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Demis Hassabis says the next frontier is AI self-improvement — and coding is where it starts, because the outputs are verifiable and synthetic data is limitless That's a compounding flywheel, and every major lab is betting on it "but raw capability without reliability is a liability"

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Haider.

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15,428 просмотров • 3 месяцев назад •via X (Twitter)

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Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)
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Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

68,231 просмотров • 2 месяцев назад

Demis Hassabis says AGI is a scientific goal worth pursuing -- a testable frontier of general intelligence. Kate Crawford pushes back: the industry is already optimized for AGI. What it lacks is alignment with goals that serve people and the planet. "When do we build AI that's actually good for everyone? These are the real benchmarks."
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Demis Hassabis says AGI is a scientific goal worth pursuing -- a testable frontier of general intelligence. Kate Crawford pushes back: the industry is already optimized for AGI. What it lacks is alignment with goals that serve people and the planet. "When do we build AI that's actually good for everyone? These are the real benchmarks."

vitrupo

29,243 просмотров • 1 год назад

Demis Hassabis says AlphaGo mastered chess in under 24 hours That same self-improvement loop could work in coding and math, where answers are verifiable Systems could close the loop, but once you remove humans from the loop, capabilities can proliferate in ways you didn't want
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Demis Hassabis says AlphaGo mastered chess in under 24 hours That same self-improvement loop could work in coding and math, where answers are verifiable Systems could close the loop, but once you remove humans from the loop, capabilities can proliferate in ways you didn't want

Haider.

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Demis Hassabis reveals the 100,000-person synthetic country that took years to hand-code, and says AI may now generate it "We tried to simulate a whole country with 100,000 living, breathing people sort of going about their daily lives and all the politics of it" "And you were supposed to create a revolution" "So it was a pretty ambitious game that we had to write by hand" "And what's amazing now, and it took years" "But what's amazing now is that we might be close to this possibility of actually just sort of generating that" The leap is not better graphics. It is the cost curve of simulation: from years of hand-coding one world to asking a model to generate it. That turns synthetic societies from bespoke software projects into a reusable AI primitive. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, at Semafor Tech
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Demis Hassabis reveals the 100,000-person synthetic country that took years to hand-code, and says AI may now generate it "We tried to simulate a whole country with 100,000 living, breathing people sort of going about their daily lives and all the politics of it" "And you were supposed to create a revolution" "So it was a pretty ambitious game that we had to write by hand" "And what's amazing now, and it took years" "But what's amazing now is that we might be close to this possibility of actually just sort of generating that" The leap is not better graphics. It is the cost curve of simulation: from years of hand-coding one world to asking a model to generate it. That turns synthetic societies from bespoke software projects into a reusable AI primitive. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, at Semafor Tech

Karl Mehta

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Strength without control is a threat. Control without strength is a liability. The world isn't safe because men are harmless. It's safer because some men are dangerous but disciplined. The line between monster and protector is self-mastery ⚡
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Strength without control is a threat. Control without strength is a liability. The world isn't safe because men are harmless. It's safer because some men are dangerous but disciplined. The line between monster and protector is self-mastery ⚡

Alpha Motivation

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Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
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Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 просмотров • 2 месяцев назад

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
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🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

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How close is DeepMind to achieving AGI? Demis Hassabis says: We are on track and DeepMind targets AGI completion by 2030 but I wouldn't be surprised if it arrives in the next decade.
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How close is DeepMind to achieving AGI? Demis Hassabis says: We are on track and DeepMind targets AGI completion by 2030 but I wouldn't be surprised if it arrives in the next decade.

Haider.

94,348 просмотров • 1 год назад

Demis Hassabis believes classical AI is enough for drug discovery, like AlphaFold has shown. But quantum computers may shine in material science, where data is scarce. They could simulate complex materials like superconductors or solar panels. DeepMind is actively exploring this frontier for breakthroughs in energy and tech. Quantum simulations might generate the missing data needed for major inventions.
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Demis Hassabis believes classical AI is enough for drug discovery, like AlphaFold has shown. But quantum computers may shine in material science, where data is scarce. They could simulate complex materials like superconductors or solar panels. DeepMind is actively exploring this frontier for breakthroughs in energy and tech. Quantum simulations might generate the missing data needed for major inventions.

Wes Roth

53,450 просмотров • 7 месяцев назад

VR founder Jaron Lanier says a future where people are paid for the data they give AI is better than one where everyone depends on billionaires to survive "artificial intelligence is a marketing term, even a religion" The software behind drug discovery and chatbots is very different, but we call it all "AI" because it serves politics, ideology
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VR founder Jaron Lanier says a future where people are paid for the data they give AI is better than one where everyone depends on billionaires to survive "artificial intelligence is a marketing term, even a religion" The software behind drug discovery and chatbots is very different, but we call it all "AI" because it serves politics, ideology

Haider.

