# Confronting Our AI Future · Closing Keynote — transcript（原文存档） | 项 | 值 | |---|---| | 形态 | 视频 / 大会闭幕主题演讲 + 炉边对谈 · 自动转录字幕（逐行带 `MM:SS` 时间码） | | 活动 | Stanford HAI「Confronting Our AI Future: Hope, Fear, and the Choices Ahead」，2026-10-28，Hoover Institution, David & Joan Traitel Building, Stanford | | 议程位置 | 4:00–4:15 PM PST「Discovery Loop: Accelerating Science and Engineering」（Jeff Dean）；4:15–4:45 PM PST「Closing Fireside with Jeff Dean and Fei-Fei Li」 | | 主讲 / 对谈 | Jeff Dean（Discovery Loop 联合创始人兼 CEO，前 Google 首席科学家）/ 李飞飞 Fei-Fei Li（Stanford HAI 创始主任、Sequoia 讲席教授、World Labs 联合创始人兼 CEO） | | 主持开场 | Stanford HAI 主持人（未具名） | | 时长 | 约 48 分钟（本份字幕末帧 `48:14`） | | 侃姐发来 | 2026-11-09 20:54，Downloads 里的 txt（59515 B / 3561 行） | | 转录性质 | **自动生成**。含口吃、重复词、断句破碎、专名误识（`fee Lee` / `John Levven` / `Oral Vignyals` / `Asimoff` 类）、漏标的说话人切换；时间码有 `05:60`、`07:60`、`14:60` 等溢出写法，原样保留 | | 来源 | 活动页 `https://hai.stanford.edu/events/confronting-our-ai-future-hope-fear-and-the-choices-ahead`；视频 `https://www.youtube.com/watch?v=ooGKNwJWdb8`（经同期中文报道与第三方摘要页交叉核对，未逐帧校验） | | 状态 | **正文一字未动、未校对**。专名校正与转录错误清单见 `../notes/2026-11-09-confronting-our-ai-future-jeff-dean-feifei-li-zh.md` | | 定位方式 | 本份**有时间码**，译文用 ¶ 段落编号 + `tMM:SS` 锚点；不补写、不外推时间码 | --- 00:00 All right, we've spent the day on hard 00:03 00:03 questions about AI. Who govern it 00:05 00:05 governs it? How fast it's moving and how 00:08 00:08 we design it around people. So for our 00:11 00:11 closing keynote, we'll turn to one of 00:13 00:13 the most hopeful promises, using AI to 00:16 00:16 accelerate scientific discovery itself. 00:19 00:19 Jeff Dean is co-founder and CEO of 00:21 00:21 Discovery Loop, a new company focused on 00:24 00:24 AI for science and engineering. And 00:26 00:26 before that he spent 27 years at Google 00:29 00:29 where he helped build systems like rapid 00:32 00:32 map reduce big table and create 00:34 00:34 tensorflow and served as chief science 00:37 00:37 scientist and co-lead of gemini. His 00:40 00:40 talk is titled discovery loop 00:42 00:42 accelerating science and engineering 00:45 00:45 and afterwards he'll be joined on stage 00:47 00:47 by professor fee Lee for a closing 00:49 00:49 conversation. FFE is the founding 00:52 00:52 director and senior fellow at Stanford 00:54 00:54 HAI, the inaugural Sequoia Professor of 00:57 00:57 Computer Science here at Stanford and 00:59 00:59 co-founder and CEO of World Labs. She 01:03 01:03 recently took on the role as special 01:04 01:04 adviser on AI to President John Levven 01:07 01:07 and as the co-chair of HAI's advisory 01:10 01:10 council. HAI would not be here today if 01:12 01:12 it weren't for her vision and commitment 01:14 01:14 to putting people at the center of AI. 01:17 01:17 And that founding vision remains at the 01:19 01:19 heart of what we do here at the 01:21 01:21 institute. So now please join me in 01:24 01:24 welcoming Jeff Dean. 01:26 01:26 [applause] 01:32 01:32 >> All right. Wonderful. Uh it's delightful 01:35 01:35 to be here uh with all of you today. Um 01:38 01:38 I'm going to talk to you about discovery 01:39 01:39 loop. Our mission is that we are trying 01:42 01:42 to build AI systems that can 01:44 01:44 automatically solve important problems 01:45 01:45 in machine learning, science, and 01:47 01:47 engineering. And we think this will 01:49 01:49 ultimately lead to more discoveries in 01:51 01:51 the world. And that's uh what we're 01:53 01:53 excited about. 01:55 01:55 So there's this common cycle across many 01:58 01:58 many disciplines of kind of the 02:00 02:00 universal loop of science and 02:02 02:02 engineering where you propose 02:03 02:03 experiments, you implement and run those 02:05 02:05 experiments, you look at the results, 02:07 02:07 you examine whatever you measurements 02:09 02:09 you can make about them and you decide 02:11 02:11 what to do next which often means doing 02:13 02:13 another experiment. 02:16 02:16 So our approach is we want to automate 02:18 02:18 these kinds of experimental loops. Um, 02:20 02:20 we think if you can drive down iteration 02:22 02:22 time, the time to run one time time 02:25 02:25 through these loops and you can propose 02:27 02:27 and automatically run many experiments, 02:29 02:29 you can learn from each evaluation so 02:31 02:31 that the subsequent experiments you 02:33 02:33 propose are better and are informed by 02:35 02:35 the previous experiments you've run and 02:37 02:37 scale both the quality and the volume of 02:40 02:40 experiments, you're going to get 02:41 02:41 breakthrough results. So that's 02:44 02:44 in essence what we're out to do. Um, if 02:48 02:48 you squint at this nice list of US 02:51 02:51 National of Academy of Engineering Grand 02:54 02:54 Challenge problems, these were problems 02:57 02:57 uh proposed as really important for us 02:59 02:59 to solve in the 21st century. And we're 03:01 03:01 a quarter of the way through roughly. 03:03 03:03 Um, if you squint at kind of the sub 03:06 03:06 problems that you can imagine need being 03:08 03:08 needed to be solved for some of these 03:10 03:10 areas, in particular the ones in red. We 03:12 03:12 think these kinds of automated discovery 03:14 03:14 loops can actually assist and help with 03:16 03:16 all of these different kinds of areas. 03:18 03:18 [snorts] 03:22 03:22 Now, one of the things that's important 03:23 03:23 to us and partly why we formed this as a 03:25 03:25 separate company is we believe 03:27 03:27 technology can have an amazingly 03:29 03:29 positive impact in the world if we do it 03:31 03:31 right. but that we all have to work to 03:34 03:34 make sure that it has this positive 03:36 03:36 impact. Um, and so we established our 03:39 03:39 company as a public benefit corporation 03:41 03:41 and our public mission is to develop 03:43 03:43 technology that addresses important 03:45 03:45 societal problems and to provide broad 03:47 03:47 access to its benefits to improve 03:50 03:50 people's lives. 03:53 03:53 So a little bit about the four of us, we 03:55 03:55 h I have uh three amazing co-founders 03:57 03:57 Sanjay Gimat Quacle and Oral Vignyals. 04:00 04:00 uh we've worked together for depending 04:02 04:02 on the pair-wise relation uh somewhere 04:04 04:04 between 14 and 30 years. Uh we really 04:07 04:07 enjoy working together on difficult 04:08 04:08 problems and we've got a lot of 04:11 04:11 different kinds of experience. So we've 04:13 04:13 helped build uh Google products. We all 04:15 04:15 came from Google. So these are all 04:17 04:17 Google centric things we've worked on. 04:19 04:19 Um lots of different products. You know 04:22 04:22 we've worked together on many different 04:23 04:23 generations. Google search, the initial 04:25 04:25 ad system, content ads, Gmail news 04:28 04:28 translate, um infrastructure systems in 04:31 04:31 the second column, uh in particular, uh 04:34 04:34 focus initially at the top part of that 04:37 04:37 column are things that help you uh uh 04:40 04:40 organize, manage large amounts of data, 04:42 04:42 run large scale computations over data 04:44 04:44 to extract interesting insights. The 04:47 04:47 bottom four things there are really 04:49 04:49 multiple generations of largecale 04:51 04:51 machine learning infrastructure to 04:53 04:53 enable training more and more capable 04:55 04:55 models and TPUs are machine learning 04:58 04:58 focused hardware that uh we were 04:59 04:59 involved in. Um then we've worked on a 05:02 05:02 bunch of different AI research problems 05:04 05:04 uh including some things that are kind 05:06 05:06 of at the core of lots of different uh 05:08 05:08 sort of uh highly capable models today. 