# (77) Demis Hassabis: We're Three Quarters of the Way to AGI - YouTube
- Transcript ID: `afpewo1gteg`
- Source tier: `youtube_auto`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (56/56 chunks)
- Original source: https://www.youtube.com/watch?v=AFpeWo1GTeg
- Raw evidence: `data/transcripts/youtube/AFpeWo1GTeg.json`
> 中文是派生翻译，英文是留存的源文本。中文未复核时，以英文和原始音视频为准。
## 00:00:02
**中文**
Demis，非常感谢。很高兴来到这里，也感谢各位到场。很荣幸在你们的巧克力工厂做客；我刚听说这件事，很期待之后的巧克力。我们马上开始：Demis 是各方面的 OG——AI 领域的原创思想家、创始人和远见者。
**English / Original**
Demis, thank you so much. >> Exciting to be here. Um, thanks everyone for coming. It's great to be here. >> We're we're so honored to have you at our chocolate factory. >> Yes, I had just heard about that. Yeah, looking forward to the chocolate afterwards. >> Excellent. Well, Demis, we're going to jump right in. We have one of the OGs in every way. Uh, original thinkers, founders, visionaries in all things AI.
## 00:00:24
**中文**
一位真正信仰 AI、也是真正科学家的 Demis。我们会先谈早期岁月、DeepMind 的早期，然后进入科学并开放提问。Demis，你曾是国际象棋神童，也创办过游戏公司。
**English / Original**
A true believer, true scientist in in Demis. Uh, we're going to spend the beginning of the conversation about the early days. Then early days of DeepMind, and then we'll get into the science and open up the room for some questions. So, let's jump right in. So, Demis, you were a chess prodigy. Uh, you also were a founder of a gaming company.
## 00:00:44
**中文**
你是神经科学家、DeepMind 创始人，如今领导一家极具影响力的大公司。这些似乎无关，但你说有一条共同主线，能解释吗？确实有，也许是我事后把它们串成主线。我很早就想做 AI，十几岁时便认定这是我能做的最重要、最有趣的事。
**English / Original**
You're a neuroscientist, and you're the founder of DeepMind, and now leader of a really big consequential company. Those seem like pretty unrelated pieces, but you've said that there's a common thread. Can you walk us through that? >> There is a common thread, and um, maybe I made it into a common thread. So, it could be a post talk, you know, sort of shaping. But, I wanted to do AI for a long time. So, I kind of decided it was the most important thing I could possibly and most interesting I could do in my teenage years.
## 00:01:16
**中文**
我选择学习和从事的事情，都是为了最终帮助我建立 DeepMind 这样的公司；约从 15、16 岁便有这个计划。游戏是一次绕路，但在 1990 年代，最前沿的技术就在游戏中，尤其是图形和硬件。今天使用的 GPU 原本为图形引擎设计；我在 1990 年代末已使用最早的 GPU。
**English / Original**
And then I picked things to study or do that I felt would eventually would help me build a company like DeepMind. So, I I had that as a plan from about 15, 16 years old. I had a detour into games because actually that was in the '90s, this is, that's where all the most cutting edge technology was being done. Obviously, not just in AI, but in graphics especially, including hardware of course. The GPUs we all use today, they were designed for graphics engines, and we use the I mean, I was using the very first GPUs back in the day in the late '90s.
## 00:01:49
**中文**
当时有大量前沿技术。我为 Bullfrog 和自己公司 Elixir Studios 制作的游戏都把 AI 作为主要玩法组件。最知名的可能是我约 17 岁制作的《主题公园》：模拟游乐园，成千上万小人来游玩、决定买什么，底层有完整的经济 AI 模型；它与《模拟城市》同属最早的一类游戏。
**English / Original**
So, there was a lot of really cutting edge technology, and then all the games I made, including all the games I did for Bullfrog, but also my own company, Elixir Studios, all involved AI as the main gameplay component. So, probably the most, you know, well-known game I made was Theme Park when I was about 17, and that was a simulation of an amusement park, and thousands of little people came into your theme park and played on your rides and decided what to buy from the shops. So, there was a whole kind of economics AI model underneath. And it was one of the first games of its type along with SimCity.
## 00:02:22
**中文**
看到它销量超过 1,000 万套、看到人们与 AI 互动的喜悦，使我决定将整个职业生涯投入其中。神经科学则为我提供大脑工作方式及算法思想的灵感；时机成熟时，我们将这些汇聚起来创办 DeepMind。早期我们也把游戏用作验证 AI 想法的试验场。
**English / Original**
And when I saw it sold, you know, 10 million plus copies, and when I saw how delighted people were to interact with the AI, you know, that was one of the things that made me think about spending my whole career on it. Um and then, of course, neuroscience is to get inspiration from how the brain works, and and and different algorithmic ideas from that, and then just bringing all those different things together for the start of DeepMind when the timing we felt was right. And then, of course, we use games as the early proving ground for our AI ideas.
