WEBVTT 00:00.000 --> 00:02.920 The following is a conversation with Greg Brockman. 00:02.920 --> 00:05.400 He's the cofounder and CTO of OpenAI, 00:05.400 --> 00:07.440 a world class research organization 00:07.440 --> 00:10.840 developing ideas in AI with a goal of eventually 00:10.840 --> 00:14.200 creating a safe and friendly artificial general 00:14.200 --> 00:18.840 intelligence, one that benefits and empowers humanity. 00:18.840 --> 00:22.520 OpenAI is not only a source of publications, algorithms, 00:22.520 --> 00:24.480 tools, and data sets. 00:24.480 --> 00:28.120 Their mission is a catalyst for an important public discourse 00:28.120 --> 00:32.720 about our future with both narrow and general intelligence 00:32.720 --> 00:34.000 systems. 00:34.000 --> 00:36.640 This conversation is part of the Artificial Intelligence 00:36.640 --> 00:39.480 Podcast at MIT and beyond. 00:39.480 --> 00:42.720 If you enjoy it, subscribe on YouTube, iTunes, 00:42.720 --> 00:44.520 or simply connect with me on Twitter 00:44.520 --> 00:48.040 at Lex Friedman, spelled F R I D. 00:48.040 --> 00:52.760 And now here's my conversation with Greg Brockman. 00:52.760 --> 00:54.640 So in high school and right after you wrote 00:54.640 --> 00:56.640 a draft of a chemistry textbook, 00:56.640 --> 00:58.120 I saw that that covers everything 00:58.120 --> 01:01.400 from basic structure of the atom to quantum mechanics. 01:01.400 --> 01:04.400 So it's clear you have an intuition and a passion 01:04.400 --> 01:09.880 for both the physical world with chemistry and non robotics 01:09.880 --> 01:13.680 to the digital world with AI, deep learning, 01:13.680 --> 01:15.360 reinforcement learning, so on. 01:15.360 --> 01:17.360 Do you see the physical world and the digital world 01:17.360 --> 01:18.600 as different? 01:18.600 --> 01:20.480 And what do you think is the gap? 01:20.480 --> 01:23.040 A lot of it actually boils down to iteration speed, 01:23.040 --> 01:25.240 that I think that a lot of what really motivates me 01:25.240 --> 01:27.720 is building things, right? 01:27.720 --> 01:29.320 Think about mathematics, for example, 01:29.320 --> 01:30.920 where you think really hard about a problem. 01:30.920 --> 01:31.680 You understand it. 01:31.680 --> 01:33.320 You're right down in this very obscure form 01:33.320 --> 01:34.560 that we call a proof. 01:34.560 --> 01:37.600 But then this is in humanity's library, right? 01:37.600 --> 01:38.400 It's there forever. 01:38.400 --> 01:40.400 This is some truth that we've discovered. 01:40.400 --> 01:43.040 And maybe only five people in your field will ever read it. 01:43.040 --> 01:45.440 But somehow you've kind of moved humanity forward. 01:45.440 --> 01:46.880 And so I actually used to really think 01:46.880 --> 01:48.680 that I was going to be a mathematician. 01:48.680 --> 01:51.400 And then I actually started writing this chemistry textbook. 01:51.400 --> 01:53.360 One of my friends told me, you'll never publish it 01:53.360 --> 01:54.880 because you don't have a PhD. 01:54.880 --> 01:58.000 So instead, I decided to build a website 01:58.000 --> 01:59.960 and try to promote my ideas that way. 01:59.960 --> 02:01.480 And then I discovered programming. 02:01.480 --> 02:05.320 And in programming, you think hard about a problem. 02:05.320 --> 02:06.080 You understand it. 02:06.080 --> 02:08.040 You're right down in a very obscure form 02:08.040 --> 02:09.720 that we call a program. 02:09.720 --> 02:12.240 But then once again, it's in humanity's library, right? 02:12.240 --> 02:14.120 And anyone can get the benefit from it. 02:14.120 --> 02:15.720 And the scalability is massive. 02:15.720 --> 02:17.720 And so I think that the thing that really appeals to me 02:17.720 --> 02:19.440 about the digital world is that you 02:19.440 --> 02:21.960 can have this insane leverage, right? 02:21.960 --> 02:24.960 A single individual with an idea is able to affect 02:24.960 --> 02:26.120 the entire planet. 02:26.120 --> 02:28.240 And that's something I think is really hard to do 02:28.240 --> 02:30.240 if you're moving around physical atoms. 02:30.240 --> 02:32.440 But you said mathematics. 02:32.440 --> 02:36.880 So if you look at the wet thing over here, our mind, 02:36.880 --> 02:39.800 do you ultimately see it as just math, 02:39.800 --> 02:41.800 as just information processing? 02:41.800 --> 02:44.880 Or is there some other magic if you've 02:44.880 --> 02:46.960 seen through biology and chemistry and so on? 02:46.960 --> 02:49.000 I think it's really interesting to think about humans 02:49.000 --> 02:51.000 as just information processing systems. 02:51.000 --> 02:54.080 And it seems like it's actually a pretty good way 02:54.080 --> 02:57.560 of describing a lot of how the world works or a lot of what 02:57.560 --> 03:01.000 we're capable of to think that, again, if you just 03:01.000 --> 03:03.640 look at technological innovations over time, 03:03.640 --> 03:05.920 that in some ways, the most transformative innovation 03:05.920 --> 03:07.760 that we've had has been the computer, right? 03:07.760 --> 03:10.560 In some ways, the internet, what has the internet done? 03:10.560 --> 03:12.760 The internet is not about these physical cables. 03:12.760 --> 03:14.560 It's about the fact that I am suddenly 03:14.560 --> 03:16.560 able to instantly communicate with any other human 03:16.560 --> 03:17.680 on the planet. 03:17.680 --> 03:19.680 I'm able to retrieve any piece of knowledge 03:19.680 --> 03:22.640 that, in some ways, the human race has ever had, 03:22.640 --> 03:26.080 and that those are these insane transformations. 03:26.080 --> 03:29.320 Do you see our society as a whole the collective 03:29.320 --> 03:31.240 as another extension of the intelligence 03:31.240 --> 03:32.280 of the human being? 03:32.280 --> 03:34.440 So if you look at the human being as an information processing 03:34.440 --> 03:36.920 system, you mentioned the internet, the networking. 03:36.920 --> 03:39.360 Do you see us all together as a civilization 03:39.360 --> 03:41.680 as a kind of intelligent system? 03:41.680 --> 03:43.560 Yeah, I think this is actually a really interesting 03:43.560 --> 03:45.840 perspective to take and to think about 03:45.840 --> 03:48.080 that you sort of have this collective intelligence 03:48.080 --> 03:49.520 of all of society. 03:49.520 --> 03:51.680 The economy itself is this superhuman machine 03:51.680 --> 03:54.440 that is optimizing something, right? 03:54.440 --> 03:56.400 And it's almost, in some ways, a company 03:56.400 --> 03:57.960 has a will of its own, right? 03:57.960 --> 03:59.400 That you have all these individuals who are all 03:59.400 --> 04:00.800 pursuing their own individual goals 04:00.800 --> 04:02.400 and thinking really hard and thinking 04:02.400 --> 04:04.640 about the right things to do, but somehow the company does 04:04.640 --> 04:07.880 something that is this emergent thing 04:07.880 --> 04:10.640 and that it's a really useful abstraction. 04:10.640 --> 04:12.440 And so I think that in some ways, 04:12.440 --> 04:14.880 we think of ourselves as the most intelligent things 04:14.880 --> 04:17.480 on the planet and the most powerful things on the planet. 04:17.480 --> 04:19.320 But there are things that are bigger than us, 04:19.320 --> 04:21.480 that are these systems that we all contribute to. 04:21.480 --> 04:25.000 And so I think actually, it's interesting to think about, 04:25.000 --> 04:27.440 if you've read Asa Geismov's foundation, right, 04:27.440 --> 04:30.160 that there's this concept of psycho history in there, 04:30.160 --> 04:31.920 which is effectively this, that if you have trillions 04:31.920 --> 04:35.200 or quadrillions of beings, then maybe you could actually 04:35.200 --> 04:39.080 predict what that huge macro being will do 04:39.080 --> 04:42.400 and almost independent of what the individuals want. 04:42.400 --> 04:44.240 And I actually have a second angle on this 04:44.240 --> 04:46.760 that I think is interesting, which is thinking about 04:46.760 --> 04:48.400 technological determinism. 04:48.400 --> 04:51.480 One thing that I actually think a lot about with OpenAI 04:51.480 --> 04:54.720 is that we're kind of coming onto this insanely 04:54.720 --> 04:57.400 transformational technology of general intelligence 04:57.400 --> 04:58.760 that will happen at some point. 04:58.760 --> 05:01.560 And there's a question of how can you take actions 05:01.560 --> 05:04.880 that will actually steer it to go better rather than worse? 05:04.880 --> 05:06.720 And that I think one question you need to ask is, 05:06.720 --> 05:09.320 as a scientist, as an event or as a creator, 05:09.320 --> 05:11.720 what impact can you have in general? 05:11.720 --> 05:12.880 You look at things like the telephone 05:12.880 --> 05:14.840 invented by two people on the same day. 05:14.840 --> 05:16.600 Like what does that mean, like what does that mean 05:16.600 --> 05:18.080 about the shape of innovation? 05:18.080 --> 05:20.160 And I think that what's going on is everyone's building 05:20.160 --> 05:21.720 on the shoulders of the same giants. 05:21.720 --> 05:23.840 And so you can kind of, you can't really hope 05:23.840 --> 05:25.720 to create something no one else ever would. 05:25.720 --> 05:27.040 You know, if Einstein wasn't born, 05:27.040 --> 05:29.200 someone else would have come up with relativity. 05:29.200 --> 05:31.000 You know, he changed the timeline a bit, right? 05:31.000 --> 05:33.000 That maybe it would have taken another 20 years, 05:33.000 --> 05:34.560 but it wouldn't be that fundamentally humanity 05:34.560 --> 05:37.360 would never discover these fundamental truths. 05:37.360 --> 05:40.440 So there's some kind of invisible momentum 05:40.440 --> 05:45.400 that some people like Einstein or OpenAI is plugging into 05:45.400 --> 05:47.800 that anybody else can also plug into. 05:47.800 --> 05:50.800 And ultimately, that wave takes us into a certain direction. 05:50.800 --> 05:51.840 That's what you mean by digitalism? 05:51.840 --> 05:52.840 That's right, that's right. 05:52.840 --> 05:54.240 And you know, this kind of seems to play out 05:54.240 --> 05:55.720 in a bunch of different ways. 05:55.720 --> 05:58.040 That there's some exponential that is being written 05:58.040 --> 05:59.960 and that the exponential itself, which one it is, 05:59.960 --> 06:01.520 changes, think about Moore's Law, 06:01.520 --> 06:04.800 an entire industry set, it's clocked to it for 50 years. 06:04.800 --> 06:06.200 Like how can that be, right? 06:06.200 --> 06:07.360 How is that possible? 06:07.360 --> 06:09.320 And yet somehow it happened. 06:09.320 --> 06:12.200 And so I think you can't hope to ever invent something 06:12.200 --> 06:13.360 that no one else will. 06:13.360 --> 06:15.360 Maybe you can change the timeline a little bit. 06:15.360 --> 06:17.400 But if you really want to make a difference, 06:17.400 --> 06:19.440 I think that the thing that you really have to do, 06:19.440 --> 06:21.320 the only real degree of freedom you have 06:21.320 --> 06:23.040 is to set the initial conditions 06:23.040 --> 06:24.960 under which a technology is born. 06:24.960 --> 06:26.680 And so you think about the internet, right? 06:26.680 --> 06:27.840 That there are lots of other competitors 06:27.840 --> 06:29.400 trying to build similar things. 06:29.400 --> 06:33.240 And the internet one, and that the initial conditions 06:33.240 --> 06:34.680 where it was created by this group 06:34.680 --> 06:37.760 that really valued people being able to be, 06:37.760 --> 06:39.120 you know, anyone being able to plug in 06:39.120 --> 06:42.480 this very academic mindset of being open and connected. 06:42.480 --> 06:44.400 And I think that the internet for the next 40 years 06:44.400 --> 06:46.360 really played out that way. 06:46.360 --> 06:47.680 You know, maybe today, 06:47.680 --> 06:49.840 things are starting to shift in a different direction, 06:49.840 --> 06:51.120 but I think that those initial conditions 06:51.120 --> 06:52.720 were really important to determine 06:52.720 --> 06:55.080 the next 40 years worth of progress. 06:55.080 --> 06:56.440 That's really beautifully put. 06:56.440 --> 06:58.800 So another example of that I think about, 06:58.800 --> 07:00.800 you know, I recently looked at it. 07:00.800 --> 07:03.800 I looked at Wikipedia, the formation of Wikipedia. 07:03.800 --> 07:05.520 And I wonder what the internet would be like 07:05.520 --> 07:07.760 if Wikipedia had ads. 07:07.760 --> 07:09.640 You know, there's an interesting argument 07:09.640 --> 07:14.280 that why they chose not to put advertisement on Wikipedia. 07:14.280 --> 07:17.800 I think Wikipedia is one of the greatest resources 07:17.800 --> 07:18.920 we have on the internet. 07:18.920 --> 07:21.280 It's extremely surprising how well it works 07:21.280 --> 07:22.960 and how well it was able to aggregate 07:22.960 --> 07:25.000 all this kind of good information. 07:25.000 --> 07:27.320 And essentially the creator of Wikipedia, 07:27.320 --> 07:29.360 I don't know, there's probably some debates there, 07:29.360 --> 07:31.200 but set the initial conditions 07:31.200 --> 07:33.240 and now it carried itself forward. 07:33.240 --> 07:34.080 That's really interesting. 07:34.080 --> 07:36.520 So the way you're thinking about AGI 07:36.520 --> 07:38.640 or artificial intelligence is you're focused on 07:38.640 --> 07:41.200 setting the initial conditions for the progress. 07:41.200 --> 07:42.320 That's right. 07:42.320 --> 07:43.160 That's powerful. 07:43.160 --> 07:45.560 Okay, so look into the future. 07:45.560 --> 07:48.160 If you create an AGI system, 07:48.160 --> 07:51.560 like one that can ace the Turing test, natural language, 07:51.560 --> 07:54.800 what do you think would be the interactions 07:54.800 --> 07:55.840 you would have with it? 07:55.840 --> 07:57.720 What do you think are the questions you would ask? 07:57.720 --> 08:00.560 Like what would be the first question you would ask? 08:00.560 --> 08:01.840 It, her, him. 08:01.840 --> 08:02.680 That's right. 08:02.680 --> 08:03.920 I think that at that point, 08:03.920 --> 08:05.960 if you've really built a powerful system 08:05.960 --> 08:08.480 that is capable of shaping the future of humanity, 08:08.480 --> 08:10.240 the first question that you really should ask 08:10.240 --> 08:12.280 is how do we make sure that this plays out well? 08:12.280 --> 08:13.960 And so that's actually the first question 08:13.960 --> 08:17.600 that I would ask a powerful AGI system is. 08:17.600 --> 08:19.160 So you wouldn't ask your colleague, 08:19.160 --> 08:22.280 you wouldn't ask like Ilya, you would ask the AGI system. 08:22.280 --> 08:24.640 Oh, we've already had the conversation with Ilya, right? 08:24.640 --> 08:25.720 And everyone here. 08:25.720 --> 08:27.480 And so you want as many perspectives 08:27.480 --> 08:29.720 and a piece of wisdom as you can 08:29.720 --> 08:31.200 for answering this question. 08:31.200 --> 08:33.120 So I don't think you necessarily defer to 08:33.120 --> 08:35.480 whatever your powerful system tells you, 08:35.480 --> 08:37.120 but you use it as one input 08:37.120 --> 08:39.280 to try to figure out what to do. 08:39.280 --> 08:40.920 But, and I guess fundamentally, 08:40.920 --> 08:42.160 what it really comes down to is 08:42.160 --> 08:43.960 if you built something really powerful 08:43.960 --> 08:45.280 and you think about, think about, for example, 08:45.280 --> 08:47.640 the creation of, of shortly after 08:47.640 --> 08:48.880 the creation of nuclear weapons, right? 08:48.880 --> 08:50.400 The most important question in the world 08:50.400 --> 08:52.800 was what's the world we're going to be like? 08:52.800 --> 08:54.880 How do we set ourselves up in a place 08:54.880 --> 08:58.320 where we're going to be able to survive as a species? 08:58.320 --> 09:00.640 With AGI, I think the question is slightly different, right? 09:00.640 --> 09:02.720 That there is a question of how do we make sure 09:02.720 --> 09:04.440 that we don't get the negative effects? 09:04.440 --> 09:06.240 But there's also the positive side, right? 09:06.240 --> 09:08.040 You imagine that, you know, like, 09:08.040 --> 09:09.720 like what will AGI be like? 09:09.720 --> 09:11.280 Like what will it be capable of? 09:11.280 --> 09:13.520 And I think that one of the core reasons 09:13.520 --> 09:15.760 that an AGI can be powerful and transformative 09:15.760 --> 09:18.920 is actually due to technological development, right? 09:18.920 --> 09:20.560 If you have something that's capable, 09:20.560 --> 09:23.880 that's capable as a human and that it's much more scalable, 09:23.880 --> 09:25.880 that you absolutely want that thing 09:25.880 --> 09:27.640 to go read the whole scientific literature 09:27.640 --> 09:30.000 and think about how to create cures for all the diseases, right? 09:30.000 --> 09:31.480 You want it to think about how to go 09:31.480 --> 09:33.360 and build technologies to help us 09:33.360 --> 09:37.320 create material abundance and to figure out societal problems 09:37.320 --> 09:38.160 that we have trouble with, 09:38.160 --> 09:40.000 like how are we supposed to clean up the environment? 09:40.000 --> 09:42.200 And, you know, maybe you want this 09:42.200 --> 09:44.120 to go and invent a bunch of little robots that will go out 09:44.120 --> 09:47.280 and be biodegradable and turn ocean debris 09:47.280 --> 09:49.640 into harmless molecules. 