05:11 05:11 Things like model distillation or 05:13 05:13 mixture of experts models. uh neural 05:15 05:15 architecture search is kind of a early 05:17 05:17 version of these kind of automated loops 05:19 05:19 that are trying to identify uh capable 05:22 05:22 um you know uh neural model 05:24 05:24 architectures for solving problems and 05:26 05:26 then applications of AI to different uh 05:29 05:29 domains including some in science. So I 05:32 05:32 was uh Quack and I were both involved in 05:34 05:34 alpha chip which is reinforcement 05:35 05:35 learning for chip design and uh 05:37 05:37 placement and routing and chip design. 05:39 05:39 Uh Oral was involved in alpha fold um 05:42 05:42 and neural weather forecasting. So good 05:45 05:45 mix of experience. Uh we've also managed 05:48 05:48 pretty large teams. So I managed uh the 05:50 05:50 Google rating team up to 600 people and 05:52 05:52 the uh Google research and AI teams up 05:55 05:55 to about 4,400 people before I decided I 05:57 05:57 was spending a lot of time in meetings 05:60 05:60 about Q2 OPEX spending. And I pressed 06:02 06:02 send on an email that took me from 4,400 06:05 06:05 to four reports. Uh and then Jeff Hinton 06:08 06:08 retired the next week. So then I had 06:10 06:10 three. Um and uh and then I was uh Oriel 06:15 06:15 and I were both uh then co-technical 06:17 06:17 leads of the Gemini project and Quac was 06:20 06:20 our post training lead. We're we're also 06:22 06:22 quite proud of many alumni of our teams 06:24 06:24 who've gone off and done lots of amazing 06:26 06:26 things including founders of companies 06:28 06:28 like Anthropic OpenAI uh physical 06:31 06:31 intelligence uh which is probably known 06:32 06:32 here in Stanford uh various other 06:35 06:35 things. Um, 06:37 06:37 and we think we have a nice blend that 06:40 06:40 mixes both AI research skills and 06:43 06:43 distributed system uh skills. So, uh, 06:46 06:46 these are the most cited AI researchers 06:48 06:48 and distributed systems researchers per 06:49 06:49 Google Scholar. Um, and there's uh us in 06:53 06:53 the red boxes. Also, the people with 06:56 06:56 stars have all worked in our teams, 06:57 06:57 which is nice. 07:01 07:01 We also have fun together. So, we've 07:04 07:04 traveled in many different continents. 07:05 07:05 Uh, there we are in London in the upper 07:08 07:08 right, Patagonia in the upper left, uh, 07:10 07:10 Vietnam in the bottom left, Quac and I 07:12 07:12 are coming home from dinner on Uberlike 07:15 07:15 moped services, and California. 07:19 07:19 Um, so, one of the things we've been 07:21 07:21 focused a lot on is how do we assemble 07:24 07:24 an amazing founding team? And so that's 07:27 07:27 what we've been up to for kind of the 07:29 07:29 first um you know uh period of existence 07:32 07:32 of the company is hiring amazing people. 07:34 07:34 Um and they've come from lots of 07:37 07:37 different places. You see all the logos. 07:39 07:39 Um many of the people on the team have 07:41 07:41 experience founding various companies or 07:43 07:43 various important projects. Uh we have a 07:45 07:45 lot of great team experience in you know 07:48 07:48 leading the Go programming language 07:51 07:51 project. uh you know uh key contributors 07:54 07:54 to lots of different models Gemini Deepc 07:56 07:56 Kimmy K3 uh the co-author of the Kimmy 07:60 07:60 K3 paper and author of the tension 08:02 08:02 residuals work joined us um recently we 08:05 08:05 have a great mix of kind of very 08:07 08:07 experienced people and people who are uh 08:10 08:10 rising amazing uh stars earlier in their 08:13 08:13 career um and roughly 10% of the company 08:16 08:16 has the ACM prize in computing and 10% 08:19 08:19 have uh III olymp medals. So, we're 08:23 08:23 we're pretty pretty excited about the 08:25 08:25 team we've hired. We're having we've 08:26 08:26 also uh really focused on hiring for um 08:30 08:30 kind of amazing uh ability to work 08:33 08:33 together, which was uh great fun. Um so, 08:37 08:37 why now? 08:39 08:39 You know, we now have these really 08:41 08:41 capable frontier models that can do lots 08:43 08:43 of amazing things. Um and partly that 08:46 08:46 came about because there's been 08:48 08:48 innovation over the last say 10 to 15 08:50 08:50 years in the AI space of you know lots 08:54 08:54 of different kinds of things. The 08:55 08:55 hardware layer has improved tremendously 08:57 08:57 in performance both TPUs and GPUs and 08:60 08:60 sort of more specialized things. 09:02 09:02 Software abstractions have gotten better 09:03 09:03 so that people can implement things they 09:05 09:05 care about more e easily and readily and 09:08 09:08 it can be mapped onto the hardware more 09:09 09:09 efficiently. Um model architectures have 09:12 09:12 improved. you know uh word tovec and 09:14 09:14 then sequence to sequence and then 09:16 09:16 transformers and have all been important 09:19 09:19 innovations in what kind of models we 09:21 09:21 should train. Uh training algorithms 09:23 09:23 have gotten a lot better. Uh things like 09:25 09:25 distillation enable very um high quality 09:28 09:28 but smaller models and inference 09:31 09:31 algorithms have actually improved quite 09:33 09:33 a lot. So you can actually apply more 09:34 09:34 compute at inference time and get better 09:37 09:37 results with things like chain of 09:39 09:39 thought or uh you know inference time 09:41 09:41 compute scaling where you try many ways 09:43 09:43 of solving the problem and then kind of 09:44 09:44 examine uh what's happened. 09:48 09:48 Um but a great base model isn't 09:50 09:50 sufficient for solving some of the 09:51 09:51 problems that we want to solve. So in 09:54 09:54 particular, we think you start with 09:55 09:55 great base models, but then you want 09:58 09:58 agents plus models to be able to really 10:01 10:01 effectively use tools to accomplish 10:03 10:03 really complicated tasks. We want the 10:05 10:05 agents and models to be especially good 10:07 10:07 at creativity. Like coming up with 10:09 10:09 amazing scientific or engineering 10:11 10:11 breakthroughs is not just kind of uh 10:14 10:14 doing things that have already been done 10:16 10:16 in a lot of cases. It's actually putting 10:18 10:18 together lots of insights, coming up 10:20 10:20 with something genuinely new and seeing 10:22 10:22 how well that works. And we also think 10:26 10:26 agents and models should learn from the 10:28 10:28 previous experiments and previous 10:29 10:29 experience. Both in trying to solve this 10:32 10:32 particular problem, but also over time 10:34 10:34 you will see systems that maybe have 10:36 10:36 solved 5,000 problems. and the 5,000 10:39 10:39 first one they can learn from the 10:41 10:41 problems that are sort of most closely 10:43 10:43 related to that particular new problem 10:45 10:45 they're trying to solve. 