## 00:02:51
**中文**
这里坐满创始人，而你不止一次、而是两次创业。请带我们回到第一次：Elixir Studios。它并非你最知名的创业公司，但取得了难以置信的成功。你如何领导它，它教会你怎样建设？
**English / Original**
>> Mhm. So, we've got a room full of founders here, and you can relate because you're a founder not just once, but twice. >> Mhm. >> Take us back to the first time. Elixir Studios. >> Mhm. >> What was that like? It's not the startup that you're most known for, >> Mhm. >> but it was one that you had incredible success with.
## 00:03:09
**中文**
我大学毕业后立即创办 Elixir Studios，且有幸在 Bullfrog Productions 工作——游戏行业早期的一家传奇工作室，可能是当时英国、欧洲最好的之一。我想做能推动 AI 的事。
**English / Original**
>> Mhm. >> How did you lead that, and what did it teach you about building? >> Well, look, we we we start I started Elixir Studios straight out of college, um and uh I was lucky enough to work at Bullfrog Productions, which, those of you know games, it was a kind of legendary game studio uh in the early days of the game industry, probably the best one in the UK in in Europe. And uh I wanted to do something that combined uh pushed AI.
## 00:03:33
**中文**
因此，当时我实际上通过游戏开发这个后门资助 AI，并推动其前沿、结合尖端创造力。我认为这仍与我们今天的蓝天研究有关。最大教训是：要领先时代 5 年，而不是 50 年。我们尝试制作《共和国》，模拟整个国家，玩家可用多种方式推翻独裁者，并模拟活生生的城市；要知道，那是在
**English / Original**
So, effectively, I was funding AI back in those days through the back door, through games development. Um and then push the the forefront of that and combine it with cutting-edge creativity. And I think that's still relevant today with the way we do our blue sky research, but maybe the biggest lesson I learned was you want to be um 5 years ahead of your time, not 50 years ahead. So, we tried to do a game called Republic at Elixir Studios which simulated a whole country, and then the idea of the game is you could sort of um overthrow, I think there was a dictator in charge of the country in any number of different ways. And we basically simulated living, breathing cities. And this is Bearing in mind, this is like
## 00:04:10
**中文**
1990 年代末的一台 Pentium 上。我们得让百万人的图形和 AI 在当时家用 PC 上运行，有些过于雄心勃勃，也确实造成问题。我从中学到：要领先时代；人人都看得懂时已太晚，但若领先 50 年，几乎不可能成功。
**English / Original**
the late '90s on a Pentium. So, we had to get all the graphics and all the AI for million people um working on uh you know, home PC at the time. So, it was a little bit ambitious. And um and maybe it was too ambitious. And it caused it caused probably some some issues. And I took that lesson with me of like, you want to be ahead of your time, you don't want to be obviously when it's obvious to everyone, it's too late. But um if you're 50 years ahead, then there's probably no way you can get it to be successful.
## 00:04:39
**中文**
说到不要太超前：2009 年你决定会有 AGI。也许当时只领先 10 年，胜过 50 年。请对这里的创始人讲讲 2009 年：你如何说服最初几位极优秀的人才，相信当时看来完全科幻的事情？
**English / Original**
>> All right. So, speaking of not being too far ahead of your time, it was 2009 and you decided there would be AGI. >> Mhm. Yes. >> [laughter] >> Maybe it was only you know, 10 years ahead of our time that time. Better than 50 years. >> So, so tell us about again, room full of founders here. Tell us about '09. How did you convince the first few brilliant talents? Cuz you pulled in really high-caliber employees, early team members. How did you convince them to believe in what seemed like total sci-fi at the time?
## 00:05:09
**中文**
我们抓住了一些有意思的线索。原以为领先 5 年，或许实际上是 10 年。Hinton 及同事刚在学界提出深度学习，但几乎没人意识到其重要性；我们也了解强化学习，认为将两者结合会有巨大进展，当时它们几乎从未结合，除玩具问题外更没有。在学术上它们是相当割裂的两部分 AI。
**English / Original**
>> Well, there was some interesting threads that I think we picked up on. I I think we thought we were 5 years ahead, but maybe we were more like 10. But it was, you know, deep learning had just been invented by Jeff Hinton and colleagues sort of in academia, but almost no one had really realized it was a big deal. Uh we knew a lot about reinforcement learning, and we felt there was huge progress to be made by combining those two techniques which almost had never been mixed together really, certainly not at any kind of anything other than toy problems. Uh in the academic subjects, they're quite two quite siloed parts of AI.
