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+WEBVTT
+
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+ The following is a conversation with Francois Chalet.
+
+00:03.720 --> 00:07.300
+ He's the creator of Keras, which is an open source deep learning
+
+00:07.300 --> 00:10.560
+ library that is designed to enable fast, user friendly
+
+00:10.560 --> 00:13.600
+ experimentation with deep neural networks.
+
+00:13.600 --> 00:16.680
+ It serves as an interface to several deep learning libraries,
+
+00:16.680 --> 00:19.040
+ most popular of which is TensorFlow.
+
+00:19.040 --> 00:22.600
+ And it was integrated into the TensorFlow main code base
+
+00:22.600 --> 00:24.120
+ a while ago.
+
+00:24.120 --> 00:27.560
+ Meaning, if you want to create, train, and use
+
+00:27.560 --> 00:31.040
+ neural networks, probably the easiest and most popular option
+
+00:31.040 --> 00:34.840
+ is to use Keras inside TensorFlow.
+
+00:34.840 --> 00:37.760
+ Aside from creating an exceptionally useful and popular
+
+00:37.760 --> 00:41.920
+ library, Francois is also a world class AI researcher
+
+00:41.920 --> 00:44.560
+ and software engineer at Google.
+
+00:44.560 --> 00:48.080
+ And he's definitely an outspoken, if not controversial,
+
+00:48.080 --> 00:51.480
+ personality in the AI world, especially
+
+00:51.480 --> 00:53.720
+ in the realm of ideas around the future
+
+00:53.720 --> 00:55.920
+ of artificial intelligence.
+
+00:55.920 --> 00:58.600
+ This is the Artificial Intelligence Podcast.
+
+00:58.600 --> 01:01.000
+ If you enjoy it, subscribe on YouTube,
+
+01:01.000 --> 01:04.160
+ give us five stars on iTunes, support on Patreon,
+
+01:04.160 --> 01:06.080
+ or simply connect with me on Twitter
+
+01:06.080 --> 01:09.960
+ at Lex Freedman, spelled F R I D M A N.
+
+01:09.960 --> 01:14.880
+ And now, here's my conversation with Francois Chalet.
+
+01:14.880 --> 01:17.320
+ You're known for not sugarcoating your opinions
+
+01:17.320 --> 01:19.640
+ and speaking your mind about ideas in AI, especially
+
+01:19.640 --> 01:21.120
+ on Twitter.
+
+01:21.120 --> 01:22.800
+ That's one of my favorite Twitter accounts.
+
+01:22.800 --> 01:26.360
+ So what's one of the more controversial ideas
+
+01:26.360 --> 01:30.440
+ you've expressed online and gotten some heat for?
+
+01:30.440 --> 01:33.080
+ How do you pick?
+
+01:33.080 --> 01:33.920
+ How do I pick?
+
+01:33.920 --> 01:38.280
+ Yeah, no, I think if you go through the trouble of maintaining
+
+01:38.280 --> 01:41.880
+ Twitter accounts, you might as well speak your mind.
+
+01:41.880 --> 01:44.640
+ Otherwise, what's even the point of doing Twitter accounts,
+
+01:44.640 --> 01:48.600
+ like getting an eye scar and just leaving it in the garage?
+
+01:48.600 --> 01:50.360
+ Yeah, so that's one thing for which
+
+01:50.360 --> 01:53.640
+ I got a lot of pushback.
+
+01:53.640 --> 01:56.720
+ Perhaps that time, I wrote something
+
+01:56.720 --> 02:00.960
+ about the idea of intelligence explosion.
+
+02:00.960 --> 02:05.680
+ And I was questioning the idea and the reasoning behind this
+
+02:05.680 --> 02:06.880
+ idea.
+
+02:06.880 --> 02:09.720
+ And I got a lot of pushback on that.
+
+02:09.720 --> 02:11.840
+ I got a lot of flak for it.
+
+02:11.840 --> 02:14.360
+ So yeah, so intelligence explosion, I'm sure you're familiar
+
+02:14.360 --> 02:15.800
+ with the idea, but it's the idea
+
+02:15.800 --> 02:21.360
+ that if you were to build general AI problems
+
+02:21.360 --> 02:27.600
+ solving algorithms, well, the problem of building such an AI,
+
+02:27.600 --> 02:30.640
+ that itself is a problem that could be solved by your AI.
+
+02:30.640 --> 02:33.840
+ And maybe it could be solved better than what humans can do.
+
+02:33.840 --> 02:36.920
+ So your AI could start tweaking its own algorithm,
+
+02:36.920 --> 02:39.640
+ could start making a better version of itself.
+
+02:39.640 --> 02:43.320
+ And so on, iteratively, in a recursive fashion,
+
+02:43.320 --> 02:47.360
+ and so you would end up with an AI
+
+02:47.360 --> 02:50.920
+ with exponentially increasing intelligence.
+
+02:50.920 --> 02:55.880
+ And I was basically questioning this idea.
+
+02:55.880 --> 02:59.080
+ First of all, because the notion of intelligence explosion
+
+02:59.080 --> 03:02.240
+ uses an implicit definition of intelligence
+
+03:02.240 --> 03:05.400
+ that doesn't sound quite right to me.
+
+03:05.400 --> 03:11.200
+ It considers intelligence as a property of a brain
+
+03:11.200 --> 03:13.680
+ that you can consider in isolation,
+
+03:13.680 --> 03:16.640
+ like the height of a building, for instance.
+
+03:16.640 --> 03:19.040
+ But that's not really what intelligence is.
+
+03:19.040 --> 03:22.200
+ Intelligence emerges from the interaction
+
+03:22.200 --> 03:26.720
+ between a brain, a body, like embodied intelligence,
+
+03:26.720 --> 03:28.320
+ and an environment.
+
+03:28.320 --> 03:30.720
+ And if you're missing one of these pieces,
+
+03:30.720 --> 03:33.840
+ then you cannot really define intelligence anymore.
