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Demis Hassabis says internally, we are working on technologies much more advanced than AlphaFold 3 The next steps go beyond protein folding into full drug discovery: chemistry, compound design, toxicity, and key properties "we need several AlphaFold-like breakthroughs, not just one"

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143,984 просмотров • 11 месяцев назад •via X (Twitter)

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Google DeepMind CEO Demis Hassabis says the Nobel Prize-winning AlphaFold team are now working on the Virtual Cell Project, an AI simulation of biological cells which in 5 years will be able to aid in drug discovery and testing

Google DeepMind CEO Demis Hassabis says the Nobel Prize-winning AlphaFold team are now working on the Virtual Cell Project, an AI simulation of biological cells which in 5 years will be able to aid in drug discovery and testing

Tsarathustra

92,806 просмотров • 1 год назад

Demis Hassabis just described the exact architecture he is building to cure all human disease and said the breakthrough will not come gradually. It will hit all at once. He is building a full platform to do it. Think of it as a dozen AlphaFold-level models, each one covering a different step in the drug discovery process, all stitched together into a single engine that can be pointed at almost any disease area on earth. AlphaFold solved protein structure. It then folded 200 million proteins in a single year. Hassabis is now building the equivalent of that for every other step like protein interactions, molecular binding, toxicity prediction, drug design, and clinical stratification. Each one its own AlphaFold. All of them connected. He is already testing it on disease profiles in pre-clinical stages right now. Most biotech analysts still model drug discovery timelines in decades. McKinsey published a report estimating AI could reduce discovery timelines by 25 to 50 percent. Hassabis is not describing a 25 percent improvement. He is describing a complete replacement of the process. Do not expect a slow drip of results. Expect nothing for a few years and then a sudden explosion where everything works at once. Demis is running the same play. At a scale that makes AlphaFold look like a warm-up act. (Watch the full interview on YouTube at Two Minute Papers channel)

Demis Hassabis just described the exact architecture he is building to cure all human disease and said the breakthrough will not come gradually. It will hit all at once. He is building a full platform to do it. Think of it as a dozen AlphaFold-level models, each one covering a different step in the drug discovery process, all stitched together into a single engine that can be pointed at almost any disease area on earth. AlphaFold solved protein structure. It then folded 200 million proteins in a single year. Hassabis is now building the equivalent of that for every other step like protein interactions, molecular binding, toxicity prediction, drug design, and clinical stratification. Each one its own AlphaFold. All of them connected. He is already testing it on disease profiles in pre-clinical stages right now. Most biotech analysts still model drug discovery timelines in decades. McKinsey published a report estimating AI could reduce discovery timelines by 25 to 50 percent. Hassabis is not describing a 25 percent improvement. He is describing a complete replacement of the process. Do not expect a slow drip of results. Expect nothing for a few years and then a sudden explosion where everything works at once. Demis is running the same play. At a scale that makes AlphaFold look like a warm-up act. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

101,315 просмотров • 1 месяц назад

🚨 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:

🚨 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 просмотров • 3 месяцев назад

The year 2017 will go down in history. As the year in which the Transformer architecture enabled us to achieve scientific breakthroughs that were previously unimaginable. Demis Hassabis and colleagues were rightly awarded the Nobel Prize in Chemistry for AlphaFold. In his TED Talk, Max Jaderberg explained why this is so incredibly important and why we are at the beginning of a scientific revolution. Check it out. Link in the commentary. We cannot rate this technological breakthrough highly enough. Nothing stays the same.

The year 2017 will go down in history. As the year in which the Transformer architecture enabled us to achieve scientific breakthroughs that were previously unimaginable. Demis Hassabis and colleagues were rightly awarded the Nobel Prize in Chemistry for AlphaFold. In his TED Talk, Max Jaderberg explained why this is so incredibly important and why we are at the beginning of a scientific revolution. Check it out. Link in the commentary. We cannot rate this technological breakthrough highly enough. Nothing stays the same.

Chubby♨️

20,607 просмотров • 1 год назад

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.

Milk Road AI

36,062 просмотров • 4 месяцев назад

Demis Hassabis says AlphaFold was just the proof point for AI solving "root node" problems. "It feels like we've packed in 10 years in one year." "The big proof point was AlphaFold... that was the proof that it was possible to do these root node type of problems." "We're exploring all the other ones now. I think material science - I'd love to do a room-temperature superconductor... better batteries... better materials of all sorts." "We're also working on fusion." This is the AI shift founders keep underestimating: Not chatbots replacing search. Models becoming a discovery engine for the bottleneck problems under everything else - proteins, materials, batteries, fusion, robotics, medicine.

