructed staff to stop booking unnecessary trips starting this week.

The upcoming Meta layoffs would be the organization’s first notable headcount cutbacks in its 18-year history. The number of Meta employees anticipated to lose their jobs may be the highest to date at a significant technology corporation in a year that has seen a retrenchment in the tech sector, even though it is smaller on a percentage basis than the cuts at Twitter Inc. this past week, which affected about half of that company’s staff.

Following are some important Q3 2022 business indicators for Meta Platforms that cause Facebook layoffs / Meta layoffs:

• Revenues fell by 4%, to $27.71 billion;
• Costs and expenses rose 19%;
• Operating income fell by 46% to $5.66 billion

Why is Meta laying off employees?

CEO Mark Zuckerberg has been under pressure from investors to reduce the firm’s investments in the metaverse. Investors are hesitant to make significant investments in disappointing metaverse offers that could take years to become lucrative, as evidenced by the company’s share price decline of more than 70% this year.

According to Zuckerberg, the metaverse is the company’s future and will take more than $10 billion in investments annually. The metaverse initiative has cost the corporation $15 billion since the start of last year. In the most recent quarter, Meta’s free cash flow decreased by 98% due to its fast-increasing expenses.

Reality Labs, the corporation’s metaverse branch, reportedly lost $3.7 billion over the last three months. The company predicted that in 2023, these losses would “increase dramatically year over year.”

“In 2023, we’re going to focus our investments on a small number of high-priority growth areas. So that means some teams will grow meaningfully, but most other teams will stay flat or shrink over the next year. In aggregate, we expect to end 2023 as either roughly the same size, or even a slightly smaller organization than we are today.”

Meta chief executive, Mark Zuckerberg

In a recent open letter to Zuckerberg, Meta’s stakeholder Altimeter Capital Management stated that the company needed to streamline by reducing capital expenditure and employee salaries. As it increased spending and shifted to the metaverse, it claimed that Meta had lost investors’ trust. Also, RBC Capital Markets analysts stated in a note last month that “Investors continue not to be receptive to Management’s road map & justification for this strategy.”

However, Zuckerberg claimed he was confident the company’s “experimental bets” would be profitable. These would ultimately turn out to be quite significant investments for the future of our company, he said.

“This is some of the most historic work we’re doing. People are going to look back on [this] decades from now and talk about the importance of the work that was done here.”

Meta chief executive, Mark Zuckerberg

As the ad industry becomes more competitive, Meta has also stated that it anticipates losing $10 billion in ad income in 2022 as a result of Apple privacy rules that allow customers to refuse to let the business to track them across apps.

Meta layoffs might be the biggest round in recent job layoffs in technology following the sector’s explosive development during the pandemic.

The Twitter alternative Mastodon social media explained: Check out the best Mastodon servers

Facebook number of employees: How many employees are at Meta?

Meta employs approximately 87,000 people altogether.

Is laid off better than fired?

Being laid off means you’ve lost your employment due to adjustments the business has made. The distinction between being fired and being laid off is that when you are fired, the employer believes that your conduct led to the termination. You didn’t necessarily do anything wrong if you were fired.

When layoffs happen, who goes first?

• In terms of layoffs, “Last-In, First-Out” applies.
• Fresher, more expensive employees are routinely let go by employers.
• In layoffs, millennials are overrepresented.

Meta’s speech-to-speech translation AI and Make-A-Video Meta AI text-to-video generator explained

Tech layoffs continue with Meta

Not just Meta, either! There are numerous tech industry layoffs taking place right now. like as:

• Twitter layoffs
• Stripe layoffs
• Lyft layoffs
• Microsoft layoffs

As more people shifted their daily lives online during the epidemic, it appeared that the IT industry was growing. However, many internet companies reported slower growth in the third quarter due to consumers and marketers rethinking their spending. Many people in the tech sector are reassessing their investments and workforce needs. Consider for a moment what transpired with the tech layoffs.

Check out the latest data breaches and hacks: CHI Health data breach, Facebook data breach, Uber security data breach, American Airlines data breach, Medibank cyber attack, and Binance hack.

Are tech layoffs will continue?

Over the past ten years, tech companies have experienced rapid expansion and exorbitant spending. However, given the impending global recession, which might be significantly longer and harsher than many think, Silicon Valley companies that announced large layoffs this week could be a leading indicator for the whole economy.

Tech companies may need to slow down their recent expansion and spending boom in favor of cost-cutting measures when possible due to the changing economic climate.

What is Facebook data breach 2022? The social media platform revealed that it had discovered over 400 fraudulent Android and iOS apps targeting internet users to steal their login information this year. According to Meta, one million people may have had their login information taken.

What should you do? How to dodge the data breach? How did Facebook data breach 2022 happen? Which apps are stealing your data? What are other Facebook data breaches till today? Keep reading…

What is Facebook data breach 2022?

According to Meta Platforms Inc., one million Facebook users may have stolen their account credentials due to security flaws with apps downloaded from Alphabet Inc. and Apple Inc.’s app stores.

On Friday, the social media platform revealed the Facebook data breach 2022. It discovered over 400 fraudulent Android and iOS apps that target internet users to steal their login information this year. Meta has contacted Apple and Google about the problem to stop customer data from being hacked further.

Apple claims that 45 of the 400 problematic apps were available on the App Store, and those apps have now been taken down. According to a report, Google uninstalled all fraudulent apps related to the Facebook data breach 2022.

According to Facebook, these hazardous apps masquerade as picture editors, mobile games, or fitness monitors.

Photo editors made up 42.6% of the malicious apps, followed by productivity tools (15.4%), phone tools (14.1%), games (11.7%), VPNs (11.7%), and lifestyle apps (4.4%) related to the Facebook data breach 2022. Interestingly, most iOS apps masqueraded as tools for managing ads for Meta and its Facebook affiliate.

“Cybercriminals know how popular these types of apps are and use these themes to trick people and steal their accounts and information. If the login information is stolen, attackers could potentially gain full access to a person’s account and do things like message their friends or access private information.”

David Agranovich, Director of Global Threat Disruption at Meta

Additionally, he provided instructions on how users can protect their accounts if they have already downloaded such programs. If you think you downloaded a harmful app and used your social media or other online credentials to log in, Agranovich advised you to remove the program from your device right away and use the steps below to secure your accounts.

Meta stated that if the login credentials were obtained, attackers “may possibly acquire full access to a person’s account and do things like message their friends or access confidential information.”

Binance Hack: Binance Smart Chain Hack explained

How to dodge the Facebook data breach 2022?

• Create new, secure passwords after a reset. Never use the same password for several websites.
• Enable two-factor authentication to add an additional layer of security to your account, preferably using the Authenticator app.
• Activate log-in alerts to receive notifications whenever someone tries to access your account. Reviewing prior sessions will help you identify the devices that have access to your account.

Facebook data breach 2022: All malicious apps that Meta warns about

Both app stores have removed 402 apps, which include 355 Android apps and 47 iOS apps.

The followings are All malicious apps that Meta warns about:

