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AGI probably needs 1-2 new breakthroughs, like continual learning, selective memory, smarter context windows, and long term planning. ~ Google DeepMind CEO Demis Hassabis But in any case, large foundation models will remain the key component of AGI. --- From 'Alex Kantrowitz' YT channel (link in comment)

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Rohan Paul

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Demis Hassabis says, We should scale current systems as far as possible, because at the minimum, it will be a key component of the final AGI system It could be the entirety of the AGI system But we'll need 1-2 more breakthroughs on the level of the transformer or AlphaGo
0:39

Demis Hassabis says, We should scale current systems as far as possible, because at the minimum, it will be a key component of the final AGI system It could be the entirety of the AGI system But we'll need 1-2 more breakthroughs on the level of the transformer or AlphaGo

Haider.

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Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."
4:40

Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."

Wes Roth

38,833 görüntüleme • 6 ay önce

🚨 BREAKING: Google DeepMind CEO Demis Hassabis says AGI could arrive by 2030. And the impact will go far beyond software. Drug discovery could shrink from 10+ years to days AI may unlock entirely new scientific breakthroughs. At AI Ascent 2026, Demis Hassabis outlined what’s coming next: But he also made one thing clear: Today’s AI is still limited. And the next 1–2 years will be critical in shaping what happens next. The bigger shift: Watch the full talk below:
26:46

🚨 BREAKING: Google DeepMind CEO Demis Hassabis says AGI could arrive by 2030. And the impact will go far beyond software. Drug discovery could shrink from 10+ years to days AI may unlock entirely new scientific breakthroughs. At AI Ascent 2026, Demis Hassabis outlined what’s coming next: But he also made one thing clear: Today’s AI is still limited. And the next 1–2 years will be critical in shaping what happens next. The bigger shift: Watch the full talk below:

Atal

18,433 görüntüleme • 2 ay önce

All remote cognitive jobs, done using only a laptop setup, is most exposed to be automated or heavily reduced by AI. ~ Shane Legg, Chief AGI Scientist and co-founder of Google DeepMind ---- Full video from 'Google DeepMind and Hannah Fry' YT channel (link in comment)
1:31

All remote cognitive jobs, done using only a laptop setup, is most exposed to be automated or heavily reduced by AI. ~ Shane Legg, Chief AGI Scientist and co-founder of Google DeepMind ---- Full video from 'Google DeepMind and Hannah Fry' YT channel (link in comment)

Rohan Paul

81,268 görüntüleme • 6 ay önce

Google Deepmind CEO gives his prediction on the final AGI architecture: Demis Hassabis just sat down on 20VC and broke down exactly how DeepMind is building toward true AGI, directly pushing back on the idea that LLMs are a dead end. He defends the current trajectory of the models, noting that “we’ve seen how successful these foundation models have been. They can do incredibly impressive things. I don’t think that’s going to go away. We’re still seeing returns from the scaling laws.” But he’s honest about the missing pieces for true superintelligence, saying “I think there might be… there’s a 50-50 chance there’s some things maybe missing that we still need to make breakthroughs in, perhaps their world models, these kinds of approaches.” And when he breaks down what the actual AGI system will look like, he frames it as one question is the LLM foundation model the key component only, or is it the total system. His answer - “I don’t think it’s going to get replaced. I think it’s going to get built on top of these foundation models, just like the way we do with our world models.”
0:53

Google Deepmind CEO gives his prediction on the final AGI architecture: Demis Hassabis just sat down on 20VC and broke down exactly how DeepMind is building toward true AGI, directly pushing back on the idea that LLMs are a dead end. He defends the current trajectory of the models, noting that “we’ve seen how successful these foundation models have been. They can do incredibly impressive things. I don’t think that’s going to go away. We’re still seeing returns from the scaling laws.” But he’s honest about the missing pieces for true superintelligence, saying “I think there might be… there’s a 50-50 chance there’s some things maybe missing that we still need to make breakthroughs in, perhaps their world models, these kinds of approaches.” And when he breaks down what the actual AGI system will look like, he frames it as one question is the LLM foundation model the key component only, or is it the total system. His answer - “I don’t think it’s going to get replaced. I think it’s going to get built on top of these foundation models, just like the way we do with our world models.”