10:48 10:48 So at this traditional experimental loop 10:50 10:50 you often have a lot of manual effort at 10:52 10:52 each stage. Um and when you try to solve 10:55 10:55 a larger problem uh that becomes even 10:57 10:57 more complicated because you have to 10:60 10:60 typically decompose those larger 11:02 11:02 problems into a bunch of subpros or 11:03 11:03 subpieces. you end up with actually a 11:07 11:07 difficult decomposition problem in and 11:09 11:09 of itself. That's the left hand side. 11:11 11:11 And then each one of these subpros or 11:13 11:13 sub areas has lots of people running 11:16 11:16 experiments, figuring out what worked, 11:18 11:18 what didn't work, trying different 11:19 11:19 approaches. Um, you end up with a lot of 11:21 11:21 coordination overhead. Uh, and resource 11:24 11:24 conflicts can occur, large system 11:26 11:26 effects. So this decomposition that you 11:28 11:28 have to do is actually quite hard. Um, 11:30 11:30 and it's hard to combine the results 11:32 11:32 across the sub problems you set out. And 11:35 11:35 perhaps even worse, the way you've 11:37 11:37 decided to decompose things in a very 11:38 11:38 large scale project is actually pretty 11:41 11:41 hard to change once you've kind of got 11:43 11:43 the project up and running and going. 11:46 11:46 And so you can't really revisit those 11:48 11:48 early decisions very effectively when 11:50 11:50 you have a uh, you know, a large scale 11:52 11:52 engineering effort like building an 11:54 11:54 airplane or training the best frontier 11:57 11:57 model or whatever. Um so what we think 12:00 12:00 is possible is you can take a relatively 12:04 12:04 small number of domain experts use 12:07 12:07 automated systems that can ideally 12:10 12:10 automatically decompose into interesting 12:12 12:12 subpros and then automatically solve 12:14 12:14 those sub problems. But uh it may be 12:18 12:18 that human guidance helps the 12:20 12:20 decomposition and then humans sort of 12:23 12:23 direct what are the important subpros to 12:25 12:25 solve and really enable a small number 12:29 12:29 of people with domain expertise to 12:31 12:31 really tackle large impactful problems. 12:34 12:34 And we think this is possible if you 12:36 12:36 build the right infrastructure. You know 12:39 12:39 you want to have tens of thousands of 12:41 12:41 automated experiments. You want a system 12:43 12:43 that can actually handle that, can 12:45 12:45 understand what is the value of running 12:47 12:47 this experiment versus running that one 12:49 12:49 and decide what to do next. Um, 12:52 12:52 automated decision-m with reinforcement 12:54 12:54 learning or evolutionary algorithms. 12:56 12:56 These earlier decisions can then be more 12:58 12:58 revisited more fluidly. Uh, and every 13:01 13:01 earlier experiment should inform every 13:03 13:03 future experiment. 13:06 13:06 So, what have we done so far? first 13:08 13:08 three weeks we did 400 interviews mostly 13:11 13:11 at Cafe Venetia on University Avenue uh 13:14 13:14 to find amazing people which I talked 13:17 13:17 about some of them uh finding office 13:19 13:19 space we're in the old Zibbo restaurant 13:22 13:22 so we have a funky funky uh space for us 13:25 13:25 uh we've built our own agent harness 13:27 13:27 that we're using for all of our own 13:29 13:29 development and for automation uh that 13:31 13:31 harness has some really nice 13:33 13:33 collaboration features so you can think 13:35 13:35 of it as a system that you interact with 13:38 13:38 uh a model uh to have conversations and 13:40 13:40 ask it to solve things, but you can also 13:42 13:42 invite your teammate in to collaborate. 13:45 13:45 And so then the two of you can talk to 13:47 13:47 uh a model and an agent. Uh and you can 13:50 13:50 also uh have other people add comments 13:52 13:52 to the thing you're trying to do or 13:54 13:54 propose a design to the to the agent 13:56 13:56 before you propose that. Um, we've built 13:60 14:00 really agent first infrastructure so 14:03 14:03 that it will scale to having tens, 14:06 14:06 hundreds of thousands of agents doing 14:08 14:08 things rather than sort of hundreds of 14:10 14:10 people doing things. Uh, we think that's 14:12 14:12 really important and we've got probably 14:14 14:14 on the order of 20 automated improvement 14:16 14:16 loops running in the way in which we 14:19 14:19 imagine uh that we want to solve 14:21 14:21 problems. uh and they're we can see that 14:23 14:23 they're running and improving uh 14:25 14:25 evaluating methods for making these 14:27 14:27 experimental iterations more effective 14:29 14:29 and faster. How do you learn from the 14:31 14:31 previous experimental results and the 14:33 14:33 previous uh measurements you're able to 14:35 14:35 take in order to um learn faster? 14:40 14:40 Um, so this is kind of where we are is 14:43 14:43 building this software loop to automate 14:46 14:46 machine learning tasks in particular 14:49 14:49 because we think that unlocks actually a 14:51 14:51 lot of uh autonomous science. Um, but 14:53 14:53 initially we're focused on how do you 14:55 14:55 make automated systems that can solve 14:57 14:57 machine learning research and machine 14:59 14:59 learning engineering problems. Uh, and 15:01 15:01 our goal is really to make domain 15:03 15:03 experts 20x to 50x more productive. You 15:05 15:05 know, we're not trying to take people 15:07 15:07 out of what we're doing. We're just 15:09 15:09 trying to make uh really empower amazing 15:12 15:12 people or or domain experts who want to 15:15 15:15 solve particular problems and enable 15:16 15:16 them to solve those problems with less 15:19 15:19 effort on their part. 15:21 15:21 Um so we think 15 years of breakthroughs 15:24 15:24 in machine learning have led to very 15:26 15:26 capable models setting the stage for 15:29 15:29 automating ML science and engineering to 15:32 15:32 dramatically accelerate discovery. Thank 15:34 15:34 you. Oh, and we're hiring apply at 15:37 15:37 discovery.com. 15:39 15:39 Thank you. [applause] 15:46 15:46 >> Oh, this one. Okay. Sorry, I took your 15:48 15:48 water then. 15:49 15:49 >> No worries. 15:51 15:51 All right, that was a great talk. 15:53 15:53 Thanks, Jeff. And uh I think this is the 15:57 15:57 last session or Okay, so we're we're 15:60 15:60 between the audience and snacks or 16:03 16:03 >> never a good place to be. 16:04 16:04 >> Yeah. So um I was hoping that we can be 16:09 16:09 a little bit nerdier for this session 16:11 16:11 and and get into a little bit of the 16:13 16:13 tech, but uh uh we're supposed to have a 16:16 16:16 mutual discussion. So I'm going to wear 16:18 16:18 the dual hat of asking you questions but 16:21 16:21 participate uh in the discussion. Um 16:24 16:24 thanks for for that talk because I was 16:27 16:27 going to ask you why as my first 16:29 16:29 question for for discovery loop. 16:31 16:31 Congratulations. 16:32 16:32 >> Thank you. 16:33 16:33 >> And pretty much all four of you are more 16:36 16:36 or less Google lifers 16:39 16:39 especially you joined Google 1999 right? 