Demis Hassabis says AlphaFold was just the proof point for AI solving "root node" problems. "It feels like we've packed in 10 years in one year." "The big proof point was AlphaFold... that was the proof that it was possible to do these root node type of problems." "We're exploring all the other ones now. I think material science - I'd love to do a room-temperature superconductor... better batteries... better materials of all sorts." "We're also working on fusion." This is the AI shift founders keep underestimating: Not chatbots replacing search. Models becoming a discovery engine for the bottleneck problems under everything else - proteins, materials, batteries, fusion, robotics, medicine.

Karl Mehta

10,273 просмотров • 1 месяц назад

People pay $80,000 to get 26 minutes with someone who thinks at this level. You just got it for fvckn free. Demis Hassabis, co-founder of Google DeepMind, 2024 Nobel Prize winner, the man who built AlphaFold and collapsed drug discovery from 10 years to days. He said AGI is achievable by 2030 and that information is more fundamental than matter or energy. Over 200k people have already watched this, most of your competitors haven't. I can't ignore one of the fastest growing AI models after reading this article...

People pay $80,000 to get 26 minutes with someone who thinks at this level. You just got it for fvckn free. Demis Hassabis, co-founder of Google DeepMind, 2024 Nobel Prize winner, the man who built AlphaFold and collapsed drug discovery from 10 years to days. He said AGI is achievable by 2030 and that information is more fundamental than matter or energy. Over 200k people have already watched this, most of your competitors haven't. I can't ignore one of the fastest growing AI models after reading this article...

Defileo🔮

29,052 просмотров • 2 месяцев назад

Demis Hassabis says AI won’t just accelerate drug discovery. It will replace the process entirely. The pharmaceutical industry finds drugs the same way it has for decades. Synthesize a compound. Test it on animals. Test it on humans. Wait years for approval. Hope the molecule doesn’t kill someone along the way. Every step is physical. Every step is slow. Every step is expensive enough to make most diseases not worth curing. Hassabis: “We’re focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it’s not toxic, and all the different properties you need for drugs to be safe.” That sounds incremental. It isn’t. AlphaFold solved protein folding. Isomorphic Labs is now working through the rest of the chain. Compound design. Toxicity screening. Safety profiling. All computational. None of it requires a lab. Hassabis: “I think we’ll have that whole drug design engine ready in the next five to 10 years.” Not a tool that assists chemists. A system that replaces the chemistry. But designing the drug was never the bottleneck that killed people. Clinical trials were. A single drug takes over a decade to move from lab to patient. Most of that time isn’t science. It’s bureaucracy, logistics, and the blunt reality of testing molecules on living tissue one dose at a time. Hassabis: “Simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that’s suitable for their genomic makeup.” Simulate the patient before you treat the patient. Map individual DNA. Model personal metabolism. Test the drug on a digital replica before it touches a vein. Not personalized medicine as a marketing phrase. Personalized medicine as an engineering output. The final wall is regulatory. The FDA exists because humans make mistakes with molecules. Every approval gate was built to catch errors that cost lives. The entire structure assumes the process is fallible. What happens when the process stops being fallible. Hassabis: “Perhaps like the animal testing is not needed anymore, maybe we can go up the dosage ladder quicker, because you can rely on these models.” He’s not speculating. He’s describing a sequence. AI-designed drugs enter the existing pipeline. A dozen compounds go through full traditional trials. Regulators collect data. They back-test model predictions against real outcomes. Hassabis: “Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models.” When the models prove more accurate than the trials they’re meant to replace, the trials become the bottleneck. Not the science. The paperwork. Animal testing shortened. Dosage ladders compressed. Entire stages of the pipeline collapsed into computation. The drug doesn’t get discovered faster. The drug gets discovered differently. The laboratory moves from a building to a server. The clinical trial moves from a hospital ward to a simulation. The patient moves from a statistic to a genome. Hassabis isn’t promising a cure for one disease. He’s describing the architecture that makes curing disease an engineering problem with a known solution path. The bottleneck was never biology. It was the speed at which humans were allowed to solve it. That speed limit is about to be revoked.

