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"Just like maths...was the perfect description language for physics, I think that AI is potentially the perfect description language for biology." Demis Hassabis envisions a new era of "digital biology" where AI helps us understand life's complex information processing. His dream? "Virtual cells" for faster, more efficient scientific discovery.

vitrupo

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28,845 次观看 • 1 年前 •via X (Twitter)

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11 条评论

vitrupo 的头像

vitrupo1 年前

Sir Demis Hassabis gives a lecture at Cambridge University:

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Yang1 年前

Want to learn how practical AI skills and automations for your business and work? Check out our 50+ step-by-step video tutorials 100% FREE 20+ hours of Ai and Automation goodness absolutely free 🥳

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O- Age 📦1 年前

@macacolibertaro everything is computer, everything is language.

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Lilith Datura1 年前

We can already do this.

cosmichaos 的头像

cosmichaos1 年前

Perfect way of thinking. The wisest way actually. Every wise person will eventually make something important for the world it doesn't matter when.

Robert Cincotta 的头像

Robert Cincotta1 年前

Can’t happen quickly enough! We have to think differently and be open to the possibilities .Just seeing how quickly AI has developed over the past year is thrilling and it’s only just scratched being applied to other fields like biology and medicine

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Desmond Wood1 年前

Don’t you just love Demis Hassabis! Such a brilliant and humble human being. 4o images are epic now

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富1 年前

my first thought was then what has David Baker been doing for year's

Joseph McCard 的头像

Joseph McCard1 年前

Does it help us understand what consciousness and life, even biological life itself are. It certainly has not helped Mike Levin. I have not seen it. 🤔

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Keshav Kumbham1 年前

Interesting!

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Ahmet MALKOCOGLU1 年前

Mükemmel...

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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.

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25,478 次观看 • 3 个月前

Demis Hassabis explains Isomorphic Labs' 10x drug discovery engine, and why solving disease could also create a multi-hundred-billion-dollar business "If you ask me, the number one thing AI can do for humanity, it would be to solve hundreds of terrible diseases. I can't imagine a better use case for AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV

Demis Hassabis explains Isomorphic Labs' 10x drug discovery engine, and why solving disease could also create a multi-hundred-billion-dollar business "If you ask me, the number one thing AI can do for humanity, it would be to solve hundreds of terrible diseases. I can't imagine a better use case for AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV

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Patrick Collison says humanity has never cured a complex disease. Not cancer. Not Alzheimer’s. Not Type 1 diabetes. His Arc Institute is trying something new: Simulate biology with AI. Test interventions before touching the body. Build a virtual cell. Test hypotheses in code. If it works, biology becomes computable.

Patrick Collison says humanity has never cured a complex disease. Not cancer. Not Alzheimer’s. Not Type 1 diabetes. His Arc Institute is trying something new: Simulate biology with AI. Test interventions before touching the body. Build a virtual cell. Test hypotheses in code. If it works, biology becomes computable.

vitrupo

1,096,611 次观看 • 1 年前

Demis Hassabis thinks mathematics has a ceiling. He thinks biology is where we hit it. Hassabis: “Machine learning is the perfect description language for biology in the same way maths is for physics.” He isn’t calling AI a tool. He’s calling it a language. For four hundred years we only had one. Newton wrote gravity in it. Maxwell wrote light. Einstein wrote spacetime itself. Every law we ever pulled out of nature came back in equations, and we decided that meant nature was written in equations. It didn’t mean that. It meant the parts that surrendered first were small enough to fit the language we already spoke. Hassabis: “The expressive power of maths is not enough to understand these highly emergent dynamical systems.” Math can put a planet’s orbit on a single line. It cannot put one protein on a page. Same universe, same laws, and one fits our notation while the other refuses. Weak signals buried under noise, correlations stacked on correlations, more moving parts turning at once than any mind can hold. Biology isn’t harder than physics. It’s more expressive than the tool we brought to it. That was never a gap in our knowledge. It was a wall in our vocabulary. Hassabis is working on the far side of that wall. He calls it a virtual cell. A running simulation of a living system that no equation could ever contain. Hassabis: “Once you learn these simulators, you could maybe extract some equations from that.” He isn’t replacing math. He’s going after math we were never going to reach on our own. The model learns the system the way you learn to catch a ball, without ever solving the equation in the air. Understanding first. Formula after. That reverses the order science has followed since Newton. We started with the equation and used it to predict the system. He starts with the system and pulls the equation out of it. What if the deepest laws of life were always there, fully written, in a language we never learned to read. Math was never the language of the universe. It was the first one we spoke. Every disease we failed to cure is a sentence in that language, sitting in the open, waiting on a reader. Every person we lost to one died on the wrong side of a translation gap. The cell was never silent. We were illiterate. Hassabis isn’t building a better microscope. He’s building a second language for reality. And the first thing it’s learning to say is life.