16:42 16:42 Yeah. So it is very clear what the macro 16:46 16:46 incredible technological um progress we 16:49 16:49 have seen the past few years but I'm 16:51 16:51 still curious was there a moment in the 16:55 16:55 past I would have guessed a year or two 16:59 16:59 years that the technology has moved into 17:03 17:03 a state that you and Qua and San and 17:06 17:06 Oreo are like we've got to do something 17:10 17:10 different and we cannot do it at Google. 17:13 17:13 >> Yeah. Yeah, I mean I think we all have 17:15 17:15 been excited about seeing automated 17:18 17:18 systems that can actually sort of help 17:21 17:21 people develop software systems in a 17:23 17:23 more automated way or um you know Quark 17:26 17:26 did a lot of work on neural architecture 17:28 17:28 search even a decade ago which you can 17:30 17:30 think of as an early version of that 17:32 17:32 kind of recursive uh sort of 17:34 17:34 self-improving system where you say 17:37 17:37 please find me great model architectures 17:38 17:38 for computer vision problems or um you 17:42 17:42 know language problems 17:44 17:44 And so we knew that kind of approach 17:46 17:46 worked. Uh but in that day you were sort 17:49 17:49 of specifying the search space as like a 17:51 17:51 junk janky kind of pile of Python code 17:54 17:54 rather than natural language as you can 17:56 17:56 today. You can sort of describe to a you 17:59 17:59 know a capable model plus agent system. 18:02 18:02 You know please go off and explore model 18:04 18:04 architectures that might help interpret 18:06 18:06 MRI images or whatever and it will go 18:08 18:08 off and do that. And you know, I think 18:11 18:11 that's just a really profound shift in 18:15 18:15 what we imagine computer systems and AI 18:19 18:19 systems to be able to do, right? And if 18:21 18:21 you can get those things working 18:23 18:23 reliably, that enables a lot of stuff. 18:25 18:25 And in particular for science and 18:27 18:27 engineering, we think that's a really 18:29 18:29 ripe set of domains for how can you 18:31 18:31 really advance positive impact in the 18:34 18:34 world. 18:35 18:35 >> I see. So it [clears throat] it was 18:36 18:36 really the the loops the self 18:39 18:39 improvement um that that got you guys 18:42 18:42 feel that you want to you want to 18:44 18:44 >> some might say we're a bit loopy but yes 18:48 18:48 >> it is and also I would also say that it 18:51 18:51 was important to us to you know we've 18:54 18:54 enjoyed many times starting very small 18:57 18:57 projects 18:58 18:58 >> and that that feeling of magic when you 19:00 19:00 have five people working on something 19:02 19:02 and you add a sixth and then you add a 19:05 19:05 seventh and 10th and so on. That that 19:07 19:07 really is fun. Um, and you can do that 19:09 19:09 in in large companies, but we also 19:11 19:11 wanted to do this as a public benefit 19:13 19:13 corporation. And we felt like that was 19:14 19:14 an important reflection of our own 19:16 19:16 values and um, you know, taking a bigger 19:19 19:19 plunge of now you have to figure out 19:22 19:22 real estate and all the other things 19:24 19:24 that involve uh, setting up a company. 19:27 19:27 Yeah. 19:27 19:27 >> Is kind of fun. 19:28 19:28 >> Yeah. Cool. Yeah. Even for myself when 19:31 19:31 we started world labs it was a moment of 19:33 19:33 seeing the technology it's different 19:35 19:35 from you guys seeing computer v vision 19:38 19:38 and uh generative models have converged 19:41 19:41 at the moment where we can create 19:44 19:44 generative models beyond just words was 19:46 19:46 the moment for for us to start world 19:49 19:49 labs. 19:49 19:49 >> Yeah I mean like the generative models 19:51 19:51 you you all produced and that are now 19:53 19:53 cap possible are pretty amazing. 19:55 19:55 >> Yeah. Okay. So let's talk about this 19:58 19:58 self uh improvement and recursive 20:01 20:01 self-improvement. This let's nerd out a 20:04 20:04 little bit. We're contemporaries in 20:06 20:06 terms of our education through the field 20:09 20:09 of machine learning. I was just thinking 20:11 20:11 in my head how many names this concept 20:16 20:16 has had in its lifetime 20:19 20:19 from me being a grad student. We had 20:21 20:21 incremental learning then we had 20:23 20:23 continuous learning. Then we had 20:25 20:25 metalarning. Then we had um um lifelong 20:30 20:30 learning and um and neuro architecture 20:34 20:34 search. You mentioned that now we have 20:37 20:37 >> self-improvement and then RSI. So do you 20:41 20:41 want to just treat the audience 20:44 20:44 especially the technical part of the 20:45 20:45 audience with some more rigorous 20:49 20:49 definition of what is self-improvement 20:52 20:52 versus what is recursive 20:54 20:54 self-improvement and my goal here is 20:57 20:57 actually to tease apart the fiction from 20:59 20:59 the the facts. 21:01 21:01 >> Sure. Yeah. I mean, I think if you think 21:03 21:03 about those loops that I talked about, 21:06 21:06 those loops can be applied to many kinds 21:08 21:08 of problems. And I I think of like 21:11 21:11 self-improving systems as one thing 21:14 21:14 where you've given the system a goal to 21:16 21:16 go off and improve you typically some 21:19 21:19 measurable objective like accuracy of an 21:22 21:22 ML model and you know it's trying 21:25 21:25 different kinds of model architectures 21:26 21:26 to improve that accuracy. Um 21:28 21:28 >> so pause there. A lot of machine 21:31 21:31 learning algorithm is capable of doing 21:33 21:33 that. 21:33 21:33 >> Yes, 21:34 21:34 >> that itself is not recursive. Correct. 21:36 21:36 >> That is a 21:37 21:37 >> so like self-improving system might be 21:40 21:40 >> we point our system at uh a bunch of MRI 21:43 21:43 data and MRI outcomes and we say I want 21:46 21:46 to develop a model to help interpret MRI 21:49 21:49 images. 21:49 21:49 >> Right? That would be a self-improving 21:51 21:51 system where you can develop that model 21:53 21:53 automatically and can then you know see 21:56 21:56 the accuracy of the different approaches 21:57 21:57 that are being tried hopefully increase 21:60 21:60 as the system runs experiments but 22:01 22:01 that's not improving the base system 22:04 22:04 that is proposing what to try next right 22:07 22:07 >> y 22:08 22:08 >> so recursive self-improvement is how do 22:10 22:10 you actually get that kind of 22:13 22:13 self-improving approach to improve the 22:16 22:16 base system that you're trying to get to 22:19 22:19 propose improve 22:21 22:21 if that makes sense. 22:22 22:22 >> Yes, it makes sense to me. But I think 22:24 22:24 it's it's a very confusing concept for 22:28 22:28 people and now 22:30 22:30 >> especially we're in the Silicon Valley 22:33 22:33 people are just using this in a very 22:34 22:34 loose way and as a scientist actually 22:37 22:37 bothers me. So I I just want to be on 22:40 22:40 this stage to make sure we define uh RSI 22:43 22:43 really well. So let's talk about the 22:45 22:45 physical limit of RSI though because 22:48 22:48 >> RSI is being hyped in a way it's almost 22:52 22:52 like 22:54 22:54 it's almost a system it has no end we 22:58 22:58 can just accelerate and and and uh to 23:02 23:02 the infinity 23:04 23:04 >> what is the hype here what is the 23:06 23:06 fiction from 23:06 23:06 >> well I mean I think 23:09 23:09 if you think about how humans improve 23:11 23:11 the capabilities of models which is what 23:13 23:13 has been happening over the past 10 or 23:14 23:14 15 years, you know, you get really 23:17 23:17 capable researchers and ML engineers 23:19 23:19 together. They run lots of experiments 23:21 23:21 at small scale. They say, "Oh, these 23:23 23:23 these ideas didn't work. These ideas did 23:26 23:26 work." Then we're going to incorporate 23:28 23:28 those into the next generation of the 23:30 23:30 the model. That's how Gemini 2 comes to 23:32 23:32 be after we had Gemini 1 is we ran lots 23:36 23:36 of experiments and and improve things. 