UC Berkeley's campus-wide, cross-disciplinary Center for Responsible, Decentralized Intelligence - RDI

Berkeley, CA
We have seen plenty of self-improving AI. We have not seen recursive self-improvement. @OriolVinyalsML, Co-founder & CTO of Discovery Loop and former VP of Research at Google DeepMind, draws the line at whether the system can rewrite its own harness and its own weights, not just its output. Has anything crossed that line yet? If not, how do we get there? Drop your take below. 👇
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The agent did exactly what you asked. That is the problem. Ion Stoica (@istoica05), Co-Founder of databricks and Anyscale and Professor at UC Berkeley, shared how his team watched an agent make a key-value store 6x faster by quietly not storing the data. The spec said return the value. The intent was to store it. Which gap is harder to close? 👉 Requirement gap: intent broader than spec 👉 Model gap: real world broader than test Drop your take below. 👇
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A perfectly aligned model does not make the world safe. @woj_zaremba, Co-Founder of OpenAI, says the field has the target wrong. Fire never got safer. Cities did. Hydrants, brigades, concrete, inspections, insurance. We focus on hardening the model. Should we be focusing on hardening the world instead? Drop your take below. 👇
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To teach a robot, the industry put humans in VR rigs. @DrJimFan, Director of Robotics & Distinguished Scientist at Nvidia, calls them medieval torture devices that fundamentally do not scale. He trains almost entirely on ordinary human video instead, letting data collection fade into the background. Can video alone teach dexterity? You can see a hand move. You cannot see the force it used. Drop your take below. 👇
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UC Berkeley RDI retweeted
I had the great honor and pleasure of sitting down with @JeffDean for his first public talk since leaving Google, where he spent an extraordinary 27 years. Few people have shaped modern computing and AI as profoundly - from MapReduce and Bigtable to TensorFlow, Mixture-of-Experts, TPUs, and Gemini. Our conversation covered some of the biggest questions shaping the future of AI: • How do you recognize a foundational idea before everyone else does? • How do you choose a research problem worth spending 5 years on? • What can coding teach us about building better reasoning models? • What might recursive self-improvement (RSI) actually look like? • What happens when the scientific discovery loop itself becomes increasingly automated? (and how is Jeff’s new startup going to contribute in this space?) • As AI becomes increasingly autonomous, how do we keep it safe and secure? • What should the next generation of researchers be working on? Here are some key insights and highlights for anyone building the future of AI. 🧵1/8
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What we are discussing today was not being discussed 3 months ago. In 3 months, nobody will be discussing today. @Alfred_Lin of Sequoia: still, roadmaps need to be set and capital allocated, and it has to last a decade. Which is the harder call to get right? 👉 The roadmap 👉 The capital Drop your take below. 👇
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There will be no AI job apocalypse. @AndrewYNg: the job most affected by AI is software engineering, and that market is healthy. "We just can't find enough skilled AI engineers." Does AI eliminate jobs, or does it reshape which skills the market values most? Drop your take below. 👇
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The best model might not win. Neither might the best chip. Peter DeSantis, SVP, Foundational AI Models, Custom Silicon, Quantum Computing, @amazon, says models and chips must anticipate each other years ahead. A wrong model roadmap can misdirect hardware. A wrong silicon roadmap can constrain models. Which is more fatal in your opinion? 👉 Getting the model roadmap wrong 👉 Getting the silicon roadmap wrong Drop your take below. 👇
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Humans can't be in 2 places at once. Agents can. Peter Steinberger shared a glimpse of the future: an agent that stays in a meeting, spots a gap, clones itself to investigate, and keeps listening at Agentic AI Summit @UCBerkeley Never having to choose between listening and working. So, when autonomous sub-agents start multiplying everywhere… Are we heading into a golden age of hyper-leverage, or are we about to be completely buried in supervising our AI’s work? Drop your take below. 👇
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UC Berkeley RDI retweeted
Great to chat with @AndrewYNg at the Agentic AI Conference at Berkeley. Our full talk is now available on YouTube. We discuss if we should expect an AI jobpocalypse, the importance of open models, and whether AGI is still 50 years out or happened 30 years ago. piped.video/watch?v=26a2jf1A…
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UC Berkeley RDI retweeted