Chris

71,716 görüntüleme • 3 ay önce

Google DeepMind CEO Demis Hassabis says we’re in the ‘foothills of the singularity’ I sat down with him to talk about what that means, curing every disease, and human meaning post-AGI: 0:00 Intro 0:45 What Demis is most excited about at I/O 1:46 Have AGI timelines shifted? 3:30 What's still missing before AGI 6:50 AI curing every disease 9:19 What diseases get cured first? 10:50 What Demis works on after AGI 11:48 Human meaning after AGI 13:50 The human skills that get more valuable 15:19 What's underhyped in AI right now
16:40

Google DeepMind CEO Demis Hassabis says we’re in the ‘foothills of the singularity’ I sat down with him to talk about what that means, curing every disease, and human meaning post-AGI: 0:00 Intro 0:45 What Demis is most excited about at I/O 1:46 Have AGI timelines shifted? 3:30 What's still missing before AGI 6:50 AI curing every disease 9:19 What diseases get cured first? 10:50 What Demis works on after AGI 11:48 Human meaning after AGI 13:50 The human skills that get more valuable 15:19 What's underhyped in AI right now

Rowan Cheung

118,832 görüntüleme • 2 ay önce

Google DeepMind CEO Demis Hassabis on AI bubble, from his new interview yesterday. Says some AI startups with tens of billions of valuations are wildly overpriced — and a correction may come. AI is overhyped in the short term, underappreciated in the medium to long term. An “AI bubble” exists in parts of the ecosystem, especially seed stage startups raising at tens of billions in valuation before proving anything, which he sees as unsustainable. However, he differentiates that from big tech, where he thinks there is real business value behind the valuations, though outcomes still depend on execution. Booms and corrections are normal for transformative tech, similar to the internet and mobile cycles. --- From 'Google DeepMind and Hannah Fry' YT channel (full link in comment)
0:34

Google DeepMind CEO Demis Hassabis on AI bubble, from his new interview yesterday. Says some AI startups with tens of billions of valuations are wildly overpriced — and a correction may come. AI is overhyped in the short term, underappreciated in the medium to long term. An “AI bubble” exists in parts of the ecosystem, especially seed stage startups raising at tens of billions in valuation before proving anything, which he sees as unsustainable. However, he differentiates that from big tech, where he thinks there is real business value behind the valuations, though outcomes still depend on execution. Booms and corrections are normal for transformative tech, similar to the internet and mobile cycles. --- From 'Google DeepMind and Hannah Fry' YT channel (full link in comment)

Rohan Paul

86,114 görüntüleme • 7 ay önce

Google DeepMind CEO Demis Hassabis on how big AGI really is: "I sometimes quantify the coming of AGI as 10 times the industrial revolution at 10 times the speed, unfolding over a decade instead of a century. Literally today, things are a bit overhyped in AI. There couldn't be any more hype in some ways. But on the other hand, I still think it's very underappreciated how revolutionary this is going to be on the timescale of about 10 years."
1:51

Google DeepMind CEO Demis Hassabis on how big AGI really is: "I sometimes quantify the coming of AGI as 10 times the industrial revolution at 10 times the speed, unfolding over a decade instead of a century. Literally today, things are a bit overhyped in AI. There couldn't be any more hype in some ways. But on the other hand, I still think it's very underappreciated how revolutionary this is going to be on the timescale of about 10 years."

Wall St Engine

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Demis Hassabis's new interview: "Society needs to hear that because we don't have long to prepare for what that means. We are standing in the foothills of the singularity now. ..which is AGI. I believe that we are only a few years away from that, maybe around 2030, plus or minus a year. " ~ Demis Hassabis, Co-Founder and CEO of Google DeepMind It is going to be enormously profound, I think. The future, in my view, is still to be written. But these next few years are going to be very critical as to which way that will go, and how we collectively want that to look.” --- IMO, The real disruption is not whether AGI arrives exactly in 2030, plus or minus a year, but whether institutions can adapt, as in post-AGI world, technology will change much faster than human systems can respond. Schools still train people for stable professions, companies still organize work around human bottlenecks, and governments still regulate after harm becomes visible. AGI, if it arrives anywhere near the frontier-lab timelines, compresses that lag into a dangerous gap. ---- From "Stanford Graduate School of Business" YouTube channel, (link in comment)
0:50