09:49.640 --> 09:54.040 And I think that that positive side 09:54.040 --> 09:55.720 is something that I think people miss 09:55.720 --> 09:58.160 sometimes when thinking about what an AGI will be like. 09:58.160 --> 10:00.280 And so I think that if you have a system 10:00.280 --> 10:01.640 that's capable of all of that, 10:01.640 --> 10:03.960 you absolutely want its advice about how do I make sure 10:03.960 --> 10:07.600 that we're using your capabilities 10:07.600 --> 10:09.200 in a positive way for humanity. 10:09.200 --> 10:11.400 So what do you think about that psychology 10:11.400 --> 10:14.800 that looks at all the different possible trajectories 10:14.800 --> 10:17.520 of an AGI system, many of which, 10:17.520 --> 10:19.960 perhaps the majority of which are positive 10:19.960 --> 10:23.320 and nevertheless focuses on the negative trajectories? 10:23.320 --> 10:24.720 I mean, you get to interact with folks, 10:24.720 --> 10:28.840 you get to think about this maybe within yourself as well. 10:28.840 --> 10:30.560 You look at Sam Harris and so on. 10:30.560 --> 10:32.720 It seems to be, sorry to put it this way, 10:32.720 --> 10:36.720 but almost more fun to think about the negative possibilities. 10:37.800 --> 10:39.560 Whatever that's deep in our psychology, 10:39.560 --> 10:40.760 what do you think about that? 10:40.760 --> 10:41.920 And how do we deal with it? 10:41.920 --> 10:44.400 Because we want AI to help us. 10:44.400 --> 10:47.880 So I think there's kind of two problems 10:47.880 --> 10:49.960 entailed in that question. 10:49.960 --> 10:52.360 The first is more of the question of, 10:52.360 --> 10:54.600 how can you even picture what a world 10:54.600 --> 10:56.600 with a new technology will be like? 10:56.600 --> 10:57.880 Now imagine we're in 1950 10:57.880 --> 11:01.040 and I'm trying to describe Uber to someone. 11:01.040 --> 11:05.360 Aps and the internet. 11:05.360 --> 11:08.920 Yeah, I mean, that's going to be extremely complicated, 11:08.920 --> 11:10.160 but it's imaginable. 11:10.160 --> 11:11.400 It's imaginable, right? 11:11.400 --> 11:14.000 But, and now imagine being in 1950 11:14.000 --> 11:15.280 and predicting Uber, right? 11:15.280 --> 11:17.680 And you need to describe the internet, 11:17.680 --> 11:18.720 you need to describe GPS, 11:18.720 --> 11:20.280 you need to describe the fact 11:20.280 --> 11:23.920 that everyone's going to have this phone in their pocket. 11:23.920 --> 11:26.160 And so I think that just the first truth 11:26.160 --> 11:28.040 is that it is hard to picture 11:28.040 --> 11:31.160 how a transformative technology will play out in the world. 11:31.160 --> 11:32.760 We've seen that before with technologies 11:32.760 --> 11:35.560 that are far less transformative than AGI will be. 11:35.560 --> 11:37.480 And so I think that one piece 11:37.480 --> 11:39.560 is that it's just even hard to imagine 11:39.560 --> 11:41.640 and to really put yourself in a world 11:41.640 --> 11:44.600 where you can predict what that positive vision 11:44.600 --> 11:45.760 would be like. 11:46.920 --> 11:49.520 And I think the second thing is that it is, 11:49.520 --> 11:53.280 I think it is always easier to support 11:53.280 --> 11:55.080 the negative side than the positive side. 11:55.080 --> 11:57.120 It's always easier to destroy than create. 11:58.200 --> 12:00.800 And, you know, less in a physical sense 12:00.800 --> 12:03.080 and more just in an intellectual sense, right? 12:03.080 --> 12:05.680 Because, you know, I think that with creating something, 12:05.680 --> 12:07.440 you need to just get a bunch of things right 12:07.440 --> 12:10.280 and to destroy, you just need to get one thing wrong. 12:10.280 --> 12:12.080 And so I think that what that means 12:12.080 --> 12:14.240 is that I think a lot of people's thinking dead ends 12:14.240 --> 12:16.880 as soon as they see the negative story. 12:16.880 --> 12:20.360 But that being said, I actually have some hope, right? 12:20.360 --> 12:23.160 I think that the positive vision 12:23.160 --> 12:26.000 is something that I think can be, 12:26.000 --> 12:27.600 is something that we can talk about. 12:27.600 --> 12:30.200 I think that just simply saying this fact of, 12:30.200 --> 12:32.000 yeah, like there's positive, there's negatives, 12:32.000 --> 12:33.600 everyone likes to dwell on the negative, 12:33.600 --> 12:35.360 people actually respond well to that message and say, 12:35.360 --> 12:37.040 huh, you're right, there's a part of this 12:37.040 --> 12:39.640 that we're not talking about, not thinking about. 12:39.640 --> 12:41.240 And that's actually something that's, 12:41.240 --> 12:43.800 I think really been a key part 12:43.800 --> 12:46.640 of how we think about AGI at OpenAI, right? 12:46.640 --> 12:48.160 You can kind of look at it as like, okay, 12:48.160 --> 12:51.000 like OpenAI talks about the fact that there are risks 12:51.000 --> 12:53.160 and yet they're trying to build this system. 12:53.160 --> 12:56.080 Like how do you square those two facts? 12:56.080 --> 12:59.120 So do you share the intuition that some people have, 12:59.120 --> 13:02.680 I mean, from Sam Harris to even Elon Musk himself, 13:02.680 --> 13:06.600 that it's tricky as you develop AGI 13:06.600 --> 13:10.400 to keep it from slipping into the existential threats, 13:10.400 --> 13:11.760 into the negative. 13:11.760 --> 13:13.640 What's your intuition about, 13:13.640 --> 13:17.720 how hard is it to keep AI development 13:17.720 --> 13:19.640 on the positive track? 13:19.640 --> 13:20.680 What's your intuition there? 13:20.680 --> 13:21.560 To answer that question, 13:21.560 --> 13:23.960 you can really look at how we structure OpenAI. 13:23.960 --> 13:25.840 So we really have three main arms. 13:25.840 --> 13:26.960 So we have capabilities, 13:26.960 --> 13:29.040 which is actually doing the technical work 13:29.040 --> 13:31.160 and pushing forward what these systems can do. 13:31.160 --> 13:35.120 There's safety, which is working on technical mechanisms 13:35.120 --> 13:36.920 to ensure that the systems we build 13:36.920 --> 13:38.480 are aligned with human values. 13:38.480 --> 13:39.640 And then there's policy, 13:39.640 --> 13:42.040 which is making sure that we have governance mechanisms, 13:42.040 --> 13:45.280 answering that question of, well, whose values? 13:45.280 --> 13:47.360 And so I think that the technical safety one 13:47.360 --> 13:50.480 is the one that people kind of talk about the most, right? 13:50.480 --> 13:52.080 You talk about, like think about, 13:52.080 --> 13:54.200 you know, all of the dystopic AI movies, 13:54.200 --> 13:55.960 a lot of that is about not having good 13:55.960 --> 13:57.520 technical safety in place. 13:57.520 --> 13:59.960 And what we've been finding is that, you know, 13:59.960 --> 14:01.360 I think that actually a lot of people 14:01.360 --> 14:02.680 look at the technical safety problem 14:02.680 --> 14:05.400 and think it's just intractable, right? 14:05.400 --> 14:07.840 This question of what do humans want? 14:07.840 --> 14:09.160 How am I supposed to write that down? 14:09.160 --> 14:11.240 Can I even write down what I want? 14:11.240 --> 14:12.080 No way. 14:13.040 --> 14:14.800 And then they stop there. 14:14.800 --> 14:16.880 But the thing is we've already built systems 14:16.880 --> 14:20.920 that are able to learn things that humans can't specify. 14:20.920 --> 14:22.920 You know, even the rules for how to recognize 14:22.920 --> 14:25.000 if there's a cat or a dog in an image. 14:25.000 --> 14:26.520 Turns out it's intractable to write that down 14:26.520 --> 14:28.400 and yet we're able to learn it. 14:28.400 --> 14:31.040 And that what we're seeing with systems we build at OpenAI 14:31.040 --> 14:33.800 and they're still in early proof of concept stage 14:33.800 --> 14:36.320 is that you are able to learn human preferences. 14:36.320 --> 14:38.920 You're able to learn what humans want from data. 14:38.920 --> 14:40.400 And so that's kind of the core focus 14:40.400 --> 14:41.760 for our technical safety team. 14:41.760 --> 14:43.800 And I think that they're actually, 14:43.800 --> 14:45.640 we've had some pretty encouraging updates 14:45.640 --> 14:48.040 in terms of what we've been able to make work. 14:48.040 --> 14:51.680 So you have an intuition and a hope that from data, 14:51.680 --> 14:53.640 you know, looking at the value alignment problem, 14:53.640 --> 14:57.040 from data we can build systems that align 14:57.040 --> 15:00.600 with the collective better angels of our nature. 15:00.600 --> 15:04.600 So align with the ethics and the morals of human beings. 15:04.600 --> 15:05.880 To even say this in a different way, 15:05.880 --> 15:08.560 I mean, think about how do we align humans, right? 15:08.560 --> 15:10.400 Think about like a human baby can grow up 15:10.400 --> 15:12.880 to be an evil person or a great person. 15:12.880 --> 15:15.200 And a lot of that is from learning from data, right? 15:15.200 --> 15:17.720 That you have some feedback as a child is growing up. 15:17.720 --> 15:19.160 They get to see positive examples. 15:19.160 --> 15:23.120 And so I think that just like the only example 15:23.120 --> 15:25.400 we have of a general intelligence 15:25.400 --> 15:28.040 that is able to learn from data 15:28.040 --> 15:31.440 to align with human values and to learn values, 15:31.440 --> 15:32.880 I think we shouldn't be surprised 15:32.880 --> 15:36.040 that we can do the same sorts of techniques 15:36.040 --> 15:37.440 or whether the same sort of techniques 15:37.440 --> 15:41.080 end up being how we solve value alignment for AGI's. 15:41.080 --> 15:42.680 So let's go even higher. 15:42.680 --> 15:44.800 I don't know if you've read the book, Sapiens. 15:44.800 --> 15:48.320 But there's an idea that, you know, 15:48.320 --> 15:50.000 that as a collective, as us human beings, 15:50.000 --> 15:54.720 we kind of develop together ideas that we hold. 15:54.720 --> 15:57.920 There's no, in that context, objective truth. 15:57.920 --> 16:00.000 We just kind of all agree to certain ideas 16:00.000 --> 16:01.440 and hold them as a collective. 16:01.440 --> 16:03.480 Did you have a sense that there is 16:03.480 --> 16:05.360 in the world of good and evil, 16:05.360 --> 16:07.560 do you have a sense that to the first approximation, 16:07.560 --> 16:10.280 there are some things that are good 16:10.280 --> 16:14.520 and that you could teach systems to behave to be good? 16:14.520 --> 16:18.440 So I think that this actually blends into our third team, 16:18.440 --> 16:19.880 which is the policy team. 16:19.880 --> 16:22.320 And this is the one, the aspect that I think people 16:22.320 --> 16:25.280 really talk about way less than they should. 16:25.280 --> 16:27.640 Because imagine that we build super powerful systems 16:27.640 --> 16:29.720 that we've managed to figure out all the mechanisms 16:29.720 --> 16:32.800 for these things to do whatever the operator wants. 16:32.800 --> 16:34.480 The most important question becomes, 16:34.480 --> 16:36.720 who's the operator, what do they want, 16:36.720 --> 16:39.400 and how is that going to affect everyone else? 16:39.400 --> 16:43.080 And I think that this question of what is good, 16:43.080 --> 16:44.720 what are those values, I mean, 16:44.720 --> 16:45.960 I think you don't even have to go 16:45.960 --> 16:48.400 to those very grand existential places 16:48.400 --> 16:50.920 to realize how hard this problem is. 16:50.920 --> 16:52.880 You just look at different countries 16:52.880 --> 16:54.520 and cultures across the world. 16:54.520 --> 16:57.120 And that there's a very different conception 16:57.120 --> 17:01.920 of how the world works and what kinds of ways 17:01.920 --> 17:03.400 that society wants to operate. 17:03.400 --> 17:07.000 And so I think that the really core question 17:07.000 --> 17:09.560 is actually very concrete. 17:09.560 --> 17:10.960 And I think it's not a question 17:10.960 --> 17:12.880 that we have ready answers to, 17:12.880 --> 17:16.560 how do you have a world where all the different countries 17:16.560 --> 17:19.720 that we have, United States, China, Russia, 17:19.720 --> 17:22.720 and the hundreds of other countries out there 17:22.720 --> 17:26.600 are able to continue to not just operate 17:26.600 --> 17:28.440 in the way that they see fit, 17:28.440 --> 17:32.520 but in the world that emerges in these, 17:32.520 --> 17:34.680 where you have these very powerful systems, 17:36.040 --> 17:37.800 operating alongside humans, 17:37.800 --> 17:39.800 ends up being something that empowers humans more, 17:39.800 --> 17:44.120 that makes human existence be a more meaningful thing 17:44.120 --> 17:46.400 and that people are happier and wealthier 17:46.400 --> 17:48.960 and able to live more fulfilling lives. 17:48.960 --> 17:51.560 It's not an obvious thing for how to design that world 17:51.560 --> 17:53.600 once you have that very powerful system. 17:53.600 --> 17:55.800 So if we take a little step back, 17:55.800 --> 17:58.200 and we're having like a fascinating conversation 17:58.200 --> 18:01.880 and open as in many ways a tech leader in the world, 18:01.880 --> 18:05.440 and yet we're thinking about these big existential questions 18:05.440 --> 18:07.000 which is fascinating, really important. 18:07.000 --> 18:09.160 I think you're a leader in that space 18:09.160 --> 18:10.840 and that's a really important space 18:10.840 --> 18:13.080 of just thinking how AI affects society 18:13.080 --> 18:14.360 in a big picture view. 18:14.360 --> 18:17.320 So Oscar Wilde said, we're all in the gutter, 18:17.320 --> 18:19.000 but some of us are looking at the stars 18:19.000 --> 18:22.320 and I think OpenAI has a charter 18:22.320 --> 18:24.600 that looks to the stars, I would say, 18:24.600 --> 18:26.880 to create intelligence, to create general intelligence, 18:26.880 --> 18:29.440 make it beneficial, safe, and collaborative. 18:29.440 --> 18:33.680 So can you tell me how that came about? 18:33.680 --> 18:36.320 How a mission like that and the path 18:36.320 --> 18:39.120 to creating a mission like that at OpenAI was founded? 18:39.120 --> 18:41.640 Yeah, so I think that in some ways 18:41.640 --> 18:45.040 it really boils down to taking a look at the landscape. 18:45.040 --> 18:47.040 So if you think about the history of AI 18:47.040 --> 18:49.920 that basically for the past 60 or 70 years, 18:49.920 --> 18:51.640 people have thought about this goal 18:51.640 --> 18:53.960 of what could happen if you could automate 18:53.960 --> 18:55.640 human intellectual labor. 18:56.680 --> 18:58.280 Imagine you can build a computer system 18:58.280 --> 19:00.560 that could do that, what becomes possible? 19:00.560 --> 19:02.400 We have a lot of sci fi that tells stories 19:02.400 --> 19:04.920 of various dystopias and increasingly you have movies 19:04.920 --> 19:06.480 like Her that tell you a little bit about 19:06.480 --> 19:09.440 maybe more of a little bit utopic vision. 19:09.440 --> 19:12.560 You think about the impacts that we've seen 19:12.560 --> 19:16.280 from being able to have bicycles for our minds 19:16.280 --> 19:20.360 and computers and that I think that the impact 19:20.360 --> 19:23.480 of computers and the internet has just far outstripped 19:23.480 --> 19:26.200 what anyone really could have predicted. 19:26.200 --> 19:27.400 And so I think that it's very clear 19:27.400 --> 19:29.360 that if you can build an AGI, 19:29.360 --> 19:31.600 it will be the most transformative technology 19:31.600 --> 19:33.040 that humans will ever create. 19:33.040 --> 19:36.840 And so what it boils down to then is a question of, 19:36.840 --> 19:38.680 well, is there a path? 19:38.680 --> 19:39.520 Is there hope? 19:39.520 --> 19:41.680 Is there a way to build such a system? 19:41.680 --> 19:43.640 And I think that for 60 or 70 years 19:43.640 --> 19:48.040 that people got excited and that ended up not being able 19:48.040 --> 19:51.480 to deliver on the hopes that people had pinned on them. 19:51.480 --> 19:54.880 And I think that then, that after two winters 19:54.880 --> 19:57.600 of AI development, that people, 19:57.600 --> 20:00.560 I think kind of almost stopped daring to dream, right? 20:00.560 --> 20:03.280 That really talking about AGI or thinking about AGI 20:03.280 --> 20:05.640 became almost this taboo in the community. 20:06.640 --> 20:08.720 But I actually think that people took the wrong lesson 20:08.720 --> 20:10.080 from AI history. 20:10.080 --> 20:12.400 And if you look back, starting in 1959 20:12.400 --> 20:14.240 is when the Perceptron was released. 20:14.240 --> 20:17.720 And this is basically one of the earliest neural networks. 20:17.720 --> 20:19.280 It was released to what was perceived 20:19.280 --> 20:20.840 as this massive overhype. 20:20.840 --> 20:22.360 So in the New York Times in 1959, 20:22.360 --> 20:26.400 you have this article saying that the Perceptron 20:26.400 --> 20:29.160 will one day recognize people, call out their names, 20:29.160 --> 20:31.480 instantly translate speech between languages. 20:31.480 --> 20:33.800 And people at the time looked at this and said, 20:33.800 --> 20:36.120 this is, your system can't do any of that. 20:36.120 --> 20:38.080 And basically spent 10 years trying to discredit 20:38.080 --> 20:40.640 the whole Perceptron direction and succeeded. 20:40.640 --> 20:41.840 And all the funding dried up. 20:41.840 --> 20:44.960 And people kind of went in other directions. 20:44.960 --> 20:46.920 And in the 80s, there was this resurgence. 20:46.920 --> 20:49.320 And I'd always heard that the resurgence in the 80s 20:49.320 --> 20:51.520 was due to the invention of back propagation 20:51.520 --> 20:53.720 and these algorithms that got people excited. 20:53.720 --> 20:55.760 But actually the causality was due to people 20:55.760 --> 20:57.200 building larger computers. 