Demis Hassabis thinks mathematics has a ceiling. He thinks biology is where we hit it. Hassabis: “Machine learning is the perfect description language for biology in the same way maths is for physics.” He isn’t calling AI a tool. He’s calling it a language. For four hundred years we only had one. Newton wrote gravity in it. Maxwell wrote light. Einstein wrote spacetime itself. Every law we ever pulled out of nature came back in equations, and we decided that meant nature was written in equations. It didn’t mean that. It meant the parts that surrendered first were small enough to fit the language we already spoke. Hassabis: “The expressive power of maths is not enough to understand these highly emergent dynamical systems.” Math can put a planet’s orbit on a single line. It cannot put one protein on a page. Same universe, same laws, and one fits our notation while the other refuses. Weak signals buried under noise, correlations stacked on correlations, more moving parts turning at once than any mind can hold. Biology isn’t harder than physics. It’s more expressive than the tool we brought to it. That was never a gap in our knowledge. It was a wall in our vocabulary. Hassabis is working on the far side of that wall. He calls it a virtual cell. A running simulation of a living system that no equation could ever contain. Hassabis: “Once you learn these simulators, you could maybe extract some equations from that.” He isn’t replacing math. He’s going after math we were never going to reach on our own. The model learns the system the way you learn to catch a ball, without ever solving the equation in the air. Understanding first. Formula after. That reverses the order science has followed since Newton. We started with the equation and used it to predict the system. He starts with the system and pulls the equation out of it. What if the deepest laws of life were always there, fully written, in a language we never learned to read. Math was never the language of the universe. It was the first one we spoke. Every disease we failed to cure is a sentence in that language, sitting in the open, waiting on a reader. Every person we lost to one died on the wrong side of a translation gap. The cell was never silent. We were illiterate. Hassabis isn’t building a better microscope. He’s building a second language for reality. And the first thing it’s learning to say is life.

Dustin

17,649 次观看 • 1 个月前

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."

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 次观看 • 1 年前

Demis Hassabis looks to history to steer the future. Oppenheimer and von Neumann warned of technologies that outpaced our ability to govern them. Now he's calling for new institutions: > A new kind of CERN > An IAEA for AI - to monitor rogue development > A “technical UN” - a wise council that represents the world

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vitrupo

54,804 次观看 • 1 年前

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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VraserX e/acc

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This thread reminded me of Thiel's take on biology and the excuses of biologists for why we aren't solving hard problems (I have a biology background and work as a scientist and still agree with him). My take is that AI won't solve biology, but it will help us understand it at a deeper level. Saying that it is impossible is a lazy take. Saying that AI will solve all problems is a lazy take too. There is an in-between where AI will support and help scientists move things forward. I am really tired of the excuses of the gatekeeper boomer scientists and the hype of the AI bros.

This thread reminded me of Thiel's take on biology and the excuses of biologists for why we aren't solving hard problems (I have a biology background and work as a scientist and still agree with him). My take is that AI won't solve biology, but it will help us understand it at a deeper level. Saying that it is impossible is a lazy take. Saying that AI will solve all problems is a lazy take too. There is an in-between where AI will support and help scientists move things forward. I am really tired of the excuses of the gatekeeper boomer scientists and the hype of the AI bros.

Linden

67,241 次观看 • 2 个月前

The smartest physicist quit physics to build AI. Here’s why that matters. In a recent interview, Demis Hassabis lays out one of the clearest origin stories for modern AI I’ve heard. He starts as a physics kid obsessed with the big stuff: reality, consciousness, why anything exists at all. Feynman was his hero. But then comes the uncomfortable realization: for decades, fundamental physics barely moved. The deepest questions stayed stuck. So he pivots. Not away from truth, but toward leverage. If the human brain is the bottleneck, don’t push harder on the same brain. Build a new one. AI, in his framing, isn’t a product. It’s the ultimate scientific instrument. A tool to smash through the ceiling that human cognition hit in physics, biology, and beyond. And the twist is beautiful: By trying to build intelligence, we’re forced to understand our own. AI becomes both microscope and mirror. This isn’t about apps or chatbots. It’s about finishing the questions physics couldn’t.