Demis Hassabis's new interview: "Society needs to hear that because we don't have long to prepare for what that means. We are standing in the foothills of the singularity now. ..which is AGI. I believe that we are only a few years away from that, maybe around 2030, plus or minus a year. " ~ Demis Hassabis, Co-Founder and CEO of Google DeepMind It is going to be enormously profound, I think. The future, in my view, is still to be written. But these next few years are going to be very critical as to which way that will go, and how we collectively want that to look.” --- IMO, The real disruption is not whether AGI arrives exactly in 2030, plus or minus a year, but whether institutions can adapt, as in post-AGI world, technology will change much faster than human systems can respond. Schools still train people for stable professions, companies still organize work around human bottlenecks, and governments still regulate after harm becomes visible. AGI, if it arrives anywhere near the frontier-lab timelines, compresses that lag into a dangerous gap. ---- From "Stanford Graduate School of Business" YouTube channel, (link in comment)

Rohan Paul

70,642 görüntüleme • 1 ay önce

Google DeepMind say true AGI will “reason, adapt, and learn continuously,” and they estimate it’s about 5-10 years away. Meta’s chief scientist Yann LeCun likewise views continual learning as a key pillar of human level AI and is optimistic about achieving it by around 2030 Currently, large language models only improve via offline updates (fine-tuning or reinforcement learning from human feedback) rather than meta learning or modular networks that update themselves gradually. Thankfully All major research teams (OpenAI, DeepMind, Anthropic, Meta, etc.) are actively working on these directions, aiming for models that continuously learn from mistakes as they interact a capability they believe will emerge as we approach true AGI later this decade 2029
1:07

Google DeepMind say true AGI will “reason, adapt, and learn continuously,” and they estimate it’s about 5-10 years away. Meta’s chief scientist Yann LeCun likewise views continual learning as a key pillar of human level AI and is optimistic about achieving it by around 2030 Currently, large language models only improve via offline updates (fine-tuning or reinforcement learning from human feedback) rather than meta learning or modular networks that update themselves gradually. Thankfully All major research teams (OpenAI, DeepMind, Anthropic, Meta, etc.) are actively working on these directions, aiming for models that continuously learn from mistakes as they interact a capability they believe will emerge as we approach true AGI later this decade 2029

Chris

284,206 görüntüleme • 10 ay önce

The CEO of Anthropic: "We might be six to twelve months away from when the model is doing most, maybe all, of what software engineers do end to end." In 30 minutes, Dario Amodei and Google DeepMind's Demis Hassabis break down the day after AGI. AGI timeline + self-improving models + the first jobs to go + who stays in control Worth more than a year of AI hot takes on your timeline
29:51

The CEO of Anthropic: "We might be six to twelve months away from when the model is doing most, maybe all, of what software engineers do end to end." In 30 minutes, Dario Amodei and Google DeepMind's Demis Hassabis break down the day after AGI. AGI timeline + self-improving models + the first jobs to go + who stays in control Worth more than a year of AI hot takes on your timeline

Phosphen

26,632 görüntüleme • 1 ay önce

The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.
1:00

The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.

Milk Road AI

130,649 görüntüleme • 3 ay önce

Demis Hassabis points out that today’s models don’t learn continuously, they are trained once and then deployed. Unlike humans, they don’t improve from experience or adapt after release. The next major leap, like “AlphaZero,” would involve self-learning systems that discover knowledge on their own. This ability to learn online and evolve in real time is a key missing component on the path to AGI. Solving this will be critical to building truly intelligent systems.
1:42

Demis Hassabis points out that today’s models don’t learn continuously, they are trained once and then deployed. Unlike humans, they don’t improve from experience or adapt after release. The next major leap, like “AlphaZero,” would involve self-learning systems that discover knowledge on their own. This ability to learn online and evolve in real time is a key missing component on the path to AGI. Solving this will be critical to building truly intelligent systems.