• Apex Race Game
• CallerPaper Show
• Video Converter Master
• Acetoon Photo Cartoon
• Photo Frame PIP Collage Maker
• ZodiHoroscope – Fortune Finder
• Ads Manager Helper
• Ad Optimization Meta
• Ad Manager For Social
• Agent John FPS Game
• Unblocked Website
• Best Fun Cam
• Psychology Facts
• Cool Photo Editor
• Cool Filter Editor
• Ads & Page
• Ads Pages Manager
• Constellation Master
• AppLock-Lock Apps & Privacy Guard
• Smooth Picture Editor
• Text Camera
• Artoon Photo Cartoon Creator
• Pics Art
• Ashtoon Cartoon Editor
• Perfect Photo Effects | Loop Photo Animator
• Bomb Master 3D
• Free Music Downloader & Free Mp3 Downloader
• Beauty Camera
• Beauty Camera Plus
• Photo Editor
• Beauty Makeup Camera
• Art Puzzle 2021
• Biscuit Coco Cam
• Blue-shoot Game
• Blur Photo
• Bluetouch Shooting Game
• Ranger Crash Game
• Bamboo VPN
• Border Sticker Camera
• Skintoon – Labs of Cartoon
• Business Planner Manage
• Classic filter camera
• PIP Camera 2022
• Candles VPN
• Perfect Puzzles
• Photo editor PIP collage maker
• ToonPrisma – 3D Photo Effect
• HD Video Player with music
• BeautyCam
• YouToon – AI Cartoon Effect
• Play Phoot Editor & Collage
• Photo Filter
• Color Call
• Photo PIP Camera
• Craftsart Cartoon Photo Tools
• Creatoon Face Editor
• Photo Video Editor:Lena Editor
• Crown Camera
• Photo Pro 2021
• Ads Manager Plus
• Maker PIP Gallery
• Pages Dashboard
• Photoquipo Cartoon Pic Effect
• Flyingfish Wallpaper
• CameraAdorn
• CreatorMould
• ElegantImage
• Deep Wallpaper-live,HD
• Videolancer – Pro Video Maker
• PIP Collage Coco
• Dress up Charming
• 3 Patti King – India Rummy
• Face Picture
• QR Barcode Scan
• Atec Sparkle Photo Editor 2022
• Enjoy Photo Editor
• Ding Ding Photo Effect Editor
• Art Photo Puzzle
• Cartoon Face Photo Editor
• Savetoon Art – Face Cartoon
• Facetoon Photo Art
• Fast VPN Proxy
• Ads Optimize
• Ads Manager For Business
• Pica Artoon Face Editor
• Photo Layout Editor
• Instapic: Photo Editor Pro, Collage Maker
• Shape Photo Editor
• Share Photo Maker
• Speedy Vpn Tunnel
• Moldish – Men’s Photo Editor
• FlyFish Speed
• Stylist Fonts for Keyboard
• PIP Editor Frame Photo
• Flash QRCode Scanner
• Transcend VPN
• Free VPN Master
• Super Tuber VPN
• Tuber VPN – Free&Secure VPN Proxy Server
• Fast Vpn Tunnel
• Photo Puzzle 2021
• Lone Hero Racing
• Share Photo Editor
• Arts Photos
• Art Filters:Photo to Painting
• Game Booster
• Vinto Cam
• Emotion Checker
• Ghost VPN Proxy
• Train Photo
• Anime Photo
• Photo Editor Pic Collage
• Train Photo Frame
• Hiyoo Hidenode pro
• All in one Doc Editor & Viewer
• Fun Wallpaper
• Hotspot Free VPN
• Highquality Purple Wallpaper
• PIP Editor Image
• Ice Selfie Beauty Cam
• Image Move Puzzle
• Business from Instagram
• Wonderful Camera
• Ora Horoscope – Fortune Finder
• Ding Ding Photo Editor
• Sketch CamPlus
• Instant Translator
• Kangaroo VPN
• Photoont – Photo Collage Edit
• Palmistry Reading Free
• Toonex Photo Editor 2022
• Male fitness
• Pic Collage & Cartoon Editor
• 4K Camera Hyper Photo Filters
• Life Run
• Lighting Creator Toon
• Lightning VPN
• Files Clear
• PhotoEditor
• Lofa – Studio Photo Cartoon
• Lucky Catcher – Catch Them All
• Lucky Number Pro
• Video Remaker
• MadToon Face Cartoon
• Magic Horoscope
• GB WA Warna Latest Version
• Free Comic Photos
• Foster – Cartoon Photo Effect
• NeoSnap Photo Editor
• Business Ads for Meta
• Business Meta Manager
• Meta Optimizer – Ads Analysis
• Meteor vpn
• Nebula Wallpaper
• Photo Editor – Frame Effect
• Full Screen Video Editor
• Nice Photo
• Sticker Maker Pro
• Mold Figure Gym
• Mood Camera
• Astro Horoscope Guide 2022
• MuMus Music Player
• Photo PIP 2022
• Camera PIP
• Nuclear VPN Proxy
• Okenyo Studio Creatoon
• Pana Camera
• Daily Fitness OL
• Onlan Cartoon Editor
• Soda Music Player
• Palmistry of Destiny
• Popular Emoji
• Hot Sexy Girls
• Perfect Photo Album
• Pewee Photoon – Photo Cartoon
• Photo PIP
• Critical Strike Ops – FPS 3D shooting Game
• Tower Defense Zone – Batmen Rush
• Simple Photo Adjuster
• Blur Effect Camera
• Photo Gaming Puzzle
• Panorama Camera
• Pip Camera 2022
• Photo editor Pro
• Sweet Summer Camera App
• Abrasive Photo Editor
• Photo Editors
• Rainbow Photo Plaze
• Photo Sticker&Camera:make you beauty
• Pic collage: photo editor&beauty image
• Camera PIP Editor
• PIP Pic Camera Photo Editor
• PIP Photo
• PIP Editor Collage
• Pixa: Photo Editor & Collage Maker
• ToonArt
• Treasures of the Pharaoh
• PIP Pic Camera Photo Editor
• Photo Collage Maker Pic
• Perfect Photo Editor
• Smart AppLock
• Cool Lock
• Pulse Music Player
• Pumpkin VPN
• Punk Vpn
• Pure-VPN:Fast Stable Secure internet Proxy
• Deep Art Effects (Beware! The malicious app listed by Meta has the package name com.quality.famous.deep and developed by Pinot Noir, and it should not be mixed with the Deep Art Effects app developed by Deep Art Effects GmbH)
• Real Driving
• Papatoon Face Art Editor
• Camera Photo Editor
• PIP Magic 2022
• Photo Video Creator with Music
• Pop Ringtone
• Christmas Sticker Camera App
• Ads Manager for Meta
• Rocket Connect
• Rush Car 3D
• Rush Hour 3D – Heavy Traffic
• Video Editor
• Safe Link
• Cartoon Illustrator
• PIPO GIF
• Surf Vpn
• Sealand Music Player
• Swarm Photo
• Perfect ProCam
• Fission Effectdo Pic Editor
• ShineStar Camera
• Light Exposure Photo Editor
• Cartoon Cam Pro
• High HD Wallpapers 4K
• Amazing Photo Puzzle
• Smart SMS Messages
• Snap Beauty Camera
• Snap Editor Pro
• Snap Face Camera
• Sandwich SnapBeauty Cam
• Sofa Cartoon – Pro Editor
• PIP 2022
• Drift Speed Racing Game
• Speed Booster
• SplitScreen PIP
• PicMix Photo Editor
• Star Line
• PIP PHOTO
• Pvideo
• Superior Speed
• SurfVPN – Fast VPN Proxy
• S-VPN Proxy
• S VPN – Fast & Safe VPN Client
• Lightly Camera
• Sweet Beauty Plus Camera
• Beauty Sweet Camera
• Speedy Turbo
• Fantastic camera
• Flying Photo
• Cartoon Keyboard Theme
• Translation Assistant
• Torrent
• Torch VPN Proxy
• Touch VPN Master
• Traffic Tour 3D
• Photazo – PIP & Cartoon Effect
• MotoM3X-PoolParty
• Tuber VPN Proxy
• Tubo VPN Master
• Secure Turbo
• Turbo Net
• Unlimited Net
• VPN Lite – Fast & Easy use VPN
• Vender Add Text on Photo
• Video Editor 2021
• Virgo VPN
• Viva VPN Booster
• Vivid Cooleditor
• Voice Changer
• Private VPN HD
• 4K Wallpaper
• Keep Step
• Pista – Cartoon Photo Effect
• Magical Daily Astrology Reader
• Brilliant Photo
• FancyPhoto
• PixEditor
• Piestra – Comic & Blend Effect
• Smart PDF Reader and Editor
• Female Fitness
• Workout Pro
• Toolkit
• Xcar Highway Race
• Xeva Photo Cartoon
• GIFs-Search Animated GIF & Stickers
• 2021 MagicCamPlus
• Enjoy Art Photo
• Xona Relaxing Sounds
• MAGIC PHOTO PUZZLE
• Cartoon Effects Photo Editor
• Photo Editor Wall
• Y VPN Master
• ZooMate VPN Proxy
• Vilatouch crash game
• Vilatouch crash car
• Online Shooter FPS
• Free Music Downloader & Music Player
• Crash Racing Game
• Beauty Camera Filter
• Action Flame FPS
• Drift Sprint Racing Game
• CityRanger Racing Game
• Thefun Camera
• Desert Hunting Game
• Try to spin
• Lunar Zodiac Horoscope
• Callshow Flash
• Grape Media Player
• Tean Music Player
• Male Fitness 2020
• 2022 Ultra Camera
• Code Name-Vulture FPS OL
• Kangaroo Fast VPN
• Muses Music Player
• Red Camera
• Snap: HD Photo Editor
• Lightning Drift Racing
• One-Sweet Camera
• Baby Camera
• BatterySafe
• Crash Race Master
• Donfan Music Player
• Mulu Music Player
• Musae Music Player
• Night Hunter Game
• Teana Music Player
• Goat Safe VPN
• SnapBeauty Cam Filter
• Mostfun Media Music
• OseaCamera
• Pomelo Music Player
• Sealod Music Player
• SeaShell Music Player
• ProFlash
• Kite camera
• Apex Crash Race
• Impostor Master Solo Kill 2021
• Instant Drag Speed Racing
• Extreme Speed Race
• NoneCanDie
• City Crash Racing Game
• Callpaper Show
• Sunday Media Player
• Flame Shock FPS
• Modern Time Camera
• Rainbow Square- Sort Puzzle
• Tea Bag Camera
• FB Advertising Optimization
• Business ADS Manager
• Ads Analytics
• FB Adverts Optimization
• FB Analytic
• FB Adverts Community
• Adverts Ai Optimize
• Very Business Manager
• FB Business Support
• Fb Ads
• Meta Optimizer
• Business Manager Pages
• Adverts Manager
• Meta Adverts Manager
• Ad Optimization Meta
• FB Pages Manager
• Business Ads
• Meta Business
• Business Suite Manager
• FB Ads Cost
• Adverts Bussiness Suite
• Business Ads Clock
• Ads & Pages
• Business Suite
• Business & Ads
• Business Manager Overview
• Business Suite Ads
• Page Suite Manager
• Business Meta Support
• Pages Manager Suite
• Business Meta Pages
• Business Suite Ads
• Ads Business Knowledge
• Page Suite Managers
• Pages Managers Suite
• Ads Business Advance
• Pages Manager Suite
• Business Suite Optimize
• Business Manager Suite
• Business Suite Managers
• Ads Business Manager
• Ads Business Suite
• Business Manager Pages
• Business Adverts Manager
• Ads Manager Suite
• Business Manager Pages
• Ads & Business Suite

As we mentioned earlier, to remove the apps from their online shops, Meta has already contacted both Apple and Google and given them a thorough report of their findings. Agranovich stated that because the action did not take place on Meta systems, it would be simpler for the company to identify the problems.

