Demis Hassabis

AI拉呱：专注于人工智能与前沿技术: 诺奖之后的终极使命：AI 真的能在 10 年内终结人类疾病吗？

No topics, substantive exchanges, or attributable remarks are supplied for this appearance, and the corpus includes an apparent duplicate entry. Skip unless fuller source material becomes available.

The transcript contains no usable speech from Demis Hassabis. No exchange can be selected as a substantive, self-contained pull quote.

AI拉呱：专注于人工智能与前沿技术: 诺奖之后的终极使命：AI 真的能在 10 年内终结人类疾病吗？

Skip: the digest contains no topics, substantive exchanges, or attributable quotes, and appears to duplicate appearance 1780. The disease-curing premise is covered far more substantially elsewhere in the corpus.

The transcript contains only two brief chunks and does not identify or include substantive speech from Demis Hassabis. No usable exchange can be selected.

跨国串门儿计划: #630.人工智能的未来：与哈萨比斯爵士和温迪·霍尔女爵士对话

No topics, substantive exchanges, or usable quotes are captured for this appearance, so there is nothing here to distinguish it from the extensive corpus. Skip unless the underlying episode is later indexed in more detail.

The provided transcript contains no clearly intelligible substantive contribution attributable to Demis Hassabis. No reliable exchange ranges can be selected.

Thrilling Threads - Conspiracy Theories, Strange Phenomena, True Crime, Unsolved Mysteries, etc!: Is AI Learning to Lie? DeepMind’s Warning on the 3-Year AGI Countdown

Skip: despite the provocative title, the transcript contains no usable contribution from Demis Hassabis. It adds nothing to a corpus already rich in his direct comments on AGI timelines and AI safety.

The transcript consists of host narration and discussion about claims attributed to Demis Hassabis and other AI researchers. It does not contain a directly identifiable substantive speech contribution from Hassabis.

View From The Top: An AI@GSB Special: Demis Hassabis Thinks We’re in the ‘Foothills of the Singularity’

This is largely familiar singularity, regulation, and student-advice material, without a usable substantive explanation of the headline-making claim. Skip unless you specifically want a brief, optimistic commencement-style message.

Right? And the world's sort of your oyster really. And I kind of envy some of you now because you're the first generation that will be AI native, just like my generation was computer and Internet native. And it's going to be in your hands in the end. The students in the room like how that future world gets built. And I think it's a very exciting time if you think about it in the right way for from the right angle and with a lot of imagination and creativity. But I think that's always been true and maybe it's more so now in changes of periods of enormous change like this that accentuates it.

Demis Hassabis argues that AGI may arrive within a few years and that society is approaching a profoundly transformative era whose direction is still undecided. He calls for broad social preparation, adaptive regulation, equitable distribution of benefits, and greater student agency in shaping an AI-native future.

Rowan's Notes: Demis Hassabis on AGI by 2030, Curing Every Disease, Life After AGI, and More

The unusually precise 2030 plus-or-minus-one-year forecast is the clearest update, but most of the discussion revisits familiar material on AGI gaps, AI-assisted drug discovery, glasses, and agent safety. Worth sampling rather than prioritizing if you have followed his recent appearances.

Omni, for example, versus three years ago when when Gemini was first released. Yeah. How have your AGI timelines kind of shifted? Have they, has it gone according to plan or has it gone faster, or slower? I think it's going according to plan. So a few years ago you probably saw maybe we even talked about in interviews I used to say 5 to 10 years, maybe that's 2 or 3 years ago. I think we're sort of exactly on track. And, and I would say my confidence interval is narrowed. So, you know, these days I'm thinking it's 2030 plus or minus a year.

Hassabis presents 2030 as his current AGI target and describes remaining obstacles in reliability, memory, continual learning, reasoning, and planning. He also discusses AI-enabled drug discovery, universal digital assistants, broad access to AI, and the safety and governance requirements of the coming agentic era.

City Arts & Lectures: Sir Demis Hassabis and Sebastian Mallaby

A worthwhile biographical detour for fans interested in Hassabis’s unusually driven childhood, but it offers little fresh AI substance. Listen for the personal material; skip if you primarily want technical insight or news.

