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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... show more

Wes Roth

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15,090 次观看 • 5 个月前 •via X (Twitter)

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Demis Hassabis on the final frontier for AI in science 🧬 : it’s not just about solving the math, it’s about having the creativity to ask the question in the first place. While coding and math are verifiable and easier to automate, natural science requires a "missing ingredient" that we haven't fully cracked yet. "Coming up with the question in the first place or coming up with the theory or the hypothesis. I think that's much, much harder and I think that's the highest level of scientific creativity.”

Demis Hassabis on the final frontier for AI in science 🧬 : it’s not just about solving the math, it’s about having the creativity to ask the question in the first place. While coding and math are verifiable and easier to automate, natural science requires a "missing ingredient" that we haven't fully cracked yet. "Coming up with the question in the first place or coming up with the theory or the hypothesis. I think that's much, much harder and I think that's the highest level of scientific creativity.”

Chris

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Demis Hassabis disagrees with Sam Altman’s idea that we’ve already passed AGI. He believes AGI has a clear scientific meaning—not just a buzzword. True AGI should match all human cognitive abilities, not just solve tasks. That includes creating new theories, making artistic breakthroughs, and inventing like Einstein or Mozart. Today’s models, in his view, are still far from that.

Demis Hassabis disagrees with Sam Altman’s idea that we’ve already passed AGI. He believes AGI has a clear scientific meaning—not just a buzzword. True AGI should match all human cognitive abilities, not just solve tasks. That includes creating new theories, making artistic breakthroughs, and inventing like Einstein or Mozart. Today’s models, in his view, are still far from that.

Wes Roth

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“Scientific progress is becoming computable.” DeepMind CEO Demis Hassabis says AI is becoming a scientific infrastructure layer, not just a productivity tool. At Google I/O, he introduced Gemini for Science: a new set of AI systems designed to help researchers read papers, write code, and generate hypotheses faster. The real shift is that research itself is starting to scale like software.

“Scientific progress is becoming computable.” DeepMind CEO Demis Hassabis says AI is becoming a scientific infrastructure layer, not just a productivity tool. At Google I/O, he introduced Gemini for Science: a new set of AI systems designed to help researchers read papers, write code, and generate hypotheses faster. The real shift is that research itself is starting to scale like software.

Jon Hernandez

25,478 次观看 • 1 个月前

📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.

📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.

Jon Hernandez

10,541 次观看 • 7 个月前

📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.

📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.

Jon Hernandez

63,723 次观看 • 6 个月前

I’m fully with Demis Hassabis on this. STEM still matters deeply, not because humans will outcode AI, but because we need to understand, steer, and define problems at a high level. At the same time, AI is blowing the doors open for creativity. When technical barriers fall, taste, judgment, and vision become the real edge. The future belongs to people who know what to build and why, not just how. Do you think schools are preparing kids for that reality or are they still training them for a world that’s already gone?

I’m fully with Demis Hassabis on this. STEM still matters deeply, not because humans will outcode AI, but because we need to understand, steer, and define problems at a high level. At the same time, AI is blowing the doors open for creativity. When technical barriers fall, taste, judgment, and vision become the real edge. The future belongs to people who know what to build and why, not just how. Do you think schools are preparing kids for that reality or are they still training them for a world that’s already gone?

VraserX e/acc

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Demis Hassabis, CEO of Google DeepMind, drops a quiet bombshell: The big question isn’t whether AI can solve problems. It’s whether AI can invent new science. Right now, it can’t. Not because of compute. Not because of data. But because it lacks something fundamental: A world model. Today’s LLMs can generate brilliant text, images, even code. But they don’t truly understand causality. They don’t know why A leads to B. They just predict patterns. Hassabis argues that real scientific discovery requires more: – Long-term planning – Stronger reasoning – And an internal model of how the world works Physics. Biology. Cause and effect. Only then can an AI run its own thought experiments. Only then do we get a true digital scientist.