20:57.200 --> 20:59.280 That you can find these articles from the 80s saying 20:59.280 --> 21:01.760 that the democratization of computing power 21:01.760 --> 21:04.040 suddenly meant that you could run these larger neural networks. 21:04.040 --> 21:06.280 And then people started to do all these amazing things, 21:06.280 --> 21:08.000 back propagation algorithm was invented. 21:08.000 --> 21:10.120 And the neural nets people were running 21:10.120 --> 21:13.000 were these tiny little like 20 neuron neural nets. 21:13.000 --> 21:15.160 What are you supposed to learn with 20 neurons? 21:15.160 --> 21:18.640 And so of course they weren't able to get great results. 21:18.640 --> 21:21.960 And it really wasn't until 2012 that this approach, 21:21.960 --> 21:24.680 that's almost the most simple, natural approach 21:24.680 --> 21:27.720 that people had come up with in the 50s, right? 21:27.720 --> 21:30.360 In some ways, even in the 40s before there were computers 21:30.360 --> 21:32.000 with the Pits McCullin neuron, 21:33.040 --> 21:37.480 suddenly this became the best way of solving problems, right? 21:37.480 --> 21:39.280 And I think there are three core properties 21:39.280 --> 21:42.120 that deep learning has that I think 21:42.120 --> 21:44.120 are very worth paying attention to. 21:44.120 --> 21:45.920 The first is generality. 21:45.920 --> 21:48.760 We have a very small number of deep learning tools, 21:48.760 --> 21:52.360 SGD, deep neural net, maybe some, you know, RL. 21:52.360 --> 21:55.600 And it solves this huge variety of problems, 21:55.600 --> 21:57.240 speech recognition, machine translation, 21:57.240 --> 22:00.200 game playing, all of these problems, 22:00.200 --> 22:01.040 small set of tools. 22:01.040 --> 22:02.760 So there's the generality. 22:02.760 --> 22:05.000 There's a second piece, which is the competence. 22:05.000 --> 22:07.040 You wanna solve any of those problems? 22:07.040 --> 22:10.640 Throughout 40 years worth of normal computer vision research 22:10.640 --> 22:13.640 replaced with a deep neural net, it's gonna work better. 22:13.640 --> 22:16.320 And there's a third piece, which is the scalability, right? 22:16.320 --> 22:18.720 That one thing that has been shown time and time again 22:18.720 --> 22:21.760 is that you, if you have a larger neural network, 22:21.760 --> 22:25.120 throw more compute, more data at it, it will work better. 22:25.120 --> 22:28.880 Those three properties together feel like essential parts 22:28.880 --> 22:30.800 of building a general intelligence. 22:30.800 --> 22:33.000 Now, it doesn't just mean that if we scale up 22:33.000 --> 22:35.200 what we have, that we will have an AGI, right? 22:35.200 --> 22:36.800 There are clearly missing pieces. 22:36.800 --> 22:38.000 There are missing ideas. 22:38.000 --> 22:40.000 We need to have answers for reasoning. 22:40.000 --> 22:44.800 But I think that the core here is that for the first time, 22:44.800 --> 22:46.880 it feels that we have a paradigm 22:46.880 --> 22:48.960 that gives us hope that general intelligence 22:48.960 --> 22:50.560 can be achievable. 22:50.560 --> 22:52.160 And so as soon as you believe that, 22:52.160 --> 22:54.480 everything else becomes into focus, right? 22:54.480 --> 22:56.560 If you imagine that you may be able to, 22:56.560 --> 22:59.920 and that the timeline I think remains uncertain, 22:59.920 --> 23:02.200 but I think that certainly within our lifetimes 23:02.200 --> 23:04.640 and possibly within a much shorter period of time 23:04.640 --> 23:06.560 than people would expect, 23:06.560 --> 23:09.360 if you can really build the most transformative technology 23:09.360 --> 23:11.720 that will ever exist, you stop thinking about yourself 23:11.720 --> 23:12.560 so much, right? 23:12.560 --> 23:14.240 And you start thinking about just like, 23:14.240 --> 23:16.440 how do you have a world where this goes well? 23:16.440 --> 23:18.160 And that you need to think about the practicalities 23:18.160 --> 23:19.560 of how do you build an organization 23:19.560 --> 23:22.000 and get together a bunch of people and resources 23:22.000 --> 23:25.160 and to make sure that people feel motivated 23:25.160 --> 23:26.800 and ready to do it. 23:28.080 --> 23:30.720 But I think that then you start thinking about, 23:30.720 --> 23:32.080 well, what if we succeed? 23:32.080 --> 23:34.280 And how do we make sure that when we succeed, 23:34.280 --> 23:35.600 that the world is actually the place 23:35.600 --> 23:38.200 that we want ourselves to exist in? 23:38.200 --> 23:41.080 And almost in the Rawlsian Vale sense of the word. 23:41.080 --> 23:43.880 And so that's kind of the broader landscape. 23:43.880 --> 23:46.680 And Open AI was really formed in 2015 23:46.680 --> 23:51.480 with that high level picture of AGI might be possible 23:51.480 --> 23:52.880 sooner than people think 23:52.880 --> 23:55.840 and that we need to try to do our best 23:55.840 --> 23:57.480 to make sure it's going to go well. 23:57.480 --> 23:59.360 And then we spent the next couple of years 23:59.360 --> 24:00.840 really trying to figure out what does that mean? 24:00.840 --> 24:01.960 How do we do it? 24:01.960 --> 24:04.800 And I think that typically with a company, 24:04.800 --> 24:07.320 you start out very small. 24:07.320 --> 24:09.000 So you want a cofounder and you build a product, 24:09.000 --> 24:11.360 you get some users, you get a product market fit, 24:11.360 --> 24:13.320 then at some point you raise some money, 24:13.320 --> 24:14.840 you hire people, you scale, 24:14.840 --> 24:17.440 and then down the road, then the big companies 24:17.440 --> 24:19.080 realize you exist and try to kill you. 24:19.080 --> 24:21.520 And for Open AI, it was basically everything 24:21.520 --> 24:22.960 in exactly the opposite order. 24:25.480 --> 24:26.760 Let me just pause for a second. 24:26.760 --> 24:27.520 He said a lot of things. 24:27.520 --> 24:31.240 And let me just admire the jarring aspect 24:31.240 --> 24:35.160 of what Open AI stands for, which is daring to dream. 24:35.160 --> 24:37.120 I mean, you said it's pretty powerful. 24:37.120 --> 24:40.080 You caught me off guard because I think that's very true. 24:40.080 --> 24:44.040 The step of just daring to dream 24:44.040 --> 24:46.720 about the possibilities of creating intelligence 24:46.720 --> 24:48.760 in a positive and a safe way, 24:48.760 --> 24:50.640 but just even creating intelligence 24:50.640 --> 24:55.640 is a much needed, refreshing catalyst 24:56.280 --> 24:57.360 for the AI community. 24:57.360 --> 24:58.800 So that's the starting point. 24:58.800 --> 25:02.840 Okay, so then formation of Open AI, what's your point? 25:02.840 --> 25:05.640 I would just say that when we were starting Open AI, 25:05.640 --> 25:07.760 that kind of the first question that we had is, 25:07.760 --> 25:12.000 is it too late to start a lab with a bunch of the best people? 25:12.000 --> 25:13.160 Right, is that even possible? 25:13.160 --> 25:14.320 That was an actual question. 25:14.320 --> 25:17.280 That was the core question of, 25:17.280 --> 25:19.320 we had this dinner in July of 2015, 25:19.320 --> 25:21.240 and that was really what we spent the whole time 25:21.240 --> 25:22.320 talking about. 25:22.320 --> 25:26.800 And because you think about kind of where AI was, 25:26.800 --> 25:30.200 is that it transitioned from being an academic pursuit 25:30.200 --> 25:32.240 to an industrial pursuit. 25:32.240 --> 25:34.240 And so a lot of the best people were in these big 25:34.240 --> 25:37.000 research labs and that we wanted to start our own one 25:37.000 --> 25:40.560 that no matter how much resources we could accumulate 25:40.560 --> 25:43.520 would be pale in comparison to the big tech companies. 25:43.520 --> 25:44.720 And we knew that. 25:44.720 --> 25:45.800 And there's a question of, 25:45.800 --> 25:47.720 are we going to be actually able to get this thing 25:47.720 --> 25:48.720 off the ground? 25:48.720 --> 25:49.760 You need critical mass. 25:49.760 --> 25:52.120 You can't just do you and a cofounder build a product, right? 25:52.120 --> 25:55.600 You really need to have a group of five to 10 people. 25:55.600 --> 25:59.480 And we kind of concluded it wasn't obviously impossible. 25:59.480 --> 26:00.840 So it seemed worth trying. 26:02.240 --> 26:04.800 Well, you're also a dreamer, so who knows, right? 26:04.800 --> 26:05.640 That's right. 26:05.640 --> 26:07.720 Okay, so speaking of that, 26:07.720 --> 26:10.520 competing with the big players, 26:11.520 --> 26:14.080 let's talk about some of the tricky things 26:14.080 --> 26:17.480 as you think through this process of growing, 26:17.480 --> 26:20.080 of seeing how you can develop these systems 26:20.080 --> 26:22.640 at a scale that competes. 26:22.640 --> 26:25.720 So you recently formed OpenAI LP, 26:26.560 --> 26:30.800 a new cap profit company that now carries the name OpenAI. 26:30.800 --> 26:33.280 So OpenAI is now this official company. 26:33.280 --> 26:36.520 The original nonprofit company still exists 26:36.520 --> 26:39.800 and carries the OpenAI nonprofit name. 26:39.800 --> 26:42.000 So can you explain what this company is, 26:42.000 --> 26:44.280 what the purpose of its creation is, 26:44.280 --> 26:48.800 and how did you arrive at the decision to create it? 26:48.800 --> 26:53.280 OpenAI, the whole entity and OpenAI LP as a vehicle 26:53.280 --> 26:55.560 is trying to accomplish the mission 26:55.560 --> 26:57.520 of ensuring that artificial general intelligence 26:57.520 --> 26:58.800 benefits everyone. 26:58.800 --> 27:00.240 And the main way that we're trying to do that 27:00.240 --> 27:01.840 is by actually trying to build 27:01.840 --> 27:03.240 general intelligence to ourselves 27:03.240 --> 27:05.920 and make sure the benefits are distributed to the world. 27:05.920 --> 27:07.200 That's the primary way. 27:07.200 --> 27:09.600 We're also fine if someone else does this, right? 27:09.600 --> 27:10.640 It doesn't have to be us. 27:10.640 --> 27:12.640 If someone else is going to build an AGI 27:12.640 --> 27:14.840 and make sure that the benefits don't get locked up 27:14.840 --> 27:18.160 in one company or with one set of people, 27:19.280 --> 27:21.160 like we're actually fine with that. 27:21.160 --> 27:25.400 And so those ideas are baked into our charter, 27:25.400 --> 27:28.400 which is kind of the foundational document 27:28.400 --> 27:31.920 that describes kind of our values and how we operate. 27:31.920 --> 27:36.360 And it's also really baked into the structure of OpenAI LP. 27:36.360 --> 27:37.960 And so the way that we've set up OpenAI LP 27:37.960 --> 27:42.160 is that in the case where we succeed, right? 27:42.160 --> 27:45.320 If we actually build what we're trying to build, 27:45.320 --> 27:47.800 then investors are able to get a return, 27:47.800 --> 27:50.400 and but that return is something that is capped. 27:50.400 --> 27:53.000 And so if you think of AGI in terms of the value 27:53.000 --> 27:54.160 that you could really create, 27:54.160 --> 27:56.320 you're talking about the most transformative technology 27:56.320 --> 27:58.000 ever created, it's gonna create, 27:58.000 --> 28:01.880 or does the magnitude more value than any existing company? 28:01.880 --> 28:05.960 And that all of that value will be owned by the world, 28:05.960 --> 28:07.880 like legally titled to the nonprofit 28:07.880 --> 28:09.560 to fulfill that mission. 28:09.560 --> 28:12.800 And so that's the structure. 28:12.800 --> 28:15.200 So the mission is a powerful one, 28:15.200 --> 28:18.920 and it's one that I think most people would agree with. 28:18.920 --> 28:22.960 It's how we would hope AI progresses. 28:22.960 --> 28:25.440 And so how do you tie yourself to that mission? 28:25.440 --> 28:29.240 How do you make sure you do not deviate from that mission 28:29.240 --> 28:34.240 that other incentives that are profit driven 28:34.560 --> 28:36.800 wouldn't don't interfere with the mission? 28:36.800 --> 28:39.560 So this was actually a really core question for us 28:39.560 --> 28:40.920 for the past couple of years, 28:40.920 --> 28:43.560 because I'd say that the way that our history went 28:43.560 --> 28:44.960 was that for the first year, 28:44.960 --> 28:46.240 we were getting off the ground, right? 28:46.240 --> 28:47.960 We had this high level picture, 28:47.960 --> 28:51.880 but we didn't know exactly how we wanted to accomplish it. 28:51.880 --> 28:53.440 And really two years ago, 28:53.440 --> 28:55.040 it's when we first started realizing 28:55.040 --> 28:56.160 in order to build AGI, 28:56.160 --> 28:58.720 we're just gonna need to raise way more money 28:58.720 --> 29:00.680 than we can as a nonprofit. 29:00.680 --> 29:02.800 We're talking many billions of dollars. 29:02.800 --> 29:05.440 And so the first question is, 29:05.440 --> 29:06.840 how are you supposed to do that 29:06.840 --> 29:08.680 and stay true to this mission? 29:08.680 --> 29:10.560 And we looked at every legal structure out there 29:10.560 --> 29:11.960 and included none of them were quite right 29:11.960 --> 29:13.400 for what we wanted to do. 29:13.400 --> 29:14.600 And I guess it shouldn't be too surprising 29:14.600 --> 29:16.920 if you're gonna do some crazy unprecedented technology 29:16.920 --> 29:17.920 that you're gonna have to come 29:17.920 --> 29:20.320 with some crazy unprecedented structure to do it in. 29:20.320 --> 29:25.320 And a lot of our conversation was with people at OpenAI, 29:26.080 --> 29:27.240 the people who really joined 29:27.240 --> 29:29.160 because they believe so much in this mission 29:29.160 --> 29:32.120 and thinking about how do we actually raise the resources 29:32.120 --> 29:35.920 to do it and also stay true to what we stand for. 29:35.920 --> 29:38.000 And the place you gotta start is to really align 29:38.000 --> 29:39.560 on what is it that we stand for, right? 29:39.560 --> 29:40.560 What are those values? 29:40.560 --> 29:41.840 What's really important to us? 29:41.840 --> 29:43.760 And so I'd say that we spent about a year 29:43.760 --> 29:46.240 really compiling the OpenAI charter. 29:46.240 --> 29:47.560 And that determines, 29:47.560 --> 29:50.240 and if you even look at the first line item in there, 29:50.240 --> 29:52.360 it says that, look, we expect we're gonna have to marshal 29:52.360 --> 29:53.760 huge amounts of resources, 29:53.760 --> 29:55.160 but we're going to make sure 29:55.160 --> 29:57.920 that we minimize conflict of interest with the mission. 29:57.920 --> 30:00.720 And that kind of aligning on all of those pieces 30:00.720 --> 30:04.240 was the most important step towards figuring out 30:04.240 --> 30:06.040 how do we structure a company 30:06.040 --> 30:08.240 that can actually raise the resources 30:08.240 --> 30:10.360 to do what we need to do. 30:10.360 --> 30:14.760 I imagine OpenAI, the decision to create OpenAI LP 30:14.760 --> 30:16.360 was a really difficult one. 30:16.360 --> 30:17.920 And there was a lot of discussions 30:17.920 --> 30:19.640 as you mentioned for a year. 30:19.640 --> 30:22.760 And there was different ideas, 30:22.760 --> 30:25.120 perhaps detractors within OpenAI, 30:26.120 --> 30:28.920 sort of different paths that you could have taken. 30:28.920 --> 30:30.240 What were those concerns? 30:30.240 --> 30:32.040 What were the different paths considered? 30:32.040 --> 30:34.080 What was that process of making that decision like? 30:34.080 --> 30:35.000 Yep. 30:35.000 --> 30:37.200 But so if you look actually at the OpenAI charter, 30:37.200 --> 30:40.880 that there's almost two paths embedded within it. 30:40.880 --> 30:44.880 There is, we are primarily trying to build AGI ourselves, 30:44.880 --> 30:47.360 but we're also okay if someone else does it. 30:47.360 --> 30:49.040 And this is a weird thing for a company. 30:49.040 --> 30:50.480 It's really interesting, actually. 30:50.480 --> 30:51.320 Yeah. 30:51.320 --> 30:53.280 But there is an element of competition 30:53.280 --> 30:56.680 that you do want to be the one that does it, 30:56.680 --> 30:59.040 but at the same time, you're okay if somebody else doesn't. 30:59.040 --> 31:01.000 We'll talk about that a little bit, that trade off, 31:01.000 --> 31:02.960 that dance that's really interesting. 31:02.960 --> 31:04.600 And I think this was the core tension 31:04.600 --> 31:06.360 as we were designing OpenAI LP 31:06.360 --> 31:08.240 and really the OpenAI strategy, 31:08.240 --> 31:11.080 is how do you make sure that both you have a shot 31:11.080 --> 31:12.640 at being a primary actor, 31:12.640 --> 31:15.840 which really requires building an organization, 31:15.840 --> 31:17.720 raising massive resources, 31:17.720 --> 31:19.440 and really having the will to go 31:19.440 --> 31:22.000 and execute on some really, really hard vision, right? 31:22.000 --> 31:23.760 You need to really sign up for a long period 31:23.760 --> 31:27.120 to go and take on a lot of pain and a lot of risk. 31:27.120 --> 31:29.000 And to do that, 31:29.000 --> 31:31.720 normally you just import the startup mindset, right? 31:31.720 --> 31:32.760 And that you think about, okay, 31:32.760 --> 31:34.240 like how do we out execute everyone? 31:34.240 --> 31:36.160 You have this very competitive angle. 31:36.160 --> 31:38.120 But you also have the second angle of saying that, 31:38.120 --> 31:41.600 well, the true mission isn't for OpenAI to build AGI. 31:41.600 --> 31:45.080 The true mission is for AGI to go well for humanity. 31:45.080 --> 31:48.080 And so how do you take all of those first actions 31:48.080 --> 31:51.320 and make sure you don't close the door on outcomes 31:51.320 --> 31:54.480 that would actually be positive and fulfill the mission? 31:54.480 --> 31:56.680 And so I think it's a very delicate balance, right? 