I think I'm not sure I'd recommend it for the parents in the audience. I think it can go many different ways from there. I mean, what I will tell you is I had a very unusual childhood, as the book chronicles. I mean, put it this way, I've never had a summer holiday ever. So I would. No, ever.

Demis Hassabis reflects on the private, mission-driven path that took him from an unusually intense childhood to AI research and leadership. He also frames AI as a technology of world-changing consequence while describing the personal motivations behind his work.

Business German Podcast: AI und AGI

The nested-learning and surprise-gating material is unusually specific relative to the corpus, but this appears to be a heavily garbled German-language recap rather than a clear firsthand Hassabis interview. Worth sampling only for those technical concepts; otherwise skip in favor of a cleaner appearance.

Es gibed ein Sogenante's surprise. Gating. Das ist wie eine ad filter Nur wicklich neue uberaschen de informationen werden in die Langfristigen ge wichte des Models uber nomen aber v mist eine KA I Oberachtent ist uber ein ubertematesches Signal. Das el Zwei norm velar signal. Wendy Vorher. Herr Sagitther K I stag von der neuen Information Abweicht Is der Feeler Gross und noss was neues das Musigmir dauer haft Merken. Wow. Yeah.

The interview discusses Google's AI strategy, continuous learning architectures, AGI evaluation, and the difficulty of distinguishing genuine transfer from benchmark memorization. It also balances safety work such as adversarial red teaming with potential scientific and medical benefits from systems including AlphaFold and AlphaGenome.

Business German Podcast: AI und AGI

Skip: this entry contains no documented topics, stump material, or notable exchanges, so there is nothing substantive to evaluate against the corpus. A separate entry for the same episode contains the actual discussion.

The transcript contains no substantive speech from Demis Hassabis. He is referenced by the host, but there is no usable exchange attributable to him.

AI Podcast Summaries from Transcripted.ai (VIDEO): AGI, Robots & Elon’s Mars Bet — Moonshots With Peter Diamandis (Condensed)

Demis Hassabis and Peter Diamandis discuss AGI, robotics, governance, AI-lab incentives, automation, longevity, synthetic biology, de-extinction, and international policy.

Moonshots with Peter Diamandis: Demis Hassabis on AGI, Robots Scale Production, and Elon’s $1T Mars-Shot Comp | EP #253

This is among the most novel appearances in the corpus, spanning resurrection through technology, GLP-1-driven longevity, post-humanoid robotics, and a concrete 2028 compute crunch. Several claims diverge sharply from Hassabis’s positions elsewhere—especially that general intelligence was effectively discovered by 2020—so speaker attribution should be verified before publication.

>> I I think there's there's a broader program here that we're just seeing the very beginning of it. If you look back at the There were strains I've spoken about this on the pod in the past of Russian Cosmism. The Russian Cosmist philosophers like Tsiolkovsky spoke about humanity's common task of taking every human who's ever lived and finding ways using technology to bring them back to life. And I think starting with extinct species is just a special case of what technology I I do think will enable us to do, which is to reach back into our past light cone, take every species that's ever lived, bring it back in whatever form, even

Demis Hassabis contributes substantive perspectives on robot scaling, AI's near-term infrastructure constraints, the AI arms race, AGI, longevity medicine, and de-extinction. His strongest standalone exchanges combine concrete forecasts with broader claims about how advanced AI and biotechnology could reshape science, markets, and civilization.

Training Data: Demis Hassabis on Building DeepMind, AlphaFold, and the Final Stretch to AGI

Much of the career story, AlphaFold pitch, and drug-discovery forecast overlaps Hassabis’s other appearances, but the discussion of simulations as experimental infrastructure for economics and biology is unusually concrete. Worth hearing for that argument and his tool-first framing of AGI, rather than for the familiar biography.

think simulations is the way we can address some of the um what we maybe think of social sciences uh like economics um and and other more humanistic subjects because um it's very difficult to do control studies in that, you know, why aren't they just sciences like physics today? Because the problem is they're emergent systems um just like biology, actually. And it's very hard to do repeated controlled experiments. You know, if you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can't run it thousands of times. But, if you could simulate things uh really accurately, then maybe there's sort of new sciences to be done where you can sort of uh rigorously sample uh from a very accurate simulator.