The smartest physicist quit physics to build AI. Here’s why that matters. In a recent interview, Demis Hassabis lays out one of the clearest origin stories for modern AI I’ve heard. He starts as a physics kid obsessed with the big stuff: reality, consciousness, why anything exists at all. Feynman was his hero. But then comes the uncomfortable realization: for decades, fundamental physics barely moved. The deepest questions stayed stuck. So he pivots. Not away from truth, but toward leverage. If the human brain is the bottleneck, don’t push harder on the same brain. Build a new one. AI, in his framing, isn’t a product. It’s the ultimate scientific instrument. A tool to smash through the ceiling that human cognition hit in physics, biology, and beyond. And the twist is beautiful: By trying to build intelligence, we’re forced to understand our own. AI becomes both microscope and mirror. This isn’t about apps or chatbots. It’s about finishing the questions physics couldn’t.

VraserX e/acc

77,489 次观看 • 8 个月前

"I think we can use AI as part of the solution if we're clever." Today Demis Hassabis was awarded the Nobel Prize in Chemistry 2024 for developing an AI model that can predicting proteins’ complex structures. He joined us earlier this year at a Nobel Prize Dialogue event and spoke about the opportunities of AI. Watch the full conversation:

"I think we can use AI as part of the solution if we're clever." Today Demis Hassabis was awarded the Nobel Prize in Chemistry 2024 for developing an AI model that can predicting proteins’ complex structures. He joined us earlier this year at a Nobel Prize Dialogue event and spoke about the opportunities of AI. Watch the full conversation:

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85,025 次观看 • 1 年前

The Bitter Lesson for Biology — Adam Green on Scaling Laws for Virtual Cells In this episode, the founder of Markov Biosciences, Adam Green, explains the "bitter lesson" for biology, the idea borrowed from Richard Sutton that large unbiased datasets and the right training objective tend to outcompete models with hard-coded rules and human priors. We talk a lot about virtual cells, sources of bias in data, and his evidence that virtual cells pre-trained on plain observational data show clean scaling laws, getting monotonically better at predicting unseen perturbations as the models grow, and beating a state-of-the-art model built specifically for that task. Our interview was about 3 hours long, but cut to 1.5 hours. My main goal was to understand Adam's perspective on virtual cells and his goal to "solve biology." Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts. Timestamps: 00:00 — Cold open 01:58 — Clinical predictions from a virtual cell 05:38 — What is a "virtual cell"? 08:01 — The problems with single-cell RNA-seq 11:31 — The urns analogy 19:54 — Observational vs. perturbation 23:29 — The bitter lesson for biology 29:06 — Geometric Plackett-Luce 38:27 — Ablations and loss function 47:23 — Cells as specimens 59:26 — Antibody-Drug conjugates 1:11:16 — Will we ever understand the cell fully?

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Niko McCarty.

28,208 次观看 • 3 个月前

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CRISPR pioneer George Church says AI is letting us steer evolution, and biology is becoming a kind of computer. “Evolution might incorporate a few base pair changes in a million years. Now we can make billions of changes in an afternoon.” AI designs the possibilities. Biology executes them. No simulation. No approximation. Just real matter evolving on command.

vitrupo

46,674 次观看 • 1 年前

“In the next 10 years we are going to be entering what I feel like is a new renaissance.” Nobel Prize laureate Demis Hassabis was awarded the Nobel Prize for using AI to predict the structure of proteins. For him, AI is a tool that will help scientists make even more discoveries in the years to come. Hassabis took part in our Nobel Prize Dialogue ‘The Future of Science With AI’ which discussed how AI might transform science in the future. Watch the full event at

“In the next 10 years we are going to be entering what I feel like is a new renaissance.” Nobel Prize laureate Demis Hassabis was awarded the Nobel Prize for using AI to predict the structure of proteins. For him, AI is a tool that will help scientists make even more discoveries in the years to come. Hassabis took part in our Nobel Prize Dialogue ‘The Future of Science With AI’ which discussed how AI might transform science in the future. Watch the full event at

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157,113 次观看 • 3 个月前