31:56.680 --> 31:59.560 And I think that going 100% one direction or the other 31:59.560 --> 32:01.320 is clearly not the correct answer. 32:01.320 --> 32:03.920 And so I think that even in terms of just how we talk about 32:03.920 --> 32:05.400 OpenAI and think about it, 32:05.400 --> 32:07.600 there's just like one thing that's always 32:07.600 --> 32:09.680 in the back of my mind is to make sure 32:09.680 --> 32:12.120 that we're not just saying OpenAI's goal 32:12.120 --> 32:14.000 is to build AGI, right? 32:14.000 --> 32:15.560 That it's actually much broader than that, right? 32:15.560 --> 32:19.360 That first of all, it's not just AGI, it's safe AGI 32:19.360 --> 32:20.320 that's very important. 32:20.320 --> 32:23.120 But secondly, our goal isn't to be the ones to build it, 32:23.120 --> 32:24.720 our goal is to make sure it goes well for the world. 32:24.720 --> 32:26.120 And so I think that figuring out, 32:26.120 --> 32:27.960 how do you balance all of those 32:27.960 --> 32:30.280 and to get people to really come to the table 32:30.280 --> 32:35.280 and compile a single document that encompasses all of that 32:36.360 --> 32:37.560 wasn't trivial. 32:37.560 --> 32:41.680 So part of the challenge here is your mission is, 32:41.680 --> 32:44.240 I would say, beautiful, empowering, 32:44.240 --> 32:47.520 and a beacon of hope for people in the research community 32:47.520 --> 32:49.200 and just people thinking about AI. 32:49.200 --> 32:51.880 So your decisions are scrutinized 32:51.880 --> 32:55.920 more than, I think, a regular profit driven company. 32:55.920 --> 32:57.400 Do you feel the burden of this 32:57.400 --> 32:58.560 in the creation of the charter 32:58.560 --> 33:00.200 and just in the way you operate? 33:00.200 --> 33:01.040 Yes. 33:03.040 --> 33:05.920 So why do you lean into the burden 33:07.040 --> 33:08.640 by creating such a charter? 33:08.640 --> 33:10.440 Why not keep it quiet? 33:10.440 --> 33:12.920 I mean, it just boils down to the mission, right? 33:12.920 --> 33:15.200 Like, I'm here and everyone else is here 33:15.200 --> 33:17.880 because we think this is the most important mission, right? 33:17.880 --> 33:19.000 Dare to dream. 33:19.000 --> 33:23.360 All right, so do you think you can be good for the world 33:23.360 --> 33:26.000 or create an AGI system that's good 33:26.000 --> 33:28.320 when you're a for profit company? 33:28.320 --> 33:32.920 From my perspective, I don't understand why profit 33:32.920 --> 33:37.640 interferes with positive impact on society. 33:37.640 --> 33:40.760 I don't understand why Google 33:40.760 --> 33:42.920 that makes most of its money from ads 33:42.920 --> 33:45.040 can't also do good for the world 33:45.040 --> 33:47.520 or other companies, Facebook, anything. 33:47.520 --> 33:50.240 I don't understand why those have to interfere. 33:50.240 --> 33:55.120 You know, you can, profit isn't the thing in my view 33:55.120 --> 33:57.240 that affects the impact of a company. 33:57.240 --> 34:00.360 What affects the impact of the company is the charter, 34:00.360 --> 34:04.160 is the culture, is the people inside 34:04.160 --> 34:07.360 and profit is the thing that just fuels those people. 34:07.360 --> 34:08.760 What are your views there? 34:08.760 --> 34:10.920 Yeah, so I think that's a really good question 34:10.920 --> 34:14.200 and there's some real like longstanding debates 34:14.200 --> 34:16.520 in human society that are wrapped up in it. 34:16.520 --> 34:18.680 The way that I think about it is just think about 34:18.680 --> 34:21.520 what are the most impactful nonprofits in the world? 34:24.000 --> 34:26.760 What are the most impactful for profits in the world? 34:26.760 --> 34:29.280 Right, it's much easier to list the for profits. 34:29.280 --> 34:30.120 That's right. 34:30.120 --> 34:32.400 And I think that there's some real truth here 34:32.400 --> 34:34.600 that the system that we set up, 34:34.600 --> 34:38.320 the system for kind of how today's world is organized 34:38.320 --> 34:41.760 is one that really allows for huge impact 34:41.760 --> 34:45.400 and that kind of part of that is that you need to be, 34:45.400 --> 34:48.080 that for profits are self sustaining 34:48.080 --> 34:51.200 and able to kind of build on their own momentum. 34:51.200 --> 34:53.080 And I think that's a really powerful thing. 34:53.080 --> 34:55.880 It's something that when it turns out 34:55.880 --> 34:57.920 that we haven't set the guardrails correctly, 34:57.920 --> 34:58.840 causes problems, right? 34:58.840 --> 35:02.720 Think about logging companies that go into the rainforest, 35:02.720 --> 35:04.680 that's really bad, we don't want that. 35:04.680 --> 35:06.520 And it's actually really interesting to me 35:06.520 --> 35:08.480 that kind of this question of 35:08.480 --> 35:11.400 how do you get positive benefits out of a for profit company? 35:11.400 --> 35:12.600 It's actually very similar to 35:12.600 --> 35:15.800 how do you get positive benefits out of an AGI, right? 35:15.800 --> 35:18.000 That you have this like very powerful system, 35:18.000 --> 35:19.680 it's more powerful than any human 35:19.680 --> 35:21.760 and it's kind of autonomous in some ways. 35:21.760 --> 35:23.800 You know, it's super human in a lot of axes 35:23.800 --> 35:25.400 and somehow you have to set the guardrails 35:25.400 --> 35:26.800 to get good things to happen. 35:26.800 --> 35:29.360 But when you do, the benefits are massive. 35:29.360 --> 35:32.920 And so I think that when I think about nonprofit 35:32.920 --> 35:36.120 versus for profit, I think just not enough happens 35:36.120 --> 35:37.800 in nonprofits, they're very pure, 35:37.800 --> 35:39.200 but it's just kind of, you know, 35:39.200 --> 35:40.840 it's just hard to do things there. 35:40.840 --> 35:44.000 And for profits in some ways, like too much happens, 35:44.000 --> 35:46.440 but if kind of shaped in the right way, 35:46.440 --> 35:47.840 it can actually be very positive. 35:47.840 --> 35:52.160 And so with OpenILP, we're picking a road in between. 35:52.160 --> 35:54.880 Now, the thing that I think is really important to recognize 35:54.880 --> 35:57.160 is that the way that we think about OpenILP 35:57.160 --> 36:00.440 is that in the world where AGI actually happens, right? 36:00.440 --> 36:01.720 In a world where we are successful, 36:01.720 --> 36:03.800 we build the most transformative technology ever, 36:03.800 --> 36:06.600 the amount of value we're going to create will be astronomical. 36:07.600 --> 36:12.600 And so then in that case, that the cap that we have 36:12.760 --> 36:15.520 will be a small fraction of the value we create. 36:15.520 --> 36:17.800 And the amount of value that goes back to investors 36:17.800 --> 36:20.000 and employees looks pretty similar to what would happen 36:20.000 --> 36:21.680 in a pretty successful startup. 36:23.760 --> 36:26.520 And that's really the case that we're optimizing for, right? 36:26.520 --> 36:28.560 That we're thinking about in the success case, 36:28.560 --> 36:32.120 making sure that the value we create doesn't get locked up. 36:32.120 --> 36:34.920 And I expect that in other for profit companies 36:34.920 --> 36:37.800 that it's possible to do something like that. 36:37.800 --> 36:39.720 I think it's not obvious how to do it, right? 36:39.720 --> 36:41.440 And I think that as a for profit company, 36:41.440 --> 36:44.240 you have a lot of fiduciary duty to your shareholders 36:44.240 --> 36:45.640 and that there are certain decisions 36:45.640 --> 36:47.520 that you just cannot make. 36:47.520 --> 36:49.080 In our structure, we've set it up 36:49.080 --> 36:52.440 so that we have a fiduciary duty to the charter, 36:52.440 --> 36:54.400 that we always get to make the decision 36:54.400 --> 36:56.720 that is right for the charter, 36:56.720 --> 36:58.800 rather than even if it comes at the expense 36:58.800 --> 37:00.680 of our own stakeholders. 37:00.680 --> 37:03.400 And so I think that when I think about 37:03.400 --> 37:04.360 what's really important, 37:04.360 --> 37:06.280 it's not really about nonprofit versus for profit. 37:06.280 --> 37:09.600 It's really a question of if you build a GI 37:09.600 --> 37:10.600 and you kind of, you know, 37:10.600 --> 37:13.080 humanity is now at this new age, 37:13.080 --> 37:15.760 who benefits, whose lives are better? 37:15.760 --> 37:17.120 And I think that what's really important 37:17.120 --> 37:20.320 is to have an answer that is everyone. 37:20.320 --> 37:23.400 Yeah, which is one of the core aspects of the charter. 37:23.400 --> 37:26.520 So one concern people have, not just with OpenAI, 37:26.520 --> 37:28.400 but with Google, Facebook, Amazon, 37:28.400 --> 37:33.400 anybody really that's creating impact at scale 37:35.000 --> 37:37.680 is how do we avoid, as your charter says, 37:37.680 --> 37:40.080 avoid enabling the use of AI or AGI 37:40.080 --> 37:43.640 to unduly concentrate power? 37:43.640 --> 37:45.920 Why would not a company like OpenAI 37:45.920 --> 37:48.640 keep all the power of an AGI system to itself? 37:48.640 --> 37:49.520 The charter. 37:49.520 --> 37:50.360 The charter. 37:50.360 --> 37:51.960 So, you know, how does the charter 37:53.120 --> 37:57.240 actualize itself in day to day? 37:57.240 --> 38:00.480 So I think that first to zoom out, right, 38:00.480 --> 38:01.880 that the way that we structure the company 38:01.880 --> 38:04.560 is so that the power for sort of, you know, 38:04.560 --> 38:06.720 dictating the actions that OpenAI takes 38:06.720 --> 38:08.600 ultimately rests with the board, right? 38:08.600 --> 38:11.720 The board of the nonprofit and the board is set up 38:11.720 --> 38:13.480 in certain ways, with certain restrictions 38:13.480 --> 38:16.280 that you can read about in the OpenAI LP blog post. 38:16.280 --> 38:19.200 But effectively the board is the governing body 38:19.200 --> 38:21.200 for OpenAI LP. 38:21.200 --> 38:24.400 And the board has a duty to fulfill the mission 38:24.400 --> 38:26.360 of the nonprofit. 38:26.360 --> 38:28.800 And so that's kind of how we tie, 38:28.800 --> 38:30.960 how we thread all these things together. 38:30.960 --> 38:32.880 Now there's a question of so day to day, 38:32.880 --> 38:34.800 how do people, the individuals, 38:34.800 --> 38:36.960 who in some ways are the most empowered ones, right? 38:36.960 --> 38:38.800 You know, the board sort of gets to call the shots 38:38.800 --> 38:41.920 at the high level, but the people who are actually executing 38:41.920 --> 38:43.120 are the employees, right? 38:43.120 --> 38:45.480 The people here on a day to day basis who have the, 38:45.480 --> 38:47.720 you know, the keys to the technical kingdom. 38:48.960 --> 38:51.720 And there I think that the answer looks a lot like, 38:51.720 --> 38:55.120 well, how does any company's values get actualized, right? 38:55.120 --> 38:56.720 And I think that a lot of that comes down to 38:56.720 --> 38:58.160 that you need people who are here 38:58.160 --> 39:01.320 because they really believe in that mission 39:01.320 --> 39:02.800 and they believe in the charter 39:02.800 --> 39:05.440 and that they are willing to take actions 39:05.440 --> 39:08.600 that maybe are worse for them, but are better for the charter. 39:08.600 --> 39:11.440 And that's something that's really baked into the culture. 39:11.440 --> 39:13.200 And honestly, I think it's, you know, 39:13.200 --> 39:14.560 I think that that's one of the things 39:14.560 --> 39:18.200 that we really have to work to preserve as time goes on. 39:18.200 --> 39:20.760 And that's a really important part of how we think 39:20.760 --> 39:23.040 about hiring people and bringing people into OpenAI. 39:23.040 --> 39:25.320 So there's people here, there's people here 39:25.320 --> 39:30.320 who could speak up and say, like, hold on a second, 39:30.840 --> 39:34.600 this is totally against what we stand for, culture wise. 39:34.600 --> 39:35.440 Yeah, yeah, for sure. 39:35.440 --> 39:37.120 I mean, I think that we actually have, 39:37.120 --> 39:38.760 I think that's like a pretty important part 39:38.760 --> 39:41.920 of how we operate and how we have, 39:41.920 --> 39:44.160 even again with designing the charter 39:44.160 --> 39:46.680 and designing OpenAI in the first place, 39:46.680 --> 39:48.760 that there has been a lot of conversation 39:48.760 --> 39:50.480 with employees here and a lot of times 39:50.480 --> 39:52.400 where employees said, wait a second, 39:52.400 --> 39:53.920 this seems like it's going in the wrong direction 39:53.920 --> 39:55.120 and let's talk about it. 39:55.120 --> 39:57.360 And so I think one thing that's, I think are really, 39:57.360 --> 39:58.880 and you know, here's actually one thing 39:58.880 --> 40:02.080 that I think is very unique about us as a small company, 40:02.080 --> 40:04.360 is that if you're at a massive tech giant, 40:04.360 --> 40:05.680 that's a little bit hard for someone 40:05.680 --> 40:08.120 who's a line employee to go and talk to the CEO 40:08.120 --> 40:10.520 and say, I think that we're doing this wrong. 40:10.520 --> 40:13.040 And you know, you'll get companies like Google 40:13.040 --> 40:15.720 that have had some collective action from employees 40:15.720 --> 40:19.400 to make ethical change around things like Maven. 40:19.400 --> 40:20.680 And so maybe there are mechanisms 40:20.680 --> 40:22.240 that other companies that work, 40:22.240 --> 40:24.480 but here, super easy for anyone to pull me aside, 40:24.480 --> 40:26.320 to pull Sam aside, to pull Eli aside, 40:26.320 --> 40:27.800 and people do it all the time. 40:27.800 --> 40:29.800 One of the interesting things in the charter 40:29.800 --> 40:31.640 is this idea that it'd be great 40:31.640 --> 40:34.240 if you could try to describe or untangle 40:34.240 --> 40:36.440 switching from competition to collaboration 40:36.440 --> 40:38.920 and late stage AGI development. 40:38.920 --> 40:39.760 It's really interesting, 40:39.760 --> 40:42.160 this dance between competition and collaboration, 40:42.160 --> 40:43.400 how do you think about that? 40:43.400 --> 40:45.000 Yeah, assuming that you can actually do 40:45.000 --> 40:47.040 the technical side of AGI development, 40:47.040 --> 40:48.960 I think there's going to be two key problems 40:48.960 --> 40:50.400 with figuring out how do you actually deploy it 40:50.400 --> 40:51.520 and make it go well. 40:51.520 --> 40:53.160 The first one of these is the run up 40:53.160 --> 40:56.360 to building the first AGI. 40:56.360 --> 40:58.920 You look at how self driving cars are being developed, 40:58.920 --> 41:00.680 and it's a competitive race. 41:00.680 --> 41:02.560 And the thing that always happens in competitive race 41:02.560 --> 41:04.160 is that you have huge amounts of pressure 41:04.160 --> 41:05.600 to get rid of safety. 41:06.800 --> 41:08.920 And so that's one thing we're very concerned about, right? 41:08.920 --> 41:12.000 Is that people, multiple teams figuring out, 41:12.000 --> 41:13.600 we can actually get there, 41:13.600 --> 41:16.680 but you know, if we took the slower path 41:16.680 --> 41:20.240 that is more guaranteed to be safe, we will lose. 41:20.240 --> 41:22.360 And so we're going to take the fast path. 41:22.360 --> 41:25.480 And so the more that we can, both ourselves, 41:25.480 --> 41:27.280 be in a position where we don't generate 41:27.280 --> 41:29.000 that competitive race, where we say, 41:29.000 --> 41:31.520 if the race is being run and that someone else 41:31.520 --> 41:33.280 is further ahead than we are, 41:33.280 --> 41:35.600 we're not going to try to leapfrog. 41:35.600 --> 41:37.200 We're going to actually work with them, right? 41:37.200 --> 41:38.800 We will help them succeed. 41:38.800 --> 41:40.440 As long as what they're trying to do 41:40.440 --> 41:42.920 is to fulfill our mission, then we're good. 41:42.920 --> 41:44.800 We don't have to build AGI ourselves. 41:44.800 --> 41:47.080 And I think that's a really important commitment from us, 41:47.080 --> 41:49.080 but it can't just be unilateral, right? 41:49.080 --> 41:50.400 I think that it's really important 41:50.400 --> 41:53.120 that other players who are serious about building AGI 41:53.120 --> 41:54.680 make similar commitments, right? 41:54.680 --> 41:56.640 And I think that, you know, again, 41:56.640 --> 41:57.840 to the extent that everyone believes 41:57.840 --> 42:00.080 that AGI should be something to benefit everyone, 42:00.080 --> 42:01.240 then it actually really shouldn't matter 42:01.240 --> 42:02.440 which company builds it. 42:02.440 --> 42:04.160 And we should all be concerned about the case 42:04.160 --> 42:06.080 where we just race so hard to get there 42:06.080 --> 42:07.640 that something goes wrong. 42:07.640 --> 42:09.600 So what role do you think government, 42:10.560 --> 42:13.840 our favorite entity has in setting policy and rules 42:13.840 --> 42:18.320 about this domain, from research to the development 42:18.320 --> 42:22.880 to early stage, to late stage AI and AGI development? 42:22.880 --> 42:25.640 So I think that, first of all, 42:25.640 --> 42:28.080 it's really important that government's in there, right? 42:28.080 --> 42:29.800 In some way, shape, or form, you know, 42:29.800 --> 42:30.920 at the end of the day, we're talking about 42:30.920 --> 42:35.080 building technology that will shape how the world operates 42:35.080 --> 42:39.040 and that there needs to be government as part of that answer. 42:39.040 --> 42:42.160 And so that's why we've done a number 42:42.160 --> 42:43.600 of different congressional testimonies. 