Demis Hassabis discusses the experiences and ideas that led him to found DeepMind, including games, neuroscience, reinforcement learning, and accelerated computing. He focuses on AI for science, especially drug discovery and simulation, and outlines a cautious progression from useful AI tools toward AGI and questions about consciousness.

跨国串门儿计划: #514.DeepMind创始人Demis Hassabis谈AGI、AlphaFold与科学发现的未来

The supplied digest contains no topics, substantive exchanges, or quote candidates, so there is nothing here to distinguish it from the extensive corpus on AGI, AlphaFold, and scientific discovery. Skip unless the source is later indexed with usable material.

The transcript contains only fragmentary, largely unintelligible text and does not identify any usable substantive contribution from Demis Hassabis. No self-contained exchange can be selected.

Build Wiz AI Show: Demis Hassabis on the Roadmap to General Intelligence

Skip: the supplied digest contains no substantive topics or notable exchanges to distinguish this appearance from Hassabis’s many AGI interviews. There is no usable evidence of unique material or even a quotable contribution.

The transcript contains discussion about Demis Hassabis and summaries of his views, but no direct or clearly attributable speech from him. Therefore, there are no usable pull-quote exchanges for the tracked person.

Y Combinator Startup Podcast: How to Build the Future: Demis Hassabis

A solid, startup-oriented conversation, but the AGI timeline and missing-capabilities discussion substantially repeat recent appearances. Worth sampling for the creative-tools speculation and advice on planning a deep-tech journey around possible AGI, rather than hearing end to end.

Then the answer would be there's nothing missing. It just was the way we were using the systems. And that might actually be the answer. It might be that today systems are capable of that. With a brilliant enough creative person using it and providing that impetus that the soul of the project and being able to probably being au fait enough with the tools to like almost be at one with the tools. I could imagine that would be happening if you experimented with the tools all day and all night, like probably many of you are doing that and you combine that with proper deep creativity, something more incredible could be done.

Demis Hassabis discusses the remaining technical challenges on the path to AGI, especially continual learning, memory, reasoning, and agentic behavior. He also reflects on creativity, persistence, and the importance of choosing deep, consequential problems that can withstand major advances in AI.

David的AI全景图: DeepMind CEO Demis Hassabis最新访谈

Skip: the supplied digest contains no topics, substantive exchanges, or excerpt candidates, so there is nothing to distinguish this appearance from the much richer corpus.

The transcript contains no usable substantive contribution from Demis Hassabis. The available chunks are too fragmentary to support a self-contained exchange.

Intelligence Squared: Demis Hassabis and Sebastian Mallaby on The Quest for Artificial General Intelligence (Part Two)

Worth hearing for a comparatively specific glimpse of DeepMind’s architectural experiments: adding AlphaGo-style planning and Monte Carlo tree search on top of transformers. It advances beyond Hassabis’s familiar list of missing AGI capabilities, though the claim of promising results remains vague.

And in terms of the architecture, you talk about Transformers and you talk about, that's 50, 50 in terms of the two different approaches, do you sense you and the team getting closer to an architecture that you're feeling more confident? Could be that pivot point? Yeah, I mean, it wouldn't be so much like, I mean, there could be something out there that's even better than Transformers, but I think that's unlikely. But I'm talking about additional things on top of the system, maybe with the thinking perhaps bringing in more AlphaGo ideas around planning Monte Carlo tree search. So we're trying out lots of these ideas and this, I would say we have a lot of promising results. But we, you know, we've got to see how that unfolds over the next year.

Hassabis discusses quantum computing, the continued importance of current AI systems, and possible additions to transformer-based architectures on the route to AGI. The strongest short exchanges emphasize reciprocal acceleration between quantum computing and AGI, alongside planning as a promising architectural direction.