The company has also contacted the potentially affected consumers to provide information on securing their data.

What a malicious apps look like?

Think twice when you want to use such applications:

• Photo editors, such as those that promise to transform you into a cartoon.
• VPNs claim to increase browsing speed or provide access to banned websites or content.
• Phone utilities like applications that promise to brighten the torch on your mobile device.
• Mobile games that make deceptive claims about their 3D graphics quality.
• Apps for healthy living, including horoscopes and fitness trackers.
• Apps for managing businesses or advertisements offering unauthorized or secret functionalities are not available in official apps for tech platforms.

Facebook data breach history

Timeline of all Facebook data breaches till today:

• Personal information for over 530 Million Facebook users was leaked in an online forum in April 2021.
• In June 2020, Facebook unintentionally provided user data to third-party developers.
• A hacker group stole 300+ million Facebook accounts’ data in December 2019.
• Data for 419 million Facebook users were discovered on an exposed server in September 2019.
• The FTC fined Facebook $5 billion and placed new privacy restrictions in July 2019.
• Facebook uploaded 1.5 million users’ email addresses without their consent in April 2019.
• 540 million Facebook user records were discovered on a public server in April 2019.
• Over 600 million Facebook passwords were kept in plaintext files discovered in March 2019.
• New York Times reported in December 2018 that Facebook shared user data without consent.
• Attackers accessed over 90 million Facebook users’ data in September 2018.
• The Facebook bug made the private posts of 14 million users public in May 2018.
• 50+ million users were affected by Cambridge Analytica Scandal in March 2018.
• A bug exposed the personal information of 6 million users in June 2013.
• Facebook’s Graph Search Rollout in January 2013 sparks privacy concerns.
• Facebook settled with the FTC over privacy complaints in November 2011.
• Facebook used a “Privacy Loophole” to share user data with advertisers found in May 2010.
• Beacon Advertising Program enabled Facebook user tracking in December 2007.

Check out the importance of cyber risk assessment

Latest data breaches

Facebook data breach 2022 just happened! Check out the other latest data breaches:

• Uber Security data breach
• American Airlines data breach
• CHI Health data breach

Consequences of data breaches: Equifax

On September 7, 2017, the credit reporting agency Equifax revealed that one of its computer networks had experienced a data leak that had exposed the personal data of 143 million customers, which later increased to 147 million. These records contained data on the names, addresses, birthdates, Social Security numbers, and credit card numbers of the consumers, all of which may be used for fraud and identity theft.

Take a closer look at how data breaches effects companies: Equifax Data breach settlement

Following the terms of the agreement, Equifax agreed to set up a fund to offer clients free credit monitoring, identity theft protection, and cash compensation of up to $20,000 per person harmed by the event. The business also has to cover court costs and fines from the government.

Make-A-Video Meta AI text-to-video generator revealed today. Meta seems to be joining the AI-art party with a powerful tool. Based on the same kind of language cues you give Dall-E, the technology creates brief, soundless video clips.

AI art is the new hype. After DALL-E 2, Stable Diffusion, Midjourney, DreamBooth AI, and Wombo Dream, Make-A-Video will make its appearance on the stage soon. The time for text-to-video generators has come. So, let’s dig deeper and find out what it promises.

What is Make-A-Video Meta AI?

Make-A-Video is an AI-powered video generator that can produce original video material from text or image prompts, much like other image synthesis tools like DALL-E and Stable Diffusion. It can also create modifications for already-existing videos.

The main technique underlying Make-A-Video builds on prior work with text-to-picture synthesis used with image generators like OpenAI’s DALL-E, which is why it has arrived earlier than some experts had predicted. Make-A-Scene, a text-to-image AI model, was introduced by Meta in July.

Instead of using labeled video data to train the Make-A-Video model (for instance, captioned descriptions of the actions shown), Meta used image synthesis data (still images trained with captions) and unlabeled video training data to train the model. As a result, the model understands the possible locations of text or image prompts in time and space. After then, it can foretell what would happen next and briefly show the scene in motion.

Meta admits the possibility of instantly producing photorealistic videos as posing some social risks. According to Meta, all AI-produced video material from Make-A-Movie has a watermark to “help viewers realize the video was generated with AI and is not a taken video,” as stated at the bottom of the announcement page.

Make-A-Video Meta AI features

Make-A-Video enables you to create quirky, original videos using only a few words or lines of text, allowing you to bring your creativity to life.

What are the features of Make-A-Video Meta AI?

• Make-A-Video with text: Meta will give three art styles for text-to-video generation:
  • Surreal
  • Realistic
  • Stylized
• From static images to the video: Add motion to a single image or add motion in the spaces between two photos.
• Making creative videos from existing videos: Based on the original, make different versions of your video.

Mark Zuckerberg also shared an informative video about Make-A-Video Meta AI on Facebook.

In addition to delivering resolutions of more than 768 by 768 pixels at 16 frames per second, it can produce clips longer than five seconds.

Make-A-Video Meta AI uses photos and descriptions to learn how the environment looks and how it is frequently described. Unlabeled movies are frequently used to teach about how the world functions. The recent advancements in text-to-image generating technologies designed to support text-to-video generation are built upon the Make-A-Video research.

You can check the Make-A-Video paper for detailed information.

How to use Make-A-Video Meta AI?

Who would have access to Make-A-Video and how or when it may be made public have not been disclosed by Meta. This part will be updated when Meta releases the instructions.

However, Meta offers a sign-up form that users can complete if they want to try it out later. In the following section, we explained how to sign up Make-A-Video Meta AI waitlist.

Artificial intelligence design: Will AI replace designers?

How to sign up Make-A-Video Meta AI waitlist?

• Go to the official Make-A-Video website.
• Find the “Sign up” option under the “Interested in trying Make-A-Video?” title. Or simply click this link.
• Fill out the form and done.

Do you know DALL-E 2 access is not for just some people anymore? Check out how to use DALL-E 2?

Make-A-Video Meta AI examples

There are some Make-A-Video Meta AI examples from the Meta AI team, although the text-to-image AI art generator is not public yet.

Since Make-A-Video isn’t currently accessible to the general public, we’ll take all these examples with a grain of salt. Still, they do represent an exciting new possible advancement in artificial intelligence.

Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence. Is artificial intelligence better than human intelligence? Let’s find out!

Facebook has announced Ego4D, a long-term project aimed at solving AI research challenges in “egocentric perception,” or first-person views. The goal is to teach AI systems to comprehend and interact with the world like humans do as opposed to in the third-person, omniscient way that most AI currently does.

It’s Facebook’s assertion that AI that understands the world from first-person could enable previously impossible augmented and virtual reality (AR/VR) experiences. But computer vision models, which would form the basis of this AI, have historically learned from millions of photos and videos captured in third-person. Next-generation AI systems might need to learn from a different kind of data — videos that show the world from the center of the action — to achieve truly egocentric perception, Facebook says.

To that end, Ego4D brings together a consortium of universities and labs across nine countries, which collected more than 2,200 hours of first-person video featuring over 700 participants in 73 cities going about their daily lives. Facebook funded the project through academic grants to each of the participating universities. And as a supplement to the work, researchers from Facebook Reality Labs (Facebook’s AR- and VR-focused research division) used Vuzix Blade smartglasses to collect an additional 400 hours of first-person video data in staged environments in research labs.

Collecting the data

According to Kristen Grauman, lead research scientist at Facebook, today’s computer vision systems don’t relate to first- and third-person perspectives in the same way that people do. For example, if you strap a computer vision system onto a rollercoaster, it likely won’t have any idea what it’s looking at — even if it’s trained on hundreds of thousands of images or videos of rollercoasters shown from the sidelines on the ground.

“For AI systems to interact with the world the way we do, the AI field needs to evolve to an entirely new paradigm of first-person perception,” Grauman said in a statement. “That means teaching AI to understand daily life activities through human eyes in the context of real-time motion, interaction, and multisensory observations.”

In this way, Ego4D is designed to tackle challenges related to embodied AI, a field aiming to develop AI systems with a physical or virtual embodiment, like robots. The concept of embodied AI draws on embodied cognition, the theory that many features of psychology — human or otherwise — are shaped by aspects of the entire body of an organism. By applying this logic to AI, researchers hope to improve the performance of AI systems like chatbots, robots, autonomous vehicles, and even smartglasses that interact with their environments, people, and other AI.

Ego4D recruited teams at partner universities to hand out off-the-shelf, head-mounted cameras (including GoPros, ZShades, and WeeViews) and other wearable sensors to research participants so that they could capture first-person, unscripted videos of their daily lives. The universities included:

1. University of Bristol
2. Georgia Tech
3. Carnegie Mellon University
4. Indiana University
5. International Institute of Information Technology
6. King Abdullah University of Science and Technology
7. University of Minnesota
8. National University of Singapore
9. University of Tokyo
10. University of Catania
11. Universidad de los Andes

The teams had participants record roughly eight-minute clips of day-to-day scenarios like grocery shopping, cooking, talking while playing games, and engaging in group activities with family and friends. Ego4D captures where the camera wearer chose to gaze at in a specific environment, what they did with their hands (and objects in front of them), and how they interacted with other people from an egocentric perspective.