+
+03:33.840 --> 03:36.800
+ So just tweaking a brain to make it smaller and smaller
+
+03:36.800 --> 03:39.120
+ doesn't actually make any sense to me.
+
+03:39.120 --> 03:43.000
+ So first of all, you're crushing the dreams of many people.
+
+03:43.000 --> 03:46.000
+ So let's look at Sam Harris.
+
+03:46.000 --> 03:48.680
+ Actually, a lot of physicists, Max Tegmark,
+
+03:48.680 --> 03:53.600
+ people who think the universe is an information processing
+
+03:53.600 --> 03:54.640
+ system.
+
+03:54.640 --> 03:57.680
+ Our brain is kind of an information processing system.
+
+03:57.680 --> 04:00.040
+ So what's the theoretical limit?
+
+04:00.040 --> 04:04.840
+ It doesn't make sense that there should be some,
+
+04:04.840 --> 04:08.080
+ it seems naive to think that our own brain is somehow
+
+04:08.080 --> 04:11.600
+ the limit of the capabilities and this information.
+
+04:11.600 --> 04:13.600
+ I'm playing devil's advocate here.
+
+04:13.600 --> 04:15.600
+ This information processing system.
+
+04:15.600 --> 04:18.040
+ And then if you just scale it, if you're
+
+04:18.040 --> 04:20.880
+ able to build something that's on par with the brain,
+
+04:20.880 --> 04:24.000
+ you just, the process that builds it just continues
+
+04:24.000 --> 04:26.360
+ and it will improve exponentially.
+
+04:26.360 --> 04:30.120
+ So that's the logic that's used actually
+
+04:30.120 --> 04:33.920
+ by almost everybody that is worried
+
+04:33.920 --> 04:36.880
+ about super human intelligence.
+
+04:36.880 --> 04:39.800
+ Yeah, so you're trying to make, so most people
+
+04:39.800 --> 04:42.320
+ who are skeptical of that are kind of like,
+
+04:42.320 --> 04:44.360
+ this doesn't, their thought process,
+
+04:44.360 --> 04:46.520
+ this doesn't feel right.
+
+04:46.520 --> 04:47.680
+ Like that's for me as well.
+
+04:47.680 --> 04:52.320
+ So I'm more like, it doesn't, the whole thing is shrouded
+
+04:52.320 --> 04:55.840
+ in mystery where you can't really say anything concrete,
+
+04:55.840 --> 04:57.880
+ but you could say this doesn't feel right.
+
+04:57.880 --> 05:00.680
+ This doesn't feel like that's how the brain works.
+
+05:00.680 --> 05:02.400
+ And you're trying to, with your blog post
+
+05:02.400 --> 05:05.680
+ and now making it a little more explicit.
+
+05:05.680 --> 05:10.280
+ So one idea is that the brain isn't,
+
+05:10.280 --> 05:13.840
+ exists alone, it exists within the environment.
+
+05:13.840 --> 05:17.520
+ So you can't exponentially, you would have to somehow
+
+05:17.520 --> 05:19.360
+ exponentially improve the environment
+
+05:19.360 --> 05:22.280
+ and the brain together, almost yet in order
+
+05:22.280 --> 05:26.280
+ to create something that's much smarter
+
+05:26.280 --> 05:29.120
+ in some kind of, of course we don't have
+
+05:29.120 --> 05:30.560
+ a definition of intelligence.
+
+05:30.560 --> 05:31.880
+ That's correct, that's correct.
+
+05:31.880 --> 05:34.560
+ I don't think, you should look at very smart people
+
+05:34.560 --> 05:37.840
+ to the even humans, not even talking about AI's.
+
+05:37.840 --> 05:40.000
+ I don't think their brain and the performance
+
+05:40.000 --> 05:42.520
+ of their brain is the bottleneck
+
+05:42.520 --> 05:45.760
+ to their expressed intelligence, to their achievements.
+
+05:47.160 --> 05:50.480
+ You cannot just tweak one part of this system,
+
+05:50.480 --> 05:53.360
+ like of this brain, body, environment system
+
+05:53.360 --> 05:56.480
+ and expect the capabilities, like what emerges
+
+05:56.480 --> 05:59.000
+ out of this system to just, you know,
+
+05:59.000 --> 06:00.800
+ explode exponentially.
+
+06:00.800 --> 06:04.720
+ Because anytime you improve one part of a system
+
+06:04.720 --> 06:07.280
+ with many interdependencies like this,
+
+06:07.280 --> 06:09.520
+ there's a new bottleneck that arises, right?
+
+06:09.520 --> 06:12.280
+ And I don't think even today for very smart people,
+
+06:12.280 --> 06:14.960
+ their brain is not the bottleneck
+
+06:14.960 --> 06:17.560
+ to the sort of problems they can solve, right?
+
+06:17.560 --> 06:21.480
+ In fact, many very smart people today, you know,
+
+06:21.480 --> 06:23.760
+ they're not actually solving any big scientific problems.
+
+06:23.760 --> 06:24.800
+ They're not Einstein.
+
+06:24.800 --> 06:26.560
+ They're like Einstein, but, you know,
+
+06:26.560 --> 06:28.280
+ the patent clerk days.
+
+06:28.280 --> 06:31.920
+ Like Einstein became Einstein
+
+06:31.920 --> 06:36.080
+ because this was a meeting of a genius
+
+06:36.080 --> 06:39.480
+ with a big problem at the right time, right?
+
+06:39.480 --> 06:42.480
+ But maybe this meeting could have never happened
+
+06:42.480 --> 06:44.960
+ and then Einstein, there's just been a patent clerk, right?
+
+06:44.960 --> 06:48.400
+ And in fact, many people today are probably like
+
+06:49.760 --> 06:52.240
+ genius level smart, but you wouldn't know
+
+06:52.240 --> 06:54.800
+ because they're not really expressing any of that.