Demis Hassabis, CEO of Google DeepMind, drops a quiet bombshell: The big question isn’t whether AI can solve problems. It’s whether AI can invent new science. Right now, it can’t. Not because of compute. Not because of data. But because it lacks something fundamental: A world model. Today’s LLMs can generate brilliant text, images, even code. But they don’t truly understand causality. They don’t know why A leads to B. They just predict patterns. Hassabis argues that real scientific discovery requires more: – Long-term planning – Stronger reasoning – And an internal model of how the world works Physics. Biology. Cause and effect. Only then can an AI run its own thought experiments. Only then do we get a true digital scientist.

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Stephen C. Meyer says Darwin’s theory of evolution has, for many, become more like a religion than a scientific theory. “Darwinian evolution functions as a kind of secular religion.” “And you can see why, because it's answering one of the most fundamental questions that any worldview or religion has to answer.” “Which is what is the thing or the entity or the process from which everything else came?” “And the neo-Darwinian explanation of the origin of new forms of life is part of the answer to that really fundamental question, which is not only a scientific question but a philosophical worldview question.” “Because it functions like a secular religion, sometimes there's resistance to a more dispassionate scientific evaluation of its merits.” Stephen C. Meyer

Stephen C. Meyer says Darwin’s theory of evolution has, for many, become more like a religion than a scientific theory. “Darwinian evolution functions as a kind of secular religion.” “And you can see why, because it's answering one of the most fundamental questions that any worldview or religion has to answer.” “Which is what is the thing or the entity or the process from which everything else came?” “And the neo-Darwinian explanation of the origin of new forms of life is part of the answer to that really fundamental question, which is not only a scientific question but a philosophical worldview question.” “Because it functions like a secular religion, sometimes there's resistance to a more dispassionate scientific evaluation of its merits.” Stephen C. Meyer

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"It’s not AI; it’s AC; Artificial Cleverness!" Sir Roger Penrose explains why current "Artificial Intelligence" is missing the most critical ingredient: consciousness. By delving into the limits of mathematical computability, he argues that while computers are masters of "computational mathematics," true intelligence requires a conscious understanding that exists beyond any algorithm. At the fundamental level, what we call AI is just high-speed calculation. To Penrose, intelligence and consciousness are inseparable; and until a machine can "understand," it’s simply being clever.

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As AI systems grow more capable, it is “not hypothetical” that AI could potentially abet biological weapons development, says Johns Hopkins professor and adviser to Anthropic, Ben Buchanan, calling it a “legitimate concern.” “They can do expert tasks in a wide variety of areas, not just in cyber, also in bio and the like,” Buchanan says. “There’s upside. AI can do a lot for medical discovery, but there’s a legitimate concern that AI is outperforming PhD-level virologists on virology questions.”

As AI systems grow more capable, it is “not hypothetical” that AI could potentially abet biological weapons development, says Johns Hopkins professor and adviser to Anthropic, Ben Buchanan, calling it a “legitimate concern.” “They can do expert tasks in a wide variety of areas, not just in cyber, also in bio and the like,” Buchanan says. “There’s upside. AI can do a lot for medical discovery, but there’s a legitimate concern that AI is outperforming PhD-level virologists on virology questions.”

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VraserX e/acc

77,489 次观看 • 6 个月前

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.

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Milk Road AI

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Demis Hassabis: it's “nonsense” to claim that today's AI chatbots have PhD intelligence “And maybe scaling will get us there, but if I was to bet, I think there are probably one or two missing breakthroughs that are still required and will come over the next five or so years.” Demis Hassabis at the All-In Summit: “You often hear some of our competitors talk about these modern systems that we have today are PhD intelligences.” “I think that's nonsense. They're not PhD intelligences.” “They have some capabilities that are PhD level, but they're not, in general, capable. And that's exactly what general intelligence should be (capable) of, of performing across the board at the PhD level.” “In fact, as we all know, interacting with today's chatbots, if you pose the question in a certain way, they can make simple mistakes with even high school maths and simple counting.” “That shouldn't be possible for a true AGI system.” “So I think that we are maybe, I would say sort of 5-10 years away from having an AGI system that's capable of doing those things.” “Another thing that's missing is continual learning, this ability to like online teach the system something new or adjust its behavior in some way.” “And so a lot of these, I think, core capabilities are still missing.”