Wes Roth

39,238 görüntüleme • 7 ay önce

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?
1:26

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?

VraserX e/acc

23,784 görüntüleme • 7 ay önce

The most skilled guy in the AI industry just said we're 1-2 breakthroughs away from AGI. And he explained exactly what's missing. Demis Hassabis runs Google DeepMind. He won the Nobel Prize in Chemistry last year. He's literally the reason why Google is considered the leader of the AI race. And he just dropped the most specific AGI timeline ever: "One or two AlphaGo-level technological breakthroughs." That's it. That's all standing between us and artificial general intelligence. But here's the thing... LLMs are NOT going to get us there. ChatGPT, Gemini, Claude - they're all hitting the same wall. They can't plan long-term. Can't create NEW ideas. Can't understand physics. Demis called them "jagged intelligences. Very good at certain things. Completely incapable of others." You've felt this yourself. You've felt this yourself. You ask ChatGPT a complex question and it sounds smart. But ask it to solve something that requires REASONING across multiple steps? It falls apart. So what ARE the 2 breakthroughs we need? Breakthrough #1: World Models AI that understands how physics actually works. How water flows. How cause and effect works. DeepMind already has early versions (Genie, Veo). The insight: If AI can GENERATE something realistic, it UNDERSTANDS it. This is the foundation for robotics and AI that interacts with reality. Breakthrough #2: Agentic Systems AI that can DO things. Not just answer questions. Plan multiple steps. Execute autonomously. Adjust when wrong. DeepMind proved this with AlphaGo in 2016 - planning 20+ moves ahead to beat the world champion. Now they're generalizing it to the real world. And here's the most interesting part: Demis says these two things are starting to CONVERGE. LLMs + World Models + Agentic Behavior = AGI And when I say converge, I mean Google is already building it. They're setting up the first fully automated scientific laboratory in the UK. No humans running experiments. AI designs the test. Robots execute it. AI analyzes results. AI adjusts and iterates. The lab will work on: → Room-temperature superconductors → Nuclear fusion materials → New battery chemistries → Climate tech breakthroughs Demis's logic is simple: "If AI can screen materials 100X faster, the energy revolution takes 10 years instead of 100." But here's the scary part: China is MONTHS behind. Not years. "They're very close to the frontier. Maybe only months behind." DeepSeek. Alibaba's Qwen models. They're catching up fast. And unlike what people thought, they're doing it WITHOUT access to the most advanced Nvidia chips. The window for the West to lead in AGI is shrinking. The economic impact? Demis: "10 times bigger than the Industrial Revolution. And maybe 10 times faster." Industrial Revolution took 100+ years and reshaped civilization. This will be 10X bigger in 1/10th the time. Mass job displacement. Economic restructuring. New industries overnight. But also: → Curing all disease → Solving climate change → Unlimited clean energy → "Radical abundance" Demis is betting DeepMind can get there first. Google spent $400 million on DeepMind in 2014. That stake is now worth 100s of billions. Because DeepMind is now the "engine room" of ALL of Google's AI. Every Gemini model. Every AI feature in Search, Gmail, Workspace. All built by DeepMind. Shipped across Google's dozens of billion-user products instantly. That distribution is their superpower. The final thing Demis said that stuck with me: "AGI is probably the most transformative moment in human history. And it's on the horizon." One or two breakthroughs and 5 years away. According to the most skilled guy in the industry.
2:40

