vtt/episode_009_small.vtt

WEBVTT

00:00.000 --> 00:05.040
 The following is a conversation from Stuart Russell. He's a professor of computer science at UC

00:05.040 --> 00:11.360
 Berkeley and a coauthor of a book that introduced me and millions of other people to the amazing world

00:11.360 --> 00:18.320
 of AI called Artificial Intelligence The Modern Approach. So it was an honor for me to have this

00:18.320 --> 00:24.480
 conversation as part of MIT course on artificial journal intelligence and the artificial intelligence

00:24.480 --> 00:30.800
 podcast. If you enjoy it, please subscribe on YouTube, iTunes or your podcast provider of choice

00:31.360 --> 00:37.600
 or simply connect with me on Twitter at Lex Freedman spelled F R I D. And now here's my

00:37.600 --> 00:46.160
 conversation with Stuart Russell. So you've mentioned in 1975 in high school you've created

00:46.160 --> 00:54.160
 one of your first AI programs that played chess. Were you ever able to build a program that

00:54.160 --> 01:02.080
 beat you at chess or another board game? So my program never beat me at chess.

01:03.520 --> 01:10.480
 I actually wrote the program at Imperial College. So I used to take the bus every Wednesday with a

01:10.480 --> 01:17.200
 box of cards this big and shove them into the card reader and they gave us eight seconds of CPU time.

01:17.200 --> 01:24.720
 It took about five seconds to read the cards in and compile the code. So we had three seconds of

01:24.720 --> 01:30.960
 CPU time, which was enough to make one move, you know, with a not very deep search. And then we

01:30.960 --> 01:34.960
 would print that move out and then we'd have to go to the back of the queue and wait to feed the

01:34.960 --> 01:40.480
 cards in again. How deep was the search? Well, are we talking about two moves? So no, I think we've

01:40.480 --> 01:48.000
 got we got an eight move, eight, you know, depth eight with alpha beta. And we had some tricks of

01:48.000 --> 01:54.480
 our own about move ordering and some pruning of the tree. And we were still able to beat that

01:54.480 --> 02:00.960
 program. Yeah, yeah, I was a reasonable chess player in my youth. I did an Othello program

02:01.680 --> 02:05.920
 and a backgammon program. So when I got to Berkeley, I worked a lot on

02:05.920 --> 02:12.560
 what we call meta reasoning, which really means reasoning about reasoning. And in the case of

02:13.200 --> 02:18.320
 a game playing program, you need to reason about what parts of the search tree you're actually

02:18.320 --> 02:23.440
 going to explore, because the search tree is enormous, you know, bigger than the number of

02:23.440 --> 02:30.960
 atoms in the universe. And the way programs succeed and the way humans succeed is by only

02:30.960 --> 02:36.160
 looking at a small fraction of the search tree. And if you look at the right fraction, you play

02:36.160 --> 02:41.360
 really well. If you look at the wrong fraction, if you waste your time thinking about things that

02:41.360 --> 02:45.840
 are never going to happen, the moves that no one's ever going to make, then you're going to lose,

02:45.840 --> 02:53.760
 because you won't be able to figure out the right decision. So that question of how machines can

02:53.760 --> 02:59.760
 manage their own computation, how they decide what to think about is the meta reasoning question.

02:59.760 --> 03:05.920
 We developed some methods for doing that. And very simply, a machine should think about

03:06.640 --> 03:11.920
 whatever thoughts are going to improve its decision quality. We were able to show that

03:12.640 --> 03:18.240
 both for a fellow, which is a standard two player game, and for backgammon, which includes

03:19.040 --> 03:24.000
 dice rolls, so it's a two player game with uncertainty. For both of those cases, we could

03:24.000 --> 03:30.480
 come up with algorithms that were actually much more efficient than the standard alpha beta search,

03:31.120 --> 03:36.560
 which chess programs at the time were using. And that those programs could beat me.

03:38.080 --> 03:44.720
 And I think you can see the same basic ideas in AlphaGo and AlphaZero today.

03:44.720 --> 03:52.560
 The way they explore the tree is using a form of meta reasoning to select what to think about

03:52.560 --> 03:57.840
 based on how useful it is to think about it. Is there any insights you can describe

03:57.840 --> 04:03.040
 without Greek symbols of how do we select which paths to go down?

04:04.240 --> 04:10.560
 There's really two kinds of learning going on. So as you say, AlphaGo learns to evaluate board

04:10.560 --> 04:17.680
 to evaluate board position. So it can look at a go board. And it actually has probably a super

04:17.680 --> 04:25.760
 human ability to instantly tell how promising that situation is. To me, the amazing thing about

04:25.760 --> 04:34.560
 AlphaGo is not that it can be the world champion with its hands tied behind his back. But the fact that

04:34.560 --> 04:41.360
 if you stop it from searching altogether, so you say, okay, you're not allowed to do

04:41.360 --> 04:46.480
 any thinking ahead. You can just consider each of your legal moves and then look at the

04:47.120 --> 04:53.280
 resulting situation and evaluate it. So what we call a depth one search. So just the immediate

04:53.280 --> 04:57.920
 outcome of your moves and decide if that's good or bad. That version of AlphaGo

04:57.920 --> 05:05.200
 can still play at a professional level. And human professionals are sitting there for

05:05.200 --> 05:13.440
 five, 10 minutes deciding what to do. And AlphaGo in less than a second can instantly intuit what

05:13.440 --> 05:18.880
 is the right move to make based on its ability to evaluate positions. And that is remarkable

05:19.760 --> 05:26.080
 because we don't have that level of intuition about go. We actually have to think about the

05:26.080 --> 05:35.200
 situation. So anyway, that capability that AlphaGo has is one big part of why it beats humans.

05:35.840 --> 05:44.560
 The other big part is that it's able to look ahead 40, 50, 60 moves into the future. And

05:46.880 --> 05:51.200
 if it was considering all possibilities, 40 or 50 or 60 moves into the future,

05:51.200 --> 06:02.240
 that would be 10 to the 200 possibilities. So way more than atoms in the universe and so on.

06:02.240 --> 06:09.680
 So it's very, very selective about what it looks at. So let me try to give you an intuition about

06:10.880 --> 06:15.360
 how you decide what to think about. It's a combination of two things. One is

06:15.360 --> 06:22.000
 how promising it is. So if you're already convinced that a move is terrible,

06:22.560 --> 06:26.560
 there's no point spending a lot more time convincing yourself that it's terrible.

06:27.520 --> 06:33.600
 Because it's probably not going to change your mind. So the real reason you think is because

06:33.600 --> 06:39.920
 there's some possibility of changing your mind about what to do. And is that changing your mind

06:39.920 --> 06:46.000
 that would result then in a better final action in the real world. So that's the purpose of thinking

06:46.800 --> 06:53.520
 is to improve the final action in the real world. And so if you think about a move that is guaranteed

06:53.520 --> 06:58.000
 to be terrible, you can convince yourself it's terrible, you're still not going to change your

06:58.000 --> 07:04.320
 mind. But on the other hand, suppose you had a choice between two moves, one of them you've

07:04.320 --> 07:10.400
 already figured out is guaranteed to be a draw, let's say. And then the other one looks a little

07:10.400 --> 07:14.000
 bit worse. Like it looks fairly likely that if you make that move, you're going to lose.

07:14.640 --> 07:20.720
 But there's still some uncertainty about the value of that move. There's still some possibility

07:20.720 --> 07:25.920
 that it will turn out to be a win. Then it's worth thinking about that. So even though it's

07:25.920 --> 07:31.280
 less promising on average than the other move, which is guaranteed to be a draw, there's still

07:31.280 --> 07:36.160
 some purpose in thinking about it because there's a chance that you'll change your mind and discover

07:36.160 --> 07:42.080
 that in fact it's a better move. So it's a combination of how good the move appears to be

07:42.080 --> 07:48.000
 and how much uncertainty there is about its value. The more uncertainty, the more it's worth thinking

07:48.000 --> 07:52.800
 about because there's a higher upside if you want to think of it that way. And of course in the

07:52.800 --> 07:59.920
 beginning, especially in the AlphaGo Zero formulation, it's everything is shrouded in

07:59.920 --> 08:06.240
 uncertainty. So you're really swimming in a sea of uncertainty. So it benefits you to

08:07.520 --> 08:11.120
 I mean, actually falling in the same process as you described, but because you're so uncertain

08:11.120 --> 08:15.280
 about everything, you basically have to try a lot of different directions.

08:15.280 --> 08:22.400
 Yeah. So the early parts of the search tree are fairly bushy that it will look at a lot

08:22.400 --> 08:27.840
 of different possibilities, but fairly quickly, the degree of certainty about some of the moves.

08:27.840 --> 08:31.760
 I mean, if a move is really terrible, you'll pretty quickly find out, right? You'll lose

08:31.760 --> 08:37.200
 half your pieces or half your territory. And then you'll say, okay, this is not worth thinking

08:37.200 --> 08:45.280
 about anymore. And then so further down, the tree becomes very long and narrow. And you're following

08:45.280 --> 08:54.800
 various lines of play, 10, 20, 30, 40, 50 moves into the future. And that again is something

08:54.800 --> 09:01.920
 that human beings have a very hard time doing mainly because they just lack the short term memory.

09:02.480 --> 09:09.440
 You just can't remember a sequence of moves. That's 50 moves long. And you can't imagine

09:09.440 --> 09:15.520
 the board correctly for that many moves into the future. Of course, the top players,

09:16.400 --> 09:19.280
 I'm much more familiar with chess, but the top players probably have,

09:19.280 --> 09:26.480
 they have echoes of the same kind of intuition instinct that in a moment's time, AlphaGo applies

09:27.280 --> 09:31.760
 when they see a board. I mean, they've seen those patterns, human beings have seen those

09:31.760 --> 09:37.680
 patterns before at the top, at the grandmaster level. It seems that there is some

09:40.000 --> 09:45.920
 similarities, or maybe it's our imagination creates a vision of those similarities, but it

09:45.920 --> 09:53.120
 feels like this kind of pattern recognition that the AlphaGo approaches are using is similar to

09:53.120 --> 10:00.560
 what human beings at the top level are using. I think there's some truth to that.

10:01.520 --> 10:08.960
 But not entirely. Yeah, I mean, I think the extent to which a human grandmaster can reliably

10:10.080 --> 10:13.680
 instantly recognize the right move and instantly recognize the values of position.

10:13.680 --> 10:19.120
 I think that's a little bit overrated. But if you sacrifice a queen, for example,

10:19.120 --> 10:23.840
 I mean, there's these, there's these beautiful games of chess with Bobby Fisher, somebody where

10:24.640 --> 10:32.720
 it's seeming to make a bad move. And I'm not sure there's a perfect degree of calculation

10:32.720 --> 10:37.440
 involved where they've calculated all the possible things that happen. But there's an

10:37.440 --> 10:45.440
 instinct there, right, that somehow adds up to. Yeah, so I think what happens is you get a sense

10:45.440 --> 10:51.680
 that there's some possibility in the position, even if you make a weird looking move, that it

10:51.680 --> 11:05.120
 opens up some lines of calculation that otherwise would be definitely bad. And it's that intuition

11:05.120 --> 11:13.920
 that there's something here in this position that might might yield a win down the set. And then

11:13.920 --> 11:20.640
 you follow that. Right. And in some sense, when a chess player is following a line in his or her

11:20.640 --> 11:27.120
 mind, they're they mentally simulating what the other person is going to do, what the opponent

11:27.120 --> 11:33.680
 is going to do. And they can do that as long as the moves are kind of forced, right, as long as

11:33.680 --> 11:39.440
 there's a we call a forcing variation where the opponent doesn't really have much choice how to

11:39.440 --> 11:45.200
 respond. And then you see if you can force them into a situation where you win. We see plenty

11:45.200 --> 11:53.520
 of mistakes, even in grandmaster games, where they just miss some simple three, four, five move

11:54.560 --> 12:00.400
 combination that wasn't particularly apparent in the position, but was still there.

12:00.400 --> 12:07.360
 That's the thing that makes us human. Yeah. So when you mentioned that in Othello, those games

12:07.360 --> 12:13.760
 were after some meta reasoning improvements and research was able to beat you. How did that make

12:13.760 --> 12:21.280
 you feel part of the meta reasoning capability that it had was based on learning. And,

12:23.280 --> 12:28.160
 and you could sit down the next day and you could just feel that it had got a lot smarter.

12:28.160 --> 12:33.280
 You know, and all of a sudden, you really felt like you're sort of pressed against

12:34.480 --> 12:40.800
 the wall because it was it was much more aggressive and was totally unforgiving of any

12:40.800 --> 12:47.760
 minor mistake that you might make. And actually, it seemed understood the game better than I did.

12:47.760 --> 12:55.520
 And Gary Kasparov has this quote where during his match against Deep Blue, he said he suddenly

12:55.520 --> 13:01.680
 felt that there was a new kind of intelligence across the board. Do you think that's a scary or

13:01.680 --> 13:10.240
 an exciting possibility for Kasparov and for yourself in the context of chess purely sort of

13:10.240 --> 13:16.720
 in this like that feeling, whatever that is, I think it's definitely an exciting feeling.

13:17.600 --> 13:23.680
 You know, this is what made me work on AI in the first place was as soon as I really understood

13:23.680 --> 13:30.080
 what a computer was, I wanted to make it smart. You know, I started out with the first program I

13:30.080 --> 13:38.640
 wrote was for the Sinclair Programmable Calculator. And I think you could write a 21 step algorithm.

13:38.640 --> 13:44.160
 That was the biggest program you could write something like that and do little arithmetic

13:44.160 --> 13:49.440
 calculations. So I think I implemented Newton's method for square roots and a few other things

13:49.440 --> 13:56.640
 like that. But then, you know, I thought, okay, if I just had more space, I could make this thing

13:56.640 --> 14:10.560
 intelligent. And so I started thinking about AI. And I think the thing that's scary is not the

14:10.560 --> 14:19.520
 chess program, because you know, chess programs, they're not in the taking over the world business.

14:19.520 --> 14:29.440
 But if you extrapolate, you know, there are things about chess that don't resemble the real

14:29.440 --> 14:37.600
 world, right? We know, we know the rules of chess. The chess board is completely visible

14:37.600 --> 14:43.280
 to the program where, of course, the real world is not. Most the real world is not visible from

14:43.280 --> 14:52.400
 wherever you're sitting, so to speak. And to overcome those kinds of problems, you need

14:52.400 --> 14:58.240
 qualitatively different algorithms. Another thing about the real world is that, you know, we

14:58.240 --> 15:07.520
 we regularly plan ahead on the timescales involving billions or trillions of steps. Now,

15:07.520 --> 15:13.760
 we don't plan those in detail. But, you know, when you choose to do a PhD at Berkeley,

15:14.800 --> 15:20.480
 that's a five year commitment that amounts to about a trillion motor control steps that you

15:20.480 --> 15:26.160
 will eventually be committed to. Including going up the stairs, opening doors,

15:26.160 --> 15:32.880
 a drinking water type. Yeah, I mean, every every finger movement while you're typing every character

15:32.880 --> 15:37.280
 of every paper and the thesis and everything. So you're not committing in advance to the specific

15:37.280 --> 15:43.760
 motor control steps, but you're still reasoning on a timescale that will eventually reduce to

15:44.400 --> 15:50.000
 trillions of motor control actions. And so for all these reasons,

15:50.000 --> 15:58.160
 you know, AlphaGo and Deep Blue and so on don't represent any kind of threat to humanity. But

15:58.160 --> 16:08.320
 they are a step towards it, right? And progress in AI occurs by essentially removing one by one

16:08.320 --> 16:14.640
 these assumptions that make problems easy, like the assumption of complete observability

16:14.640 --> 16:21.120
 of the situation, right? We remove that assumption, you need a much more complicated kind of computing

16:22.160 --> 16:26.000
 design and you need something that actually keeps track of all the things you can't see

16:26.000 --> 16:31.920
 and tries to estimate what's going on. And there's inevitable uncertainty in that. So it becomes a

16:31.920 --> 16:38.160
 much more complicated problem. But, you know, we are removing those assumptions, we are starting to

16:38.160 --> 16:44.400
 have algorithms that can cope with much longer timescales, cope with uncertainty that can cope

16:44.400 --> 16:53.360
 with partial observability. And so each of those steps sort of magnifies by a thousand the range

16:53.360 --> 16:58.400
 of things that we can do with AI systems. So the way I started in AI, I wanted to be a psychiatrist

16:58.400 --> 17:03.840
 for a long time and understand the mind in high school, and of course program and so on. And I

17:03.840 --> 17:10.640
 showed up University of Illinois to an AI lab and they said, okay, I don't have time for you, but here

17:10.640 --> 17:18.480
 is a book, AI Modern Approach, I think it was the first edition at the time. Here, go learn this.

17:18.480 --> 17:23.120
 And I remember the lay of the land was, well, it's incredible that we solved chess, but we'll

17:23.120 --> 17:30.480
 never solve go. I mean, it was pretty certain that go in the way we thought about systems that reason

17:31.520 --> 17:36.080
 wasn't possible to solve. And now we've solved it. So it's a very... Well, I think I would have said

17:36.080 --> 17:44.080
 that it's unlikely we could take the kind of algorithm that was used for chess and just get

17:44.080 --> 17:55.680
 it to scale up and work well for go. And at the time, what we thought was that in order to solve

17:55.680 --> 18:01.600
 go, we would have to do something similar to the way humans manage the complexity of go,

18:01.600 --> 18:06.960
 which is to break it down into kind of sub games. So when a human thinks about a go board,

18:06.960 --> 18:12.480
 they think about different parts of the board as sort of weakly connected to each other.

18:12.480 --> 18:16.880
 And they think about, okay, within this part of the board, here's how things could go.

18:16.880 --> 18:20.560
 In that part of board, here's how things could go. And then you try to sort of couple those

18:20.560 --> 18:26.400
 two analyses together and deal with the interactions and maybe revise your views of how things are

18:26.400 --> 18:32.160
 going to go in each part. And then you've got maybe five, six, seven, 10 parts of the board. And

18:33.440 --> 18:40.640
 that actually resembles the real world much more than chess does. Because in the real world,

18:41.440 --> 18:49.200
 we have work, we have home life, we have sport, whatever different kinds of activities, shopping,

18:49.200 --> 18:57.040
 these all are connected to each other, but they're weakly connected. So when I'm typing a paper,

18:58.480 --> 19:03.600
 I don't simultaneously have to decide which order I'm going to get the milk and the butter.

19:04.400 --> 19:09.760
 That doesn't affect the typing. But I do need to realize, okay, better finish this

19:10.320 --> 19:14.080
 before the shops close because I don't have anything, I don't have any food at home.

19:14.080 --> 19:20.560
 So there's some weak connection, but not in the way that chess works, where everything is tied

19:20.560 --> 19:27.600
 into a single stream of thought. So the thought was that go to solve go would have to make progress

19:27.600 --> 19:31.600
 on stuff that would be useful for the real world. And in a way, AlphaGo is a little bit disappointing

19:32.480 --> 19:38.160
 because the program designed for AlphaGo is actually not that different from

19:38.160 --> 19:45.840
 from Deep Blue or even from Arthur Samuel's Jacob playing program from the 1950s.

19:48.160 --> 19:54.560
 And in fact, the two things that make AlphaGo work is one is this amazing ability to evaluate

19:54.560 --> 19:59.200
 the positions. And the other is the meta reasoning capability, which allows it to

19:59.200 --> 20:06.960
 to explore some paths in the tree very deeply and to abandon other paths very quickly.

20:06.960 --> 20:14.640
 So this word meta reasoning, while technically correct, inspires perhaps the wrong

20:16.000 --> 20:21.360
 degree of power that AlphaGo has, for example, the word reasoning is a powerful word. So let me

20:21.360 --> 20:29.840
 ask you sort of, do you were part of the symbolic AI world for a while, like where AI was, there's

20:29.840 --> 20:38.960
 a lot of excellent interesting ideas there that unfortunately met a winter. And so do you think

20:38.960 --> 20:46.800
 it reemerges? Oh, so I would say, yeah, it's not quite as simple as that. So the AI winter,

20:46.800 --> 20:54.400
 the first winter that was actually named as such was the one in the late 80s.

20:56.400 --> 21:00.880
 And that came about because in the mid 80s, there was

21:03.280 --> 21:10.480
 really a concerted attempt to push AI out into the real world using what was called

21:10.480 --> 21:17.280
 expert system technology. And for the most part, that technology was just not ready for prime

21:17.280 --> 21:27.200
 time. They were trying in many cases to do a form of uncertain reasoning, judgment combinations of

21:27.200 --> 21:34.640
 evidence diagnosis, those kinds of things, which was simply invalid. And when you try to apply

21:34.640 --> 21:40.960
 invalid reasoning methods to real problems, you can fudge it for small versions of the problem.

21:40.960 --> 21:47.200
 But when it starts to get larger, the thing just falls apart. So many companies found that

21:49.040 --> 21:53.440
 the stuff just didn't work. And they were spending tons of money on consultants to

21:53.440 --> 21:59.600
 try to make it work. And there were other practical reasons, like they were asking

21:59.600 --> 22:07.760
 the companies to buy incredibly expensive Lisp machine workstations, which were literally

22:07.760 --> 22:17.680
 between $50,000 and $100,000 in 1980s money, which would be between $150,000 and $300,000 per

22:17.680 --> 22:24.000
 workstation in current prices. Then the bottom line, they weren't seeing a profit from it.

22:24.000 --> 22:29.840
 Yeah. In many cases, I think there were some successes. There's no doubt about that. But

22:30.880 --> 22:37.760
 people, I would say, over invested. Every major company was starting an AI department just like

22:37.760 --> 22:45.840
 now. And I worry a bit that we might see similar disappointments, not because the

22:45.840 --> 22:57.600
 current technology is invalid, but it's limited in its scope. And it's almost the dual of the

22:57.600 --> 23:03.360
 scope problems that expert systems had. What have you learned from that hype cycle? And

23:03.360 --> 23:09.760
 what can we do to prevent another winter, for example? Yeah. So when I'm giving talks these

23:09.760 --> 23:17.520
 days, that's one of the warnings that I give. So there's two part warning slide. One is that

23:18.480 --> 23:24.000
 rather than data being the new oil, data is the new snake oil. That's a good line. And then

23:26.000 --> 23:35.440
 the other is that we might see a very visible failure in some of the major application areas.

23:35.440 --> 23:42.400
 And I think self driving cars would be the flagship. And I think

23:43.600 --> 23:48.560
 when you look at the history, so the first self driving car was on the freeway,

23:51.200 --> 24:00.400
 driving itself, changing lanes, overtaking in 1987. And so it's more than 30 years.

24:00.400 --> 24:06.720
 And that kind of looks like where we are today, right? Prototypes on the freeway,

24:06.720 --> 24:13.760
 changing lanes and overtaking. Now, I think significant progress has been made, particularly

24:13.760 --> 24:20.560
 on the perception side. So we worked a lot on autonomous vehicles in the early, mid 90s at

24:20.560 --> 24:29.040
 Berkeley. And we had our own big demonstrations. We put congressmen into self driving cars and

24:29.040 --> 24:36.000
 had them zooming along the freeway. And the problem was clearly perception.

24:37.520 --> 24:42.880
 At the time, the problem was perception. Yeah. So in simulation, with perfect perception,

24:42.880 --> 24:47.200
 you could actually show that you can drive safely for a long time, even if the other cars

24:47.200 --> 24:55.360
 are misbehaving and so on. But simultaneously, we worked on machine vision for detecting cars and

24:55.360 --> 25:03.040
 tracking pedestrians and so on. And we couldn't get the reliability of detection and tracking

25:03.040 --> 25:11.440
 up to a high enough level, particularly in bad weather conditions, nighttime rainfall.

25:11.440 --> 25:16.000
 Good enough for demos, but perhaps not good enough to cover the general operation.

25:16.000 --> 25:20.800
 Yeah. So the thing about driving is, so suppose you're a taxi driver and you drive every day,

25:20.800 --> 25:27.360
 eight hours a day for 10 years, that's 100 million seconds of driving. And any one of those

25:27.360 --> 25:33.280
 seconds, you can make a fatal mistake. So you're talking about eight nines of reliability.

25:34.960 --> 25:43.840
 Now, if your vision system only detects 98.3% of the vehicles, that's sort of one

25:43.840 --> 25:52.720
 on a bit nine reliability. So you have another seven orders of magnitude to go. And this is

25:52.720 --> 25:57.920
 what people don't understand. They think, oh, because I had a successful demo, I'm pretty much

25:57.920 --> 26:07.440
 done. But you're not even within seven orders of magnitude of being done. And that's the difficulty.

26:07.440 --> 26:14.320
 And it's not, can I follow a white line? That's not the problem. We follow a white line all the

26:14.320 --> 26:22.160
 way across the country. But it's the weird stuff that happens. It's all the edge cases. Yeah.

26:22.160 --> 26:30.640
 The edge case, other drivers doing weird things. So if you talk to Google, so they had actually

26:30.640 --> 26:36.560
 a very classical architecture where you had machine vision, which would detect all the

26:36.560 --> 26:41.920
 other cars and pedestrians and the white lines and the road signs. And then basically,

26:42.480 --> 26:49.680
 that was fed into a logical database. And then you had a classical 1970s rule based expert system

26:52.000 --> 26:55.680
 telling you, okay, if you're in the middle lane, and there's a bicyclist in the right lane,

26:55.680 --> 27:03.040
 who is signaling this, then then do that, right? And what they found was that every day that go

27:03.040 --> 27:07.760
 out and there'd be another situation that the rules didn't cover. So they come to a traffic

27:07.760 --> 27:11.680
 circle and there's a little girl riding her bicycle the wrong way around the traffic circle.

27:11.680 --> 27:17.520
 Okay, what do you do? We don't have a rule. Oh my God. Okay, stop. And then they come back

27:17.520 --> 27:24.400
 and add more rules. And they just found that this was not really converging. And if you think about

27:24.400 --> 27:31.280
 it, right, how do you deal with an unexpected situation, meaning one that you've never previously

27:31.280 --> 27:37.200
 encountered and the sort of the reasoning required to figure out the solution for that

27:37.200 --> 27:42.800
 situation has never been done. It doesn't match any previous situation in terms of the kind of

27:42.800 --> 27:49.520
 reasoning you have to do. Well, in chess programs, this happens all the time. You're constantly

27:49.520 --> 27:54.560
 coming up with situations you haven't seen before. And you have to reason about them and you have

27:54.560 --> 27:59.840
 to think about, okay, here are the possible things I could do. Here are the outcomes. Here's how

27:59.840 --> 28:04.560
 desirable the outcomes are and then pick the right one. In the 90s, we were saying, okay,

28:04.560 --> 28:08.160
 this is how you're going to have to do automated vehicles. They're going to have to have a look

28:08.160 --> 28:14.400
 ahead capability. But the look ahead for driving is more difficult than it is for chess. Because

28:14.400 --> 28:20.720
 of humans. Right, there's humans and they're less predictable than chess pieces. Well,

28:20.720 --> 28:28.240
 then you have an opponent in chess who's also somewhat unpredictable. But for example, in chess,

28:28.240 --> 28:33.600
 you always know the opponent's intention. They're trying to beat you. Whereas in driving, you don't

28:33.600 --> 28:39.040
 know, is this guy trying to turn left or has he just forgotten to turn off his turn signal? Or is

28:39.040 --> 28:45.680
 he drunk? Or is he changing the channel on his radio or whatever it might be, you got to try and

28:45.680 --> 28:52.560
 figure out the mental state, the intent of the other drivers to forecast the possible evolutions

28:52.560 --> 28:58.160
 of their trajectories. And then you got to figure out, okay, which is the trajectory for me that's

28:58.160 --> 29:04.000
 going to be safest. And those all interact with each other because the other drivers are going

29:04.000 --> 29:09.120
 to react to your trajectory and so on. So, you know, they've got the classic merging onto the

29:09.120 --> 29:14.640
 freeway problem where you're kind of racing a vehicle that's already on the freeway and you're

29:14.640 --> 29:17.680
 are you going to pull ahead of them or are you going to let them go first and pull in behind

29:17.680 --> 29:23.680
 and you get this sort of uncertainty about who's going first. So all those kinds of things

29:23.680 --> 29:34.720
 mean that you need a decision making architecture that's very different from either a rule based

29:34.720 --> 29:41.360
 system or it seems to me a kind of an end to end neural network system. You know, so just as Alpha

29:41.360 --> 29:47.360
 Go is pretty good when it doesn't do any look ahead, but it's way, way, way, way better when it does.

29:47.360 --> 29:52.720
 I think the same is going to be true for driving. You can have a driving system that's pretty good

29:54.080 --> 29:59.280
 when it doesn't do any look ahead, but that's not good enough. You know, and we've already seen

29:59.920 --> 30:07.440
 multiple deaths caused by poorly designed machine learning algorithms that don't really

30:07.440 --> 30:13.600
 understand what they're doing. Yeah, and on several levels, I think it's on the perception side,

30:13.600 --> 30:19.520
 there's mistakes being made by those algorithms where the perception is very shallow on the

30:19.520 --> 30:26.720
 planning side, the look ahead, like you said, and the thing that we come up against that's

30:28.560 --> 30:32.080
 really interesting when you try to deploy systems in the real world is

30:33.280 --> 30:37.680
 you can't think of an artificial intelligence system as a thing that responds to the world always.

30:38.320 --> 30:41.600
 You have to realize that it's an agent that others will respond to as well.

30:41.600 --> 30:47.200
 Well, so in order to drive successfully, you can't just try to do obstacle avoidance.

30:47.840 --> 30:51.520
 You can't pretend that you're invisible, right? You're the invisible car.

30:52.400 --> 30:57.280
 It doesn't work that way. I mean, but you have to assert, yet others have to be scared of you,

30:57.280 --> 31:04.160
 just there's this tension, there's this game. So we study a lot of work with pedestrians.

31:04.160 --> 31:09.360
 If you approach pedestrians as purely an obstacle avoidance, so you're doing look

31:09.360 --> 31:15.040
 ahead as in modeling the intent, they're not going to take advantage of you.

31:15.040 --> 31:20.080
 They're not going to respect you at all. There has to be a tension, a fear, some amount of

31:20.080 --> 31:26.720
 uncertainty. That's how we have created. Or at least just a kind of a resoluteness.

31:28.000 --> 31:32.000
 You have to display a certain amount of resoluteness. You can't be too tentative.

31:32.000 --> 31:42.480
 Yeah. So the solutions then become pretty complicated. You get into game theoretic

31:42.480 --> 31:50.960
 analyses. So at Berkeley now, we're working a lot on this kind of interaction between machines

31:50.960 --> 32:03.600
 and humans. And that's exciting. So my colleague, Anka Dragan, actually, if you formulate the problem

32:03.600 --> 32:08.800
 game theoretically and you just let the system figure out the solution, it does interesting,

32:08.800 --> 32:16.640
 unexpected things. Like sometimes at a stop sign, if no one is going first, the car will

32:16.640 --> 32:23.200
 actually back up a little. It's just to indicate to the other cars that they should go. And that's

32:23.200 --> 32:28.480
 something it invented entirely by itself. That's interesting. We didn't say this is the language

32:28.480 --> 32:36.240
 of communication at stop signs. It figured it out. That's really interesting. So let me one just

32:36.240 --> 32:42.960
 step back for a second. Just this beautiful philosophical notion. So Pamela McCordick in

32:42.960 --> 32:50.320
 1979 wrote AI began with the ancient wish to forge the gods. So when you think about the

32:50.320 --> 32:57.520
 history of our civilization, do you think that there is an inherent desire to create,

32:58.960 --> 33:05.680
 let's not say gods, but to create superintelligence? Is it inherent to us? Is it in our genes,

33:05.680 --> 33:13.680
 that the natural arc of human civilization is to create things that are of greater and greater

33:13.680 --> 33:21.680
 power and perhaps echoes of ourselves? So to create the gods, as Pamela said.

33:21.680 --> 33:34.160
 It may be. I mean, we're all individuals, but certainly we see over and over again in history

33:35.760 --> 33:42.320
 individuals who thought about this possibility. Hopefully, I'm not being too philosophical here.

33:42.320 --> 33:48.560
 But if you look at the arc of this, where this is going and we'll talk about AI safety,

33:48.560 --> 33:55.840
 we'll talk about greater and greater intelligence, do you see that when you created the Othello

33:55.840 --> 34:01.680
 program and you felt this excitement, what was that excitement? Was it the excitement of a tinkerer

34:01.680 --> 34:10.240
 who created something cool, like a clock? Or was there a magic, or was it more like a child being

34:10.240 --> 34:17.520
 born? Yeah. So I mean, I certainly understand that viewpoint. And if you look at the light

34:17.520 --> 34:26.640
 hill report, so in the 70s, there was a lot of controversy in the UK about AI and whether it

34:26.640 --> 34:34.720
 was for real and how much the money the government should invest. So it's a long story, but the

34:34.720 --> 34:43.280
 government commissioned a report by Lighthill, who was a physicist, and he wrote a very damning

34:43.280 --> 34:53.920
 report about AI, which I think was the point. And he said that these are frustrated men who

34:54.480 --> 35:05.760
 unable to have children would like to create life as a kind of replacement, which I think is

35:05.760 --> 35:21.600
 really pretty unfair. But there is a kind of magic, I would say, when you build something

35:25.680 --> 35:29.760
 and what you're building in is really just you're building in some understanding of the

35:29.760 --> 35:37.120
 principles of learning and decision making. And to see those principles actually then

35:37.840 --> 35:47.920
 turn into intelligent behavior in specific situations, it's an incredible thing. And

35:47.920 --> 35:58.480
 that is naturally going to make you think, okay, where does this end?

36:00.080 --> 36:08.240
 And so there's a there's magical, optimistic views of word and whatever your view of optimism is,

36:08.240 --> 36:13.360
 whatever your view of utopia is, it's probably different for everybody. But you've often talked

36:13.360 --> 36:26.080
 about concerns you have of how things might go wrong. So I've talked to Max Tegmark. There's a

36:26.080 --> 36:33.360
 lot of interesting ways to think about AI safety. You're one of the seminal people thinking about

36:33.360 --> 36:39.360
 this problem amongst sort of being in the weeds of actually solving specific AI problems,

36:39.360 --> 36:44.080
 you're also thinking about the big picture of where we're going. So can you talk about

36:44.080 --> 36:49.200
 several elements of it? Let's just talk about maybe the control problem. So this idea of

36:50.800 --> 36:58.720
 losing ability to control the behavior of our AI system. So how do you see that? How do you see

36:58.720 --> 37:04.480
 that coming about? What do you think we can do to manage it?

37:04.480 --> 37:11.520
 Well, so it doesn't take a genius to realize that if you make something that's smarter than you,

37:11.520 --> 37:20.320
 you might have a problem. Alan Turing wrote about this and gave lectures about this,

37:21.600 --> 37:32.480
 1951. He did a lecture on the radio. And he basically says, once the machine thinking method

37:32.480 --> 37:45.600
 starts, very quickly, they'll outstrip humanity. And if we're lucky, we might be able to turn off

37:45.600 --> 37:52.160
 the power at strategic moments, but even so, our species would be humbled. And actually,

37:52.160 --> 37:56.240
 I think it was wrong about that. If it's a sufficiently intelligent machine, it's not

37:56.240 --> 38:00.160
 going to let you switch it off. It's actually in competition with you.

38:00.160 --> 38:05.840
 So what do you think is meant just for a quick tangent if we shut off this

38:05.840 --> 38:08.800
 super intelligent machine that our species would be humbled?

38:11.840 --> 38:20.560
 I think he means that we would realize that we are inferior, that we only survive by the skin

38:20.560 --> 38:27.440
 of our teeth because we happen to get to the off switch just in time. And if we hadn't,

38:27.440 --> 38:34.400
 then we would have lost control over the earth. So are you more worried when you think about

38:34.400 --> 38:41.600
 this stuff about super intelligent AI or are you more worried about super powerful AI that's not

38:41.600 --> 38:49.760
 aligned with our values? So the paperclip scenarios kind of... I think so the main problem I'm

38:49.760 --> 38:58.960
 working on is the control problem, the problem of machines pursuing objectives that are, as you

38:58.960 --> 39:06.720
 say, not aligned with human objectives. And this has been the way we've thought about AI

39:06.720 --> 39:15.120
 since the beginning. You build a machine for optimizing and then you put in some objective

39:15.120 --> 39:25.520
 and it optimizes. And we can think of this as the king Midas problem. Because if the king Midas

39:26.480 --> 39:32.640
 put in this objective, everything I touch should turn to gold and the gods, that's like the machine,

39:32.640 --> 39:39.360
 they said, okay, done. You now have this power and of course his food and his drink and his family

39:39.360 --> 39:50.080
 all turned to gold and then he dies of misery and starvation. It's a warning, it's a failure mode that

39:50.080 --> 39:56.160
 pretty much every culture in history has had some story along the same lines. There's the

39:56.160 --> 40:01.920
 genie that gives you three wishes and third wish is always, please undo the first two wishes because

40:01.920 --> 40:11.920
 I messed up. And when Arthur Samuel wrote his checker playing program, which learned to play

40:11.920 --> 40:16.800
 checkers considerably better than Arthur Samuel could play and actually reached a pretty decent

40:16.800 --> 40:25.040
 standard, Norbert Wiener, who was one of the major mathematicians of the 20th century, he's sort of

40:25.040 --> 40:32.560
 the father of modern automation control systems. He saw this and he basically extrapolated

40:33.360 --> 40:43.680
 as Turing did and said, okay, this is how we could lose control. And specifically that

40:45.520 --> 40:50.960
 we have to be certain that the purpose we put into the machine is the purpose which we really

40:50.960 --> 40:59.760
 desire. And the problem is, we can't do that. Right. You mean we're not, it's a very difficult

40:59.760 --> 41:05.440
 to encode, to put our values on paper is really difficult, or you're just saying it's impossible?

41:09.120 --> 41:15.360
 The line is great between the two. So theoretically, it's possible, but in practice,

41:15.360 --> 41:23.520
 it's extremely unlikely that we could specify correctly in advance the full range of concerns

41:23.520 --> 41:29.360
 of humanity. You talked about cultural transmission of values, I think is how humans to human

41:29.360 --> 41:36.320
 transmission of values happens, right? Well, we learn, yeah, I mean, as we grow up, we learn about

41:36.320 --> 41:42.640
 the values that matter, how things should go, what is reasonable to pursue and what isn't

41:42.640 --> 41:47.920
 reasonable to pursue. I think machines can learn in the same kind of way. Yeah. So I think that

41:49.120 --> 41:54.480
 what we need to do is to get away from this idea that you build an optimizing machine and then you

41:54.480 --> 42:03.200
 put the objective into it. Because if it's possible that you might put in a wrong objective, and we

42:03.200 --> 42:08.880
 already know this is possible because it's happened lots of times, right? That means that the machine

42:08.880 --> 42:17.760
 should never take an objective that's given as gospel truth. Because once it takes the objective

42:17.760 --> 42:26.800
 as gospel truth, then it believes that whatever actions it's taking in pursuit of that objective

42:26.800 --> 42:31.200
 are the correct things to do. So you could be jumping up and down and saying, no, no, no, no,

42:31.200 --> 42:36.480
 you're going to destroy the world, but the machine knows what the true objective is and is pursuing

42:36.480 --> 42:42.640
 it and tough luck to you. And this is not restricted to AI, right? This is, I think,

42:43.360 --> 42:48.880
 many of the 20th century technologies, right? So in statistics, you minimize a loss function,

42:48.880 --> 42:54.320
 the loss function is exogenously specified in control theory, you minimize a cost function,

42:54.320 --> 42:59.840
 in operations research, you maximize a reward function, and so on. So in all these disciplines,

42:59.840 --> 43:08.560
 this is how we conceive of the problem. And it's the wrong problem. Because we cannot specify

43:08.560 --> 43:15.360
 with certainty the correct objective, right? We need uncertainty, we need the machine to be

43:15.360 --> 43:19.440
 uncertain about what it is that it's supposed to be maximizing.

43:19.440 --> 43:25.200
 It's my favorite idea of yours. I've heard you say somewhere, well, I shouldn't pick favorites,

43:25.200 --> 43:32.640
 but it just sounds beautiful. We need to teach machines humility. It's a beautiful way to put

43:32.640 --> 43:40.320
 it. I love it. That they're humble. They know that they don't know what it is they're supposed

43:40.320 --> 43:48.240
 to be doing. And that those objectives, I mean, they exist, they're within us, but we may not

43:48.240 --> 43:56.160
 be able to explicate them. We may not even know how we want our future to go.

43:57.040 --> 44:06.800
 So exactly. And a machine that's uncertain is going to be differential to us. So if we say,

44:06.800 --> 44:11.840
 don't do that, well, now the machines learn something a bit more about our true objectives,

44:11.840 --> 44:16.480
 because something that it thought was reasonable in pursuit of our objective,

44:16.480 --> 44:20.800
 it turns out not to be so now it's learned something. So it's going to defer because it

44:20.800 --> 44:30.240
 wants to be doing what we really want. And that point, I think, is absolutely central

44:30.240 --> 44:37.920
 to solving the control problem. And it's a different kind of AI when you take away this

44:37.920 --> 44:44.560
 idea that the objective is known, then in fact, a lot of the theoretical frameworks that we're so

44:44.560 --> 44:53.520
 familiar with, you know, mark off decision processes, goal based planning, you know,

44:53.520 --> 44:59.280
 standard game research, all of these techniques actually become inapplicable.

45:01.040 --> 45:11.120
 And you get a more complicated problem because because now the interaction with the human becomes

45:11.120 --> 45:20.400
 part of the problem. Because the human by making choices is giving you more information about

45:21.280 --> 45:25.360
 the true objective and that information helps you achieve the objective better.

45:26.640 --> 45:32.000
 And so that really means that you're mostly dealing with game theoretic problems where you've

45:32.000 --> 45:38.000
 got the machine and the human and they're coupled together, rather than a machine going off by itself

45:38.000 --> 45:43.600
 with a fixed objective. Which is fascinating on the machine and the human level that we,

45:44.400 --> 45:51.920
 when you don't have an objective means you're together coming up with an objective. I mean,

45:51.920 --> 45:56.160
 there's a lot of philosophy that, you know, you could argue that life doesn't really have meaning.

45:56.160 --> 46:01.680
 We we together agree on what gives it meaning and we kind of culturally create

46:01.680 --> 46:08.560
 things that give why the heck we are in this earth anyway. We together as a society create

46:08.560 --> 46:13.680
 that meaning and you have to learn that objective. And one of the biggest, I thought that's where

46:13.680 --> 46:19.200
 you were going to go for a second. One of the biggest troubles we run into outside of statistics

46:19.200 --> 46:26.240
 and machine learning and AI in just human civilization is when you look at, I came from,

46:26.240 --> 46:32.160
 I was born in the Soviet Union. And the history of the 20th century, we ran into the most trouble,

46:32.160 --> 46:40.160
 us humans, when there was a certainty about the objective. And you do whatever it takes to achieve

46:40.160 --> 46:46.480
 that objective, whether you're talking about Germany or communist Russia, you get into trouble

46:46.480 --> 46:52.960
 with humans. And I would say with corporations, in fact, some people argue that we don't have

46:52.960 --> 46:57.840
 to look forward to a time when AI systems take over the world, they already have. And they call

46:57.840 --> 47:04.880
 corporations, right? That corporations happen to be using people as components right now.

47:05.920 --> 47:11.680
 But they are effectively algorithmic machines, and they're optimizing an objective, which is

47:11.680 --> 47:18.080
 quarterly profit that isn't aligned with overall well being of the human race. And they are

47:18.080 --> 47:24.160
 destroying the world. They are primarily responsible for our inability to tackle climate change.

47:24.960 --> 47:30.400
 So I think that's one way of thinking about what's going on with corporations. But

47:31.840 --> 47:39.680
 I think the point you're making is valid, that there are many systems in the real world where

47:39.680 --> 47:48.480
 we've sort of prematurely fixed on the objective and then decoupled the machine from those that

47:48.480 --> 47:54.720
 are supposed to be serving. And I think you see this with government, right? Government is supposed

47:54.720 --> 48:02.720
 to be a machine that serves people. But instead, it tends to be taken over by people who have their

48:02.720 --> 48:08.160
 own objective and use government to optimize that objective, regardless of what people want.

48:08.160 --> 48:16.080
 Do you find appealing the idea of almost arguing machines where you have multiple AI systems with

48:16.080 --> 48:22.400
 a clear fixed objective? We have in government the red team and the blue team that are very fixed

48:22.400 --> 48:28.240
 on their objectives. And they argue, and it kind of maybe would disagree, but it kind of seems to

48:28.240 --> 48:39.440
 make it work somewhat that the duality of it, okay, let's go 100 years back when there was still

48:39.440 --> 48:44.480
 was going on or at the founding of this country, there was disagreements and that disagreement is

48:44.480 --> 48:52.160
 where so it was a balance between certainty and forced humility because the power was distributed.

48:52.160 --> 49:05.280
 Yeah, I think that the nature of debate and disagreement argument takes as a premise the idea

49:05.280 --> 49:12.800
 that you could be wrong, which means that you're not necessarily absolutely convinced that your

49:12.800 --> 49:19.520
 objective is the correct one. If you were absolutely convinced, there'd be no point

49:19.520 --> 49:24.160
 in having any discussion or argument because you would never change your mind. And there wouldn't

49:24.160 --> 49:32.080
 be any sort of synthesis or anything like that. So I think you can think of argumentation as an

49:32.080 --> 49:44.640
 implementation of a form of uncertain reasoning. I've been reading recently about utilitarianism

49:44.640 --> 49:54.960
 and the history of efforts to define in a sort of clear mathematical way a if you like a formula for

49:54.960 --> 50:02.320
 moral or political decision making. And it's really interesting that the parallels between

50:02.320 --> 50:08.720
 the philosophical discussions going back 200 years and what you see now in discussions about

50:08.720 --> 50:15.040
 existential risk because it's almost exactly the same. So someone would say, okay, well,

50:15.040 --> 50:21.600
 here's a formula for how we should make decisions. So utilitarianism is roughly each person has a

50:21.600 --> 50:27.680
 utility function and then we make decisions to maximize the sum of everybody's utility.

50:28.720 --> 50:36.480
 And then people point out, well, in that case, the best policy is one that leads to

50:36.480 --> 50:42.480
 the enormously vast population, all of whom are living a life that's barely worth living.

50:43.520 --> 50:50.640
 And this is called the repugnant conclusion. And another version is that we should maximize

50:51.200 --> 50:57.680
 pleasure and that's what we mean by utility. And then you'll get people effectively saying,

50:57.680 --> 51:02.480
 well, in that case, we might as well just have everyone hooked up to a heroin drip. And they

51:02.480 --> 51:08.720
 didn't use those words. But that debate was happening in the 19th century, as it is now

51:09.920 --> 51:17.600
 about AI, that if we get the formula wrong, we're going to have AI systems working towards

51:17.600 --> 51:22.080
 an outcome that in retrospect, would be exactly wrong.

51:22.080 --> 51:26.960
 Do you think there's has beautifully put so the echoes are there. But do you think,

51:26.960 --> 51:34.640
 I mean, if you look at Sam Harris, our imagination worries about the AI version of that, because

51:34.640 --> 51:44.640
 of the speed at which the things going wrong in the utilitarian context could happen.

51:45.840 --> 51:47.280
 Is that a worry for you?

51:47.280 --> 51:55.360
 Yeah, I think that in most cases, not in all, but if we have a wrong political idea,

51:55.360 --> 52:01.200
 we see it starting to go wrong. And we're not completely stupid. And so we said, okay,

52:02.000 --> 52:10.160
 maybe that was a mistake. Let's try something different. And also, we're very slow and inefficient

52:10.160 --> 52:15.520
 about implementing these things and so on. So you have to worry when you have corporations

52:15.520 --> 52:20.800
 or political systems that are extremely efficient. But when we look at AI systems,

52:20.800 --> 52:27.840
 or even just computers in general, right, they have this different characteristic

52:28.400 --> 52:35.040
 from ordinary human activity in the past. So let's say you were a surgeon. You had some idea

52:35.040 --> 52:40.480
 about how to do some operation, right? Well, and let's say you were wrong, right, that that way

52:40.480 --> 52:45.840
 of doing the operation would mostly kill the patient. Well, you'd find out pretty quickly,

52:45.840 --> 52:56.000
 like after three, maybe three or four tries, right? But that isn't true for pharmaceutical

52:56.000 --> 53:03.040
 companies, because they don't do three or four operations. They manufacture three or four billion

53:03.040 --> 53:08.800
 pills and they sell them. And then they find out maybe six months or a year later that, oh,

53:08.800 --> 53:14.880
 people are dying of heart attacks or getting cancer from this drug. And so that's why we have the FDA,

53:14.880 --> 53:22.960
 right? Because of the scalability of pharmaceutical production. And there have been some unbelievably

53:22.960 --> 53:34.320
 bad episodes in the history of pharmaceuticals and adulteration of products and so on that have

53:34.320 --> 53:37.520
 killed tens of thousands or paralyzed hundreds of thousands of people.

53:39.360 --> 53:43.280
 Now, with computers, we have that same scalability problem that you can

53:43.280 --> 53:49.520
 sit there and type for i equals one to five billion, two, right? And all of a sudden,

53:49.520 --> 53:55.360
 you're having an impact on a global scale. And yet we have no FDA, right? There's absolutely no

53:55.360 --> 54:02.480
 controls at all over what a bunch of undergraduates with too much caffeine can do to the world.

54:03.440 --> 54:09.600
 And, you know, we look at what happened with Facebook, well, social media in general, and

54:09.600 --> 54:18.480
 click through optimization. So you have a simple feedback algorithm that's trying to just optimize

54:18.480 --> 54:24.080
 click through, right? That sounds reasonable, right? Because you don't want to be feeding people

54:24.080 --> 54:33.200
 ads that they don't care about or not interested in. And you might even think of that process as

54:33.200 --> 54:42.160
 simply adjusting the the feeding of ads or news articles or whatever it might be to match people's

54:42.160 --> 54:50.000
 preferences, right? Which sounds like a good idea. But in fact, that isn't how the algorithm works,

54:50.880 --> 54:59.760
 right? You make more money. The algorithm makes more money. If it can better predict what people

54:59.760 --> 55:06.400
 are going to click on, because then it can feed them exactly that, right? So the way to maximize

55:06.400 --> 55:13.360
 click through is actually to modify the people, to make them more predictable. And one way to do

55:13.360 --> 55:21.280
 that is to feed them information which will change their behavior and preferences towards

55:21.920 --> 55:27.600
 extremes that make them predictable. Whatever is the nearest extreme or the nearest predictable

55:27.600 --> 55:33.840
 point, that's where you're going to end up. And the machines will force you there.

55:34.480 --> 55:40.400
 Now, and I think there's a reasonable argument to say that this, among other things, is

55:40.400 --> 55:48.880
 contributing to the destruction of democracy in the world. And where was the oversight

55:50.160 --> 55:55.600
 of this process? Where were the people saying, okay, you would like to apply this algorithm to

55:55.600 --> 56:01.120
 five billion people on the face of the earth? Can you show me that it's safe? Can you show

56:01.120 --> 56:07.040
 me that it won't have various kinds of negative effects? No, there was no one asking that question.

56:07.040 --> 56:14.800
 There was no one placed between the undergrads with too much caffeine and the human race.

56:15.520 --> 56:20.480
 It's just they just did it. And some way outside the scope of my knowledge,

56:20.480 --> 56:27.120
 so economists would argue that the invisible hand, so the capitalist system, it was the

56:27.120 --> 56:32.480
 oversight. So if you're going to corrupt society with whatever decision you make as a company,

56:32.480 --> 56:38.640
 then that's going to be reflected in people not using your product. That's one model of oversight.

56:39.280 --> 56:48.000
 We shall see. But in the meantime, but you might even have broken the political system

56:48.000 --> 56:54.960
 that enables capitalism to function. Well, you've changed it. So we should see. Yeah.

56:54.960 --> 57:01.360
 Change is often painful. So my question is absolutely, it's fascinating. You're absolutely

57:01.360 --> 57:07.840
 right that there was zero oversight on algorithms that can have a profound civilization changing

57:09.120 --> 57:15.440
 effect. So do you think it's possible? I mean, I haven't, have you seen government?

57:15.440 --> 57:22.800
 So do you think it's possible to create regulatory bodies oversight over AI algorithms,

57:22.800 --> 57:28.400
 which are inherently such cutting edge set of ideas and technologies?

57:30.960 --> 57:37.520
 Yeah, but I think it takes time to figure out what kind of oversight, what kinds of controls.

57:37.520 --> 57:42.960
 I mean, it took time to design the FDA regime. Some people still don't like it and they want

57:42.960 --> 57:50.400
 to fix it. And I think there are clear ways that it could be improved. But the whole notion that

57:50.400 --> 57:56.400
 you have stage one, stage two, stage three, and here are the criteria for what you have to do

57:56.400 --> 58:02.000
 to pass a stage one trial, right? We haven't even thought about what those would be for algorithms.

58:02.000 --> 58:10.320
 So I mean, I think there are, there are things we could do right now with regard to bias, for

58:10.320 --> 58:19.040
 example, we have a pretty good technical handle on how to detect algorithms that are propagating

58:19.040 --> 58:26.720
 bias that exists in data sets, how to debias those algorithms, and even what it's going to cost you

58:26.720 --> 58:34.480
 to do that. So I think we could start having some standards on that. I think there are things to do

58:34.480 --> 58:41.840
 with impersonation and falsification that we could, we could work on. So I think, yeah.

58:43.360 --> 58:50.240
 Or the very simple point. So impersonation is a machine acting as if it was a person.

58:51.440 --> 58:58.800
 I can't see a real justification for why we shouldn't insist that machine self identify as

58:58.800 --> 59:08.480
 machines. Where is the social benefit in fooling people into thinking that this is really a person

59:08.480 --> 59:15.120
 when it isn't? I don't mind if it uses a human like voice that's easy to understand. That's fine.

59:15.120 --> 59:22.000
 But it should just say, I'm a machine in some form. And now many people are speaking to that.

59:22.800 --> 59:26.400
 I would think relatively obvious facts. So I think most people... Yeah. I mean,

59:26.400 --> 59:32.400
 there is actually a law in California that bans impersonation, but only in certain

59:33.280 --> 59:41.280
 restricted circumstances. So for the purpose of engaging in a fraudulent transaction and for

59:41.280 --> 59:48.160
 the purpose of modifying someone's voting behavior. So those are the circumstances where

59:48.160 --> 59:55.520
 machines have to self identify. But I think, arguably, it should be in all circumstances.

59:56.400 --> 1:00:03.280
 And then when you talk about deep fakes, we're just at the beginning. But already,

1:00:03.280 --> 1:00:10.480
 it's possible to make a movie of anybody saying anything in ways that are pretty hard to detect.

1:00:11.520 --> 1:00:15.040
 Including yourself because you're on camera now and your voice is coming through with high

1:00:15.040 --> 1:00:19.360
 resolution. Yeah. So you could take what I'm saying and replace it with pretty much anything

1:00:19.360 --> 1:00:24.400
 else you wanted me to be saying. And even it would change my lips and facial expressions to fit.

1:00:26.960 --> 1:00:35.280
 And there's actually not much in the way of real legal protection against that.

1:00:35.920 --> 1:00:38.640
 I think in the commercial area, you could say, yeah, that's...

1:00:38.640 --> 1:00:46.000
 You're using my brand and so on. There are rules about that. But in the political sphere, I think,

1:00:47.600 --> 1:00:52.560
 at the moment, it's anything goes. So that could be really, really damaging.

1:00:53.920 --> 1:01:02.400
 And let me just try to make not an argument, but try to look back at history and say something

1:01:02.400 --> 1:01:09.680
 dark, in essence, is while regulation seems to be... Oversight seems to be exactly the

1:01:09.680 --> 1:01:14.480
 right thing to do here. It seems that human beings, what they naturally do is they wait

1:01:14.480 --> 1:01:20.080
 for something to go wrong. If you're talking about nuclear weapons, you can't talk about

1:01:20.080 --> 1:01:26.000
 nuclear weapons being dangerous until somebody actually, like the United States drops the bomb,

1:01:26.000 --> 1:01:34.960
 or Chernobyl melting. Do you think we will have to wait for things going wrong in a way that's

1:01:34.960 --> 1:01:39.840
 obviously damaging to society, not an existential risk, but obviously damaging?

1:01:42.320 --> 1:01:48.000
 Or do you have faith that... I hope not. But I mean, I think we do have to look at history.

1:01:48.000 --> 1:01:57.600
 So the two examples you gave, nuclear weapons and nuclear power, are very, very interesting because

1:01:59.520 --> 1:02:07.840
 nuclear weapons, we knew in the early years of the 20th century that atoms contained a huge

1:02:07.840 --> 1:02:13.280
 amount of energy. We had E equals MC squared. We knew the mass differences between the different

1:02:13.280 --> 1:02:20.640
 atoms and their components, and we knew that you might be able to make an incredibly powerful

1:02:20.640 --> 1:02:28.000
 explosive. So H.G. Wells wrote science fiction book, I think, in 1912. Frederick Soddy, who was the

1:02:28.000 --> 1:02:35.760
 guy who discovered isotopes as a Nobel Prize winner, he gave a speech in 1915 saying that

1:02:37.840 --> 1:02:42.160
 one pound of this new explosive would be the equivalent of 150 tons of dynamite,

1:02:42.160 --> 1:02:50.720
 which turns out to be about right. And this was in World War I, so he was imagining how much worse

1:02:51.360 --> 1:02:57.600
 the world war would be if we were using that kind of explosive. But the physics establishment

1:02:57.600 --> 1:03:05.760
 simply refused to believe that these things could be made. Including the people who were making it.

1:03:06.400 --> 1:03:11.280
 Well, so they were doing the nuclear physics. I mean, eventually were the ones who made it.

1:03:11.280 --> 1:03:21.280
 You talk about Fermi or whoever. Well, so up to the development was mostly theoretical. So it was

1:03:21.280 --> 1:03:28.320
 people using sort of primitive kinds of particle acceleration and doing experiments at the level

1:03:28.320 --> 1:03:36.480
 of single particles or collections of particles. They weren't yet thinking about how to actually

1:03:36.480 --> 1:03:40.160
 make a bomb or anything like that. But they knew the energy was there and they figured if they

1:03:40.160 --> 1:03:46.720
 understood it better, it might be possible. But the physics establishment, their view, and I think

1:03:46.720 --> 1:03:51.840
 because they did not want it to be true, their view was that it could not be true.

1:03:53.360 --> 1:04:00.240
 That this could not provide a way to make a super weapon. And there was this famous

1:04:01.120 --> 1:04:08.240
 speech given by Rutherford, who was the sort of leader of nuclear physics. And it was on

1:04:08.240 --> 1:04:14.800
 September 11, 1933. And he said, you know, anyone who talks about the possibility of

1:04:14.800 --> 1:04:21.600
 obtaining energy from transformation of atoms is talking complete moonshine. And the next

1:04:22.800 --> 1:04:28.560
 morning, Leo Zillard read about that speech and then invented the nuclear chain reaction.

1:04:28.560 --> 1:04:35.920
 And so as soon as he invented, as soon as he had that idea, that you could make a chain reaction

1:04:35.920 --> 1:04:40.640
 with neutrons because neutrons were not repelled by the nucleus so they could enter the nucleus

1:04:40.640 --> 1:04:48.480
 and then continue the reaction. As soon as he has that idea, he instantly realized that the world

1:04:48.480 --> 1:04:58.160
 was in deep doo doo. Because this is 1933, right? Hitler had recently come to power in Germany.

1:04:58.160 --> 1:05:09.280
 Zillard was in London. He eventually became a refugee and he came to the US. And in the

1:05:09.280 --> 1:05:14.880
 process of having the idea about the chain reaction, he figured out basically how to make

1:05:14.880 --> 1:05:22.800
 a bomb and also how to make a reactor. And he patented the reactor in 1934. But because

1:05:22.800 --> 1:05:28.480
 of the situation, the great power conflict situation that he could see happening,

1:05:29.200 --> 1:05:38.160
 he kept that a secret. And so between then and the beginning of World War II,

1:05:39.600 --> 1:05:48.000
 people were working, including the Germans, on how to actually create neutron sources,

1:05:48.000 --> 1:05:54.720
 what specific fission reactions would produce neutrons of the right energy to continue the

1:05:54.720 --> 1:06:02.480
 reaction. And that was demonstrated in Germany, I think in 1938, if I remember correctly. The first

1:06:03.760 --> 1:06:17.280
 nuclear weapon patent was 1939 by the French. So this was actually going on well before World War

1:06:17.280 --> 1:06:22.720
 II really got going. And then the British probably had the most advanced capability

1:06:22.720 --> 1:06:27.920
 in this area. But for safety reasons, among others, and bless just sort of just resources,

1:06:28.720 --> 1:06:33.520
 they moved the program from Britain to the US. And then that became Manhattan Project.

1:06:34.480 --> 1:06:37.920
 So the reason why we couldn't

1:06:37.920 --> 1:06:48.320
 have any kind of oversight of nuclear weapons and nuclear technology was because we were basically

1:06:48.320 --> 1:06:57.520
 already in an arms race in a war. But you mentioned in the 20s and 30s, so what are the echoes?

1:07:00.000 --> 1:07:04.320
 The way you've described the story, I mean, there's clearly echoes. What do you think most AI

1:07:04.320 --> 1:07:11.440
 researchers, folks who are really close to the metal, they really are not concerned about AI,

1:07:11.440 --> 1:07:16.960
 they don't think about it, whether it's they don't want to think about it. But what are the,

1:07:16.960 --> 1:07:23.760
 yeah, why do you think that is? What are the echoes of the nuclear situation to the current AI

1:07:23.760 --> 1:07:33.120
 situation? And what can we do about it? I think there is a kind of motivated cognition, which is

1:07:33.120 --> 1:07:40.640
 a term in psychology means that you believe what you would like to be true, rather than what is

1:07:40.640 --> 1:07:50.240
 true. And it's unsettling to think that what you're working on might be the end of the human race,

1:07:50.800 --> 1:07:58.400
 obviously. So you would rather instantly deny it and come up with some reason why it couldn't be

1:07:58.400 --> 1:08:07.440
 true. And I collected a long list of regions that extremely intelligent, competent AI scientists

1:08:08.080 --> 1:08:16.560
 have come up with for why we shouldn't worry about this. For example, calculators are superhuman at

1:08:16.560 --> 1:08:21.680
 arithmetic and they haven't taken over the world, so there's nothing to worry about. Well, okay,

1:08:21.680 --> 1:08:29.440
 my five year old could have figured out why that was an unreasonable and really quite weak argument.

1:08:31.520 --> 1:08:40.800
 Another one was, while it's theoretically possible that you could have superhuman AI

1:08:41.760 --> 1:08:46.480
 destroy the world, it's also theoretically possible that a black hole could materialize

1:08:46.480 --> 1:08:51.760
 right next to the earth and destroy humanity. I mean, yes, it's theoretically possible,

1:08:51.760 --> 1:08:56.720
 quantum theoretically, extremely unlikely that it would just materialize right there.

1:08:58.400 --> 1:09:04.720
 But that's a completely bogus analogy because if the whole physics community on earth was working

1:09:04.720 --> 1:09:11.680
 to materialize a black hole in near earth orbit, wouldn't you ask them, is that a good idea? Is

1:09:11.680 --> 1:09:19.040
 that going to be safe? What if you succeed? And that's the thing. The AI community is sort of

1:09:19.040 --> 1:09:26.240
 refused to ask itself, what if you succeed? And initially, I think that was because it was too

1:09:26.240 --> 1:09:35.520
 hard, but Alan Turing asked himself that and he said, we'd be toast. If we were lucky, we might

1:09:35.520 --> 1:09:40.000
 be able to switch off the power but probably we'd be toast. But there's also an aspect

1:09:40.000 --> 1:09:49.680
 that because we're not exactly sure what the future holds, it's not clear exactly so technically

1:09:49.680 --> 1:09:58.480
 what to worry about, sort of how things go wrong. And so there is something it feels like, maybe

1:09:58.480 --> 1:10:04.400
 you can correct me if I'm wrong, but there's something paralyzing about worrying about something

1:10:04.400 --> 1:10:10.000
 that logically is inevitable. But you don't really know what that will look like.

1:10:10.720 --> 1:10:19.440
 Yeah, I think that's a reasonable point. And it's certainly in terms of existential risks,

1:10:19.440 --> 1:10:26.800
 it's different from asteroid collides with the earth, which again is quite possible. It's

1:10:26.800 --> 1:10:33.040
 happened in the past. It'll probably happen again. We don't know right now. But if we did detect an

1:10:33.040 --> 1:10:39.200
 asteroid that was going to hit the earth in 75 years time, we'd certainly be doing something

1:10:39.200 --> 1:10:43.600
 about it. Well, it's clear there's got big rock and we'll probably have a meeting and see what

1:10:43.600 --> 1:10:49.040
 do we do about the big rock with AI. Right, with AI. I mean, there are very few people who think it's

1:10:49.040 --> 1:10:54.400
 not going to happen within the next 75 years. I know Rod Brooks doesn't think it's going to happen.

1:10:55.200 --> 1:11:00.880
 Maybe Andrew Ng doesn't think it's happened. But a lot of the people who work day to day,

1:11:00.880 --> 1:11:07.360
 you know, as you say, at the rock face, they think it's going to happen. I think the median

1:11:08.640 --> 1:11:14.320
 estimate from AI researchers is somewhere in 40 to 50 years from now. Or maybe, you know,

1:11:14.320 --> 1:11:20.000
 I think in Asia, they think it's going to be even faster than that. I'm a little bit

1:11:21.280 --> 1:11:25.840
 more conservative. I think it probably take longer than that. But I think, you know, as

1:11:25.840 --> 1:11:32.400
 happened with nuclear weapons, it can happen overnight that you have these breakthroughs.

1:11:32.400 --> 1:11:38.240
 And we need more than one breakthrough. But, you know, it's on the order of half a dozen.

1:11:38.800 --> 1:11:43.920
 This is a very rough scale. But so half a dozen breakthroughs of that nature

1:11:45.840 --> 1:11:53.600
 would have to happen for us to reach superhuman AI. But the AI research community is

1:11:53.600 --> 1:12:00.640
 vast now, the massive investments from governments, from corporations, tons of really,

1:12:00.640 --> 1:12:05.760
 really smart people. You just have to look at the rate of progress in different areas of AI

1:12:05.760 --> 1:12:10.800
 to see that things are moving pretty fast. So to say, oh, it's just going to be thousands of years.

1:12:11.920 --> 1:12:18.160
 I don't see any basis for that. You know, I see, you know, for example, the

1:12:18.160 --> 1:12:28.640
 Stanford 100 year AI project, which is supposed to be sort of, you know, the serious establishment view,

1:12:29.520 --> 1:12:33.440
 their most recent report actually said it's probably not even possible.

1:12:34.160 --> 1:12:34.720
 Oh, wow.

1:12:35.280 --> 1:12:42.960
 Right. Which if you want a perfect example of people in denial, that's it. Because, you know,

1:12:42.960 --> 1:12:49.760
 for the whole history of AI, we've been saying to philosophers who said it wasn't possible. Well,

1:12:49.760 --> 1:12:53.520
 you have no idea what you're talking about. Of course, it's possible. Right. Give me an

1:12:53.520 --> 1:12:59.760
 argument for why it couldn't happen. And there isn't one. Right. And now, because people are

1:12:59.760 --> 1:13:04.000
 worried that maybe AI might get a bad name, or I just don't want to think about this,

1:13:05.200 --> 1:13:09.520
 they're saying, okay, well, of course, it's not really possible. You know, imagine, right? Imagine

1:13:09.520 --> 1:13:16.000
 if, you know, the leaders of the cancer biology community got up and said, well, you know, of

1:13:16.000 --> 1:13:23.680
 course, curing cancer, it's not really possible. There'd be a complete outrage and dismay. And,

1:13:25.040 --> 1:13:30.800
 you know, I find this really a strange phenomenon. So,

1:13:30.800 --> 1:13:38.640
 okay, so if you accept it as possible, and if you accept that it's probably going to happen,

1:13:40.560 --> 1:13:44.400
 the point that you're making that, you know, how does it go wrong?

1:13:46.320 --> 1:13:51.600
 A valid question without that, without an answer to that question, then you're stuck with what I

1:13:51.600 --> 1:13:56.160
 call the gorilla problem, which is, you know, the problem that the gorillas face, right? They

1:13:56.160 --> 1:14:02.800
 made something more intelligent than them, namely us a few million years ago, and now they're in

1:14:02.800 --> 1:14:09.360
 deep doo doo. So there's really nothing they can do. They've lost the control. They failed to solve

1:14:09.360 --> 1:14:16.240
 the control problem of controlling humans. And so they've lost. So we don't want to be in that

1:14:16.240 --> 1:14:22.320
 situation. And if the gorilla problem is the only formulation you have, there's not a lot you can do.

1:14:22.320 --> 1:14:28.240
 Right. Other than to say, okay, we should try to stop. You know, we should just not make the humans

1:14:28.240 --> 1:14:33.120
 or, or in this case, not make the AI. And I think that's really hard to do.

1:14:35.120 --> 1:14:42.480
 To, I'm not actually proposing that that's a feasible course of action. And I also think that,

1:14:43.040 --> 1:14:46.080
 you know, if properly controlled AI could be incredibly beneficial.

1:14:46.080 --> 1:14:54.960
 So the, but it seems to me that there's a, there's a consensus that one of the major

1:14:54.960 --> 1:15:02.320
 failure modes is this loss of control that we create AI systems that are pursuing incorrect

1:15:02.320 --> 1:15:11.040
 objectives. And because the AI system believes it knows what the objective is, it has no incentive

1:15:11.040 --> 1:15:16.800
 to listen to us anymore, so to speak, right? It's just carrying out the,

1:15:17.920 --> 1:15:22.160
 the strategy that it, it has computed as being the optimal solution.

1:15:24.320 --> 1:15:31.040
 And, you know, it may be that in the process, it needs to acquire more resources to increase the

1:15:31.600 --> 1:15:37.360
 possibility of success or prevent various failure modes by defending itself against interference.

1:15:37.360 --> 1:15:42.640
 And so that collection of problems, I think, is something we can address.

1:15:45.280 --> 1:15:55.360
 The other problems are roughly speaking, you know, misuse, right? So even if we solve the control

1:15:55.360 --> 1:16:00.960
 problem, we make perfectly safe controllable AI systems. Well, why, you know, why does Dr.

1:16:00.960 --> 1:16:05.600
 Evil going to use those, right? He wants to just take over the world and he'll make unsafe AI systems

1:16:05.600 --> 1:16:12.000
 that then get out of control. So that's one problem, which is sort of a, you know, partly a

1:16:12.000 --> 1:16:20.880
 policing problem, partly a sort of a cultural problem for the profession of how we teach people

1:16:21.760 --> 1:16:26.560
 what kinds of AI systems are safe. You talk about autonomous weapon system and how pretty much

1:16:26.560 --> 1:16:31.920
 everybody agrees that there's too many ways that that can go horribly wrong. You have this great

1:16:31.920 --> 1:16:36.560
 Slotabots movie that kind of illustrates that beautifully. Well, I want to talk about that.

1:16:36.560 --> 1:16:41.440
 That's another, there's another topic I'm happy to talk about the, I just want to mention that

1:16:41.440 --> 1:16:48.080
 what I see is the third major failure mode, which is overuse, not so much misuse, but overuse of AI,

1:16:49.680 --> 1:16:55.280
 that we become overly dependent. So I call this the warly problems. If you've seen the warly,

1:16:55.280 --> 1:17:00.800
 the movie, all right, all the humans are on the spaceship and the machines look after everything

1:17:00.800 --> 1:17:07.680
 for them. And they just watch TV and drink big gulps. And they're all sort of obese and stupid.

1:17:07.680 --> 1:17:17.040
 And they sort of totally lost any notion of human autonomy. And, you know, so in effect, right,

1:17:18.240 --> 1:17:23.520
 this would happen like the slow boiling frog, right, we would gradually turn over

1:17:24.320 --> 1:17:28.480
 more and more of the management of our civilization to machines as we are already doing.

1:17:28.480 --> 1:17:34.560
 And this, you know, this, if this process continues, you know, we sort of gradually

1:17:34.560 --> 1:17:41.440
 switch from sort of being the masters of technology to just being the guests, right?

1:17:41.440 --> 1:17:45.840
 So, so we become guests on a cruise ship, you know, which is fine for a week, but not,

1:17:46.480 --> 1:17:53.520
 not for the rest of eternity, right? You know, and it's almost irreversible, right? Once you,

1:17:53.520 --> 1:18:00.000
 once you lose the incentive to, for example, you know, learn to be an engineer or a doctor

1:18:00.800 --> 1:18:08.000
 or a sanitation operative or any other of the, the infinitely many ways that we

1:18:08.000 --> 1:18:13.200
 maintain and propagate our civilization. You know, if you, if you don't have the

1:18:13.200 --> 1:18:18.400
 incentive to do any of that, you won't. And then it's really hard to recover.

1:18:18.400 --> 1:18:23.440
 And of course they add just one of the technologies that could, that third failure mode result in that.

1:18:23.440 --> 1:18:27.360
 There's probably other technology in general detaches us from.

1:18:28.400 --> 1:18:33.440
 It does a bit, but the, the, the difference is that in terms of the knowledge to,

1:18:34.080 --> 1:18:39.360
 to run our civilization, you know, up to now we've had no alternative, but to put it into

1:18:39.360 --> 1:18:44.400
 people's heads, right? And if you, if you, software with Google, I mean, so software in

1:18:44.400 --> 1:18:51.600
 general, so computers in general, but, but the, you know, the knowledge of how, you know, how

1:18:51.600 --> 1:18:56.560
 a sanitation system works, you know, that's an AI has to understand that it's no good putting it

1:18:56.560 --> 1:19:02.960
 into Google. So, I mean, we, we've always put knowledge in on paper, but paper doesn't run

1:19:02.960 --> 1:19:07.520
 our civilization. It only runs when it goes from the paper into people's heads again, right? So

1:19:07.520 --> 1:19:13.920
 we've always propagated civilization through human minds and we've spent about a trillion

1:19:13.920 --> 1:19:19.440
 person years doing that literally, right? You, you can work it out. It's about, right? There's

1:19:19.440 --> 1:19:25.120
 about just over a hundred billion people who've ever lived and each of them has spent about 10

1:19:25.120 --> 1:19:30.640
 years learning stuff to keep their civilization going. And so that's a trillion person years we

1:19:30.640 --> 1:19:35.760
 put into this effort. Beautiful way to describe all of civilization. And now we're, you know,

1:19:35.760 --> 1:19:39.840
 we're in danger of throwing that away. So this is a problem that AI can't solve. It's not a

1:19:39.840 --> 1:19:47.120
 technical problem. It's a, you know, and if we do our job right, the AI systems will say, you know,

1:19:47.120 --> 1:19:52.800
 the human race doesn't in the long run want to be passengers in a cruise ship. The human race

1:19:52.800 --> 1:19:59.840
 wants autonomy. This is part of human preferences. So we, the AI systems are not going to do this

1:19:59.840 --> 1:20:05.440
 stuff for you. You've got to do it for yourself, right? I'm not going to carry you to the top of

1:20:05.440 --> 1:20:11.840
 Everest in an autonomous helicopter. You have to climb it if you want to get the benefit and so on. So

1:20:14.160 --> 1:20:20.160
 but I'm afraid that because we are short sighted and lazy, we're going to override the AI systems.

1:20:20.880 --> 1:20:27.520
 And, and there's an amazing short story that I recommend to everyone that I talk to about this

1:20:27.520 --> 1:20:36.080
 called the machine stops written in 1909 by Ian Forster, who, you know, wrote novels about the

1:20:36.080 --> 1:20:41.200
 British Empire and sort of things that became costume dramas on the BBC. But he wrote this one

1:20:41.200 --> 1:20:49.280
 science fiction story, which is an amazing vision of the future. It has, it has basically iPads.

1:20:49.280 --> 1:20:57.680
 It has video conferencing. It has MOOCs. It has computer and computer induced obesity. I mean,

1:20:57.680 --> 1:21:02.960
 literally, the whole thing is what people spend their time doing is giving online courses or

1:21:02.960 --> 1:21:07.920
 listening to online courses and talking about ideas. But they never get out there in the real

1:21:07.920 --> 1:21:13.680
 world. They don't really have a lot of face to face contact. Everything is done online.

1:21:13.680 --> 1:21:19.680
 You know, so all the things we're worrying about now were described in the story and and then the

1:21:19.680 --> 1:21:26.640
 human race becomes more and more dependent on the machine loses knowledge of how things really run

1:21:27.600 --> 1:21:35.200
 and then becomes vulnerable to collapse. And so it's a it's a pretty unbelievably amazing

1:21:35.200 --> 1:21:41.520
 story for someone writing in 1909 to imagine all this. Plus, yeah. So there's very few people

1:21:41.520 --> 1:21:46.080
 that represent artificial intelligence more than you, Stuart Russell.

1:21:46.960 --> 1:21:50.880
 If you say it's okay, that's very kind. So it's all my fault.

1:21:50.880 --> 1:21:59.680
 It's all your fault. No, right. You're often brought up as the person. Well, Stuart Russell,

1:22:00.560 --> 1:22:04.960
 like the AI person is worried about this. That's why you should be worried about it.

1:22:06.080 --> 1:22:11.280
 Do you feel the burden of that? I don't know if you feel that at all. But when I talk to people,

1:22:11.280 --> 1:22:16.800
 like from you talk about people outside of computer science, when they think about this,

1:22:16.800 --> 1:22:22.560
 Stuart Russell is worried about AI safety, you should be worried too. Do you feel the burden

1:22:22.560 --> 1:22:31.520
 of that? I mean, in a practical sense, yeah, because I get, you know, a dozen, sometimes

1:22:31.520 --> 1:22:39.600
 25 invitations a day to talk about it, to give interviews, to write press articles and so on.

1:22:39.600 --> 1:22:47.120
 So in that very practical sense, I'm seeing that people are concerned and really interested about

1:22:47.120 --> 1:22:53.680
 this. Are you worried that you could be wrong, as all good scientists are? Of course. I worry about

1:22:53.680 --> 1:23:00.400
 that all the time. I mean, that's always been the way that I've worked, you know, is like I have an

1:23:00.400 --> 1:23:06.320
 argument in my head with myself, right? So I have some idea. And then I think, okay,

1:23:06.320 --> 1:23:11.680
 okay, how could that be wrong? Or did someone else already have that idea? So I'll go and

1:23:12.800 --> 1:23:18.240
 search in as much literature as I can to see whether someone else already thought of that

1:23:18.240 --> 1:23:25.600
 or even refuted it. So, you know, right now, I'm reading a lot of philosophy because,

1:23:25.600 --> 1:23:37.920
 you know, in the form of the debates over utilitarianism and other kinds of moral formulas,

1:23:37.920 --> 1:23:44.320
 shall we say, people have already thought through some of these issues. But, you know,

1:23:44.320 --> 1:23:51.280
 what one of the things I'm not seeing in a lot of these debates is this specific idea about

1:23:51.280 --> 1:23:58.560
 the importance of uncertainty in the objective, that this is the way we should think about machines

1:23:58.560 --> 1:24:06.800
 that are beneficial to humans. So this idea of provably beneficial machines based on explicit

1:24:06.800 --> 1:24:15.200
 uncertainty in the objective, you know, it seems to be, you know, my gut feeling is this is the core

1:24:15.200 --> 1:24:21.200
 of it. It's going to have to be elaborated in a lot of different directions. And they're a lot

1:24:21.200 --> 1:24:27.440
 of beneficial. Yeah, but they're, I mean, it has to be, right? We can't afford, you know,

1:24:27.440 --> 1:24:33.040
 hand wavy beneficial. Because there are, you know, whenever we do hand wavy stuff, there are

1:24:33.040 --> 1:24:38.080
 loopholes. And the thing about super intelligent machines is they find the loopholes. You know,

1:24:38.080 --> 1:24:44.320
 just like, you know, tax evaders, if you don't write your tax law properly, people will find

1:24:44.320 --> 1:24:53.440
 the loopholes and end up paying no tax. And so you should think of it this way. And getting those

1:24:53.440 --> 1:25:03.440
 definitions right, you know, it is really a long process, you know, so you can you can define

1:25:03.440 --> 1:25:07.760
 mathematical frameworks. And within that framework, you can prove mathematical theorems that, yes,

1:25:07.760 --> 1:25:12.800
 this will, you know, this this theoretical entity will be provably beneficial to that theoretical

1:25:12.800 --> 1:25:20.160
 entity. But that framework may not match the real world in some crucial way. So the long process

1:25:20.160 --> 1:25:27.120
 thinking through it to iterating and so on. Last question. Yep. You have 10 seconds to answer it.

1:25:27.120 --> 1:25:34.480
 What is your favorite sci fi movie about AI? I would say interstellar has my favorite robots.

1:25:34.480 --> 1:25:42.160
 Oh, beats space. Yeah, yeah, yeah. So so Tars, the robots, one of the robots in interstellar is

1:25:42.160 --> 1:25:52.080
 the way robots should behave. And I would say X Machina is in some ways the one, the one that

1:25:52.080 --> 1:25:58.000
 makes you think in a nervous kind of way about about where we're going.

1:25:58.000 --> 1:26:13.920
 Well, Stuart, thank you so much for talking today. Pleasure.