Demis Hassabis says AI won’t just accelerate drug discovery. It will replace the process entirely. The pharmaceutical industry finds drugs the same way it has for decades. Synthesize a compound. Test it on animals. Test it on humans. Wait years for approval. Hope the molecule doesn’t kill someone along the way. Every step is physical. Every step is slow. Every step is expensive enough to make most diseases not worth curing. Hassabis: “We’re focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it’s not toxic, and all the different properties you need for drugs to be safe.” That sounds incremental. It isn’t. AlphaFold solved protein folding. Isomorphic Labs is now working through the rest of the chain. Compound design. Toxicity screening. Safety profiling. All computational. None of it requires a lab. Hassabis: “I think we’ll have that whole drug design engine ready in the next five to 10 years.” Not a tool that assists chemists. A system that replaces the chemistry. But designing the drug was never the bottleneck that killed people. Clinical trials were. A single drug takes over a decade to move from lab to patient. Most of that time isn’t science. It’s bureaucracy, logistics, and the blunt reality of testing molecules on living tissue one dose at a time. Hassabis: “Simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that’s suitable for their genomic makeup.” Simulate the patient before you treat the patient. Map individual DNA. Model personal metabolism. Test the drug on a digital replica before it touches a vein. Not personalized medicine as a marketing phrase. Personalized medicine as an engineering output. The final wall is regulatory. The FDA exists because humans make mistakes with molecules. Every approval gate was built to catch errors that cost lives. The entire structure assumes the process is fallible. What happens when the process stops being fallible. Hassabis: “Perhaps like the animal testing is not needed anymore, maybe we can go up the dosage ladder quicker, because you can rely on these models.” He’s not speculating. He’s describing a sequence. AI-designed drugs enter the existing pipeline. A dozen compounds go through full traditional trials. Regulators collect data. They back-test model predictions against real outcomes. Hassabis: “Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models.” When the models prove more accurate than the trials they’re meant to replace, the trials become the bottleneck. Not the science. The paperwork. Animal testing shortened. Dosage ladders compressed. Entire stages of the pipeline collapsed into computation. The drug doesn’t get discovered faster. The drug gets discovered differently. The laboratory moves from a building to a server. The clinical trial moves from a hospital ward to a simulation. The patient moves from a statistic to a genome. Hassabis isn’t promising a cure for one disease. He’s describing the architecture that makes curing disease an engineering problem with a known solution path. The bottleneck was never biology. It was the speed at which humans were allowed to solve it. That speed limit is about to be revoked.

Dustin

43,980 просмотров • 4 месяцев назад

#WATCH | Delhi: On new indigenous systems that DRDO is looking to develop in near future, DRDO Chairman Samir V Kamat says, "Our focus is going to be on aero-engines. Our focus is going to be on unmanned combat aerial vehicle. We are also looking at working in several deep-tech technologies which will be essential for all our systems going ahead. So, we are looking at quantum techonologies, at AIML (Artificial Intelligence and Machine Learning) technologies, advanced materials and these technologies will go into any system that we develop."

#WATCH | Delhi: On new indigenous systems that DRDO is looking to develop in near future, DRDO Chairman Samir V Kamat says, "Our focus is going to be on aero-engines. Our focus is going to be on unmanned combat aerial vehicle. We are also looking at working in several deep-tech technologies which will be essential for all our systems going ahead. So, we are looking at quantum techonologies, at AIML (Artificial Intelligence and Machine Learning) technologies, advanced materials and these technologies will go into any system that we develop."

ANI

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may have leaked 3 alphas about Alice and $ALICE on the stage at the CoinGecko event. My Neighbor Alice will much more than just a game, and we are working on creating a universe which goes beyond the core game. 1. Working on making the game available on all devices 2. Currently testing the new in-game economy. More updates on the token economy may follow 3. We believe in remixing IPs and experiences beyond gaming, with partnerships and integrations to be announced soon

may have leaked 3 alphas about Alice and $ALICE on the stage at the CoinGecko event. My Neighbor Alice will much more than just a game, and we are working on creating a universe which goes beyond the core game. 1. Working on making the game available on all devices 2. Currently testing the new in-game economy. More updates on the token economy may follow 3. We believe in remixing IPs and experiences beyond gaming, with partnerships and integrations to be announced soon

Antonio

78,832 просмотров • 1 год назад

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

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

360,550 просмотров • 3 месяцев назад

⚡️🚨IDF Chief of Staff Lt. Gen. Eyal Zamir says the military has launched an "offensive campaign" against Hezbollah that will likely last several days. "We have begun an offensive campaign against Hezbollah. We are not just on the defense, now we go on the offense," he says during an assessment following Hezbollah's rocket and drone attacks on Israel overnight. "We need to prepare for several days of fighting, many. We need strong defensive readiness and continuous offensive preparedness, in waves," he says.