Demis Hassabis: it's “nonsense” to claim that today's AI chatbots have PhD intelligence “And maybe scaling will get us there, but if I was to bet, I think there are probably one or two missing breakthroughs that are still required and will come over the next five or so years.” Demis Hassabis at the All-In Summit: “You often hear some of our competitors talk about these modern systems that we have today are PhD intelligences.” “I think that's nonsense. They're not PhD intelligences.” “They have some capabilities that are PhD level, but they're not, in general, capable. And that's exactly what general intelligence should be (capable) of, of performing across the board at the PhD level.” “In fact, as we all know, interacting with today's chatbots, if you pose the question in a certain way, they can make simple mistakes with even high school maths and simple counting.” “That shouldn't be possible for a true AGI system.” “So I think that we are maybe, I would say sort of 5-10 years away from having an AGI system that's capable of doing those things.” “Another thing that's missing is continual learning, this ability to like online teach the system something new or adjust its behavior in some way.” “And so a lot of these, I think, core capabilities are still missing.”

The All-In Podcast

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Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Karl Mehta

14,946 次观看 • 10 天前

I've seen a lot of AI coding tools, but most are just copilots in a sidebar or in a black box form like Claude code. This one is different. I recently came across the new TRAE SOLO, and it’s not an assistant... It's more like a full AI engineer who plans and executes entire projects. The key point is that it has a GUI, which allows the development process to be clearly visible.

I've seen a lot of AI coding tools, but most are just copilots in a sidebar or in a black box form like Claude code. This one is different. I recently came across the new TRAE SOLO, and it’s not an assistant... It's more like a full AI engineer who plans and executes entire projects. The key point is that it has a GUI, which allows the development process to be clearly visible.

Francesco Ciulla

75,035 次观看 • 8 个月前

.Naval: The easiest way to see that AI is not taking jobs but creating opportunities is to go learn AI and then reapply for the job that rejected you. Watch how they pull you in. “My last startup, Airchat, could not have existed without AI because we needed the transcription/translation. And even the current thing I’m working on—it’s not an AI company, but it cannot exist without AI. It is relying on AI. Even at AngelList, we’re significantly adopting AI. Like everywhere you turn, it’s more opportunity, more opportunity, more opportunity. And people like to go on Twitter—or the artist formerly known as Twitter—and basically, they like to exaggerate. Like, ‘Oh my God, we’ve hit AGI.’ ‘Oh my God, I just replaced all my mid-level engineers.’ ‘Oh my God, I’ve stopped hiring.’ To me, that’s moronic. The two valid perspectives are the one-man entrepreneur shows, where there’s one guy or one gal, and they’re scaling up like crazy thanks to AI. Or there are people who are embracing AI and being like, ‘I need to hire, and I need to hire anyone who can even spell ‘AI’—anyone who’s even used AI. Just come on in, come on in.’ Again, I would say the easiest way to see that AI is not taking jobs but creating opportunities is this: go brush up on your AI, learn a little bit, watch a few videos, use the AI, tinker with it, and then go reapply for that job that rejected you. Watch how they pull you in.”