42:43.600 --> 42:46.440 We interact with a number of different lawmakers 42:46.440 --> 42:50.040 and that right now, a lot of our message to them 42:50.040 --> 42:54.360 is that it's not the time for regulation, 42:54.360 --> 42:56.400 it is the time for measurement, right? 42:56.400 --> 42:59.080 That our main policy recommendation is that people, 42:59.080 --> 43:00.680 and you know, the government does this all the time 43:00.680 --> 43:04.880 with bodies like NIST, spend time trying to figure out 43:04.880 --> 43:07.920 just where the technology is, how fast it's moving, 43:07.920 --> 43:11.200 and can really become literate and up to speed 43:11.200 --> 43:13.520 with respect to what to expect. 43:13.520 --> 43:15.240 So I think that today, the answer really 43:15.240 --> 43:17.320 is about measurement. 43:17.320 --> 43:20.160 And I think that there will be a time and place 43:20.160 --> 43:21.720 where that will change. 43:21.720 --> 43:24.840 And I think it's a little bit hard to predict exactly 43:24.840 --> 43:27.120 what exactly that trajectory should look like. 43:27.120 --> 43:31.080 So there will be a point at which regulation, 43:31.080 --> 43:34.200 federal in the United States, the government steps in 43:34.200 --> 43:39.200 and helps be the, I don't wanna say the adult in the room, 43:39.520 --> 43:42.400 to make sure that there is strict rules, 43:42.400 --> 43:45.200 maybe conservative rules that nobody can cross. 43:45.200 --> 43:47.400 Well, I think there's kind of maybe two angles to it. 43:47.400 --> 43:49.800 So today with narrow AI applications, 43:49.800 --> 43:51.960 that I think there are already existing bodies 43:51.960 --> 43:54.880 that are responsible and should be responsible for regulation. 43:54.880 --> 43:57.040 You think about, for example, with self driving cars, 43:57.040 --> 43:59.440 that you want the national highway. 44:00.720 --> 44:02.920 Yeah, exactly to be regulated in that. 44:02.920 --> 44:04.040 That makes sense, right? 44:04.040 --> 44:04.960 That basically what we're saying 44:04.960 --> 44:08.120 is that we're going to have these technological systems 44:08.120 --> 44:10.600 that are going to be performing applications 44:10.600 --> 44:12.280 that humans already do. 44:12.280 --> 44:14.800 Great, we already have ways of thinking about standards 44:14.800 --> 44:16.160 and safety for those. 44:16.160 --> 44:18.880 So I think actually empowering those regulators today 44:18.880 --> 44:20.040 is also pretty important. 44:20.040 --> 44:24.760 And then I think for AGI, that there's going to be a point 44:24.760 --> 44:26.040 where we'll have better answers. 44:26.040 --> 44:27.640 And I think that maybe a similar approach 44:27.640 --> 44:30.520 of first measurement and start thinking about 44:30.520 --> 44:31.640 what the rules should be. 44:31.640 --> 44:32.640 I think it's really important 44:32.640 --> 44:36.280 that we don't prematurely squash progress. 44:36.280 --> 44:40.160 I think it's very easy to kind of smother a budding field. 44:40.160 --> 44:42.160 And I think that's something to really avoid. 44:42.160 --> 44:43.760 But I don't think that the right way of doing it 44:43.760 --> 44:46.920 is to say, let's just try to blaze ahead 44:46.920 --> 44:50.280 and not involve all these other stakeholders. 44:51.480 --> 44:56.240 So you've recently released a paper on GPT2 44:56.240 --> 45:01.240 language modeling, but did not release the full model 45:02.040 --> 45:05.280 because you had concerns about the possible negative effects 45:05.280 --> 45:07.480 of the availability of such model. 45:07.480 --> 45:10.680 It's outside of just that decision, 45:10.680 --> 45:14.360 and it's super interesting because of the discussion 45:14.360 --> 45:17.000 at a societal level, the discourse it creates. 45:17.000 --> 45:19.320 So it's fascinating in that aspect. 45:19.320 --> 45:22.880 But if you think that's the specifics here at first, 45:22.880 --> 45:25.920 what are some negative effects that you envisioned? 45:25.920 --> 45:28.600 And of course, what are some of the positive effects? 45:28.600 --> 45:30.640 Yeah, so again, I think to zoom out, 45:30.640 --> 45:34.040 like the way that we thought about GPT2 45:34.040 --> 45:35.800 is that with language modeling, 45:35.800 --> 45:38.560 we are clearly on a trajectory right now 45:38.560 --> 45:40.880 where we scale up our models 45:40.880 --> 45:44.480 and we get qualitatively better performance, right? 45:44.480 --> 45:47.360 GPT2 itself was actually just a scale up 45:47.360 --> 45:50.680 of a model that we've released in the previous June, right? 45:50.680 --> 45:52.880 And we just ran it at much larger scale 45:52.880 --> 45:53.880 and we got these results 45:53.880 --> 45:57.240 where suddenly starting to write coherent pros, 45:57.240 --> 46:00.040 which was not something we'd seen previously. 46:00.040 --> 46:01.320 And what are we doing now? 46:01.320 --> 46:05.760 Well, we're gonna scale up GPT2 by 10x by 100x by 1000x 46:05.760 --> 46:07.840 and we don't know what we're gonna get. 46:07.840 --> 46:10.120 And so it's very clear that the model 46:10.120 --> 46:12.840 that we released last June, 46:12.840 --> 46:16.440 I think it's kind of like, it's a good academic toy. 46:16.440 --> 46:18.920 It's not something that we think is something 46:18.920 --> 46:20.440 that can really have negative applications 46:20.440 --> 46:21.680 or to the extent that it can, 46:21.680 --> 46:24.360 that the positive of people being able to play with it 46:24.360 --> 46:28.280 is far outweighs the possible harms. 46:28.280 --> 46:32.600 You fast forward to not GPT2, but GPT20, 46:32.600 --> 46:34.720 and you think about what that's gonna be like. 46:34.720 --> 46:38.200 And I think that the capabilities are going to be substantive. 46:38.200 --> 46:41.120 And so there needs to be a point in between the two 46:41.120 --> 46:43.480 where you say, this is something 46:43.480 --> 46:45.200 where we are drawing the line 46:45.200 --> 46:48.000 and that we need to start thinking about the safety aspects. 46:48.000 --> 46:50.160 And I think for GPT2, we could have gone either way. 46:50.160 --> 46:52.720 And in fact, when we had conversations internally 46:52.720 --> 46:54.760 that we had a bunch of pros and cons 46:54.760 --> 46:58.160 and it wasn't clear which one outweighed the other. 46:58.160 --> 46:59.840 And I think that when we announced 46:59.840 --> 47:02.160 that, hey, we decide not to release this model, 47:02.160 --> 47:03.600 then there was a bunch of conversation 47:03.600 --> 47:05.200 where various people said it's so obvious 47:05.200 --> 47:06.360 that you should have just released it. 47:06.360 --> 47:07.520 There are other people that said it's so obvious 47:07.520 --> 47:08.840 you should not have released it. 47:08.840 --> 47:10.960 And I think that that almost definitionally means 47:10.960 --> 47:13.800 that holding it back was the correct decision. 47:13.800 --> 47:17.000 If it's not obvious whether something is beneficial 47:17.000 --> 47:19.720 or not, you should probably default to caution. 47:19.720 --> 47:22.440 And so I think that the overall landscape 47:22.440 --> 47:23.760 for how we think about it 47:23.760 --> 47:25.920 is that this decision could have gone either way. 47:25.920 --> 47:27.960 There are great arguments in both directions. 47:27.960 --> 47:30.080 But for future models down the road, 47:30.080 --> 47:32.320 and possibly sooner than you'd expect, 47:32.320 --> 47:33.880 because scaling these things up doesn't actually 47:33.880 --> 47:36.800 take that long, those ones, 47:36.800 --> 47:39.600 you're definitely not going to want to release into the wild. 47:39.600 --> 47:42.640 And so I think that we almost view this as a test case 47:42.640 --> 47:45.360 and to see, can we even design, 47:45.360 --> 47:47.960 how do you have a society or how do you have a system 47:47.960 --> 47:50.520 that goes from having no concept of responsible disclosure 47:50.520 --> 47:53.440 where the mere idea of not releasing something 47:53.440 --> 47:55.960 for safety reasons is unfamiliar 47:55.960 --> 47:57.440 to a world where you say, okay, 47:57.440 --> 47:58.720 we have a powerful model. 47:58.720 --> 47:59.720 Let's at least think about it. 47:59.720 --> 48:01.280 Let's go through some process. 48:01.280 --> 48:02.680 And you think about the security community. 48:02.680 --> 48:03.880 It took them a long time 48:03.880 --> 48:05.960 to design responsible disclosure. 48:05.960 --> 48:07.200 You think about this question of, 48:07.200 --> 48:08.800 well, I have a security exploit. 48:08.800 --> 48:09.760 I send it to the company. 48:09.760 --> 48:12.000 The company is like, tries to prosecute me 48:12.000 --> 48:14.760 or just ignores it. 48:14.760 --> 48:16.080 What do I do? 48:16.080 --> 48:17.320 And so the alternatives of, 48:17.320 --> 48:19.120 oh, I just always publish your exploits. 48:19.120 --> 48:20.200 That doesn't seem good either. 48:20.200 --> 48:21.600 And so it really took a long time 48:21.600 --> 48:25.320 and it was bigger than any individual. 48:25.320 --> 48:27.080 It's really about building a whole community 48:27.080 --> 48:28.760 that believe that, okay, we'll have this process 48:28.760 --> 48:30.160 where you send it to the company 48:30.160 --> 48:31.680 if they don't act at a certain time, 48:31.680 --> 48:33.120 then you can go public 48:33.120 --> 48:34.440 and you're not a bad person. 48:34.440 --> 48:36.240 You've done the right thing. 48:36.240 --> 48:38.680 And I think that in AI, 48:38.680 --> 48:41.400 part of the response to GPT2 just proves 48:41.400 --> 48:44.200 that we don't have any concept of this. 48:44.200 --> 48:47.080 So that's the high level picture. 48:47.080 --> 48:48.720 And so I think that, 48:48.720 --> 48:51.240 I think this was a really important move to make. 48:51.240 --> 48:54.000 And we could have maybe delayed it for GPT3, 48:54.000 --> 48:56.080 but I'm really glad we did it for GPT2. 48:56.080 --> 48:57.760 And so now you look at GPT2 itself 48:57.760 --> 48:59.440 and you think about the substance of, okay, 48:59.440 --> 49:01.320 what are potential negative applications? 49:01.320 --> 49:04.120 So you have this model that's been trained on the internet, 49:04.120 --> 49:06.520 which is also going to be a bunch of very biased data, 49:06.520 --> 49:09.600 a bunch of very offensive content in there. 49:09.600 --> 49:13.240 And you can ask it to generate content for you 49:13.240 --> 49:14.600 on basically any topic, right? 49:14.600 --> 49:15.440 You just give it a prompt 49:15.440 --> 49:16.800 and it'll just start writing 49:16.800 --> 49:19.120 and it writes content like you see on the internet, 49:19.120 --> 49:21.960 you know, even down to like saying advertisement 49:21.960 --> 49:24.200 in the middle of some of its generations. 49:24.200 --> 49:26.200 And you think about the possibilities 49:26.200 --> 49:29.280 for generating fake news or abusive content. 49:29.280 --> 49:30.120 And, you know, it's interesting 49:30.120 --> 49:31.880 seeing what people have done with, you know, 49:31.880 --> 49:34.400 we released a smaller version of GPT2 49:34.400 --> 49:37.480 and the people have done things like try to generate, 49:37.480 --> 49:40.760 you know, take my own Facebook message history 49:40.760 --> 49:43.360 and generate more Facebook messages like me 49:43.360 --> 49:47.360 and people generating fake politician content 49:47.360 --> 49:49.520 or, you know, there's a bunch of things there 49:49.520 --> 49:51.920 where you at least have to think, 49:51.920 --> 49:54.720 is this going to be good for the world? 49:54.720 --> 49:56.320 There's the flip side, which is I think 49:56.320 --> 49:57.840 that there's a lot of awesome applications 49:57.840 --> 50:01.640 that we really want to see like creative applications 50:01.640 --> 50:04.000 in terms of if you have sci fi authors 50:04.000 --> 50:06.760 that can work with this tool and come with cool ideas, 50:06.760 --> 50:09.720 like that seems awesome if we can write better sci fi 50:09.720 --> 50:11.360 through the use of these tools. 50:11.360 --> 50:13.080 And we've actually had a bunch of people right into us 50:13.080 --> 50:16.160 asking, hey, can we use it for, you know, 50:16.160 --> 50:18.360 a variety of different creative applications? 50:18.360 --> 50:21.880 So the positive are actually pretty easy to imagine. 50:21.880 --> 50:26.880 There are, you know, the usual NLP applications 50:26.880 --> 50:30.960 that are really interesting, but let's go there. 50:30.960 --> 50:32.960 It's kind of interesting to think about a world 50:32.960 --> 50:37.960 where, look at Twitter, where not just fake news 50:37.960 --> 50:42.960 but smarter and smarter bots being able to spread 50:43.040 --> 50:47.400 in an interesting complex networking way in information 50:47.400 --> 50:50.800 that just floods out us regular human beings 50:50.800 --> 50:52.880 with our original thoughts. 50:52.880 --> 50:57.880 So what are your views of this world with GPT 20? 50:58.760 --> 51:01.600 Right, how do we think about, again, 51:01.600 --> 51:03.560 it's like one of those things about in the 50s 51:03.560 --> 51:08.560 trying to describe the internet or the smartphone. 51:08.720 --> 51:09.960 What do you think about that world, 51:09.960 --> 51:11.400 the nature of information? 51:12.920 --> 51:16.760 One possibility is that we'll always try to design systems 51:16.760 --> 51:19.680 that identify a robot versus human 51:19.680 --> 51:21.280 and we'll do so successfully. 51:21.280 --> 51:24.600 And so we'll authenticate that we're still human. 51:24.600 --> 51:27.520 And the other world is that we just accept the fact 51:27.520 --> 51:30.360 that we're swimming in a sea of fake news 51:30.360 --> 51:32.120 and just learn to swim there. 51:32.120 --> 51:34.800 Well, have you ever seen the, there's a, you know, 51:34.800 --> 51:39.800 popular meme of a robot with a physical arm and pen 51:41.520 --> 51:43.440 clicking the I'm not a robot button? 51:43.440 --> 51:44.280 Yeah. 51:44.280 --> 51:48.560 I think the truth is that really trying to distinguish 51:48.560 --> 51:52.160 between robot and human is a losing battle. 51:52.160 --> 51:53.800 Ultimately, you think it's a losing battle? 51:53.800 --> 51:55.520 I think it's a losing battle ultimately, right? 51:55.520 --> 51:57.800 I think that that is that in terms of the content, 51:57.800 --> 51:59.360 in terms of the actions that you can take. 51:59.360 --> 52:01.200 I mean, think about how captures have gone, right? 52:01.200 --> 52:02.920 The captures used to be a very nice, simple. 52:02.920 --> 52:06.320 You just have this image, all of our OCR is terrible. 52:06.320 --> 52:08.880 You put a couple of artifacts in it, you know, 52:08.880 --> 52:11.040 humans are gonna be able to tell what it is 52:11.040 --> 52:13.840 an AI system wouldn't be able to today. 52:13.840 --> 52:15.720 Like I could barely do captures. 52:15.720 --> 52:18.360 And I think that this is just kind of where we're going. 52:18.360 --> 52:20.400 I think captures where we're a moment in time thing. 52:20.400 --> 52:22.520 And as AI systems become more powerful, 52:22.520 --> 52:25.520 that there being human capabilities that can be measured 52:25.520 --> 52:29.360 in a very easy automated way that the AIs will not be 52:29.360 --> 52:31.120 capable of, I think that's just like, 52:31.120 --> 52:34.160 it's just an increasingly hard technical battle. 52:34.160 --> 52:36.240 But it's not that all hope is lost, right? 52:36.240 --> 52:39.760 And you think about how do we already authenticate 52:39.760 --> 52:40.600 ourselves, right? 52:40.600 --> 52:41.760 That, you know, we have systems. 52:41.760 --> 52:43.440 We have social security numbers. 52:43.440 --> 52:46.560 If you're in the U S or, you know, you have, you have, 52:46.560 --> 52:48.920 you know, ways of identifying individual people 52:48.920 --> 52:51.880 and having real world identity tied to digital identity 52:51.880 --> 52:54.880 seems like a step towards, you know, 52:54.880 --> 52:56.200 authenticating the source of content 52:56.200 --> 52:58.240 rather than the content itself. 52:58.240 --> 53:00.000 Now, there are problems with that. 53:00.000 --> 53:03.000 How can you have privacy and anonymity in a world 53:03.000 --> 53:05.440 where the only content you can really trust is, 53:05.440 --> 53:06.560 or the only way you can trust content 53:06.560 --> 53:08.560 is by looking at where it comes from. 53:08.560 --> 53:11.400 And so I think that building out good reputation networks 53:11.400 --> 53:14.080 maybe one possible solution. 53:14.080 --> 53:16.280 But yeah, I think that this question is not 53:16.280 --> 53:17.720 an obvious one. 53:17.720 --> 53:19.320 And I think that we, you know, 53:19.320 --> 53:20.880 maybe sooner than we think we'll be in a world 53:20.880 --> 53:23.800 where, you know, today I often will read a tweet 53:23.800 --> 53:25.960 and be like, do I feel like a real human wrote this? 53:25.960 --> 53:27.560 Or, you know, do I feel like this was like genuine? 53:27.560 --> 53:30.160 I feel like I can kind of judge the content a little bit. 53:30.160 --> 53:32.640 And I think in the future, it just won't be the case. 53:32.640 --> 53:36.880 You look at, for example, the FCC comments on net neutrality. 53:36.880 --> 53:39.880 It came out later that millions of those were auto generated 53:39.880 --> 53:41.960 and that the researchers were able to do various 53:41.960 --> 53:44.040 statistical techniques to do that. 53:44.040 --> 53:47.160 What do you do in a world where those statistical techniques 53:47.160 --> 53:48.000 don't exist? 53:48.000 --> 53:49.120 It's just impossible to tell the difference 53:49.120 --> 53:50.640 between humans and AI's. 53:50.640 --> 53:53.960 And in fact, the most persuasive arguments 53:53.960 --> 53:57.200 are written by AI, all that stuff. 