Some footage was paired with 3D scans, motion data from inertial measurement units, and eye tracking. The data was de-identified in a three-step process that involved human review of all video files, automated reviews, and a human review of automated blurring, Facebook says — excepting for participants who consented to share their audio and unblurred faces.

Potential bias

In computer vision datasets, poor representation can result in harm, particularly given that the AI field generally lacks clear descriptions of bias. Previous research has found that ImageNet and OpenImages — two large, publicly available image datasets — are U.S. and Euro-centric, encoding humanlike biases about race, ethnicity, gender, weight, and more. Models trained on these datasets perform worse on images from Global South countries. For example, images of grooms are classified with lower accuracy when they come from Ethiopia and Pakistan, compared to images of grooms from the United States. And because of how images of words like “wedding” or “spices” are presented in distinctly different cultures, object recognition systems can fail to classify many of these objects when they come from the Global South.

Tech giants have historically deployed flawed models into production. For example, Zoom’s virtual backgrounds and Twitter’s automatic photo-cropping tool have been shown to disfavor people with darker-colored skin. Google Photos once labeled Black people as “gorillas,” and Google Cloud Vision, Google’s computer vision service, was found to have labeled an image of a dark-skinned person holding a thermometer “gun” while labeling a similar image with a light-skinned person “electronic device.” More recently, an audit revealed that OpenAI’s Contrastive Language-Image Pre-training (CLIP), an AI model trained to recognize a range of visual concepts in images and associate them with their names, is susceptible to biases against people of certain genders and age ranges.

In an effort to diversify Ego4D, Facebook says that participants were recruited via word of mouth, ads, and community bulletin boards from the U.K., Italy, India, Japan, Saudi Arabia, Singapore, and the U.S. across varying ages (97 were over 50 years old), professions (bakers, carpenters, landscapers, mechanics, etc.), and genders (45% were female, one identified as nonbinary, and three preferred not to say a gender). The company also says it’s working on expanding the project to incorporate data from partners in additional countries including Colombia and Rwanda.

But Facebook declined to say whether it took into account accessibility and users with major mobility issues. Disabled people might have gaits, or patterns of limb movements, that appear different to an algorithm trained on footage of able-bodied people. Some people with disabilities also have a stagger or slurred speech related to neurological issues, mental or emotional disturbance, or hypoglycemia, and these characteristics may cause an algorithm to perform worse if the training dataset isn’t sufficiently inclusive.

In a paper describing Ego4D, Facebook researchers and other contributors concede that biases exist in the Ego4D dataset. The locations are a long way from complete coverage of the globe, they write, while the camera wearers are generally located in urban or college town areas. Moreover, the pandemic led to ample footage for “stay-at-home scenarios” such as cooking, cleaning, and crafts, with more limited video at public events. In addition, since battery life prohibited daylong filming, the videos in Ego4D tend to contain more “active” portions of a participant’s day.

Benchmarks

In addition to the datasets, Ego4D introduces new research benchmarks of tasks, which Grauman believes is equally as important as data collection. “A major milestone for this project has been to distill what it means to have intelligent egocentric perception,” she said. “[This is] where we recall the past, anticipate the future, and interact with people and objects.”

The benchmarks include:

1. Episodic memory: AI could answer freeform questions and extend personal memory by retrieving key moments in past videos. To do this, the model must localize the response to a query within past video frames — and, when relevant, further provide 3D spatial directions in the environment.
2. Forecasting: AI could understand how the camera wearer’s actions might affect the future state of the world, in terms of where the person is likely to move and what objects they’re likely to touch. Forecasting actions requires not only recognizing what has happened but looking ahead to anticipate next moves.
3. Hand-object interaction: Learning how hands interact with objects is crucial for coaching and instructing on daily tasks. AI must detect first-person human-object interactions, recognize grasps, and detect object state changes. This thrust is also motivated by robot learning, where a robot could gain experience vicariously through people’s experience observed in video.
4. Audiovisual diarization: Humans use sound to understand the world and identify who said what and when. AI of the future could too.
5. Social interaction: Beyond recognizing sight and sound cues, understanding social interactions is core to any intelligent AI assistant. A socially intelligent AI would understand who is speaking to whom and who is paying attention to whom.

Building these benchmarks required annotating the Ego4D datasets with labels. Labels — the annotations from which AI models learn relationships in data — also bear the hallmarks of inequality. A major venue for crowdsourcing labeling work is Amazon Mechanical Turk, but an estimated less than 2% of Mechanical Turk workers come from the Global South, with the vast majority originating from the U.S. and India.

For its part, Facebook says it leveraged third-party annotators who were given instructions to watch a five-minute clip, summarize it, and then rewatch it, pausing to write sentences about things the camera wearer did. The company collected “a wide variety” of label types, it claims, including narrations describing the camera wearer’s activity, spatial and temporal labels on objects and actions, and multimodal speech transcription. In total, thousands of hours of video were transcribed and millions of annotations were compiled, with sampling criteria spanning the video data from partners in the consortium.

“Ego4D annotations are done by crowdsourced workers in two sites in Africa. This means that there will be at least subtle ways in which the language-based narrations are biased towards their local word choices,” the Ego4D researchers wrote in the paper.

Future steps

It’s early days, but Facebook says it’s working on assistant-inspired research prototypes that can understand the world around them better by drawing on knowledge rooted in the physical environment. “Not only will AI start to understand the world around it better, it could one day be personalized at an individual level — it could know your favorite coffee mug or guide your itinerary for your next family trip,” Grauman said.

Facebook says that in the coming months, the Ego4D university consortium will release its data. Early next year, the company plans to launch a challenge that’ll invite researchers to develop AI that understands the first-person perspectives of daily activities.

The efforts coincide with the rebranding of Facebook’s VR social network, Facebook Horizon, to Horizon Worlds last week. With Horizon Worlds, which remains in closed beta, Facebook aims to make available creation tools to developers so that they can design environments comparable to those in rival apps like Rec Room, Microsoft-owned AltSpace, and VRChat. Ego4D, if successful in its goals, could give Facebook a leg up in a lucrative market — Rec Room and VRChat have billion-dollar valuations despite being pre-revenue.

“Ultimately — for now, at least — this is just a very clean and large dataset. So in isolation, it’s not particularly notable or interesting. But it does imply a lot of investment in the future of ‘egocentric’ AI, and the idea of cameras recording our lives from a first-person perspective,” Mike Cook, an AI researcher at Queen Mary University, told VentureBeat via email. “I think I’d mainly argue that this is not actually addressing a pressing challenge or problem in AI … unless you’re a major tech firm that wants to sell wearable cameras. It does tell you a bit more about Facebook’s future plans, but … just because they’re pumping money into it doesn’t mean it’s necessarily going to become significant.”

Beyond egocentric, perspective-aware AI, high-quality graphics, and avatar systems, Facebook’s vision for the “metaverse” — a VR universe of games and entertainment — is underpinned by its Quest VR headsets and forthcoming AR glasses. In the case of the latter, the social network recently launched Ray-Ban Stories, a pair of smartglasses developed in collaboration with Ray-Ban that capture photos and videos with built-in cameras and microphones. And Facebook continues to refine the technologies it acquired from Ctrl-labs, a New York-based startup developing a wristband that translates neuromuscular signals into machine-interpretable commands.

Progress toward Facebook’s vision of the metaverse has been slowed by technical and political challenges, however.

CEO Mark Zuckerberg recently called AR glasses “one of the hardest technical challenges of the decade,” akin to “fitting a supercomputer in the frame of glasses.” Ctrl-labs head Andrew Bosworth has conceded that its tech is “years away” from consumers, and Facebook’s VR headset has yet to overcome limitations plaguing the broader industry like blurry imagery, virtual reality sickness, and the “screen door effect.”

Unclear, too, is the effect that an internal product slowdown might have on Facebook’s metaverse-related efforts. Last week, The Wall Street Journal reported that Facebook has delayed the rollout of products in recent days amid articles and hearings related to internal documents showing harms from its platforms. According to the piece, a team within the company is examining all in-house research that could potentially damage Facebook’s image if made public, conducting  “reputational reviews” to examine how Facebook might be criticized.

To preempt criticism of its VR and AR initiatives, Facebook says it’s soliciting proposals for research to learn about making social VR safer and to explore the impact AR and VR can have on bystanders, particularly underrepresented communities. The company also says it doesn’t plan to make Ego4D publicly available, instead requiring researchers to seek “time-limited” access to the data to review and assent to license terms from each Ego4D partner. Lastly, Facebook says it has placed restrictions on the use of images from the dataset, preventing the training of algorithms on headshots.

This article was originally published on VentureBeat and is reproduced with permission.

If you are a Data Scientist wondering what companies could have the most career opportunities or an employer looking to hire the best data science talent but aren’t sure what titles to use in your job listings — a recent report using Diffbot’s Knowledge Graph could hold some answers for you.

According to Glassdoor, a Data Scientist is a person who “utilizes their analytical, statistical, and programming skills to collect, analyze, and interpret large data sets. They then use this information to develop data-driven solutions to difficult business challenges. Data Scientists commonly have a bachelor’s degree in statistics, math, computer science, or economics. Data Scientists have a wide range of technical competencies including: statistics and machine learning, coding languages, databases, machine learning, and reporting technologies.”

