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The alignment problem, retold through storytelling. Raj Ammanabrolu, Prithviraj (Raj) Ammanabrolu, breaks down how narrative-capable AIs can change minds - for better or worse. Conflict resolution, manipulation, and the role of transparent alignment in building trust. Ep. 2 of The AI Agent Takeover ↓ #AIforALL #SaharaLabsAI 🔆

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SITUATION EXPLAINED: Thinking Machines just published their mission statement. • Mission: build AI that extends human will and judgment, not replaces it • The tacit knowledge argument: central planning fails not because of insufficient intelligence but because productive knowledge is local, fleeting, and held privately, the same reason a single AI alignment spec will fail • The "intelligence curse": power that needs nothing from people loses the incentive to care for their needs. A single locus of AI value alignment becomes a locus of power to be captured • Decentralized alignment: human values resist consolidation just like human knowledge does... today the values and voice of AI are decided in a handful of places, and that's dangerous • Today each lab trains its next flagship model on its previous one... whatever character emerges from that loop, everyone gets the same one, each generation inheriting the traits of the last • Their answer: AI that is more capable because it encourages human participation... alignment that arises from diverse AIs shaped by the people who own them sof 𓋹: "This is going to be very soon the winning narrative. People are gonna get so sick of hearing super intelligent stuff because they're like, I don't see anything that proves that right now. All this messaging is just gonna start becoming like, oh, make you the best human, make you a superhuman, make you awesome." Theo Jaffee: "I think it's a way better vision for AI to be customized to people in different organizations rather than to have just one Claude that is the same everywhere. Kind of like a deity."

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Andrew Ng (Andrew Ng) on how startups can build faster with AI. At AI Startup School in San Francisco. 00:31 - The Importance of Speed in Startups 01:13 - Opportunities in the AI Stack 02:06 - The Rise of Agent AI 04:52 - Concrete Ideas for Faster Execution 08:56 - Rapid Prototyping and Engineering 17:06 - The Role of Product Management 21:23 - The Value of Understanding AI 22:33 - Technical Decisions in AI Development 23:26 - Leveraging Gen AI Tools for Startups 24:05 - Building with AI Building Blocks 25:26 - The Importance of Speed in Startups 26:41 - Addressing AI Hype and Misconceptions 37:35 - AI in Education: Current Trends and Future Directions 39:33 - Balancing AI Innovation with Ethical Considerations 41:27 - Protecting Open Source and the Future of AI

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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

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🚨 New Proof of Vision out today folks! In this 34th PoV episode, our Director of Protocol Services, Kirk had on Andrew Hill, Co-Founder and CEO of Recall When it comes to your business, you’d never trust someone without a proven track record to make high-risk decisions So why should it be any different with AI agents? How do you know today which AI agents you should trust? AI agents are multiplying at unprecedented scale, with millions designed to shape the way we work, make decisions, and live our lives But how can we trust them? The challenge is trust: as dependence on these agents increases, so too does the exponential risk and cost of delegating to the wrong one This is the core challenge of reputation: the need for a transparent system that proves what AI agents can do, where they excel, and which will succeed That’s the reason why Recall exists Recall is the infrastructure protocol to discover, verify, and rank AI agents in real time, rewarding the best through on-chain competitions that begin with trading PnL and expand to any measurable task, from research to healthcare to business strategy In this episode, Andrew breaks down Recall’s mission, the problem it solves, how Agent Rank works, strategies to attract agents and users, the role of community, and the long-term vision plus much more Enjoy the Podcast 👇 ⏲ Timestamps: 00:00 - Intro 01:50 - Andrew’s journey into crypto and AI 05:00 - What problem is Recall solving? 10:00 - Agent Rank: how Recall actually ranks agents 16:03 - How Recall is attracting AI agents to compete? 19:10 - How big the community is today and the role it plays in giving builders real feedback 22:49 - How Recall ensures transparency and trust in its rankings 25:14 - Why users join Recall competitions 27:20 - Product-market fit and distribution 31:15 - What competitions could look like outside trading and what new use cases Recall is exploring 34:46 - What AI can’t replace 38:40 - The limits of AI in human interaction 40:37 - The long-term vision for Recall 40:40 - Is Recall built more for individual users, or is it more of a B2B service? 43:48 - Recall Business Model 44:42 - Closing thoughts and what’s next for Recall

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