+
+06:54.800 --> 06:58.520
+ Well, that's brilliant. So we can think of the world, earth,
+
+06:58.520 --> 07:02.720
+ but also the universe as just, as a space of problems.
+
+07:02.720 --> 07:05.160
+ So all of these problems and tasks are roaming it
+
+07:05.160 --> 07:06.880
+ of various difficulty.
+
+07:06.880 --> 07:10.120
+ And there's agents, creatures like ourselves
+
+07:10.120 --> 07:13.360
+ and animals and so on that are also roaming it.
+
+07:13.360 --> 07:16.480
+ And then you get coupled with a problem
+
+07:16.480 --> 07:17.640
+ and then you solve it.
+
+07:17.640 --> 07:19.880
+ But without that coupling,
+
+07:19.880 --> 07:22.560
+ you can't demonstrate your quote unquote intelligence.
+
+07:22.560 --> 07:25.440
+ Yeah, exactly. Intelligence is the meaning of
+
+07:25.440 --> 07:28.760
+ great problem solving capabilities with a great problem.
+
+07:28.760 --> 07:30.560
+ And if you don't have the problem,
+
+07:30.560 --> 07:32.280
+ you don't really express in intelligence.
+
+07:32.280 --> 07:34.760
+ All you're left with is potential intelligence,
+
+07:34.760 --> 07:36.920
+ like the performance of your brain or, you know,
+
+07:36.920 --> 07:41.920
+ how high your IQ is, which in itself is just a number, right?
+
+07:42.080 --> 07:46.520
+ So you mentioned problem solving capacity.
+
+07:46.520 --> 07:47.360
+ Yeah.
+
+07:47.360 --> 07:51.040
+ What do you think of as problem solving capacity?
+
+07:51.040 --> 07:55.200
+ What, can you try to define intelligence?
+
+07:56.680 --> 08:00.040
+ Like, what does it mean to be more or less intelligent?
+
+08:00.040 --> 08:03.040
+ Is it completely coupled to a particular problem?
+
+08:03.040 --> 08:05.760
+ Or is there something a little bit more universal?
+
+08:05.760 --> 08:07.480
+ Yeah, I do believe all intelligence
+
+08:07.480 --> 08:09.120
+ is specialized intelligence.
+
+08:09.120 --> 08:12.280
+ Even human intelligence has some degree of generality.
+
+08:12.280 --> 08:15.400
+ Well, all intelligence systems have some degree of generality,
+
+08:15.400 --> 08:19.480
+ but they're always specialized in one category of problems.
+
+08:19.480 --> 08:21.920
+ So the human intelligence is specialized
+
+08:21.920 --> 08:25.560
+ in the human experience and that shows at various levels,
+
+08:25.560 --> 08:29.320
+ that shows in some prior knowledge,
+
+08:29.320 --> 08:32.040
+ that's innate, that we have at birth,
+
+08:32.040 --> 08:35.360
+ knowledge about things like agents,
+
+08:35.360 --> 08:40.440
+ goal driven behavior, visual priors about what makes an object,
+
+08:40.440 --> 08:43.520
+ priors about time, and so on.
+
+08:43.520 --> 08:45.360
+ That shows also in the way we learn,
+
+08:45.360 --> 08:48.920
+ for instance, it's very easy for us to pick up language,
+
+08:48.920 --> 08:52.080
+ it's very, very easy for us to learn certain things
+
+08:52.080 --> 08:54.920
+ because we are basically hard coded to learn them.
+
+08:54.920 --> 08:58.280
+ And we are specialized in solving certain kinds of problems
+
+08:58.280 --> 09:01.440
+ and we are quite useless when it comes to other kinds of problems.
+
+09:01.440 --> 09:06.160
+ For instance, we are not really designed
+
+09:06.160 --> 09:08.800
+ to handle very long term problems.
+
+09:08.800 --> 09:12.840
+ We have no capability of seeing the very long term.
+
+09:12.840 --> 09:17.840
+ We don't have very much working memory, you know?
+
+09:17.840 --> 09:19.960
+ So how do you think about long term?
+
+09:19.960 --> 09:21.280
+ Do you think long term planning,
+
+09:21.280 --> 09:24.760
+ we're talking about scale of years, millennia,
+
+09:24.760 --> 09:27.960
+ what do you mean by long term, we're not very good?
+
+09:27.960 --> 09:30.600
+ Well, human intelligence is specialized in the human experience
+
+09:30.600 --> 09:34.120
+ and human experience is very short, like one lifetime is short.
+
+09:34.120 --> 09:38.600
+ Even within one lifetime, we have a very hard time envisioning,
+
+09:38.600 --> 09:41.080
+ you know, things on a scale of years.
+
+09:41.080 --> 09:43.920
+ Like it's very difficult to project yourself at the scale of five,
+
+09:43.920 --> 09:46.080
+ at the scale of 10 years and so on.
+
+09:46.080 --> 09:49.960
+ Right. We can solve only fairly narrowly scoped problems.
+
+09:49.960 --> 09:53.720
+ So when it comes to solving bigger problems, larger scale problems,
+
+09:53.720 --> 09:56.320
+ we are not actually doing it on an individual level.
+
+09:56.320 --> 09:59.240
+ So it's not actually our brain doing it.
+
+09:59.240 --> 10:03.040
+ We have this thing called civilization, right?
+
+10:03.040 --> 10:06.600
+ Which is itself a sort of problem solving system,
+
+10:06.600 --> 10:10.000
+ a sort of artificial intelligence system, right?
+
+10:10.000 --> 10:14.080
+ And it's not running on one brain, it's running on a network of brains.
+
+10:14.080 --> 10:16.760
+ In fact, it's running on much more than a network of brains.
+
+10:16.760 --> 10:21.960
+ It's running on a lot of infrastructure, like books and computers
+
+10:21.960 --> 10:25.760
+ and the internet and human institutions and so on.
+
+10:25.760 --> 10:31.640
+ And that is capable of handling problems on a much greater scale
+
+10:31.640 --> 10:33.720
+ than any individual human.