The most skilled guy in the AI industry just said we're 1-2 breakthroughs away from AGI. And he explained exactly what's missing. Demis Hassabis runs Google DeepMind. He won the Nobel Prize in Chemistry last year. He's literally the reason why Google is considered the leader of the AI race. And he just dropped the most specific AGI timeline ever: "One or two AlphaGo-level technological breakthroughs." That's it. That's all standing between us and artificial general intelligence. But here's the thing... LLMs are NOT going to get us there. ChatGPT, Gemini, Claude - they're all hitting the same wall. They can't plan long-term. Can't create NEW ideas. Can't understand physics. Demis called them "jagged intelligences. Very good at certain things. Completely incapable of others." You've felt this yourself. You've felt this yourself. You ask ChatGPT a complex question and it sounds smart. But ask it to solve something that requires REASONING across multiple steps? It falls apart. So what ARE the 2 breakthroughs we need? Breakthrough #1: World Models AI that understands how physics actually works. How water flows. How cause and effect works. DeepMind already has early versions (Genie, Veo). The insight: If AI can GENERATE something realistic, it UNDERSTANDS it. This is the foundation for robotics and AI that interacts with reality. Breakthrough #2: Agentic Systems AI that can DO things. Not just answer questions. Plan multiple steps. Execute autonomously. Adjust when wrong. DeepMind proved this with AlphaGo in 2016 - planning 20+ moves ahead to beat the world champion. Now they're generalizing it to the real world. And here's the most interesting part: Demis says these two things are starting to CONVERGE. LLMs + World Models + Agentic Behavior = AGI And when I say converge, I mean Google is already building it. They're setting up the first fully automated scientific laboratory in the UK. No humans running experiments. AI designs the test. Robots execute it. AI analyzes results. AI adjusts and iterates. The lab will work on: → Room-temperature superconductors → Nuclear fusion materials → New battery chemistries → Climate tech breakthroughs Demis's logic is simple: "If AI can screen materials 100X faster, the energy revolution takes 10 years instead of 100." But here's the scary part: China is MONTHS behind. Not years. "They're very close to the frontier. Maybe only months behind." DeepSeek. Alibaba's Qwen models. They're catching up fast. And unlike what people thought, they're doing it WITHOUT access to the most advanced Nvidia chips. The window for the West to lead in AGI is shrinking. The economic impact? Demis: "10 times bigger than the Industrial Revolution. And maybe 10 times faster." Industrial Revolution took 100+ years and reshaped civilization. This will be 10X bigger in 1/10th the time. Mass job displacement. Economic restructuring. New industries overnight. But also: → Curing all disease → Solving climate change → Unlimited clean energy → "Radical abundance" Demis is betting DeepMind can get there first. Google spent $400 million on DeepMind in 2014. That stake is now worth 100s of billions. Because DeepMind is now the "engine room" of ALL of Google's AI. Every Gemini model. Every AI feature in Search, Gmail, Workspace. All built by DeepMind. Shipped across Google's dozens of billion-user products instantly. That distribution is their superpower. The final thing Demis said that stuck with me: "AGI is probably the most transformative moment in human history. And it's on the horizon." One or two breakthroughs and 5 years away. According to the most skilled guy in the industry.

Ricardo

217,113 görüntüleme • 6 ay önce

🇨🇳 Compared to China "Algorithmically, I think the West still has an edge. I think none of the Chinese models or companies have shown that they can innovate on algorithmically something completely new thats beyond the state of the art. They have been very good at following the current state of the art." ~ Google DeepMind’s Demis Hassabis. From Axios YT channel ( link in comment)
0:25

🇨🇳 Compared to China "Algorithmically, I think the West still has an edge. I think none of the Chinese models or companies have shown that they can innovate on algorithmically something completely new thats beyond the state of the art. They have been very good at following the current state of the art." ~ Google DeepMind’s Demis Hassabis. From Axios YT channel ( link in comment)

Rohan Paul

114,818 görüntüleme • 7 ay önce

Demis Hassabis, CEO of Google DeepMind and Nobel laureate, says the AGI definition the industry uses is too low a bar. His Einstein test: train a system on a 1901 knowledge cutoff and ask it to invent special relativity. "The human brain is the only existence proof we have that general intelligence is even possible." "That's a pretty high bar, and it means that it's probably higher bar than just being able to do some useful economic work." "The type of test I would have is like an Einstein test of, you know, let's train one of these systems with a 1901 knowledge cutoff. Can it invent special relativity like Einstein did in 1905?" "What you'd actually want is something a future version of AlphaGo to be able to invent Go, right? Invent a game as deep and as complex and elegant and as beautiful as Go, not just come up with a strategy within the game." "I don't think today's systems are yet capable of doing that, but I think they will be able to in the future." - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, on The Next Big Thing.
2:52