53:57.200 --> 53:58.600 It's not sci fi anymore. 53:58.600 --> 54:01.320 You look at GPT2 making a great argument for why recycling 54:01.320 --> 54:02.560 is bad for the world. 54:02.560 --> 54:04.440 You got to read that and be like, huh, you're right. 54:04.440 --> 54:06.520 We are addressing just the symptoms. 54:06.520 --> 54:08.120 Yeah, that's quite interesting. 54:08.120 --> 54:11.320 I mean, ultimately it boils down to the physical world 54:11.320 --> 54:13.680 being the last frontier of proving. 54:13.680 --> 54:16.080 So you said like basically networks of people, 54:16.080 --> 54:19.400 humans vouching for humans in the physical world. 54:19.400 --> 54:22.960 And somehow the authentication ends there. 54:22.960 --> 54:24.560 I mean, if I had to ask you, 54:25.520 --> 54:28.160 I mean, you're way too eloquent for a human. 54:28.160 --> 54:31.240 So if I had to ask you to authenticate, 54:31.240 --> 54:33.120 like prove how do I know you're not a robot 54:33.120 --> 54:34.920 and how do you know I'm not a robot? 54:34.920 --> 54:35.760 Yeah. 54:35.760 --> 54:40.520 I think that's so far were in this space, 54:40.520 --> 54:42.120 this conversation we just had, 54:42.120 --> 54:44.000 the physical movements we did 54:44.000 --> 54:47.040 is the biggest gap between us and AI systems 54:47.040 --> 54:49.360 is the physical manipulation. 54:49.360 --> 54:51.280 So maybe that's the last frontier. 54:51.280 --> 54:53.040 Well, here's another question is, 54:53.040 --> 54:57.320 why is solving this problem important, right? 54:57.320 --> 54:59.080 Like what aspects are really important to us? 54:59.080 --> 55:01.200 And I think that probably where we'll end up 55:01.200 --> 55:03.600 is we'll hone in on what do we really want 55:03.600 --> 55:06.400 out of knowing if we're talking to a human. 55:06.400 --> 55:09.480 And I think that again, this comes down to identity. 55:09.480 --> 55:11.760 And so I think that the internet of the future, 55:11.760 --> 55:14.840 I expect to be one that will have lots of agents out there 55:14.840 --> 55:16.320 that will interact with you. 55:16.320 --> 55:17.880 But I think that the question of, 55:17.880 --> 55:21.520 is this real flesh and blood human 55:21.520 --> 55:23.800 or is this an automated system? 55:23.800 --> 55:25.800 May actually just be less important. 55:25.800 --> 55:27.360 Let's actually go there. 55:27.360 --> 55:32.360 It's GPT2 is impressive and let's look at GPT20. 55:32.440 --> 55:37.440 Why is it so bad that all my friends are GPT20? 55:37.440 --> 55:42.440 Why is it so important on the internet? 55:43.320 --> 55:47.360 Do you think to interact with only human beings? 55:47.360 --> 55:50.640 Why can't we live in a world where ideas can come 55:50.640 --> 55:52.960 from models trained on human data? 55:52.960 --> 55:55.720 Yeah, I think this is actually a really interesting question. 55:55.720 --> 55:56.560 This comes back to the, 55:56.560 --> 55:59.560 how do you even picture a world with some new technology? 55:59.560 --> 56:02.080 And I think that one thing that I think is important 56:02.080 --> 56:04.760 is, you know, let's say honesty. 56:04.760 --> 56:07.520 And I think that if you have, you know, almost in the 56:07.520 --> 56:11.120 Turing test style sense of technology, 56:11.120 --> 56:13.200 you have AIs that are pretending to be humans 56:13.200 --> 56:15.800 and deceiving you, I think that is, you know, 56:15.800 --> 56:17.560 that feels like a bad thing, right? 56:17.560 --> 56:19.720 I think that it's really important that we feel like 56:19.720 --> 56:21.280 we're in control of our environment, right? 56:21.280 --> 56:23.400 That we understand who we're interacting with. 56:23.400 --> 56:25.880 And if it's an AI or a human, 56:25.880 --> 56:28.680 that that's not something that we're being deceived about. 56:28.680 --> 56:30.240 But I think that the flip side of, 56:30.240 --> 56:32.680 can I have as meaningful of an interaction with an AI 56:32.680 --> 56:34.240 as I can with a human? 56:34.240 --> 56:36.880 Well, I actually think here you can turn to sci fi. 56:36.880 --> 56:40.040 And her, I think is a great example of asking this very 56:40.040 --> 56:40.880 question, right? 56:40.880 --> 56:42.800 And one thing I really love about her is it really starts 56:42.800 --> 56:45.800 out almost by asking how meaningful are human 56:45.800 --> 56:47.280 virtual relationships, right? 56:47.280 --> 56:51.200 And then you have a human who has a relationship with an AI 56:51.200 --> 56:54.320 and that you really start to be drawn into that, right? 56:54.320 --> 56:56.960 And that all of your emotional buttons get triggered 56:56.960 --> 56:59.000 in the same way as if there was a real human that was on 56:59.000 --> 57:00.400 the other side of that phone. 57:00.400 --> 57:03.800 And so I think that this is one way of thinking about it, 57:03.800 --> 57:07.160 is that I think that we can have meaningful interactions 57:07.160 --> 57:09.720 and that if there's a funny joke, 57:09.720 --> 57:11.320 some sense it doesn't really matter if it was written 57:11.320 --> 57:14.600 by a human or an AI, but what you don't want in a way 57:14.600 --> 57:17.360 where I think we should really draw hard lines is deception. 57:17.360 --> 57:20.200 And I think that as long as we're in a world where, 57:20.200 --> 57:22.640 you know, why do we build AI systems at all, right? 57:22.640 --> 57:25.000 The reason we want to build them is to enhance human lives, 57:25.000 --> 57:26.680 to make humans be able to do more things, 57:26.680 --> 57:29.040 to have humans feel more fulfilled. 57:29.040 --> 57:32.040 And if we can build AI systems that do that, 57:32.040 --> 57:33.200 you know, sign me up. 57:33.200 --> 57:35.160 So the process of language modeling, 57:37.120 --> 57:38.760 how far do you think it take us? 57:38.760 --> 57:40.680 Let's look at movie HER. 57:40.680 --> 57:45.040 Do you think a dialogue, natural language conversation 57:45.040 --> 57:47.840 is formulated by the Turing test, for example, 57:47.840 --> 57:50.760 do you think that process could be achieved through 57:50.760 --> 57:53.160 this kind of unsupervised language modeling? 57:53.160 --> 57:56.960 So I think the Turing test in its real form 57:56.960 --> 57:58.680 isn't just about language, right? 57:58.680 --> 58:00.560 It's really about reasoning too, right? 58:00.560 --> 58:01.920 That to really pass the Turing test, 58:01.920 --> 58:03.880 I should be able to teach calculus 58:03.880 --> 58:05.520 to whoever's on the other side 58:05.520 --> 58:07.480 and have it really understand calculus 58:07.480 --> 58:09.320 and be able to, you know, go and solve 58:09.320 --> 58:11.280 new calculus problems. 58:11.280 --> 58:13.960 And so I think that to really solve the Turing test, 58:13.960 --> 58:16.440 we need more than what we're seeing with language models. 58:16.440 --> 58:18.720 We need some way of plugging in reasoning. 58:18.720 --> 58:22.400 Now, how different will that be from what we already do? 58:22.400 --> 58:23.880 That's an open question, right? 58:23.880 --> 58:25.480 It might be that we need some sequence 58:25.480 --> 58:27.200 of totally radical new ideas, 58:27.200 --> 58:29.560 or it might be that we just need to kind of shape 58:29.560 --> 58:31.920 our existing systems in a slightly different way. 58:33.040 --> 58:34.640 But I think that in terms of how far 58:34.640 --> 58:35.920 language modeling will go, 58:35.920 --> 58:37.520 it's already gone way further 58:37.520 --> 58:39.760 than many people would have expected, right? 58:39.760 --> 58:40.960 I think that things like, 58:40.960 --> 58:42.720 and I think there's a lot of really interesting angles 58:42.720 --> 58:45.920 to poke in terms of how much does GPT2 58:45.920 --> 58:47.880 understand physical world? 58:47.880 --> 58:49.360 Like, you know, you read a little bit 58:49.360 --> 58:52.360 about fire underwater in GPT2. 58:52.360 --> 58:54.200 So it's like, okay, maybe it doesn't quite understand 58:54.200 --> 58:55.680 what these things are. 58:55.680 --> 58:58.560 But at the same time, I think that you also see 58:58.560 --> 59:00.640 various things like smoke coming from flame, 59:00.640 --> 59:02.680 and you know, a bunch of these things that GPT2, 59:02.680 --> 59:04.880 it has no body, it has no physical experience, 59:04.880 --> 59:07.280 it's just statically read data. 59:07.280 --> 59:11.680 And I think that if the answer is like, 59:11.680 --> 59:14.600 we don't know yet, and these questions though, 59:14.600 --> 59:16.240 we're starting to be able to actually ask them 59:16.240 --> 59:18.720 to physical systems, to real systems that exist, 59:18.720 --> 59:19.880 and that's very exciting. 59:19.880 --> 59:21.160 Do you think, what's your intuition? 59:21.160 --> 59:24.040 Do you think if you just scale language modeling, 59:24.040 --> 59:29.040 like significantly scale, that reasoning can emerge 59:29.320 --> 59:31.320 from the same exact mechanisms? 59:31.320 --> 59:34.960 I think it's unlikely that if we just scale GPT2, 59:34.960 --> 59:38.600 that we'll have reasoning in the full fledged way. 59:38.600 --> 59:39.760 And I think that there's like, 59:39.760 --> 59:41.520 the type signature is a little bit wrong, right? 59:41.520 --> 59:44.560 That like, there's something we do with, 59:44.560 --> 59:45.800 that we call thinking, right? 59:45.800 --> 59:47.640 Where we spend a lot of compute, 59:47.640 --> 59:49.160 like a variable amount of compute 59:49.160 --> 59:50.680 to get to better answers, right? 59:50.680 --> 59:53.040 I think a little bit harder, I get a better answer. 59:53.040 --> 59:55.160 And that that kind of type signature 59:55.160 --> 59:58.880 isn't quite encoded in a GPT, right? 59:58.880 --> 1:00:01.880 GPT will kind of like, it's spent a long time 1:00:01.880 --> 1:00:03.640 in it's like evolutionary history, 1:00:03.640 --> 1:00:04.680 baking and all this information, 1:00:04.680 --> 1:00:07.000 getting very, very good at this predictive process. 1:00:07.000 --> 1:00:10.320 And then at runtime, I just kind of do one forward pass 1:00:10.320 --> 1:00:13.240 and am able to generate stuff. 1:00:13.240 --> 1:00:15.560 And so, there might be small tweaks 1:00:15.560 --> 1:00:18.040 to what we do in order to get the type signature, right? 1:00:18.040 --> 1:00:21.040 For example, well, it's not really one forward pass, right? 1:00:21.040 --> 1:00:22.640 You generate symbol by symbol. 1:00:22.640 --> 1:00:25.560 And so, maybe you generate like a whole sequence of thoughts 1:00:25.560 --> 1:00:28.200 and you only keep like the last bit or something. 1:00:28.200 --> 1:00:29.840 But I think that at the very least, 1:00:29.840 --> 1:00:32.160 I would expect you have to make changes like that. 1:00:32.160 --> 1:00:35.520 Yeah, just exactly how we, you said think 1:00:35.520 --> 1:00:38.400 is the process of generating thought by thought 1:00:38.400 --> 1:00:40.360 in the same kind of way, like you said, 1:00:40.360 --> 1:00:43.640 keep the last bit, the thing that we converge towards. 1:00:45.000 --> 1:00:47.280 And I think there's another piece which is interesting, 1:00:47.280 --> 1:00:50.240 which is this out of distribution generalization, right? 1:00:50.240 --> 1:00:52.600 That like thinking somehow lets us do that, right? 1:00:52.600 --> 1:00:54.400 That we have an experience of thing 1:00:54.400 --> 1:00:56.080 and yet somehow we just kind of keep refining 1:00:56.080 --> 1:00:58.040 our mental model of it. 1:00:58.040 --> 1:01:01.160 This is again, something that feels tied to 1:01:01.160 --> 1:01:03.360 whatever reasoning is. 1:01:03.360 --> 1:01:05.720 And maybe it's a small tweak to what we do. 1:01:05.720 --> 1:01:08.080 Maybe it's many ideas and we'll take as many decades. 1:01:08.080 --> 1:01:11.920 Yeah, so the assumption there, generalization 1:01:11.920 --> 1:01:14.160 out of distribution is that it's possible 1:01:14.160 --> 1:01:16.880 to create new ideas. 1:01:18.160 --> 1:01:20.840 It's possible that nobody's ever created any new ideas. 1:01:20.840 --> 1:01:25.360 And then with scaling GPT2 to GPT20, 1:01:25.360 --> 1:01:30.360 you would essentially generalize to all possible thoughts 1:01:30.520 --> 1:01:34.200 as humans can have, just to play devil's advocate. 1:01:34.200 --> 1:01:37.280 Right, I mean, how many new story ideas 1:01:37.280 --> 1:01:39.120 have we come up with since Shakespeare, right? 1:01:39.120 --> 1:01:40.160 Yeah, exactly. 1:01:41.600 --> 1:01:44.680 It's just all different forms of love and drama and so on. 1:01:44.680 --> 1:01:45.800 Okay. 1:01:45.800 --> 1:01:47.520 Not sure if you read Biddle Lesson, 1:01:47.520 --> 1:01:49.400 a recent blog post by Rich Sutton. 1:01:49.400 --> 1:01:50.880 Yep, I have. 1:01:50.880 --> 1:01:53.720 He basically says something that echoes 1:01:53.720 --> 1:01:55.480 some of the ideas that you've been talking about, 1:01:55.480 --> 1:01:58.320 which is, he says the biggest lesson 1:01:58.320 --> 1:02:00.680 that can be read from 70 years of AI research 1:02:00.680 --> 1:02:03.880 is that general methods that leverage computation 1:02:03.880 --> 1:02:07.920 are ultimately going to ultimately win out. 1:02:07.920 --> 1:02:08.960 Do you agree with this? 1:02:08.960 --> 1:02:13.520 So basically open AI in general about the ideas 1:02:13.520 --> 1:02:15.880 you're exploring about coming up with methods, 1:02:15.880 --> 1:02:20.120 whether it's GPT2 modeling or whether it's open AI5, 1:02:20.120 --> 1:02:23.160 playing Dota, where a general method 1:02:23.160 --> 1:02:27.160 is better than a more fine tuned, expert tuned method. 1:02:29.760 --> 1:02:32.200 Yeah, so I think that, well, one thing that I think 1:02:32.200 --> 1:02:33.800 was really interesting about the reaction 1:02:33.800 --> 1:02:36.480 to that blog post was that a lot of people have read this 1:02:36.480 --> 1:02:39.440 as saying that compute is all that matters. 1:02:39.440 --> 1:02:41.360 And that's a very threatening idea, right? 1:02:41.360 --> 1:02:43.720 And I don't think it's a true idea either, right? 1:02:43.720 --> 1:02:45.800 It's very clear that we have algorithmic ideas 1:02:45.800 --> 1:02:47.920 that have been very important for making progress. 1:02:47.920 --> 1:02:50.720 And to really build AI, you wanna push as far as you can 1:02:50.720 --> 1:02:52.760 on the computational scale, and you wanna push 1:02:52.760 --> 1:02:55.520 as far as you can on human ingenuity. 1:02:55.520 --> 1:02:57.040 And so I think you need both. 1:02:57.040 --> 1:02:58.320 But I think the way that you phrase the question 1:02:58.320 --> 1:02:59.640 is actually very good, right? 1:02:59.640 --> 1:03:02.200 That it's really about what kind of ideas 1:03:02.200 --> 1:03:04.040 should we be striving for? 1:03:04.040 --> 1:03:07.600 And absolutely, if you can find a scalable idea, 1:03:07.600 --> 1:03:08.640 you pour more compute into it, 1:03:08.640 --> 1:03:11.400 you pour more data into it, it gets better. 1:03:11.400 --> 1:03:13.800 Like that's the real Holy Grail. 1:03:13.800 --> 1:03:16.600 And so I think that the answer to the question, 1:03:16.600 --> 1:03:19.920 I think is yes, that's really how we think about it. 1:03:19.920 --> 1:03:22.760 And that part of why we're excited about the power 1:03:22.760 --> 1:03:25.320 of deep learning and the potential for building AGI 1:03:25.320 --> 1:03:27.600 is because we look at the systems that exist 1:03:27.600 --> 1:03:29.720 in the most successful AI systems, 1:03:29.720 --> 1:03:32.680 and we realize that you scale those up, 1:03:32.680 --> 1:03:34.000 they're gonna work better. 1:03:34.000 --> 1:03:36.320 And I think that that scalability is something 1:03:36.320 --> 1:03:37.160 that really gives us hope 1:03:37.160 --> 1:03:39.600 for being able to build transformative systems. 1:03:39.600 --> 1:03:43.240 So I'll tell you, this is partially an emotional, 1:03:43.240 --> 1:03:45.760 you know, a thing that a response that people often have, 1:03:45.760 --> 1:03:49.280 if compute is so important for state of the art performance, 1:03:49.280 --> 1:03:50.760 you know, individual developers, 1:03:50.760 --> 1:03:52.960 maybe a 13 year old sitting somewhere in Kansas 1:03:52.960 --> 1:03:55.040 or something like that, you know, they're sitting, 1:03:55.040 --> 1:03:56.760 they might not even have a GPU 1:03:56.760 --> 1:04:00.080 and or may have a single GPU, a 1080 or something like that. 1:04:00.080 --> 1:04:02.640 And there's this feeling like, well, 1:04:02.640 --> 1:04:07.280 how can I possibly compete or contribute to this world of AI 1:04:07.280 --> 1:04:09.840 if scale is so important? 1:04:09.840 --> 1:04:11.920 So if you can comment on that, 1:04:11.920 --> 1:04:14.320 and in general, do you think we need to also 1:04:14.320 --> 1:04:18.800 in the future focus on democratizing compute resources 1:04:18.800 --> 1:04:22.680 more or as much as we democratize the algorithms? 1:04:22.680 --> 1:04:23.960 Well, so the way that I think about it 1:04:23.960 --> 1:04:28.880 is that there's this space of possible progress, right? 1:04:28.880 --> 1:04:30.920 There's a space of ideas and sort of systems 1:04:30.920 --> 1:04:32.960 that will work, that will move us forward. 1:04:32.960 --> 1:04:34.840 And there's a portion of that space, 1:04:34.840 --> 1:04:35.760 and to some extent, 1:04:35.760 --> 1:04:37.960 an increasingly significant portion of that space 1:04:37.960 --> 1:04:41.080 that does just require massive compute resources. 