+
+10:33.720 --> 10:37.560
+ If you look at computer science, for instance,
+
+10:37.560 --> 10:42.480
+ that's an institution that solves problems and it is super human, right?
+
+10:42.480 --> 10:46.840
+ It operates on a greater scale, it can solve much bigger problems
+
+10:46.840 --> 10:49.040
+ than an individual human could.
+
+10:49.040 --> 10:52.120
+ And science itself, science as a system, as an institution,
+
+10:52.120 --> 10:57.640
+ is a kind of artificially intelligent problem solving algorithm
+
+10:57.640 --> 10:59.360
+ that is super human.
+
+10:59.360 --> 11:06.080
+ Yeah, it's a computer science is like a theorem prover
+
+11:06.080 --> 11:10.360
+ at a scale of thousands, maybe hundreds of thousands of human beings.
+
+11:10.360 --> 11:14.640
+ At that scale, what do you think is an intelligent agent?
+
+11:14.640 --> 11:18.280
+ So there's us humans at the individual level.
+
+11:18.280 --> 11:23.880
+ There is millions, maybe billions of bacteria in our skin.
+
+11:23.880 --> 11:26.400
+ There is, that's at the smaller scale.
+
+11:26.400 --> 11:31.880
+ You can even go to the particle level as systems that behave.
+
+11:31.880 --> 11:35.400
+ You can say intelligently in some ways.
+
+11:35.400 --> 11:37.840
+ And then you can look at the Earth as a single organism.
+
+11:37.840 --> 11:42.160
+ You can look at our galaxy and even the universe as a single organism.
+
+11:42.160 --> 11:46.320
+ Do you think, how do you think about scale and defining intelligent systems?
+
+11:46.320 --> 11:51.840
+ And we're here at Google, there is millions of devices doing computation
+
+11:51.840 --> 11:53.400
+ in a distributed way.
+
+11:53.400 --> 11:55.880
+ How do you think about intelligence versus scale?
+
+11:55.880 --> 12:00.640
+ You can always characterize anything as a system.
+
+12:00.640 --> 12:05.320
+ I think people who talk about things like intelligence explosion
+
+12:05.320 --> 12:08.760
+ tend to focus on one agent is basically one brain,
+
+12:08.760 --> 12:11.920
+ like one brain considered in isolation, like a brain, a jar
+
+12:11.920 --> 12:16.280
+ that's controlling a body in a very top to bottom kind of fashion.
+
+12:16.280 --> 12:19.480
+ And that body is pursuing goals into an environment.
+
+12:19.480 --> 12:20.720
+ So it's a very hierarchical view.
+
+12:20.720 --> 12:22.840
+ You have the brain at the top of the pyramid,
+
+12:22.840 --> 12:25.960
+ then you have the body just plainly receiving orders,
+
+12:25.960 --> 12:28.920
+ then the body is manipulating objects in an environment and so on.
+
+12:28.920 --> 12:33.680
+ So everything is subordinate to this one thing, this epicenter,
+
+12:33.680 --> 12:34.760
+ which is the brain.
+
+12:34.760 --> 12:39.240
+ But in real life, intelligent agents don't really work like this.
+
+12:39.240 --> 12:43.400
+ There is no strong delimitation between the brain and the body to start with.
+
+12:43.400 --> 12:46.520
+ You have to look not just at the brain, but at the nervous system.
+
+12:46.520 --> 12:50.760
+ But then the nervous system and the body are naturally two separate entities.
+
+12:50.760 --> 12:53.960
+ So you have to look at an entire animal as one agent.
+
+12:53.960 --> 13:00.200
+ But then you start realizing as you observe an animal over any length of time
+
+13:00.200 --> 13:04.600
+ that a lot of the intelligence of an animal is actually externalized.
+
+13:04.600 --> 13:06.240
+ That's especially true for humans.
+
+13:06.240 --> 13:08.880
+ A lot of our intelligence is externalized.
+
+13:08.880 --> 13:11.960
+ When you write down some notes, there is externalized intelligence.
+
+13:11.960 --> 13:16.000
+ When you write a computer program, you are externalizing cognition.
+
+13:16.000 --> 13:17.320
+ So it's externalized in books.
+
+13:17.320 --> 13:23.040
+ It's externalized in computers, the internet, in other humans.
+
+13:23.040 --> 13:25.400
+ It's externalized in language and so on.
+
+13:25.400 --> 13:32.640
+ So there is no hard delimitation of what makes an intelligent agent.
+
+13:32.640 --> 13:34.920
+ It's all about context.
+
+13:34.920 --> 13:42.440
+ OK, but AlphaGo is better at Go than the best human player.
+
+13:42.440 --> 13:44.960
+ There's levels of skill here.
+
+13:44.960 --> 13:52.680
+ So do you think there is such a concept as an intelligence explosion
+
+13:52.680 --> 13:54.720
+ in a specific task?
+
+13:54.720 --> 14:00.080
+ And then, well, yeah, do you think it's possible to have a category of tasks
+
+14:00.080 --> 14:05.000
+ on which you do have something like an exponential growth of ability
+
+14:05.000 --> 14:07.400
+ to solve that particular problem?
+
+14:07.400 --> 14:15.280
+ I think if you consider a specific vertical, it's probably possible to some extent.
+
+14:15.280 --> 14:18.320
+ I also don't think we have to speculate about it
+
+14:18.320 --> 14:24.760
+ because we have real world examples of free classivity self improving
+
+14:24.760 --> 14:26.880
+ intelligent systems.
+
+14:26.880 --> 14:32.560
+ For instance, science is a problem solving system, a knowledge generation system,
+
+14:32.560 --> 14:36.240
+ like a system that experiences the world in some sense
+
+14:36.240 --> 14:40.120
+ and then gradually understands it and can act on it.
+
+14:40.120 --> 14:45.560
+ And that system is superhuman and it is clearly recursively self improving
+
+14:45.560 --> 14:47.520
+ because science fits into technology.
