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Self-learning worked in AlphaGo because the world was a clean rulebook, says Demis Hassabis. Real life has no perfect rulebook, so long-term planning needs a strong “world model” to simulate what happens next. Without that simulation, a robot has to guess in the real world—where mistakes can be costly...

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Wes Roth

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15,757 Aufrufe • vor 7 Monaten •via X (Twitter)

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Demis Hassabis says an “AlphaFold moment” for the physical world could be close—real robots doing useful tasks reliably in the real world. Gemini was built multimodal so a future assistant can understand what you see on a phone or glasses. That same “understand the world” ability is a direct bridge into robotics, says Demis Hassabis. Physical intelligence becomes real when robots stop being demos and start being dependable helpers.
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Demis Hassabis says an “AlphaFold moment” for the physical world could be close—real robots doing useful tasks reliably in the real world. Gemini was built multimodal so a future assistant can understand what you see on a phone or glasses. That same “understand the world” ability is a direct bridge into robotics, says Demis Hassabis. Physical intelligence becomes real when robots stop being demos and start being dependable helpers.

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Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:
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“We think we have a competitive advantage.” - Demis Hassibis, Head of Google DeepMind / AI efforts While Anthropic and OpenAI seek to win via training on “text” (written language), Demis Hassabis says Google Gemini is attempting a different path: Multimodal. Gemini aims to be the most effective model in the real world: physics, robotics, voice, visual, sensory, & text. The bet is multimodal unlocks more of what humanity really cares about.
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“We think we have a competitive advantage.” - Demis Hassibis, Head of Google DeepMind / AI efforts While Anthropic and OpenAI seek to win via training on “text” (written language), Demis Hassabis says Google Gemini is attempting a different path: Multimodal. Gemini aims to be the most effective model in the real world: physics, robotics, voice, visual, sensory, & text. The bet is multimodal unlocks more of what humanity really cares about.

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Demis Hassabis says unlimited compute might let AI predict the consequences of a decision, not the future Economics today is limited because real-world choices cannot be tested twice under the same conditions "but an accurate enough simulator might let you run the counterfactual"
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Demis Hassabis says unlimited compute might let AI predict the consequences of a decision, not the future Economics today is limited because real-world choices cannot be tested twice under the same conditions "but an accurate enough simulator might let you run the counterfactual"

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Demis Hassabis says true AGI is a theoretical benchmark set by the human brain — the only proven architecture for general intelligence today’s models are impressive but inconsistent; anyone can find flaws within minutes. "real AGI should be so strong that it would take experts months to spot a weakness"
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Demis Hassabis says true AGI is a theoretical benchmark set by the human brain — the only proven architecture for general intelligence today’s models are impressive but inconsistent; anyone can find flaws within minutes. "real AGI should be so strong that it would take experts months to spot a weakness"

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Demis Hassabis is calling on philosophers to step in. He says technologists shouldn't decide AGI's future alone. “We need a new Kant or Wittgenstein to help map out where society should go next.” Politics, ethics, and theology will be essential to navigating a post-AGI world.
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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

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In all seriousness, the name General Intuition comes from this Demis Hassabis talk, as the north star of the lab is a general agent, where "Play the World" applies to scientific problems that can become games too! Another example:
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In all seriousness, the name General Intuition comes from this Demis Hassabis talk, as the north star of the lab is a general agent, where "Play the World" applies to scientific problems that can become games too! Another example:

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Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!
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Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

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Without World Models, There Is No AGI. Google Just Proved It. If AGI ever happens, it will not come from bigger chatbots alone. From the very start of this interview, one thing is crystal clear: without world models, we will never reach AGI. And right now, Google is leading with its world simulator Genie 3. Here is the core of what Demis Hassabis explains in this conversation: • World models are the missing core of AGI Hassabis says his deepest long term focus has always been world models and simulations. Not just language. Not just prediction. Actual internal simulations of reality. • LLMs are impressive, but incomplete Language models understand more about the world than expected because human language encodes a lot of reality. Still, language is only a shadow of the real thing. • What text can never fully teach Reality includes things text struggles to express: •3D space and spatial dynamics •Physical causality and mechanics •Sensorimotor experience like movement, force, smell, or balance • Experience beats description To close the gap, AI must learn from interaction and experience, not just static text. That is how you build an internal world simulator. • Why Genie 3 matters With Google DeepMind pushing systems like Genie 3, AI starts to model reality itself, not just talk about it. • Robots and real world assistants depend on this True robotics, smart glasses, and universal assistants require AI that understands the physical world you live in, not just your screen. Bottom line: AGI will not emerge from better text prediction. It will emerge from systems that can simulate, predict, and understand reality itself. Right now, Google is clearly ahead on that path. Curious what you think. Are world models the real AGI unlock, or just another stepping stone?
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Without World Models, There Is No AGI. Google Just Proved It. If AGI ever happens, it will not come from bigger chatbots alone. From the very start of this interview, one thing is crystal clear: without world models, we will never reach AGI. And right now, Google is leading with its world simulator Genie 3. Here is the core of what Demis Hassabis explains in this conversation: • World models are the missing core of AGI Hassabis says his deepest long term focus has always been world models and simulations. Not just language. Not just prediction. Actual internal simulations of reality. • LLMs are impressive, but incomplete Language models understand more about the world than expected because human language encodes a lot of reality. Still, language is only a shadow of the real thing. • What text can never fully teach Reality includes things text struggles to express: •3D space and spatial dynamics •Physical causality and mechanics •Sensorimotor experience like movement, force, smell, or balance • Experience beats description To close the gap, AI must learn from interaction and experience, not just static text. That is how you build an internal world simulator. • Why Genie 3 matters With Google DeepMind pushing systems like Genie 3, AI starts to model reality itself, not just talk about it. • Robots and real world assistants depend on this True robotics, smart glasses, and universal assistants require AI that understands the physical world you live in, not just your screen. Bottom line: AGI will not emerge from better text prediction. It will emerge from systems that can simulate, predict, and understand reality itself. Right now, Google is clearly ahead on that path. Curious what you think. Are world models the real AGI unlock, or just another stepping stone?

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Demis Hassabis says our brains are likely to be approximate Turing machines. AlphaFold showed why that matters: protein folding looked like a quantum problem, but a classical neural network could model it well enough to solve it. The world may be quantum, but understanding it may only need the right approximation.
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Demis Hassabis says our brains are likely to be approximate Turing machines. AlphaFold showed why that matters: protein folding looked like a quantum problem, but a classical neural network could model it well enough to solve it. The world may be quantum, but understanding it may only need the right approximation.

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Demis Hassabis describes a surreal setup: one AI playing inside the mind of another DeepMind SIMA agent can now operate within Genie 3 -- taking actions in a world that Genie generates in real time "an AI creates the world, another acts inside it" this setup could unlock unlimited training data for robotics and AGI
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Demis Hassabis describes a surreal setup: one AI playing inside the mind of another DeepMind SIMA agent can now operate within Genie 3 -- taking actions in a world that Genie generates in real time "an AI creates the world, another acts inside it" this setup could unlock unlimited training data for robotics and AGI

Haider.

215,914 Aufrufe • vor 1 Jahr

The best way to get robust, high-quality robot performance is through reinforcement learning; but RL in either the real world or a traditional simulation has lots of limitations. Instead, Jiazhi Yang in RISE does RL in a compositional world model. Learn more ->
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The best way to get robust, high-quality robot performance is through reinforcement learning; but RL in either the real world or a traditional simulation has lots of limitations. Instead, Jiazhi Yang in RISE does RL in a compositional world model. Learn more ->

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34,102 Aufrufe • vor 2 Monaten