⚡️🚨IDF Chief of Staff Lt. Gen. Eyal Zamir says the military has launched an "offensive campaign" against Hezbollah that will likely last several days. "We have begun an offensive campaign against Hezbollah. We are not just on the defense, now we go on the offense," he says during an assessment following Hezbollah's rocket and drone attacks on Israel overnight. "We need to prepare for several days of fighting, many. We need strong defensive readiness and continuous offensive preparedness, in waves," he says.

Middle East Observer

53,286 просмотров • 5 месяцев назад

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

Karl Mehta

46,918 просмотров • 1 месяц назад

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

Ian Miles Cheong

93,975 просмотров • 4 месяцев назад

The interview with Demis Hassabis - the tl;dr (summary) about scaling, AGI and much more: 1. Solving the "Root Node" Problems: DeepMind isn't just building chatbots; they are using AI to solve the hardest scientific problems. After the success of AlphaFold, they are now targeting materials science (room-temperature superconductors, better batteries) and even nuclear fusion to unlock unlimited clean energy. 2. The "Jagged Intelligence" Paradox: Current AI models are in a weird spot—they can win gold medals at the International Math Olympiad but still fail at basic logic puzzles. Hassabis calls this "jagged intelligence." The goal isn't just more data, but fixing these inconsistencies to make models reliable across the board. 3. Scaling is Not Dead (But it’s Changing): Despite rumors of hitting a "data wall," Hassabis says we haven't seen a hard limit yet. However, we are seeing diminishing returns. His bet? Getting to AGI will require 50% scaling and 50% architectural innovation. It’s no longer just about making the models bigger; it’s about making them smarter. 4. The Missing Piece: System 2 Thinking: Today's models are passive—they just spit out an answer. To reach AGI, we need systems that can "think" before they speak. This involves planning, reasoning, and double-checking their own work (similar to human "System 2" thinking) rather than just predicting the next word. 5. Rise of World Models: The next big frontier is "World Models" (like their project Genie). AI needs to understand the physics of the world—gravity, object permanence, and cause-and-effect—not just language. This is crucial for building helpful digital agents and robots that can navigate real-life situations. 6. Is the Universe Computable? On a philosophical level, Hassabis believes that everything in the universe might be computable. His life's work is testing the limits of the "Turing Machine." If we can build an AGI that simulates the human mind perfectly, we might finally understand what (if anything) makes human consciousness unique. 7. Bigger than the Industrial Revolution: We need to prepare for a shift that is 10x faster and bigger than the Industrial Revolution. If AI solves energy (fusion) and labor, we might enter a "post-scarcity" world. Hassabis warns that society, economics, and governments need to adapt quickly to ensure these benefits are shared by everyone, not just a few. And since this is the most important aspect, here is the clip about post labor economy:

The interview with Demis Hassabis - the tl;dr (summary) about scaling, AGI and much more: 1. Solving the "Root Node" Problems: DeepMind isn't just building chatbots; they are using AI to solve the hardest scientific problems. After the success of AlphaFold, they are now targeting materials science (room-temperature superconductors, better batteries) and even nuclear fusion to unlock unlimited clean energy. 2. The "Jagged Intelligence" Paradox: Current AI models are in a weird spot—they can win gold medals at the International Math Olympiad but still fail at basic logic puzzles. Hassabis calls this "jagged intelligence." The goal isn't just more data, but fixing these inconsistencies to make models reliable across the board. 3. Scaling is Not Dead (But it’s Changing): Despite rumors of hitting a "data wall," Hassabis says we haven't seen a hard limit yet. However, we are seeing diminishing returns. His bet? Getting to AGI will require 50% scaling and 50% architectural innovation. It’s no longer just about making the models bigger; it’s about making them smarter. 4. The Missing Piece: System 2 Thinking: Today's models are passive—they just spit out an answer. To reach AGI, we need systems that can "think" before they speak. This involves planning, reasoning, and double-checking their own work (similar to human "System 2" thinking) rather than just predicting the next word. 5. Rise of World Models: The next big frontier is "World Models" (like their project Genie). AI needs to understand the physics of the world—gravity, object permanence, and cause-and-effect—not just language. This is crucial for building helpful digital agents and robots that can navigate real-life situations. 6. Is the Universe Computable? On a philosophical level, Hassabis believes that everything in the universe might be computable. His life's work is testing the limits of the "Turing Machine." If we can build an AGI that simulates the human mind perfectly, we might finally understand what (if anything) makes human consciousness unique. 7. Bigger than the Industrial Revolution: We need to prepare for a shift that is 10x faster and bigger than the Industrial Revolution. If AI solves energy (fusion) and labor, we might enter a "post-scarcity" world. Hassabis warns that society, economics, and governments need to adapt quickly to ensure these benefits are shared by everyone, not just a few. And since this is the most important aspect, here is the clip about post labor economy:

Chubby♨️

27,473 просмотров • 8 месяцев назад

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.