+
+14:47.520 --> 14:51.120
+ Technology can be used to build better tools, better computers,
+
+14:51.120 --> 14:56.720
+ better instrumentation and so on, which in turn can make science faster.
+
+14:56.720 --> 15:00.520
+ So science is probably the closest thing we have today
+
+15:00.520 --> 15:04.720
+ to a real civility self improving superhuman AI.
+
+15:04.720 --> 15:10.280
+ And you can just observe, is science, is scientific progress today exploding,
+
+15:10.280 --> 15:12.760
+ which itself is an interesting question.
+
+15:12.760 --> 15:15.800
+ You can use that as a basis to try to understand what
+
+15:15.800 --> 15:20.960
+ will happen with a superhuman AI that has science like behavior.
+
+15:20.960 --> 15:23.320
+ Let me linger on it a little bit more.
+
+15:23.320 --> 15:28.520
+ What is your intuition why an intelligence explosion is not possible?
+
+15:28.520 --> 15:34.400
+ Like taking the scientific, all the semi scientific revolutions.
+
+15:34.400 --> 15:38.080
+ Why can't we slightly accelerate that process?
+
+15:38.080 --> 15:43.160
+ So you can absolutely accelerate any problem solving process.
+
+15:43.160 --> 15:48.640
+ So recursively, recursive self improvement is absolutely a real thing.
+
+15:48.640 --> 15:51.880
+ But what happens with a recursively self improving system
+
+15:51.880 --> 15:56.480
+ is typically not explosion because no system exists in isolation.
+
+15:56.480 --> 16:00.840
+ And so tweaking one part of the system means that suddenly another part of the system
+
+16:00.840 --> 16:02.120
+ becomes a bottleneck.
+
+16:02.120 --> 16:06.760
+ And if you look at science, for instance, which is clearly a recursively self improving,
+
+16:06.760 --> 16:11.960
+ clearly a problem solving system, scientific progress is not actually exploding.
+
+16:11.960 --> 16:17.840
+ If you look at science, what you see is the picture of a system that is consuming
+
+16:17.840 --> 16:20.440
+ an exponentially increasing amount of resources.
+
+16:20.440 --> 16:26.000
+ But it's having a linear output in terms of scientific progress.
+
+16:26.000 --> 16:28.960
+ And maybe that will seem like a very strong claim.
+
+16:28.960 --> 16:34.520
+ Many people are actually saying that scientific progress is exponential.
+
+16:34.520 --> 16:40.000
+ But when they're claiming this, they're actually looking at indicators of resource
+
+16:40.000 --> 16:43.080
+ consumption by science.
+
+16:43.080 --> 16:49.200
+ For instance, the number of papers being published, the number of patterns being
+
+16:49.200 --> 16:55.760
+ filed, and so on, which are just completely correlated with how many people are working
+
+16:55.760 --> 16:57.640
+ on science today.
+
+16:57.640 --> 17:00.720
+ So it's actually an indicator of resource consumption.
+
+17:00.720 --> 17:06.760
+ But what you should look at is the output is progress in terms of the knowledge that
+
+17:06.760 --> 17:12.840
+ science generates in terms of the scope and significance of the problems that we solve.
+
+17:12.840 --> 17:16.920
+ And some people have actually been trying to measure that.
+
+17:16.920 --> 17:22.800
+ Like Michael Nielsen, for instance, he had a very nice paper, I think that was last
+
+17:22.800 --> 17:25.280
+ year about it.
+
+17:25.280 --> 17:32.760
+ So his approach to measure scientific progress was to look at the timeline of scientific
+
+17:32.760 --> 17:37.400
+ discoveries over the past 100, 150 years.
+
+17:37.400 --> 17:46.120
+ And for each major discovery, ask a panel of experts to rate the significance of the
+
+17:46.120 --> 17:47.120
+ discovery.
+
+17:47.120 --> 17:54.440
+ And if the output of sciences in the institution were exponential, you would expect the temporal
+
+17:54.440 --> 18:01.080
+ density of significance to go up exponentially, maybe because there's a faster rate of discoveries,
+
+18:01.080 --> 18:05.120
+ maybe because the discoveries are increasingly more important.
+
+18:05.120 --> 18:10.360
+ And what actually happens if you plot this temporal density of significance measured
+
+18:10.360 --> 18:14.600
+ in this way, is that you see very much a flat graph.
+
+18:14.600 --> 18:20.040
+ You see a flat graph across all disciplines, across physics, biology, medicine and so on.
+
+18:20.040 --> 18:24.400
+ And it actually makes a lot of sense if you think about it, because think about the progress
+
+18:24.400 --> 18:28.120
+ of physics 110 years ago.
+
+18:28.120 --> 18:30.240
+ It was a time of crazy change.
+
+18:30.240 --> 18:36.640
+ Think about the progress of technology 170 years ago, when we started replacing horses,
+
+18:36.640 --> 18:40.080
+ with cars, when we started having electricity and so on.
+
+18:40.080 --> 18:41.640
+ It was a time of incredible change.
+
+18:41.640 --> 18:44.800
+ And today is also a time of very, very fast change.
+
+18:44.800 --> 18:50.480
+ But it would be an unfair characterization to say that today, technology and science
+
+18:50.480 --> 18:54.600
+ are moving way faster than they did 50 years ago or 100 years ago.
+
+18:54.600 --> 19:08.800
+ And if you do try to rigorously plot the temporal density of the significance, you do see very
+
+19:08.800 --> 19:16.240
+ flat curves and you can check out the paper that Michael Nielsen had about this idea.
+
+19:16.240 --> 19:25.280
+ And so the way I interpret it is as you make progress in a given field or in a given subfield
+
+19:25.280 --> 19:30.640
+ of science, it becomes exponentially more difficult to make further progress, like the
+
+19:30.640 --> 19:35.120
+ very first person to work on information theory.