DATA SCIENCE COMPANIES: IBM tops the list of employers

Of all the top tech companies, it is no surprise that IBM has the largest Data Science workforce. Amazon and Microsoft have similar amounts of Data Science employees. Despite their popularity, Google and Apple are in the bottom two. Why is this the case? It could have something to do with their attitude to how to attract and retain a data scientist. The report does not clearly mention the reasons for these rankings. 

However, Data Scientists want to work for companies that provide them with the right challenges, the right tools, the right level of empowerment, and the right training and development. When these four come together harmoniously, it provides the right space for Data Scientists to thrive and excel at their jobs in their companies.

TOP FIVE COUNTRIES WITH DATA SCIENCE PROFESSIONALS: USA, India, UK, France, Canada

The United States contains more people with data science job titles than any other country. Glassdoor actually names “Data Scientist as the best job in the United States for 2019.”  After the United States are the following countries in this order:

• India
• United Kingdom
• France
• Canada
• Australia
• Germany
• Netherlands
• Italy
• Spain
• China

China has the least amount of data science job titles at 1,829 compared to the United States’ number of 152, 608. But what is the scenario for Data Scientists in Europe? What is the demand and supply? 

Key findings indicate that demand for Data Scientists far outweighs supply in Europe. The existence of a combination of established corporations and up-and-coming startups have given Data Scientists many great options to choose where they want to work. 

MOST SOUGHT AFTER DATA SCIENCE JOB ROLES: Data Scientist, Data Engineer and Database Administrator.

Among all companies, the most common job roles are Data Scientist, Data Engineer and Database Administrator. Data Scientist is the most common job role among all companies, with Database Administrator coming in at second place. If you remove Database Administrator, you find that Microsoft leads the way in terms of data science employees. This means that the reason for IBM’s lead in its data science workforce could largely be due to its sheer amount of Database Administrators. Unsurprisingly, across every job title in data science, males outnumber females 3:1 or more.  It is also interesting to note that this ratio only exists within the Database Administrator category. At the Data Scientist category, the ratio reads 6:1.

It also comes to no surprise that Data Scientist ranks number 1 in LinkedIn’s Top 10. It has a job score of 4.7, job satisfaction rating of 4.3 with 6,510 open positions paying a median base salary of $108,000 in the U.S. However, it is important to note that these positions do not work in isolation. A move towards Data Science collaboration is increasing the need for Data Scientists who can work alone and in a team as well. By utilizing the strengths of all the different job roles mentioned above, data science projects in companies remain manageable and their goals become more attainable. The main takeaway is that despite the vast amount of job titles, each role brings its own unique expertise to the table. 

DATA COLLECTION AND ANALYSIS

Diffbot is an AI startup whose Knowledge Graph automatically and instantly extracts structured data from any website. After rendering every web page and browser, it interprets them based on formatting, content, and web page type. With its record linking technology, Diffbot found the people currently employed in the data science industry at a point in time to provide an accurate representation of the statistics mentioned in this article. 

For over the last decade, some of the most successful companies on earth have made their riches by mining user data and selling it to advertisers. The big question is whether this will continue to be a sustainable business model with the ever-mounting scrutiny on data privacy and if not – what’s the alternative?

Many say the Cambridge Analytica scandal sparked a great data awakening by bringing to light the ways in which some companies were amassing and monetizing personal data about their users. As a result, Facebook was recently slapped with a record $5 billion fine and new privacy checks following a year-long probe by the US regulators into the Cambridge Analytica scandal and other data privacy breaches.

As The Verge pointed out though, Facebook had previously settled similar charges in 2011, but such slaps on the wrist don’t seem to be an effective deterrent. While a $5 billion fine sounds highly punitive, many in the industry doubt that this would solve the privacy problem overnight. (Especially when you consider that Facebook made $22 billion in profit alone last year.)

This isn’t a problem that is exclusive to the giants of Silicon Valley. In Europe, hefty fines have also recently been meted out to British Airways and Marriott for data breaches. As data protection complaints have doubled year-on-year, regulators will be getting tougher on companies to ensure their compliance with GDPR (General Data Protection Regulation).

Meanwhile, GDPR has driven a global movement as governments outside the EU, from Australia to Brazil, are set to introduce similar data protection regulations. The GDPR policy has helped to create greater awareness about data protection among the masses. The European Commission’s March 2019 Eurobarometer survey showed that about 67% of European citizens surveyed are aware of GDPR.

The convergence of a compliance culture within organizations, stricter data privacy regulations globally, and consumers becoming more aware of their rights will continue to have a huge impact on businesses that profit from personal data, and even any business which collects it.

The situation demands urgency as the stakes have never been higher. According to a report by Gartner, by 2020, personal data will represent the largest area of privacy risk for 70% of organizations, up from 10% in 2018.

Monetizing Data While Maximizing Privacy

Better privacy for individuals doesn’t mean it’s bad for business. On the contrary, companies can use this opportunity to establish trust with customers while becoming more thoughtful and innovative about their approach to data monetization.

Here are the three key factors organizations need to know about monetizing their data while respecting privacy and complying with regulations: 

1. Your business data could be valuable to those you might not have thought of..
   
   For many firms, data monetization has been inextricably linked with the personal data of their customers. However, they could be collecting, generating or archiving other types of non-personal data that could be valuable to certain end users. That is, the alternative data that may even be overlooked by the business handling it.
   
   In fact, there are many use cases for such alternative data in the world of investing when every bit of timely information helps to gain an edge. This is where anonymized and aggregated data matters most and personally identifiable information has zero value. What matters most to economists and asset managers is how many soft drinks Coca Cola is selling across Europe this quarter, not whether John Doe bought a Coke. The focus is never on the who but the what and how much.
   
   
2. Technology has made it easier to extract the needle from the haystack…
   Most companies have more data than they know what to do with. Forrester reported that on average, between 60% to 73% of all data within an enterprise goes unused. But new tools and technologies have made it easier to mine and process huge amounts of raw data into insights. These insights could serve as timely intelligence to those in other sectors, like economists, analysts or investors looking to identify patterns and trends.
   
   Valuable or insightful data is simply good-quality data. And while data is always described as one of the most valuable enterprise assets, it’s not often treated like one. In order for firms to unlock the full power of data, they need to approach it as thoughtfully as any traditional asset. They will need to carefully consider issues like data architecture management and data quality management. If data is not their core business, then they need to find the right tech partners to ensure their data meets standards that enable the generation of insights.
   
   
3. Aggregation and enrichment of data make it more valuable…
   Your company’s raw data by itself can be one-dimensional. But integrating data from different companies and sectors can provide a more complete and nuanced picture.
   
   For instance, a firm working with vendors across the country might have data on national beverage sales. It could track these sales and provide additional insights back to the vendors as a value-add to help them improve sales and promotions. The company could also share this data with beverage brands so they can finetune and optimize marketing by city. This would allow the company to monetize its data and open up a new revenue stream, without ever sharing any sensitive information that would jeopardize its relationship with customers.
   
   When information is provided in an aggregated form, it’s a safe and secure way of delivering an exceptional level of insight without compromising privacy. It allows economic, social and commercial questions to be answered without revealing any individual’s details.

The growing focus on privacy doesn’t mean data monetization has been taken off the table. Data will always be an important and valuable asset for any organization, but it needs to be harnessed with the full respect of individual rights to privacy.

It’s no news that unstructured data has been a highly sought after source since its inception, first for determining public topical insights and now for training machine learning algorithms. The critical question to answer is whether you should outsource the collection to overcome business challenges or not? Mike Madarasz explains why it could be worth it!

Welcome to the new world of analytics! Brands are hiring data scientists to overcome challenges and shortcomings of single-point solutions. However, better algorithms and better data are not enough. There is no magic button that drives actionable insights to solve business challenges. At least not yet. Combine the right technology with experienced operators and subject-matter expertise and now you have something of substance. Technical skills are not enough to satisfy market expectations. We need data scientists, engineers, designers, people who understand data, people who are creative with data and who have empathy for the insight-challenged community that’s now represented and shaped by experienced practitioners. The tide is now turning toward making data and sophisticated algorithms king. 

Social media data, for instance, is truly a global phenomenon. Everyone uses a few popular sites but you have many regionally dominant platforms. Even without different apps, each culture and region have its own social media nuances, and you have to account for that when analyzing data. Furthermore, incorporating social data, adds substantial depth to a variety of use cases in practically any research question and can be informed by at least one of these data sets.

But what sources are relevant? Twitter or Facebook? Reddit or YouTube? Or maybe various forums, blogs and reviews? Maybe it’s just one source or maybe it’s all of them. Many factors contribute to the relevance of a data source as it pertains to a specific use case. However, regardless if you are using these datasets for research or training your machine learning algorithms, they can be invaluable.    

The data analytics community essentially builds cars, which require gas. And data is the gasoline that fuels these sophisticated engines. So, the million-dollar question is, “how can I access it?” Understanding how to access unstructured data sources like online conversation, is an integral, yet tricky, part of the equation. With today’s compliance and access standards more scrutinized than ever before, knowing how to best prepare for that from a licensing and technical perspective in order to maximize the opportunity for successful analytics is essential. For example, social media today looks nothing like it did 15 years ago. Data has become more complex, more global, and has more uses than anyone could have predicted. We are talking about hundreds of millions of data points from millions of sources. According to market experts, more data has been created just in the past two years than in the entire previous history of the human race. And within five years there will be over 50 billion smart connected devices in the world, all developed to collect, analyze and share data. Just accessing the raw data isn’t enough anymore because it’s so nuanced that you need to have it sifted and parsed to make any real use of it. 

