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🧬A trust layer for genomic AI powered by OriginTrail, has been implemented under the ELSA - European Lighthouse on Secure and Safe AI funded by the EU. Trace Labs - Trusted Context for AI Agents's Jurij Skornik demonstrated it in action ↯ 00:00 - Introduction 00:30 - Key challenges... show more

OriginTrail

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🛍️ Bain & Company estimates that retailers can increase revenue by 5% to 10% through AI personalization tools. AI agent powered by OriginTrail & GS1 standards, delivers a personalized purchase experience. Go check out the demo by Jurij Skornik at #GS1GlobalForum2025! 👇 01:25 The shift from attention to intention economy 03:51 How AI agents power this shift 06:22 The role of DKG & GS1 in ethical AI, trust & verifiability 13:08 Live demo of the GS1 x OriginTrail DKG-powered AI agent

🛍️ Bain & Company estimates that retailers can increase revenue by 5% to 10% through AI personalization tools. AI agent powered by OriginTrail & GS1 standards, delivers a personalized purchase experience. Go check out the demo by Jurij Skornik at #GS1GlobalForum2025! 👇 01:25 The shift from attention to intention economy 03:51 How AI agents power this shift 06:22 The role of DKG & GS1 in ethical AI, trust & verifiability 13:08 Live demo of the GS1 x OriginTrail DKG-powered AI agent

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As ever, a fascinating chat with my friend Jordi Visser on AI, crypto, and hedge funds' future... Enjoy! 00:00:00 Introduction 00:00:54 The Big Picture: AI, Crypto, and Macro 00:02:25 Jordie Visser’s Background in Macro and Finance 00:03:30 AI’s Disruptive Impact on the Economy 00:06:32 Rethinking GDP and Productivity 00:08:59 The Future of Labor and Profit Margins 00:10:49 The Rise of the Speculative Economy 00:14:41 Generational Shifts in Investing and Risk-taking 00:16:18 AI and Crypto: A Global Network Effect 00:19:38 Stablecoins and AI-Driven Market Volume Growth 00:24:28 The End of Traditional Hedge Funds? 00:31:16 Deep Research and the Power of AI Analysis 00:35:29 The Acceleration of AI and its Economic Impact 00:41:02 The Growing Crypto Adoption and Its Role in the Future Economy 00:44:22 Shifting from Fear to Optimism in Markets 00:50:27 The Political and Economic Challenges Ahead 00:56:01 Quantum Computing and the Next Leap in AI 01:02:06 Closing Thoughts: Preparing for a Radical Future

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I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

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Matt Dancho (Business Science)

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Matt Dancho (Business Science)

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AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

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EigenCloud

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We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways

We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways

Epoch AI

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New podcast with Garry Tan, farbood and Daniel Francis. Live in the Future! 00:00 Guest Intros 02:35 Live in the Future 03:58 Will AI Outsmart us? 07:43 In the Anthropic Breadline 09:59 The Tech Genie Is Out 12:33 We Invested in COVID?! 14:25 Good Writing Is Novelty 18:50 Living Like It’s 2028 24:32 Truth dot ai 30:18 Does China have the Weights? 35:38 Everyone has AI Anxiety 39:32 Have Your Agent Talk to My Agent 42:01 What if Open Source takes the Lead? 44:03 The Sun is Setting on Google 48:00 Ride the AGI 50:46 Will There be Startups? 54:05 Defending Taiwan 1:00:05 The California Empire 1:01:26 If the U.S. Falls 1:03:11 Universal Basic Robot 1:06:01 Humans as AI Handlers

New podcast with Garry Tan, farbood and Daniel Francis. Live in the Future! 00:00 Guest Intros 02:35 Live in the Future 03:58 Will AI Outsmart us? 07:43 In the Anthropic Breadline 09:59 The Tech Genie Is Out 12:33 We Invested in COVID?! 14:25 Good Writing Is Novelty 18:50 Living Like It’s 2028 24:32 Truth dot ai 30:18 Does China have the Weights? 35:38 Everyone has AI Anxiety 39:32 Have Your Agent Talk to My Agent 42:01 What if Open Source takes the Lead? 44:03 The Sun is Setting on Google 48:00 Ride the AGI 50:46 Will There be Startups? 54:05 Defending Taiwan 1:00:05 The California Empire 1:01:26 If the U.S. Falls 1:03:11 Universal Basic Robot 1:06:01 Humans as AI Handlers