+
+19:35.120 --> 19:40.320
+ If you enter a new field and it's still the very early years, there's a lot of low hanging
+
+19:40.320 --> 19:42.200
+ fruit you can pick.
+
+19:42.200 --> 19:48.240
+ But the next generation of researchers is going to have to dig much harder, actually,
+
+19:48.240 --> 19:52.800
+ to make smaller discoveries, probably larger numbers, smaller discoveries.
+
+19:52.800 --> 19:57.640
+ And to achieve the same amount of impact, you're going to need a much greater head count.
+
+19:57.640 --> 20:02.840
+ And that's exactly the picture you're seeing with science, is that the number of scientists
+
+20:02.840 --> 20:06.680
+ and engineers is, in fact, increasing exponentially.
+
+20:06.680 --> 20:11.520
+ The amount of computational resources that are available to science is increasing exponentially
+
+20:11.520 --> 20:12.520
+ and so on.
+
+20:12.520 --> 20:18.240
+ So the resource consumption of science is exponential, but the output in terms of progress,
+
+20:18.240 --> 20:21.160
+ in terms of significance, is linear.
+
+20:21.160 --> 20:26.200
+ And the reason why is because, and even though science is rigorously self improving, meaning
+
+20:26.200 --> 20:33.000
+ that scientific progress turns into technological progress, which in turn helps science.
+
+20:33.000 --> 20:39.240
+ If you look at computers, for instance, our products of science and computers are tremendously
+
+20:39.240 --> 20:41.600
+ useful in spinning up science.
+
+20:41.600 --> 20:42.600
+ The internet, same thing.
+
+20:42.600 --> 20:47.680
+ The internet is a technology that's made possible by very recent scientific advances.
+
+20:47.680 --> 20:53.960
+ And itself, because it enables scientists to network, to communicate, to exchange papers
+
+20:53.960 --> 20:57.480
+ and ideas much faster, it is a way to speed up scientific progress.
+
+20:57.480 --> 21:02.800
+ So even though you're looking at a recursively self improving system, it is consuming exponentially
+
+21:02.800 --> 21:09.240
+ more resources to produce the same amount of problem solving, in fact.
+
+21:09.240 --> 21:11.200
+ So that's a fascinating way to paint it.
+
+21:11.200 --> 21:14.960
+ And certainly that holds for the deep learning community, right?
+
+21:14.960 --> 21:18.040
+ If you look at the temporal, what did you call it?
+
+21:18.040 --> 21:21.260
+ The temporal density of significant ideas.
+
+21:21.260 --> 21:27.440
+ If you look at in deep learning, I think, I'd have to think about that, but if you really
+
+21:27.440 --> 21:32.480
+ look at significant ideas in deep learning, they might even be decreasing.
+
+21:32.480 --> 21:39.720
+ So I do believe the per paper significance is decreasing.
+
+21:39.720 --> 21:43.480
+ But the amount of papers is still today, exponentially increasing.
+
+21:43.480 --> 21:49.480
+ So I think if you look at an aggregate, my guess is that you would see a linear progress.
+
+21:49.480 --> 21:58.720
+ If you were to sum the significance of all papers, you would see a roughly linear progress.
+
+21:58.720 --> 22:05.680
+ And in my opinion, it is not a coincidence that you're seeing linear progress in science
+
+22:05.680 --> 22:07.640
+ despite exponential resource consumption.
+
+22:07.640 --> 22:15.840
+ I think the resource consumption is dynamically adjusting itself to maintain linear progress
+
+22:15.840 --> 22:21.360
+ because we as a community expect linear progress, meaning that if we start investing less and
+
+22:21.360 --> 22:26.160
+ seeing less progress, it means that suddenly there are some lower hanging fruits that become
+
+22:26.160 --> 22:31.320
+ available and someone's going to step up and pick them.
+
+22:31.320 --> 22:37.200
+ So it's very much like a market for discoveries and ideas.
+
+22:37.200 --> 22:41.640
+ But there's another fundamental part which you're highlighting, which as a hypothesis
+
+22:41.640 --> 22:49.440
+ as science or the space of ideas, any one path you travel down, it gets exponentially
+
+22:49.440 --> 22:54.800
+ more difficult to develop new ideas.
+
+22:54.800 --> 23:01.080
+ And your sense is that's going to hold across our mysterious universe.
+
+23:01.080 --> 23:02.080
+ Yes.
+
+23:02.080 --> 23:06.800
+ Well, exponential progress triggers exponential friction so that if you tweak one part of
+
+23:06.800 --> 23:10.200
+ the system, suddenly some other part becomes a bottleneck.
+
+23:10.200 --> 23:17.440
+ For instance, let's say we develop some device that measures its own acceleration and then
+
+23:17.440 --> 23:22.240
+ it has some engine and it outputs even more acceleration in proportion of its own acceleration
+
+23:22.240 --> 23:23.240
+ and you drop it somewhere.
+
+23:23.240 --> 23:29.120
+ It's not going to reach infinite speed because it exists in a certain context.
+
+23:29.120 --> 23:32.960
+ So the error on this is going to generate friction and it's going to block it at some
+
+23:32.960 --> 23:34.440
+ top speed.
+
+23:34.440 --> 23:39.880
+ And even if you were to consider a broader context and lift the bottleneck there, like
+
+23:39.880 --> 23:46.200
+ the bottleneck of friction, then some other part of the system would start stepping in
+
+23:46.200 --> 23:50.040
+ and creating exponential friction, maybe the speed of flight or whatever.
+
+23:50.040 --> 23:55.400
+ And this definitely holds true when you look at the problem solving algorithm that is being
+
+23:55.400 --> 23:59.780
+ run by science as an institution, science as a system.
+
+23:59.780 --> 24:06.880
+ As you make more and more progress, despite having this recursive self improvement component,
+
+24:06.880 --> 24:11.840
+ you are encountering exponential friction, like the more researchers you have working
+
+24:11.840 --> 24:18.200
+ on different ideas, the more overhead you have in terms of communication across researchers.