Which begs the question, buy vs. build?

Data aggregation challenges ensue as the activity can be time-consuming, costly and very difficult to do effectively. I’d compare it to renting an office. Do you want to have to find your own source of water? Electricity? Of course not, but gathering those things is not in your wheelhouse and your energy is best used on other things. The insights from social media data today is right up there with water and power when it comes to keeping a business functioning. You need it, but just like you don’t want to run your own pipes and your own power plant, you shouldn’t have to find social media data relevant to your requirements.

There are viable options to identify, index and make unstructured data available in a structured way, and enable access to social media content from a vast array of sources in near-real to real-time delivery mechanisms. Standardized data sets support business intelligence algorithms and predictive modelling by providing on-demand access to years of historical data.

 Social data is a basic necessity and should be delivered to a business already mined and sifted so that analytics systems can do their work. Even if you have a truly huge business, and want full vertical integration, you can do your own social media mining but it’s still probably not worth it. At the end of the day, if you are a Data Scientist or Analyst, your time is best spent focusing on your core competency rather than tedious data collection.  

With the rise of machine learning, the ability to analyze data is only getting better. That’s why we’ll be able to look at information much more quickly and organize it more precisely, which is great because there will be so much more useful data coming in. Our whole world is online, and IoT alone is going to load-up systems worldwide with more data than we can conceive. Better machine learning and heuristics are necessary just to keep up with the flood of information we’re expecting to see, and the companies that are best positioned to thrive in this digital tomorrow are the ones who make the best use of all this information. To do that you need the best, smoothest access to well-organized data.

Everyone in the data chain has to worry about the question of ethics and security. Individuals want to protect their data. Companies and researchers want to learn from it. Big data, like many other emerging areas of technology, suffer from very real ethical problems. Regulations, governing bodies, and even general understanding of ethics are struggling to get up-to-date. Worse still, it’s not just mega-corporations using that data, it’s researchers and the thousands of small companies and studies worldwide.

One study from Facebook in 2014 has long been the poster child for misuse of social data in science. Most ethical guidelines for big data use consider maintaining the reputation of scientific research a key requirement, meaning Facebook researchers had some explaining to do. Yet, the data collected was not particularly sensitive. The experiment included tweaking users’ news feeds to contain either more positive or more negative stories to see whether that influenced users’ emotions and subsequent posts. There was an amount of outrage, and shock, but were Facebook’s actions actually unethical?

First and foremost, the looming question is “did users give their consent?” If an ordinary user doesn’t provide consent to be a human guinea pig, why would they ever suspect they’re being tracked and analyzed? However, despite the missing consent, there might not be any perceived problem by researchers, given the minimal risk to users and anonymization of data. Whether Facebook’s data collection methods were appropriate had ethicists split down the middle, which is an increasingly common occurrence in data. In the end, the hammer did not come down, as the project was conducted by Facebook for internal purposes, with outsiders from Cornell University providing only analysis, and nothing more. The fact Facebook is a private company also completely changed the rules, making it tough to say they did anything legitimately wrong.

While ethicists can find plenty to disagree on, many agree that academics and scientific researchers should be held to higher standards than the bare minimum, or what they can scrape by with. In the wake of the emotion-study debacle, Facebook enacted a new research review board and updated terms of service. Many other companies, including Microsoft, joined suit to avoid upsetting users in the future. While users are prepared for their data to be taken and used, that doesn’t mean they’re comfortable with it. Many would-be-test-subjects view data and data collection in a very negative light. The idea of academics collecting data in what many define as a “creepy” manner does harm individuals’ respect for research as a whole.

When Ethicists Argue and Guidelines Get Ignored

Interestingly, there are already several rules (official and unofficial) for researchers to follow. Associations, government bodies, and even research companies have laid out extensive guidelines for researchers to abide by. In many instances, however, these guidelines are anything but legally binding.

There are several points that make up a general understanding of ethical data collection and usage, the most obvious being consent, which, as seen with Facebook, doesn’t always apply in the way users expect it to. Other points include that used data was reasonably linked to the topic and study, a point that may interest researchers, but doesn’t make users feel any safer. A general requirement to not in any way harm users does exist, but, when it comes to not terrifying them with “creepy” out-of-the-blue data collection, that can be also be difficult.

This is one reason review boards exist. Institutions often have their own Institutional Review Board, made up of researchers who are left to judge what is and isn’t ethical when a study gets to a sticky spot. Many of these judges, however, aren’t professional ethicists. The boards are often made up of scientists, a fact that likely made much more sense before the time of big data. Such boards may be fit to preside over medical studies, where a subject the risk is clear as life and death, but the complexities of technology and data rights is proving to be a tough grey area.

Casting aside legitimate ethicists in favor of what seems to be common sense may have played a role in the recent OKCupid data disaster, wherein researchers scraped data on 70,000 OKCupid users and then made their results public. When asked whether any attempt to anonymize the dataset had been made, head researcher Emil Kirkegaard tweeted only, “No. Data is already public.” Just about every news source has already jumped into the conversation, most arguing the situation is much more complex than Kirkegaard describes. Simply existing on the internet does not mean data is truly public, or that researchers have a right to it. Perhaps the most glaringly obvious problem behind Kirkegaards’ dismissal is that he dismisses the existence, and possible harm caused to, his unwitting test subjects, which should be a primary consideration during any academic research. While arguing the ethics of the situation (which seem to be more along the lines of “just how unethical is this” as opposed to “is this unethical or not”), perhaps the most jarring point is that Kierkegaard is just a graduate student. Not every graduate student sparks an internet-wide freak out and ethics debate, and it’s the wide-spread power of big data that made it happen.

It doesn’t take much to scrape data. It takes relatively little to gather 70,000 profiles worth of data, and that’s why users and ethicists care so much. The OKCupid researchers’ first attempt at data collection included creating a profile and letting a bot scrape suggested profiles, meaning just about any female in and near Denmark could have become unsuspecting test subjects. While this particular study was for science as opposed to explicitly nefarious reasons, the almost deceptive use of an OKCupid profile to scrape data from users is still unsettling. That data, which includes full usernames, could also be used to form full profiles of users, from their location to preferences and answers to site-related questions.

This leads to the problem of big data’s future uses. Now, there’s not just data; there’s an effort to combine all existing data to create thorough user profiles. Putting apparently public data together leads to much more than a dating profile, but a very exact look at an individual or specific group of individuals. Unforeseeable problems with collected data only make the case for a more unified, and strict, stance on the ethics of data more important. Even if research groups and universities establish guidelines for ethical data collection and usage, it means nothing without a relatively uniform understanding of what is and isn’t allowed—not to mention, respect for the test subjects and their privacy.

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Deep Learning is one of the key parts of data science. As data becomes increasingly important and accessible, today’s biggest companies are rapidly investing in deep learning. In fact, it is considered to be so vital to future technologies that many are sharing their own results and discoveries with the public. Researchers have been playing with the idea of deep learning for decades, but it has only blossomed in recent years. With companies like Facebook and Google pouring funds and resources into research, consumers are finally seeing the results of deep learning for themselves. Rather than staying behind closed doors, ordinary folks are already face-to-face with deep learning and don’t even know it.

DeepFace. The appropriately named project from Facebook is “closing the gap to human-level performance in face verification.” If you’ve ever gone to tag a picture, only to have completely random name pop up, you’ve experienced poor facial recognition. The inabilities of current recognition software has created a population that simply distrusts it entirely, and has spawned plenty of failure compilations and web articles. Many facial recognition programs use thousands of engineered features to fuel the recognition process. DeepFace, on the other hand, is based in the development of an effective deep neural net that leverages a huge dataset of faces, allowing them to have a real understanding of general traits and features. This shift in approach has revamped the process of facial recognition, and DeepFace reached an accuracy of 97.35% on their Labeled Faces in the Wild (LFW) dataset. That is near human-level performance, and might just be better than some humans. More importantly, the results completely overshadow other methods, reducing the error rates of other state of the art programs by over 27%.

Mike Schroepfer, the chief technology officer at Facebook, shares even more amazing numbers:

“Teaching computers to be able to detect and differentiate between objects — to train them to understand what the patterns in the pixels mean — is something the Facebook AI Research (FAIR) team has been working on for the last year. They’ve made huge progress in a short time. FAIR has been able to create more than a 60 percent improvement in its object detection and segmentation technology in a year.”

Facebook isn’t the only one using deep learning to change our day-to-day experiences. Self-driving cars would be almost impossible without deep learning. It is vital that the car be able to recognize millions of details and to respond to complex situations with speed. Deep learning is the key to that future. Nvidia has already decided to create GPU’s for the express purpose of enabling deep learning capabilities. This will be a great platform for the driverless car to evolve. Nvidia Drive PX, the newest addition, is explicitly marketed as “the world’s most advanced autonomous car platform—combining deep learning, sensor fusion, and surround vision to change the driving experience.” Even in marketing, it is clear that deep learning is one the few key ingredients to successful driverless car models.

Of course, their love for deep learning doesn’t stop there. It continues into their own courses on the topic. The fact that deep neural networks have proven to be massive game changers has moved Nvidia to share their tools with outside parties. In 2014, the company noted the increased importance of deep learning and opted to share their own library of primitives for deep neural networks, the cuDNN. This is barely even the tip of the iceberg for companies developing and sharing deep learning tools, solutions and data. One smart move from Google just opened up doors for data scientists, and also for the field as a whole.

