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+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.
+