Demis Hassabis, CEO of Google DeepMind and Nobel laureate, says the AGI definition the industry uses is too low a bar. His Einstein test: train a system on a 1901 knowledge cutoff and ask it to invent special relativity. "The human brain is the only existence proof we have that general intelligence is even possible." "That's a pretty high bar, and it means that it's probably higher bar than just being able to do some useful economic work." "The type of test I would have is like an Einstein test of, you know, let's train one of these systems with a 1901 knowledge cutoff. Can it invent special relativity like Einstein did in 1905?" "What you'd actually want is something a future version of AlphaGo to be able to invent Go, right? Invent a game as deep and as complex and elegant and as beautiful as Go, not just come up with a strategy within the game." "I don't think today's systems are yet capable of doing that, but I think they will be able to in the future." - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, on The Next Big Thing.

Karl Mehta

71,877 görüntüleme • 7 gün önce

Here's my conversation with Demis Hassabis, CEO of Google DeepMind, all about the future of AI & AGI, simulating biology & physics, video games, programming, video generation, world models, Gemini 3, scaling laws, compute, P vs NP, complexity, energy (solar & fusion), and much more. This was a fun & fascinating super-technical conversation. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:06 - Learnable patterns in nature 5:48 - Computation and P vs NP 14:26 - Veo 3 and understanding reality 18:50 - Video games 30:52 - AlphaEvolve 36:53 - AI research 41:17 - Simulating a biological organism 46:00 - Origin of life 52:15 - Path to AGI 1:03:01 - Scaling laws 1:06:17 - Compute 1:09:04 - Future of energy 1:13:00 - Human nature 1:17:54 - Google and the race to AGI 1:35:53 - Competition and AI talent 1:42:27 - Future of programming 1:48:53 - John von Neumann 1:58:07 - p(doom) 2:02:50 - Humanity 2:05:56 - Consciousness and quantum computation 2:12:06 - David Foster Wallace 2:19:20 - Education and research
2:28:14

Here's my conversation with Demis Hassabis, CEO of Google DeepMind, all about the future of AI & AGI, simulating biology & physics, video games, programming, video generation, world models, Gemini 3, scaling laws, compute, P vs NP, complexity, energy (solar & fusion), and much more. This was a fun & fascinating super-technical conversation. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:06 - Learnable patterns in nature 5:48 - Computation and P vs NP 14:26 - Veo 3 and understanding reality 18:50 - Video games 30:52 - AlphaEvolve 36:53 - AI research 41:17 - Simulating a biological organism 46:00 - Origin of life 52:15 - Path to AGI 1:03:01 - Scaling laws 1:06:17 - Compute 1:09:04 - Future of energy 1:13:00 - Human nature 1:17:54 - Google and the race to AGI 1:35:53 - Competition and AI talent 1:42:27 - Future of programming 1:48:53 - John von Neumann 1:58:07 - p(doom) 2:02:50 - Humanity 2:05:56 - Consciousness and quantum computation 2:12:06 - David Foster Wallace 2:19:20 - Education and research

Lex Fridman

1,455,273 görüntüleme • 1 yıl önce

Demis Hassabis, CEO of Google DeepMind: "Agents are the first step to AGI, and we're just getting started. One engineer with agents already does what took 1000 back in the 2000s." This is the shift you can't ignore anymore, unless you want to be left behind. 40 minutes of pure insight from a Nobel laureate on where AI is really headed. Watch it, then read the step by step guide for beginners on building agents below.
39:05

Demis Hassabis, CEO of Google DeepMind: "Agents are the first step to AGI, and we're just getting started. One engineer with agents already does what took 1000 back in the 2000s." This is the shift you can't ignore anymore, unless you want to be left behind. 40 minutes of pure insight from a Nobel laureate on where AI is really headed. Watch it, then read the step by step guide for beginners on building agents below.

Anatoli Kopadze

34,739 görüntüleme • 16 gün önce

Demis Hassabis (Demis Hassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives
40:56

Demis Hassabis (Demis Hassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives

Y Combinator

358,228 görüntüleme • 3 ay önce