23:39 23:39 So that is kind of a normal process of 23:44 23:44 research and engineering. And I think 23:46 23:46 when you start to apply automation to 23:48 23:48 that, yes, you can get some uh 23:50 23:50 improvements in how fast that can 23:52 23:52 happen. You know, people will start off 23:55 23:55 an experiment, the experiment will 23:56 23:56 finish at 2 a.m. They're not into the 23:58 23:58 desk until 8 a.m. to look at the 23:60 24:00 results. So if you have an automated 24:02 24:02 system, it can look at the results, 24:04 24:04 figure out what to do next in a much 24:05 24:05 more automated way. can maybe make a lot 24:08 24:08 better decisions across um an entire 24:11 24:11 large project than each individual 24:13 24:13 making decisions about what to do next. 24:15 24:15 Um so but I think if you think about 24:19 24:19 what needs to be involved in creating 24:23 24:23 the base model and making it better, 24:25 24:25 there's lots of actual training and 24:28 24:28 experimentation that needs to be done 24:30 24:30 and that's not going to happen 24:31 24:31 infinitely fast, right? Like there's 24:33 24:33 just actual physics involved. you need 24:35 24:35 to run some computations. You need to 24:37 24:37 look at the results. The computers need 24:39 24:39 to like do their thing and you need to 24:41 24:41 save the data, look at the have another 24:43 24:43 model look at the results in order to 24:45 24:45 decide what to do next. So I think some 24:47 24:47 people have this illusion that you're 24:49 24:49 going to be able to run this loop 24:51 24:51 infinitely fast. And I just find that 24:54 24:54 kind of uh implausible. 24:56 24:56 >> Yeah. 24:57 24:57 >> And it's good to be grounded in reality. 24:59 24:59 Having automated systems that can try to 25:02 25:02 improve things is definitely going to 25:04 25:04 improve the rate at which we can improve 25:06 25:06 those systems, but it's not going to 25:08 25:08 make them go infinitely fast. 25:10 25:10 >> I think this is a really important 25:11 25:11 point. Actually, I was I I was not 25:14 25:14 applying for a job at Discovery Loop, 25:16 25:16 but I was at the cafe Valencia with 25:19 25:19 Jeff. 25:20 25:20 >> We we have coffee for many reasons cuz 25:23 25:23 we like coffee 25:24 25:24 >> and we have been talking about this RSI, 25:26 25:26 you know, hype and facts. We were 25:29 25:29 actually prepping for this panel 25:30 25:30 >> apparently. But I was thinking, okay, 25:33 25:33 this is something I haven't run by you, 25:35 25:35 but tell me if you agree or you can 25:37 25:37 disagree with me, that's totally fine. I 25:39 25:39 was thinking about humans. Are we a 25:42 25:42 self-improving system or are we a RSI 25:45 25:45 system? And I think we're definitely a 25:49 25:49 self-improving system. I suck at 25:51 25:51 cooking. And if I were to try to cook 25:55 25:55 omelette, the first time will really I 25:58 25:58 will burn everything and sec I would 25:60 25:60 have learned and the second time I would 26:02 26:02 do it a little better. Maybe after 10 26:04 26:04 times I'll have a reasonable omelette 26:06 26:06 that I will eat. My kids will not eat. 26:08 26:08 And that is self-improving. 26:11 26:11 >> But the RSI part has to do with my 26:14 26:14 brain. In this process of learning how 26:17 26:17 to cook omelette, first of all, my brain 26:20 26:20 has to be somehow altered. Some neurono 26:25 26:25 connectivity needs to be altered enough 26:27 26:27 that I have a better system to to cook 26:32 26:32 omelette. Maybe I can grow the six 26:34 26:34 finger 26:36 26:36 if if if I'm we're talking about 26:38 26:38 hardware improvement. And then by 26:40 26:40 cooking more, 26:41 26:41 >> maybe you're AI generated. 26:43 26:43 >> Exactly. [laughter] 26:44 26:44 And then the by cooking more omelette my 26:47 26:47 brain will be further improved. And if 26:50 26:50 that loop happens humans is a RSI 26:53 26:53 system. Is that the right analogy? 26:55 26:55 >> Yeah. I mean I I I think the answer is 26:57 26:57 humans are both right. 26:58 26:58 >> Right. I mean I think through your life 27:02 27:02 you learn a lot of things which is 27:04 27:04 self-improving like you learn how to 27:05 27:05 cook an omelette, you learn how to do 27:07 27:07 calculus problems but you also learn 27:11 27:11 how you learn. 27:12 27:12 >> Yeah. And that learning how to learn 27:14 27:14 actually is what makes you 27:16 27:16 self-improving uh and recursively 27:19 27:19 self-improving because now you can learn 27:21 27:21 to do more things more quickly based on 27:24 27:24 you understanding what kinds of 27:26 27:26 knowledge and the ways to get 27:28 27:28 information presented to you actually 27:30 27:30 makes you most effectively pick it up. 27:32 27:32 >> Yeah. and possibly recursive self-learn 27:37 27:37 recursive self-improvement is po 27:40 27:40 possibly a evolutionary algorithm right 27:43 27:43 so anyway I don't want to go down the 27:45 27:45 rabbit hole but I think it's just a rare 27:47 27:47 opportunity to have this discussion in 27:50 27:50 public because we have so much hype 27:51 27:51 right now about 27:52 27:52 >> concept I mean there's a lot of hype 27:54 27:54 about like oh it's going to go 27:55 27:55 infinitely fast and it's scary I mean I 27:57 27:57 think if you look at it as a pragmatic 27:59 27:59 engineering system doing things it 28:02 28:02 becomes 28:03 28:03 less scary and more understandable. 28:05 28:05 >> Yeah. So, you talked a lot about 28:07 28:07 discovery loop, but you were still a 28:10 28:10 little vague about what scientific 28:13 28:13 problem you want to work on. So, I want 28:15 28:15 to just push you a little bit to the 28:17 28:17 extent you're willing to review. 28:19 28:19 >> Yeah. 28:20 28:20 >> There are so many scientific problems. 28:22 28:22 >> Yes. 28:23 28:23 >> That benefits the society. Which one are 28:26 28:26 you passionate about right now? 28:28 28:28 >> Yeah. So uh 28:31 28:31 I I I for a long time I've been very 28:33 28:33 interested in AI for healthcare. I think 28:35 28:35 that's an area that we will do work in 28:37 28:37 not right now but down the road because 28:39 28:39 I think it's a super important area and 28:42 28:42 there's that would be very aligned with 28:44 28:44 our sort of uh public benefit 28:46 28:46 corporation mission. Um right now we are 28:50 28:50 focused on the kinds of science and 28:52 28:52 engineering problems where you can do 28:54 28:54 the entire loop in digital form because 28:56 28:56 we think that's the one where you it's 28:58 28:58 very amenable to speeding up that entire 28:60 28:60 loop. If you have like actual physical 29:02 29:02 processes involved that be that becomes 29:04 29:04 harder to speed up the loop. There are 29:06 29:06 obviously many important disciplines 29:08 29:08 where you do need physical processes. Um 29:11 29:11 and we have some hybrid you know 29:13 29:13 approaches that we can speed things up 29:15 29:15 in those domains. 