1:04:41.080 --> 1:04:44.760 And for that, I think that the answer is kind of clear 1:04:44.760 --> 1:04:47.960 and that part of why we have the structure that we do 1:04:47.960 --> 1:04:49.640 is because we think it's really important 1:04:49.640 --> 1:04:50.600 to be pushing the scale 1:04:50.600 --> 1:04:53.840 and to be building these large clusters and systems. 1:04:53.840 --> 1:04:55.920 But there's another portion of the space 1:04:55.920 --> 1:04:57.880 that isn't about the large scale compute, 1:04:57.880 --> 1:04:59.960 that are these ideas that, and again, 1:04:59.960 --> 1:05:02.200 I think that for the ideas to really be impactful 1:05:02.200 --> 1:05:04.200 and really shine, that they should be ideas 1:05:04.200 --> 1:05:05.840 that if you scale them up, 1:05:05.840 --> 1:05:08.840 would work way better than they do at small scale. 1:05:08.840 --> 1:05:11.160 But you can discover them without massive 1:05:11.160 --> 1:05:12.760 computational resources. 1:05:12.760 --> 1:05:15.200 And if you look at the history of recent developments, 1:05:15.200 --> 1:05:17.680 you think about things like the GAN or the VAE, 1:05:17.680 --> 1:05:20.920 that these are ones that I think you could come up with them 1:05:20.920 --> 1:05:22.720 without having, and in practice, 1:05:22.720 --> 1:05:24.520 people did come up with them without having 1:05:24.520 --> 1:05:26.560 massive, massive computational resources. 1:05:26.560 --> 1:05:28.000 Right, I just talked to Ian Goodfellow, 1:05:28.000 --> 1:05:31.600 but the thing is the initial GAN 1:05:31.600 --> 1:05:34.200 produced pretty terrible results, right? 1:05:34.200 --> 1:05:36.880 So only because it was in a very specific, 1:05:36.880 --> 1:05:38.640 only because they're smart enough to know 1:05:38.640 --> 1:05:41.520 that this is quite surprising to generate anything 1:05:41.520 --> 1:05:43.160 that they know. 1:05:43.160 --> 1:05:46.040 Do you see a world, or is that too optimistic and dreamer, 1:05:46.040 --> 1:05:49.760 like, to imagine that the compute resources 1:05:49.760 --> 1:05:52.200 are something that's owned by governments 1:05:52.200 --> 1:05:55.040 and provided as a utility? 1:05:55.040 --> 1:05:57.120 Actually, to some extent, this question reminds me 1:05:57.120 --> 1:06:00.280 of a blog post from one of my former professors 1:06:00.280 --> 1:06:02.440 at Harvard, this guy, Matt Welch, 1:06:02.440 --> 1:06:03.760 who was a systems professor. 1:06:03.760 --> 1:06:05.280 I remember sitting in his tenure talk, right, 1:06:05.280 --> 1:06:08.800 and that he had literally just gotten tenure. 1:06:08.800 --> 1:06:10.960 He went to Google for the summer, 1:06:10.960 --> 1:06:15.680 and then decided he wasn't going back to academia, right? 1:06:15.680 --> 1:06:17.760 And kind of in his blog post, he makes this point 1:06:17.760 --> 1:06:20.800 that, look, as a systems researcher, 1:06:20.800 --> 1:06:23.040 that I come up with these cool system ideas, 1:06:23.040 --> 1:06:25.080 right, and kind of build a little proof of concept, 1:06:25.080 --> 1:06:27.080 and the best thing I could hope for 1:06:27.080 --> 1:06:30.120 is that the people at Google or Yahoo, 1:06:30.120 --> 1:06:32.600 which was around at the time, 1:06:32.600 --> 1:06:35.400 will implement it and actually make it work at scale, right? 1:06:35.400 --> 1:06:36.640 That's like the dream for me, right? 1:06:36.640 --> 1:06:38.000 I build the little thing, and they turn it into 1:06:38.000 --> 1:06:40.000 the big thing that's actually working. 1:06:40.000 --> 1:06:43.360 And for him, he said, I'm done with that. 1:06:43.360 --> 1:06:45.320 I want to be the person who's actually doing 1:06:45.320 --> 1:06:47.200 building and deploying. 1:06:47.200 --> 1:06:49.560 And I think that there's a similar dichotomy here, right? 1:06:49.560 --> 1:06:52.400 I think that there are people who really actually 1:06:52.400 --> 1:06:55.240 find value, and I think it is a valuable thing to do, 1:06:55.240 --> 1:06:57.440 to be the person who produces those ideas, right, 1:06:57.440 --> 1:06:58.840 who builds the proof of concept. 1:06:58.840 --> 1:07:00.600 And yeah, you don't get to generate 1:07:00.600 --> 1:07:02.760 the coolest possible GAN images, 1:07:02.760 --> 1:07:04.480 but you invented the GAN, right? 1:07:04.480 --> 1:07:07.560 And so there's a real trade off there. 1:07:07.560 --> 1:07:09.040 And I think that that's a very personal choice, 1:07:09.040 --> 1:07:10.840 but I think there's value in both sides. 1:07:10.840 --> 1:07:14.600 So do you think creating AGI, something, 1:07:14.600 --> 1:07:19.600 or some new models, we would see echoes of the brilliance 1:07:20.440 --> 1:07:22.240 even at the prototype level. 1:07:22.240 --> 1:07:24.080 So you would be able to develop those ideas 1:07:24.080 --> 1:07:27.240 without scale, the initial seeds. 1:07:27.240 --> 1:07:30.680 So take a look at, I always like to look at examples 1:07:30.680 --> 1:07:32.680 that exist, right, look at real precedent. 1:07:32.680 --> 1:07:36.240 And so take a look at the June 2018 model 1:07:36.240 --> 1:07:39.200 that we released that we scaled up to turn to GPT2. 1:07:39.200 --> 1:07:41.280 And you can see that at small scale, 1:07:41.280 --> 1:07:42.800 it set some records, right? 1:07:42.800 --> 1:07:44.800 This was the original GPT. 1:07:44.800 --> 1:07:46.840 We actually had some cool generations. 1:07:46.840 --> 1:07:49.840 They weren't nearly as amazing and really stunning 1:07:49.840 --> 1:07:52.000 as the GPT2 ones, but it was promising. 1:07:52.000 --> 1:07:53.040 It was interesting. 1:07:53.040 --> 1:07:55.280 And so I think it is the case that with a lot 1:07:55.280 --> 1:07:58.280 of these ideas that you see promise at small scale, 1:07:58.280 --> 1:08:00.800 but there isn't an asterisk here, a very big asterisk, 1:08:00.800 --> 1:08:05.240 which is sometimes we see behaviors that emerge 1:08:05.240 --> 1:08:07.280 that are qualitatively different 1:08:07.280 --> 1:08:09.080 from anything we saw at small scale. 1:08:09.080 --> 1:08:12.600 And that the original inventor of whatever algorithm 1:08:12.600 --> 1:08:15.520 looks at and says, I didn't think it could do that. 1:08:15.520 --> 1:08:17.400 This is what we saw in Dota, right? 1:08:17.400 --> 1:08:19.320 So PPO was created by John Shulman, 1:08:19.320 --> 1:08:20.560 who's a researcher here. 1:08:20.560 --> 1:08:24.680 And with Dota, we basically just ran PPO 1:08:24.680 --> 1:08:26.520 at massive, massive scale. 1:08:26.520 --> 1:08:29.120 And there's some tweaks in order to make it work, 1:08:29.120 --> 1:08:31.520 but fundamentally it's PPO at the core. 1:08:31.520 --> 1:08:35.280 And we were able to get this longterm planning, 1:08:35.280 --> 1:08:38.680 these behaviors to really play out on a time scale 1:08:38.680 --> 1:08:40.760 that we just thought was not possible. 1:08:40.760 --> 1:08:42.680 And John looked at that and was like, 1:08:42.680 --> 1:08:44.240 I didn't think it could do that. 1:08:44.240 --> 1:08:45.480 That's what happens when you're at three orders 1:08:45.480 --> 1:08:48.400 of magnitude more scale than you tested at. 1:08:48.400 --> 1:08:50.600 Yeah, but it still has the same flavors of, 1:08:50.600 --> 1:08:55.600 you know, at least echoes of the expected billions. 1:08:56.000 --> 1:08:57.880 Although I suspect with GPT, 1:08:57.880 --> 1:09:01.800 it's scaled more and more, you might get surprising things. 1:09:01.800 --> 1:09:03.200 So yeah, you're right. 1:09:03.200 --> 1:09:06.360 It's interesting that it's difficult to see 1:09:06.360 --> 1:09:09.320 how far an idea will go when it's scaled. 1:09:09.320 --> 1:09:11.080 It's an open question. 1:09:11.080 --> 1:09:13.080 Well, so to that point with Dota and PPO, 1:09:13.080 --> 1:09:15.040 like I mean, here's a very concrete one, right? 1:09:15.040 --> 1:09:16.680 It's like, it's actually one thing 1:09:16.680 --> 1:09:17.720 that's very surprising about Dota 1:09:17.720 --> 1:09:20.400 that I think people don't really pay that much attention to. 1:09:20.400 --> 1:09:22.360 Is the decree of generalization 1:09:22.360 --> 1:09:24.560 out of distribution that happens, right? 1:09:24.560 --> 1:09:26.320 That you have this AI that's trained 1:09:26.320 --> 1:09:28.880 against other bots for its entirety, 1:09:28.880 --> 1:09:30.360 the entirety of its existence. 1:09:30.360 --> 1:09:31.440 Sorry to take a step back. 1:09:31.440 --> 1:09:36.440 Can you talk through, you know, a story of Dota, 1:09:37.240 --> 1:09:42.040 a story of leading up to opening I5 and that past, 1:09:42.040 --> 1:09:43.920 and what was the process of self playing 1:09:43.920 --> 1:09:45.440 and so on of training on this? 1:09:45.440 --> 1:09:46.280 Yeah, yeah, yeah. 1:09:46.280 --> 1:09:47.120 So with Dota. 1:09:47.120 --> 1:09:47.960 What is Dota? 1:09:47.960 --> 1:09:50.000 Dota is a complex video game 1:09:50.000 --> 1:09:51.320 and we started training, 1:09:51.320 --> 1:09:52.720 we started trying to solve Dota 1:09:52.720 --> 1:09:55.680 because we felt like this was a step towards the real world 1:09:55.680 --> 1:09:58.040 relative to other games like Chess or Go, right? 1:09:58.040 --> 1:09:59.160 Those very cerebral games 1:09:59.160 --> 1:10:00.480 where you just kind of have this board 1:10:00.480 --> 1:10:01.880 of very discreet moves. 1:10:01.880 --> 1:10:04.040 Dota starts to be much more continuous time. 1:10:04.040 --> 1:10:06.200 So you have this huge variety of different actions 1:10:06.200 --> 1:10:07.680 that you have a 45 minute game 1:10:07.680 --> 1:10:09.360 with all these different units 1:10:09.360 --> 1:10:11.840 and it's got a lot of messiness to it 1:10:11.840 --> 1:10:14.480 that really hasn't been captured by previous games. 1:10:14.480 --> 1:10:17.320 And famously all of the hard coded bots for Dota 1:10:17.320 --> 1:10:18.400 were terrible, right? 1:10:18.400 --> 1:10:19.920 It's just impossible to write anything good for it 1:10:19.920 --> 1:10:21.240 because it's so complex. 1:10:21.240 --> 1:10:23.280 And so this seemed like a really good place 1:10:23.280 --> 1:10:25.240 to push what's the state of the art 1:10:25.240 --> 1:10:26.800 in reinforcement learning. 1:10:26.800 --> 1:10:29.000 And so we started by focusing on the one versus one 1:10:29.000 --> 1:10:32.360 version of the game and we're able to solve that. 1:10:32.360 --> 1:10:33.880 We're able to beat the world champions 1:10:33.880 --> 1:10:37.240 and the learning, the skill curve 1:10:37.240 --> 1:10:38.960 was this crazy exponential, right? 1:10:38.960 --> 1:10:41.000 It was like constantly we were just scaling up, 1:10:41.000 --> 1:10:43.240 that we were fixing bugs and that you look 1:10:43.240 --> 1:10:46.600 at the skill curve and it was really a very, very smooth one. 1:10:46.600 --> 1:10:47.440 So it's actually really interesting 1:10:47.440 --> 1:10:50.000 to see how that like human iteration loop 1:10:50.000 --> 1:10:52.680 yielded very steady exponential progress. 1:10:52.680 --> 1:10:55.160 And to one side note, first of all, 1:10:55.160 --> 1:10:57.080 it's an exceptionally popular video game. 1:10:57.080 --> 1:10:59.400 The side effect is that there's a lot 1:10:59.400 --> 1:11:01.920 of incredible human experts at that video game. 1:11:01.920 --> 1:11:05.200 So the benchmark that you're trying to reach is very high. 1:11:05.200 --> 1:11:07.840 And the other, can you talk about the approach 1:11:07.840 --> 1:11:10.600 that was used initially and throughout training 1:11:10.600 --> 1:11:12.040 these agents to play this game? 1:11:12.040 --> 1:11:12.880 Yep. 1:11:12.880 --> 1:11:14.400 And so the approach that we used is self play. 1:11:14.400 --> 1:11:17.320 And so you have two agents that don't know anything. 1:11:17.320 --> 1:11:18.640 They battle each other, 1:11:18.640 --> 1:11:20.760 they discover something a little bit good 1:11:20.760 --> 1:11:22.000 and now they both know it. 1:11:22.000 --> 1:11:24.520 And they just get better and better and better without bound. 1:11:24.520 --> 1:11:27.040 And that's a really powerful idea, right? 1:11:27.040 --> 1:11:30.160 That we then went from the one versus one version 1:11:30.160 --> 1:11:32.400 of the game and scaled up to five versus five, right? 1:11:32.400 --> 1:11:34.280 So you think about kind of like with basketball 1:11:34.280 --> 1:11:35.440 where you have this like team sport 1:11:35.440 --> 1:11:37.640 and you need to do all this coordination 1:11:37.640 --> 1:11:40.920 and we were able to push the same idea, 1:11:40.920 --> 1:11:45.920 the same self play to really get to the professional level 1:11:45.920 --> 1:11:48.880 at the full five versus five version of the game. 1:11:48.880 --> 1:11:52.400 And the things that I think are really interesting here 1:11:52.400 --> 1:11:54.760 is that these agents in some ways 1:11:54.760 --> 1:11:56.760 they're almost like an insect like intelligence, right? 1:11:56.760 --> 1:11:59.920 Where they have a lot in common with how an insect is trained, 1:11:59.920 --> 1:12:00.760 right? 1:12:00.760 --> 1:12:02.640 An insect kind of lives in this environment for a very long time 1:12:02.640 --> 1:12:05.280 or the ancestors of this insect have been around 1:12:05.280 --> 1:12:07.000 for a long time and had a lot of experience. 1:12:07.000 --> 1:12:09.680 I think it's baked into this agent. 1:12:09.680 --> 1:12:12.720 And it's not really smart in the sense of a human, right? 1:12:12.720 --> 1:12:14.560 It's not able to go and learn calculus, 1:12:14.560 --> 1:12:17.000 but it's able to navigate its environment extremely well. 1:12:17.000 --> 1:12:18.480 And it's able to handle unexpected things 1:12:18.480 --> 1:12:22.080 in the environment that's never seen before, pretty well. 1:12:22.080 --> 1:12:24.800 And we see the same sort of thing with our Dota bots, right? 1:12:24.800 --> 1:12:26.720 That they're able to, within this game, 1:12:26.720 --> 1:12:28.440 they're able to play against humans, 1:12:28.440 --> 1:12:30.000 which is something that never existed 1:12:30.000 --> 1:12:31.360 in its evolutionary environment. 1:12:31.360 --> 1:12:34.400 Totally different play styles from humans versus the bots. 1:12:34.400 --> 1:12:37.200 And yet it's able to handle it extremely well. 1:12:37.200 --> 1:12:40.400 And that's something that I think was very surprising to us 1:12:40.400 --> 1:12:43.440 was something that doesn't really emerge 1:12:43.440 --> 1:12:47.200 from what we've seen with PPO at smaller scale, right? 1:12:47.200 --> 1:12:48.560 And the kind of scale we're running this stuff at 1:12:48.560 --> 1:12:51.920 was I could take 100,000 CPU cores, 1:12:51.920 --> 1:12:54.040 running with like hundreds of GPUs. 1:12:54.040 --> 1:12:59.040 It was probably about something like hundreds of years 1:12:59.040 --> 1:13:03.800 of experience going into this bot every single real day. 1:13:03.800 --> 1:13:06.200 And so that scale is massive. 1:13:06.200 --> 1:13:08.400 And we start to see very different kinds of behaviors 1:13:08.400 --> 1:13:10.760 out of the algorithms that we all know and love. 1:13:10.760 --> 1:13:15.160 Dota, you mentioned, beat the world expert 1v1. 1:13:15.160 --> 1:13:21.160 And then you weren't able to win 5v5 this year 1:13:21.160 --> 1:13:24.080 at the best players in the world. 1:13:24.080 --> 1:13:26.640 So what's the comeback story? 1:13:26.640 --> 1:13:27.680 First of all, talk through that. 1:13:27.680 --> 1:13:29.480 That was an exceptionally exciting event. 1:13:29.480 --> 1:13:33.160 And what's the following months in this year look like? 1:13:33.160 --> 1:13:33.760 Yeah, yeah. 1:13:33.760 --> 1:13:38.640 So one thing that's interesting is that we lose all the time. 1:13:38.640 --> 1:13:40.040 Because we play here. 1:13:40.040 --> 1:13:42.840 So the Dota team at OpenAI, we play the bot 1:13:42.840 --> 1:13:45.800 against better players than our system all the time. 1:13:45.800 --> 1:13:47.400 Or at least we used to, right? 1:13:47.400 --> 1:13:50.680 Like the first time we lost publicly was we went up 1:13:50.680 --> 1:13:53.480 on stage at the international and we played against some 1:13:53.480 --> 1:13:54.800 of the best teams in the world. 1:13:54.800 --> 1:13:56.320 And we ended up losing both games. 1:13:56.320 --> 1:13:58.520 But we give them a run for their money, right? 1:13:58.520 --> 1:14:01.440 That both games were kind of 30 minutes, 25 minutes. 1:14:01.440 --> 1:14:04.200 And they went back and forth, back and forth, back and forth. 1:14:04.200 --> 1:14:06.360 And so I think that really shows that we're 1:14:06.360 --> 1:14:08.280 at the professional level. 1:14:08.280 --> 1:14:09.640 And that kind of looking at those games, 1:14:09.640 --> 1:14:12.280 we think that the coin could have gone a different direction 1:14:12.280 --> 1:14:13.560 and we could have had some wins. 1:14:13.560 --> 1:14:16.200 And so that was actually very encouraging for us. 1:14:16.200 --> 1:14:18.360 And you know, it's interesting because the international was 1:14:18.360 --> 1:14:19.720 at a fixed time, right? 1:14:19.720 --> 1:14:22.680 So we knew exactly what day we were going to be playing. 1:14:22.680 --> 1:14:25.480 And we pushed as far as we could, as fast as we could. 1:14:25.480 --> 1:14:28.040 Two weeks later, we had a bot that had an 80% win rate 1:14:28.040 --> 1:14:30.120 versus the one that played at TI. 1:14:30.120 --> 1:14:31.720 So the March of Progress, you know, 1:14:31.720 --> 1:14:33.480 that you should think of as a snapshot rather 1:14:33.480 --> 1:14:34.920 than as an end state. 1:14:34.920 --> 1:14:39.000 And so in fact, we'll be announcing our finals pretty soon. 1:14:39.000 --> 1:14:42.760 I actually think that we'll announce our final match 1:14:42.760 --> 1:14:45.240 prior to this podcast being released. 1:14:45.240 --> 1:14:49.240 So there should be, we'll be playing against the world 1:14:49.240 --> 1:14:49.720 champions. 1:14:49.720 --> 1:14:52.520 And you know, for us, it's really less about, 1:14:52.520 --> 1:14:55.400 like the way that we think about what's upcoming 1:14:55.400 --> 1:14:59.000 is the final milestone, the final competitive milestone 1:14:59.000 --> 1:15:00.280 for the project, right? 1:15:00.280 --> 1:15:02.760 That our goal in all of this isn't really 1:15:02.760 --> 1:15:05.160 about beating humans at Dota. 1:15:05.160 --> 1:15:06.760 Our goal is to push the state of the art 1:15:06.760 --> 1:15:07.800 in reinforcement learning. 1:15:07.800 --> 1:15:08.920 And we've done that, right? 1:15:08.920 --> 1:15:10.680 And we've actually learned a lot from our system 1:15:10.680 --> 1:15:13.320 and that we have, you know, I think a lot of exciting 1:15:13.320 --> 1:15:14.680 next steps that we want to take. 1:15:14.680 --> 1:15:16.440 And so, you know, kind of the final showcase 1:15:16.440 --> 1:15:18.760 of what we built, we're going to do this match. 1:15:18.760 --> 1:15:21.240 But for us, it's not really the success or failure 1:15:21.240 --> 1:15:23.800 to see, you know, do we have the coin flip go 1:15:23.800 --> 1:15:24.840 in our direction or against. 1:15:25.880 --> 1:15:28.680 Where do you see the field of deep learning 1:15:28.680 --> 1:15:30.680 heading in the next few years? 1:15:31.720 --> 1:15:35.480 Where do you see the work in reinforcement learning 1:15:35.480 --> 1:15:40.360 perhaps heading and more specifically with OpenAI, 1:15:41.160 --> 1:15:43.480 all the exciting projects that you're working on, 1:15:44.280 --> 1:15:46.360 what does 2019 hold for you? 1:15:46.360 --> 1:15:47.400 Massive scale. 1:15:47.400 --> 1:15:47.880 Scale. 1:15:47.880 --> 1:15:49.480 I will put an atrocious on that and just say, 1:15:49.480 --> 1:15:52.200 you know, I think that it's about ideas plus scale. 1:15:52.200 --> 1:15:52.840 You need both. 1:15:52.840 --> 1:15:54.920 So that's a really good point. 1:15:54.920 --> 1:15:57.720 So the question, in terms of ideas, 1:15:58.520 --> 1:16:02.200 you have a lot of projects that are exploring 1:16:02.200 --> 1:16:04.280 different areas of intelligence. 1:16:04.280 --> 1:16:07.480 And the question is, when you think of scale, 1:16:07.480 --> 1:16:09.560 do you think about growing the scale 1:16:09.560 --> 1:16:10.680 of those individual projects, 1:16:10.680 --> 1:16:12.600 or do you think about adding new projects? 1:16:13.160 --> 1:16:17.320 And sorry, if you were thinking about adding new projects, 1:16:17.320 --> 1:16:19.800 or if you look at the past, what's the process 1:16:19.800 --> 1:16:21.960 of coming up with new projects and new ideas? 1:16:21.960 --> 1:16:22.680 Yep. 1:16:22.680 --> 1:16:24.600 So we really have a life cycle of project here. 1:16:25.240 --> 1:16:27.320 So we start with a few people just working 1:16:27.320 --> 1:16:28.440 on a small scale idea. 1:16:28.440 --> 1:16:30.520 And language is actually a very good example of this, 1:16:30.520 --> 1:16:32.440 that it was really, you know, one person here 1:16:32.440 --> 1:16:34.840 who was pushing on language for a long time. 1:16:34.840 --> 1:16:36.680 I mean, then you get signs of life, right? 1:16:36.680 --> 1:16:38.440 And so this is like, let's say, you know, 1:16:38.440 --> 1:16:42.600 with the original GPT, we had something that was interesting. 1:16:42.600 --> 1:16:44.760 And we said, okay, it's time to scale this, right? 1:16:44.760 --> 1:16:45.960 It's time to put more people on it, 1:16:45.960 --> 1:16:48.120 put more computational resources behind it, 1:16:48.120 --> 1:16:51.560 and then we just kind of keep pushing and keep pushing. 1:16:51.560 --> 1:16:52.920 And the end state is something that looks like 1:16:52.920 --> 1:16:55.400 Dota or Robotics, where you have a large team of, 1:16:55.400 --> 1:16:57.800 you know, 10 or 15 people that are running things 1:16:57.800 --> 1:17:00.680 at very large scale, and that you're able to really have 1:17:00.680 --> 1:17:04.280 material engineering and, you know, 1:17:04.280 --> 1:17:06.520 sort of machine learning science coming together 1:17:06.520 --> 1:17:10.200 to make systems that work and get material results 1:17:10.200 --> 1:17:11.560 that just would have been impossible otherwise. 1:17:12.200 --> 1:17:13.560 So we do that whole life cycle. 1:17:13.560 --> 1:17:16.600 We've done it a number of times, you know, typically end to end. 1:17:16.600 --> 1:17:19.960 It's probably two years or so to do it. 1:17:19.960 --> 1:17:21.720 You know, the organization's been around for three years, 1:17:21.720 --> 1:17:23.000 so maybe we'll find that we also have 1:17:23.000 --> 1:17:24.760 longer life cycle projects. 1:17:24.760 --> 1:17:27.480 But, you know, we work up to those. 1:17:27.480 --> 1:17:30.280 So one team that we're actually just starting, 1:17:30.280 --> 1:17:32.200 Illy and I, are kicking off a new team 1:17:32.200 --> 1:17:35.080 called the Reasoning Team, and this is to really try to tackle 1:17:35.080 --> 1:17:37.400 how do you get neural networks to reason? 1:17:37.400 --> 1:17:41.400 And we think that this will be a long term project. 1:17:41.400 --> 1:17:42.840 It's one that we're very excited about. 1:17:42.840 --> 1:17:46.200 In terms of reasoning, super exciting topic, 1:17:47.400 --> 1:17:52.200 what kind of benchmarks, what kind of tests of reasoning 1:17:52.200 --> 1:17:53.800 do you envision? 1:17:53.800 --> 1:17:55.880 What would, if you set back, 1:17:55.880 --> 1:17:59.240 whatever drink, and you would be impressed 1:17:59.240 --> 1:18:01.640 that this system is able to do something, 1:18:01.640 --> 1:18:02.760 what would that look like? 1:18:02.760 --> 1:18:03.800 Theorem proving. 1:18:03.800 --> 1:18:04.840 Theorem proving. 1:18:04.840 --> 1:18:09.480 So some kind of logic, and especially mathematical logic. 1:18:09.480 --> 1:18:10.440 I think so, right? 1:18:10.440 --> 1:18:12.440 And I think that there's kind of other problems 1:18:12.440 --> 1:18:14.520 that are dual to theorem proving in particular. 1:18:14.520 --> 1:18:16.840 You know, you think about programming, 1:18:16.840 --> 1:18:19.960 you think about even like security analysis of code, 1:18:19.960 --> 1:18:24.200 that these all kind of capture the same sorts of core reasoning 1:18:24.200 --> 1:18:27.480 and being able to do some out of distribution generalization. 1:18:28.440 --> 1:18:31.880 It would be quite exciting if OpenAI Reasoning Team 1:18:31.880 --> 1:18:33.880 was able to prove that P equals NP. 1:18:33.880 --> 1:18:35.080 That would be very nice. 1:18:35.080 --> 1:18:37.720 It would be very, very exciting especially. 1:18:37.720 --> 1:18:39.080 If it turns out that P equals NP, 1:18:39.080 --> 1:18:40.120 that'll be interesting too. 1:18:40.120 --> 1:18:45.160 It would be ironic and humorous. 1:18:45.160 --> 1:18:51.800 So what problem stands out to you as the most exciting 1:18:51.800 --> 1:18:55.720 and challenging impactful to the work for us as a community 1:18:55.720 --> 1:18:58.440 in general and for OpenAI this year? 1:18:58.440 --> 1:18:59.480 You mentioned reasoning. 1:18:59.480 --> 1:19:01.320 I think that's a heck of a problem. 1:19:01.320 --> 1:19:01.480 Yeah. 1:19:01.480 --> 1:19:02.760 So I think reasoning is an important one. 1:19:02.760 --> 1:19:04.840 I think it's going to be hard to get good results in 2019. 1:19:05.480 --> 1:19:07.480 You know, again, just like we think about the lifecycle, 1:19:07.480 --> 1:19:07.960 takes time. 1:19:08.600 --> 1:19:11.320 I think for 2019, language modeling seems to be kind of 1:19:11.320 --> 1:19:12.520 on that ramp, right? 1:19:12.520 --> 1:19:14.760 It's at the point that we have a technique that works. 1:19:14.760 --> 1:19:17.000 We want to scale 100x, 1000x, see what happens. 1:19:18.040 --> 1:19:18.360 Awesome. 1:19:18.360 --> 1:19:21.800 Do you think we're living in a simulation? 1:19:21.800 --> 1:19:24.520 I think it's hard to have a real opinion about it. 1:19:25.560 --> 1:19:26.200 It's actually interesting. 1:19:26.200 --> 1:19:29.960 I separate out things that I think can have yield 1:19:29.960 --> 1:19:31.880 materially different predictions about the world 1:19:32.520 --> 1:19:35.640 from ones that are just kind of fun to speculate about. 1:19:35.640 --> 1:19:37.800 And I kind of view simulation as more like, 1:19:37.800 --> 1:19:40.200 is there a flying teapot between Mars and Jupiter? 1:19:40.200 --> 1:19:43.800 Like, maybe, but it's a little bit hard to know 1:19:43.800 --> 1:19:45.000 what that would mean for my life. 1:19:45.000 --> 1:19:46.360 So there is something actionable. 1:19:46.360 --> 1:19:50.680 So some of the best work opening as done 1:19:50.680 --> 1:19:52.200 is in the field of reinforcement learning. 1:19:52.760 --> 1:19:56.520 And some of the success of reinforcement learning 1:19:56.520 --> 1:19:59.080 come from being able to simulate the problem you're trying 1:19:59.080 --> 1:20:00.040 to solve. 1:20:00.040 --> 1:20:03.560 So do you have a hope for reinforcement, 1:20:03.560 --> 1:20:05.160 for the future of reinforcement learning 1:20:05.160 --> 1:20:06.920 and for the future of simulation? 1:20:06.920 --> 1:20:09.000 Like, whether we're talking about autonomous vehicles 1:20:09.000 --> 1:20:12.760 or any kind of system, do you see that scaling? 1:20:12.760 --> 1:20:16.280 So we'll be able to simulate systems and, hence, 1:20:16.280 --> 1:20:19.400 be able to create a simulator that echoes our real world 1:20:19.400 --> 1:20:22.520 and proving once and for all, even though you're denying it 1:20:22.520 --> 1:20:23.800 that we're living in a simulation. 1:20:24.840 --> 1:20:26.360 I feel like I've used that for questions, right? 1:20:26.360 --> 1:20:28.200 So, you know, kind of at the core there of, like, 1:20:28.200 --> 1:20:30.280 can we use simulation for self driving cars? 1:20:31.080 --> 1:20:33.720 Take a look at our robotic system, DACTL, right? 1:20:33.720 --> 1:20:37.720 That was trained in simulation using the Dota system, in fact. 1:20:37.720 --> 1:20:39.560 And it transfers to a physical robot. 1:20:40.280 --> 1:20:42.120 And I think everyone looks at our Dota system, 1:20:42.120 --> 1:20:43.400 they're like, okay, it's just a game. 1:20:43.400 --> 1:20:45.080 How are you ever going to escape to the real world? 1:20:45.080 --> 1:20:47.320 And the answer is, well, we did it with the physical robot, 1:20:47.320 --> 1:20:48.600 the no one could program. 1:20:48.600 --> 1:20:50.840 And so I think the answer is simulation goes a lot further 1:20:50.840 --> 1:20:53.400 than you think if you apply the right techniques to it. 1:20:54.040 --> 1:20:55.400 Now, there's a question of, you know, 1:20:55.400 --> 1:20:57.400 are the beings in that simulation going to wake up 1:20:57.400 --> 1:20:58.520 and have consciousness? 1:20:59.480 --> 1:21:02.840 I think that one seems a lot harder to, again, reason about. 1:21:02.840 --> 1:21:05.240 I think that, you know, you really should think about, like, 1:21:05.240 --> 1:21:07.800 where exactly does human consciousness come from 1:21:07.800 --> 1:21:09.000 in our own self awareness? 1:21:09.000 --> 1:21:10.600 And, you know, is it just that, like, 1:21:10.600 --> 1:21:12.280 once you have, like, a complicated enough neural net, 1:21:12.280 --> 1:21:14.440 do you have to worry about the agent's feeling pain? 1:21:15.720 --> 1:21:17.560 And, you know, I think there's, like, 1:21:17.560 --> 1:21:19.320 interesting speculation to do there. 1:21:19.320 --> 1:21:22.920 But, you know, again, I think it's a little bit hard to know for sure. 1:21:22.920 --> 1:21:24.840 Well, let me just keep with the speculation. 1:21:24.840 --> 1:21:28.040 Do you think to create intelligence, general intelligence, 1:21:28.600 --> 1:21:33.000 you need one consciousness and two a body? 1:21:33.000 --> 1:21:34.920 Do you think any of those elements are needed, 1:21:34.920 --> 1:21:38.360 or is intelligence something that's orthogonal to those? 1:21:38.360 --> 1:21:41.560 I'll stick to the kind of, like, the non grand answer first, 1:21:41.560 --> 1:21:41.720 right? 1:21:41.720 --> 1:21:43.960 So the non grand answer is just to look at, 1:21:43.960 --> 1:21:45.560 you know, what are we already making work? 1:21:45.560 --> 1:21:47.640 You look at GPT2, a lot of people would have said 1:21:47.640 --> 1:21:49.320 that to even get these kinds of results, 1:21:49.320 --> 1:21:50.920 you need real world experience. 1:21:50.920 --> 1:21:52.440 You need a body, you need grounding. 1:21:52.440 --> 1:21:54.920 How are you supposed to reason about any of these things? 1:21:54.920 --> 1:21:56.360 How are you supposed to, like, even kind of know 1:21:56.360 --> 1:21:57.960 about smoke and fire and those things 1:21:57.960 --> 1:21:59.560 if you've never experienced them? 1:21:59.560 --> 1:22:03.000 And GPT2 shows that you can actually go way further 1:22:03.000 --> 1:22:05.640 than that kind of reasoning would predict. 1:22:05.640 --> 1:22:09.240 So I think that in terms of, do we need consciousness? 1:22:09.240 --> 1:22:10.360 Do we need a body? 1:22:10.360 --> 1:22:11.880 It seems the answer is probably not, right? 1:22:11.880 --> 1:22:13.640 That we could probably just continue to push 1:22:13.640 --> 1:22:14.680 kind of the systems we have. 1:22:14.680 --> 1:22:16.520 They already feel general. 1:22:16.520 --> 1:22:19.080 They're not as competent or as general 1:22:19.080 --> 1:22:21.640 or able to learn as quickly as an AGI would, 1:22:21.640 --> 1:22:24.680 but, you know, they're at least like kind of proto AGI 1:22:24.680 --> 1:22:28.040 in some way, and they don't need any of those things. 1:22:28.040 --> 1:22:31.640 Now, let's move to the grand answer, which is, you know, 1:22:31.640 --> 1:22:34.840 if our neural nets consciousness, 1:22:34.840 --> 1:22:37.240 nets conscious already, would we ever know? 1:22:37.240 --> 1:22:38.680 How can we tell, right? 1:22:38.680 --> 1:22:40.920 And, you know, here's where the speculation starts 1:22:40.920 --> 1:22:44.760 to become, you know, at least interesting or fun 1:22:44.760 --> 1:22:46.200 and maybe a little bit disturbing, 1:22:46.200 --> 1:22:47.880 depending on where you take it. 1:22:47.880 --> 1:22:51.080 But it certainly seems that when we think about animals, 1:22:51.080 --> 1:22:53.080 that there's some continuum of consciousness. 1:22:53.080 --> 1:22:56.040 You know, my cat, I think, is conscious in some way, right? 1:22:56.040 --> 1:22:58.040 You know, not as conscious as a human. 1:22:58.040 --> 1:22:59.880 And you could imagine that you could build 1:22:59.880 --> 1:23:01.000 a little consciousness meter, right? 1:23:01.000 --> 1:23:02.840 You point at a cat, it gives you a little reading, 1:23:02.840 --> 1:23:06.200 you point at a human, it gives you much bigger reading. 1:23:06.200 --> 1:23:07.960 What would happen if you pointed one of those 1:23:07.960 --> 1:23:09.800 at a Dota neural net? 1:23:09.800 --> 1:23:11.960 And if you're training this massive simulation, 1:23:11.960 --> 1:23:14.600 do the neural nets feel pain? 1:23:14.600 --> 1:23:16.760 You know, it becomes pretty hard to know 1:23:16.760 --> 1:23:20.040 that the answer is no, and it becomes pretty hard 1:23:20.040 --> 1:23:22.360 to really think about what that would mean 1:23:22.360 --> 1:23:25.160 if the answer were yes. 1:23:25.160 --> 1:23:27.400 And it's very possible, you know, for example, 1:23:27.400 --> 1:23:29.400 you could imagine that maybe the reason 1:23:29.400 --> 1:23:31.400 that humans have consciousness 1:23:31.400 --> 1:23:35.000 is because it's a convenient computational shortcut, right? 1:23:35.000 --> 1:23:36.920 If you think about it, if you have a being 1:23:36.920 --> 1:23:39.320 that wants to avoid pain, which seems pretty important 1:23:39.320 --> 1:23:41.000 to survive in this environment 1:23:41.000 --> 1:23:43.640 and wants to, like, you know, eat food, 1:23:43.640 --> 1:23:45.400 then maybe the best way of doing it 1:23:45.400 --> 1:23:47.080 is to have a being that's conscious, right? 1:23:47.080 --> 1:23:49.480 That, you know, in order to succeed in the environment, 1:23:49.480 --> 1:23:51.080 you need to have those properties 1:23:51.080 --> 1:23:52.600 and how are you supposed to implement them? 1:23:52.600 --> 1:23:55.240 And maybe this consciousness is a way of doing that. 1:23:55.240 --> 1:23:57.720 If that's true, then actually maybe we should expect 1:23:57.720 --> 1:23:59.880 that really competent reinforcement learning agents 1:23:59.880 --> 1:24:01.960 will also have consciousness. 1:24:01.960 --> 1:24:03.240 But, you know, that's a big if. 1:24:03.240 --> 1:24:04.760 And I think there are a lot of other arguments 1:24:04.760 --> 1:24:05.880 that you can make in other directions. 1:24:06.680 --> 1:24:08.360 I think that's a really interesting idea 1:24:08.360 --> 1:24:11.400 that even GPT2 has some degree of consciousness. 1:24:11.400 --> 1:24:14.200 That's something that's actually not as crazy 1:24:14.200 --> 1:24:14.760 to think about. 1:24:14.760 --> 1:24:17.720 It's useful to think about as we think about 1:24:17.720 --> 1:24:19.800 what it means to create intelligence of a dog, 1:24:19.800 --> 1:24:24.360 intelligence of a cat, and the intelligence of a human. 1:24:24.360 --> 1:24:30.760 So, last question, do you think we will ever fall in love, 1:24:30.760 --> 1:24:33.560 like in the movie, Her, with an artificial intelligence system 1:24:34.360 --> 1:24:36.200 or an artificial intelligence system 1:24:36.200 --> 1:24:38.440 falling in love with a human? 1:24:38.440 --> 1:24:38.920 I hope so. 1:24:40.120 --> 1:24:43.640 If there's any better way to end it is on love. 1:24:43.640 --> 1:24:45.560 So, Greg, thanks so much for talking today. 1:24:45.560 --> 1:24:55.560 Thank you for having me.