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]