.Naval: The easiest way to see that AI is not taking jobs but creating opportunities is to go learn AI and then reapply for the job that rejected you. Watch how they pull you in. “My last startup, Airchat, could not have existed without AI because we needed the transcription/translation. And even the current thing I’m working on—it’s not an AI company, but it cannot exist without AI. It is relying on AI. Even at AngelList, we’re significantly adopting AI. Like everywhere you turn, it’s more opportunity, more opportunity, more opportunity. And people like to go on Twitter—or the artist formerly known as Twitter—and basically, they like to exaggerate. Like, ‘Oh my God, we’ve hit AGI.’ ‘Oh my God, I just replaced all my mid-level engineers.’ ‘Oh my God, I’ve stopped hiring.’ To me, that’s moronic. The two valid perspectives are the one-man entrepreneur shows, where there’s one guy or one gal, and they’re scaling up like crazy thanks to AI. Or there are people who are embracing AI and being like, ‘I need to hire, and I need to hire anyone who can even spell ‘AI’—anyone who’s even used AI. Just come on in, come on in.’ Again, I would say the easiest way to see that AI is not taking jobs but creating opportunities is this: go brush up on your AI, learn a little bit, watch a few videos, use the AI, tinker with it, and then go reapply for that job that rejected you. Watch how they pull you in.”

Arjun Khemani

167,733 次观看 • 1 年前

Jensen Huang recently said he wants his engineers to spend 0% of their time coding. At first, it sounds absurd — until you realize what he really means. Coding is becoming automated. The new value isn’t in typing instructions for machines… …it’s in teaching machines what’s worth building. Jensen’s message is clear: The future belongs to engineers who understand systems, context, and creativity — not just syntax. As AI takes over execution, human intelligence must rise to orchestration. It’s not about writing code anymore. It’s about writing the future. What do you think — will the best engineers of 2026 still code, or will they design intelligence itself? #ArtificialIntelligence #AI #Coding #Technology #FutureOfWork #Leadership #Innovation #Automation #MachineLearning #AIandHumanity

Jensen Huang recently said he wants his engineers to spend 0% of their time coding. At first, it sounds absurd — until you realize what he really means. Coding is becoming automated. The new value isn’t in typing instructions for machines… …it’s in teaching machines what’s worth building. Jensen’s message is clear: The future belongs to engineers who understand systems, context, and creativity — not just syntax. As AI takes over execution, human intelligence must rise to orchestration. It’s not about writing code anymore. It’s about writing the future. What do you think — will the best engineers of 2026 still code, or will they design intelligence itself? #ArtificialIntelligence #AI #Coding #Technology #FutureOfWork #Leadership #Innovation #Automation #MachineLearning #AIandHumanity

Pascal Bornet

65,454 次观看 • 5 个月前

Greg Brockman says the long-term future is clear: AI will run on its own computer, manage fleets of agents, and solve tasks in parallel. Brockman imagines a workflow where you review your agent’s work in the morning, guiding it like a partner, but models are not yet smart enough for that. He explains that today’s reality is AI helping directly in the terminal or editor, making coding workflows familiar but more efficient.

Greg Brockman says the long-term future is clear: AI will run on its own computer, manage fleets of agents, and solve tasks in parallel. Brockman imagines a workflow where you review your agent’s work in the morning, guiding it like a partner, but models are not yet smart enough for that. He explains that today’s reality is AI helping directly in the terminal or editor, making coding workflows familiar but more efficient.

Wes Roth

39,230 次观看 • 10 个月前

Sir Roger Penrose argues that current AI is a misnomer because it lacks the one ingredient essential for true intelligence: Understanding. By applying the lens of mathematical logic, he explains that while machines are masters of following rules, the ability to understand why those rules work is a non-computational process. To Penrose, the leap from calculation to comprehension requires more than just better code; it requires Consciousness. At the fundamental level, intelligence isn't just data processing; it's the conscious awareness that gives meaning to the math. Credit: TheInstituteOfArtAndIdeas

Sir Roger Penrose argues that current AI is a misnomer because it lacks the one ingredient essential for true intelligence: Understanding. By applying the lens of mathematical logic, he explains that while machines are masters of following rules, the ability to understand why those rules work is a non-computational process. To Penrose, the leap from calculation to comprehension requires more than just better code; it requires Consciousness. At the fundamental level, intelligence isn't just data processing; it's the conscious awareness that gives meaning to the math. Credit: TheInstituteOfArtAndIdeas

Cosmos Archive

75,631 次观看 • 4 个月前

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.

Rohan Paul

84,464 次观看 • 7 个月前