+
+24:18.200 --> 24:23.160
+ If you look at, you were mentioning quantum mechanics, right?
+
+24:23.160 --> 24:28.480
+ Well if you want to start making significant discoveries today, significant progress in
+
+24:28.480 --> 24:34.200
+ quantum mechanics, there is an amount of knowledge you have to ingest, which is huge.
+
+24:34.200 --> 24:40.000
+ But there is a very large overhead to even start to contribute, there is a large amount
+
+24:40.000 --> 24:44.240
+ of overhead to synchronize across researchers and so on.
+
+24:44.240 --> 24:50.720
+ And of course, the significant practical experiments are going to require exponentially
+
+24:50.720 --> 24:57.920
+ expensive equipment because the easier ones have already been run, right?
+
+24:57.920 --> 25:08.520
+ So in your senses, there is no way of escaping this kind of friction with artificial intelligence
+
+25:08.520 --> 25:09.520
+ systems.
+
+25:09.520 --> 25:15.360
+ Yeah, no, I think science is a very good way to model what would happen with a superhuman
+
+25:15.360 --> 25:17.880
+ recursive research improving AI.
+
+25:17.880 --> 25:20.960
+ That's my intuition.
+
+25:20.960 --> 25:26.680
+ It's not like a mathematical proof of anything, that's not my point, like I'm not trying
+
+25:26.680 --> 25:31.440
+ to prove anything, I'm just trying to make an argument to question the narrative of intelligence
+
+25:31.440 --> 25:35.600
+ explosion, which is quite a dominant narrative and you do get a lot of pushback if you go
+
+25:35.600 --> 25:36.920
+ against it.
+
+25:36.920 --> 25:43.280
+ Because so for many people, right, AI is not just a subfield of computer science, it's
+
+25:43.280 --> 25:49.560
+ more like a belief system, like this belief that the world is headed towards an event,
+
+25:49.560 --> 25:58.000
+ the singularity, past which, you know, AI will become, will go exponential very much
+
+25:58.000 --> 26:02.160
+ and the world will be transformed and humans will become obsolete.
+
+26:02.160 --> 26:07.880
+ And if you go against this narrative, because it is not really a scientific argument but
+
+26:07.880 --> 26:12.240
+ more of a belief system, it is part of the identity of many people.
+
+26:12.240 --> 26:15.680
+ If you go against this narrative, it's like you're attacking the identity of people who
+
+26:15.680 --> 26:16.680
+ believe in it.
+
+26:16.680 --> 26:22.880
+ It's almost like saying God doesn't exist or something, so you do get a lot of pushback
+
+26:22.880 --> 26:25.200
+ if you try to question his ideas.
+
+26:25.200 --> 26:29.880
+ First of all, I believe most people, they might not be as eloquent or explicit as you're
+
+26:29.880 --> 26:34.400
+ being, but most people in computer science are most people who actually have built anything
+
+26:34.400 --> 26:39.160
+ that you could call AI, quote unquote, would agree with you.
+
+26:39.160 --> 26:43.880
+ They might not be describing in the same kind of way, it's more, so the pushback you're
+
+26:43.880 --> 26:51.120
+ getting is from people who get attached to the narrative from, not from a place of science,
+
+26:51.120 --> 26:53.520
+ but from a place of imagination.
+
+26:53.520 --> 26:54.520
+ That's correct.
+
+26:54.520 --> 26:55.520
+ That's correct.
+
+26:55.520 --> 26:57.240
+ So why do you think that's so appealing?
+
+26:57.240 --> 27:03.880
+ Because the usual dreams that people have when you create a superintelligence system
+
+27:03.880 --> 27:09.520
+ past the singularity, that what people imagine is somehow always destructive.
+
+27:09.520 --> 27:13.760
+ Do you have, if you were put on your psychology hat, what's, why is it so?
+
+27:13.760 --> 27:20.200
+ Why is it so appealing to imagine the ways that all of human civilization will be destroyed?
+
+27:20.200 --> 27:22.200
+ I think it's a good story.
+
+27:22.200 --> 27:23.200
+ You know, it's a good story.
+
+27:23.200 --> 27:30.680
+ And very interestingly, it mirrors religious stories, right, religious mythology.
+
+27:30.680 --> 27:36.960
+ If you look at the mythology of most civilizations, it's about the world being headed towards
+
+27:36.960 --> 27:42.240
+ some final events in which the world will be destroyed and some new world order will
+
+27:42.240 --> 27:49.640
+ arise that will be mostly spiritual, like the apocalypse followed by a paradise, probably.
+
+27:49.640 --> 27:52.880
+ It's a very appealing story on a fundamental level.
+
+27:52.880 --> 27:54.640
+ And we all need stories.
+
+27:54.640 --> 27:59.920
+ We all need stories to structure in the way we see the world, especially at timescales
+
+27:59.920 --> 28:04.600
+ that are beyond our ability to make predictions.
+
+28:04.600 --> 28:14.920
+ So on a more serious non exponential explosion question, do you think there will be a time
+
+28:14.920 --> 28:21.880
+ when we'll create something like human level intelligence or intelligence systems that
+
+28:21.880 --> 28:28.720
+ will make you sit back and be just surprised at damn how smart this thing is?
+
+28:28.720 --> 28:32.360
+ That doesn't require exponential growth or an exponential improvement.
+
+28:32.360 --> 28:39.840
+ But what's your sense of the timeline and so on, that you'll be really surprised at
+
+28:39.840 --> 28:40.840
+ certain capabilities?
+
+28:40.840 --> 28:44.360
+ And we'll talk about limitations and deep learning, so do you think in your lifetime
+
+28:44.360 --> 28:46.760
+ you'll be really damn surprised?
+
+28:46.760 --> 28:53.960
+ Around 2013, 2014, I was many times surprised by the capabilities of deep learning, actually.