Intelligence Squared: Demis Hassabis and Sebastian Mallaby on The Quest for Artificial General Intelligence (Part One)

A notably candid piece of institutional history: Hassabis says withholding Google’s early LaMDA product was, in retrospect, probably the wrong decision and explains the reliability concerns behind it. This stands apart from his more familiar AGI forecasts and Google-competitiveness material.

Well, look, actually the story is pretty complicated in the sense that the leading labs at the time, us included, and actually both DeepMind and the Google Lab, Google Brain, had large language models. In fact, the Google Brain side of it had a product, a very early one, it was called Lambda in those days. And there'd been a conscious decision not to release that. And it was obviously, in retrospect, probably the wrong decision, but you can sort of see. And I wasn't involved in that because we were at the time just supposed to be working on research.

Demis Hassabis discusses why Google was cautious about releasing early language models, given their tendency to produce incorrect information. He says ChatGPT’s rapid adoption revealed broad, creative uses for imperfect systems and showed that the technology was ready to reach the public.

AI Podcast Summaries from Transcripted.ai (VIDEO): Demis Hassabis on AGI, Safety & Scaling — 20VC (Condensed)

Demis Hassabis discusses AGI, scaling, continual learning, AI safety and governance, drug discovery, energy, jobs, and the future impact of advanced AI.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch: 20VC: DeepMind's Demis Hassabis on Why AGI is Bigger than the Industrial Revolution | Why LLMs Will Not Commoditise & We Have Not Hit Scaling Laws | Bottlenecks in AI & The Energy Crisis Caused By AI | Whether AI Will Do More to Harm or Help Inequality

Mostly familiar Hassabis material: the AGI definition and timeline, missing capabilities, safety risks, scaling, and energy arguments all recur elsewhere in the corpus. Skippable for regular followers, though the retrospective on DeepMind’s original forecast and the explanation of compute as an experimental workbench add some value.

Is that closer than you thought? Has that changed over time? Not really. I mean, actually when you when you uh it's funny um my co-founder Shane Legg, who's chief scientist here, um uh when we started out DeepMind back in 2010, he used to write blog posts sort of predicting about uh when AGI would happen. And bearing in mind in 2010 when we started, almost nobody was working in AI and everyone thought AI uh basically didn't work. It was a it was a dead [laughter] end. No, and but they're still there on the internet for people to check and uh we used to do this extrapolation of compute and algorithmic uh progress, and basically we predicted around 20 years it would take from when we started out, and I think we're pretty

Demis Hassabis discusses AGI's definition, likely timeline, technical bottlenecks, and the capabilities still missing from current systems. He also addresses AI safety, economic disruption, and the possibility that AI-driven advances in infrastructure and energy technology could offset the technology's costs.

BBC News: Urgent research needed to tackle AI threats, says Google AI boss | BBC News

A concise but largely familiar overview of Hassabis’s standard benefits-versus-risks case. The strongest fresh angle is his practical defense of STEM education alongside a shift toward taste, creativity, and judgment; otherwise, this is skippable for anyone who has heard his longer interviews.

will there come a time when AI will prompt itself? >> Yeah. Well, I think it's still very important to have a STEM education. I would still say Uh like if you know how to code or do math, even though these systems will stop being able to code, you still need to orchestrate them and actually understand what it is they're doing and give them the problem that they need to solve. So, I think the technical if you have a technical background, I think you'll still be at an advantage in using these systems. But, I think the exciting thing is that it's if these systems are able to code, it's going to open up the space for creatives to actually make and build many more things. I'm going to type in to make more quickly. So, I think it'll kind of expand who's able to create. And then maybe the the key thing becomes taste and creativity and judgment. The

Demis Hassabis says AI offers major benefits but also creates risks from harmful actors and increasingly autonomous systems, making urgent safety research necessary. He also discusses education, creativity, and DeepMind's effort to balance ambitious development with responsible deployment.