29:17 29:17 So let's look at digital domains of 29:20 29:20 science and engineering. We are 29:22 29:22 initially focused on machine learning 29:24 29:24 research and machine learning 29:25 29:25 engineering itself and uh also hardware 29:29 29:29 design because we think those three 29:31 29:31 areas are you know it's good to have a 29:32 29:32 few different areas you focus on not try 29:34 29:34 to focus on everything but um those are 29:38 29:38 ones where we have domain expertise 29:40 29:40 ourselves and we think it's a really 29:42 29:42 important domain because it unlocks 29:44 29:44 other things. So for example in 29:46 29:46 healthcare if you have a system that can 29:48 29:48 automatically solve machine learning 29:51 29:51 engineering tasks 29:53 29:53 then you can point it at lots of 29:54 29:54 problems in in health or you know 29:56 29:56 material science or or whatever and say 29:59 29:59 I would like an amazing model 30:01 30:01 specialized to this particular task 30:03 30:03 which is say self-improvement but not 30:05 30:05 recursive self-improvement. Um so that 30:09 30:09 uh I think is how I would describe us. 30:11 30:11 We're focused on those three domains 30:14 30:14 right now, but we have ambitions to go 30:17 30:17 beyond. 30:18 30:18 >> Okay. So, I was trained in physics when 30:22 30:22 I was undergrad and um in in physics we 30:28 30:28 have this division between 30:30 30:30 experimentalists and theorists, right? I 30:33 30:33 was just thinking about your loop the 30:35 30:35 the way you draw it. you call it propose 30:37 30:37 uh experiment but in physics we tend to 30:40 30:40 call it hypothesis 30:42 30:42 form hypothesis and then you go through 30:44 30:44 um experiments and evaluation and all 30:47 30:47 that I was just thinking the way how 30:50 30:50 powerful AI agents or these uh 30:52 30:52 self-improvement loops whether it's 30:54 30:54 recursive or not doesn't matter is it 30:58 30:58 philosophically taking a stance that it 31:02 31:02 is more a experimental experimentalist 31:05 31:05 approach approach to a scientific 31:07 31:07 question because it's very much about 31:10 31:10 proposing hypothesis and parallel um 31:13 31:13 parallelized experiments 31:16 31:16 um as opposed to the more mysterious 31:20 31:20 theoretical way of doing scientific 31:22 31:22 discovery. You know to this day you 31:25 31:25 would wonder 31:27 31:27 great theorists like Newton like 31:31 31:31 Einstein they don't do that much 31:33 31:33 experiments they read about them but 31:35 31:35 they don't do it that way so do you feel 31:38 31:38 this is 31:40 31:40 skewing towards experimentalism 31:42 31:42 >> I don't think necessarily but I it 31:46 31:46 probably there's a bit more leaning in 31:47 31:47 that direction I do think you could do 31:49 31:49 great theoretical work with a system 31:51 31:51 like this where you can propose 31:53 31:53 Can you see if you can come up with a 31:55 31:55 theory that explains all of this data? 31:58 31:58 >> And I think it could iterate and try to 31:60 32:00 find kind of the minimal description 32:03 32:03 theory that helps explain experimental 32:05 32:05 data. You know, I think a lot of 32:06 32:06 experimental or theoretical physicists 32:09 32:09 are seeing what's happening in the 32:11 32:11 physics community in the experimental 32:13 32:13 domain and then trying to come up with 32:16 32:16 theories that explain, you know, some of 32:18 32:18 the things that people are observing and 32:19 32:19 don't understand yet. So I I think that 32:21 32:21 that's certainly possible and I think 32:23 32:23 actually it's not good to think of these 32:26 32:26 as separate disciplines. 32:28 32:28 >> Right. 32:28 32:28 >> Right. So theoretical physics, 32:30 32:30 experimental physics, you know, 32:32 32:32 chemistry. 32:33 32:33 If you think about a system that 32:35 32:35 actually has say performed 32:38 32:38 a mil solved a million problems with a 32:40 32:40 100 million experiments across a 100 32:43 32:43 domains of science. you know, it's very 32:45 32:45 likely going to have a lot of insights 32:47 32:47 that derive from the fact that it has 32:49 32:49 seen lots of different kinds of, you 32:51 32:51 know, experimental results and seen like 32:53 32:53 what worked and what didn't work in sort 32:56 32:56 of the neighborhood of of interesting 32:58 32:58 problems you're going to ask it to solve 32:59 32:59 in the future. And that I think doesn't 33:02 33:02 have hard domain boundaries the way we 33:05 33:05 as people sometimes tend to instill 33:08 33:08 with, you know, academic department 33:10 33:10 boundaries or whatever. One of the 33:11 33:11 things I like about HAI is it's like a a 33:14 33:14 pan institutional thing that gets 33:16 33:16 everyone talking together. So, 33:18 33:18 >> okay. Well, we have to talk about math 33:19 33:19 then. Um, in fact, when we were 33:22 33:22 proposing to have to invite you to this 33:25 33:25 panel, I I actually was going to did 33:28 33:28 invite Terrence to so I called him but 33:31 33:31 he couldn't come. So, let's talk about 33:33 33:33 math a little bit. Um I have to say 33:38 33:38 I had mixed emotions when we see a 33:42 33:42 serious it was first Navier Stokes and 33:45 33:45 there 720 33:47 33:47 you know papers and then there's more 33:49 33:49 that was just released uh by open AI 33:52 33:52 yesterday that uh AI solving math it's 33:55 33:55 not it's obviously it's not that I don't 33:59 33:59 like what's being discovered it's just 34:01 34:01 that math and the century old, 34:05 34:05 millennial old math problem is such a 34:07 34:07 pinnacle of human intelligence and and 34:10 34:10 mathematicians see not only technical 34:15 34:15 solutions in them, they also see beauty. 34:17 34:17 They they and and now we have a 34:20 34:20 completely different machinery that goes 34:23 34:23 about these problems. Right. Yeah. 34:25 34:25 >> So Terrace and I actually talked quite a 34:29 34:29 bit about that and he recently also just 34:31 34:31 gave a talk about math 2.0 O and talk 34:34 34:34 about how AI can help solve certain type 34:37 34:37 of math problems, but he also does 34:40 34:40 insist there are other math problems 34:43 34:43 that are out of the realm of AI, 34:45 34:45 especially if we don't have solutions, 34:47 34:47 there's no data. What's your take of 34:51 34:51 this pretty 34:54 34:54 big tsunami change in math in the past 34:57 34:57 few literally weeks? Yeah, I mean 34:60 34:60 clearly the models and the sort of 35:03 35:03 agent-based systems that can use those 35:05 35:05 models in highly parallel, you know, 35:08 35:08 agent uh activity are capable of solving 35:11 35:11 really hard unopen, you know, unsolved 35:14 35:14 problems in mathematics and that is a a 35:17 35:17 revelation of what is possible now. I 35:20 35:20 think people are find it finding it 35:22 35:22 dissatisfying and a little uh uh 35:26 35:26 discomforting because often those proofs 35:29 35:29 involve like 500 pages of lean or 35:32 35:32 something. So it's machine checked but 35:34 35:34 no one quite understands the elegant 35:38 35:38 simple way to get to that result. And so 35:40 35:40 I think um it is good to know that the 35:44 35:44 problem is proven or disproven. But 35:48 35:48 understanding how to develop really 35:50 35:50 elegant techniques to come to that 35:52 35:52 result is often what happens as 35:56 35:56 mathematician human mathematicians are 35:58 35:58 wrestling with problems and those often 35:60 35:60 generate new kinds of mathematics that 36:01 36:01 can be used to tackle other kinds of 36:03 36:03 problems. So, 36:05 36:05 >> so you're saying new math pro solutions 36:07 36:07 only come from elegant solutions. 