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Facebook F8 Takeaways – The Future is Private

F8, what was an 8- hour hackathon is now Facebook’s annual 2-day conference for developers, creators and entrepreneurs all around the world.

Conducted in McEnergy Convention Center in San Jose, CEO Mark Zuckerberg stressed his vision of building a privacy-focused social platform “as a product”  as he debuted the newest version of the company’s core app.

Digital Equivalent of a Living Room:

With the expansion of the digital world, Privacy fills the vacuum with a unique sense of purpose — giving us the power to be ourselves. F8 spent much time discussing privacy upgrades and improvements to social impact from the client side. The problem area of concern being security, algorithm fairness, privacy, misinformation, inclusion safety and care, accessibility, election integrity and content policy.

“For the last 15 years or so, we have focused on building Facebook and Instagram into the digital equivalent of town squares. But I believe that the future is private and over time, a private social platform will be even more important in our lives than digital town squares. So today, we’re going to start talking about what this could look like as a product”, said Zuckerberg which worked to set the tone for the rest of the conference. The core techs being implemented to resolve the problem area for every product team are computer vision, natural language processing, encryption, data framework, speech recognition, text-to-speech, liability tools, AI infrastructure, OCR and embedding.

Zuckerberg aims to change their business trajectory to win back the trust of the users by focusing their vision on 6 privacy principles for every one of their digital platforms.

  • Private Interactions
  • Encryption
  • Reduced Permanence
  • Safety
  • Interoperability
  • Secure data storage

“This isn’t just about building features,” Zuckerberg said. “We need to change a lot of ways we run this company.”

Privacy First Approach:

Facebook:
Initially designed as an alternative to the then social-media-champion, MySpace; Facebook’s design, flexibility and the key focus on amplifying social connections and distribution of public information, rocketed to become the social media sovereign within a span of 5 years.

In early 2018, plagued by public data breaches and scandals, the social media giant was under heavy scrutiny for its management of user data. Zuckerberg didn’t dodge the issue at F8.
“I know we don’t have the strongest reputation on privacy right now, but I’m committed to doing this well and starting a new chapter for our products.” He meant it as a joke that wasn’t.
Instead of what Facebook is, F8 was about what Facebook wants to be.

The first thing to have been rolled out in the conference is FB5 with its big redesign making it lighter, faster and cleaner.De-emphasising its news feed and prioritizing groups and events. “Friends” are  no longer the centre of the experience. With the launch focus has been made to build a community and make “communities as central as friends”.

Messenger:
The Facebook Messenger also got an overhaul for its upcoming LightSpeed with a rebuilt architecture making it 2x faster, 7x smaller, simpler, more reliable and more secure. With the last year messenger launch M4, it was the first step towards the vision.
“People’s communication styles are migrating toward messaging way faster than anyone thought,” said Stan Chudnovsky, head of Messenger. “And people want to communicate with businesses the same way.” With messages being end-to-end encrypted, the messenger is now the fastest and most secure messaging platform.
For business, an automated system has been created that allows customers to book an appointment through messenger.

The all-new desktop app has some new features for business users. It also allows its users to host group video calls and collaborate on projects. The AI smart camera is using the “pose detection” tech to give a hasslefree and even more life like experience.

Instagram:
Instagram updates basically focused on giving the users the ability to shop directly from the makers and “Support the people who make”, and raise funds within the app.
Instagram is also testing hiding the total number of likes a post receives to bring back the focus on connection than posting for likes.
Stories now don’t have to start with the camera anymore. Users can now get more creative with their stories. They can now raise money for charitable causes with a new donation sticker on their stories.

Finally, the Instagram camera will be updated with the “create mode” allowing to post effects and interactive stickers without having to take a photo or record a video.

Whatsapp:
Whatsapp updates deliver a private and intimate experience with end-to-end encryption. It now allows users to send their location privately with their friends and families. The company rolled out a product catalogue feature for small WhatsApp businesses and payment process that is being tested in India.

Zuckerberg left the audience with one final notion:
“This is about building the kind of future we want to live in. To build a world where we can be ourselves and live freely and know that our private moments are only going to be seen by the people they want, where we can come together around community and commerce, where we build in the tools that we need to keep us safe from the beginning and prevent harm and we then are able to focus on all the good people are able to do. Both in private and in public, both the living room and the town squares.”

How do you think Facebook’s new direction would affect the users?  We’re hoping to see some more updates?
Let us know by commenting.
To know us in person, drop a Hi at hello@mantralabsglobal.com

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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