AI & Beyond: AGI Showdown: Hassabis vs. Amodei on Humanity’s Next Chapter Panelist

Skip unless you want a condensed, secondhand recap. Nearly all substantive material duplicates the Radio Davos interview and another Build Wiz summary, with no meaningful new reporting or direct exchange.

falsifiability problem. because of that physical verification step. He thinks the Nobel laurate timeline might be stretched out a bit longer, maybe the end of the decade rather than next year. >> He also mentioned something really profound about creativity. He said, "Current AI is great at solving a problem you give it, but it lacks the ability to come up with the question in the first place." >> That's the missing ingredient he referred to, hypothesis generation. It's the highest level of scientific creativity. Looking at the world and asking, "Why is the sky blue?" or how does gravity actually work? >> It can answer the question, but it can't ask the question. >> Exactly. It lacks that spark of scientific curiosity that identifies the problem in the first place. Demis believes we haven't quite cracked that

The transcript presents Demis Hassabis as more cautious than Dario Amodei about the speed of AI progress, especially where physical experiments and scientific hypothesis generation remain necessary. It also attributes to him positions favoring international AI safety cooperation, adaptation to an AI-shaped labor market, and optimism that humanity retains agency over its future.

Radio Davos: The day after AGI: Two 'rock stars' of AI on what it will mean for humanity

This is a broad, substantive discussion, but nearly all of Hassabis’s material overlaps with other supplied appearances, including close recaps of this same exchange. Most fans can skip it unless they want the primary conversation rather than a summary.

>> Um I think there's definitely a risk. I think um I think that's kind of reasonable. there's fear and there's worries about these things like jobs and livelihoods. Um I think there's a couple of things that I mean it's going to be very complicated the next few years I think geopolitically but also the various factors here like we want to and we're trying to do this with AlphaFold and our science work and isomorphic our spinout companies solve all disease cure diseases come up with new energy sources. I think as a society it's clear we'd want that. I think maybe the balance of what the industry is doing is not enough balance towards those types of activities. I think we should have a lot more examples. I know Dario agris of like alpha fold like things that help sort of unequivocal good in the world and I think actually it's incumbent on

Demis Hassabis presents AGI as a potentially transformative scientific tool whose arrival remains uncertain but could be accelerated by AI-assisted development. He balances optimism about medicine, science, and human ingenuity with urgent warnings about autonomy, misuse, labor disruption, geopolitical competition, and the need for coordination and safety work.

Fortune 500: Titans and Disruptors of Industry: Google’s AI boss made OpenAI issue code red. Now he wants to solve disease. | Titans and Disruptors

A solid overview, but most of the AlphaFold, agents, product integration, and abundance material is familiar from other appearances. Worth sampling chiefly for the unusually direct account of merging DeepMind and Google Brain; otherwise, regular followers can skip.

Mind and Google Brain. And actually, I think often, as a collector, we don't get enough credit for the fact that, you know, I think about 90% of the modern AI industry is built on technology or discoveries made by one of those two groups, from transformers to AlphaGo and deep reinforcement learning. So we have, when we still have, I think, the deepest and broadest research bench. So we have incredible talent, I think, better than anywhere else in the world, by a long way. But it was getting complicated having two groups, especially given the amount of compute needed in this scaling era. So that was really why we had to put the two groups together, so we could pull all of the talents together working on a single project in Gemini.

Demis Hassabis discusses the strategic decision to sell DeepMind to Google, the scientific and medical ambitions behind AlphaFold and Isomorphic, and the organizational choices that accelerated Google's AI work. He also forecasts near-term growth in AI agents and longer-term gains in health, science, energy, and exploration.

RECORTES DE HISTORIA Y CIENCIA: Del Estrés al Cosmos: IA, Voyager y la Crisis Climática.

No hay una intervención hablada atribuible directamente a Demis Hassabis; el episodio solo lo menciona o resume mediante la narración. Los oyentes que busquen una aparición real deberían saltárselo.

El material incluye referencias a ideas atribuidas a Demis Hassabis sobre inteligencia artificial general, memoria, creatividad y propósito humano. Sin embargo, no contiene una contribución hablada identificable del entrevistado que pueda seleccionarse como intercambio.