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

130,875 просмотров • 4 месяцев назад

#WATCH | ANI National Security Summit 2.0: DRDO Chairman Dr Samir V Kamat says, "For short-range ballistic missiles, the Pralay is now in the final stages of testing. With respect to hypersonic, we are working on two programs, the hypersonic glide missile and the hypersonic cruise missiles. The glide missile will come out first. We should be doing the first trials fairly soon. And that is at a more advanced stage than the cruise missile. The cruise missile program has not yet been sanctioned, although we are working on the various technologies which will get into the cruise missile..." "For short-range ballistic missiles, the Pralay is now in the final stages of testing and should be ready. Then we have some of our strategic missiles, which can be converted to tactical usage for the medium range and the long range. With respect to hypersonic, we are working on two programs, the hypersonic glide missile and the hypersonic cruise missiles. The hypersonic cruise missiles is one which has a scramjet engine and it is powered during its flight. The hypersonic glide missile is a missile which uses a booster to give it initial velocity, and then it just glides without any powering. The glide missile will come out first. We should be doing the first trials fairly soon. And that is at a more advanced stage than the cruise missile. The cruise missile program has not yet been sanctioned, although we are working on the various technologies which will get into the cruise missile. Recently, we have done a scramjet propulsion for more than 1,000 seconds. So that's been a major achievement, and once the program is sanctioned, we'll convert the scramjet propulsion into a working missile system. And I think that should take about five years after the sanction."

#WATCH | ANI National Security Summit 2.0: DRDO Chairman Dr Samir V Kamat says, "For short-range ballistic missiles, the Pralay is now in the final stages of testing. With respect to hypersonic, we are working on two programs, the hypersonic glide missile and the hypersonic cruise missiles. The glide missile will come out first. We should be doing the first trials fairly soon. And that is at a more advanced stage than the cruise missile. The cruise missile program has not yet been sanctioned, although we are working on the various technologies which will get into the cruise missile..." "For short-range ballistic missiles, the Pralay is now in the final stages of testing and should be ready. Then we have some of our strategic missiles, which can be converted to tactical usage for the medium range and the long range. With respect to hypersonic, we are working on two programs, the hypersonic glide missile and the hypersonic cruise missiles. The hypersonic cruise missiles is one which has a scramjet engine and it is powered during its flight. The hypersonic glide missile is a missile which uses a booster to give it initial velocity, and then it just glides without any powering. The glide missile will come out first. We should be doing the first trials fairly soon. And that is at a more advanced stage than the cruise missile. The cruise missile program has not yet been sanctioned, although we are working on the various technologies which will get into the cruise missile. Recently, we have done a scramjet propulsion for more than 1,000 seconds. So that's been a major achievement, and once the program is sanctioned, we'll convert the scramjet propulsion into a working missile system. And I think that should take about five years after the sanction."

ANI

25,700 просмотров • 3 месяцев назад

#WATCH | Kurnool, Andhra Pradesh: DRDO Chairman Dr Samir V Kamat says, "...This is just the beginning of the journey. The synergy that this lab has achieved with other DRDO labs, industry and academia, I am sure we will reach our destination soon... We are also working on other high energy systems like high energy microwaves, electromagnetic pulse. So we are working on a number of technologies that will give us Star Wars capability. What you saw today was one of the components of Star Wars technologies..."

#WATCH | Kurnool, Andhra Pradesh: DRDO Chairman Dr Samir V Kamat says, "...This is just the beginning of the journey. The synergy that this lab has achieved with other DRDO labs, industry and academia, I am sure we will reach our destination soon... We are also working on other high energy systems like high energy microwaves, electromagnetic pulse. So we are working on a number of technologies that will give us Star Wars capability. What you saw today was one of the components of Star Wars technologies..."

ANI

34,148 просмотров • 1 год назад