Industry Leaders Want to Share Tools and Data

If there was any question just how important deep learning is to innovation, Google Brain, Google’s Deep Learning branch, has a big surprise. Vincent Vanhoucke is a principal scientist at Google. He is also a technical lead and manager in their deep learning infrastructure team. His next role will include being a professor for an upcoming free 3-month course on Udacity. Deep Learning has played a huge role at Google in the recent past, including Voice Search, a project Vanhoucke was involved in, himself. He describes the course as an opportunity for data scientists and machine learning students to learn the skills necessary to move to the forefront of career opportunities and innovative emergent technologies.

“Our overall goal in designing this course was to provide the machine learning enthusiast a rapid and direct path to solving real and interesting problems with deep learning techniques, and we’re now very excited to share what we’ve built!”

TensorFlow is one central topic of the course. This machine learning software library is not only from Google—it was made open source in November 2015. It gives tools and data away to the community for free. It allows smaller companies to leverage the data that tech giants already possess. Facebook has also done their own similar “community service.” Many of their AI teams actively use the open source software Torch. Last year, they made their own Torch optimized deep-learning modules open source. As the Facebook team said themselves, “progress in science and technology accelerates when scientists share not just their results, but also their tools and methods.”

Though these companies could continue to hog all the data for themselves, they are making clear efforts to share it and create a movement throughout the entire scientific community. So what does Google really get out of this course? Prospects. Academics and smaller companies can’t create as quickly as companies like Google because they lack the necessary data and tools. By sharing some of their information and teaching people how to use it, Google is also opening up their own future in the market. They, too, need data from outside developers and sources. This method of sharing generates more talent, knowledge and ideas that will offer returns in time. Google knows Deep Learning is the future: “Machine Learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge.”

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featured image source: GoogleResearch

The second day at F8, Facebook’s annual developer conference, saw quite a few updates from the social networking giant.

Facebook’s Chief Technology Officer, Mike Schroepfer, addressed their “long-term technology investments” in Connectivity Lab, Facebook AI Research and Oculus.

Speaking about Facebook’s AI endeavour, Schroepfer said demonstrated how they have progressed so far. Working with researchers in the field Facebook is tapping a relatively new advancement in technology called Memory Networks “which enables a machine to perform relatively sophisticated question answering.” Not being limited to Q&A, the AI system can mne images and recognize actions like sports in videos, as VentureBeat points out.

“You can really get these systems to understand deep, minute differences,” Schroepfer said.

A paper was published in October last year, through Cornell University, describing a new class of learning models called memory networks. Facebook spoke about it later in November and since then has been making strides in the direction.

It is becoming common practice for tech heavyweights to invest in Artificial Intelligence and Facebook is no exception.

Facebook’s video recognition advancements surface at the wake of revelations made by a startup called Clarifai about technology that surpasses image recognition to video analysis.

Image credit: Facebook

Dataminr, a real-time information discovery outfit, has picked up $130 million in growth capital in a Series D funding round, it announced on Tuesday after Wall Street Journal broke the news.

“This newly raised capital will enable Dataminr to meet the tremendous global demand for our products, expand into new verticals, and integrate valuable new datasets into our algorithmic engine to enhance our Twitter-based signals and broaden our offering. The explosion of real-time public data will only continue in the years to come and Dataminr’s ambition is to continue being the leader in discovering valuable signals from within this exponentially broadening sea of data,” explained CEO and Founder of Dataminr, Ted Bailey.

Founded in 2009, Dataminr’s proprietary algorithm allows for instant analyses of public tweets and other publicly available data to provide its clients with the earliest warning for ‘breaking news, real-world events, off the radar content, and emerging trends.’

It can identify and filter out valuable real-time information providing relevant insight and contextual analytics. Dataminr’s boasts customers in finance, the public sector, news, security, and crisis management.

The latest investment puts the company at a $700 million valuation. The round was led by Fidelity Management and Research Company, with additional institutional investments from Wellington Management Company LLP, Credit Suisse NEXT Investors, and other existing Dataminr investors Venrock and Institutional Venture Partners. Some of the other investors include Vikram Pandit, former CEO of Citigroup, Tom Glocer, former CEO of Reuters, Glynn Capital and Goldman Sachs.

Image credit: Dataminr

Airbnb open sourced a web-based data exploration and SQL query tool for Hadoop called Airpal, built on Facebook’s PrestoDB, that enables query execution to facilitate data analysis, Thursday.

Making the announcement, James Mayfield, product lead at Airbnb explains that working with SQL for ‘exploration and investigation’ is not easy.

“Remembering how a query was written, copying and pasting from the command line, and running multiple terminal windows can slow down analysis and be frustrating. Additionally, when diverse teams are using SQL for analytics, the learning curve can be steep for beginners, so good UI tools can help drive adoption and promote knowledge sharing,” he wrote.

Airpal was deployed for internal use, about a year ago and since then, Mayfield notes, it has had over a third of all employees issue a query.

Airbnb lists out the key features of Airpal as follows:

• optional access controls for users
• ability to search and find tables
• see metadata, partitions, schemas, and sample rows
• write queries in an easy-to-read editor
• submit queries through a web interface
• track query progress
• get the results back through the browser as a CSV
• create new Hive table based on the results of a query
• save queries once written
• searchable history of all queries run within the tool

Software engineer Andy Kramolisch pointed out that earlier Airbnb utilized Amazon‘s Redshift cloud data warehouse for its speed but eventually it proved less user friendly and required managing and replicating data from Hive.

(Featured image credit: Airbnb)

I’ve previously written a lot on data mining in the abstract; now, I want to start taking you through some practical applications. Welcome to the fascinating world of the recommendation engine- this post will walk through the concepts, and later posts will teach you how to implement your own.

What we will learn:

I’ll begin our tour by answering four basic questions:

1. What is a recommendation engine?
2. What is the difference between real life recommendation engine and online recommendation engines?
3. Why should we use recommendation engines?
4. What are the different types of recommendation engines?

What is a Recommendation Engine ?

Wiki Definition: Recommendation Engines are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that user would give to an item.

dataaspirant Definition:  Recommendation Engine is a black box which analysis some set of users and shows the items which a single user may like.

Offline Recommendation Engines

In the external world, we can think of the people around us as recommendation engines.

• Your family and friends as clothes recommendation engines: With the thousands of style options now available to us, we often rely on friends and family to recommend stores, styles and tell us what looks good on us.
• Your Professors and book recommendation engines: When want to research or better understand a concept, our Professors can lead us to the titles which best suit our needs
• Your friends as movie recommendation engines: If you have friends who know your cinematic tastes well, you’re likely to trust their movie recommendations over a random stranger’s picks.

Notice that all of these “offline recommenders” know something about you. They know your style, taste or area of study, and thus can make more informed decisions about what to recommendations would benefit you most. It is this personalisation- based on getting to “know” you- that online recommenders aim to emulate.

Online Recommendation Engines

Facebook: “People You May Know”

Facebook users a recommender system to suggest Facebook users you may know offline. The system is trained on personal data mutual friends, where you went to school, places of work and mutual networks (pages, groups, etc.), to learn who might be in your offline & offline network.

Netflix: “Other Movies You Might Enjoy”

When you fill out your Taste Preferences or rate movies and TV shows, you’re helping Netflix to filter through the thousands of selections to get a better idea of what you might like to watch. Factors that Netflix algorithm uses to make such recommendations include:

• The genre of movies and TV shows available
• Your streaming history, and previous ratings you’ve made.
• The combined ratings of all Netflix members who have similar tastes in titles to you.

LinkedIn: “Jobs You May be Interested In”

The Jobs You May Be Interested In feature shows jobs posted on LinkedIn that match your profile in some way. These recommendations shown based on the titles and descriptions in your previous experience, and the skills other users have “endorsed”.

Amazon: “Customers Who Bought This Item Also Bought…

Amazon’s algorithm crunches data on all of its millions of customer baskets, to figure out which items are frequently bought together. This can lead to huge returns- for example, if you’re buying an electrical item, and see a recommendation for the cables or batteries it requires beneath it, you’re very likely to purchase both the core product and the accessories from Amazon.

Why Should We Use Recommendation Engines?

In the immortal words of Steve Jobs: “A lot of times, people don’t know what they want until you show it to them.” Customers may love your movie, your product, your job opening- but they may not know it exists. The job of the recommender system is to open the customer/user up to a whole new products and possibilities, which they would not think to directly search for themselves.

What Are the Different Types of Recommendation Engines?

Let me introduce you to three very important types of recommender systems:

• Collaborative Filtering
• Content-Based Filtering
• Hybrid Recommendation Systems

Collaborative Filtering

Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an “understanding” of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation.

Content Based Filtering

Content-based filtering methods are based on a description of the item and a profile of the user’s preference. In a content-based recommendation system, keywords are used to describe the items; beside, a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past (or is examining in the present). In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended. This approach has its roots in information retrieval and information filtering research.

Hybrid Recommendation Systems

Recent research has demonstrated that a hybrid approach, combining collaborative filtering and content-based filtering could be more effective in some cases. Hybrid approaches can be implemented in several ways, by making content-based and collaborative-based predictions separately and then combining them, by adding content-based capabilities to a collaborative-based approach (and vice versa), or by unifying the approaches into one model. Several studies empirically compare the performance of the hybrid with the pure collaborative and content-based methods and demonstrate that the hybrid methods can provide more accurate recommendations than pure approaches. These methods can also be used to overcome some of the common problems in recommendation systems such as cold start and the sparsity problem.