Squawk Pod: Davos 2026: Google DeepMind CEO Demis Hassabis 1/24/26

A solid but heavily overlapping Davos-era update on Gemini, Google’s distribution advantages, and uneven AI-market froth. The older-chip deployment detail is the freshest material; listeners familiar with Hassabis’s recent competitive-positioning interviews can otherwise skip.

Yeah, well I think that's one of the advantage we have is we're full stack. So I think we're the only organization really that has a frontier lap and our own chips and TPUs and our cloud business. So we have a lot of sort of ways of utilizing any spare compute anywhere on our, on our, you know, on our, on our systems and data centers. And maybe as compute gets older, the older generations you start moving them towards serving or maybe like labeling data for you so you can always utilize, you know, even quite older sets of generations of chips for useful, useful work.

Demis Hassabis discusses Gemini's recent progress, its expansion across Google's products, and the advantages created by Google's research, infrastructure, hardware, and distribution. He also addresses AI-market valuations, future competition, chip efficiency, and how personalization may shape user loyalty.

硅谷声研所: EP37｜对话DeepMind CEO：AGI如何发展，谷歌AI眼镜何时到来？

Skip: the supplied digest contains no topics, substantive exchanges, or excerpt candidates to distinguish this appearance from the much fuller AGI and AI-glasses discussions elsewhere in the corpus.

The transcript contains no reliably identifiable or usable contribution from Demis Hassabis. The available chunks are too fragmentary to support grounded topics or pull-quote candidates.

The Morning Brief: ET@Davos: Demis Hassabis on China, Apple and AGI

Worth hearing chiefly for the newsworthy Apple endorsement and his unusually direct advice that countries such as India should apply existing frontier models rather than build their own. The China, AlphaFold, and post-scarcity material substantially repeats other appearances.

>> Indeed. Indeed. What can you tell us about your deal with Apple? >> Yeah, it's a very exciting and and um you know very important deal for us. I think it's a great endorsement in some ways of the progress we've made with things like Gemini. you know, Apple ran a very rigorous evaluation period on uh as I understand it all the leading models and Gemini had the best um overall capabilities. So, we're very excited about that. We're very excited about you know helping Apple with their whole ecosystem and and Apple intelligence. So, I think it's uh you know a great deal for us.

Demis Hassabis discusses Google DeepMind’s shift from foundational research toward faster product delivery, along with openness, the Apple partnership, and AI strategy for countries such as India. He also assesses quantum computing, China’s narrowing capability gap, and the possibility that AGI could accelerate science and produce a post-scarcity future.

Build Wiz AI Show: The World after AGI - Dario Amodei and Demis Hasssabis Panelist

Skip unless you want a brief secondhand recap: every substantive point closely repeats the fuller Radio Davos and AI & Beyond appearances, often through host paraphrase rather than Hassabis directly. The Great Filter material is the most distinctive, but it is already covered elsewhere in the corpus.

filter, but interestingly both of them rejected that idea. They didn't buy the Dumer argument. No, Dennis had this really optimistic take. He thinks the Great Filter wasn't technology, it was biology. He thinks the really hard part was just getting from single cells to complex multicellular life. So we've already passed the hardest test. That's his view. The future is ours to write. We don't see aliens because intelligent life is just incredibly rare, not because it keeps blowing itself up. Which? Puts a massive responsibility back on us, doesn't. It if we're the only ones, we really better not mess this up.

The transcript is a host-led recap of a discussion involving Demis Hassabis, so his contributions are reported rather than presented as direct, speaker-labeled remarks. The strongest usable exchanges cover verification limits, AI economics, safety coordination, labor-market advice, and Demis's optimistic interpretation of the Great Filter.

Moneycontrol Podcast: 5003: TCS woos OpenAI, DeepMind’s Hassabis on Indian AI & make way for Apple Pay | MC Editor's Picks

Skip: the supplied digest contains no attributable Hassabis exchange, topics, or notable material to assess. It appears to be a news roundup rather than a substantive appearance.

Demis Hassabis is mentioned in the episode introduction as having discussed India's AI priorities, but no transcript chunk contains his substantive speech. Therefore, no usable exchange range can be extracted.