Naval

755,678 görüntüleme • 1 ay önce

Anglofuturism interviewed James Wise, Chair of the government's Sovereign AI Unit. 00:01:57 - Navigating AI from within government in Britain 00:11:30 - Why full-stack AI isn't the goal of sovereignty 00:24:59 - Are we investing enough in Britain's AI future? 00:33:19 - Overcoming capital constraints for British AI innovation 00:41:58 - How to leverage Britain's unique advantage in healthcare 01:10:52 - Britain needs an entrepreneurial culture shift You can find more Anglofuturism on YouTube, Spotify, Apple Podcasts, and Substack.

Anglofuturism interviewed James Wise, Chair of the government's Sovereign AI Unit. 00:01:57 - Navigating AI from within government in Britain 00:11:30 - Why full-stack AI isn't the goal of sovereignty 00:24:59 - Are we investing enough in Britain's AI future? 00:33:19 - Overcoming capital constraints for British AI innovation 00:41:58 - How to leverage Britain's unique advantage in healthcare 01:10:52 - Britain needs an entrepreneurial culture shift You can find more Anglofuturism on YouTube, Spotify, Apple Podcasts, and Substack.

Aeron Laffere

17,610 görüntüleme • 1 ay önce

Dr. Fei-Fei Li (Fei-Fei Li), the Godmother of AI, on the origin of computer vision and why spatial intelligence is the next frontier. At AI Startup School in San Francisco. 01:00 - Origins of ImageNet 02:15 - The Dream to Make Machines See 03:28 - A paradigm shift in AI 05:50 - AlexNet and Deep Learning 08:00 - Evolving Computer Vision 12:20 - Building World Labs 13:00 - The next frontier in AI 14:20 - Why Spatial Intelligence Is Harder Than Language 18:40 - Technical Barriers for Vision-Based AI 20:00 - AI is more than LLMs 25:50 - Fei Fei’s Journey 29:30 - Mentoring some of the legends of AI 33:00 - Audience Q&A

Dr. Fei-Fei Li (Fei-Fei Li), the Godmother of AI, on the origin of computer vision and why spatial intelligence is the next frontier. At AI Startup School in San Francisco. 01:00 - Origins of ImageNet 02:15 - The Dream to Make Machines See 03:28 - A paradigm shift in AI 05:50 - AlexNet and Deep Learning 08:00 - Evolving Computer Vision 12:20 - Building World Labs 13:00 - The next frontier in AI 14:20 - Why Spatial Intelligence Is Harder Than Language 18:40 - Technical Barriers for Vision-Based AI 20:00 - AI is more than LLMs 25:50 - Fei Fei’s Journey 29:30 - Mentoring some of the legends of AI 33:00 - Audience Q&A

Y Combinator

194,705 görüntüleme • 1 yıl önce

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NEAR AI Ecosystem - "What Did You Ship This Week?" Ep. 2 🚢 Yesterday’s episode was another banger shipstream covering the latest deployments on the NEAR AI stack, with some impressive demos and updates (plus an unexpected comeback) from projects building truly open AI. Timestamps & Highlights: 00:00 - Welcome & Introduction by Cameron.near 02:53 - NEAR AI: Proof Of Response Paper + Auditor RFP by Eric Winer 10:36 - Doppelgangers AI: AI Voice Chat, Voice Cloning, AI Personality Marketplace and Voice Agent Demo by Andrew 20:55 - Proximity Labs: Shade Agents by Kendall 30:10 - Phala: DeepSeek R1 on RedPill w. Private ML SDK, elizaOS on Phala TEE Cloud and Dstack Decentralized KMS by Hang Yin 42:30 - Intellex AI Protocol: Agent Swarms for Real Use-Cases by Omar Saadoun 53:45 - Jutsu | Work Smarter with AI Agents: NEAR AI Typescript SDK, Live Demo and Future Roadmap by Zahidul Islam 1:03:30 - Closing Remarks & Upcoming Events What's left to say? NEAR is the blockchain for AI. Don't fade the full recording:

NEARWEEK

18,083 görüntüleme • 1 yıl önce

Every frontier lab's stated plan for AGI 'crunch-time' is 'use AI to make AI safe.' I spoke with Ajeya Cotra – influential AI forecaster – who has been trying to figure out whether this crazy-sounding plan could actually work, and if so how: • Ajeya’s impressive track record identifying where things are going (00:00:41) • How the hell can smart people disagree 1000-fold about AGI & economic growth (00:02:31) • AI companies might go dark as AGI gets close (00:30:39) • Everyone's default plan: use AI to make AI safe (00:47:21) • White-knuckling it through automated AI R&D (01:12:01) • Donors should switch from buying human researchers to buying inference (01:24:42) • Will frontier AI even be for sale during an intelligence explosion? (01:32:03) • Pre-crunch prep: what we should do right now (01:43:59) • A grantmaking trial by fire at Coefficient Giving (01:47:03) • Sabbatical and reflections on effective altruism (02:07:45) • EA as an incubator for avant-garde causes others won't touch (02:46:55) On the 80,000 Hours Podcast, links below - enjoy!

Every frontier lab's stated plan for AGI 'crunch-time' is 'use AI to make AI safe.' I spoke with Ajeya Cotra – influential AI forecaster – who has been trying to figure out whether this crazy-sounding plan could actually work, and if so how: • Ajeya’s impressive track record identifying where things are going (00:00:41) • How the hell can smart people disagree 1000-fold about AGI & economic growth (00:02:31) • AI companies might go dark as AGI gets close (00:30:39) • Everyone's default plan: use AI to make AI safe (00:47:21) • White-knuckling it through automated AI R&D (01:12:01) • Donors should switch from buying human researchers to buying inference (01:24:42) • Will frontier AI even be for sale during an intelligence explosion? (01:32:03) • Pre-crunch prep: what we should do right now (01:43:59) • A grantmaking trial by fire at Coefficient Giving (01:47:03) • Sabbatical and reflections on effective altruism (02:07:45) • EA as an incubator for avant-garde causes others won't touch (02:46:55) On the 80,000 Hours Podcast, links below - enjoy!

Rob Wiblin

40,746 görüntüleme • 6 ay önce

The talent race: How countries and companies win by empowering youth with AI? Hear from Mark Cuban Foundation AI Bootcamps, H-FARM, Azerbaijan’s Ministry of Youth & Sports & OriginTrail on how to: 05:43 – Bring student ideas to market with safe AI 10:25 – Use AI to help athletes win at the highest level 16:23 – Balance AI risks & benefits in education 20:00 – Identify & nurture entrepreneurial talent 23:17 – Align governments, educators & accelerators on responsible AI 35:54 – AzerFit, a trusted national sports data platform powered by OriginTrail

The talent race: How countries and companies win by empowering youth with AI? Hear from Mark Cuban Foundation AI Bootcamps, H-FARM, Azerbaijan’s Ministry of Youth & Sports & OriginTrail on how to: 05:43 – Bring student ideas to market with safe AI 10:25 – Use AI to help athletes win at the highest level 16:23 – Balance AI risks & benefits in education 20:00 – Identify & nurture entrepreneurial talent 23:17 – Align governments, educators & accelerators on responsible AI 35:54 – AzerFit, a trusted national sports data platform powered by OriginTrail

OriginTrail

11,323 görüntüleme • 8 ay önce

Andrew Ng just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to agentic knowledge Graphs • 03:07 - Construction of agentic Graphs • 14:00 - Architecture of multi-agent systems • 23:00 - Building agentic graphs with Google ADK • 01:06:03 - Why Graphsare the future of agentic AI Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

Andrew Ng just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to agentic knowledge Graphs • 03:07 - Construction of agentic Graphs • 14:00 - Architecture of multi-agent systems • 23:00 - Building agentic graphs with Google ADK • 01:06:03 - Why Graphsare the future of agentic AI Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

Codez

464,658 görüntüleme • 1 ay önce

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 görüntüleme • 3 ay önce