36:10 36:10 >> No, but I think the like the elegant an 36:13 36:13 elegant solution often comes up with a 36:15 36:15 cool technique that can then be applied 36:17 36:17 to other problems. Um, and I don't think 36:20 36:20 people are necessarily seeing the crisp 36:24 36:24 elegant technique in some of these very 36:25 36:25 long machine generated proofs. But 36:28 36:28 knowing that something is solvable is 36:31 36:31 actually uh in lots of different 36:33 36:33 domains. If you know something is 36:36 36:36 possible, it actually makes you strive 36:38 36:38 harder and understand that you can do it 36:41 36:41 and then you actually can do it perhaps 36:43 36:43 a different way or in a more elegant 36:45 36:45 way. And so I think actually yes, it's 36:48 36:48 kind of upsetting about 36:52 36:52 you know the way in which so many of 36:54 36:54 these problems are being solved 36:55 36:55 simultaneously. 36:56 36:56 But I do think you know that is now 36:59 36:59 possible. So people have to figure out 37:01 37:01 how does how do we take advantage of 37:03 37:03 this capability to do even more math. 37:07 37:07 >> We can we can talk so much more about 37:09 37:09 this. But I did promise Jeff I'm going 37:12 37:12 to give him a pop quiz on stage 37:14 37:14 [laughter] and I did not tell him what 37:16 37:16 the problem is. Um but it's okay if you 37:18 37:18 don't know how to answer it because I 37:20 37:20 did not remember myself. Um 37:23 37:23 >> great. No pressure. 37:24 37:24 >> Yeah. [laughter] 37:26 37:26 Do you remember the significance of this 37:28 37:28 date? June 12th, 2017. 37:32 37:32 >> June 12th, 2017. 37:34 37:34 >> Don't cheat with AI. 37:35 37:35 >> Oh, well, is that the Transformer paper 37:37 37:37 on archive? 37:39 37:39 >> Amazing. A+. Okay. We have to uh Yes, 37:43 37:43 that's uh I I had to check which date it 37:47 37:47 was. Did you read the transformer paper 37:49 37:49 on June 12th? 37:51 37:51 >> No, probably June 10th. 37:53 37:53 >> Oh, yes, that's true. you had to approve 37:55 37:55 that probably. So um you know your 37:59 37:59 career I I want to reflect a little bit 38:02 38:02 um you know we can talk so much you you 38:06 38:06 one of your slides showed the four of 38:08 38:08 you collectively have done so much at 38:09 38:09 Google and for our field and uh but 38:13 38:13 let's just use that day as kind of a 38:16 38:16 historical date some kind of beginning 38:18 38:18 of you know the generative model era 38:22 38:22 just share with us in this journey since 38:26 38:26 almost 10 years, nine years now. 38:29 38:29 What in your mind, maybe we don't have 38:32 38:32 time to say all of them, but the top 38:34 38:34 most significant 38:36 38:36 technical moment um that for yourself 38:40 38:40 personally and I also want to follow up 38:43 38:43 by asking you what and intellectually 38:46 38:46 honestly what did you predict correctly 38:50 38:50 and what did you predict incorrectly? 38:54 38:54 >> Yeah. Uh let's see. Um I mean I think a 38:59 38:59 handful of things stand out among a 39:02 39:02 bunch of major improvements to all the 39:05 39:05 things we did like that slide that had 39:06 39:06 all those different advances. You know I 39:09 39:09 think obviously transformers are a much 39:12 39:12 more efficient model architecture for 39:14 39:14 tackling these problems. The the idea 39:16 39:16 that you save all the states you went 39:18 39:18 through and then you attend to them 39:20 39:20 rather than trying to update a small 39:22 39:22 vector with every state and and forget 39:24 39:24 the previous ones was a really great 39:26 39:26 insight and has led to um you know many 39:32 39:32 many many advances because the models 39:34 39:34 can now learn so much more with so much 39:38 39:38 less computation than earlier say 39:40 39:40 LSTMbased models. Um, you know, I think 39:44 39:44 uh uh the distillation work that Oral uh 39:48 39:48 I and Jeff Hinton did uh which was 39:50 39:50 actually rejected from Nurips in 2016 39:53 39:53 saying it's unlikely to have any impact. 39:55 39:55 >> We should have a website that collects 39:57 39:57 all the rejected papers that 39:59 39:59 >> I mean that one's pretty good. We threw 39:60 39:60 it on archive. It has 40,000 citations 40:02 40:02 or something. Um, so I mean that I think 40:05 40:05 has been key to making really capable 40:10 40:10 inference efficient models because you 40:12 40:12 can take a very large model you've 40:13 40:13 trained that's really unwieldy and 40:15 40:15 expensive and you can use use it as the 40:17 40:17 teacher to teach your now nimble 40:20 40:20 lightweight uh um student model is a 40:23 40:23 good thing to say in a university. 40:26 40:26 >> Um and so that that's been good. Thee 40:28 40:28 architecture I think has proven to be 40:31 40:31 good. I think one one thing I kind of 40:33 40:33 thought was that was going to be the 40:34 40:34 beginning of a much more exotic set of 40:37 40:37 model architectures. I think you know 40:39 40:39 the MOE architecture is you you branch 40:41 40:41 out to a bunch of experts and then you 40:43 40:43 come back and then you branch out again 40:45 40:45 and you come back and you learn which 40:47 40:47 experts are good at which things which 40:49 40:49 is super useful. Gives your model a lot 40:51 40:51 of capacity but you then activate only a 40:53 40:53 tiny portion of it for any given token 40:56 40:56 or task or whatever. But it's still very 40:59 40:59 regular like in most all the experts are 41:02 41:02 the same size. Uh they come back 41:04 41:04 together immediately. I thought we were 41:06 41:06 going to start to have like a 41:07 41:07 flourishing of like really wild exotic 41:10 41:10 more organic looking neural network 41:12 41:12 architectures with like columns of 41:15 41:15 neural activity and they come back 41:17 41:17 together and some like very uh 41:20 41:20 infrequent but uh large cross 41:23 41:23 connections that would let you connect 41:25 41:25 distal parts of your your neural net. We 41:27 41:27 haven't done that yet. 41:29 41:29 >> I mean, we thought about that for neuro 41:31 41:31 architecture search and that didn't pan 41:33 41:33 out either, right? 