Big Technology Podcast: Google DeepMind CEO Demis Hassabis: AI's Next Breakthroughs, AGI Timeline, Google's AI Glasses Bet

A solid overview, but most of the AGI, glasses, and AlphaFold material substantially overlaps Hassabis’s other appearances. The unusually candid discussion of advertising versus assistant trust is the main reason not to skip it.

dichotomy I see is that um uh uh uh if you if you want an assistant that works for you what is the most important thing trust. Okay. So trust and security and privacy. um because you want to share potentially your life with that assistant and you want to be confident that it's it's working on your you know behalf and uh and and with your best interests and so you know you got to be careful I think there are ways one could do it but you got to be careful that it doesn't the advertising model doesn't bleed into that and confuse the the user uh as to what you know what is this assistant recommending you and I think um you know that's going to be an interesting challenge uh in that space

Demis Hassabis discusses the technical gaps he believes remain before AGI, including continual learning, memory, world models, and long-term reasoning, while defining AGI as a system with the full range of human cognitive abilities. He also outlines Google's case for AI glasses, stresses that personal assistants must preserve trust and privacy, and argues that AI will create new forms of human adaptation and scientific discovery.

Bloomberg Tech: Netflix’s Amended Offer Puts Pressure on Paramount

This brief appearance largely repeats Hassabis’s familiar account of Google DeepMind’s resurgence and China’s rapid catch-up. Skip unless you specifically want his blunt characterization of the Western reaction to DeepSeek.

I think it was a massive overreaction in the West it was impressive, and I think it shows that the Chinese are very capable that the deleting companies. I think companies like Buy Dance actually, I would say are the most capable, and there may be only six months behind, not one or two years behind the front So I think that's what Deepseak showed.

Demis Hassabis says Google DeepMind has regained state-of-the-art standing by combining its long research history with faster product execution. He views Chinese AI companies as highly capable and relatively close to the frontier, but says it remains uncertain whether they can independently push beyond it.

The Tech Download: The man behind Google’s AI machine: DeepMind CEO Demis Hassabis

A competent overview, but nearly every major answer recurs elsewhere in the corpus with greater depth or specificity. Skip unless you want a compact survey of Hassabis’s standard positions on AGI, science, energy, risk, and competition.

Well, look, my passion for and my whole reason I spent my whole career on AI is, I think, eventually will be the ultimate tool for science. And of course, we've shown that with things like Alpha Fold and all of the science work we've been doing over the last decade. But there's still a long way to go in terms of, can an AI actually come up with a new hypothesis itself, not just solve a conjecture that is already out there, which would be already useful and impressive. But can it actually come up with a new conjecture, a new a new idea about how the world might work? And so far, these systems can't do that. They don't really have the capability to do that. So there

Demis Hassabis discusses the remaining technical requirements for AGI, including reasoning, continual learning, creativity, and world models, while maintaining a five-to-ten-year horizon. He also addresses AI's energy demands, social risks, industry valuations, China's competitive position, and the prospect of a new era of AI-driven scientific discovery.

BIMPRAXIS: Demis Hassabis: De los modelos de lenguaje (LLM) a los modelos de mundo (WM)

La aparición repasa ideas conocidas sobre modelos de mundo, AGI y una transformación social de escala industrial, con menos precisión y profundidad que otras entrevistas del corpus. Puede omitirse salvo que interese especialmente el ángulo filosófico sobre información y conciencia.

Increible. Proposito de la vida y la conscientia. La bicion de hazabis estan bertiginosa in su ambicion como reflexiva, in sus consequencias absolutamente y para terminar deja flotando una idea que los esquemas el especula con que quiz fundamental de no se alla materia nilinerjia. Si no la informacion. La informacion. Si si todo desduna proteina asta un pensamiento es en ultima instancia un systema de processamiento de informacion.

Las intervenciones seleccionadas de Demis Hassabis se concentran en los modelos de mundo, la inteligencia artificial general y sus consecuencias sociales. También exploran preguntas más abstractas sobre la conciencia y la información como fundamento de los sistemas físicos y mentales.