## 00:05:44
**中文**
我们也看到算力、当时的 GPU 会非常有用——现在用 TPU——加速计算产业会很有帮助。我的博士和博士后工作及早期同伴都是计算神经科学家；我们认为从大脑获得足够多可用思想与原则，包括强化学习最终可扩展至 AGI。因此我们觉得具备了要素，甚至像在守护一个秘密，因为学界和产业界几乎没人相信会有重大进展。
**English / Original**
Um then we could see the compute uh the GPUs at the time were going to be really useful. Of course, we use TPUs now, um but the accelerated computing industry was was was going to be very helpful. Uh and then we also felt at the end of my PhD and post-doc and some of the other people I got together were computational neuroscientists that we had enough ideas and principles um from the brain that could be useful, including the idea that reinforcement learning uh could eventually scale uh to AGI. So, um so we felt we had these ingredients and we almost felt like we were keepers of a secret because no one uh either in academia or industry really believed that any big progress was possible. In
## 00:06:23
**中文**
事实上，许多学界人士会对我们翻白眼，因为我们要研究 AGI 或当时所谓强 AI；他们说，大家 1990 年代都试过，知道行不通。我在 MIT 做博士后，那里曾是专家系统和逻辑语言系统的中心；如今听来令人惊讶，但当时我已觉得它过时了，可英国剑桥和 MIT 仍这样做。
**English / Original**
fact, a lot of the people in academia uh used to roll out that that, you know, literally rolled their eyes up at us when we were sort of suggesting we would work on AGI or strong AI, as it was sometimes called at the time, um because it was like, "Well, we know this doesn't work. So, you know, everyone tried it in the '90s." You know, I did my post-doc at MIT, which was the which was the sort of center uh uh point for expert systems. Um uh and first all the logic language systems. I mean, it seems amazing to think that now, but I was already feeling that that was archaic then, but they they, you know, that's still how it was done both in Cambridge in the UK and also in MIT,
## 00:06:59
**中文**
这反而使我更确信我们找对了方向：至少如果失败，也会以不同于 1990 年代人们尝试 AGI 时的方式失败。因此无论如何都值得做；当然它是研究，我们不知道一定成功，但若不成，至少会以原创的方式失败。
**English / Original**
these big centers of traditional AI. Um and it felt like, but actually that convinced me even more that we were onto something because at least if we were going to fail, we would fail in a different way than people had failed, you know, to get to AGI in the '90s. So, that felt like it was worth doing uh no matter what, even if obviously it was research, we didn't know for sure it would be successful, but at least we would we would fail in a original way if it didn't work.
## 00:07:25
**中文**
早期信念是否有共同的难点，必须向自己或早期追随者证明什么？无论发生什么，我本都会一生投入 AI。结果表明，发展落在我们预期中极其惊人、极为乐观的那一侧。
**English / Original**
>> Was there any common sticking point in that early belief? Was there something that you had to prove either to yourself or to your early followers to get them on board? >> Well, it was I mean, we had Put it this way. I would have been spent my life on AI um no matter what had happened. So, as it's turned out, it's gone you know, it's sort of gone on the absolutely amazing side of the optimistic side of what we thought.
## 00:07:48
**中文**
我们需要不断验证的是，关键技术要素能否真正组合并扩展；早期研究的每一次成功都使团队更确信这条路径。
**English / Original**
Still actually within what we were predicting in 2010, we thought it would be a 20-year mission. And I think we're basically exactly on track as a field for that, and obviously we played our part in that. But even if it hadn't transpired that way, and it was still now niche subject, that's all that's what I would still be doing because I felt it was the most important technology ever. It feels obvious to me, you know, our original state mission statement at DeepMind was step one, solve solve intelligence, I build AGI.
## 00:08:16
**中文**
DeepMind 以科学突破为中心，而不是短期产品；这要求人才愿意面对高度不确定性，并共同相信长期使命。
**English / Original**
Step two, use it to solve everything else. So, it was always I always thought it was the most important technology that could ever be invented, but also the most interesting one. So, as a tool for science, as an interesting artifact in itself, and actually as one of the best ways to understand our own minds, you know, like the nature of consciousness, dreaming, creativity, all of these questions I had as a neuroscientist, I felt one of the things that was missing was an analysis tool like AI, but also a comparison, a that you could do sort of a controlled experiment, study, and compare two different systems against each other.
## 00:08:51
**中文**
今天的进展显示，早期关于学习、规划与通用能力的判断正在逐步得到验证，但仍有重要的科学问题未解决。
**English / Original**
>> Mhm. Let's talk about AI for science. You've been early to that, you've been a believer, and you've been really a purist about this. This is the driving mission. What about the way you set up DeepMind and set the culture has positioned it to be on the constant forefront of AI for science? >> Well, that was the the ultimate goal, at least for me, my personal passion. There's one There's my own drive to build AI, which was to advance science and medicine and and our understanding of the world. It's my expression of that mission was to sort of do it in a meta way, right? Build the build the the ultimate tool, and then come back when that was ready and use it to make breakthroughs in science, things
## 00:09:30
**中文**
我仍认为 AGI 是一项科学使命：建成极其强大的工具，同时以负责任方式理解并引导其影响。
**English / Original**
like AlphaFold that we've done, and I think many more things. So, we've always had that at the heart of what we've been trying to do at DeepMind. So, actually, we've had an AI for science group division led by Pushmeet Kohli that has existed for nearly a decade now. Actually, pretty much the day after we got back from Seoul in the AlphaGo match, which is sort of 10 years to the month now, is when we started formally started the AI for science efforts cuz I was waiting for the algorithms to be powerful enough and the ideas to be general enough and for me, you know, cracking Go was that point, that time that we thought, "Okay, now we're ready to really apply these ideas to important
## 00:10:10
**中文**
AI 最值得期待的用途之一，是治愈疾病、延长健康寿命，并帮助医学；随后还包括材料科学、环境与能源等重要领域，未来几年 AI 都将发挥巨大作用。
**English / Original**
real-world problems starting with these big scientific challenges." So, we've always had that in mind as the the most beneficial use of of AI. Like, what could be better than using it to, you know, cure diseases and give us healthier lifespans and and to help with medicine, followed, obviously, by other really important areas like material science and the environment and energy and these kinds of topics, which I think AI is also going to be have a huge part to play in the next few years.
## 00:10:37
**中文**
AI 如何在生物学中突破？你深度参与 Isomorphic，也一直坚信 AI 可治愈疾病。何时生物学会迎来语言和编程那样的时刻？我认为 AlphaFold 已带来过这种时刻：蛋白质折叠是 50 年重大挑战，而了解蛋白质三维结构对设计药物、理解生物学极其重要；但它只是药物发现流程的一部分。
**English / Original**
>> And how does AI break through in biology? You're deeply involved with Isomorphic. This is an area of deep passion. You have been a purist on the potential of AI to cure diseases from the very beginning. When do we have the type of moment that we've had in language and coding but in biology? >> Yeah, well, I I mean, I'd argue we've already had one of those moments with AlphaFold. So, you know, this is a 50-year grand challenge, protein folding, and the 3D structure of proteins is incredibly important thing to know about if you want to design medicines or if you want to understand biology. Of course, it's only one part of the drug discovery process, an important part, but it's
## 00:11:16
**中文**
Isomorphic Labs 是我们后来的拆分公司，我也很享受运营它。它要构建生物化学、化学领域的相邻技术，自动设计能匹配并结合蛋白质正确部位的化合物。现在我们知道蛋白质的形状、表面有什么以及要靶向什么。
**English / Original**
only one part. So, Isomorphic Labs, which is our, you know, later spin-out, having a lot of fun running that as well, and it is is to build adjacent technologies in more biochemistry and chemistry space that can actually design the compounds automatically to kind of fit and bind to the right part of the protein. So, we now know the protein that the shape of the protein, we know that what's on the on the surface of the protein and what we have to target.
## 00:11:42
**中文**
但现在需构建能强力结合目标部位、理想情况下不结合其他任何部位的正确化合物，否则会有毒性副作用。梦想是把 99% 的探索和时间都在计算机内完成，只把湿实验室留给验证。我认为未来几年可做到，将平均约 10 年的药物发现缩至数月、甚至数周，也许有一天缩至数日。
**English / Original**
Um and but now we've got to build the right compound that of course binds strongly to where you want it to bind on the target of interest, but doesn't bind to anything else ideally cuz that would be a a toxic side effect. So, um the dream is to do almost all the exploration uh which is 99% of the of the work and the time in silico and then save the the the the wet lab step just for the validation step. Right? So, that would be um you know, I think if we can do that and and I think we can get there in the next few years, um I think we could reduce drug discovery times instead of that for taking like, you know, an average of 10 years down to months, maybe even weeks and perhaps even days
## 00:12:22
**中文**
届时所有疾病可能触手可及，个性化医疗也会成为可能，例如在基础药物上的个性化变体。整个医学与药物发现领域将在未来几年被革命性改变。
**English / Original**
one day. And um and then I think then all disease could be in reach and I think things like personalized medicine will become possible. Uh you know, like personalized variations off of base medicines. So, uh I think the whole of uh the whole medical area, drug discovery areas is going to be revolutionized in the next in next few years.
## 00:12:41
**中文**
你常谈 AI for Science。AI 是否会创造新科学？就像工业革命与热力学，教育体系会教什么根本新内容？我认为会有几件事。首先，对 AI 系统本身的理解和分析会成为一门完整的、近似工程的科学；我们创造的人工物极其有趣，也极其复杂。
**English / Original**
>> Brilliant. You talked a lot about AI for science. >> Mhm. >> Do you think that at some point AI will create new sciences? Al uh industrial revolution and thermodynamics, will there be something new taught fundamentally in our education system? And if so, what would it be like? >> Well, I think there's several things uh that along those lines that I think we is going to happen. So, first of all, um the understanding and the analysis of AI systems themselves, I think it's going to become a whole science, a kind of engineering science. These incredible incredibly interesting artifacts that we are building and um they're incredibly
## 00:13:23
**中文**
它们最终会像人类心智和大脑一样复杂，因此需要被研究，以便远超今天地充分理解其工作方式。机制可解释性是其中一部分，但还可做得更多；这将成为一门科学。
**English / Original**
complex as well. As complex eventually they'll be as complex as the human mind and the brain. And so, they'll need to be studied so we can understand fully, way beyond where we we are today, how these systems work. So, I think there's a whole kind of field mech and tarp is part of that, but there's a lot more, I think, that we can do to analyze uh these systems. So, that'll be a science.
## 00:13:45
**中文**
AI 本身也许会解锁新科学。我尤其兴奋的是用于模拟的 AI。我喜欢模拟，写的游戏都有 AI 且都是模拟。模拟也许能处理经济学等社会科学及更多人文学科，因为它们是涌现系统，难以做重复、受控实验。
**English / Original**
But, I think also um AI itself will maybe unlock new sciences, which is maybe what you're getting at. Um I the one I'm particularly excited about is AI for simulations. So, I I love simulations. All my all the games I wrote not only had AI, but they were simulations. And I 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
## 00:14:26
**中文**
例如加息 0.5%，必须在现实世界中实施并观察，虽有理论却无法重复数千次。但若能极其准确地模拟事物，也许可从准确模拟器中严格抽样，开展新科学。
**English / Original**
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.
## 00:14:45
**中文**
这会让我们在今天非常不确定的领域做出更好的决定。要得到这种准确模拟，需要什么？世界模型？我们在 AI 中大量做“学习模拟器”的工作；在未知数学或系统太复杂、无法直接写出足够准确的专用模拟器的领域尤其如此。
**English / Original**
Um and then I think that will turn that give allow us to make much better decisions in these today what are very uncertain uh domains. >> What will it take to get to those extremely accurate simulations? World models? What kind of science is necessary and engineering to get >> Yeah, well, look, I I mean, I'm thinking a lot about that in we do a ton of that work at AI like learning simulators is basically what it'd be. So, you know, these are in domains where you can't We don't know the mathematics of it well enough or it's perhaps too complex. We can't just write We can't just write a uh uh uh directly down a special case simulator. Um Um, it's just not accurate enough, doesn't capture all the
## 00:15:22
**中文**
天气就是例子。我们有 WeatherNext，它是世界上最准确的一类天气模拟器，且比气象学家使用的模型快得多。能控制天气吗？不能，我也不确定那是否是好主意。第一步是更好地理解它。
**English / Original**
variables. Um, we're doing that it was done in with weather. Um, we have the most accurate kind of weather simulator in the world, Weather Next. And it's far faster than what the meteorologists use. >> weather yet? >> No, we can't. We can't, no. And I'm not sure that would be a good idea. But the the first step is to understand it better.
## 00:15:41
**中文**
在生物学中，我们正研究我所谓的“虚拟细胞”。生物学是高度动态的涌现系统；我认为机器学习对生物学就像数学对物理学，是完美的描述语言，因为这里有大量弱信号、弱相关性和超出人脑可分析量的数据，其中仍有联系、相关性和有趣因果性。
**English / Original**
Um, and but then even biology, you know, we we're working on a kind of what I call a virtual cell. So, you have hugely um, dynamical emergent system. And I think biology is perfect uh, sort of machine learning is perfect description language for biology in the same way math is for physics. Because I think in biology and in lots of these natural systems you have loads of weak signals, weak correlations, tons of data, far too much than any human mind can analyze. But there are connections and correlations and interesting causalities within that mass of data.
## 00:16:14
**中文**
因此机器学习是描述此类系统的完美工具；迄今数学还做不到，要么复杂到顶尖数学家也难以处理，要么数学表达能力不足以理解这些高度涌现的动态系统。
**English / Original**
So, I think it's sort of it's always struck me that machine learning is the perfect tool to describe those kinds of systems. Where until today, you know, mathematics hasn't been able to do that. Either because we can't manage it as as top mathematicians cuz too complex or the expressive power of math is not enough for to understand these sort of highly emergent dynamical systems.
## 00:16:38
**中文**
是否也由于其混乱和随机性？当然。学习这些模拟器后，或许会出现另一门新科学：可以从隐式、直觉式模拟器中提取显式方程。
**English / Original**
>> Is it also because of the messiness and stochastic nature of Yeah, sure. >> And and I mean eventually you could by the way, once you learn these simulators, it maybe there's maybe there's another branch of new branch of science. You could maybe extract some equations from the once you have the simulator. So, you have this sort of implicit simulator or intuitive simulator and then maybe you could extract explicit equations from that.
## 00:17:02
**中文**
部分因为可以随意抽样它。会像麦克斯韦方程那样根本吗？也许。我们不知道这类涌现系统是否存在这样的方程；但若存在，我不认为我们无法用这些方法找到它们。
**English / Original**
Um, because partly cuz you could also sample it as many times as you want. >> As fundamental as Maxwell's or >> Maybe. If if I don't know that exists for such emergent systems, um, but if they do exist, I don't see we we won't be able to find them in this with this with this methods. >> That would be amazing. You've talked about this theory that the basic building block of of everything in the universe could be information like. This is more theoretical. How do you think about it that and what does that mean for a traditional classical Turing computer?
## 00:17:33
**中文**
你谈过宇宙一切的基本构件可能是信息，这更具理论性。你怎样想，它对传统经典图灵计算机意味着什么？我认为能量和物质当然等价，但信息也有相似的等价性。物质的组织、结构，尤其抵抗熵的生物学，本质可看作信息处理系统。
**English / Original**
>> Well, look, I think you can of course all the famous, you know, equals mc squared and all the stuff Einstein did and energy and matter are kind of equivalent. But I actually think information has a kind of equivalence in the same way. So you can think of, you know, the organization of matter and structure and especially things like biology that are resisting entropy as basically information processing systems at their heart. So I think one can convert all of those three kind of quantities into each other, but I have this feeling information is most fundamental. So it's a little bit the opposite way around to the classic physicist thought in the 1920s and things where, you know, it's
## 00:18:10
**中文**
能量与物质是首要的。
**English / Original**
sort of energy and matter primary.
## 00:18:13
**中文**
我认为以信息优先来理解世界和宇宙更好。若这是真的——我认为有相当多证据——AI 在某种意义上比我们想象的更深刻；它本就很深刻，因为它也关乎组织、理解、构建信息或对象。我看来 AI 就是信息处理。
**English / Original**
I actually think it's a better to to better way to understand the world the universe is to think about it as information first. And if that's true, and I think there's quite a lot of evidence for that, then of course AI is even more sort of profound in a sense >> Totally. >> than we think and it's already pretty profound because it's it's also about organizing information and understanding information and constructing information or objects. So AI in my opinion is all about information processing.
## 00:18:44
**中文**
若以信息处理作为首要透镜，这些领域之间有很深联系。经典图灵机能计算一切吗？我有时把我们称为图灵的冠军。图灵机、Alan Turing 是我最敬仰的科学英雄之一。
**English / Original**
So I think there's something sort of very deeply connected with with these different areas if you think of it through the lens of information processing as the primary way to think about it. And do you think a classical Turing machine will be able to compute everything? Well, I sometimes think you know, I sometimes sort of think about what we're doing and refer to ourselves as Turing's champion because Turing machines, I think Alan Turing is one of my all time, you know, favorite scientific heroes.
## 00:19:12
**中文**
他的工作奠定了计算机、计算机科学和 AI 的基础。图灵机结果是最深刻的成果之一：一切可计算的事物都能由相对简单的机器描述计算。我认为大脑很可能是近似图灵机。
**English / Original**
I think what he did obviously laid the foundations for the computers for computer science but also AI. And I think it's one of the most profound results ever is the Turing machine result. You know, that everything that is computable can be computed by relatively simple description of a machine. So I think our brains are likely to be approximate Turing machines.
## 00:19:35
**中文**
图灵机、量子计算机和量子系统间的联系很有趣。但 AlphaGo，尤其 AlphaFold 显示，作为现代神经网络的经典图灵机能够建模蛋白质折叠这类原本被认为是量子系统的问题；它涉及极小粒子，人们或许会认为必须考虑水键等一切量子效应。
**English / Original**
And I think it's interesting to think about the connection between Turing machines and quantum computers and quantum systems. But I think at least what we've shown with things like AlphaGo and especially AlphaFold is that classical Turing machine obviously in the guise of a modern neural network it can model what was thought to be in the case of protein folding is a quantum system. You know, in some sense it's very you know, it's dealing with very small particles and one might think you'd have to take into account all the quantum effects of the water bonds and all sorts of things.
## 00:20:11
**中文**
但事实证明，在经典系统上可获得近似最优的解。因此许多我们认为需量子系统建模或运行的事物，若以正确方式思考，可能可在经典系统上建模。你一直把 AI 说成一种工具，像历代望远镜、显微镜和星盘。
**English / Original**
But it turns out you can get to an approximate optimal sort of solution on a classical system. So it may turn out that a lot of things that we think are would need a quantum system to model or run might be modelable on a classical system if thought about in the right way. >> So you've talked about AI consistently as a tool. Like a telescope or a microscope astrolabe through the centuries.
## 00:20:39
**中文**
当机器几乎可模拟任何事物时，它何时不再是工具？我强烈认为，在构建 AGI 的使命和旅程中，应先建造一种极其智能、有用、精确的工具，再跨越下一道卢比孔河。这已足够深刻；工具当然会变得越来越自主、越来越像智能体，我们正处在
**English / Original**
When you think about a machine that can model almost anything let's say it can't even model quantum systems like you pointed out. When does it stop becoming a tool and will that ever happen? >> Well, my my my feeling strong feeling is we should in this sort of mission to and journey to build AGI those of us on that journey many of people in this room, you know, I feel like it would be best to build a tool first an incredibly intelligent and useful and precise tool and then cross the the the next sort of Rubicon. That's already profound enough and has, you know, of course the tool could start becoming more and more autonomous and agent-like that we're all seeing. We're in the
## 00:21:21
**中文**
智能体时代之中。
**English / Original**
midst of that the agent era now.
## 00:21:24
**中文**
但还有更进一步的问题：它是否有能动性？是否有意识？这些都要解决，但我建议作为第二步处理。也许可在第一步用工具帮助我们处理这些深刻问题，理想情况下还可更好理解自己的大脑和心智，并比今天精确得多地定义意识。
**English / Original**
But then there's a further step of like, you know, does it have agency? Is it conscious? These sorts of questions which are also going to be questions we're going to need to address, but I would um I would recommend we do that as a second step. Perhaps using the tool in the first step to help us with those next profound questions and ideally also we could understand our own brain and and minds better and define things like consciousness a lot more precisely than we can today.
## 00:21:53
**中文**
你估计意识定义会是什么样？我没有什么超出数千年哲学的新见解。但一些成分显然是必要、可能不充分的：自我意识、自我与他者的概念、某种时间连续性。完整定义显然仍是开放问题；我曾与许多伟大哲学家讨论，包括不久前去世的 Daniel Dennett。
**English / Original**
>> Do you have estimations of what that definition of consciousness might look like? >> No, I mean I haven't got much to add beyond that thousands of years of philosophy hasn't said already, but I mean it's very clear to me that it's obvious some components are going to be needed. Probably necessary but not sufficient. Like things like self-awareness and, you know, the idea of self and other, some kind of continuity over time. So some of these things are clearly needed for anything that might look like consciousness, but I mean obviously it's an open question as to as to what the full definition is and I've talked to many of the great philosophers about that. Daniel Dennett obviously sadly passed away recently,
## 00:22:33
**中文**
几年前我们进行过长谈。问题之一是系统如何行为：是否像有意识系统那样行为？接近 AGI 时一些 AI 系统可能会做到。但我们为何认为彼此有意识？一是行为像有意识者，二是我们运行在相同基质上；若两者都成立，最简约的推断是你体验着与我相同的东西。
**English / Original**
but we had a long conversation a few years back about this and I think um you know, one of the issues is how does a system behave? Does it behave like a conscious system? So that's you know, you could argue some of the AI systems might end up being able to do that as they get close to AGI. But then there's still the question of why do you you know, why do we think each other are conscious? One is the way we're behaving. We're behaving like conscious beings, but the other thing is we're we're we're running on the same substrate. So I think if both those things are true, then it's parsimonious to imagine you're you're experiencing the same thing I'm experiencing, which is why we don't have that debate
## 00:23:09
**中文**
因此通常不会争论彼此是否有意识。但人工系统永远不会与我们有基质等价性，所以很难完全弥合这个差距。可以从行为观察，但体验方面呢？AGI 之后或许有办法；但这超出今天 AI for Science 的讨论范围。
**English / Original**
about, you know, normally about as are each other conscious. But, I think we'll obviously we'll never have the substrate equivalence with an artificial system. So, there'll there'll always be a I think it'll be hard to to completely close that gap. So, you can look at it behaviorally, but but what about experientially? There are probably some ways to do that post AGI, but let me speak about out of scope today even for AI for science discussion.
## 00:23:33
**中文**
很好。我们马上开放现场提问。你提到哲学家，康德和斯宾诺莎是你的两位最爱：康德强调义务，斯宾诺莎近乎决定论。你如何连接这两种信念，又如何看待世界？
**English / Original**
Yeah. >> Brilliant. So, we're going to open the room to questions in just a moment. Get your questions ready. But, you brought up philosophers. You've mentioned Kant and Spinoza as two of your two of your favorite philosophers. Kant is this you know, deontological, highly duty-driven philosopher. Spinoza almost has this deterministic view of the universe. How do you kind of connect those two beliefs? And where where is your thinking of how the world looks?
## 00:23:59
**中文**
我喜欢他们，是因为读神经科学博士时，康德关于心智创造现实的论述让我印象深刻，我认为基本正确。这也是研究心智、研究大脑如何工作的一层理由。我最终关心现实本质，因此必须理解心智如何解释它。
**English / Original**
>> Well, the reason I like those two they they stuck out for me is that I think Kant when when I was doing my PhD in neuroscience, you know, his his sort of statements about the mind creates reality, right? I think that's basically true. And and so, another reason to study the mind, right? How the brain works. And I'm interested ultimately in the nature of reality. So, we have to understand how the mind is interpreting that.
## 00:24:25
**中文**
这就是我从康德那里获得的。斯宾诺莎则更多关乎某种精神维度：若以科学为工具理解宇宙，就是以真正深刻的方式理解宇宙如何运作的深层奥秘。当我做科学、从事 AI、构建工具时，感觉我们正在阅读宇宙的语言。
**English / Original**
>> Mhm. >> And so, I think that's for me what I took from Kant. And then Spinoza is more about the you could almost call spiritual dimension of like, well, if you're trying to understand the universe using science in my case as the tool, you're sort of understanding some deep mystery about how the universe works, right? In a in a really uh uh kind of deep way. And that's what I feel I'm we're doing and I'm doing when, you know, I do my science and we work on AI and we're building these tools is somehow we're kind of reading the language of of the universe.
## 00:25:03
**中文**
说得很美。Demis，科学家、演说家、哲学家。结束前快速问几个问题：AGI 的发布年份，押大还是押小？或者拒绝问题前提。
**English / Original**
>> Beautiful. What a beautiful way to say what you do every day. Demis, scientist, orator, and philosopher. We will, before we wrap, do a couple of rapid-fire questions. >> Okay. >> Thank you for >> [laughter] >> finishing He's not seen these yet. Over under on distribution year of AGI. >> Oh, wow. Um >> Or reject premise of question.
## 00:25:25
**中文**
不，2030 年。我对此一直相当一致。好，2030。实现 AGI 时必读的书、诗或论文？我最喜欢的书是 David Deutsch 的《现实的结构》，我认为仍然适用；希望借 AGI 解答书中的问题，那是我的后 AGI 工作。
**English / Original**
>> No, 2030. I've been pretty consistent about that. >> Okay, 2030. Must read book, poem, or paper for when we achieve AGI. >> Oh, wow. Um As for when we achieve once we achieve it, um Well, my favorite book is The Fabric of Reality by David Deutsch, so I think that still holds. I'd hope to answer the questions in that book with with the AGI. That's my post-AGI work.
## 00:25:51
**中文**
很棒。DeepMind 至今最自豪的时刻？我们很幸运有许多，可能是 AlphaFold。好。再问几个游戏问题：若进行高风险回合制策略游戏，例如《文明》《The Battle of Polytopia》，可从历史选一名科学家加入团队——爱因斯坦、图灵、牛顿——你选谁？
**English / Original**
>> Brilliant. >> Yeah. >> Proudest moments so far in DeepMind. >> Oh, wow. Um we've been lucky to have a lot. I mean, probably AlphaFold. >> Okay. >> Yeah. >> Now, a couple games questions. If you were engaged in a high-stakes strategy game, turn-based strategy game. >> Okay. >> Civ, Polytopia, strategy games. And you can select one scientist from history. We're thinking the the Einsteins, the Turings, Newtons. Who would you select to be on your team?
## 00:26:19
**中文**
我的团队？是。天啊。可能是冯·诺伊曼。我认为策略游戏需要博弈论家，而我认为他是最好的。他会是好队友。很合理。Demis，你无所不能；非常感谢你的到来。请大家一起感谢 Demis。
**English / Original**
>> On my team? >> On your team. >> Oh, gosh. >> [laughter] >> Um probably von Neumann, I think. I mean, he's Yeah, you want a game theorist, I think. And I think I think I think I think he's the best. >> Makes sense. >> I feel he'd be a good teammate. Yeah. >> All right. >> Yeah. >> [snorts] >> Well, Demis, you do it all. Thank you so much for being with us. Please join me in thanking Demis.
## 00:26:45
**中文**
［掌声］
**English / Original**
>> [applause]