41:34 41:34 >> Yeah. I mean, the the models that come 41:36 41:36 out of neural architecture search do 41:38 41:38 look much more organic than the kind of 41:40 41:40 human design ones where you have like a 41:42 41:42 bunch of layers. Occasionally, you have 41:44 41:44 a skip connection every layer. The 41:46 41:46 neural architecture search ones have 41:48 41:48 like these crazy like 41:50 41:50 >> connect this one up to 17 layers above 41:52 41:52 and do do something funky. 41:54 41:54 >> Yep. So, so that was the one thing you 41:56 41:56 think you predicted wrong? 41:58 41:58 >> Well, I mean, I predicted many things 42:00 42:00 wrong. Uh, that's one that that kind of 42:02 42:02 stands out as like I thought thee push 42:05 42:05 would go more in that direction. 42:08 42:08 >> Um, 42:10 42:10 you know, I think uh 42:14 42:14 coding capability has proven to be 42:16 42:16 really important. I didn't necessarily 42:17 42:17 foresee that was such a key unlocker for 42:21 42:21 making these models really capable in 42:23 42:23 solving lots of different kinds of 42:24 42:24 problems. 42:25 42:25 >> Um, so 42:26 42:26 >> that that one I I'm with you. We all 42:30 42:30 kind of know but did not 42:33 42:33 predict the level of profoundness. 42:36 42:36 >> So like in the Gemini effort, it was one 42:38 42:38 of the things we focused on but it was 42:39 42:39 not like the thing we focused on. 42:43 42:43 >> Okay. Well, there's one thing that 42:45 42:45 obviously we've been nerding out a 42:47 42:47 little bit there. We're in the HAI 42:50 42:50 conference, so we have to talk about the 42:51 42:51 letter H. The one thing I wonder if you 42:56 42:56 predicted and how do you feel about that 42:59 42:59 is all the societal impact of AI and 43:02 43:02 it's not just impact part of the impact 43:06 43:06 is there's jobs there's the 43:09 43:09 psychological anxiety there is the the 43:12 43:12 the the the right now the almost extreme 43:15 43:15 talk about human extinction and 43:19 43:19 alignment and all that. Well, first of 43:21 43:21 all, which one which one worries you the 43:25 43:25 most? 43:26 43:26 >> Yeah. I mean, I think there this is a 43:30 43:30 incredibly powerful and general 43:32 43:32 technology and it's influencing nearly 43:34 43:34 every aspect of society, right? Like all 43:37 43:37 the things that it can do in mathematics 43:38 43:38 or in coding or in generating images or 43:43 43:43 um you know helping autonomous vehicles 43:45 43:45 drive. So it's unlike previous 43:49 43:49 technological revolutions which were a 43:51 43:51 little bit more narrowly contained and 43:52 43:52 affected deeply five or 10 fields. This 43:56 43:56 is simultaneously affecting lots and 43:58 43:58 lots of things in the world. I think 43:60 43:60 that's a 44:01 44:01 you know it's a source of excitement 44:05 44:05 apprehension you know fear all the 44:08 44:08 things. I actually was a co-author with 44:10 44:10 an amazing set of co-authors on a paper 44:13 44:13 called shaping AI's impact on billions 44:15 44:15 of people. Uh it has my only paper with 44:18 44:18 its own website because we had an 44:19 44:19 inspired co-author shapingai.com. 44:22 44:22 Um but really we looked at seven 44:24 44:24 different areas including uh employment 44:27 44:27 and e and economy um AI for science uh 44:32 44:32 you know dis misinformation 44:34 44:34 disinformation 44:36 44:36 uh media 44:37 44:37 uh and um a few other uh domains and we 44:42 44:42 talked to lots of domain experts in each 44:43 44:43 of those domains and I think that was a 44:45 44:45 a great experience because we got their 44:47 44:47 perspective on how AI was already in 44:50 44:50 impacting their fields and how it was 44:52 44:52 likely to impact fields in the future. I 44:54 44:54 mean, the one I worry about the most is 44:56 44:56 just 44:58 44:58 how do we capture 44:60 44:60 as much of the upsides of what AI can do 45:02 45:02 across all these different domains while 45:05 45:05 minimizing the downsides in many many 45:07 45:07 areas that it uh can affect negatively. 45:10 45:10 And I think that is not purely a 45:13 45:13 technical problem. That is a technical 45:15 45:15 problem, a regulatory and policymaker 45:18 45:18 problem, a citizen problem. and we all 45:22 45:22 need to kind of figure out how do we 45:24 45:24 navigate this uh new technology to make 45:27 45:27 sure that it serves us all extremely 45:29 45:29 well and doesn't uh harm us in ways that 45:33 45:33 we think it could in you know so that's 45:35 45:35 things like making sure when people's 45:38 45:38 jobs are affected they have good ways to 45:40 45:40 adapt to what is now needed in the world 45:43 45:43 and and can you know be um you know 45:47 45:47 gainfully employed and and have a 45:50 45:50 prosperous existence and I think that is 45:52 45:52 a you know a a multiaceted thing that 45:56 45:56 needs to be tackled by lots of people in 45:57 45:57 the world. 45:58 45:58 >> So one thing this is my last question 46:01 46:01 one thing I know about you Jeff that 46:03 46:03 most of the public doesn't know is how 46:05 46:05 much you care about the young generation 46:08 46:08 we don't get to be on stage too often 46:12 46:12 but in my memory the last time we're in 46:15 46:15 a conference together was Grace Hopper 46:18 46:18 conference. Oh, 46:19 46:19 >> you know, you you were going there for 46:20 46:21 [clears throat] multiple years. 46:22 46:22 >> So, right now we're on a university 46:24 46:24 campus 46:25 46:25 >> and frankly this generation of college 46:29 46:29 students are anxious. 46:31 46:31 >> They they're even Stanford students are 46:34 46:34 anxious about jobs, anxious about the 46:37 46:37 future of economy. And as someone who 46:41 46:41 has, you know, such a successful career, 46:45 46:45 been an an and a pioneer in AI and also, 46:49 46:49 you know, just just 46:52 46:52 seeing the future in ways that the 46:54 46:54 public doesn't see yet. What is your 46:57 46:57 parting words for for the students in 46:60 46:60 this audience who are feeling that 47:02 47:02 anxiety and possibly also hope? Yeah, I 47:05 47:05 mean I think it I understand the anxiety 47:09 47:09 for sure because the world is changing 47:11 47:11 quickly and I think if you're planning 47:13 47:13 for a future, it's very hard to plan for 47:16 47:16 a future that is changing quickly. 47:18 47:18 >> Yeah. Um, and so, you know, I think the 47:22 47:22 general advice I have is try to 47:24 47:24 understand what new technology is 47:27 47:27 starting to exist or will exist 47:29 47:29 relatively soon and make sure that you 47:32 47:32 understand how to use that technology 47:34 47:34 for whatever it is you are most 47:36 47:36 passionate about, right? That could be, 47:39 47:39 you know, um, being a doctor, that could 47:42 47:42 be, you know, uh, helping write 47:45 47:45 literature. I could be, you know, 47:47 47:47 writing software more effectively. All 47:49 47:49 of these things, I think, can be aided 47:52 47:52 and empowered by AI, but there's still 47:54 47:54 that human element of like humans figure 47:56 47:56 out what we want to collectively do. 47:59 47:59 >> And you should figure out what it is you 48:02 48:02 want to do and be the best you can at 48:05 48:05 it. And that means using all the tools 48:07 48:07 that are now available. 48:09 48:09 >> So, human passion is still timeless in 48:12 48:12 the age of AI. Thank you, Jeff. Yeah. 48:14 48:14 >> Thank you. [applause]