Netflix is a good example of a hybrid system. They make recommendations by comparing the watching and searching habits of similar users (i.e. collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering).

I hope you liked today’s post. In the next installment, we’re going to learn about these three recommendation systems in the bigger picture, and learn how to implement them. Any questions? Leave a comment below.

Featured Image Credit: clasesdeperiodismo / Foter / CC BY-SA
Body Image Credits: Yuriy Trubitsyn / dataaspirant
Original source can be found here.

Deep learning innovator, AlchemyAPI has been acquired by tech giant IBM in a bid to further develop its “next generation cognitive computing applications,” essentially adding to Watson’s deep learning potential.

AlchemyAPI’s deep learning platform allows structuring of cognitive-infused applications with advanced data analysis capabilities such as taxonomy categorization, entity and keyword extraction, sentiment analysis and web page cleaning, processing billions of API calls per month across 36 countries in eight languages.

“Our ability to draw upon both internal and external sources of innovation, from IBM Research to acquisitions like AlchemyAPI, remain central to our strategy of bringing Watson to new markets, industries and regions,” said Mike Rhodin, senior VP at IBM Watson.

Founder and CEO of AlchemyAPI, Elliot Turner said: “We founded AlchemyAPI with the mission of democratizing deep learning artificial intelligence for real-time analysis of unstructured data and giving the world’s developers access to these capabilities to innovate. As part of IBM’s Watson unit, we have an infinite opportunity to further that goal.”

As a direct outcome of the acquisition AlchemyAPI’s deep learning technology will be integrated into the core Watson platform. This will enhance Watson’s ability to ‘quickly identify hierarchies and understand relationships within large volume data sets,’ explains the news release making the announcement.

Watson will be able to “ingest, train and learn the “long-tail” of various data domains – including general business and target industries, as well as address the need to manage constantly evolving ontologies.”

This acquisition also makes available to the Watson community a host of cognitive computing APIs like language analysis APIs to address new types of text and visual recognition and the ability to automatically detect, label and extract important details from image data.

Furthermore,40,000 developers who had been working with AlchemyAPI will now be part of the Watson ecosystem. The details of the acquisition remain undisclosed. This move is in line with heavyweights like Facebook and Google who have made investments in deep learning earlier.

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(Image credit: AlchemyAPI)

Some companies really get big data. Not only do they realise size matters – they understand you also have to know what to do with it. Here’s a list of seven companies I think are at the top of the game, when it comes to cutting-edge use of data to strategically achieve business goals. If you run a business yourself and are interested in big data projects, there is something to be learned from every one of these. So in no particular order …

General Electric

GE – with its fingers in every pie from finance to aviation to power, is perfectly positioned to benefit from its championing of “The internet of things”. They clearly see that IOT – the concept that every device can be networked and learn to communicate with other devices in the same way that computers do – is key to huge efficiency savings and potentially revolutionary business change.

As a result they are heavily investing in what they call the Industrial Internet – the subset of IOT dedicated to industrial devices and equipment. Aircraft engines are being fitted with arrays of sensors capable of detecting and measuring the slightest changes, meaning that fine-tuning for efficiency is possible to a higher standard than ever before. And the same is just as true with their medical equipment and power station turbines. In 2012 the company announced it was investing $1 billion into its data projects over four years.

Like other companies mentioned here, it also makes the technology driving its data operations available to other businesses, by licencing its GE Predictivity services. For more on GE see my article: How GE Is Using Big Data To Drive Business Performance.

IBM

In 2003 50,000 IBM staff took part in online interviews where they were asked about key business issues, and the direction they thought the company should be heading in. Those interviews were fed into textual analysis software designed to pick out the most common phrases and themes, which became new company objectives.

This was forward-thinking in many ways and encapsulates the idea of a company transforming itself into a data enterprise. Those at the top had come to the conclusion that data in most fields will always trump opinion – even their opinion – and surrendered themselves to something of a destruction of the ego; “letting go” (temporarily) of the reigns and seeing what direction the company would head, steered by science and statistics, rather than the possibly jaded or entrenched ideas and opinions of directors and senior managers.

Since then IBM has reinvented itself as a data powerhouse, at the forefront of the current boom in business-to-business data infrastructure services. It offers hardware and software for maintaining big databases, such as its DB2 database application and SPSS analytics application, among many other products and services.

It has also become an ambassador for the concept of big data, publishing several papers on how companies can exploit its potential for innovation and increased profits. Books have been written on the turbulent history of this particular tech giant, but by embracing big data with such enthusiasm, they are entering a new chapter.

Amazon

Amazon not only brought big data to the masses, it made it personal – and customer service was changed forever. One of the shortfalls of online shopping for early adopters of the habit was the lack of a sales assistant or shopkeeper to explain the products and, by getting to know you, helping you find whatever it is you need to solve a particular problem in your life.

With its recommendations and reviews-base structure, Amazon introduced us to the super-powered sales assistant – equipped with a super memory retaining every customer transaction and able to offer lightning-quick, and most importantly accurate, suggestions. In fact, it’s got so good at this that according to rumor (based on patent applications) it is planning to begin predictive shipping – automatically sending out parcels of books, DVDs, videogames and gadgets based on what it thinks its users will want to pay for.

Amazon is clearly not blind to the vital role data has played in its success, and has used the vast revenues (if not profits) built up by pioneering online retailing to invest in also providing data services. Much like IBM mentioned above it provides infrastructure to allow other businesses to capitalize on data gathering, storage and analysis enterprises. For more on Amazon, see my posts How Amazon Uses Big Data to Boost It’s Performance and Amazon: Using Big Data To Read Your Mind.

Facebook

Facebook has revolutionized the way we communicate with each other, from staying in touch with relatives to organizing weekend activities with friends. There was instant messaging and email before it, but Facebook invited users to build the world’s largest directory of people. It then made them all accessible to each other – depending, in theory, on privacy settings determined by each user. With 1.32 billion active users, it is still by far the world’s largest social network.

In the process, it has collected probably the biggest database of personal information in the history of the world. Its users upload 30 billion pieces of content between them every day, resulting in over 300 petabytes (3 million gigabytes) of information. It has used this information to draw in advertisers, generating $2.68 million in advertising revenue during the last quarter.

This year the company made moves in an unexpected direction by purchasing the upcoming Oculus Rift virtual reality technology for $2 billion. Speculation says the company is looking ahead to times when we want to be able to experience greater levels of interaction with our data (or our friends, in their Facebook digitized form) than current flat screen technology allows. For more on Facebook, see my postFacebook’s Big Data: Equal Parts Exciting and Terrifying.

Google

No list of the top big data businesses would be complete without mentioning the still-undisputed king of search. Like Facebook, it turned data collection and analysis into a business model by providing a service ostensibly for free, then selling on information it gathers about us by monitoring the way we use that service.

Search is still the key service it provides – and since the early days when its algorithms were first recognized for their superiority at matching what the user is typing, with what they are looking for, they have continued to evolve – moving towards a standard of “natural language processing” which is planned to one day let us converse with computers as easily as with people.

Its activities have often caught the public imagination. From the blistering speeds that it reports (“smugly” as one comedian described it) it has trawled millions of web pages to find what you’re looking for, to the breathtaking scope of Google Earth, consistently providing services that people want to use – for education, business or just passing time.

Google offers a range of services – now collected at the Business Hub – to aid with promotion, and has also moved firmly into providing more heavyweight big data services to businesses. These include BigQuery – its analysis engine, and Google Cloud Storage services. For more on Google, see my article: Wow! Big Data At Google.

Cloudera

Less well-known than the other companies I’ve mentioned here, Cloudera has emerged in recent years as one of the most prominent suppliers of Apache Hadoop solutions. Apache Hadoop, as I’ve mentioned before is a suite of software applications designed for running big data enterprise operations. Although open-source (free) in its raw state, an industry has sprung up providing companies with custom-configured systems, intended to simplify the process of data gathering and analysis. Cloudera is a leader in this field, and clearly realises the obligation it owes to the free technology on which it is built, returning a share of its profits to the voluntary foundation which maintains Hadoop. For more on Hadoop, see may article: What’s Hadoop? Here’s a Simple Explanation For Everyone.

Kaggle

Another newcomer – built from the ground up as a big data business, rather than a dinosaur forcing itself to evolve. Kaggle pioneered data science as competition – offering rewards for solving various challenges faced by industry.

Companies post problems they are attempting to overcome – for example, to match movies on a streaming service with what the customer may want to watch next, alongside sample data sets. Prize money is then awarded to the solution which most comprehensively trumps their existing methods.

Clients who have benefited from the 150,000-strong army of data scientists – some professional, some amateur – Kaggle can call for help with any problem, include NASA, Google, Wikipedia and Microsoft. For more on Kaggle, see my article: The Amazing Big Data World of Kaggle and the Crowd-Sourced Data Scientist.

This post was originally published on LinkedIn.

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Bernard Marr – the ‘Big Data Guru’ – is one of the world’s most highly respected voices anywhere when it comes to data in business. He is a highly acclaimed keynote speaker and advises companies and government organizations on how to use big data, analytics and metrics to improve strategic decision-making and boost company performance. His new book is: Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance.