WEBVTT 00:00.000 --> 00:02.360 The following is a conversation with Jeff Hawkins. 00:02.360 --> 00:04.140 He's the founder of the Redwood Center 00:04.140 --> 00:06.560 for Theoretical and Neuroscience in 2002 00:06.560 --> 00:08.980 and New Menta in 2005. 00:08.980 --> 00:11.920 In his 2004 book titled On Intelligence 00:11.920 --> 00:13.880 and in the research before and after, 00:13.880 --> 00:16.200 he and his team have worked to reverse engineer 00:16.200 --> 00:19.160 the New York Cortex and propose artificial intelligence 00:19.160 --> 00:21.360 architectures approaches and ideas 00:21.360 --> 00:23.640 that are inspired by the human brain. 00:23.640 --> 00:25.960 These ideas include hierarchical temporal memory, 00:25.960 --> 00:30.080 HTM from 2004, and New Work, The Thousand's Brain's Theory 00:30.080 --> 00:33.800 of Intelligence from 2017, 18, and 19. 00:33.800 --> 00:36.120 Jeff's ideas have been an inspiration 00:36.120 --> 00:38.960 to many who have looked for progress beyond the current 00:38.960 --> 00:41.760 machine learning approaches, but they have also received 00:41.760 --> 00:44.680 criticism for lacking a body of empirical evidence 00:44.680 --> 00:46.240 supporting the models. 00:46.240 --> 00:49.040 This is always a challenge when seeking more than small 00:49.040 --> 00:51.440 incremental steps forward in AI. 00:51.440 --> 00:54.120 Jeff is a brilliant mind and many of the ideas 00:54.120 --> 00:56.520 he has developed and aggregated from neuroscience 00:56.520 --> 00:59.160 are worth understanding and thinking about. 00:59.160 --> 01:00.960 There are limits to deep learning 01:00.960 --> 01:02.960 as it is currently defined. 01:02.960 --> 01:05.800 Forward progress in AI is shrouded in mystery. 01:05.800 --> 01:07.800 My hope is that conversations like this 01:07.800 --> 01:11.480 can help provide an inspiring spark for new ideas. 01:11.480 --> 01:14.060 This is the Artificial Intelligence Podcast. 01:14.060 --> 01:16.200 If you enjoy it, subscribe on YouTube, 01:16.200 --> 01:18.680 iTunes, or simply connect with me on Twitter 01:18.680 --> 01:21.560 at Lex Freedman spelled F R I D. 01:21.560 --> 01:25.360 And now here's my conversation with Jeff Hawkins. 01:26.800 --> 01:29.880 Are you more interested in understanding the human brain 01:29.880 --> 01:32.040 or in creating artificial systems 01:32.040 --> 01:34.680 that have many of the same qualities, 01:34.680 --> 01:38.600 but don't necessarily require that you actually understand 01:38.600 --> 01:41.520 the underpinning workings of our mind? 01:41.520 --> 01:44.020 So there's a clear answer to that question. 01:44.020 --> 01:46.800 My primary interest is understanding the human brain. 01:46.800 --> 01:51.520 No question about it, but I also firmly believe 01:51.520 --> 01:53.880 that we will not be able to create 01:53.880 --> 01:55.040 fully intelligent machines 01:55.040 --> 01:57.280 until we understand how the human brain works. 01:57.280 --> 02:00.120 So I don't see those as separate problems. 02:00.120 --> 02:01.720 I think there's limits to what can be done 02:01.720 --> 02:02.640 with machine intelligence 02:02.640 --> 02:04.040 if you don't understand the principles 02:04.040 --> 02:05.680 by which the brain works. 02:05.680 --> 02:07.880 And so I actually believe that studying the brain 02:07.880 --> 02:11.960 is actually the fastest way to get to machine intelligence. 02:11.960 --> 02:14.640 And within that, let me ask the impossible question. 02:14.640 --> 02:17.160 How do you not define, but at least think 02:17.160 --> 02:19.400 about what it means to be intelligent? 02:19.400 --> 02:22.240 So I didn't try to answer that question first. 02:22.240 --> 02:24.480 We said, let's just talk about how the brain works. 02:24.480 --> 02:26.680 And let's figure out how certain parts of the brain, 02:26.680 --> 02:29.880 mostly the neocortex, but some other parts too, 02:29.880 --> 02:32.320 the parts of the brain most associated with intelligence. 02:32.320 --> 02:35.800 And let's discover the principles by how they work. 02:35.800 --> 02:39.320 Because intelligence isn't just like some mechanism 02:39.320 --> 02:40.640 and it's not just some capabilities. 02:40.640 --> 02:43.000 It's like, okay, we don't even know where to begin 02:43.000 --> 02:44.000 on this stuff. 02:44.000 --> 02:49.000 And so now that we've made a lot of progress on this, 02:49.000 --> 02:50.480 after we've made a lot of progress 02:50.480 --> 02:53.200 on how the neocortex works, and we can talk about that, 02:53.200 --> 02:54.560 I now have a very good idea 02:54.560 --> 02:57.200 what's gonna be required to make intelligent machines. 02:57.200 --> 02:59.600 I can tell you today, some of the things 02:59.600 --> 03:02.120 are gonna be necessary, I believe, 03:02.120 --> 03:03.480 to create intelligent machines. 03:03.480 --> 03:04.600 Well, so we'll get there. 03:04.600 --> 03:07.440 We'll get to the neocortex and some of the theories 03:07.440 --> 03:09.200 of how the whole thing works. 03:09.200 --> 03:11.720 And you're saying, as we understand more and more 03:12.720 --> 03:14.800 about the neocortex, about our own human mind, 03:14.800 --> 03:17.720 we'll be able to start to more specifically define 03:17.720 --> 03:18.680 what it means to be intelligent. 03:18.680 --> 03:21.880 It's not useful to really talk about that until... 03:21.880 --> 03:23.560 I don't know if it's not useful. 03:23.560 --> 03:26.200 Look, there's a long history of AI, as you know. 03:26.200 --> 03:28.920 And there's been different approaches taken to it. 03:28.920 --> 03:30.160 And who knows? 03:30.160 --> 03:32.280 Maybe they're all useful. 03:32.280 --> 03:37.280 So the good old fashioned AI, the expert systems, 03:37.360 --> 03:38.960 the current convolutional neural networks, 03:38.960 --> 03:40.440 they all have their utility. 03:41.280 --> 03:43.800 They all have a value in the world. 03:43.800 --> 03:45.280 But I would think almost everyone agreed 03:45.280 --> 03:46.640 that none of them are really intelligent 03:46.640 --> 03:49.880 in a sort of a deep way that humans are. 03:49.880 --> 03:53.600 And so it's just the question of how do you get 03:53.600 --> 03:56.440 from where those systems were or are today 03:56.440 --> 03:59.240 to where a lot of people think we're gonna go. 03:59.240 --> 04:02.320 And there's a big, big gap there, a huge gap. 04:02.320 --> 04:06.240 And I think the quickest way of bridging that gap 04:06.240 --> 04:08.840 is to figure out how the brain does that. 04:08.840 --> 04:10.160 And then we can sit back and look and say, 04:10.160 --> 04:13.000 oh, what are these principles that the brain works on 04:13.000 --> 04:15.160 are necessary and which ones are not? 04:15.160 --> 04:16.640 Clearly, we don't have to build this in, 04:16.640 --> 04:18.520 and tellage machines aren't gonna be built 04:18.520 --> 04:22.760 out of organic living cells. 04:22.760 --> 04:24.720 But there's a lot of stuff that goes on the brain 04:24.720 --> 04:25.920 that's gonna be necessary. 04:25.920 --> 04:30.320 So let me ask maybe, before we get into the fun details, 04:30.320 --> 04:33.080 let me ask maybe a depressing or a difficult question. 04:33.080 --> 04:36.240 Do you think it's possible that we will never 04:36.240 --> 04:38.120 be able to understand how our brain works, 04:38.120 --> 04:41.840 that maybe there's aspects to the human mind 04:41.840 --> 04:46.160 like we ourselves cannot introspectively get to the core, 04:46.160 --> 04:48.120 that there's a wall you eventually hit? 04:48.120 --> 04:50.200 Yeah, I don't believe that's the case. 04:52.040 --> 04:53.240 I have never believed that's the case. 04:53.240 --> 04:55.760 There's not been a single thing we've ever, 04:55.760 --> 04:57.760 humans have ever put their minds to that we've said, 04:57.760 --> 05:00.320 oh, we reached the wall, we can't go any further. 05:00.320 --> 05:01.680 People keep saying that. 05:01.680 --> 05:03.400 People used to believe that about life, you know, 05:03.400 --> 05:04.480 Alain Vitao, right? 05:04.480 --> 05:06.360 There's like, what's the difference in living matter 05:06.360 --> 05:07.280 and nonliving matter? 05:07.280 --> 05:09.120 Something special you never understand. 05:09.120 --> 05:10.640 We no longer think that. 05:10.640 --> 05:14.720 So there's no historical evidence that suggests this is the case 05:14.720 --> 05:17.600 and I just never even consider that's a possibility. 05:17.600 --> 05:21.840 I would also say today, we understand so much 05:21.840 --> 05:22.800 about the neocortex. 05:22.800 --> 05:25.480 We've made tremendous progress in the last few years 05:25.480 --> 05:29.160 that I no longer think of as an open question. 05:30.000 --> 05:32.560 The answers are very clear to me and the pieces 05:32.560 --> 05:34.800 that we don't know are clear to me, 05:34.800 --> 05:37.440 but the framework is all there and it's like, oh, okay, 05:37.440 --> 05:38.600 we're gonna be able to do this. 05:38.600 --> 05:39.960 This is not a problem anymore. 05:39.960 --> 05:42.680 It just takes time and effort, but there's no mystery, 05:42.680 --> 05:44.040 a big mystery anymore. 05:44.040 --> 05:47.800 So then let's get into it for people like myself 05:47.800 --> 05:52.800 who are not very well versed in the human brain, 05:52.960 --> 05:53.840 except my own. 05:54.800 --> 05:57.320 Can you describe to me at the highest level, 05:57.320 --> 05:59.120 what are the different parts of the human brain 05:59.120 --> 06:02.080 and then zooming in on the neocortex, 06:02.080 --> 06:05.480 the parts of the neocortex and so on, a quick overview. 06:05.480 --> 06:06.640 Yeah, sure. 06:06.640 --> 06:10.800 The human brain, we can divide it roughly into two parts. 06:10.800 --> 06:14.200 There's the old parts, lots of pieces, 06:14.200 --> 06:15.680 and then there's the new part. 06:15.680 --> 06:18.040 The new part is the neocortex. 06:18.040 --> 06:20.440 It's new because it didn't exist before mammals. 06:20.440 --> 06:23.000 The only mammals have a neocortex and in humans 06:23.000 --> 06:24.760 and primates is very large. 06:24.760 --> 06:29.400 In the human brain, the neocortex occupies about 70 to 75% 06:29.400 --> 06:30.640 of the volume of the brain. 06:30.640 --> 06:32.080 It's huge. 06:32.080 --> 06:34.840 And the old parts of the brain are, 06:34.840 --> 06:36.000 there's lots of pieces there. 06:36.000 --> 06:38.760 There's a spinal cord and there's the brainstem 06:38.760 --> 06:40.240 and the cerebellum and the different parts 06:40.240 --> 06:42.040 of the basal ganglion and so on. 06:42.040 --> 06:42.960 In the old parts of the brain, 06:42.960 --> 06:44.800 you have the autonomic regulation, 06:44.800 --> 06:46.280 like breathing and heart rate. 06:46.280 --> 06:48.240 You have basic behaviors. 06:48.240 --> 06:49.960 So like walking and running are controlled 06:49.960 --> 06:51.400 by the old parts of the brain. 06:51.400 --> 06:53.080 All the emotional centers of the brain 06:53.080 --> 06:53.920 are in the old part of the brain. 06:53.920 --> 06:55.080 So when you feel anger or hungry, 06:55.080 --> 06:56.080 lust or things like that, 06:56.080 --> 06:57.880 those are all in the old parts of the brain. 06:59.080 --> 07:02.160 And we associate with the neocortex 07:02.160 --> 07:03.320 all the things we think about 07:03.320 --> 07:05.760 as sort of high level perception. 07:05.760 --> 07:10.760 And cognitive functions, anything from seeing and hearing 07:10.920 --> 07:14.560 and touching things to language, to mathematics 07:14.560 --> 07:16.920 and engineering and science and so on. 07:16.920 --> 07:19.760 Those are all associated with the neocortex. 07:19.760 --> 07:21.760 And they're certainly correlated. 07:21.760 --> 07:24.000 Our abilities in those regards are correlated 07:24.000 --> 07:25.800 with the relative size of our neocortex 07:25.800 --> 07:27.960 compared to other mammals. 07:27.960 --> 07:30.520 So that's like the rough division. 07:30.520 --> 07:32.760 And you obviously can't understand 07:32.760 --> 07:35.160 the neocortex completely isolated, 07:35.160 --> 07:37.040 but you can understand a lot of it 07:37.040 --> 07:38.720 with just a few interfaces 07:38.720 --> 07:40.320 to the old parts of the brain. 07:40.320 --> 07:44.960 And so it gives you a system to study. 07:44.960 --> 07:48.040 The other remarkable thing about the neocortex 07:48.040 --> 07:49.880 compared to the old parts of the brain 07:49.880 --> 07:52.880 is the neocortex is extremely uniform. 07:52.880 --> 07:55.720 It's not visually or anatomically, 07:55.720 --> 07:58.800 or it's very, it's like a, 07:58.800 --> 08:00.080 I always like to say it's like the size 08:00.080 --> 08:03.720 of a dinner napkin, about two and a half millimeters thick. 08:03.720 --> 08:06.000 And it looks remarkably the same everywhere. 08:06.000 --> 08:07.920 Everywhere you look in that two and a half millimeters 08:07.920 --> 08:10.080 is this detailed architecture. 08:10.080 --> 08:11.560 And it looks remarkably the same everywhere. 08:11.560 --> 08:12.600 And that's a cross species, 08:12.600 --> 08:15.360 a mouse versus a cat and a dog and a human. 08:15.360 --> 08:17.080 Where if you look at the old parts of the brain, 08:17.080 --> 08:19.640 there's lots of little pieces do specific things. 08:19.640 --> 08:22.040 So it's like the old parts of a brain evolved, 08:22.040 --> 08:23.640 like this is the part that controls heart rate 08:23.640 --> 08:24.840 and this is the part that controls this 08:24.840 --> 08:25.800 and this is this kind of thing. 08:25.800 --> 08:27.200 And that's this kind of thing. 08:27.200 --> 08:30.080 And these evolve for eons of a long, long time 08:30.080 --> 08:31.600 and they have those specific functions. 08:31.600 --> 08:33.240 And all of a sudden mammals come along 08:33.240 --> 08:35.200 and they got this thing called the neocortex 08:35.200 --> 08:38.200 and it got large by just replicating the same thing 08:38.200 --> 08:39.440 over and over and over again. 08:39.440 --> 08:42.680 This is like, wow, this is incredible. 08:42.680 --> 08:46.240 So all the evidence we have, 08:46.240 --> 08:50.040 and this is an idea that was first articulated 08:50.040 --> 08:52.080 in a very cogent and beautiful argument 08:52.080 --> 08:55.720 by a guy named Vernon Malkassel in 1978, I think it was, 08:56.880 --> 09:01.640 that the neocortex all works on the same principle. 09:01.640 --> 09:05.320 So language, hearing, touch, vision, engineering, 09:05.320 --> 09:07.040 all these things are basically underlying 09:07.040 --> 09:10.400 or all built in the same computational substrate. 09:10.400 --> 09:11.880 They're really all the same problem. 09:11.880 --> 09:14.880 So the low level of the building blocks all look similar. 09:14.880 --> 09:16.320 Yeah, and they're not even that low level. 09:16.320 --> 09:17.920 We're not talking about like neurons. 09:17.920 --> 09:19.960 We're talking about this very complex circuit 09:19.960 --> 09:23.560 that exists throughout the neocortex is remarkably similar. 09:23.560 --> 09:26.400 It is, it's like, yes, you see variations of it here 09:26.400 --> 09:29.680 and they're more of the cell, that's not old and so on. 09:29.680 --> 09:31.840 But what Malkassel argued was, 09:31.840 --> 09:35.640 it says, you know, if you take a section on neocortex, 09:35.640 --> 09:38.640 why is one a visual area and one is a auditory area? 09:38.640 --> 09:41.240 Or why is, and his answer was, 09:41.240 --> 09:43.240 it's because one is connected to eyes 09:43.240 --> 09:45.440 and one is connected to ears. 09:45.440 --> 09:47.840 Literally, you mean just as most closest 09:47.840 --> 09:50.440 in terms of the number of connections to the sensor? 09:50.440 --> 09:52.920 Literally, if you took the optic nerve 09:52.920 --> 09:55.320 and attached it to a different part of the neocortex, 09:55.320 --> 09:58.000 that part would become a visual region. 09:58.000 --> 10:00.400 This actually, this experiment was actually done 10:00.400 --> 10:05.000 by Murgankasur in developing, I think it was lemurs, 10:05.000 --> 10:06.720 I can't remember what it was, it's some animal. 10:06.720 --> 10:08.560 And there's a lot of evidence to this. 10:08.560 --> 10:09.960 You know, if you take a blind person, 10:09.960 --> 10:12.240 a person is born blind at birth, 10:12.240 --> 10:15.480 they're born with a visual neocortex. 10:15.480 --> 10:18.320 It doesn't, may not get any input from the eyes 10:18.320 --> 10:21.280 because of some congenital defect or something. 10:21.280 --> 10:24.720 And that region becomes, does something else. 10:24.720 --> 10:27.000 It picks up another task. 10:27.000 --> 10:32.000 So, and it's, so it's this very complex thing. 10:32.280 --> 10:33.760 It's not like, oh, they're all built on neurons. 10:33.760 --> 10:36.480 No, they're all built in this very complex circuit. 10:36.480 --> 10:40.280 And somehow that circuit underlies everything. 10:40.280 --> 10:44.760 And so this is, it's called the common cortical algorithm, 10:44.760 --> 10:47.960 if you will, some scientists just find it hard to believe. 10:47.960 --> 10:50.040 And they just say, I can't believe that's true. 10:50.040 --> 10:52.080 But the evidence is overwhelming in this case. 10:52.080 --> 10:54.320 And so a large part of what it means 10:54.320 --> 10:56.440 to figure out how the brain creates intelligence 10:56.440 --> 10:59.840 and what is intelligence in the brain 10:59.840 --> 11:02.040 is to understand what that circuit does. 11:02.040 --> 11:05.040 If you can figure out what that circuit does, 11:05.040 --> 11:06.920 as amazing as it is, then you can, 11:06.920 --> 11:10.480 then you understand what all these other cognitive functions are. 11:10.480 --> 11:13.280 So if you were to sort of put neocortex 11:13.280 --> 11:15.160 outside of your book on intelligence, 11:15.160 --> 11:17.480 you look, if you wrote a giant tome, 11:17.480 --> 11:19.800 a textbook on the neocortex, 11:19.800 --> 11:23.760 and you look maybe a couple of centuries from now, 11:23.760 --> 11:26.520 how much of what we know now would still be accurate 11:26.520 --> 11:27.680 two centuries from now. 11:27.680 --> 11:30.840 So how close are we in terms of understanding? 11:30.840 --> 11:33.000 I have to speak from my own particular experience here. 11:33.000 --> 11:36.440 So I run a small research lab here. 11:36.440 --> 11:38.040 It's like any other research lab. 11:38.040 --> 11:39.440 I'm sort of the principal investigator. 11:39.440 --> 11:40.280 There's actually two of us, 11:40.280 --> 11:42.560 and there's a bunch of other people. 11:42.560 --> 11:43.840 And this is what we do. 11:43.840 --> 11:44.960 We started the neocortex, 11:44.960 --> 11:46.960 and we publish our results and so on. 11:46.960 --> 11:48.520 So about three years ago, 11:49.840 --> 11:52.480 we had a real breakthrough in this field. 11:52.480 --> 11:53.320 Just tremendous breakthrough. 11:53.320 --> 11:56.520 We started, we now publish, I think, three papers on it. 11:56.520 --> 12:00.200 And so I have a pretty good understanding 12:00.200 --> 12:02.320 of all the pieces and what we're missing. 12:02.320 --> 12:06.280 I would say that almost all the empirical data 12:06.280 --> 12:08.520 we've collected about the brain, which is enormous. 12:08.520 --> 12:10.320 If you don't know the neuroscience literature, 12:10.320 --> 12:13.960 it's just incredibly big. 12:13.960 --> 12:16.840 And it's, for the most part, all correct. 12:16.840 --> 12:20.240 It's facts and experimental results 12:20.240 --> 12:22.960 and measurements and all kinds of stuff. 12:22.960 --> 12:25.800 But none of that has been really assimilated 12:25.800 --> 12:27.840 into a theoretical framework. 12:27.840 --> 12:32.240 It's data without, in the language of Thomas Kuhn, 12:32.240 --> 12:35.280 the historian, it would be sort of a preparadigm science. 12:35.280 --> 12:38.160 Lots of data, but no way to fit it in together. 12:38.160 --> 12:39.520 I think almost all of that's correct. 12:39.520 --> 12:42.120 There's gonna be some mistakes in there. 12:42.120 --> 12:43.240 And for the most part, 12:43.240 --> 12:45.480 there aren't really good cogent theories 12:45.480 --> 12:47.240 about how to put it together. 12:47.240 --> 12:50.040 It's not like we have two or three competing good theories, 12:50.040 --> 12:51.520 which ones are right and which ones are wrong. 12:51.520 --> 12:53.720 It's like, yeah, people just scratching their heads 12:53.720 --> 12:55.560 throwing things, you know, some people giving up 12:55.560 --> 12:57.560 on trying to figure out what the whole thing does. 12:57.560 --> 13:00.960 In fact, there's very, very few labs that we do 13:00.960 --> 13:03.280 that focus really on theory 13:03.280 --> 13:06.760 and all this unassimilated data and trying to explain it. 13:06.760 --> 13:08.880 So it's not like we've got it wrong. 13:08.880 --> 13:11.120 It's just that we haven't got it at all. 13:11.120 --> 13:15.040 So it's really, I would say, pretty early days 13:15.040 --> 13:18.360 in terms of understanding the fundamental theories, 13:18.360 --> 13:20.240 forces of the way our mind works. 13:20.240 --> 13:21.080 I don't think so. 13:21.080 --> 13:23.760 I would have said that's true five years ago. 13:25.360 --> 13:28.600 So as I said, we had some really big breakthroughs 13:28.600 --> 13:30.800 on this recently and we started publishing papers on this. 13:30.800 --> 13:34.240 So you can get to that. 13:34.240 --> 13:36.760 But so I don't think it's, you know, I'm an optimist 13:36.760 --> 13:38.280 and from where I sit today, 13:38.280 --> 13:39.440 most people would disagree with this, 13:39.440 --> 13:41.640 but from where I sit today, from what I know, 13:43.240 --> 13:44.920 it's not super early days anymore. 13:44.920 --> 13:46.840 We are, you know, the way these things go 13:46.840 --> 13:48.200 is it's not a linear path, right? 13:48.200 --> 13:49.840 You don't just start accumulating 13:49.840 --> 13:50.800 and get better and better and better. 13:50.800 --> 13:52.920 No, you got all the stuff you've collected. 13:52.920 --> 13:53.760 None of it makes sense. 13:53.760 --> 13:55.640 All these different things are just sort of around. 13:55.640 --> 13:57.120 And then you're going to have some breaking points 13:57.120 --> 13:59.400 all of a sudden, oh my God, now we got it right. 13:59.400 --> 14:01.120 That's how it goes in science. 14:01.120 --> 14:04.480 And I personally feel like we passed that little thing 14:04.480 --> 14:06.320 about a couple of years ago. 14:06.320 --> 14:07.560 All that big thing a couple of years ago. 14:07.560 --> 14:09.600 So we can talk about that. 14:09.600 --> 14:11.000 Time will tell if I'm right, 14:11.000 --> 14:12.640 but I feel very confident about it. 14:12.640 --> 14:15.120 That's when we'll just say it on tape like this. 14:15.120 --> 14:18.040 At least very optimistic. 14:18.040 --> 14:20.160 So let's, before those few years ago, 14:20.160 --> 14:23.200 let's take a step back to HTM, 14:23.200 --> 14:25.960 the hierarchical temporal memory theory, 14:25.960 --> 14:27.480 which you first proposed on intelligence 14:27.480 --> 14:29.280 and went through a few different generations. 14:29.280 --> 14:31.200 Can you describe what it is, 14:31.200 --> 14:33.560 how it would evolve through the three generations 14:33.560 --> 14:35.360 since you first put it on paper? 14:35.360 --> 14:39.240 Yeah, so one of the things that neuroscientists 14:39.240 --> 14:42.920 just sort of missed for many, many years. 14:42.920 --> 14:45.720 And especially people were thinking about theory 14:45.720 --> 14:47.720 was the nature of time in the brain. 14:47.720 --> 14:50.440 Brain's process, information through time, 14:50.440 --> 14:53.280 the information coming into the brain is constantly changing. 14:53.280 --> 14:56.160 The patterns from my speech right now, 14:56.160 --> 14:58.520 if you're listening to it at normal speed, 14:58.520 --> 15:00.080 would be changing on your ears 15:00.080 --> 15:02.680 about every 10 milliseconds or so, you'd have a change. 15:02.680 --> 15:05.320 This constant flow, when you look at the world, 15:05.320 --> 15:06.800 your eyes are moving constantly, 15:06.800 --> 15:08.240 three to five times a second, 15:08.240 --> 15:09.920 and the input's completely, completely. 15:09.920 --> 15:11.800 If I were to touch something like a coffee cup 15:11.800 --> 15:13.880 as I move my fingers, the input changes. 15:13.880 --> 15:16.840 So this idea that the brain works on time 15:16.840 --> 15:19.640 changing patterns is almost completely, 15:19.640 --> 15:21.080 or was almost completely missing 15:21.080 --> 15:23.520 from a lot of the basic theories like fears of vision 15:23.520 --> 15:24.360 and so on. 15:24.360 --> 15:26.280 It's like, oh no, we're gonna put this image in front of you 15:26.280 --> 15:28.360 and flash it and say, what is it? 15:28.360 --> 15:31.120 A convolutional neural network's worked that way today, right? 15:31.120 --> 15:33.280 Classified this picture. 15:33.280 --> 15:35.120 But that's not what vision is like. 15:35.120 --> 15:37.760 Vision is this sort of crazy time based pattern 15:37.760 --> 15:39.080 that's going all over the place, 15:39.080 --> 15:40.920 and so is touch and so is hearing. 15:40.920 --> 15:42.880 So the first part of a hierarchical temporal memory 15:42.880 --> 15:44.280 was the temporal part. 15:44.280 --> 15:47.680 It's to say, you won't understand the brain, 15:47.680 --> 15:49.360 nor will you understand intelligent machines 15:49.360 --> 15:51.720 unless you're dealing with time based patterns. 15:51.720 --> 15:54.760 The second thing was, the memory component of it was, 15:54.760 --> 15:59.760 is to say that we aren't just processing input, 15:59.760 --> 16:02.000 we learn a model of the world. 16:02.000 --> 16:04.000 And the memory stands for that model. 16:04.000 --> 16:06.640 The point of the brain, part of the neocortex, 16:06.640 --> 16:07.840 it learns a model of the world. 16:07.840 --> 16:10.840 We have to store things that are experiences 16:10.840 --> 16:13.520 in a form that leads to a model of the world. 16:13.520 --> 16:15.080 So we can move around the world, 16:15.080 --> 16:16.240 we can pick things up and do things 16:16.240 --> 16:17.520 and navigate and know how it's going on. 16:17.520 --> 16:19.320 So that's what the memory referred to. 16:19.320 --> 16:22.320 And many people just, they were thinking about like, 16:22.320 --> 16:24.480 certain processes without memory at all. 16:24.480 --> 16:26.120 They're just like processing things. 16:26.120 --> 16:28.320 And then finally, the hierarchical component 16:28.320 --> 16:31.640 was a reflection to that the neocortex, 16:31.640 --> 16:33.920 although it's just a uniform sheet of cells, 16:33.920 --> 16:36.920 different parts of it project to other parts, 16:36.920 --> 16:38.680 which project to other parts. 16:38.680 --> 16:42.400 And there is a sort of rough hierarchy in terms of that. 16:42.400 --> 16:46.000 So the hierarchical temporal memory is just saying, 16:46.000 --> 16:47.720 look, we should be thinking about the brain 16:47.720 --> 16:52.720 as time based, model memory based and hierarchical processing. 16:54.760 --> 16:58.160 And that was a placeholder for a bunch of components 16:58.160 --> 17:00.720 that we would then plug into that. 17:00.720 --> 17:02.600 We still believe all those things I just said, 17:02.600 --> 17:06.960 but we now know so much more that I'm stopping to use 17:06.960 --> 17:08.200 the word hierarchical temporal memory yet 17:08.200 --> 17:11.320 because it's insufficient to capture the stuff we know. 17:11.320 --> 17:12.960 So again, it's not incorrect, 17:12.960 --> 17:15.800 but I now know more and I would rather describe it 17:15.800 --> 17:16.800 more accurately. 17:16.800 --> 17:20.360 Yeah, so you're basically, we could think of HTM 17:20.360 --> 17:24.800 as emphasizing that there's three aspects of intelligence 17:24.800 --> 17:25.920 that are important to think about 17:25.920 --> 17:28.880 whatever the eventual theory converges to. 17:28.880 --> 17:32.480 So in terms of time, how do you think of nature of time 17:32.480 --> 17:33.880 across different time scales? 17:33.880 --> 17:36.800 So you mentioned things changing, 17:36.800 --> 17:39.160 sensory inputs changing every 10, 20 minutes. 17:39.160 --> 17:40.520 What about every few minutes? 17:40.520 --> 17:42.120 Every few months and years? 17:42.120 --> 17:44.840 Well, if you think about a neuroscience problem, 17:44.840 --> 17:49.640 the brain problem, neurons themselves can stay active 17:49.640 --> 17:51.560 for certain periods of time. 17:51.560 --> 17:53.280 They're parts of the brain where they stay active 17:53.280 --> 17:56.680 for minutes, so you could hold a certain perception 17:56.680 --> 18:01.320 or an activity for a certain period of time, 18:01.320 --> 18:04.480 but not most of them don't last that long. 18:04.480 --> 18:07.160 And so if you think about your thoughts 18:07.160 --> 18:09.080 or the activity neurons, 18:09.080 --> 18:10.680 if you're gonna wanna involve something 18:10.680 --> 18:11.920 that happened a long time ago, 18:11.920 --> 18:14.400 even just this morning, for example, 18:14.400 --> 18:16.360 the neurons haven't been active throughout that time. 18:16.360 --> 18:17.800 So you have to store that. 18:17.800 --> 18:20.720 So by I ask you, what did you have for breakfast today? 18:20.720 --> 18:22.000 That is memory. 18:22.000 --> 18:24.160 That is, you've built it into your model of the world now. 18:24.160 --> 18:27.880 You remember that and that memory is in the synapses, 18:27.880 --> 18:30.080 it's basically in the formation of synapses. 18:30.080 --> 18:35.080 And so you're sliding into what used to different time scales. 18:36.760 --> 18:38.280 There's time scales of which we are 18:38.280 --> 18:40.440 like understanding my language and moving about 18:40.440 --> 18:41.840 and seeing things rapidly and over time. 18:41.840 --> 18:44.280 That's the time scales of activities of neurons. 18:44.280 --> 18:46.200 But if you wanna get in longer time scales, 18:46.200 --> 18:48.840 then it's more memory and we have to invoke those memories 18:48.840 --> 18:50.960 to say, oh, yes, well, now I can remember 18:50.960 --> 18:54.160 what I had for breakfast because I stored that someplace. 18:54.160 --> 18:58.200 I may forget it tomorrow, but I'd store it for now. 18:58.200 --> 19:01.600 So does memory also need to have, 19:02.880 --> 19:06.240 so the hierarchical aspect of reality 19:06.240 --> 19:07.720 is not just about concepts, 19:07.720 --> 19:08.800 it's also about time. 19:08.800 --> 19:10.280 Do you think of it that way? 19:10.280 --> 19:12.840 Yeah, time is infused in everything. 19:12.840 --> 19:15.560 It's like, you really can't separate it out. 19:15.560 --> 19:19.560 If I ask you, what is your, how's the brain 19:19.560 --> 19:21.360 learn a model of this coffee cup here? 19:21.360 --> 19:23.200 I have a coffee cup, then I met the coffee cup. 19:23.200 --> 19:26.000 I said, well, time is not an inherent property 19:26.000 --> 19:28.520 of the model I have of this cup, 19:28.520 --> 19:31.440 whether it's a visual model or tactile model. 19:31.440 --> 19:32.600 I can sense it through time, 19:32.600 --> 19:34.880 but the model itself doesn't really have much time. 19:34.880 --> 19:36.560 If I asked you, if I say, well, 19:36.560 --> 19:39.000 what is the model of my cell phone? 19:39.000 --> 19:41.480 My brain has learned a model of the cell phones. 19:41.480 --> 19:43.360 If you have a smartphone like this, 19:43.360 --> 19:45.680 and I said, well, this has time aspects to it. 19:45.680 --> 19:48.040 I have expectations when I turn it on, 19:48.040 --> 19:49.480 what's gonna happen, what water, 19:49.480 --> 19:51.960 how long it's gonna take to do certain things, 19:51.960 --> 19:54.040 if I bring up an app, what sequences, 19:54.040 --> 19:56.520 and so I have instant, it's like melodies in the world, 19:56.520 --> 19:58.560 you know, melody has a sense of time. 19:58.560 --> 20:01.200 So many things in the world move and act, 20:01.200 --> 20:03.720 and there's a sense of time related to them. 20:03.720 --> 20:08.280 Some don't, but most things do actually. 20:08.280 --> 20:12.120 So it's sort of infused throughout the models of the world. 20:12.120 --> 20:13.720 You build a model of the world, 20:13.720 --> 20:16.400 you're learning the structure of the objects in the world, 20:16.400 --> 20:17.840 and you're also learning 20:17.840 --> 20:19.760 how those things change through time. 20:20.760 --> 20:23.920 Okay, so it really is just a fourth dimension 20:23.920 --> 20:25.280 that's infused deeply, 20:25.280 --> 20:26.760 and you have to make sure 20:26.760 --> 20:30.960 that your models of intelligence incorporate it. 20:30.960 --> 20:34.840 So, like you mentioned, the state of neuroscience 20:34.840 --> 20:36.000 is deeply empirical. 20:36.000 --> 20:40.120 A lot of data collection, it's, you know, 20:40.120 --> 20:43.120 that's where it is, you mentioned Thomas Kuhn, right? 20:43.120 --> 20:44.560 Yeah. 20:44.560 --> 20:48.040 And then you're proposing a theory of intelligence, 20:48.040 --> 20:50.520 and which is really the next step, 20:50.520 --> 20:52.920 the really important step to take, 20:52.920 --> 20:57.920 but why is HTM, or what we'll talk about soon, 20:57.920 --> 21:01.160 the right theory? 21:01.160 --> 21:05.160 So is it more in this, is it backed by intuition, 21:05.160 --> 21:09.160 is it backed by evidence, is it backed by a mixture of both? 21:09.160 --> 21:12.800 Is it kind of closer to where string theory is in physics, 21:12.800 --> 21:15.800 where there's mathematical components 21:15.800 --> 21:18.160 which show that, you know what, 21:18.160 --> 21:20.160 it seems that this, 21:20.160 --> 21:23.560 it fits together too well for it not to be true, 21:23.560 --> 21:25.360 which is where string theory is. 21:25.360 --> 21:28.080 Is that where you're kind of thinking? 21:28.080 --> 21:30.080 It's a mixture of all those things, 21:30.080 --> 21:32.080 although definitely where we are right now, 21:32.080 --> 21:34.080 it's definitely much more on the empirical side 21:34.080 --> 21:36.080 than, let's say, string theory. 21:36.080 --> 21:39.080 The way this goes about, we're theorists, right? 21:39.080 --> 21:41.080 So we look at all this data, 21:41.080 --> 21:43.080 and we're trying to come up with some sort of model 21:43.080 --> 21:45.080 that explains it, basically, 21:45.080 --> 21:47.080 and there's, unlike string theory, 21:47.080 --> 21:50.080 there's vast more amounts of empirical data here 21:50.080 --> 21:54.080 than I think that most physicists deal with. 21:54.080 --> 21:57.080 And so our challenge is to sort through that 21:57.080 --> 22:01.080 and figure out what kind of constructs would explain this. 22:01.080 --> 22:04.080 And when we have an idea, 22:04.080 --> 22:06.080 you come up with a theory of some sort, 22:06.080 --> 22:08.080 you have lots of ways of testing it. 22:08.080 --> 22:10.080 First of all, I am, you know, 22:10.080 --> 22:14.080 there are 100 years of assimilated, 22:14.080 --> 22:16.080 und assimilated empirical data from neuroscience. 22:16.080 --> 22:18.080 So we go back and repapers, and we say, 22:18.080 --> 22:20.080 oh, did someone find this already? 22:20.080 --> 22:23.080 We can predict X, Y, and Z, 22:23.080 --> 22:25.080 and maybe no one's even talked about it 22:25.080 --> 22:27.080 since 1972 or something, 22:27.080 --> 22:29.080 but we go back and find that, and we say, 22:29.080 --> 22:31.080 oh, either it can support the theory 22:31.080 --> 22:33.080 or it can invalidate the theory. 22:33.080 --> 22:35.080 And then we say, okay, we have to start over again. 22:35.080 --> 22:37.080 Oh, no, it's support. Let's keep going with that one. 22:37.080 --> 22:40.080 So the way I kind of view it, 22:40.080 --> 22:43.080 when we do our work, we come up, 22:43.080 --> 22:45.080 we look at all this empirical data, 22:45.080 --> 22:47.080 and it's what I call it is a set of constraints. 22:47.080 --> 22:49.080 We're not interested in something that's biologically inspired. 22:49.080 --> 22:52.080 We're trying to figure out how the actual brain works. 22:52.080 --> 22:55.080 So every piece of empirical data is a constraint on a theory. 22:55.080 --> 22:57.080 If you have the correct theory, 22:57.080 --> 22:59.080 it needs to explain every pin, right? 22:59.080 --> 23:02.080 So we have this huge number of constraints on the problem, 23:02.080 --> 23:05.080 which initially makes it very, very difficult. 23:05.080 --> 23:07.080 If you don't have many constraints, 23:07.080 --> 23:09.080 you can make up stuff all the day. 23:09.080 --> 23:11.080 You can say, oh, here's an answer to how you can do this, 23:11.080 --> 23:13.080 you can do that, you can do this. 23:13.080 --> 23:15.080 But if you consider all biology as a set of constraints, 23:15.080 --> 23:17.080 all neuroscience as a set of constraints, 23:17.080 --> 23:19.080 and even if you're working in one little part of the Neocortex, 23:19.080 --> 23:21.080 for example, there are hundreds and hundreds of constraints. 23:21.080 --> 23:23.080 There are a lot of empirical constraints 23:23.080 --> 23:25.080 that it's very, very difficult initially 23:25.080 --> 23:27.080 to come up with a theoretical framework for that. 23:27.080 --> 23:31.080 But when you do, and it solves all those constraints at once, 23:31.080 --> 23:33.080 you have a high confidence 23:33.080 --> 23:36.080 that you got something close to correct. 23:36.080 --> 23:39.080 It's just mathematically almost impossible not to be. 23:39.080 --> 23:43.080 So that's the curse and the advantage of what we have. 23:43.080 --> 23:47.080 The curse is we have to meet all these constraints, 23:47.080 --> 23:49.080 which is really hard. 23:49.080 --> 23:51.080 But when you do meet them, 23:51.080 --> 23:53.080 then you have a great confidence 23:53.080 --> 23:55.080 that you've discovered something. 23:55.080 --> 23:58.080 In addition, then we work with scientific labs. 23:58.080 --> 24:00.080 So we'll say, oh, there's something we can't find, 24:00.080 --> 24:02.080 we can predict something, 24:02.080 --> 24:04.080 but we can't find it anywhere in the literature. 24:04.080 --> 24:07.080 So we will then, we have people we collaborated with, 24:07.080 --> 24:09.080 we'll say, sometimes they'll say, you know what, 24:09.080 --> 24:11.080 I have some collected data, which I didn't publish, 24:11.080 --> 24:13.080 but we can go back and look at it 24:13.080 --> 24:15.080 and see if we can find that, 24:15.080 --> 24:17.080 which is much easier than designing a new experiment. 24:17.080 --> 24:20.080 You know, neuroscience experiments take a long time, years. 24:20.080 --> 24:23.080 So although some people are doing that now too. 24:23.080 --> 24:27.080 So, but between all of these things, 24:27.080 --> 24:29.080 I think it's a reasonable, 24:29.080 --> 24:32.080 it's actually a very, very good approach. 24:32.080 --> 24:35.080 We are blessed with the fact that we can test our theories 24:35.080 --> 24:37.080 out to yin and yang here, 24:37.080 --> 24:39.080 because there's so much on a similar data, 24:39.080 --> 24:41.080 and we can also falsify our theories very easily, 24:41.080 --> 24:43.080 which we do often. 24:43.080 --> 24:46.080 So it's kind of reminiscent to whenever that was with Copernicus, 24:46.080 --> 24:49.080 you know, when you figure out that the sun is at the center, 24:49.080 --> 24:53.080 the solar system as opposed to Earth, 24:53.080 --> 24:55.080 the pieces just fall into place. 24:55.080 --> 24:59.080 Yeah, I think that's the general nature of the Ha moments, 24:59.080 --> 25:02.080 is in Copernicus, it could be, 25:02.080 --> 25:05.080 you could say the same thing about Darwin, 25:05.080 --> 25:07.080 you could say the same thing about, you know, 25:07.080 --> 25:09.080 about the double helix, 25:09.080 --> 25:13.080 that people have been working on a problem for so long, 25:13.080 --> 25:14.080 and have all this data, 25:14.080 --> 25:15.080 and they can't make sense of it, they can't make sense of it. 25:15.080 --> 25:17.080 But when the answer comes to you, 25:17.080 --> 25:19.080 and everything falls into place, 25:19.080 --> 25:21.080 it's like, oh my gosh, that's it. 25:21.080 --> 25:23.080 That's got to be right. 25:23.080 --> 25:28.080 I asked both Jim Watson and Francis Crick about this. 25:28.080 --> 25:30.080 I asked them, you know, 25:30.080 --> 25:33.080 when you were working on trying to discover the structure 25:33.080 --> 25:35.080 of the double helix, 25:35.080 --> 25:38.080 and when you came up with the sort of, 25:38.080 --> 25:42.080 the structure that ended up being correct, 25:42.080 --> 25:44.080 but it was sort of a guess, you know, 25:44.080 --> 25:46.080 it wasn't really verified yet. 25:46.080 --> 25:48.080 I said, did you know that it was right? 25:48.080 --> 25:50.080 And they both said, absolutely. 25:50.080 --> 25:52.080 We absolutely knew it was right. 25:52.080 --> 25:55.080 And it doesn't matter if other people didn't believe it or not, 25:55.080 --> 25:57.080 we knew it was right, they'd get around to thinking it 25:57.080 --> 25:59.080 and agree with it eventually anyway. 25:59.080 --> 26:01.080 And that's the kind of thing you hear a lot with scientists 26:01.080 --> 26:04.080 who really are studying a difficult problem, 26:04.080 --> 26:07.080 and I feel that way too, about our work. 26:07.080 --> 26:10.080 Have you talked to Crick or Watson about the problem 26:10.080 --> 26:15.080 you're trying to solve, the, of finding the DNA of the brain? 26:15.080 --> 26:16.080 Yeah. 26:16.080 --> 26:19.080 In fact, Francis Crick was very interested in this, 26:19.080 --> 26:21.080 in the latter part of his life. 26:21.080 --> 26:23.080 And in fact, I got interested in brains 26:23.080 --> 26:26.080 by reading an essay he wrote in 1979 26:26.080 --> 26:28.080 called Thinking About the Brain. 26:28.080 --> 26:30.080 And that was when I decided 26:30.080 --> 26:33.080 I'm going to leave my profession of computers and engineering 26:33.080 --> 26:35.080 and become a neuroscientist. 26:35.080 --> 26:37.080 Just reading that one essay from Francis Crick. 26:37.080 --> 26:39.080 I got to meet him later in life. 26:39.080 --> 26:43.080 I got to, I spoke at the Salk Institute 26:43.080 --> 26:44.080 and he was in the audience 26:44.080 --> 26:47.080 and then I had a tea with him afterwards. 26:47.080 --> 26:50.080 You know, he was interested in a different problem. 26:50.080 --> 26:52.080 He was focused on consciousness. 26:52.080 --> 26:54.080 The easy problem, right? 26:54.080 --> 26:58.080 Well, I think it's the red herring 26:58.080 --> 27:01.080 and so we weren't really overlapping a lot there. 27:01.080 --> 27:05.080 Jim Watson, who's still alive, 27:05.080 --> 27:07.080 is also interested in this problem 27:07.080 --> 27:11.080 and when he was director of the Coltsman Harbor Laboratories, 27:11.080 --> 27:13.080 he was really sort of behind 27:13.080 --> 27:16.080 moving in the direction of neuroscience there. 27:16.080 --> 27:19.080 And so he had a personal interest in this field 27:19.080 --> 27:23.080 and I have met with him numerous times. 27:23.080 --> 27:25.080 And in fact, the last time, 27:25.080 --> 27:27.080 a little bit over a year ago, 27:27.080 --> 27:30.080 I gave a talk at Coltsman Harbor Labs 27:30.080 --> 27:34.080 about the progress we were making in our work. 27:34.080 --> 27:39.080 And it was a lot of fun because he said, 27:39.080 --> 27:41.080 well, you wouldn't be coming here 27:41.080 --> 27:42.080 unless you had something important to say, 27:42.080 --> 27:44.080 so I'm going to go attend your talk. 27:44.080 --> 27:46.080 So he sat in the very front row. 27:46.080 --> 27:50.080 Next to him was the director of the lab, Bruce Stillman. 27:50.080 --> 27:52.080 So these guys were in the front row of this auditorium, right? 27:52.080 --> 27:54.080 So nobody else in the auditorium wants to sit in the front row 27:54.080 --> 27:57.080 because there's Jim Watson there as the director. 27:57.080 --> 28:03.080 And I gave a talk and then I had dinner with Jim afterwards. 28:03.080 --> 28:06.080 But there's a great picture of my colleague, 28:06.080 --> 28:08.080 Subitai Amantik, where I'm up there 28:08.080 --> 28:11.080 sort of expiring the basics of this new framework we have. 28:11.080 --> 28:13.080 And Jim Watson's on the edge of his chair. 28:13.080 --> 28:15.080 He's literally on the edge of his chair, 28:15.080 --> 28:17.080 like, internally staring up at the screen. 28:17.080 --> 28:21.080 And when he discovered the structure of DNA, 28:21.080 --> 28:25.080 the first public talk he gave was at Coltsman Harbor Labs. 28:25.080 --> 28:27.080 And there's a picture, there's a famous picture 28:27.080 --> 28:29.080 of Jim Watson standing at the whiteboard 28:29.080 --> 28:31.080 with an overhead thing pointing at something, 28:31.080 --> 28:33.080 pointing at the double helix at this pointer. 28:33.080 --> 28:35.080 And it actually looks a lot like the picture of me. 28:35.080 --> 28:37.080 So there was a sort of funny, there's an area talking about the brain 28:37.080 --> 28:39.080 and there's Jim Watson staring up at the tent. 28:39.080 --> 28:41.080 And of course, there was, you know, whatever, 28:41.080 --> 28:44.080 60 years earlier he was standing pointing at the double helix. 28:44.080 --> 28:47.080 It's one of the great discoveries in all of, you know, 28:47.080 --> 28:50.080 whatever, by all the science, all science and DNA. 28:50.080 --> 28:54.080 So it's the funny that there's echoes of that in your presentation. 28:54.080 --> 28:58.080 Do you think in terms of evolutionary timeline and history, 28:58.080 --> 29:01.080 the development of the neocortex was a big leap? 29:01.080 --> 29:06.080 Or is it just a small step? 29:06.080 --> 29:09.080 So, like, if we ran the whole thing over again, 29:09.080 --> 29:12.080 from the birth of life on Earth, 29:12.080 --> 29:15.080 how likely would we develop the mechanism of the neocortex? 29:15.080 --> 29:17.080 Okay, well, those are two separate questions. 29:17.080 --> 29:19.080 One, was it a big leap? 29:19.080 --> 29:21.080 And one was how likely it is, okay? 29:21.080 --> 29:23.080 They're not necessarily related. 29:23.080 --> 29:25.080 Maybe correlated. 29:25.080 --> 29:28.080 And we don't really have enough data to make a judgment about that. 29:28.080 --> 29:30.080 I would say definitely it was a big leap. 29:30.080 --> 29:31.080 And I can tell you why. 29:31.080 --> 29:34.080 I don't think it was just another incremental step. 29:34.080 --> 29:36.080 I'll get that in a moment. 29:36.080 --> 29:38.080 I don't really have any idea how likely it is. 29:38.080 --> 29:41.080 If we look at evolution, we have one data point, 29:41.080 --> 29:43.080 which is Earth, right? 29:43.080 --> 29:45.080 Life formed on Earth billions of years ago, 29:45.080 --> 29:48.080 whether it was introduced here or it created it here 29:48.080 --> 29:50.080 or someone introduced it we don't really know, 29:50.080 --> 29:51.080 but it was here early. 29:51.080 --> 29:55.080 It took a long, long time to get to multicellular life. 29:55.080 --> 29:58.080 And then from multicellular life, 29:58.080 --> 30:02.080 it took a long, long time to get the neocortex. 30:02.080 --> 30:05.080 And we've only had the neocortex for a few hundred thousand years. 30:05.080 --> 30:07.080 So that's like nothing. 30:07.080 --> 30:09.080 Okay, so is it likely? 30:09.080 --> 30:13.080 Well, certainly it isn't something that happened right away on Earth. 30:13.080 --> 30:15.080 And there were multiple steps to get there. 30:15.080 --> 30:17.080 So I would say it's probably not going to something that would happen 30:17.080 --> 30:20.080 instantaneously on other planets that might have life. 30:20.080 --> 30:23.080 It might take several billion years on average. 30:23.080 --> 30:24.080 Is it likely? 30:24.080 --> 30:25.080 I don't know. 30:25.080 --> 30:28.080 But you'd have to survive for several billion years to find out. 30:28.080 --> 30:29.080 Probably. 30:29.080 --> 30:30.080 Is it a big leap? 30:30.080 --> 30:35.080 Yeah, I think it is a qualitative difference 30:35.080 --> 30:38.080 in all other evolutionary steps. 30:38.080 --> 30:40.080 I can try to describe that if you'd like. 30:40.080 --> 30:42.080 Sure, in which way? 30:42.080 --> 30:44.080 Yeah, I can tell you how. 30:44.080 --> 30:48.080 Pretty much, let's start with a little preface. 30:48.080 --> 30:54.080 Maybe the things that humans are able to do do not have obvious 30:54.080 --> 30:59.080 survival advantages precedent. 30:59.080 --> 31:00.080 We create music. 31:00.080 --> 31:03.080 Is there a really survival advantage to that? 31:03.080 --> 31:04.080 Maybe, maybe not. 31:04.080 --> 31:05.080 What about mathematics? 31:05.080 --> 31:09.080 Is there a real survival advantage to mathematics? 31:09.080 --> 31:10.080 You can stretch it. 31:10.080 --> 31:13.080 You can try to figure these things out, right? 31:13.080 --> 31:18.080 But most of evolutionary history, everything had immediate survival 31:18.080 --> 31:19.080 advantages to it. 31:19.080 --> 31:22.080 I'll tell you a story, which I like. 31:22.080 --> 31:25.080 It may not be true. 31:25.080 --> 31:29.080 But the story goes as follows. 31:29.080 --> 31:34.080 Organisms have been evolving since the beginning of life here on Earth. 31:34.080 --> 31:37.080 Adding this sort of complexity onto that and this sort of complexity onto that. 31:37.080 --> 31:40.080 And the brain itself is evolved this way. 31:40.080 --> 31:44.080 There's an old part, an older part, an older, older part to the brain that kind of just 31:44.080 --> 31:47.080 keeps calming on new things and we keep adding capabilities. 31:47.080 --> 31:52.080 When we got to the neocortex, initially it had a very clear survival advantage 31:52.080 --> 31:56.080 in that it produced better vision and better hearing and better touch and maybe 31:56.080 --> 31:58.080 a new place and so on. 31:58.080 --> 32:04.080 But what I think happens is that evolution took a mechanism, and this is in our 32:04.080 --> 32:08.080 recent theory, but it took a mechanism that evolved a long time ago for 32:08.080 --> 32:10.080 navigating in the world, for knowing where you are. 32:10.080 --> 32:14.080 These are the so called grid cells and place cells of that old part of the brain. 32:14.080 --> 32:21.080 And it took that mechanism for building maps of the world and knowing where you are 32:21.080 --> 32:26.080 on those maps and how to navigate those maps and turns it into a sort of a slim 32:26.080 --> 32:29.080 down idealized version of it. 32:29.080 --> 32:32.080 And that idealized version could now apply to building maps of other things, 32:32.080 --> 32:36.080 maps of coffee cups and maps of phones, maps of mathematics. 32:36.080 --> 32:40.080 Concepts, yes, and not just almost, exactly. 32:40.080 --> 32:44.080 And it just started replicating this stuff. 32:44.080 --> 32:46.080 You just think more and more and more. 32:46.080 --> 32:51.080 So we went from being sort of dedicated purpose neural hardware to solve certain 32:51.080 --> 32:56.080 problems that are important to survival to a general purpose neural hardware 32:56.080 --> 33:02.080 that could be applied to all problems and now it's escaped the orbit of survival. 33:02.080 --> 33:08.080 It's, we are now able to apply it to things which we find enjoyment, you know, 33:08.080 --> 33:13.080 but aren't really clearly survival characteristics. 33:13.080 --> 33:19.080 And that it seems to only have happened in humans to the large extent. 33:19.080 --> 33:24.080 And so that's what's going on where we sort of have, we've sort of escaped the 33:24.080 --> 33:28.080 gravity of evolutionary pressure in some sense in the near cortex. 33:28.080 --> 33:32.080 And it now does things which are not, that are really interesting, 33:32.080 --> 33:36.080 discovering models of the universe, which may not really help us. 33:36.080 --> 33:37.080 It doesn't matter. 33:37.080 --> 33:41.080 How does it help us surviving knowing that there might be multiple verses or that 33:41.080 --> 33:44.080 there might be, you know, the age of the universe or how do, you know, 33:44.080 --> 33:46.080 various stellar things occur? 33:46.080 --> 33:47.080 It doesn't really help us survive at all. 33:47.080 --> 33:50.080 But we enjoy it and that's what happened. 33:50.080 --> 33:53.080 Or at least not in the obvious way, perhaps. 33:53.080 --> 33:58.080 It is required, if you look at the entire universe in an evolutionary way, 33:58.080 --> 34:03.080 it's required for us to do interplanetary travel and therefore survive past our own fun. 34:03.080 --> 34:05.080 But you know, let's not get too quick. 34:05.080 --> 34:07.080 Yeah, but, you know, evolution works at one time frame. 34:07.080 --> 34:11.080 It's survival, if you think of survival of the phenotype, 34:11.080 --> 34:13.080 survival of the individual. 34:13.080 --> 34:16.080 What you're talking about there is spans well beyond that. 34:16.080 --> 34:22.080 So there's no genetic, I'm not transferring any genetic traits to my children. 34:22.080 --> 34:25.080 That are going to help them survive better on Mars. 34:25.080 --> 34:27.080 Totally different mechanism. 34:27.080 --> 34:32.080 So let's get into the new, as you've mentioned, this idea, 34:32.080 --> 34:35.080 I don't know if you have a nice name, thousand. 34:35.080 --> 34:37.080 We call it the thousand brain theory of intelligence. 34:37.080 --> 34:38.080 I like it. 34:38.080 --> 34:44.080 So can you talk about this idea of spatial view of concepts and so on? 34:44.080 --> 34:45.080 Yeah. 34:45.080 --> 34:49.080 So can I just describe sort of the, there's an underlying core discovery, 34:49.080 --> 34:51.080 which then everything comes from that. 34:51.080 --> 34:55.080 That's a very simple, this is really what happened. 34:55.080 --> 35:00.080 We were deep into problems about understanding how we build models of stuff in the world 35:00.080 --> 35:03.080 and how we make predictions about things. 35:03.080 --> 35:07.080 And I was holding a coffee cup just like this in my hand. 35:07.080 --> 35:10.080 And I had my finger was touching the side, my index finger. 35:10.080 --> 35:15.080 And then I moved it to the top and I was going to feel the rim at the top of the cup. 35:15.080 --> 35:18.080 And I asked myself a very simple question. 35:18.080 --> 35:22.080 I said, well, first of all, let's say I know that my brain predicts what it's going to feel 35:22.080 --> 35:23.080 before it touches it. 35:23.080 --> 35:25.080 You can just think about it and imagine it. 35:25.080 --> 35:28.080 And so we know that the brain's making predictions all the time. 35:28.080 --> 35:31.080 So the question is, what does it take to predict that? 35:31.080 --> 35:33.080 And there's a very interesting answer. 35:33.080 --> 35:36.080 First of all, it says the brain has to know it's touching a coffee cup. 35:36.080 --> 35:38.080 It has to have a model of a coffee cup. 35:38.080 --> 35:43.080 It needs to know where the finger currently is on the cup, relative to the cup. 35:43.080 --> 35:46.080 Because when I make a movement, it needs to know where it's going to be on the cup 35:46.080 --> 35:50.080 after the movement is completed, relative to the cup. 35:50.080 --> 35:53.080 And then it can make a prediction about what it's going to sense. 35:53.080 --> 35:56.080 So this told me that the neocortex, which is making this prediction, 35:56.080 --> 35:59.080 needs to know that it's sensing it's touching a cup. 35:59.080 --> 36:02.080 And it needs to know the location of my finger relative to that cup 36:02.080 --> 36:04.080 in a reference frame of the cup. 36:04.080 --> 36:06.080 It doesn't matter where the cup is relative to my body. 36:06.080 --> 36:08.080 It doesn't matter its orientation. 36:08.080 --> 36:09.080 None of that matters. 36:09.080 --> 36:13.080 It's where my finger is relative to the cup, which tells me then that the neocortex 36:13.080 --> 36:17.080 has a reference frame that's anchored to the cup. 36:17.080 --> 36:19.080 Because otherwise, I wouldn't be able to say the location 36:19.080 --> 36:21.080 and I wouldn't be able to predict my new location. 36:21.080 --> 36:24.080 And then we quickly, very instantly, you can say, 36:24.080 --> 36:26.080 well, every part of my skin could touch this cup 36:26.080 --> 36:28.080 and therefore every part of my skin is making predictions 36:28.080 --> 36:30.080 and every part of my skin must have a reference frame 36:30.080 --> 36:33.080 that it's using to make predictions. 36:33.080 --> 36:39.080 So the big idea is that throughout the neocortex, 36:39.080 --> 36:47.080 there are, everything is being stored and referenced in reference frames. 36:47.080 --> 36:49.080 You can think of them like XYZ reference frames, 36:49.080 --> 36:50.080 but they're not like that. 36:50.080 --> 36:52.080 We know a lot about the neural mechanisms for this. 36:52.080 --> 36:55.080 But the brain thinks in reference frames. 36:55.080 --> 36:58.080 And as an engineer, if you're an engineer, this is not surprising. 36:58.080 --> 37:01.080 You'd say, if I were to build a CAD model of the coffee cup, 37:01.080 --> 37:03.080 well, I would bring it up in some CAD software 37:03.080 --> 37:05.080 and I would assign some reference frame and say, 37:05.080 --> 37:07.080 this features at this location and so on. 37:07.080 --> 37:10.080 But the fact that this, the idea that this is occurring 37:10.080 --> 37:14.080 throughout the neocortex everywhere, it was a novel idea. 37:14.080 --> 37:20.080 And then a zillion things fell into place after that, a zillion. 37:20.080 --> 37:23.080 So now we think about the neocortex as processing information 37:23.080 --> 37:25.080 quite differently than we used to do it. 37:25.080 --> 37:28.080 We used to think about the neocortex as processing sensory data 37:28.080 --> 37:30.080 and extracting features from that sensory data 37:30.080 --> 37:32.080 and then extracting features from the features 37:32.080 --> 37:35.080 very much like a deep learning network does today. 37:35.080 --> 37:36.080 But that's not how the brain works at all. 37:36.080 --> 37:39.080 The brain works by assigning everything, 37:39.080 --> 37:41.080 every input, everything to reference frames, 37:41.080 --> 37:44.080 and there are thousands, hundreds of thousands of them 37:44.080 --> 37:47.080 active at once in your neocortex. 37:47.080 --> 37:49.080 It's a surprising thing to think about, 37:49.080 --> 37:51.080 but once you sort of internalize this, 37:51.080 --> 37:54.080 you understand that it explains almost every, 37:54.080 --> 37:57.080 almost all the mysteries we've had about this structure. 37:57.080 --> 38:00.080 So one of the consequences of that is that 38:00.080 --> 38:04.080 every small part of the neocortex, say a millimeter square, 38:04.080 --> 38:06.080 and there's 150,000 of those. 38:06.080 --> 38:08.080 So it's about 150,000 square millimeters. 38:08.080 --> 38:11.080 If you take every little square millimeter of the cortex, 38:11.080 --> 38:13.080 it's got some input coming into it, 38:13.080 --> 38:15.080 and it's going to have reference frames 38:15.080 --> 38:17.080 where it's assigning that input to. 38:17.080 --> 38:21.080 And each square millimeter can learn complete models of objects. 38:21.080 --> 38:22.080 So what do I mean by that? 38:22.080 --> 38:23.080 If I'm touching the coffee cup, 38:23.080 --> 38:25.080 well, if I just touch it in one place, 38:25.080 --> 38:27.080 I can't learn what this coffee cup is 38:27.080 --> 38:29.080 because I'm just feeling one part. 38:29.080 --> 38:32.080 But if I move it around the cup and touch it in different areas, 38:32.080 --> 38:34.080 I can build up a complete model of the cup 38:34.080 --> 38:36.080 because I'm now filling in that three dimensional map, 38:36.080 --> 38:37.080 which is the coffee cup. 38:37.080 --> 38:39.080 I can say, oh, what am I feeling in all these different locations? 38:39.080 --> 38:40.080 That's the basic idea. 38:40.080 --> 38:42.080 It's more complicated than that. 38:42.080 --> 38:46.080 But so through time, and we talked about time earlier, 38:46.080 --> 38:48.080 through time, even a single column, 38:48.080 --> 38:50.080 which is only looking at, or a single part of the cortex, 38:50.080 --> 38:52.080 which is only looking at a small part of the world, 38:52.080 --> 38:54.080 can build up a complete model of an object. 38:54.080 --> 38:57.080 And so if you think about the part of the brain, 38:57.080 --> 38:59.080 which is getting input from all my fingers, 38:59.080 --> 39:01.080 so they're spread across the top of your head here. 39:01.080 --> 39:03.080 This is the somatosensory cortex. 39:03.080 --> 39:07.080 There's columns associated with all the different areas of my skin. 39:07.080 --> 39:10.080 And what we believe is happening is that 39:10.080 --> 39:12.080 all of them are building models of this cup, 39:12.080 --> 39:15.080 every one of them, or things. 39:15.080 --> 39:18.080 Not every column or every part of the cortex 39:18.080 --> 39:19.080 builds models of everything, 39:19.080 --> 39:21.080 but they're all building models of something. 39:21.080 --> 39:26.080 And so when I touch this cup with my hand, 39:26.080 --> 39:29.080 there are multiple models of the cup being invoked. 39:29.080 --> 39:30.080 If I look at it with my eyes, 39:30.080 --> 39:32.080 there are again many models of the cup being invoked, 39:32.080 --> 39:34.080 because each part of the visual system, 39:34.080 --> 39:36.080 the brain doesn't process an image. 39:36.080 --> 39:38.080 That's a misleading idea. 39:38.080 --> 39:40.080 It's just like your fingers touching the cup, 39:40.080 --> 39:43.080 so different parts of my retina are looking at different parts of the cup. 39:43.080 --> 39:45.080 And thousands and thousands of models of the cup 39:45.080 --> 39:47.080 are being invoked at once. 39:47.080 --> 39:49.080 And they're all voting with each other, 39:49.080 --> 39:50.080 trying to figure out what's going on. 39:50.080 --> 39:52.080 So that's why we call it the thousand brains theory of intelligence, 39:52.080 --> 39:54.080 because there isn't one model of a cup. 39:54.080 --> 39:56.080 There are thousands of models of this cup. 39:56.080 --> 39:58.080 There are thousands of models of your cell phone, 39:58.080 --> 40:01.080 and about cameras and microphones and so on. 40:01.080 --> 40:03.080 It's a distributed modeling system, 40:03.080 --> 40:05.080 which is very different than what people have thought about it. 40:05.080 --> 40:07.080 So that's a really compelling and interesting idea. 40:07.080 --> 40:09.080 I have two first questions. 40:09.080 --> 40:12.080 So one, on the ensemble part of everything coming together, 40:12.080 --> 40:14.080 you have these thousand brains. 40:14.080 --> 40:19.080 How do you know which one has done the best job of forming the cup? 40:19.080 --> 40:20.080 Great question. Let me try to explain. 40:20.080 --> 40:23.080 There's a problem that's known in neuroscience 40:23.080 --> 40:25.080 called the sensor fusion problem. 40:25.080 --> 40:26.080 Yes. 40:26.080 --> 40:28.080 And so the idea is something like, 40:28.080 --> 40:29.080 oh, the image comes from the eye. 40:29.080 --> 40:30.080 There's a picture on the retina. 40:30.080 --> 40:32.080 And it gets projected to the neocortex. 40:32.080 --> 40:35.080 Oh, by now it's all sped out all over the place, 40:35.080 --> 40:37.080 and it's kind of squirrely and distorted, 40:37.080 --> 40:39.080 and pieces are all over the, you know, 40:39.080 --> 40:41.080 it doesn't look like a picture anymore. 40:41.080 --> 40:43.080 When does it all come back together again? 40:43.080 --> 40:44.080 Right? 40:44.080 --> 40:46.080 Or you might say, well, yes, but I also, 40:46.080 --> 40:48.080 I also have sounds or touches associated with the cup. 40:48.080 --> 40:50.080 So I'm seeing the cup and touching the cup. 40:50.080 --> 40:52.080 How do they get combined together again? 40:52.080 --> 40:54.080 So this is called the sensor fusion problem. 40:54.080 --> 40:57.080 As if all these disparate parts have to be brought together 40:57.080 --> 40:59.080 into one model someplace. 40:59.080 --> 41:01.080 That's the wrong idea. 41:01.080 --> 41:03.080 The right idea is that you get all these guys voting. 41:03.080 --> 41:05.080 There's auditory models of the cup, 41:05.080 --> 41:07.080 there's visual models of the cup, 41:07.080 --> 41:09.080 there's tactile models of the cup. 41:09.080 --> 41:11.080 In the vision system, there might be ones 41:11.080 --> 41:13.080 that are more focused on black and white, 41:13.080 --> 41:14.080 ones versioned on color. 41:14.080 --> 41:15.080 It doesn't really matter. 41:15.080 --> 41:17.080 There's just thousands and thousands of models of this cup. 41:17.080 --> 41:18.080 And they vote. 41:18.080 --> 41:20.080 They don't actually come together in one spot. 41:20.080 --> 41:22.080 Just literally think of it this way. 41:22.080 --> 41:25.080 Imagine you have, each columns are like about the size 41:25.080 --> 41:26.080 of a little piece of spaghetti. 41:26.080 --> 41:27.080 Okay? 41:27.080 --> 41:28.080 Like a two and a half millimeters tall 41:28.080 --> 41:30.080 and about a millimeter in white. 41:30.080 --> 41:33.080 They're not physical like, but you can think of them that way. 41:33.080 --> 41:36.080 And each one's trying to guess what this thing is or touching. 41:36.080 --> 41:38.080 Now they can, they can do a pretty good job 41:38.080 --> 41:40.080 if they're allowed to move over time. 41:40.080 --> 41:42.080 So I can reach my hand into a black box and move my finger 41:42.080 --> 41:44.080 around an object and if I touch enough space, 41:44.080 --> 41:46.080 it's like, okay, I know what it is. 41:46.080 --> 41:48.080 But often we don't do that. 41:48.080 --> 41:50.080 Often I can just reach and grab something with my hand 41:50.080 --> 41:51.080 all at once and I get it. 41:51.080 --> 41:53.080 Or if I had to look through the world through a straw, 41:53.080 --> 41:55.080 so I'm only invoking one little column, 41:55.080 --> 41:57.080 I can only see part of something because I have to move 41:57.080 --> 41:58.080 the straw around. 41:58.080 --> 42:00.080 But if I open my eyes to see the whole thing at once. 42:00.080 --> 42:02.080 So what we think is going on is all these little pieces 42:02.080 --> 42:05.080 of spaghetti, all these little columns in the cortex 42:05.080 --> 42:08.080 are all trying to guess what it is that they're sensing. 42:08.080 --> 42:10.080 They'll do a better guess if they have time 42:10.080 --> 42:11.080 and can move over time. 42:11.080 --> 42:13.080 So if I move my eyes and move my fingers. 42:13.080 --> 42:16.080 But if they don't, they have a, they have a poor guess. 42:16.080 --> 42:19.080 It's a, it's a probabilistic guess of what they might be touching. 42:19.080 --> 42:22.080 Now imagine they can post their probability 42:22.080 --> 42:24.080 at the top of little piece of spaghetti. 42:24.080 --> 42:25.080 Each one of them says, I think, 42:25.080 --> 42:27.080 and it's not really a probability distribution. 42:27.080 --> 42:29.080 It's more like a set of possibilities in the brain. 42:29.080 --> 42:31.080 It doesn't work as a probability distribution. 42:31.080 --> 42:33.080 It works as more like what we call a union. 42:33.080 --> 42:35.080 You could say, and one column says, 42:35.080 --> 42:39.080 I think it could be a coffee cup, a soda can or a water bottle. 42:39.080 --> 42:42.080 And another column says, I think it could be a coffee cup 42:42.080 --> 42:45.080 or a, you know, telephone or camera or whatever. 42:45.080 --> 42:46.080 Right. 42:46.080 --> 42:49.080 And all these guys are saying what they think it might be. 42:49.080 --> 42:51.080 And there's these long range connections 42:51.080 --> 42:53.080 in certain layers in the cortex. 42:53.080 --> 42:57.080 So there's some layers in some cell types in each column 42:57.080 --> 42:59.080 send the projections across the brain. 42:59.080 --> 43:01.080 And that's the voting occurs. 43:01.080 --> 43:04.080 And so there's a simple associative memory mechanism. 43:04.080 --> 43:07.080 We've described this in a recent paper and we've modeled this 43:07.080 --> 43:11.080 that says they can all quickly settle on the only 43:11.080 --> 43:14.080 or the one best answer for all of them. 43:14.080 --> 43:17.080 If there is a single best answer, they all vote and say, 43:17.080 --> 43:19.080 yep, it's got to be the coffee cup. 43:19.080 --> 43:21.080 And at that point, they all know it's a coffee cup. 43:21.080 --> 43:23.080 And at that point, everyone acts as if it's a coffee cup. 43:23.080 --> 43:24.080 Yeah, we know it's a coffee. 43:24.080 --> 43:26.080 Even though I've only seen one little piece of this world, 43:26.080 --> 43:28.080 I know it's a coffee cup I'm touching or I'm seeing or whatever. 43:28.080 --> 43:31.080 And so you can think of all these columns are looking 43:31.080 --> 43:33.080 at different parts and different places, 43:33.080 --> 43:35.080 different sensory input, different locations. 43:35.080 --> 43:36.080 They're all different. 43:36.080 --> 43:40.080 But this layer that's doing the voting, it solidifies. 43:40.080 --> 43:43.080 It crystallizes and says, oh, we all know what we're doing. 43:43.080 --> 43:46.080 And so you don't bring these models together in one model, 43:46.080 --> 43:49.080 you just vote and there's a crystallization of the vote. 43:49.080 --> 43:50.080 Great. 43:50.080 --> 43:56.080 That's at least a compelling way to think about the way you 43:56.080 --> 43:58.080 form a model of the world. 43:58.080 --> 44:00.080 Now, you talk about a coffee cup. 44:00.080 --> 44:04.080 Do you see this as far as I understand that you were proposing 44:04.080 --> 44:07.080 this as well, that this extends to much more than coffee cups? 44:07.080 --> 44:09.080 Yeah, it does. 44:09.080 --> 44:11.080 Or at least the physical world. 44:11.080 --> 44:14.080 It expands to the world of concepts. 44:14.080 --> 44:15.080 Yeah, it does. 44:15.080 --> 44:18.080 And well, the first, the primary phase of evidence for that 44:18.080 --> 44:21.080 is that the regions of the neocortex that are associated 44:21.080 --> 44:24.080 with language or high level thought or mathematics or things 44:24.080 --> 44:26.080 like that, they look like the regions of the neocortex 44:26.080 --> 44:28.080 that process vision and hearing and touch. 44:28.080 --> 44:31.080 They don't look any different or they look only marginally 44:31.080 --> 44:32.080 different. 44:32.080 --> 44:36.080 And so one would say, well, if Vernon Mountcastle, 44:36.080 --> 44:39.080 who proposed that all the parts of the neocortex 44:39.080 --> 44:42.080 are the same thing, if he's right, then the parts 44:42.080 --> 44:44.080 that are doing language or mathematics or physics 44:44.080 --> 44:46.080 are working on the same principle. 44:46.080 --> 44:48.080 They must be working on the principle of reference frames. 44:48.080 --> 44:51.080 So that's a little odd thought. 44:51.080 --> 44:55.080 But of course, we had no prior idea how these things happen. 44:55.080 --> 44:57.080 So let's go with that. 44:57.080 --> 45:01.080 And in our recent paper, we talked a little bit about that. 45:01.080 --> 45:03.080 I've been working on it more since. 45:03.080 --> 45:05.080 I have better ideas about it now. 45:05.080 --> 45:08.080 I'm sitting here very confident that that's what's happening. 45:08.080 --> 45:11.080 And I can give you some examples to help you think about that. 45:11.080 --> 45:13.080 It's not that we understand it completely, 45:13.080 --> 45:15.080 but I understand it better than I've described it in any paper 45:15.080 --> 45:16.080 so far. 45:16.080 --> 45:18.080 But we did put that idea out there. 45:18.080 --> 45:22.080 It's a good place to start. 45:22.080 --> 45:25.080 And the evidence would suggest it's how it's happening. 45:25.080 --> 45:27.080 And then we can start tackling that problem one piece at a time. 45:27.080 --> 45:29.080 What does it mean to do high level thought? 45:29.080 --> 45:30.080 What does it mean to do language? 45:30.080 --> 45:34.080 How would that fit into a reference framework? 45:34.080 --> 45:38.080 I don't know if you could tell me if there's a connection, 45:38.080 --> 45:42.080 but there's an app called Anki that helps you remember different concepts. 45:42.080 --> 45:46.080 And they talk about like a memory palace that helps you remember 45:46.080 --> 45:50.080 completely random concepts by trying to put them in a physical space 45:50.080 --> 45:52.080 in your mind and putting them next to each other. 45:52.080 --> 45:54.080 It's called the method of loci. 45:54.080 --> 45:57.080 For some reason, that seems to work really well. 45:57.080 --> 46:00.080 Now that's a very narrow kind of application of just remembering some facts. 46:00.080 --> 46:03.080 But that's a very, very telling one. 46:03.080 --> 46:04.080 Yes, exactly. 46:04.080 --> 46:09.080 So this seems like you're describing a mechanism why this seems to work. 46:09.080 --> 46:13.080 So basically the way what we think is going on is all things you know, 46:13.080 --> 46:17.080 all concepts, all ideas, words, everything, you know, 46:17.080 --> 46:20.080 are stored in reference frames. 46:20.080 --> 46:24.080 And so if you want to remember something, 46:24.080 --> 46:27.080 you have to basically navigate through a reference frame the same way 46:27.080 --> 46:28.080 a rat navigates to a man. 46:28.080 --> 46:31.080 Even the same way my finger rat navigates to this coffee cup. 46:31.080 --> 46:33.080 You are moving through some space. 46:33.080 --> 46:37.080 And so if you have a random list of things you would ask to remember 46:37.080 --> 46:39.080 by assigning them to a reference frame, 46:39.080 --> 46:42.080 you've already know very well to see your house, right? 46:42.080 --> 46:44.080 And the idea of the method of loci is you can say, 46:44.080 --> 46:46.080 okay, in my lobby, I'm going to put this thing. 46:46.080 --> 46:48.080 And then the bedroom, I put this one. 46:48.080 --> 46:49.080 I go down the hall, I put this thing. 46:49.080 --> 46:51.080 And then you want to recall those facts. 46:51.080 --> 46:52.080 So recall those things. 46:52.080 --> 46:53.080 You just walk mentally. 46:53.080 --> 46:54.080 You walk through your house. 46:54.080 --> 46:57.080 You're mentally moving through a reference frame that you already had. 46:57.080 --> 47:00.080 And that tells you there's two things that are really important about that. 47:00.080 --> 47:03.080 It tells us the brain prefers to store things in reference frames. 47:03.080 --> 47:08.080 And the method of recalling things or thinking, if you will, 47:08.080 --> 47:11.080 is to move mentally through those reference frames. 47:11.080 --> 47:13.080 You could move physically through some reference frames, 47:13.080 --> 47:16.080 like I could physically move through the reference frame of this coffee cup. 47:16.080 --> 47:18.080 I can also mentally move through the reference frame of the coffee cup, 47:18.080 --> 47:19.080 imagining me touching it. 47:19.080 --> 47:22.080 But I can also mentally move my house. 47:22.080 --> 47:26.080 And so now we can ask ourselves, are all concepts stored this way? 47:26.080 --> 47:32.080 There was some recent research using human subjects in fMRI. 47:32.080 --> 47:36.080 And I'm going to apologize for not knowing the name of the scientists who did this. 47:36.080 --> 47:41.080 But what they did is they put humans in this fMRI machine, 47:41.080 --> 47:42.080 which was one of these imaging machines. 47:42.080 --> 47:46.080 And they gave the humans tasks to think about birds. 47:46.080 --> 47:49.080 So they had different types of birds, and birds that looked big and small 47:49.080 --> 47:51.080 and long necks and long legs, things like that. 47:51.080 --> 47:56.080 And what they could tell from the fMRI was a very clever experiment. 47:56.080 --> 48:00.080 You get to tell when humans were thinking about the birds, 48:00.080 --> 48:05.080 that the birds, the knowledge of birds was arranged in a reference frame 48:05.080 --> 48:08.080 similar to the ones that are used when you navigate in a room. 48:08.080 --> 48:10.080 These are called grid cells. 48:10.080 --> 48:14.080 And there are grid cell like patterns of activity in the neocortex when they do this. 48:14.080 --> 48:18.080 So that, it's a very clever experiment. 48:18.080 --> 48:22.080 And what it basically says is that even when you're thinking about something abstract 48:22.080 --> 48:24.080 and you're not really thinking about it as a reference frame, 48:24.080 --> 48:27.080 it tells us the brain is actually using a reference frame. 48:27.080 --> 48:29.080 And it's using the same neural mechanisms. 48:29.080 --> 48:32.080 These grid cells are the basic same neural mechanisms that we propose 48:32.080 --> 48:36.080 that grid cells, which exist in the old part of the brain, the entomonic cortex, 48:36.080 --> 48:40.080 that that mechanism is now similar mechanism, is used throughout the neocortex. 48:40.080 --> 48:44.080 It's the same nature to preserve this interesting way of creating reference frames. 48:44.080 --> 48:49.080 And so now they have empirical evidence that when you think about concepts like birds 48:49.080 --> 48:53.080 that you're using reference frames that are built on grid cells. 48:53.080 --> 48:55.080 So that's similar to the method of loci. 48:55.080 --> 48:57.080 But in this case, the birds are related so that makes, 48:57.080 --> 49:01.080 they create their own reference frame, which is consistent with bird space. 49:01.080 --> 49:03.080 And when you think about something, you go through that. 49:03.080 --> 49:04.080 You can make the same example. 49:04.080 --> 49:06.080 Let's take a math mathematics. 49:06.080 --> 49:08.080 Let's say you want to prove a conjecture. 49:08.080 --> 49:09.080 Okay. 49:09.080 --> 49:10.080 What is a conjecture? 49:10.080 --> 49:13.080 A conjecture is a statement you believe to be true, 49:13.080 --> 49:15.080 but you haven't proven it. 49:15.080 --> 49:17.080 And so it might be an equation. 49:17.080 --> 49:19.080 I want to show that this is equal to that. 49:19.080 --> 49:21.080 And you have some places you start with. 49:21.080 --> 49:23.080 You say, well, I know this is true and I know this is true. 49:23.080 --> 49:26.080 And I think that maybe to get to the final proof, 49:26.080 --> 49:28.080 I need to go through some intermediate results. 49:28.080 --> 49:33.080 What I believe is happening is literally these equations 49:33.080 --> 49:36.080 or these points are assigned to a reference frame, 49:36.080 --> 49:38.080 a mathematical reference frame. 49:38.080 --> 49:40.080 And when you do mathematical operations, 49:40.080 --> 49:42.080 a simple one might be multiply or divide, 49:42.080 --> 49:44.080 maybe a little plus transform or something else. 49:44.080 --> 49:47.080 That is like a movement in the reference frame of the math. 49:47.080 --> 49:50.080 And so you're literally trying to discover a path 49:50.080 --> 49:56.080 from one location to another location in a space of mathematics. 49:56.080 --> 49:58.080 And if you can get to these intermediate results, 49:58.080 --> 50:00.080 then you know your map is pretty good 50:00.080 --> 50:03.080 and you know you're using the right operations. 50:03.080 --> 50:06.080 Much of what we think about is solving hard problems 50:06.080 --> 50:09.080 is designing the correct reference frame for that problem, 50:09.080 --> 50:12.080 how to organize the information, and what behaviors 50:12.080 --> 50:15.080 I want to use in that space to get me there. 50:15.080 --> 50:19.080 Yeah, so if you dig in on an idea of this reference frame, 50:19.080 --> 50:21.080 whether it's the math, you start a set of axioms 50:21.080 --> 50:24.080 to try to get to proving the conjecture. 50:24.080 --> 50:27.080 Can you try to describe, maybe take a step back, 50:27.080 --> 50:30.080 how you think of the reference frame in that context? 50:30.080 --> 50:35.080 Is it the reference frame that the axioms are happy in? 50:35.080 --> 50:38.080 Is it the reference frame that might contain everything? 50:38.080 --> 50:41.080 Is it a changing thing as you... 50:41.080 --> 50:43.080 You have many, many reference frames. 50:43.080 --> 50:45.080 In fact, the way the thousand brain theories of intelligence 50:45.080 --> 50:48.080 says that every single thing in the world has its own reference frame. 50:48.080 --> 50:50.080 So every word has its own reference frames. 50:50.080 --> 50:52.080 And we can talk about this. 50:52.080 --> 50:55.080 The mathematics work out this is no problem for neurons to do this. 50:55.080 --> 50:58.080 But how many reference frames does the coffee cup have? 50:58.080 --> 51:03.080 Well, let's say you ask how many reference frames 51:03.080 --> 51:07.080 could the column in my finger that's touching the coffee cup have 51:07.080 --> 51:10.080 because there are many, many models of the coffee cup. 51:10.080 --> 51:12.080 So there is no model of the coffee cup. 51:12.080 --> 51:14.080 There are many models of the coffee cup. 51:14.080 --> 51:17.080 And you can say, well, how many different things can my finger learn? 51:17.080 --> 51:19.080 Is this the question you want to ask? 51:19.080 --> 51:21.080 Imagine I say every concept, every idea, 51:21.080 --> 51:23.080 everything you've ever know about that you can say, 51:23.080 --> 51:28.080 I know that thing has a reference frame associated with it. 51:28.080 --> 51:30.080 And what we do when we build composite objects, 51:30.080 --> 51:34.080 we assign reference frames to point another reference frame. 51:34.080 --> 51:37.080 So my coffee cup has multiple components to it. 51:37.080 --> 51:38.080 It's got a limb. 51:38.080 --> 51:39.080 It's got a cylinder. 51:39.080 --> 51:40.080 It's got a handle. 51:40.080 --> 51:43.080 And those things have their own reference frames. 51:43.080 --> 51:45.080 And they're assigned to a master reference frame, 51:45.080 --> 51:46.080 which is called this cup. 51:46.080 --> 51:48.080 And now I have this mental logo on it. 51:48.080 --> 51:50.080 Well, that's something that exists elsewhere in the world. 51:50.080 --> 51:51.080 It's its own thing. 51:51.080 --> 51:52.080 So it has its own reference frame. 51:52.080 --> 51:56.080 So we now have to say, how can I assign the mental logo reference frame 51:56.080 --> 51:59.080 onto the cylinder or onto the coffee cup? 51:59.080 --> 52:04.080 So we talked about this in the paper that came out in December 52:04.080 --> 52:06.080 of this last year. 52:06.080 --> 52:09.080 The idea of how you can assign reference frames to reference frames, 52:09.080 --> 52:10.080 how neurons could do this. 52:10.080 --> 52:14.080 So my question is, even though you mentioned reference frames a lot, 52:14.080 --> 52:18.080 I almost feel it's really useful to dig into how you think 52:18.080 --> 52:20.080 of what a reference frame is. 52:20.080 --> 52:22.080 It was already helpful for me to understand that you think 52:22.080 --> 52:26.080 of reference frames as something there is a lot of. 52:26.080 --> 52:29.080 OK, so let's just say that we're going to have some neurons 52:29.080 --> 52:32.080 in the brain, not many actually, 10,000, 20,000, 52:32.080 --> 52:34.080 are going to create a whole bunch of reference frames. 52:34.080 --> 52:35.080 What does it mean? 52:35.080 --> 52:37.080 What is a reference frame? 52:37.080 --> 52:40.080 First of all, these reference frames are different than the ones 52:40.080 --> 52:42.080 you might be used to. 52:42.080 --> 52:43.080 We know lots of reference frames. 52:43.080 --> 52:45.080 For example, we know the Cartesian coordinates, 52:45.080 --> 52:47.080 XYZ, that's a type of reference frame. 52:47.080 --> 52:50.080 We know longitude and latitude. 52:50.080 --> 52:52.080 That's a different type of reference frame. 52:52.080 --> 52:55.080 If I look at a printed map, it might have columns, 52:55.080 --> 52:59.080 A through M and rows, 1 through 20, 52:59.080 --> 53:01.080 that's a different type of reference frame. 53:01.080 --> 53:04.080 It's kind of a Cartesian reference frame. 53:04.080 --> 53:07.080 The interesting thing about the reference frames in the brain, 53:07.080 --> 53:09.080 and we know this because these have been established 53:09.080 --> 53:12.080 through neuroscience studying the entorhinal cortex. 53:12.080 --> 53:13.080 So I'm not speculating here. 53:13.080 --> 53:16.080 This is known neuroscience in an old part of the brain. 53:16.080 --> 53:18.080 The way these cells create reference frames, 53:18.080 --> 53:20.080 they have no origin. 53:20.080 --> 53:24.080 So what it's more like, you have a point, 53:24.080 --> 53:26.080 a point in some space, 53:26.080 --> 53:29.080 and you, given a particular movement, 53:29.080 --> 53:32.080 you can then tell what the next point should be. 53:32.080 --> 53:34.080 And you can then tell what the next point would be. 53:34.080 --> 53:35.080 And so on. 53:35.080 --> 53:40.080 You can use this to calculate how to get from one point to another. 53:40.080 --> 53:43.080 So how do I get from my house to my home, 53:43.080 --> 53:45.080 or how do I get my finger from the side of my cup 53:45.080 --> 53:46.080 to the top of the cup? 53:46.080 --> 53:52.080 How do I get from the axioms to the conjecture? 53:52.080 --> 53:54.080 So it's a different type of reference frame. 53:54.080 --> 53:57.080 And I can, if you want, I can describe in more detail. 53:57.080 --> 53:59.080 I can paint a picture how you might want to think about that. 53:59.080 --> 54:00.080 It's really helpful to think. 54:00.080 --> 54:02.080 It's something you can move through. 54:02.080 --> 54:03.080 Yeah. 54:03.080 --> 54:08.080 But is it helpful to think of it as spatial in some sense, 54:08.080 --> 54:09.080 or is there something? 54:09.080 --> 54:11.080 No, it's definitely spatial. 54:11.080 --> 54:13.080 It's spatial in a mathematical sense. 54:13.080 --> 54:14.080 How many dimensions? 54:14.080 --> 54:16.080 Can it be a crazy number of dimensions? 54:16.080 --> 54:17.080 Well, that's an interesting question. 54:17.080 --> 54:20.080 In the old part of the brain, the entorhinal cortex, 54:20.080 --> 54:22.080 they studied rats. 54:22.080 --> 54:24.080 And initially, it looks like, oh, this is just two dimensional. 54:24.080 --> 54:27.080 It's like the rat is in some box in a maze or whatever, 54:27.080 --> 54:29.080 and they know whether the rat is using these two dimensional 54:29.080 --> 54:32.080 reference frames and know where it is in the maze. 54:32.080 --> 54:35.080 We say, OK, well, what about bats? 54:35.080 --> 54:38.080 That's a mammal, and they fly in three dimensional space. 54:38.080 --> 54:39.080 How do they do that? 54:39.080 --> 54:41.080 They seem to know where they are, right? 54:41.080 --> 54:44.080 So this is a current area of active research, 54:44.080 --> 54:47.080 and it seems like somehow the neurons in the entorhinal cortex 54:47.080 --> 54:50.080 can learn three dimensional space. 54:50.080 --> 54:55.080 We just, two members of our team, along with Ilefet from MIT, 54:55.080 --> 54:59.080 just released a paper this literally last week, 54:59.080 --> 55:03.080 it's on bioarchive, where they show that you can, 55:03.080 --> 55:06.080 the way these things work, and unless you want to, 55:06.080 --> 55:10.080 I won't get into the detail, but grid cells 55:10.080 --> 55:12.080 can represent any n dimensional space. 55:12.080 --> 55:15.080 It's not inherently limited. 55:15.080 --> 55:18.080 You can think of it this way, if you had two dimensional, 55:18.080 --> 55:21.080 the way it works is you had a bunch of two dimensional slices. 55:21.080 --> 55:22.080 That's the way these things work. 55:22.080 --> 55:24.080 There's a whole bunch of two dimensional models, 55:24.080 --> 55:27.080 and you can slice up any n dimensional space 55:27.080 --> 55:29.080 with two dimensional projections. 55:29.080 --> 55:31.080 And you could have one dimensional models. 55:31.080 --> 55:34.080 So there's nothing inherent about the mathematics 55:34.080 --> 55:36.080 about the way the neurons do this, 55:36.080 --> 55:39.080 which constrained the dimensionality of the space, 55:39.080 --> 55:41.080 which I think was important. 55:41.080 --> 55:44.080 So obviously, I have a three dimensional map of this cup. 55:44.080 --> 55:46.080 Maybe it's even more than that, I don't know. 55:46.080 --> 55:48.080 But it's a clearly three dimensional map of the cup. 55:48.080 --> 55:50.080 I don't just have a projection of the cup. 55:50.080 --> 55:52.080 But when I think about birds, 55:52.080 --> 55:53.080 or when I think about mathematics, 55:53.080 --> 55:55.080 perhaps it's more than three dimensions. 55:55.080 --> 55:56.080 Who knows? 55:56.080 --> 56:00.080 So in terms of each individual column 56:00.080 --> 56:04.080 building up more and more information over time, 56:04.080 --> 56:06.080 do you think that mechanism is well understood? 56:06.080 --> 56:10.080 In your mind, you've proposed a lot of architectures there. 56:10.080 --> 56:14.080 Is that a key piece, or is it, is the big piece, 56:14.080 --> 56:16.080 the thousand brain theory of intelligence, 56:16.080 --> 56:18.080 the ensemble of it all? 56:18.080 --> 56:19.080 Well, I think they're both big. 56:19.080 --> 56:21.080 I mean, clearly the concept, as a theorist, 56:21.080 --> 56:23.080 the concept is most exciting, right? 56:23.080 --> 56:24.080 A high level concept. 56:24.080 --> 56:25.080 A high level concept. 56:25.080 --> 56:26.080 This is a totally new way of thinking about 56:26.080 --> 56:27.080 how the near characteristics work. 56:27.080 --> 56:29.080 So that is appealing. 56:29.080 --> 56:31.080 It has all these ramifications. 56:31.080 --> 56:34.080 And with that, as a framework for how the brain works, 56:34.080 --> 56:35.080 you can make all kinds of predictions 56:35.080 --> 56:36.080 and solve all kinds of problems. 56:36.080 --> 56:38.080 Now we're trying to work through many of these details right now. 56:38.080 --> 56:40.080 Okay, how do the neurons actually do this? 56:40.080 --> 56:42.080 Well, it turns out, if you think about grid cells 56:42.080 --> 56:44.080 and place cells in the old parts of the brain, 56:44.080 --> 56:46.080 there's a lot that's known about them, 56:46.080 --> 56:47.080 but there's still some mysteries. 56:47.080 --> 56:49.080 There's a lot of debate about exactly the details, 56:49.080 --> 56:50.080 how these work, and what are the signs. 56:50.080 --> 56:52.080 And we have that same level of detail, 56:52.080 --> 56:54.080 that same level of concern. 56:54.080 --> 56:56.080 What we spend here, most of our time doing, 56:56.080 --> 56:59.080 is trying to make a very good list 56:59.080 --> 57:02.080 of the things we don't understand yet. 57:02.080 --> 57:04.080 That's the key part here. 57:04.080 --> 57:05.080 What are the constraints? 57:05.080 --> 57:07.080 It's not like, oh, this seems to work, we're done. 57:07.080 --> 57:09.080 It's like, okay, it kind of works, 57:09.080 --> 57:11.080 but these are other things we know it has to do, 57:11.080 --> 57:13.080 and it's not doing those yet. 57:13.080 --> 57:15.080 I would say we're well on the way here. 57:15.080 --> 57:17.080 We're not done yet. 57:17.080 --> 57:20.080 There's a lot of trickiness to this system, 57:20.080 --> 57:23.080 but the basic principles about how different layers 57:23.080 --> 57:27.080 in the neocortex are doing much of this, we understand. 57:27.080 --> 57:29.080 But there's some fundamental parts 57:29.080 --> 57:30.080 that we don't understand as well. 57:30.080 --> 57:34.080 So what would you say is one of the harder open problems, 57:34.080 --> 57:37.080 or one of the ones that have been bothering you, 57:37.080 --> 57:39.080 keeping you up at night the most? 57:39.080 --> 57:41.080 Well, right now, this is a detailed thing 57:41.080 --> 57:43.080 that wouldn't apply to most people, okay? 57:43.080 --> 57:44.080 Sure. 57:44.080 --> 57:45.080 But you want me to answer that question? 57:45.080 --> 57:46.080 Yeah, please. 57:46.080 --> 57:49.080 We've talked about, as if, oh, to predict 57:49.080 --> 57:51.080 what you're going to sense on this coffee cup, 57:51.080 --> 57:54.080 I need to know where my finger's going to be on the coffee cup. 57:54.080 --> 57:56.080 That is true, but it's insufficient. 57:56.080 --> 57:59.080 Think about my finger touching the edge of the coffee cup. 57:59.080 --> 58:02.080 My finger can touch it at different orientations. 58:02.080 --> 58:05.080 I can touch it at my finger around here. 58:05.080 --> 58:06.080 And that doesn't change. 58:06.080 --> 58:09.080 I can make that prediction, and somehow, 58:09.080 --> 58:10.080 so it's not just the location. 58:10.080 --> 58:13.080 There's an orientation component of this as well. 58:13.080 --> 58:15.080 This is known in the old part of the brain, too. 58:15.080 --> 58:17.080 There's things called head direction cells, 58:17.080 --> 58:18.080 which way the rat is facing. 58:18.080 --> 58:20.080 It's the same kind of basic idea. 58:20.080 --> 58:23.080 So if my finger were a rat, you know, in three dimensions, 58:23.080 --> 58:25.080 I have a three dimensional orientation, 58:25.080 --> 58:27.080 and I have a three dimensional location. 58:27.080 --> 58:29.080 If I was a rat, I would have a, 58:29.080 --> 58:31.080 I think it was a two dimensional location, 58:31.080 --> 58:33.080 or one dimensional orientation, like this, 58:33.080 --> 58:35.080 which way is it facing? 58:35.080 --> 58:38.080 So how the two components work together, 58:38.080 --> 58:41.080 how does it, I combine orientation, 58:41.080 --> 58:43.080 the orientation of my sensor, 58:43.080 --> 58:47.080 as well as the location, 58:47.080 --> 58:49.080 is a tricky problem. 58:49.080 --> 58:52.080 And I think I've made progress on it. 58:52.080 --> 58:55.080 So at a bigger version of that, 58:55.080 --> 58:57.080 the perspective is super interesting, 58:57.080 --> 58:58.080 but super specific. 58:58.080 --> 58:59.080 Yeah, I warned you. 58:59.080 --> 59:01.080 No, no, no, it's really good, 59:01.080 --> 59:04.080 but there's a more general version of that. 59:04.080 --> 59:06.080 Do you think context matters? 59:06.080 --> 59:10.080 The fact that we are in a building in North America, 59:10.080 --> 59:15.080 that we, in the day and age where we have mugs, 59:15.080 --> 59:18.080 I mean, there's all this extra information 59:18.080 --> 59:22.080 that you bring to the table about everything else in the room 59:22.080 --> 59:24.080 that's outside of just the coffee cup. 59:24.080 --> 59:25.080 Of course it is. 59:25.080 --> 59:27.080 How does it get connected, do you think? 59:27.080 --> 59:30.080 Yeah, and that is another really interesting question. 59:30.080 --> 59:32.080 I'm going to throw that under the rubric 59:32.080 --> 59:34.080 or the name of attentional problems. 59:34.080 --> 59:36.080 First of all, we have this model. 59:36.080 --> 59:37.080 I have many, many models. 59:37.080 --> 59:39.080 And also the question, does it matter? 59:39.080 --> 59:41.080 Well, it matters for certain things. 59:41.080 --> 59:42.080 Of course it does. 59:42.080 --> 59:44.080 Maybe what we think about as a coffee cup 59:44.080 --> 59:47.080 in another part of the world is viewed as something completely different. 59:47.080 --> 59:51.080 Or maybe our logo, which is very benign in this part of the world, 59:51.080 --> 59:53.080 it means something very different in another part of the world. 59:53.080 --> 59:56.080 So those things do matter. 59:56.080 --> 1:00:00.080 I think the way to think about it as the following, 1:00:00.080 --> 1:00:01.080 one way to think about it, 1:00:01.080 --> 1:00:03.080 is we have all these models of the world. 1:00:03.080 --> 1:00:06.080 And we model everything. 1:00:06.080 --> 1:00:08.080 And as I said earlier, I kind of snuck it in there. 1:00:08.080 --> 1:00:12.080 Our models are actually, we build composite structures. 1:00:12.080 --> 1:00:15.080 So every object is composed of other objects, 1:00:15.080 --> 1:00:16.080 which are composed of other objects, 1:00:16.080 --> 1:00:18.080 and they become members of other objects. 1:00:18.080 --> 1:00:21.080 So this room is chairs and a table and a room and walls and so on. 1:00:21.080 --> 1:00:24.080 Now we can just arrange these things in a certain way. 1:00:24.080 --> 1:00:27.080 And you go, oh, that's in the Nementa conference room. 1:00:27.080 --> 1:00:32.080 So, and what we do is when we go around the world, 1:00:32.080 --> 1:00:34.080 when we experience the world, 1:00:34.080 --> 1:00:36.080 by walking to a room, for example, 1:00:36.080 --> 1:00:38.080 the first thing I do is like, oh, I'm in this room. 1:00:38.080 --> 1:00:39.080 Do I recognize the room? 1:00:39.080 --> 1:00:42.080 Then I can say, oh, look, there's a table here. 1:00:42.080 --> 1:00:44.080 And by attending to the table, 1:00:44.080 --> 1:00:46.080 I'm then assigning this table in a context of the room. 1:00:46.080 --> 1:00:48.080 Then I say, oh, on the table, there's a coffee cup. 1:00:48.080 --> 1:00:50.080 Oh, and on the table, there's a logo. 1:00:50.080 --> 1:00:52.080 And in the logo, there's the word Nementa. 1:00:52.080 --> 1:00:54.080 So if you look in the logo, there's the letter E. 1:00:54.080 --> 1:00:56.080 And look, it has an unusual surf. 1:00:56.080 --> 1:00:59.080 It doesn't actually, but I pretend it does. 1:00:59.080 --> 1:01:05.080 So the point is your attention is kind of drilling deep in and out 1:01:05.080 --> 1:01:07.080 of these nested structures. 1:01:07.080 --> 1:01:09.080 And I can pop back up and I can pop back down. 1:01:09.080 --> 1:01:11.080 I can pop back up and I can pop back down. 1:01:11.080 --> 1:01:13.080 So when I attend to the coffee cup, 1:01:13.080 --> 1:01:15.080 I haven't lost the context of everything else, 1:01:15.080 --> 1:01:19.080 but it's sort of, there's this sort of nested structure. 1:01:19.080 --> 1:01:22.080 The attention filters the reference frame formation 1:01:22.080 --> 1:01:24.080 for that particular period of time. 1:01:24.080 --> 1:01:25.080 Yes. 1:01:25.080 --> 1:01:28.080 It basically, a moment to moment, you attend the subcomponents 1:01:28.080 --> 1:01:30.080 and then you can attend the subcomponents to subcomponents. 1:01:30.080 --> 1:01:31.080 You can move up and down. 1:01:31.080 --> 1:01:32.080 You can move up and down. 1:01:32.080 --> 1:01:33.080 We do that all the time. 1:01:33.080 --> 1:01:35.080 You're not even, now that I'm aware of it, 1:01:35.080 --> 1:01:37.080 I'm very conscious of it. 1:01:37.080 --> 1:01:40.080 But most people don't even think about this. 1:01:40.080 --> 1:01:42.080 You know, you just walk in a room and you don't say, 1:01:42.080 --> 1:01:43.080 oh, I looked at the chair and I looked at the board 1:01:43.080 --> 1:01:44.080 and looked at that word on the board 1:01:44.080 --> 1:01:45.080 and I looked over here. 1:01:45.080 --> 1:01:46.080 What's going on? 1:01:46.080 --> 1:01:47.080 Right. 1:01:47.080 --> 1:01:50.080 So what percentage of your day are you deeply aware of this? 1:01:50.080 --> 1:01:53.080 In what part can you actually relax and just be Jeff? 1:01:53.080 --> 1:01:55.080 Me personally, like my personal day. 1:01:55.080 --> 1:01:56.080 Yeah. 1:01:56.080 --> 1:02:01.080 Unfortunately, I'm afflicted with too much of the former. 1:02:01.080 --> 1:02:03.080 Well, unfortunately or unfortunately. 1:02:03.080 --> 1:02:04.080 Yeah. 1:02:04.080 --> 1:02:05.080 You don't think it's useful? 1:02:05.080 --> 1:02:06.080 Oh, it is useful. 1:02:06.080 --> 1:02:07.080 Totally useful. 1:02:07.080 --> 1:02:09.080 I think about this stuff almost all the time. 1:02:09.080 --> 1:02:13.080 And one of my primary ways of thinking is 1:02:13.080 --> 1:02:14.080 when I'm asleep at night, 1:02:14.080 --> 1:02:16.080 I always wake up in the middle of the night 1:02:16.080 --> 1:02:19.080 and I stay awake for at least an hour with my eyes shut 1:02:19.080 --> 1:02:21.080 in sort of a half sleep state thinking about these things. 1:02:21.080 --> 1:02:23.080 I come up with answers to problems very often 1:02:23.080 --> 1:02:25.080 in that sort of half sleeping state. 1:02:25.080 --> 1:02:27.080 I think about on my bike ride, I think about on walks. 1:02:27.080 --> 1:02:29.080 I'm just constantly thinking about this. 1:02:29.080 --> 1:02:34.080 I have to almost schedule time to not think about this stuff 1:02:34.080 --> 1:02:37.080 because it's very, it's mentally taxing. 1:02:37.080 --> 1:02:39.080 Are you, when you're thinking about this stuff, 1:02:39.080 --> 1:02:41.080 are you thinking introspectively, 1:02:41.080 --> 1:02:43.080 like almost taking a step outside of yourself 1:02:43.080 --> 1:02:45.080 and trying to figure out what is your mind doing right now? 1:02:45.080 --> 1:02:48.080 I do that all the time, but that's not all I do. 1:02:48.080 --> 1:02:50.080 I'm constantly observing myself. 1:02:50.080 --> 1:02:52.080 So as soon as I started thinking about grid cells, 1:02:52.080 --> 1:02:54.080 for example, and getting into that, 1:02:54.080 --> 1:02:57.080 I started saying, oh, well, grid cells can have my place of sense 1:02:57.080 --> 1:02:58.080 in the world. 1:02:58.080 --> 1:02:59.080 That's where you know where you are. 1:02:59.080 --> 1:03:01.080 And it's interesting, we always have a sense of where we are 1:03:01.080 --> 1:03:02.080 unless we're lost. 1:03:02.080 --> 1:03:05.080 And so I started at night when I got up to go to the bathroom, 1:03:05.080 --> 1:03:07.080 I would start trying to do it completely with my eyes closed 1:03:07.080 --> 1:03:09.080 all the time and I would test my sense of grid cells. 1:03:09.080 --> 1:03:13.080 I would walk five feet and say, okay, I think I'm here. 1:03:13.080 --> 1:03:14.080 Am I really there? 1:03:14.080 --> 1:03:15.080 What's my error? 1:03:15.080 --> 1:03:17.080 And then I would calculate my error again and see how the errors 1:03:17.080 --> 1:03:18.080 accumulate. 1:03:18.080 --> 1:03:20.080 So even something as simple as getting up in the middle of the 1:03:20.080 --> 1:03:22.080 night to go to the bathroom, I'm testing these theories out. 1:03:22.080 --> 1:03:23.080 It's kind of fun. 1:03:23.080 --> 1:03:25.080 I mean, the coffee cup is an example of that too. 1:03:25.080 --> 1:03:30.080 So I think I find that these sort of everyday introspections 1:03:30.080 --> 1:03:32.080 are actually quite helpful. 1:03:32.080 --> 1:03:34.080 It doesn't mean you can ignore the science. 1:03:34.080 --> 1:03:38.080 I mean, I spend hours every day reading ridiculously complex 1:03:38.080 --> 1:03:39.080 papers. 1:03:39.080 --> 1:03:41.080 That's not nearly as much fun, 1:03:41.080 --> 1:03:44.080 but you have to sort of build up those constraints and the knowledge 1:03:44.080 --> 1:03:47.080 about the field and who's doing what and what exactly they think 1:03:47.080 --> 1:03:48.080 is happening here. 1:03:48.080 --> 1:03:51.080 And then you can sit back and say, okay, let's try to have pieces 1:03:51.080 --> 1:03:52.080 all together. 1:03:52.080 --> 1:03:56.080 Let's come up with some, you know, I'm very in this group here 1:03:56.080 --> 1:03:58.080 and people, they know they do this. 1:03:58.080 --> 1:03:59.080 I do this all the time. 1:03:59.080 --> 1:04:01.080 I come in with these introspective ideas and say, well, 1:04:01.080 --> 1:04:02.080 there we ever thought about this. 1:04:02.080 --> 1:04:04.080 Now watch, well, let's all do this together. 1:04:04.080 --> 1:04:06.080 And it's helpful. 1:04:06.080 --> 1:04:10.080 It's not, as long as you don't, if all you did was that, 1:04:10.080 --> 1:04:12.080 then you're just making up stuff, right? 1:04:12.080 --> 1:04:15.080 But if you're constraining it by the reality of the neuroscience, 1:04:15.080 --> 1:04:17.080 then it's really helpful. 1:04:17.080 --> 1:04:22.080 So let's talk a little bit about deep learning and the successes 1:04:22.080 --> 1:04:28.080 in the applied space of neural networks, ideas of training model 1:04:28.080 --> 1:04:31.080 on data and these simple computational units, 1:04:31.080 --> 1:04:37.080 artificial neurons that with back propagation have statistical 1:04:37.080 --> 1:04:42.080 ways of being able to generalize from the training set on to 1:04:42.080 --> 1:04:44.080 data that similar to that training set. 1:04:44.080 --> 1:04:48.080 So where do you think are the limitations of those approaches? 1:04:48.080 --> 1:04:52.080 What do you think are strengths relative to your major efforts 1:04:52.080 --> 1:04:55.080 of constructing a theory of human intelligence? 1:04:55.080 --> 1:04:56.080 Yeah. 1:04:56.080 --> 1:04:58.080 Well, I'm not an expert in this field. 1:04:58.080 --> 1:04:59.080 I'm somewhat knowledgeable. 1:04:59.080 --> 1:05:00.080 So, but I'm not. 1:05:00.080 --> 1:05:02.080 A little bit in just your intuition. 1:05:02.080 --> 1:05:04.080 Well, I have a little bit more than intuition, 1:05:04.080 --> 1:05:07.080 but I just want to say like, you know, one of the things that you asked me, 1:05:07.080 --> 1:05:09.080 do I spend all my time thinking about neuroscience? 1:05:09.080 --> 1:05:10.080 I do. 1:05:10.080 --> 1:05:12.080 That's to the exclusion of thinking about things like convolutional neural 1:05:12.080 --> 1:05:13.080 networks. 1:05:13.080 --> 1:05:15.080 But I try to stay current. 1:05:15.080 --> 1:05:18.080 So look, I think it's great the progress they've made. 1:05:18.080 --> 1:05:19.080 It's fantastic. 1:05:19.080 --> 1:05:23.080 And as I mentioned earlier, it's very highly useful for many things. 1:05:23.080 --> 1:05:27.080 The models that we have today are actually derived from a lot of 1:05:27.080 --> 1:05:28.080 neuroscience principles. 1:05:28.080 --> 1:05:31.080 They are distributed processing systems and distributed memory systems, 1:05:31.080 --> 1:05:33.080 and that's how the brain works. 1:05:33.080 --> 1:05:36.080 And they use things that we might call them neurons, 1:05:36.080 --> 1:05:37.080 but they're really not neurons at all. 1:05:37.080 --> 1:05:39.080 So we can just, they're not really neurons. 1:05:39.080 --> 1:05:42.080 So they're distributed processing systems. 1:05:42.080 --> 1:05:47.080 And nature of hierarchy that came also from neuroscience. 1:05:47.080 --> 1:05:50.080 And so there's a lot of things, the learning rules, basically, 1:05:50.080 --> 1:05:52.080 not backprop, but other, you know, sort of heavy entire learning. 1:05:52.080 --> 1:05:55.080 I'll be curious to say they're not neurons at all. 1:05:55.080 --> 1:05:56.080 Can you describe in which way? 1:05:56.080 --> 1:06:00.080 I mean, some of it is obvious, but I'd be curious if you have specific 1:06:00.080 --> 1:06:02.080 ways in which you think are the biggest differences. 1:06:02.080 --> 1:06:06.080 Yeah, we had a paper in 2016 called Why Neurons of Thousands of Synapses. 1:06:06.080 --> 1:06:11.080 And if you read that paper, you'll know what I'm talking about here. 1:06:11.080 --> 1:06:14.080 A real neuron in the brain is a complex thing. 1:06:14.080 --> 1:06:18.080 Let's just start with the synapses on it, which is a connection between neurons. 1:06:18.080 --> 1:06:24.080 Real neurons can everywhere from five to 30,000 synapses on them. 1:06:24.080 --> 1:06:30.080 The ones near the cell body, the ones that are close to the soma, the cell body, 1:06:30.080 --> 1:06:33.080 those are like the ones that people model in artificial neurons. 1:06:33.080 --> 1:06:35.080 There's a few hundred of those. 1:06:35.080 --> 1:06:37.080 Maybe they can affect the cell. 1:06:37.080 --> 1:06:39.080 They can make the cell become active. 1:06:39.080 --> 1:06:43.080 95% of the synapses can't do that. 1:06:43.080 --> 1:06:44.080 They're too far away. 1:06:44.080 --> 1:06:47.080 So if you activate one of those synapses, it just doesn't affect the cell body 1:06:47.080 --> 1:06:49.080 enough to make any difference. 1:06:49.080 --> 1:06:50.080 Any one of them individually. 1:06:50.080 --> 1:06:53.080 Any one of them individually, or even if you do a mass of them. 1:06:53.080 --> 1:06:57.080 What real neurons do is the following. 1:06:57.080 --> 1:07:04.080 If you activate, or you get 10 to 20 of them active at the same time, 1:07:04.080 --> 1:07:06.080 meaning they're all receiving an input at the same time, 1:07:06.080 --> 1:07:10.080 and those 10 to 20 synapses or 40 synapses are within a very short distance 1:07:10.080 --> 1:07:13.080 on the dendrite, like 40 microns, a very small area. 1:07:13.080 --> 1:07:17.080 So if you activate a bunch of these right next to each other at some distant place, 1:07:17.080 --> 1:07:21.080 what happens is it creates what's called the dendritic spike. 1:07:21.080 --> 1:07:24.080 And dendritic spike travels through the dendrites 1:07:24.080 --> 1:07:27.080 and can reach the soma or the cell body. 1:07:27.080 --> 1:07:31.080 Now, when it gets there, it changes the voltage, 1:07:31.080 --> 1:07:33.080 which is sort of like going to make the cell fire, 1:07:33.080 --> 1:07:35.080 but never enough to make the cell fire. 1:07:35.080 --> 1:07:38.080 It's sort of what we call, it says we depolarize the cell. 1:07:38.080 --> 1:07:41.080 You raise the voltage a little bit, but not enough to do anything. 1:07:41.080 --> 1:07:42.080 It's like, well, what good is that? 1:07:42.080 --> 1:07:44.080 And then it goes back down again. 1:07:44.080 --> 1:07:50.080 So we proposed a theory, which I'm very confident in basics are, 1:07:50.080 --> 1:07:54.080 is that what's happening there is those 95% of the synapses 1:07:54.080 --> 1:07:58.080 are recognizing dozens to hundreds of unique patterns. 1:07:58.080 --> 1:08:01.080 They can write, you know, about 10, 20 synapses at a time, 1:08:01.080 --> 1:08:04.080 and they're acting like predictions. 1:08:04.080 --> 1:08:07.080 So the neuron actually is a predictive engine on its own. 1:08:07.080 --> 1:08:11.080 It can fire when it gets enough, what they call proximal input from those ones 1:08:11.080 --> 1:08:15.080 near the cell fire, but it can get ready to fire from dozens to hundreds 1:08:15.080 --> 1:08:17.080 of patterns that it recognizes from the other guys. 1:08:17.080 --> 1:08:22.080 And the advantage of this to the neuron is that when it actually does produce 1:08:22.080 --> 1:08:27.080 a spike in action potential, it does so slightly sooner than it would have otherwise. 1:08:27.080 --> 1:08:29.080 And so what could just slightly sooner? 1:08:29.080 --> 1:08:33.080 Well, the slightly sooner part is it, there's all the neurons in the, 1:08:33.080 --> 1:08:36.080 the excited throwing neurons in the brain are surrounded by these inhibitory neurons, 1:08:36.080 --> 1:08:40.080 and they're very fast, the inhibitory neurons, these baskets all. 1:08:40.080 --> 1:08:44.080 And if I get my spike out a little bit sooner than someone else, 1:08:44.080 --> 1:08:46.080 I inhibit all my neighbors around me, right? 1:08:46.080 --> 1:08:49.080 And what you end up with is a different representation. 1:08:49.080 --> 1:08:52.080 You end up with a representation that matches your prediction. 1:08:52.080 --> 1:08:55.080 It's a sparser representation, meaning fewer neurons are active, 1:08:55.080 --> 1:08:57.080 but it's much more specific. 1:08:57.080 --> 1:09:04.080 And so we showed how networks of these neurons can do very sophisticated temporal prediction, basically. 1:09:04.080 --> 1:09:10.080 So this summarizes real neurons in the brain are time based prediction engines, 1:09:10.080 --> 1:09:17.080 and there's no concept of this at all in artificial, what we call point neurons. 1:09:17.080 --> 1:09:19.080 I don't think you can mail the brain without them. 1:09:19.080 --> 1:09:25.080 I don't think you can build intelligence without them because it's where a large part of the time comes from. 1:09:25.080 --> 1:09:31.080 These are predictive models and the time is, there's a prior prediction and an action, 1:09:31.080 --> 1:09:34.080 and it's inherent through every neuron in the neocortex. 1:09:34.080 --> 1:09:38.080 So I would say that point neurons sort of model a piece of that, 1:09:38.080 --> 1:09:45.080 and not very well at that either, but, you know, like, for example, synapses are very unreliable, 1:09:45.080 --> 1:09:49.080 and you cannot assign any precision to them. 1:09:49.080 --> 1:09:52.080 So even one digit of precision is not possible. 1:09:52.080 --> 1:09:57.080 So the way real neurons work is they don't add these, they don't change these weights accurately, 1:09:57.080 --> 1:09:59.080 like artificial neural networks do. 1:09:59.080 --> 1:10:03.080 They basically form new synapses, and so what you're trying to always do is 1:10:03.080 --> 1:10:09.080 detect the presence of some 10 to 20 active synapses at the same time as opposed, 1:10:09.080 --> 1:10:11.080 and they're almost binary. 1:10:11.080 --> 1:10:14.080 It's like, because you can't really represent anything much finer than that. 1:10:14.080 --> 1:10:18.080 So these are the kind of, and I think that's actually another essential component 1:10:18.080 --> 1:10:24.080 because the brain works on sparse patterns, and all that mechanism is based on sparse patterns, 1:10:24.080 --> 1:10:28.080 and I don't actually think you could build real brains or machine intelligence 1:10:28.080 --> 1:10:30.080 without incorporating some of those ideas. 1:10:30.080 --> 1:10:34.080 It's hard to even think about the complexity that emerges from the fact that 1:10:34.080 --> 1:10:40.080 the timing of the firing matters in the brain, the fact that you form new synapses, 1:10:40.080 --> 1:10:44.080 and everything you just mentioned in the past couple minutes. 1:10:44.080 --> 1:10:47.080 Trust me, if you spend time on it, you can get your mind around it. 1:10:47.080 --> 1:10:49.080 It's not like it's no longer a mystery to me. 1:10:49.080 --> 1:10:53.080 No, but sorry, as a function in a mathematical way, 1:10:53.080 --> 1:10:58.080 can you start getting an intuition about what gets it excited, what not, 1:10:58.080 --> 1:11:00.080 and what kind of representation? 1:11:00.080 --> 1:11:04.080 Yeah, it's not as easy as there are many other types of neural networks 1:11:04.080 --> 1:11:10.080 that are more amenable to pure analysis, especially very simple networks. 1:11:10.080 --> 1:11:12.080 You know, oh, I have four neurons, and they're doing this. 1:11:12.080 --> 1:11:16.080 Can we describe them mathematically what they're doing type of thing? 1:11:16.080 --> 1:11:19.080 Even the complexity of convolutional neural networks today, 1:11:19.080 --> 1:11:23.080 it's sort of a mystery. They can't really describe the whole system. 1:11:23.080 --> 1:11:25.080 And so it's different. 1:11:25.080 --> 1:11:31.080 My colleague, Subitain Ahmad, he did a nice paper on this. 1:11:31.080 --> 1:11:34.080 You can get all the stuff on our website if you're interested. 1:11:34.080 --> 1:11:38.080 Talking about sort of mathematical properties of sparse representations, 1:11:38.080 --> 1:11:42.080 and so what we can do is we can show mathematically, for example, 1:11:42.080 --> 1:11:46.080 why 10 to 20 synapses to recognize a pattern is the correct number, 1:11:46.080 --> 1:11:48.080 is the right number you'd want to use. 1:11:48.080 --> 1:11:50.080 And by the way, that matches biology. 1:11:50.080 --> 1:11:55.080 We can show mathematically some of these concepts about the show 1:11:55.080 --> 1:12:01.080 why the brain is so robust to noise and error and fallout and so on. 1:12:01.080 --> 1:12:05.080 We can show that mathematically as well as empirically in simulations. 1:12:05.080 --> 1:12:08.080 But the system can't be analyzed completely. 1:12:08.080 --> 1:12:12.080 Any complex system can, and so that's out of the realm. 1:12:12.080 --> 1:12:19.080 But there is mathematical benefits and intuitions that can be derived from mathematics. 1:12:19.080 --> 1:12:21.080 And we try to do that as well. 1:12:21.080 --> 1:12:23.080 Most of our papers have a section about that. 1:12:23.080 --> 1:12:28.080 So I think it's refreshing and useful for me to be talking to you about deep neural networks, 1:12:28.080 --> 1:12:36.080 because your intuition basically says that we can't achieve anything like intelligence with artificial neural networks. 1:12:36.080 --> 1:12:37.080 Well, not in the current form. 1:12:37.080 --> 1:12:38.080 Not in the current form. 1:12:38.080 --> 1:12:40.080 I'm sure we can do it in the ultimate form, sure. 1:12:40.080 --> 1:12:43.080 So let me dig into it and see what your thoughts are there a little bit. 1:12:43.080 --> 1:12:49.080 So I'm not sure if you read this little blog post called Bitter Lesson by Rich Sutton recently. 1:12:49.080 --> 1:12:51.080 He's a reinforcement learning pioneer. 1:12:51.080 --> 1:12:53.080 I'm not sure if you're familiar with him. 1:12:53.080 --> 1:13:02.080 His basic idea is that all the stuff we've done in AI in the past 70 years, he's one of the old school guys. 1:13:02.080 --> 1:13:10.080 The biggest lesson learned is that all the tricky things we've done don't, you know, they benefit in the short term. 1:13:10.080 --> 1:13:20.080 But in the long term, what wins out is a simple general method that just relies on Moore's law on computation getting faster and faster. 1:13:20.080 --> 1:13:21.080 This is what he's saying. 1:13:21.080 --> 1:13:23.080 This is what has worked up to now. 1:13:23.080 --> 1:13:25.080 This is what has worked up to now. 1:13:25.080 --> 1:13:31.080 If you're trying to build a system, if we're talking about, he's not concerned about intelligence. 1:13:31.080 --> 1:13:38.080 He's concerned about a system that works in terms of making predictions on applied, narrow AI problems. 1:13:38.080 --> 1:13:41.080 That's what the discussion is about. 1:13:41.080 --> 1:13:50.080 That you just try to go as general as possible and wait years or decades for the computation to make it actually possible. 1:13:50.080 --> 1:13:54.080 Is he saying that as a criticism or is he saying this is a prescription of what we ought to be doing? 1:13:54.080 --> 1:13:55.080 Well, it's very difficult. 1:13:55.080 --> 1:13:57.080 He's saying this is what has worked. 1:13:57.080 --> 1:14:03.080 And yes, a prescription, but it's a difficult prescription because it says all the fun things you guys are trying to do. 1:14:03.080 --> 1:14:05.080 We are trying to do. 1:14:05.080 --> 1:14:07.080 He's part of the community. 1:14:07.080 --> 1:14:11.080 He's saying it's only going to be short term gains. 1:14:11.080 --> 1:14:19.080 This all leads up to a question, I guess, on artificial neural networks and maybe our own biological neural networks. 1:14:19.080 --> 1:14:24.080 Do you think if we just scale things up significantly? 1:14:24.080 --> 1:14:28.080 Take these dumb artificial neurons, the point neurons. 1:14:28.080 --> 1:14:30.080 I like that term. 1:14:30.080 --> 1:14:36.080 If we just have a lot more of them, do you think some of the elements that we see in the brain 1:14:36.080 --> 1:14:38.080 may start emerging? 1:14:38.080 --> 1:14:39.080 No, I don't think so. 1:14:39.080 --> 1:14:43.080 We can do bigger problems of the same type. 1:14:43.080 --> 1:14:50.080 I mean, it's been pointed out by many people that today's convolutional neural networks aren't really much different than the ones we had quite a while ago. 1:14:50.080 --> 1:14:56.080 We just, they're bigger and train more and we have more labeled data and so on. 1:14:56.080 --> 1:15:03.080 But I don't think you can get to the kind of things I know the brain can do and that we think about as intelligence by just scaling it up. 1:15:03.080 --> 1:15:12.080 So that may be, it's a good description of what's happened in the past, what's happened recently with the reemergence of artificial neural networks. 1:15:12.080 --> 1:15:17.080 It may be a good prescription for what's going to happen in the short term. 1:15:17.080 --> 1:15:19.080 But I don't think that's the path. 1:15:19.080 --> 1:15:20.080 I've said that earlier. 1:15:20.080 --> 1:15:21.080 There's an alternate path. 1:15:21.080 --> 1:15:29.080 I should mention to you, by the way, that we've made sufficient progress on our, the whole cortical theory in the last few years. 1:15:29.080 --> 1:15:40.080 But last year, we decided to start actively pursuing how we get these ideas embedded into machine learning. 1:15:40.080 --> 1:15:45.080 That's, again, being led by my colleague, and he's more of a machine learning guy. 1:15:45.080 --> 1:15:47.080 I'm more of an neuroscience guy. 1:15:47.080 --> 1:15:58.080 So this is now our, I wouldn't say our focus, but it is now an equal focus here because we need to proselytize what we've learned. 1:15:58.080 --> 1:16:03.080 And we need to show how it's beneficial to the machine learning. 1:16:03.080 --> 1:16:05.080 So we're putting, we have a plan in place right now. 1:16:05.080 --> 1:16:07.080 In fact, we just did our first paper on this. 1:16:07.080 --> 1:16:09.080 I can tell you about that. 1:16:09.080 --> 1:16:15.080 But, you know, one of the reasons I want to talk to you is because I'm trying to get more people in the machine learning community to say, 1:16:15.080 --> 1:16:17.080 I need to learn about this stuff. 1:16:17.080 --> 1:16:21.080 And maybe we should just think about this a bit more about what we've learned about the brain. 1:16:21.080 --> 1:16:23.080 And what are those team, what have they done? 1:16:23.080 --> 1:16:25.080 Is that useful for us? 1:16:25.080 --> 1:16:32.080 Yeah, so is there elements of all the, the cortical theory that things we've been talking about that may be useful in the short term? 1:16:32.080 --> 1:16:34.080 Yes, in the short term, yes. 1:16:34.080 --> 1:16:41.080 This is the, sorry to interrupt, but the, the open question is it, it certainly feels from my perspective that in the long term, 1:16:41.080 --> 1:16:44.080 some of the ideas we've been talking about will be extremely useful. 1:16:44.080 --> 1:16:46.080 The question is whether in the short term. 1:16:46.080 --> 1:16:51.080 Well, this is a, always what we, I would call the entrepreneur's dilemma. 1:16:51.080 --> 1:16:59.080 You have this long term vision, oh, we're going to all be driving electric cars or we're all going to have computers or we're all going to whatever. 1:16:59.080 --> 1:17:03.080 And, and you're at some point in time and you say, I can see that long term vision. 1:17:03.080 --> 1:17:04.080 I'm sure it's going to happen. 1:17:04.080 --> 1:17:07.080 How do I get there without killing myself, you know, without going out of business? 1:17:07.080 --> 1:17:09.080 That's the challenge. 1:17:09.080 --> 1:17:10.080 That's the dilemma. 1:17:10.080 --> 1:17:11.080 That's the really difficult thing to do. 1:17:11.080 --> 1:17:13.080 So we're facing that right now. 1:17:13.080 --> 1:17:17.080 So ideally what you'd want to do is find some steps along the way that you can get there incrementally. 1:17:17.080 --> 1:17:20.080 You don't have to like throw it all out and start over again. 1:17:20.080 --> 1:17:25.080 The first thing that we've done is we focus on these sparse representations. 1:17:25.080 --> 1:17:30.080 So just in case you don't know what that means or some of the listeners don't know what that means. 1:17:30.080 --> 1:17:37.080 In the brain, if I have like 10,000 neurons, what you would see is maybe 2% of them active at a time. 1:17:37.080 --> 1:17:41.080 You don't see 50%, you don't see 30%, you might see 2%. 1:17:41.080 --> 1:17:42.080 And it's always like that. 1:17:42.080 --> 1:17:44.080 For any set of sensory inputs. 1:17:44.080 --> 1:17:45.080 It doesn't matter anything. 1:17:45.080 --> 1:17:47.080 It doesn't matter any part of the brain. 1:17:47.080 --> 1:17:51.080 But which neurons differs? 1:17:51.080 --> 1:17:52.080 Which neurons are active? 1:17:52.080 --> 1:17:53.080 Yeah. 1:17:53.080 --> 1:17:54.080 So let me put this. 1:17:54.080 --> 1:17:56.080 Let's say I take 10,000 neurons that are representing something. 1:17:56.080 --> 1:17:58.080 They're sitting there in a little block together. 1:17:58.080 --> 1:18:00.080 It's a teeny little block of neurons, 10,000 neurons. 1:18:00.080 --> 1:18:01.080 And they're representing a location. 1:18:01.080 --> 1:18:02.080 They're representing a cop. 1:18:02.080 --> 1:18:04.080 They're representing the input from my sensors. 1:18:04.080 --> 1:18:05.080 I don't know. 1:18:05.080 --> 1:18:06.080 It doesn't matter. 1:18:06.080 --> 1:18:07.080 It's representing something. 1:18:07.080 --> 1:18:10.080 The way the representations occur, it's always a sparse representation. 1:18:10.080 --> 1:18:12.080 Meaning it's a population code. 1:18:12.080 --> 1:18:15.080 So which 200 cells are active tells me what's going on. 1:18:15.080 --> 1:18:18.080 It's not individual cells aren't that important at all. 1:18:18.080 --> 1:18:20.080 It's the population code that matters. 1:18:20.080 --> 1:18:23.080 And when you have sparse population codes, 1:18:23.080 --> 1:18:26.080 then all kinds of beautiful properties come out of them. 1:18:26.080 --> 1:18:29.080 So the brain uses sparse population codes that we've written 1:18:29.080 --> 1:18:32.080 and described these benefits in some of our papers. 1:18:32.080 --> 1:18:37.080 So they give this tremendous robustness to the systems. 1:18:37.080 --> 1:18:39.080 You know, brains are incredibly robust. 1:18:39.080 --> 1:18:42.080 Neurons are dying all the time and spasming and synapses falling apart. 1:18:42.080 --> 1:18:45.080 And, you know, all the time and it keeps working. 1:18:45.080 --> 1:18:52.080 So what Subitai and Louise, one of our other engineers here have done, 1:18:52.080 --> 1:18:56.080 have shown that they're introducing sparseness into convolutional neural networks. 1:18:56.080 --> 1:18:58.080 Now other people are thinking along these lines, 1:18:58.080 --> 1:19:00.080 but we're going about it in a more principled way, I think. 1:19:00.080 --> 1:19:06.080 And we're showing that if you enforce sparseness throughout these convolutional neural networks, 1:19:06.080 --> 1:19:13.080 in both the sort of which neurons are active and the connections between them, 1:19:13.080 --> 1:19:15.080 that you get some very desirable properties. 1:19:15.080 --> 1:19:20.080 So one of the current hot topics in deep learning right now are these adversarial examples. 1:19:20.080 --> 1:19:23.080 So, you know, you give me any deep learning network 1:19:23.080 --> 1:19:27.080 and I can give you a picture that looks perfect and you're going to call it, you know, 1:19:27.080 --> 1:19:30.080 you're going to say the monkey is, you know, an airplane. 1:19:30.080 --> 1:19:32.080 So that's a problem. 1:19:32.080 --> 1:19:36.080 And DARPA just announced some big thing and we're trying to, you know, have some contests for this. 1:19:36.080 --> 1:19:40.080 But if you enforce sparse representations here, 1:19:40.080 --> 1:19:41.080 many of these problems go away. 1:19:41.080 --> 1:19:45.080 They're much more robust and they're not easy to fool. 1:19:45.080 --> 1:19:48.080 So we've already shown some of those results, 1:19:48.080 --> 1:19:53.080 just literally in January or February, just like last month we did that. 1:19:53.080 --> 1:19:59.080 And you can, I think it's on bioarchive right now or on iCry, you can read about it. 1:19:59.080 --> 1:20:02.080 But so that's like a baby step. 1:20:02.080 --> 1:20:04.080 Okay. That's a take something from the brain. 1:20:04.080 --> 1:20:05.080 We know, we know about sparseness. 1:20:05.080 --> 1:20:06.080 We know why it's important. 1:20:06.080 --> 1:20:08.080 We know what it gives the brain. 1:20:08.080 --> 1:20:09.080 So let's try to enforce that onto this. 1:20:09.080 --> 1:20:12.080 What's your intuition why sparsity leads to robustness? 1:20:12.080 --> 1:20:14.080 Because it feels like it would be less robust. 1:20:14.080 --> 1:20:17.080 Why would you feel the rest robust to you? 1:20:17.080 --> 1:20:24.080 So it, it just feels like if the fewer neurons are involved, 1:20:24.080 --> 1:20:26.080 the more fragile the representation. 1:20:26.080 --> 1:20:28.080 Yeah, but I didn't say there was lots of few. 1:20:28.080 --> 1:20:30.080 I said, let's say 200. 1:20:30.080 --> 1:20:31.080 That's a lot. 1:20:31.080 --> 1:20:32.080 There's still a lot. 1:20:32.080 --> 1:20:33.080 Yeah. 1:20:33.080 --> 1:20:35.080 So here's an intuition for it. 1:20:35.080 --> 1:20:37.080 This is a bit technical. 1:20:37.080 --> 1:20:41.080 So for, you know, for engineers, machine learning people this be easy, 1:20:41.080 --> 1:20:44.080 but God's listeners, maybe not. 1:20:44.080 --> 1:20:46.080 If you're trying to classify something, 1:20:46.080 --> 1:20:50.080 you're trying to divide some very high dimensional space into different pieces, A and B. 1:20:50.080 --> 1:20:55.080 And you're trying to create some point where you say all these points in this high dimensional space are A 1:20:55.080 --> 1:20:57.080 and all these points in this high dimensional space are B. 1:20:57.080 --> 1:21:03.080 And if you have points that are close to that line, it's not very robust. 1:21:03.080 --> 1:21:07.080 It works for all the points you know about, but it's, it's not very robust 1:21:07.080 --> 1:21:10.080 because you can just move a little bit and you've crossed over the line. 1:21:10.080 --> 1:21:14.080 When you have sparse representations, imagine I pick, I have, 1:21:14.080 --> 1:21:18.080 I'm going to pick 200 cells active out of, out of 10,000. 1:21:18.080 --> 1:21:19.080 Okay. 1:21:19.080 --> 1:21:20.080 So I have 200 cells active. 1:21:20.080 --> 1:21:24.080 Now let's say I pick randomly another, a different representation, 200. 1:21:24.080 --> 1:21:27.080 The overlap between those is going to be very small, just a few. 1:21:27.080 --> 1:21:36.080 I can pick millions of samples randomly of 200 neurons and not one of them will overlap more than just a few. 1:21:36.080 --> 1:21:43.080 So one way to think about it is if I want to fool one of these representations to look like one of those other representations, 1:21:43.080 --> 1:21:46.080 I can't move just one cell or two cells or three cells or four cells. 1:21:46.080 --> 1:21:48.080 I have to move 100 cells. 1:21:48.080 --> 1:21:52.080 And that makes them robust. 1:21:52.080 --> 1:21:56.080 In terms of further, so you mentioned sparsity. 1:21:56.080 --> 1:21:57.080 Will we be the next thing? 1:21:57.080 --> 1:21:58.080 Yeah. 1:21:58.080 --> 1:21:59.080 Okay. 1:21:59.080 --> 1:22:00.080 So we have, we picked one. 1:22:00.080 --> 1:22:02.080 We don't know if it's going to work well yet. 1:22:02.080 --> 1:22:08.080 So again, we're trying to come up incremental ways of moving from brain theory to add pieces to machine learning, 1:22:08.080 --> 1:22:12.080 current machine learning world in one step at a time. 1:22:12.080 --> 1:22:20.080 So the next thing we're going to try to do is, is sort of incorporate some of the ideas of the, the thousand brains theory that you have many, 1:22:20.080 --> 1:22:22.080 many models and that are voting. 1:22:22.080 --> 1:22:23.080 Now that idea is not new. 1:22:23.080 --> 1:22:26.080 There's a mixture of models that's been around for a long time. 1:22:26.080 --> 1:22:29.080 But the way the brain does it is a little different. 1:22:29.080 --> 1:22:36.080 And, and the way it votes is different and the kind of way it represents uncertainty is different. 1:22:36.080 --> 1:22:43.080 So we're just starting this work, but we're going to try to see if we can sort of incorporate some of the principles of voting 1:22:43.080 --> 1:22:53.080 or principles of a thousand brain theory, like lots of simple models that talk to each other in a, in a very certain way. 1:22:53.080 --> 1:23:07.080 And can we build more machines and systems that learn faster and, and also, well, mostly are multimodal and robust to multimodal type of issues. 1:23:07.080 --> 1:23:15.080 So one of the challenges there is, you know, the machine learning computer vision community has certain sets of benchmarks. 1:23:15.080 --> 1:23:18.080 So it's a test based on which they compete. 1:23:18.080 --> 1:23:29.080 And I would argue, especially from your perspective, that those benchmarks aren't that useful for testing the aspects that the brain is good at or intelligent. 1:23:29.080 --> 1:23:31.080 They're not really testing intelligence. 1:23:31.080 --> 1:23:41.080 They're very fine and has been extremely useful for developing specific mathematical models, but it's not useful in the long term for creating intelligence. 1:23:41.080 --> 1:23:46.080 So do you think you also have a role in proposing better tests? 1:23:46.080 --> 1:23:50.080 Yeah, this is a very, you've identified a very serious problem. 1:23:50.080 --> 1:23:57.080 First of all, the tests that they have are the tests that they want, not the tests of the other things that we're trying to do. 1:23:57.080 --> 1:24:01.080 Right. You know, what are the, so on. 1:24:01.080 --> 1:24:10.080 The second thing is sometimes these to be competitive in these tests, you have to have huge data sets and huge computing power. 1:24:10.080 --> 1:24:13.080 And so, you know, and we don't have that here. 1:24:13.080 --> 1:24:18.080 We don't have it as well as other big teams that big companies do. 1:24:18.080 --> 1:24:20.080 So there's numerous issues there. 1:24:20.080 --> 1:24:26.080 You know, we come at it, you know, we're our approach to this is all based on, in some sense, you might argue elegance. 1:24:26.080 --> 1:24:30.080 You know, we're coming at it from like a theoretical base that we think, oh my God, this is so clearly elegant. 1:24:30.080 --> 1:24:31.080 This is how brains work. 1:24:31.080 --> 1:24:32.080 This is what intelligence is. 1:24:32.080 --> 1:24:35.080 But the machine learning world has gotten in this phase where they think it doesn't matter. 1:24:35.080 --> 1:24:39.080 Doesn't matter what you think, as long as you do, you know, 0.1% better on this benchmark. 1:24:39.080 --> 1:24:41.080 That's what that's all that matters. 1:24:41.080 --> 1:24:43.080 And that's a problem. 1:24:43.080 --> 1:24:46.080 You know, we have to figure out how to get around that. 1:24:46.080 --> 1:24:47.080 That's a challenge for us. 1:24:47.080 --> 1:24:50.080 That's one of the challenges we have to deal with. 1:24:50.080 --> 1:24:53.080 So I agree you've identified a big issue. 1:24:53.080 --> 1:24:55.080 It's difficult for those reasons. 1:24:55.080 --> 1:25:02.080 But, you know, part of the reasons I'm talking to you here today is I hope I'm going to get some machine learning people to say, 1:25:02.080 --> 1:25:03.080 I'm going to read those papers. 1:25:03.080 --> 1:25:04.080 Those might be some interesting ideas. 1:25:04.080 --> 1:25:08.080 I'm tired of doing this 0.1% improvement stuff, you know. 1:25:08.080 --> 1:25:21.080 Well, that's why I'm here as well, because I think machine learning now as a community is at a place where the next step is needs to be orthogonal to what has received success in the past. 1:25:21.080 --> 1:25:27.080 You see other leaders saying this, machine learning leaders, you know, Jeff Hinton with his capsules idea. 1:25:27.080 --> 1:25:33.080 Many people have gotten up saying, you know, we're going to hit road, maybe we should look at the brain, you know, things like that. 1:25:33.080 --> 1:25:37.080 So hopefully that thinking will occur organically. 1:25:37.080 --> 1:25:43.080 And then we're in a nice position for people to come and look at our work and say, well, what can we learn from these guys? 1:25:43.080 --> 1:25:49.080 Yeah, MIT is just launching a billion dollar computing college that's centered around this idea. 1:25:49.080 --> 1:25:51.080 On this idea of what? 1:25:51.080 --> 1:25:59.080 Well, the idea that, you know, the humanities, psychology, neuroscience have to work all together to get to build the S. 1:25:59.080 --> 1:26:02.080 Yeah, I mean, Stanford just did this human center today, I think. 1:26:02.080 --> 1:26:10.080 I'm a little disappointed in these initiatives because, you know, they're focusing on sort of the human side of it, 1:26:10.080 --> 1:26:17.080 and it can very easily slip into how humans interact with intelligent machines, which is nothing wrong with that. 1:26:17.080 --> 1:26:20.080 But that's not, that is orthogonal to what we're trying to do. 1:26:20.080 --> 1:26:22.080 We're trying to say, like, what is the essence of intelligence? 1:26:22.080 --> 1:26:23.080 I don't care. 1:26:23.080 --> 1:26:31.080 In fact, I want to build intelligent machines that aren't emotional, that don't smile at you, that, you know, that aren't trying to tuck you in at night. 1:26:31.080 --> 1:26:38.080 Yeah, there is that pattern that you, when you talk about understanding humans is important for understanding intelligence. 1:26:38.080 --> 1:26:47.080 You start slipping into topics of ethics or, yeah, like you said, the interactive elements as opposed to, no, no, no, let's zoom in on the brain, 1:26:47.080 --> 1:26:51.080 study what the human brain, the baby, the... 1:26:51.080 --> 1:26:53.080 Let's study what a brain does. 1:26:53.080 --> 1:26:57.080 And then we can decide which parts of that we want to recreate in some system. 1:26:57.080 --> 1:27:00.080 But until you have that theory about what the brain does, what's the point? 1:27:00.080 --> 1:27:03.080 You know, it's just, you're going to be wasting time, I think. 1:27:03.080 --> 1:27:09.080 Just to break it down on the artificial neural network side, maybe you can speak to this on the, on the biologic neural network side, 1:27:09.080 --> 1:27:13.080 the process of learning versus the process of inference. 1:27:13.080 --> 1:27:22.080 Maybe you can explain to me, what, is there a difference between, you know, in artificial neural networks, there's a difference between the learning stage and the inference stage? 1:27:22.080 --> 1:27:23.080 Yeah. 1:27:23.080 --> 1:27:25.080 Do you see the brain as something different? 1:27:25.080 --> 1:27:33.080 One of the big distinctions that people often say, I don't know how correct it is, is artificial neural networks need a lot of data. 1:27:33.080 --> 1:27:34.080 They're very inefficient learning. 1:27:34.080 --> 1:27:35.080 Yeah. 1:27:35.080 --> 1:27:42.080 Do you see that as a correct distinction from the biology of the human brain, that the human brain is very efficient? 1:27:42.080 --> 1:27:44.080 Or is that just something we deceive ourselves with? 1:27:44.080 --> 1:27:45.080 No, it is efficient, obviously. 1:27:45.080 --> 1:27:47.080 We can learn new things almost instantly. 1:27:47.080 --> 1:27:50.080 And so what elements do you think... 1:27:50.080 --> 1:27:51.080 Yeah, I can talk about that. 1:27:51.080 --> 1:27:52.080 You brought up two issues there. 1:27:52.080 --> 1:28:00.080 So remember I talked early about the constraints, we always feel, well, one of those constraints is the fact that brains are continually learning. 1:28:00.080 --> 1:28:03.080 That's not something we said, oh, we can add that later. 1:28:03.080 --> 1:28:11.080 That's something that was upfront, had to be there from the start, made our problems harder. 1:28:11.080 --> 1:28:19.080 But we showed, going back to the 2016 paper on sequence memory, we showed how that happens, how the brains infer and learn at the same time. 1:28:19.080 --> 1:28:22.080 And our models do that. 1:28:22.080 --> 1:28:26.080 They're not two separate phases or two separate sets of time. 1:28:26.080 --> 1:28:33.080 I think that's a big, big problem in AI, at least for many applications, not for all. 1:28:33.080 --> 1:28:34.080 So I can talk about that. 1:28:34.080 --> 1:28:37.080 It gets detailed. 1:28:37.080 --> 1:28:46.080 There are some parts of the neocortex in the brain where actually what's going on, there's these cycles of activity in the brain. 1:28:46.080 --> 1:28:54.080 And there's very strong evidence that you're doing more of inference on one part of the phase and more of learning on the other part of the phase. 1:28:54.080 --> 1:28:58.080 So the brain can actually sort of separate different populations of cells that are going back and forth like this. 1:28:58.080 --> 1:29:01.080 But in general, I would say that's an important problem. 1:29:01.080 --> 1:29:05.080 We have all of our networks that we've come up with do both. 1:29:05.080 --> 1:29:08.080 They're continuous learning networks. 1:29:08.080 --> 1:29:10.080 And you mentioned benchmarks earlier. 1:29:10.080 --> 1:29:12.080 Well, there are no benchmarks about that. 1:29:12.080 --> 1:29:13.080 Exactly. 1:29:13.080 --> 1:29:19.080 So we have to like, we get in our little soapbox and hey, by the way, this is important. 1:29:19.080 --> 1:29:21.080 And here's the mechanism for doing that. 1:29:21.080 --> 1:29:26.080 But until you can prove it to someone in some commercial system or something, it's a little harder. 1:29:26.080 --> 1:29:33.080 So one of the things I had to linger on that is in some ways to learn the concept of a coffee cup. 1:29:33.080 --> 1:29:38.080 You only need this one coffee cup and maybe some time alone in a room with it. 1:29:38.080 --> 1:29:43.080 So the first thing is I imagine I reach my hand into a black box and I'm reaching, I'm trying to touch something. 1:29:43.080 --> 1:29:47.080 I don't know up front if it's something I already know or if it's a new thing. 1:29:47.080 --> 1:29:50.080 And I have to, I'm doing both at the same time. 1:29:50.080 --> 1:29:53.080 I don't say, oh, let's see if it's a new thing. 1:29:53.080 --> 1:29:55.080 Oh, let's see if it's an old thing. 1:29:55.080 --> 1:29:56.080 I don't do that. 1:29:56.080 --> 1:29:59.080 As I go, my brain says, oh, it's new or it's not new. 1:29:59.080 --> 1:30:02.080 And if it's new, I start learning what it is. 1:30:02.080 --> 1:30:06.080 And by the way, it starts learning from the get go, even if it's going to recognize it. 1:30:06.080 --> 1:30:09.080 So they're not separate problems. 1:30:09.080 --> 1:30:10.080 And so that's the thing there. 1:30:10.080 --> 1:30:13.080 The other thing you mentioned was the fast learning. 1:30:13.080 --> 1:30:17.080 So I was just talking about continuous learning, but there's also fast learning. 1:30:17.080 --> 1:30:20.080 Literally, I can show you this coffee cup and I say, here's a new coffee cup. 1:30:20.080 --> 1:30:21.080 It's got the logo on it. 1:30:21.080 --> 1:30:22.080 Take a look at it. 1:30:22.080 --> 1:30:23.080 Done. 1:30:23.080 --> 1:30:24.080 You're done. 1:30:24.080 --> 1:30:27.080 You can predict what it's going to look like, you know, in different positions. 1:30:27.080 --> 1:30:29.080 So I can talk about that too. 1:30:29.080 --> 1:30:30.080 Yes. 1:30:30.080 --> 1:30:34.080 In the brain, the way learning occurs. 1:30:34.080 --> 1:30:36.080 I mentioned this earlier, but I mentioned it again. 1:30:36.080 --> 1:30:40.080 The way learning occurs, I imagine I have a section of a dendrite of a neuron. 1:30:40.080 --> 1:30:43.080 And I want to learn, I'm going to learn something new. 1:30:43.080 --> 1:30:44.080 It just doesn't matter what it is. 1:30:44.080 --> 1:30:46.080 I'm just going to learn something new. 1:30:46.080 --> 1:30:48.080 I need to recognize a new pattern. 1:30:48.080 --> 1:30:52.080 So what I'm going to do is I'm going to form new synapses. 1:30:52.080 --> 1:30:57.080 New synapses, we're going to rewire the brain onto that section of the dendrite. 1:30:57.080 --> 1:31:02.080 Once I've done that, everything else that neuron has learned is not affected by it. 1:31:02.080 --> 1:31:06.080 Now, it's because it's isolated to that small section of the dendrite. 1:31:06.080 --> 1:31:09.080 They're not all being added together, like a point neuron. 1:31:09.080 --> 1:31:13.080 So if I learn something new on this segment here, it doesn't change any of the learning 1:31:13.080 --> 1:31:15.080 that occur anywhere else in that neuron. 1:31:15.080 --> 1:31:18.080 So I can add something without affecting previous learning. 1:31:18.080 --> 1:31:20.080 And I can do it quickly. 1:31:20.080 --> 1:31:24.080 Now, let's talk, we can talk about the quickness, how it's done in real neurons. 1:31:24.080 --> 1:31:27.080 You might say, well, doesn't it take time to form synapses? 1:31:27.080 --> 1:31:30.080 Yes, it can take maybe an hour to form a new synapse. 1:31:30.080 --> 1:31:32.080 We can form memories quicker than that. 1:31:32.080 --> 1:31:35.080 And I can explain that happens too, if you want. 1:31:35.080 --> 1:31:38.080 But it's getting a bit neurosciencey. 1:31:38.080 --> 1:31:40.080 That's great. 1:31:40.080 --> 1:31:43.080 But is there an understanding of these mechanisms at every level? 1:31:43.080 --> 1:31:48.080 So from the short term memories and the forming new connections. 1:31:48.080 --> 1:31:51.080 So this idea of synaptogenesis, the growth of new synapses, 1:31:51.080 --> 1:31:54.080 that's well described, as well understood. 1:31:54.080 --> 1:31:56.080 And that's an essential part of learning. 1:31:56.080 --> 1:31:57.080 That is learning. 1:31:57.080 --> 1:31:58.080 That is learning. 1:31:58.080 --> 1:32:00.080 Okay. 1:32:00.080 --> 1:32:04.080 You know, back, you know, going back many, many years, people, you know, 1:32:04.080 --> 1:32:08.080 was, what's his name, the psychologist proposed, 1:32:08.080 --> 1:32:10.080 Hebb, Donald Hebb. 1:32:10.080 --> 1:32:13.080 He proposed that learning was the modification of the strength 1:32:13.080 --> 1:32:15.080 of a connection between two neurons. 1:32:15.080 --> 1:32:19.080 People interpreted that as the modification of the strength of a synapse. 1:32:19.080 --> 1:32:21.080 He didn't say that. 1:32:21.080 --> 1:32:24.080 He just said there's a modification between the effect of one neuron and another. 1:32:24.080 --> 1:32:28.080 So synaptogenesis is totally consistent with Donald Hebb said. 1:32:28.080 --> 1:32:31.080 But anyway, there's these mechanisms, the growth of new synapse. 1:32:31.080 --> 1:32:34.080 You can go online, you can watch a video of a synapse growing in real time. 1:32:34.080 --> 1:32:36.080 It's literally, you can see this little thing going. 1:32:36.080 --> 1:32:38.080 It's pretty impressive. 1:32:38.080 --> 1:32:40.080 So that those mechanisms are known. 1:32:40.080 --> 1:32:43.080 Now, there's another thing that we've speculated and we've written about, 1:32:43.080 --> 1:32:48.080 which is consistent with no neuroscience, but it's less proven. 1:32:48.080 --> 1:32:49.080 And this is the idea. 1:32:49.080 --> 1:32:51.080 How do I form a memory really, really quickly? 1:32:51.080 --> 1:32:53.080 Like instantaneous. 1:32:53.080 --> 1:32:56.080 If it takes an hour to grow a synapse, like that's not instantaneous. 1:32:56.080 --> 1:33:01.080 So there are types of synapses called silent synapses. 1:33:01.080 --> 1:33:04.080 They look like a synapse, but they don't do anything. 1:33:04.080 --> 1:33:05.080 They're just sitting there. 1:33:05.080 --> 1:33:10.080 It's like if an action potential comes in, it doesn't release any neurotransmitter. 1:33:10.080 --> 1:33:12.080 Some parts of the brain have more of these than others. 1:33:12.080 --> 1:33:14.080 For example, the hippocampus has a lot of them, 1:33:14.080 --> 1:33:18.080 which is where we associate most short term memory with. 1:33:18.080 --> 1:33:22.080 So what we speculated, again, in that 2016 paper, 1:33:22.080 --> 1:33:26.080 we proposed that the way we form very quick memories, 1:33:26.080 --> 1:33:29.080 very short term memories, or quick memories, 1:33:29.080 --> 1:33:34.080 is that we convert silent synapses into active synapses. 1:33:34.080 --> 1:33:38.080 It's like saying a synapse has a zero weight and a one weight. 1:33:38.080 --> 1:33:41.080 But the long term memory has to be formed by synaptogenesis. 1:33:41.080 --> 1:33:43.080 So you can remember something really quickly 1:33:43.080 --> 1:33:46.080 by just flipping a bunch of these guys from silent to active. 1:33:46.080 --> 1:33:49.080 It's not from 0.1 to 0.15. 1:33:49.080 --> 1:33:52.080 It doesn't do anything until it releases transmitter. 1:33:52.080 --> 1:33:56.080 If I do that over a bunch of these, I've got a very quick short term memory. 1:33:56.080 --> 1:34:01.080 So I guess the lesson behind this is that most neural networks today are fully connected. 1:34:01.080 --> 1:34:04.080 Every neuron connects every other neuron from layer to layer. 1:34:04.080 --> 1:34:06.080 That's not correct in the brain. 1:34:06.080 --> 1:34:07.080 We don't want that. 1:34:07.080 --> 1:34:08.080 We actually don't want that. 1:34:08.080 --> 1:34:09.080 It's bad. 1:34:09.080 --> 1:34:13.080 You want a very sparse connectivity so that any neuron connects 1:34:13.080 --> 1:34:15.080 to some subset of the neurons in the other layer, 1:34:15.080 --> 1:34:18.080 and it does so on a dendrite by dendrite segment basis. 1:34:18.080 --> 1:34:21.080 So it's a very parcelated out type of thing. 1:34:21.080 --> 1:34:25.080 And that then learning is not adjusting all these weights, 1:34:25.080 --> 1:34:29.080 but learning is just saying, OK, connect to these 10 cells here right now. 1:34:29.080 --> 1:34:32.080 In that process, you know, with artificial neural networks, 1:34:32.080 --> 1:34:37.080 it's a very simple process of back propagation that adjusts the weights. 1:34:37.080 --> 1:34:39.080 The process of synaptogenesis. 1:34:39.080 --> 1:34:40.080 Synaptogenesis. 1:34:40.080 --> 1:34:41.080 Synaptogenesis. 1:34:41.080 --> 1:34:42.080 It's even easier. 1:34:42.080 --> 1:34:43.080 It's even easier. 1:34:43.080 --> 1:34:44.080 It's even easier. 1:34:44.080 --> 1:34:49.080 Back propagation requires something that really can't happen in brains. 1:34:49.080 --> 1:34:51.080 This back propagation of this error signal. 1:34:51.080 --> 1:34:52.080 They really can't happen. 1:34:52.080 --> 1:34:55.080 People are trying to make it happen in brains, but it doesn't happen in brain. 1:34:55.080 --> 1:34:57.080 This is pure Hebbian learning. 1:34:57.080 --> 1:34:59.080 Well, synaptogenesis is pure Hebbian learning. 1:34:59.080 --> 1:35:03.080 It's basically saying there's a population of cells over here that are active right now, 1:35:03.080 --> 1:35:05.080 and there's a population of cells over here active right now. 1:35:05.080 --> 1:35:08.080 How do I form connections between those active cells? 1:35:08.080 --> 1:35:11.080 And it's literally saying this guy became active. 1:35:11.080 --> 1:35:15.080 These 100 neurons here became active before this neuron became active. 1:35:15.080 --> 1:35:17.080 So form connections to those ones. 1:35:17.080 --> 1:35:18.080 That's it. 1:35:18.080 --> 1:35:20.080 There's no propagation of error, nothing. 1:35:20.080 --> 1:35:26.080 All the networks we do, all models we have work on almost completely on Hebbian learning, 1:35:26.080 --> 1:35:33.080 but on dendritic segments and multiple synaptoses at the same time. 1:35:33.080 --> 1:35:36.080 So now let's turn the question that you already answered, 1:35:36.080 --> 1:35:38.080 and maybe you can answer it again. 1:35:38.080 --> 1:35:43.080 If you look at the history of artificial intelligence, where do you think we stand? 1:35:43.080 --> 1:35:45.080 How far are we from solving intelligence? 1:35:45.080 --> 1:35:47.080 You said you were very optimistic. 1:35:47.080 --> 1:35:48.080 Yeah. 1:35:48.080 --> 1:35:49.080 Can you elaborate on that? 1:35:49.080 --> 1:35:55.080 Yeah, it's always the crazy question to ask, because no one can predict the future. 1:35:55.080 --> 1:35:56.080 Absolutely. 1:35:56.080 --> 1:35:58.080 So I'll tell you a story. 1:35:58.080 --> 1:36:02.080 I used to run a different neuroscience institute called the Redwood Neuroscience Institute, 1:36:02.080 --> 1:36:07.080 and we would hold these symposiums, and we'd get like 35 scientists from around the world to come together. 1:36:07.080 --> 1:36:09.080 And I used to ask them all the same question. 1:36:09.080 --> 1:36:13.080 I would say, well, how long do you think it'll be before we understand how the New York Cortex works? 1:36:13.080 --> 1:36:17.080 And everyone went around the room, and they had introduced the name, and they had to answer that question. 1:36:17.080 --> 1:36:22.080 So I got, the typical answer was 50 to 100 years. 1:36:22.080 --> 1:36:24.080 Some people would say 500 years. 1:36:24.080 --> 1:36:25.080 Some people said never. 1:36:25.080 --> 1:36:27.080 I said, why are you a neuroscience institute? 1:36:27.080 --> 1:36:28.080 Never. 1:36:28.080 --> 1:36:30.080 It's good pay. 1:36:30.080 --> 1:36:33.080 It's interesting. 1:36:33.080 --> 1:36:37.080 But it doesn't work like that. 1:36:37.080 --> 1:36:39.080 As I mentioned earlier, these are step functions. 1:36:39.080 --> 1:36:41.080 Things happen, and then bingo, they happen. 1:36:41.080 --> 1:36:43.080 You can't predict that. 1:36:43.080 --> 1:36:45.080 I feel I've already passed a step function. 1:36:45.080 --> 1:36:53.080 So if I can do my job correctly over the next five years, then meaning I can proselytize these ideas. 1:36:53.080 --> 1:36:55.080 I can convince other people they're right. 1:36:55.080 --> 1:37:01.080 We can show that machine learning people should pay attention to these ideas. 1:37:01.080 --> 1:37:04.080 Then we're definitely in an under 20 year time frame. 1:37:04.080 --> 1:37:09.080 If I can do those things, if I'm not successful in that, and this is the last time anyone talks to me, 1:37:09.080 --> 1:37:15.080 and no one reads our papers, and I'm wrong or something like that, then I don't know. 1:37:15.080 --> 1:37:17.080 But it's not 50 years. 1:37:17.080 --> 1:37:22.080 It's the same thing about electric cars. 1:37:22.080 --> 1:37:24.080 How quickly are they going to populate the world? 1:37:24.080 --> 1:37:27.080 It probably takes about a 20 year span. 1:37:27.080 --> 1:37:28.080 It'll be something like that. 1:37:28.080 --> 1:37:31.080 But I think if I can do what I said, we're starting it. 1:37:31.080 --> 1:37:35.080 Of course, there could be other use of step functions. 1:37:35.080 --> 1:37:42.080 It could be everybody gives up on your ideas for 20 years, and then all of a sudden somebody picks it up again. 1:37:42.080 --> 1:37:44.080 Wait, that guy was onto something. 1:37:44.080 --> 1:37:46.080 That would be a failure on my part. 1:37:46.080 --> 1:37:49.080 Think about Charles Babbage. 1:37:49.080 --> 1:37:55.080 Charles Babbage used to invented the computer back in the 1800s. 1:37:55.080 --> 1:37:59.080 Everyone forgot about it until 100 years later. 1:37:59.080 --> 1:38:03.080 This guy figured this stuff out a long time ago, but he was ahead of his time. 1:38:03.080 --> 1:38:09.080 As I said, I recognize this is part of any entrepreneur's challenge. 1:38:09.080 --> 1:38:11.080 I use entrepreneur broadly in this case. 1:38:11.080 --> 1:38:13.080 I'm not meaning like I'm building a business trying to sell something. 1:38:13.080 --> 1:38:15.080 I'm trying to sell ideas. 1:38:15.080 --> 1:38:20.080 This is the challenge as to how you get people to pay attention to you. 1:38:20.080 --> 1:38:24.080 How do you get them to give you positive or negative feedback? 1:38:24.080 --> 1:38:27.080 How do you get the people to act differently based on your ideas? 1:38:27.080 --> 1:38:30.080 We'll see how well we do on that. 1:38:30.080 --> 1:38:34.080 There's a lot of hype behind artificial intelligence currently. 1:38:34.080 --> 1:38:43.080 Do you, as you look to spread the ideas that are in your cortical theory, the things you're working on, 1:38:43.080 --> 1:38:47.080 do you think there's some possibility we'll hit an AI winter once again? 1:38:47.080 --> 1:38:49.080 It's certainly a possibility. 1:38:49.080 --> 1:38:51.080 That's something you worry about? 1:38:51.080 --> 1:38:54.080 I guess, do I worry about it? 1:38:54.080 --> 1:38:58.080 I haven't decided yet if that's good or bad for my mission. 1:38:58.080 --> 1:38:59.080 That's true. 1:38:59.080 --> 1:39:04.080 That's very true because it's almost like you need the winter to refresh the pallet. 1:39:04.080 --> 1:39:08.080 Here's what you want to have it. 1:39:08.080 --> 1:39:15.080 To the extent that everyone is so thrilled about the current state of machine learning and AI, 1:39:15.080 --> 1:39:20.080 and they don't imagine they need anything else, it makes my job harder. 1:39:20.080 --> 1:39:24.080 If everything crashed completely and every student left the field, 1:39:24.080 --> 1:39:26.080 and there was no money for anybody to do anything, 1:39:26.080 --> 1:39:29.080 and it became an embarrassment to talk about machine intelligence and AI, 1:39:29.080 --> 1:39:31.080 that wouldn't be good for us either. 1:39:31.080 --> 1:39:33.080 You want the soft landing approach, right? 1:39:33.080 --> 1:39:37.080 You want enough people, the senior people in AI and machine learning to say, 1:39:37.080 --> 1:39:39.080 you know, we need other approaches. 1:39:39.080 --> 1:39:40.080 We really need other approaches. 1:39:40.080 --> 1:39:42.080 Damn, we need other approaches. 1:39:42.080 --> 1:39:43.080 Maybe we should look to the brain. 1:39:43.080 --> 1:39:44.080 Okay, let's look to the brain. 1:39:44.080 --> 1:39:45.080 Who's got some brain ideas? 1:39:45.080 --> 1:39:49.080 Okay, let's start a little project on the side here trying to do brain idea related stuff. 1:39:49.080 --> 1:39:51.080 That's the ideal outcome we would want. 1:39:51.080 --> 1:39:53.080 So I don't want a total winter, 1:39:53.080 --> 1:39:57.080 and yet I don't want it to be sunny all the time either. 1:39:57.080 --> 1:40:02.080 So what do you think it takes to build a system with human level intelligence 1:40:02.080 --> 1:40:06.080 where once demonstrated, you would be very impressed? 1:40:06.080 --> 1:40:08.080 So does it have to have a body? 1:40:08.080 --> 1:40:18.080 Does it have to have the C word we used before consciousness as an entirety in a holistic sense? 1:40:18.080 --> 1:40:23.080 First of all, I don't think the goal is to create a machine that is human level intelligence. 1:40:23.080 --> 1:40:24.080 I think it's a false goal. 1:40:24.080 --> 1:40:26.080 Back to Turing, I think it was a false statement. 1:40:26.080 --> 1:40:28.080 We want to understand what intelligence is, 1:40:28.080 --> 1:40:31.080 and then we can build intelligent machines of all different scales, 1:40:31.080 --> 1:40:33.080 all different capabilities. 1:40:33.080 --> 1:40:35.080 You know, a dog is intelligent. 1:40:35.080 --> 1:40:38.080 You know, that would be pretty good to have a dog, you know, 1:40:38.080 --> 1:40:41.080 but what about something that doesn't look like an animal at all in different spaces? 1:40:41.080 --> 1:40:45.080 So my thinking about this is that we want to define what intelligence is, 1:40:45.080 --> 1:40:48.080 agree upon what makes an intelligent system. 1:40:48.080 --> 1:40:52.080 We can then say, okay, we're now going to build systems that work on those principles, 1:40:52.080 --> 1:40:57.080 or some subset of them, and we can apply them to all different types of problems. 1:40:57.080 --> 1:41:00.080 And the kind, the idea, it's not computing. 1:41:00.080 --> 1:41:05.080 We don't ask, if I take a little, you know, little one chip computer, 1:41:05.080 --> 1:41:08.080 I don't say, well, that's not a computer because it's not as powerful as this, you know, 1:41:08.080 --> 1:41:09.080 big server over here. 1:41:09.080 --> 1:41:11.080 No, no, because we know that what the principles are computing on, 1:41:11.080 --> 1:41:14.080 and I can apply those principles to a small problem or into a big problem. 1:41:14.080 --> 1:41:16.080 And same, intelligence needs to get there. 1:41:16.080 --> 1:41:17.080 We have to say, these are the principles. 1:41:17.080 --> 1:41:19.080 I can make a small one, a big one. 1:41:19.080 --> 1:41:20.080 I can make them distributed. 1:41:20.080 --> 1:41:21.080 I can put them on different sensors. 1:41:21.080 --> 1:41:23.080 They don't have to be human like at all. 1:41:23.080 --> 1:41:25.080 Now, you did bring up a very interesting question about embodiment. 1:41:25.080 --> 1:41:27.080 Does it have to have a body? 1:41:27.080 --> 1:41:30.080 It has to have some concept of movement. 1:41:30.080 --> 1:41:33.080 It has to be able to move through these reference frames. 1:41:33.080 --> 1:41:35.080 I talked about earlier, whether it's physically moving, 1:41:35.080 --> 1:41:38.080 like I need, if I'm going to have an AI that understands coffee cups, 1:41:38.080 --> 1:41:42.080 it's going to have to pick up the coffee cup and touch it and look at it with its eyes 1:41:42.080 --> 1:41:45.080 and hands or something equivalent to that. 1:41:45.080 --> 1:41:51.080 If I have a mathematical AI, maybe it needs to move through mathematical spaces. 1:41:51.080 --> 1:41:55.080 I could have a virtual AI that lives in the Internet 1:41:55.080 --> 1:42:00.080 and its movements are traversing links and digging into files, 1:42:00.080 --> 1:42:04.080 but it's got a location that it's traveling through some space. 1:42:04.080 --> 1:42:08.080 You can't have an AI that just takes some flash thing input, 1:42:08.080 --> 1:42:10.080 you know, we call it flash inference. 1:42:10.080 --> 1:42:13.080 Here's a pattern done. 1:42:13.080 --> 1:42:16.080 No, it's movement pattern, movement pattern, movement pattern. 1:42:16.080 --> 1:42:18.080 Attention, digging, building, building structure, 1:42:18.080 --> 1:42:20.080 just trying to figure out the model of the world. 1:42:20.080 --> 1:42:25.080 So some sort of embodiment, whether it's physical or not, has to be part of it. 1:42:25.080 --> 1:42:28.080 So self awareness in the way to be able to answer where am I? 1:42:28.080 --> 1:42:31.080 You bring up self awareness, it's a different topic, self awareness. 1:42:31.080 --> 1:42:37.080 No, the very narrow definition, meaning knowing a sense of self enough to know 1:42:37.080 --> 1:42:40.080 where am I in the space where essentially. 1:42:40.080 --> 1:42:43.080 The system needs to know its location, 1:42:43.080 --> 1:42:48.080 where each component of the system needs to know where it is in the world at that point in time. 1:42:48.080 --> 1:42:51.080 So self awareness and consciousness. 1:42:51.080 --> 1:42:56.080 Do you think, one, from the perspective of neuroscience and neurocortex, 1:42:56.080 --> 1:42:59.080 these are interesting topics, solvable topics, 1:42:59.080 --> 1:43:04.080 do you have any ideas of why the heck it is that we have a subjective experience at all? 1:43:04.080 --> 1:43:05.080 Yeah, I have a lot of questions. 1:43:05.080 --> 1:43:08.080 And is it useful, or is it just a side effect of us? 1:43:08.080 --> 1:43:10.080 It's interesting to think about. 1:43:10.080 --> 1:43:16.080 I don't think it's useful as a means to figure out how to build intelligent machines. 1:43:16.080 --> 1:43:21.080 It's something that systems do, and we can talk about what it is, 1:43:21.080 --> 1:43:25.080 that are like, well, if I build a system like this, then it would be self aware. 1:43:25.080 --> 1:43:28.080 Or if I build it like this, it wouldn't be self aware. 1:43:28.080 --> 1:43:30.080 So that's a choice I can have. 1:43:30.080 --> 1:43:32.080 It's not like, oh my God, it's self aware. 1:43:32.080 --> 1:43:37.080 I heard an interview recently with this philosopher from Yale. 1:43:37.080 --> 1:43:38.080 I can't remember his name. 1:43:38.080 --> 1:43:39.080 I apologize for that. 1:43:39.080 --> 1:43:41.080 But he was talking about, well, if these computers were self aware, 1:43:41.080 --> 1:43:43.080 then it would be a crime to unplug them. 1:43:43.080 --> 1:43:45.080 And I'm like, oh, come on. 1:43:45.080 --> 1:43:46.080 I unplug myself every night. 1:43:46.080 --> 1:43:47.080 I go to sleep. 1:43:47.080 --> 1:43:49.080 Is that a crime? 1:43:49.080 --> 1:43:51.080 I plug myself in again in the morning. 1:43:51.080 --> 1:43:53.080 There I am. 1:43:53.080 --> 1:43:56.080 People get kind of bent out of shape about this. 1:43:56.080 --> 1:44:02.080 I have very detailed understanding or opinions about what it means to be conscious 1:44:02.080 --> 1:44:04.080 and what it means to be self aware. 1:44:04.080 --> 1:44:07.080 I don't think it's that interesting a problem. 1:44:07.080 --> 1:44:08.080 You talked about Christoph Koch. 1:44:08.080 --> 1:44:10.080 He thinks that's the only problem. 1:44:10.080 --> 1:44:12.080 I didn't actually listen to your interview with him. 1:44:12.080 --> 1:44:15.080 But I know him, and I know that's the thing. 1:44:15.080 --> 1:44:18.080 He also thinks intelligence and consciousness are disjoint. 1:44:18.080 --> 1:44:21.080 So I mean, it's not, you don't have to have one or the other. 1:44:21.080 --> 1:44:23.080 I just agree with that. 1:44:23.080 --> 1:44:24.080 I just totally disagree with that. 1:44:24.080 --> 1:44:26.080 So where's your thoughts and consciousness? 1:44:26.080 --> 1:44:28.080 Where does it emerge from? 1:44:28.080 --> 1:44:30.080 Then we have to break it down to the two parts. 1:44:30.080 --> 1:44:32.080 Because consciousness isn't one thing. 1:44:32.080 --> 1:44:34.080 That's part of the problem with that term. 1:44:34.080 --> 1:44:36.080 It means different things to different people. 1:44:36.080 --> 1:44:38.080 And there's different components of it. 1:44:38.080 --> 1:44:40.080 There is a concept of self awareness. 1:44:40.080 --> 1:44:44.080 That can be very easily explained. 1:44:44.080 --> 1:44:46.080 You have a model of your own body. 1:44:46.080 --> 1:44:48.080 The neocortex models things in the world. 1:44:48.080 --> 1:44:50.080 And it also models your own body. 1:44:50.080 --> 1:44:53.080 And then it has a memory. 1:44:53.080 --> 1:44:55.080 It can remember what you've done. 1:44:55.080 --> 1:44:57.080 So it can remember what you did this morning. 1:44:57.080 --> 1:44:59.080 It can remember what you had for breakfast and so on. 1:44:59.080 --> 1:45:02.080 And so I can say to you, okay, Lex, 1:45:02.080 --> 1:45:06.080 were you conscious this morning when you had your bagel? 1:45:06.080 --> 1:45:08.080 And you'd say, yes, I was conscious. 1:45:08.080 --> 1:45:11.080 Now, what if I could take your brain and revert all the synapses 1:45:11.080 --> 1:45:13.080 back to the state they were this morning? 1:45:13.080 --> 1:45:15.080 And then I said to you, Lex, 1:45:15.080 --> 1:45:17.080 were you conscious when you ate the bagel? 1:45:17.080 --> 1:45:18.080 And he said, no, I wasn't conscious. 1:45:18.080 --> 1:45:20.080 I said, here's a video of eating the bagel. 1:45:20.080 --> 1:45:22.080 And he said, I wasn't there. 1:45:22.080 --> 1:45:25.080 That's not possible because I must have been unconscious at that time. 1:45:25.080 --> 1:45:27.080 So we can just make this one to one correlation 1:45:27.080 --> 1:45:30.080 between memory of your body's trajectory through the world 1:45:30.080 --> 1:45:32.080 over some period of time. 1:45:32.080 --> 1:45:34.080 And the ability to recall that memory 1:45:34.080 --> 1:45:36.080 is what you would call conscious. 1:45:36.080 --> 1:45:38.080 I was conscious of that. It's self awareness. 1:45:38.080 --> 1:45:41.080 And any system that can recall, 1:45:41.080 --> 1:45:43.080 memorize what it's done recently 1:45:43.080 --> 1:45:46.080 and bring that back and invoke it again 1:45:46.080 --> 1:45:48.080 would say, yeah, I'm aware. 1:45:48.080 --> 1:45:51.080 I remember what I did. All right, I got it. 1:45:51.080 --> 1:45:54.080 That's an easy one. Although some people think that's a hard one. 1:45:54.080 --> 1:45:57.080 The more challenging part of consciousness 1:45:57.080 --> 1:45:59.080 is this is one that's sometimes used 1:45:59.080 --> 1:46:01.080 by the word Aqualia, 1:46:01.080 --> 1:46:04.080 which is, you know, why does an object seem red? 1:46:04.080 --> 1:46:06.080 Or what is pain? 1:46:06.080 --> 1:46:08.080 And why does pain feel like something? 1:46:08.080 --> 1:46:10.080 Why do I feel redness? 1:46:10.080 --> 1:46:12.080 So why do I feel a little painless in a way? 1:46:12.080 --> 1:46:14.080 And then I could say, well, why does sight 1:46:14.080 --> 1:46:16.080 seems different than hearing? You know, it's the same problem. 1:46:16.080 --> 1:46:18.080 It's really, you know, these are all just neurons. 1:46:18.080 --> 1:46:21.080 And so how is it that why does looking at you 1:46:21.080 --> 1:46:24.080 feel different than, you know, hearing you? 1:46:24.080 --> 1:46:26.080 It feels different, but there's just neurons in my head. 1:46:26.080 --> 1:46:28.080 They're all doing the same thing. 1:46:28.080 --> 1:46:30.080 So that's an interesting question. 1:46:30.080 --> 1:46:32.080 The best treatise I've read about this 1:46:32.080 --> 1:46:34.080 is by a guy named Oregon. 1:46:34.080 --> 1:46:38.080 He wrote a book called Why Red Doesn't Sound Like a Bell. 1:46:38.080 --> 1:46:42.080 It's a little, it's not a trade book, easy to read, 1:46:42.080 --> 1:46:45.080 but it, and it's an interesting question. 1:46:45.080 --> 1:46:47.080 Take something like color. 1:46:47.080 --> 1:46:49.080 Color really doesn't exist in the world. 1:46:49.080 --> 1:46:51.080 It's not a property of the world. 1:46:51.080 --> 1:46:54.080 Property of the world that exists is light frequency, 1:46:54.080 --> 1:46:57.080 and that gets turned into we have certain cells 1:46:57.080 --> 1:46:59.080 in the retina that respond to different frequencies 1:46:59.080 --> 1:47:00.080 different than others. 1:47:00.080 --> 1:47:02.080 And so when they enter the brain, you just have a bunch 1:47:02.080 --> 1:47:04.080 of axons that are firing at different rates, 1:47:04.080 --> 1:47:06.080 and from that we perceive color. 1:47:06.080 --> 1:47:08.080 But there is no color in the brain. 1:47:08.080 --> 1:47:11.080 I mean, there's no color coming in on those synapses. 1:47:11.080 --> 1:47:14.080 It's just a correlation between some axons 1:47:14.080 --> 1:47:17.080 and some property of frequency. 1:47:17.080 --> 1:47:19.080 And that isn't even color itself. 1:47:19.080 --> 1:47:21.080 Frequency doesn't have a color. 1:47:21.080 --> 1:47:23.080 It's just what it is. 1:47:23.080 --> 1:47:25.080 So then the question is, well, why does it even 1:47:25.080 --> 1:47:27.080 appear to have a color at all? 1:47:27.080 --> 1:47:30.080 Just as you're describing it, there seems to be a connection 1:47:30.080 --> 1:47:32.080 to those ideas of reference frames. 1:47:32.080 --> 1:47:38.080 I mean, it just feels like consciousness having the subject, 1:47:38.080 --> 1:47:42.080 assigning the feeling of red to the actual color 1:47:42.080 --> 1:47:47.080 or to the wavelength is useful for intelligence. 1:47:47.080 --> 1:47:49.080 Yeah, I think that's a good way of putting it. 1:47:49.080 --> 1:47:51.080 It's useful as a predictive mechanism, 1:47:51.080 --> 1:47:53.080 or useful as a generalization idea. 1:47:53.080 --> 1:47:55.080 It's a way of grouping things together to say 1:47:55.080 --> 1:47:58.080 it's useful to have a model like this. 1:47:58.080 --> 1:48:02.080 Think about the well known syndrome that people 1:48:02.080 --> 1:48:06.080 who've lost a limb experience called phantom limbs. 1:48:06.080 --> 1:48:11.080 And what they claim is they can have their arms removed 1:48:11.080 --> 1:48:13.080 but they feel the arm. 1:48:13.080 --> 1:48:15.080 Not only feel it, they know it's there. 1:48:15.080 --> 1:48:16.080 It's there. 1:48:16.080 --> 1:48:17.080 I know it's there. 1:48:17.080 --> 1:48:19.080 They'll swear to you that it's there. 1:48:19.080 --> 1:48:20.080 And then they can feel pain in their arm. 1:48:20.080 --> 1:48:22.080 And they'll feel pain in their finger. 1:48:22.080 --> 1:48:25.080 And if they move their non existent arm behind their back, 1:48:25.080 --> 1:48:27.080 then they feel the pain behind their back. 1:48:27.080 --> 1:48:30.080 So this whole idea that your arm exists 1:48:30.080 --> 1:48:31.080 is a model of your brain. 1:48:31.080 --> 1:48:34.080 It may or may not really exist. 1:48:34.080 --> 1:48:38.080 And just like, but it's useful to have a model of something 1:48:38.080 --> 1:48:40.080 that sort of correlates to things in the world 1:48:40.080 --> 1:48:42.080 so you can make predictions about what would happen 1:48:42.080 --> 1:48:43.080 when those things occur. 1:48:43.080 --> 1:48:44.080 It's a little bit of a fuzzy, 1:48:44.080 --> 1:48:46.080 but I think you're getting quite towards the answer there. 1:48:46.080 --> 1:48:51.080 It's useful for the model to express things certain ways 1:48:51.080 --> 1:48:53.080 that we can then map them into these reference frames 1:48:53.080 --> 1:48:55.080 and make predictions about them. 1:48:55.080 --> 1:48:57.080 I need to spend more time on this topic. 1:48:57.080 --> 1:48:58.080 It doesn't bother me. 1:48:58.080 --> 1:49:00.080 Do you really need to spend more time on this? 1:49:00.080 --> 1:49:01.080 Yeah. 1:49:01.080 --> 1:49:04.080 It does feel special that we have subjective experience, 1:49:04.080 --> 1:49:07.080 but I'm yet to know why. 1:49:07.080 --> 1:49:08.080 I'm just personally curious. 1:49:08.080 --> 1:49:10.080 It's not necessary for the work we're doing here. 1:49:10.080 --> 1:49:12.080 I don't think I need to solve that problem 1:49:12.080 --> 1:49:14.080 to build intelligent machines at all. 1:49:14.080 --> 1:49:15.080 Not at all. 1:49:15.080 --> 1:49:19.080 But there is sort of the silly notion that you described briefly 1:49:19.080 --> 1:49:22.080 that doesn't seem so silly to us humans is, 1:49:22.080 --> 1:49:26.080 you know, if you're successful building intelligent machines, 1:49:26.080 --> 1:49:29.080 it feels wrong to then turn them off. 1:49:29.080 --> 1:49:32.080 Because if you're able to build a lot of them, 1:49:32.080 --> 1:49:36.080 it feels wrong to then be able to, you know, 1:49:36.080 --> 1:49:38.080 to turn off the... 1:49:38.080 --> 1:49:39.080 Well, why? 1:49:39.080 --> 1:49:41.080 Let's break that down a bit. 1:49:41.080 --> 1:49:43.080 As humans, why do we fear death? 1:49:43.080 --> 1:49:46.080 There's two reasons we fear death. 1:49:46.080 --> 1:49:48.080 Well, first of all, I'll say when you're dead, it doesn't matter. 1:49:48.080 --> 1:49:49.080 Okay. 1:49:49.080 --> 1:49:50.080 You're dead. 1:49:50.080 --> 1:49:51.080 So why do we fear death? 1:49:51.080 --> 1:49:53.080 We fear death for two reasons. 1:49:53.080 --> 1:49:57.080 One is because we are programmed genetically to fear death. 1:49:57.080 --> 1:50:02.080 That's a survival and propaganda in the genes thing. 1:50:02.080 --> 1:50:06.080 And we also are programmed to feel sad when people we know die. 1:50:06.080 --> 1:50:08.080 We don't feel sad for someone we don't know dies. 1:50:08.080 --> 1:50:09.080 There's people dying right now. 1:50:09.080 --> 1:50:10.080 They're only scared to say, 1:50:10.080 --> 1:50:11.080 I don't feel bad about them because I don't know them. 1:50:11.080 --> 1:50:13.080 But I knew they might feel really bad. 1:50:13.080 --> 1:50:18.080 So again, these are old brain genetically embedded things 1:50:18.080 --> 1:50:20.080 that we fear death. 1:50:20.080 --> 1:50:23.080 Outside of those uncomfortable feelings, 1:50:23.080 --> 1:50:25.080 there's nothing else to worry about. 1:50:25.080 --> 1:50:27.080 Well, wait, hold on a second. 1:50:27.080 --> 1:50:30.080 Do you know the denial of death by Beckard? 1:50:30.080 --> 1:50:36.080 You know, there's a thought that death is, you know, 1:50:36.080 --> 1:50:43.080 our whole conception of our world model kind of assumes immortality. 1:50:43.080 --> 1:50:47.080 And then death is this terror that underlies it all. 1:50:47.080 --> 1:50:50.080 So like, well, some people's world model, not mine. 1:50:50.080 --> 1:50:51.080 But okay. 1:50:51.080 --> 1:50:54.080 So what, what Becker would say is that you're just living in an illusion. 1:50:54.080 --> 1:50:58.080 You've constructed illusion for yourself because it's such a terrible terror. 1:50:58.080 --> 1:51:02.080 The fact that this illusion, the illusion that death doesn't matter. 1:51:02.080 --> 1:51:05.080 You're still not coming to grips with the illusion of what? 1:51:05.080 --> 1:51:08.080 That death is going to happen. 1:51:08.080 --> 1:51:10.080 Oh, it's not going to happen. 1:51:10.080 --> 1:51:11.080 You're, you're actually operating. 1:51:11.080 --> 1:51:13.080 You haven't, even though you said you've accepted it, 1:51:13.080 --> 1:51:16.080 you haven't really accepted the notion of death is what you say. 1:51:16.080 --> 1:51:21.080 So it sounds like it sounds like you disagree with that notion. 1:51:21.080 --> 1:51:22.080 I mean, totally. 1:51:22.080 --> 1:51:27.080 Like, I literally every night, every night I go to bed, it's like dying. 1:51:27.080 --> 1:51:28.080 Little deaths. 1:51:28.080 --> 1:51:29.080 Little deaths. 1:51:29.080 --> 1:51:32.080 And if I didn't wake up, it wouldn't matter to me. 1:51:32.080 --> 1:51:35.080 Only if I knew that was going to happen would it be bothersome. 1:51:35.080 --> 1:51:37.080 If I didn't know it was going to happen, how would I know? 1:51:37.080 --> 1:51:39.080 Then I would worry about my wife. 1:51:39.080 --> 1:51:40.080 Yeah. 1:51:40.080 --> 1:51:44.080 So imagine, imagine I was a loner and I lived in Alaska and I lived them out there and there 1:51:44.080 --> 1:51:45.080 was no animals. 1:51:45.080 --> 1:51:46.080 Nobody knew I existed. 1:51:46.080 --> 1:51:48.080 I was just eating these roots all the time. 1:51:48.080 --> 1:51:50.080 And nobody knew I was there. 1:51:50.080 --> 1:51:53.080 And one day I didn't wake up. 1:51:53.080 --> 1:51:56.080 Where, what, what pain in the world would there exist? 1:51:56.080 --> 1:52:01.080 Well, so most people that think about this problem would say that you're just deeply enlightened 1:52:01.080 --> 1:52:04.080 or are completely delusional. 1:52:04.080 --> 1:52:05.080 Wow. 1:52:05.080 --> 1:52:14.080 But I would say, I would say that's a very enlightened way to see the world is that that's the rational one. 1:52:14.080 --> 1:52:15.080 Well, I think it's rational. 1:52:15.080 --> 1:52:16.080 That's right. 1:52:16.080 --> 1:52:22.080 But the fact is we don't, I mean, we really don't have an understanding of why the heck 1:52:22.080 --> 1:52:26.080 it is we're born and why we die and what happens after we die. 1:52:26.080 --> 1:52:27.080 Well, maybe there isn't a reason. 1:52:27.080 --> 1:52:28.080 Maybe there is. 1:52:28.080 --> 1:52:30.080 So I'm interested in those big problems too, right? 1:52:30.080 --> 1:52:33.080 You know, you, you interviewed Max Tagmark, you know, and there's people like that, right? 1:52:33.080 --> 1:52:35.080 I'm interested in those big problems as well. 1:52:35.080 --> 1:52:41.080 And in fact, when I was young, I made a list of the biggest problems I could think of. 1:52:41.080 --> 1:52:43.080 First, why does anything exist? 1:52:43.080 --> 1:52:46.080 Second, why did we have the laws of physics that we have? 1:52:46.080 --> 1:52:49.080 Third, is life inevitable? 1:52:49.080 --> 1:52:50.080 And why is it here? 1:52:50.080 --> 1:52:52.080 Fourth, is intelligence inevitable? 1:52:52.080 --> 1:52:53.080 And why is it here? 1:52:53.080 --> 1:52:58.080 I stopped there because I figured if you can make a truly intelligent system, we'll be, 1:52:58.080 --> 1:53:03.080 that'll be the quickest way to answer the first three questions. 1:53:03.080 --> 1:53:04.080 I'm serious. 1:53:04.080 --> 1:53:05.080 Yeah. 1:53:05.080 --> 1:53:09.080 And so I said, my mission, you know, you asked me earlier, my first mission is to understand 1:53:09.080 --> 1:53:12.080 the brain, but I felt that is the shortest way to get to true machine intelligence. 1:53:12.080 --> 1:53:16.080 And I want to get to true machine intelligence because even if it doesn't occur in my lifetime, 1:53:16.080 --> 1:53:19.080 other people will benefit from it because I think it'll occur in my lifetime. 1:53:19.080 --> 1:53:21.080 But, you know, 20 years, you never know. 1:53:21.080 --> 1:53:27.080 And, but that will be the quickest way for us to, you know, we can make super mathematicians. 1:53:27.080 --> 1:53:29.080 We can make super space explorers. 1:53:29.080 --> 1:53:36.080 We can make super physicists brains that do these things and that can run experiments 1:53:36.080 --> 1:53:37.080 that we can't run. 1:53:37.080 --> 1:53:40.080 We don't have the abilities to manipulate things and so on. 1:53:40.080 --> 1:53:42.080 But we can build and tell the machines to do all those things. 1:53:42.080 --> 1:53:48.080 And with the ultimate goal of finding out the answers to the other questions. 1:53:48.080 --> 1:53:56.080 Let me ask, you know, the depressing and difficult question, which is once we achieve that goal, 1:53:56.080 --> 1:54:03.080 do you, of creating, no, of understanding intelligence, do you think we would be happier, 1:54:03.080 --> 1:54:05.080 more fulfilled as a species? 1:54:05.080 --> 1:54:08.080 The understanding intelligence or understanding the answers to the big questions? 1:54:08.080 --> 1:54:09.080 Understanding intelligence. 1:54:09.080 --> 1:54:11.080 Oh, totally. 1:54:11.080 --> 1:54:12.080 Totally. 1:54:12.080 --> 1:54:14.080 It would be far more fun place to live. 1:54:14.080 --> 1:54:15.080 You think so? 1:54:15.080 --> 1:54:16.080 Oh, yeah. 1:54:16.080 --> 1:54:17.080 Why not? 1:54:17.080 --> 1:54:22.080 I just put aside this, you know, terminator nonsense and, and, and, and just think about, 1:54:22.080 --> 1:54:26.080 you can think about the, we can talk about the risk of AI if you want. 1:54:26.080 --> 1:54:27.080 I'd love to. 1:54:27.080 --> 1:54:28.080 So let's talk about. 1:54:28.080 --> 1:54:30.080 But I think the world is far better knowing things. 1:54:30.080 --> 1:54:32.080 We're always better than no things. 1:54:32.080 --> 1:54:33.080 Do you think it's better? 1:54:33.080 --> 1:54:38.080 Is it a better place to live in that I know that our planet is one of many in the solar system 1:54:38.080 --> 1:54:40.080 and the solar system is one of many in the galaxy? 1:54:40.080 --> 1:54:44.080 I think it's a more, I dread, I used to, I sometimes think like, God, what would be like 1:54:44.080 --> 1:54:47.080 the 300 years ago, I'd be looking up the sky, I can't understand anything. 1:54:47.080 --> 1:54:48.080 Oh my God. 1:54:48.080 --> 1:54:50.080 I'd be like going to bed every night going, what's going on here? 1:54:50.080 --> 1:54:54.080 Well, I mean, in some sense, I agree with you, but I'm not exactly sure. 1:54:54.080 --> 1:54:55.080 So I'm also a scientist. 1:54:55.080 --> 1:55:01.080 So I have, I share your views, but I'm not, we're, we're like rolling down the hill together. 1:55:01.080 --> 1:55:03.080 What's down the hill? 1:55:03.080 --> 1:55:05.080 I feel for climbing a hill. 1:55:05.080 --> 1:55:07.080 Whatever we're getting, we're getting closer to enlightenment. 1:55:07.080 --> 1:55:08.080 Whatever. 1:55:08.080 --> 1:55:12.080 We're climbing, we're getting pulled up a hill. 1:55:12.080 --> 1:55:14.080 Pulled up by our curiosity. 1:55:14.080 --> 1:55:17.080 We're pulling ourselves up the hill by our curiosity. 1:55:17.080 --> 1:55:19.080 Yeah, sycophers are doing the same thing with the rock. 1:55:19.080 --> 1:55:21.080 Yeah, yeah, yeah. 1:55:21.080 --> 1:55:29.080 But okay, our happiness aside, do you have concerns about, you know, you talk about Sam Harris, Elon Musk, 1:55:29.080 --> 1:55:32.080 of existential threats of intelligence systems? 1:55:32.080 --> 1:55:34.080 No, I'm not worried about existential threats at all. 1:55:34.080 --> 1:55:36.080 There are some things we really do need to worry about. 1:55:36.080 --> 1:55:38.080 Even today's AI, we have things we have to worry about. 1:55:38.080 --> 1:55:43.080 We have to worry about privacy and about how it impacts false beliefs in the world. 1:55:43.080 --> 1:55:48.080 And we have real problems that, and things to worry about with today's AI. 1:55:48.080 --> 1:55:51.080 And that will continue as we create more intelligent systems. 1:55:51.080 --> 1:55:59.080 There's no question, you know, the whole issue about, you know, making intelligent armament and weapons is something that really we have to think about carefully. 1:55:59.080 --> 1:56:01.080 I don't think of those as existential threats. 1:56:01.080 --> 1:56:09.080 I think those are the kind of threats we always face and we'll have to face them here and we'll have to deal with them. 1:56:09.080 --> 1:56:17.080 We can talk about what people think are the existential threats, but when I hear people talking about them, they all sound hollow to me. 1:56:17.080 --> 1:56:21.080 They're based on ideas, they're based on people who really have no idea what intelligence is. 1:56:21.080 --> 1:56:26.080 And if they knew what intelligence was, they wouldn't say those things. 1:56:26.080 --> 1:56:29.080 So those are not experts in the field, you know. 1:56:29.080 --> 1:56:31.080 So there's two, right? 1:56:31.080 --> 1:56:33.080 So one is like superintelligence. 1:56:33.080 --> 1:56:42.080 So a system that becomes far, far superior in reasoning ability than us humans. 1:56:42.080 --> 1:56:45.080 And how is that an existential threat? 1:56:45.080 --> 1:56:49.080 So there's a lot of ways in which it could be. 1:56:49.080 --> 1:57:00.080 One way is us humans are actually irrational, inefficient and get in the way of not happiness, 1:57:00.080 --> 1:57:05.080 but whatever the objective function is of maximizing that objective function and superintelligence. 1:57:05.080 --> 1:57:07.080 The paperclip problem and things like that. 1:57:07.080 --> 1:57:09.080 So the paperclip problem, but with the superintelligence. 1:57:09.080 --> 1:57:10.080 Yeah, yeah, yeah. 1:57:10.080 --> 1:57:15.080 So we already faced this threat in some sense. 1:57:15.080 --> 1:57:17.080 They're called bacteria. 1:57:17.080 --> 1:57:21.080 These are organisms in the world that would like to turn everything into bacteria. 1:57:21.080 --> 1:57:23.080 And they're constantly morphing. 1:57:23.080 --> 1:57:26.080 They're constantly changing to evade our protections. 1:57:26.080 --> 1:57:33.080 And in the past, they have killed huge swaths of populations of humans on this planet. 1:57:33.080 --> 1:57:38.080 So if you want to worry about something that's going to multiply endlessly, we have it. 1:57:38.080 --> 1:57:43.080 And I'm far more worried in that regard, I'm far more worried that some scientists in the laboratory 1:57:43.080 --> 1:57:47.080 will create a super virus or a super bacteria that we cannot control. 1:57:47.080 --> 1:57:49.080 That is a more existential threat. 1:57:49.080 --> 1:57:54.080 Putting an intelligence thing on top of it actually seems to make it less existential to me. 1:57:54.080 --> 1:57:56.080 It's like, it limits its power. 1:57:56.080 --> 1:57:57.080 It limits where it can go. 1:57:57.080 --> 1:57:59.080 It limits the number of things it can do in many ways. 1:57:59.080 --> 1:58:02.080 A bacteria is something you can't even see. 1:58:02.080 --> 1:58:04.080 So that's only one of those problems. 1:58:04.080 --> 1:58:05.080 Yes, exactly. 1:58:05.080 --> 1:58:09.080 So the other one, just in your intuition about intelligence, 1:58:09.080 --> 1:58:12.080 when you think about intelligence of us humans, 1:58:12.080 --> 1:58:14.080 do you think of that as something, 1:58:14.080 --> 1:58:18.080 if you look at intelligence on a spectrum from zero to us humans, 1:58:18.080 --> 1:58:24.080 do you think you can scale that to something far superior to all the mechanisms we've been talking about? 1:58:24.080 --> 1:58:27.080 I want to make another point here, Alex, before I get there. 1:58:27.080 --> 1:58:30.080 Intelligence is the neocortex. 1:58:30.080 --> 1:58:32.080 It is not the entire brain. 1:58:32.080 --> 1:58:36.080 The goal is not to make a human. 1:58:36.080 --> 1:58:38.080 The goal is not to make an emotional system. 1:58:38.080 --> 1:58:41.080 The goal is not to make a system that wants to have sex and reproduce. 1:58:41.080 --> 1:58:42.080 Why would I build that? 1:58:42.080 --> 1:58:44.080 If I want to have a system that wants to reproduce and have sex, 1:58:44.080 --> 1:58:47.080 make bacteria, make computer viruses. 1:58:47.080 --> 1:58:48.080 Those are bad things. 1:58:48.080 --> 1:58:49.080 Don't do that. 1:58:49.080 --> 1:58:50.080 Those are really bad. 1:58:50.080 --> 1:58:51.080 Don't do those things. 1:58:51.080 --> 1:58:53.080 Regulate those. 1:58:53.080 --> 1:58:56.080 But if I just say, I want an intelligent system, 1:58:56.080 --> 1:58:58.080 why doesn't it have to have any human like emotions? 1:58:58.080 --> 1:59:00.080 Why does it even care if it lives? 1:59:00.080 --> 1:59:02.080 Why does it even care if it has food? 1:59:02.080 --> 1:59:04.080 It doesn't care about those things. 1:59:04.080 --> 1:59:07.080 It's just in a trance thinking about mathematics, 1:59:07.080 --> 1:59:12.080 or it's out there just trying to build the space for it on Mars. 1:59:12.080 --> 1:59:15.080 That's a choice we make. 1:59:15.080 --> 1:59:17.080 Don't make human like things. 1:59:17.080 --> 1:59:18.080 Don't make replicating things. 1:59:18.080 --> 1:59:19.080 Don't make things that have emotions. 1:59:19.080 --> 1:59:21.080 Just stick to the neocortex. 1:59:21.080 --> 1:59:24.080 That's a view, actually, that I share, but not everybody shares, 1:59:24.080 --> 1:59:28.080 in the sense that you have faith and optimism about us as engineers 1:59:28.080 --> 1:59:31.080 and systems, humans as builders of systems, 1:59:31.080 --> 1:59:35.080 to not put in different stupid things. 1:59:35.080 --> 1:59:37.080 This is why I mentioned the bacteria one, 1:59:37.080 --> 1:59:40.080 because you might say, well, some person's going to do that. 1:59:40.080 --> 1:59:42.080 Well, some person today could create a bacteria 1:59:42.080 --> 1:59:46.080 that's resistant to all the known antibacterial agents. 1:59:46.080 --> 1:59:49.080 So we already have that threat. 1:59:49.080 --> 1:59:51.080 We already know this is going on. 1:59:51.080 --> 1:59:52.080 It's not a new threat. 1:59:52.080 --> 1:59:56.080 So just accept that, and then we have to deal with it, right? 1:59:56.080 --> 1:59:59.080 Yeah, so my point is nothing to do with intelligence. 1:59:59.080 --> 2:00:02.080 Intelligence is a separate component that you might apply 2:00:02.080 --> 2:00:05.080 to a system that wants to reproduce and do stupid things. 2:00:05.080 --> 2:00:07.080 Let's not do that. 2:00:07.080 --> 2:00:10.080 Yeah, in fact, it is a mystery why people haven't done that yet. 2:00:10.080 --> 2:00:14.080 My dad as a physicist believes that the reason, 2:00:14.080 --> 2:00:19.080 for example, nuclear weapons haven't proliferated amongst evil people. 2:00:19.080 --> 2:00:25.080 So one belief that I share is that there's not that many evil people in the world 2:00:25.080 --> 2:00:32.080 that would use whether it's bacteria or nuclear weapons, 2:00:32.080 --> 2:00:35.080 or maybe the future AI systems to do bad. 2:00:35.080 --> 2:00:37.080 So the fraction is small. 2:00:37.080 --> 2:00:40.080 And the second is that it's actually really hard, technically. 2:00:40.080 --> 2:00:45.080 So the intersection between evil and competent is small. 2:00:45.080 --> 2:00:47.080 And by the way, to really annihilate humanity, 2:00:47.080 --> 2:00:51.080 you'd have to have sort of the nuclear winter phenomenon, 2:00:51.080 --> 2:00:54.080 which is not one person shooting or even 10 bombs. 2:00:54.080 --> 2:00:58.080 You'd have to have some automated system that detonates a million bombs, 2:00:58.080 --> 2:01:00.080 or whatever many thousands we have. 2:01:00.080 --> 2:01:03.080 So it's extreme evil combined with extreme competence. 2:01:03.080 --> 2:01:06.080 And despite building some stupid system that would automatically, 2:01:06.080 --> 2:01:10.080 you know, Dr. Strangelup type of thing, you know, 2:01:10.080 --> 2:01:14.080 I mean, look, we could have some nuclear bomb go off in some major city in the world. 2:01:14.080 --> 2:01:17.080 I think that's actually quite likely, even in my lifetime. 2:01:17.080 --> 2:01:20.080 I don't think that's an unlikely thing, and it would be a tragedy. 2:01:20.080 --> 2:01:23.080 But it won't be an existential threat. 2:01:23.080 --> 2:01:27.080 And it's the same as, you know, the virus of 1917 or whatever it was, 2:01:27.080 --> 2:01:29.080 you know, the influenza. 2:01:29.080 --> 2:01:33.080 These bad things can happen and the plague and so on. 2:01:33.080 --> 2:01:35.080 We can't always prevent it. 2:01:35.080 --> 2:01:37.080 We always try, but we can't. 2:01:37.080 --> 2:01:41.080 But they're not existential threats until we combine all those crazy things together. 2:01:41.080 --> 2:01:45.080 So on the spectrum of intelligence from zero to human, 2:01:45.080 --> 2:01:51.080 do you have a sense of whether it's possible to create several orders of magnitude 2:01:51.080 --> 2:01:54.080 or at least double that of human intelligence, 2:01:54.080 --> 2:01:56.080 to talk about neural cortex? 2:01:56.080 --> 2:01:58.080 I think it's the wrong thing to say, double the intelligence. 2:01:58.080 --> 2:02:01.080 Break it down into different components. 2:02:01.080 --> 2:02:04.080 Can I make something that's a million times faster than a human brain? 2:02:04.080 --> 2:02:06.080 Yes, I can do that. 2:02:06.080 --> 2:02:10.080 Could I make something that is, has a lot more storage than a human brain? 2:02:10.080 --> 2:02:13.080 Yes, I can do that. More copies come. 2:02:13.080 --> 2:02:16.080 Can I make something that attaches to different sensors than a human brain? 2:02:16.080 --> 2:02:17.080 Yes, I can do that. 2:02:17.080 --> 2:02:19.080 Could I make something that's distributed? 2:02:19.080 --> 2:02:23.080 We talked earlier about the departure of neural cortex voting. 2:02:23.080 --> 2:02:25.080 They don't have to be co located. 2:02:25.080 --> 2:02:29.080 They can be all around the places. I could do that too. 2:02:29.080 --> 2:02:32.080 Those are the levers I have, but is it more intelligent? 2:02:32.080 --> 2:02:35.080 What depends what I train in on? What is it doing? 2:02:35.080 --> 2:02:37.080 So here's the thing. 2:02:37.080 --> 2:02:46.080 Let's say larger neural cortex and or whatever size that allows for higher and higher hierarchies 2:02:46.080 --> 2:02:49.080 to form, we're talking about reference frames and concepts. 2:02:49.080 --> 2:02:53.080 So I could, could I have something that's a super physicist or a super mathematician? Yes. 2:02:53.080 --> 2:02:59.080 And the question is, once you have a super physicist, will they be able to understand something? 2:02:59.080 --> 2:03:03.080 Do you have a sense that it will be orders, like us compared to ants? 2:03:03.080 --> 2:03:04.080 Could we ever understand it? 2:03:04.080 --> 2:03:05.080 Yeah. 2:03:05.080 --> 2:03:11.080 Most people cannot understand general relativity. 2:03:11.080 --> 2:03:13.080 It's a really hard thing to get. 2:03:13.080 --> 2:03:17.080 I mean, you can paint it in a fuzzy picture, stretchy space, you know? 2:03:17.080 --> 2:03:18.080 Yeah. 2:03:18.080 --> 2:03:23.080 But the field equations to do that and the deep intuitions are really, really hard. 2:03:23.080 --> 2:03:26.080 And I've tried, I'm unable to do it. 2:03:26.080 --> 2:03:32.080 It's easy to get special relative, but general relative, man, that's too much. 2:03:32.080 --> 2:03:35.080 And so we already live with this to some extent. 2:03:35.080 --> 2:03:40.080 The vast majority of people can't understand actually what the vast majority of other people actually know. 2:03:40.080 --> 2:03:45.080 We're just either we don't have the effort to or we can't or we don't have time or just not smart enough, whatever. 2:03:45.080 --> 2:03:48.080 So, but we have ways of communicating. 2:03:48.080 --> 2:03:51.080 Einstein has spoken in a way that I can understand. 2:03:51.080 --> 2:03:54.080 He's given me analogies that are useful. 2:03:54.080 --> 2:04:00.080 I can use those analogies for my own work and think about, you know, concepts that are similar. 2:04:00.080 --> 2:04:02.080 It's not stupid. 2:04:02.080 --> 2:04:04.080 It's not like he's existed in some other plane. 2:04:04.080 --> 2:04:06.080 There's no connection to my plane in the world here. 2:04:06.080 --> 2:04:07.080 So that will occur. 2:04:07.080 --> 2:04:09.080 It already has occurred. 2:04:09.080 --> 2:04:12.080 That's when my point at this story is it already has occurred. 2:04:12.080 --> 2:04:14.080 We live it every day. 2:04:14.080 --> 2:04:21.080 One could argue that with we create machine intelligence that think a million times faster than us that it'll be so far we can't make the connections. 2:04:21.080 --> 2:04:29.080 But, you know, at the moment, everything that seems really, really hard to figure out in the world when you actually figure it out is not that hard. 2:04:29.080 --> 2:04:32.080 You know, almost everyone can understand the multiverses. 2:04:32.080 --> 2:04:34.080 Almost everyone can understand quantum physics. 2:04:34.080 --> 2:04:38.080 Almost everyone can understand these basic things, even though hardly any people could figure those things out. 2:04:38.080 --> 2:04:40.080 Yeah, but really understand. 2:04:40.080 --> 2:04:43.080 But you don't need to really, only a few people really understand. 2:04:43.080 --> 2:04:49.080 You need to only understand the projections, the sprinkles of the useful insights from that. 2:04:49.080 --> 2:04:51.080 That was my example of Einstein, right? 2:04:51.080 --> 2:04:55.080 His general theory of relativity is one thing that very, very, very few people can get. 2:04:55.080 --> 2:04:59.080 And what if we just said those other few people are also artificial intelligences? 2:04:59.080 --> 2:05:01.080 How bad is that? 2:05:01.080 --> 2:05:02.080 In some sense they are, right? 2:05:02.080 --> 2:05:03.080 Yeah, they say already. 2:05:03.080 --> 2:05:05.080 I mean, Einstein wasn't a really normal person. 2:05:05.080 --> 2:05:07.080 He had a lot of weird quirks. 2:05:07.080 --> 2:05:09.080 And so the other people who work with him. 2:05:09.080 --> 2:05:15.080 So, you know, maybe they already were sort of this actual plane of intelligence that we live with it already. 2:05:15.080 --> 2:05:17.080 It's not a problem. 2:05:17.080 --> 2:05:20.080 It's still useful and, you know. 2:05:20.080 --> 2:05:24.080 So do you think we are the only intelligent life out there in the universe? 2:05:24.080 --> 2:05:29.080 I would say that intelligent life has and will exist elsewhere in the universe. 2:05:29.080 --> 2:05:31.080 I'll say that. 2:05:31.080 --> 2:05:39.080 There is a question about contemporaneous intelligence life, which is hard to even answer when we think about relativity and the nature of space time. 2:05:39.080 --> 2:05:43.080 We can't say what exactly is this time someplace else in the world. 2:05:43.080 --> 2:05:54.080 But I think it's, you know, I do worry a lot about the filter idea, which is that perhaps intelligent species don't last very long. 2:05:54.080 --> 2:05:56.080 And so we haven't been around very long. 2:05:56.080 --> 2:06:02.080 As a technological species, we've been around for almost nothing, you know, what, 200 years or something like that. 2:06:02.080 --> 2:06:08.080 And we don't have any data, a good data point on whether it's likely that we'll survive or not. 2:06:08.080 --> 2:06:12.080 So do I think that there have been intelligent life elsewhere in the universe? 2:06:12.080 --> 2:06:14.080 Almost certainly, of course. 2:06:14.080 --> 2:06:16.080 In the past, in the future, yes. 2:06:16.080 --> 2:06:18.080 Does it survive for a long time? 2:06:18.080 --> 2:06:19.080 I don't know. 2:06:19.080 --> 2:06:25.080 This is another reason I'm excited about our work, is our work meaning the general world of AI. 2:06:25.080 --> 2:06:31.080 I think we can build intelligent machines that outlast us. 2:06:31.080 --> 2:06:34.080 You know, they don't have to be tied to Earth. 2:06:34.080 --> 2:06:39.080 They don't have to, you know, I'm not saying they're recreating, you know, you know, aliens. 2:06:39.080 --> 2:06:44.080 I'm just saying, if I asked myself, and this might be a good point to end on here. 2:06:44.080 --> 2:06:47.080 If I asked myself, you know, what's special about our species? 2:06:47.080 --> 2:06:49.080 We're not particularly interesting physically. 2:06:49.080 --> 2:06:51.080 We're not, we don't fly. 2:06:51.080 --> 2:06:52.080 We're not good swimmers. 2:06:52.080 --> 2:06:53.080 We're not very fast. 2:06:53.080 --> 2:06:54.080 We're not very strong, you know. 2:06:54.080 --> 2:06:55.080 It's our brain. 2:06:55.080 --> 2:06:56.080 That's the only thing. 2:06:56.080 --> 2:07:01.080 And we are the only species on this planet that's built the model of the world that extends beyond what we can actually sense. 2:07:01.080 --> 2:07:09.080 We're the only people who know about the far side of the moon and other universes and other galaxies and other stars and what happens in the atom. 2:07:09.080 --> 2:07:12.080 That knowledge doesn't exist anywhere else. 2:07:12.080 --> 2:07:13.080 It's only in our heads. 2:07:13.080 --> 2:07:14.080 Cats don't do it. 2:07:14.080 --> 2:07:15.080 Dogs don't do it. 2:07:15.080 --> 2:07:16.080 Monkeys don't do it. 2:07:16.080 --> 2:07:18.080 That is what we've created that's unique. 2:07:18.080 --> 2:07:19.080 Not our genes. 2:07:19.080 --> 2:07:20.080 It's knowledge. 2:07:20.080 --> 2:07:24.080 And if I ask me, what is the legacy of humanity? 2:07:24.080 --> 2:07:25.080 What should our legacy be? 2:07:25.080 --> 2:07:26.080 It should be knowledge. 2:07:26.080 --> 2:07:30.080 We should preserve our knowledge in a way that it can exist beyond us. 2:07:30.080 --> 2:07:38.080 And I think the best way of doing that, in fact, you have to do it, is to have to go along with intelligent machines to understand that knowledge. 2:07:38.080 --> 2:07:44.080 It's a very broad idea, but we should be thinking, I call it a state planning for humanity. 2:07:44.080 --> 2:07:49.080 We should be thinking about what we want to leave behind when as a species we're no longer here. 2:07:49.080 --> 2:07:51.080 And that will happen sometime. 2:07:51.080 --> 2:07:52.080 Sooner or later, it's going to happen. 2:07:52.080 --> 2:07:58.080 And understanding intelligence and creating intelligence gives us a better chance to prolong. 2:07:58.080 --> 2:08:00.080 It does give us a better chance to prolong life. 2:08:00.080 --> 2:08:01.080 Yes. 2:08:01.080 --> 2:08:03.080 It gives us a chance to live on other planets. 2:08:03.080 --> 2:08:07.080 But even beyond that, I mean, our solar system will disappear one day. 2:08:07.080 --> 2:08:09.080 It's given enough time. 2:08:09.080 --> 2:08:10.080 So I don't know. 2:08:10.080 --> 2:08:14.080 I doubt we will ever be able to travel to other things. 2:08:14.080 --> 2:08:18.080 But we could tell the stars, but we could send intelligent machines to do that. 2:08:18.080 --> 2:08:29.080 Do you have an optimistic, a hopeful view of our knowledge of the echoes of human civilization living through the intelligent systems we create? 2:08:29.080 --> 2:08:30.080 Oh, totally. 2:08:30.080 --> 2:08:32.080 Well, I think the intelligent systems are greater. 2:08:32.080 --> 2:08:39.080 In some sense, the vessel for bringing them beyond Earth or making them last beyond humans themselves. 2:08:39.080 --> 2:08:41.080 So how do you feel about that? 2:08:41.080 --> 2:08:44.080 That they won't be human, quote unquote. 2:08:44.080 --> 2:08:48.080 Human, what is human? Our species are changing all the time. 2:08:48.080 --> 2:08:52.080 Human today is not the same as human just 50 years ago. 2:08:52.080 --> 2:08:54.080 What is human? Do we care about our genetics? 2:08:54.080 --> 2:08:56.080 Why is that important? 2:08:56.080 --> 2:08:59.080 As I point out, our genetics are no more interesting than a bacterium's genetics. 2:08:59.080 --> 2:09:01.080 It's no more interesting than a monkey's genetics. 2:09:01.080 --> 2:09:07.080 What we have, what's unique and what's valuable is our knowledge, what we've learned about the world. 2:09:07.080 --> 2:09:09.080 And that is the rare thing. 2:09:09.080 --> 2:09:11.080 That's the thing we want to preserve. 2:09:11.080 --> 2:09:15.080 Who cares about our genes? 2:09:15.080 --> 2:09:17.080 It's the knowledge. 2:09:17.080 --> 2:09:19.080 That's a really good place to end. 2:09:19.080 --> 2:09:42.080 Thank you so much for talking to me.