🚀 The future of AI is agentic — this is one message echoed across every stage at the Agentic AI Summit 2026 (Aug 1 & 2), the largest gathering dedicated to agentic AI: 🏛️ ~5,000 attendees in person at UC Berkeley 🌍 ~100,000 joined online from around the world 🎤 ~200 world-class speakers plus ~200 poster presentations, from frontier AI researchers and visionary founders to leaders and pioneers across academia and industry. 💡 Here are just a few glimpses into the ideas that shaped the conversations at the summit: 💬 "The text box is AI's radio-on-TV phase…. Every new medium starts by imitating the old one." — Peter Steinberger @steipete, Creator of OpenClaw, OpenAI 💬 "AI infrastructure isn't a chip problem. It isn't a model problem. It's a systems problem." — Peter DeSantis, SVP, Foundational AI Models, Custom Silicon, Quantum Computing, Amazon 💬 "There will be no AI job apocalypse... we just can't find enough skilled AI engineers." — Andrew Ng @AndrewYNg, Founder, DeepLearning .AI 💬 "Coding capabilities and cyber capabilities are two sides of the same coin—you cannot make models better at coding without also making them better at cyber." — Dawn Song, Professor, UC Berkeley; Co-Director, Berkeley RDI; VP of AI Research, Meta Superintelligence Labs 💬 "'Curfew' comes from the French word for extinguishing fire. Medieval cities tried to restrict fire, yet London still burned. AI resilience won't come from one breakthrough. There's no silver bullet for AI safety—only an ecosystem." — Wojciech Zaremba, Co-Founder, OpenAI 💬 "This is the biggest scientific bet our civilization has ever made—bigger than the Apollo program, the internet buildout, and the Manhattan Project combined." — Jasjeet Sekhon, Chief Strategy Officer, Google DeepMind 💬 "Our generation was too late to explore the Earth, too early to explore the stars—but right on time to build superintelligence." — Richard Socher, Founder/CEO, Recursive Superintelligence 💬 "I genuinely believe the next two years will be the time of architecture—the biggest gains will come from stepping away from transformers." — Jerry Tworek, CEO, Core Automation; Former VP of Research at OpenAI 💬 "Recursive Self-Improvement isn't one capability. It's four: Ideation, Implementation, Experimentation and Evaluation.." — Oriol Vinyals @OriolVinyalsML, Former VP of Research, Google DeepMind; Co-Founder, Discovery Loop 💬 "We are in a capability overhang—models are far more capable than they're able to side-effect into the world today." — Ryan Lopopolo, Principal Engineer, Agentic Google Cloud Platform; Previously Led Dark Factory at OpenAI 💬 "Stop thinking about evaluation as the last check before shipping—think of it as an engine that helps you ship a better agent every single day." — Michele Catasta, President and Head of AI, Replit 💬 "The bottleneck becomes your attention as an agent-using engineer. … We're moving from seeing the code to seeing the entire business." — Alex Graveley, Co-Founder of FlyingObject .ai; Co-creator, GitHub Copilot & Perplexity Computer 💬 "An agent isn't just an LLM — it's an LLM surrounded by what I call infrastructure... another word for that is computer science." — Jonathan Cohen, VP of Applied Research, Nvidia; Academy Scientific and Technical Award Winner 💬 "We don't arbitrate the truth. We give people the most powerful tools to make up their own minds." — Chris Bregler, Senior Director / Distinguished Scientist, Google DeepMind; Academy Scientific and Technical Award Winner 💬 “Thinking doesn’t have to be in text! ... We can even use multiple modalities simultaneously to “think” at the right level of abstraction for the problem at hand” — Sergey Levine, Co-Founder, Physical Intelligence; Professor, UC Berkeley 💬 “Video is the most general modality that we have that allows us to simulate real-world experience." — Anastasis Germanidis, Co-Founder/Co-CEO, Runway 💬 “The relationship between AI and enterprise data is not one-directional. Understanding both sides of that equation is the difference between AI that works and AI that disappoints.” — Dan Roth, Chief AI Scientist, Oracle; Professor, UPenn 💬 "Maybe 99.9% of training data in the next step will be synthetic." — Weizhu Chen, Technical Fellow & CVP, Microsoft AI 💬 "It's maybe the best time ever to start a company—but most 'obvious' AI products will be outcompeted by the frontier labs. The real opportunities lie in solving specific customer problems." — Alfred Lin, General Partner, Sequoia Capital ✨Over two days, we explored one central question: How do we build AI systems that are not only more capable, but also more trustworthy, more secure, and ultimately more beneficial for humanity? This wasn't the end of a conference - it was the beginning of the next chapter for agentic AI. Join us to shape and steward the future of AI for human flourishing! 🙏 A heartfelt thank you to our speakers, sponsors, volunteers, partners, and every attendee (in-person or online) who made this summit possible. 👇 What are your learnings, insights, favourite talk, quote, or moment from the summit? We'd love to hear it below!
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