64,901 просмотров • 3 месяцев назад

In a world where data is constantly extracted and exploited, CESS is building the decentralized data value infrastructure to take it back. 👊 Web3 starts with ownership—and it starts with your data. In CESS, every storage node is a secure vault. Your data. Your rules. Your future. 🔥 🎥 Watch how CESS is rewriting the future below👇 #CESS #Web3 #Blockchain #Data #DePIN #Crypto #AI
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In a world where data is constantly extracted and exploited, CESS is building the decentralized data value infrastructure to take it back. 👊 Web3 starts with ownership—and it starts with your data. In CESS, every storage node is a secure vault. Your data. Your rules. Your future. 🔥 🎥 Watch how CESS is rewriting the future below👇 #CESS #Web3 #Blockchain #Data #DePIN #Crypto #AI

CESS Network

142,778 просмотров • 1 год назад

Introducing Ricursive Intelligence, a frontier AI lab enabling a recursive self-improvement loop between AI and the chips that fuel it. Learn more at
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Introducing Ricursive Intelligence, a frontier AI lab enabling a recursive self-improvement loop between AI and the chips that fuel it. Learn more at

Ricursive Intelligence

518,251 просмотров • 9 месяцев назад

Demis Hassabis explains that tasks like coding and math are easier to automate because they offer clear, verifiable outputs. However, scientific discovery like creating new hypotheses or theories is much harder for AI. It’s not just about solving known problems, but inventing new questions, which requires a higher level of creativity. He believes we're still missing key capabilities needed for that level of AI. While progress is rapid, reaching true scientific innovation without human input remains uncertain.
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Demis Hassabis explains that tasks like coding and math are easier to automate because they offer clear, verifiable outputs. However, scientific discovery like creating new hypotheses or theories is much harder for AI. It’s not just about solving known problems, but inventing new questions, which requires a higher level of creativity. He believes we're still missing key capabilities needed for that level of AI. While progress is rapid, reaching true scientific innovation without human input remains uncertain.

Wes Roth

15,090 просмотров • 7 месяцев назад

Elon Musk says humans may exit the AI development loop entirely by next year. Musk: “Humans are gradually getting less and less in the loop on the recursive self-improvement. Every successive model is built by the one before it.”
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Elon Musk says humans may exit the AI development loop entirely by next year. Musk: “Humans are gradually getting less and less in the loop on the recursive self-improvement. Every successive model is built by the one before it.”

illuminatibot

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Everyone's thinks Leopold's latest 13F is bearish AI. But they're wrong. he just told us where the next major AI bottleneck is: Power and memory infrastructure... and he's placing a massive $3.5B bet on it. The thesis is simple, but the trade is less obvious: -> The AI semiconductor trade is too crowded. Nvidia, AMD and Intel are up 200%+ over the last year. -> The next unlock? Powering the GPUs... his massive bloom energy and corweave prove it But there's also a genius move he's executing with corweave and bitcoin mining... We covered it all on todays episode 👇
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Everyone's thinks Leopold's latest 13F is bearish AI. But they're wrong. he just told us where the next major AI bottleneck is: Power and memory infrastructure... and he's placing a massive $3.5B bet on it. The thesis is simple, but the trade is less obvious: -> The AI semiconductor trade is too crowded. Nvidia, AMD and Intel are up 200%+ over the last year. -> The next unlock? Powering the GPUs... his massive bloom energy and corweave prove it But there's also a genius move he's executing with corweave and bitcoin mining... We covered it all on todays episode 👇

Limitless

53,958 просмотров • 3 месяцев назад

Sundar Pichai says only a few labs are genuinely at the frontier, and then there is a big gap The perception of who is leading shifts every 4 to 6 weeks because each lab's release cycle peaks at different times "if recursive self-improvement emerges, it becomes a societal conversation, not a single-company decision"
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Sundar Pichai says only a few labs are genuinely at the frontier, and then there is a big gap The perception of who is leading shifts every 4 to 6 weeks because each lab's release cycle peaks at different times "if recursive self-improvement emerges, it becomes a societal conversation, not a single-company decision"

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can AI write engaging news that people can trust? introducing ✨Data2Story: a data journalist agent. give it raw data, it generate a verifiable, multimodal article. 🔍verifiable: every claim is evidence-grounded, traces back to data, code, or a cited source. 🔮multimodal: the article is a generative UI — images, videos, audio, interactive charts. not just readable, but trustworthy and playable. 🧵1/N

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33,193 просмотров • 2 месяцев назад