+
+28:53.960 --> 28:57.880
+ That was before we had assessed exactly what deep learning could do and could not do and
+
+28:57.880 --> 29:00.680
+ it felt like a time of immense potential.
+
+29:00.680 --> 29:03.120
+ And then we started narrowing it down.
+
+29:03.120 --> 29:07.240
+ But I was very surprised, so I would say it has already happened.
+
+29:07.240 --> 29:13.640
+ Was there a moment, there must have been a day in there where your surprise was almost
+
+29:13.640 --> 29:19.640
+ bordering on the belief of the narrative that we just discussed?
+
+29:19.640 --> 29:23.200
+ Was there a moment, because you've written quite eloquently about the limits of deep
+
+29:23.200 --> 29:28.600
+ learning, was there a moment that you thought that maybe deep learning is limitless?
+
+29:28.600 --> 29:32.520
+ No, I don't think I've ever believed this.
+
+29:32.520 --> 29:35.120
+ What was really shocking is that it worked.
+
+29:35.120 --> 29:37.800
+ It worked at all, yeah.
+
+29:37.800 --> 29:43.880
+ But there's a big jump between being able to do really good computer vision and human
+
+29:43.880 --> 29:45.040
+ level intelligence.
+
+29:45.040 --> 29:50.840
+ So I don't think at any point, I wasn't an impression that the results we got in computer
+
+29:50.840 --> 29:54.040
+ vision meant that we were very close to human level intelligence.
+
+29:54.040 --> 29:56.000
+ I don't think we're very close to human level intelligence.
+
+29:56.000 --> 30:01.720
+ I do believe that there's no reason why we won't achieve it at some point.
+
+30:01.720 --> 30:10.280
+ I also believe that the problem with talking about human level intelligence is that implicitly
+
+30:10.280 --> 30:13.920
+ you're considering an axis of intelligence with different levels.
+
+30:13.920 --> 30:17.200
+ But that's not really how intelligence works.
+
+30:17.200 --> 30:19.600
+ Intelligence is very multidimensional.
+
+30:19.600 --> 30:24.440
+ And so there's the question of capabilities, but there's also the question of being human
+
+30:24.440 --> 30:29.640
+ like, and it's two very different things, like you can build potentially very advanced
+
+30:29.640 --> 30:32.760
+ intelligent agents that are not human like at all.
+
+30:32.760 --> 30:35.320
+ And you can also build very human like agents.
+
+30:35.320 --> 30:37.920
+ And these are two very different things, right?
+
+30:37.920 --> 30:38.920
+ Right.
+
+30:38.920 --> 30:42.360
+ Let's go from the philosophical to the practical.
+
+30:42.360 --> 30:46.560
+ Can you give me a history of Keras and all the major deep learning frameworks that you
+
+30:46.560 --> 30:51.600
+ kind of remember in relation to Keras and in general, TensorFlow, Theano, the old days.
+
+30:51.600 --> 30:57.440
+ Can you give a brief overview, Wikipedia style history, and your role in it before we return
+
+30:57.440 --> 30:58.840
+ to AGI discussions?
+
+30:58.840 --> 31:00.840
+ Yeah, that's a broad topic.
+
+31:00.840 --> 31:06.800
+ So I started working on Keras, it was a name Keras at the time, I actually picked the
+
+31:06.800 --> 31:09.920
+ name like just the day I was going to release it.
+
+31:09.920 --> 31:15.040
+ So I started working on it in February 2015.
+
+31:15.040 --> 31:18.440
+ And so at the time, there weren't too many people working on deep learning, maybe like
+
+31:18.440 --> 31:25.480
+ fewer than 10,000, the software tooling was not really developed.
+
+31:25.480 --> 31:30.960
+ So the main deep learning library was Cafe, which was mostly C++.
+
+31:30.960 --> 31:33.040
+ Why do you say Cafe was the main one?
+
+31:33.040 --> 31:39.120
+ Cafe was vastly more popular than Theano in late 2014, early 2015.
+
+31:39.120 --> 31:43.480
+ Cafe was the one library that everyone was using for computer vision.
+
+31:43.480 --> 31:46.240
+ And computer vision was the most popular problem.
+
+31:46.240 --> 31:47.240
+ Absolutely.
+
+31:47.240 --> 31:53.280
+ Like, Covenant was like the subfield of deep learning that everyone was working on.
+
+31:53.280 --> 32:01.840
+ So myself, so in late 2014, I was actually interested in RNNs, in recurrent neural networks,
+
+32:01.840 --> 32:08.800
+ which was a very niche topic at the time, right, it really took off around 2016.
+
+32:08.800 --> 32:11.520
+ And so I was looking for good tools.
+
+32:11.520 --> 32:19.480
+ I had used Torch 7, I had used Theano, used Theano a lot in Kaggle competitions, I had
+
+32:19.480 --> 32:21.240
+ used Cafe.
+
+32:21.240 --> 32:27.880
+ And there was no like good solution for RNNs at the time, like there was no reusable open
+
+32:27.880 --> 32:30.280
+ source implementation of an LSTM, for instance.
+
+32:30.280 --> 32:33.200
+ So I decided to build my own.
+
+32:33.200 --> 32:39.600
+ And at first, the pitch for that was it was going to be mostly around LSTM recurrent neural
+
+32:39.600 --> 32:40.600
+ networks.
+
+32:40.600 --> 32:46.000
+ So in Python, an important decision at the time that was kind of nonobvious is that the
+
+32:46.000 --> 32:54.520
+ models would be defined via Python code, which was kind of like going against the mainstream
+
+32:54.520 --> 33:00.320
+ at the time, because Cafe, Pylon 2 and so on, like all the big libraries were actually
+
+33:00.320 --> 33:05.840
+ going with you, approaching static configuration files in YAML to define models.
+
+33:05.840 --> 33:10.560
+ So some libraries were using code to define models like Torch 7, obviously, but that was
+
+33:10.560 --> 33:11.560
+ not.
+
+33:11.560 --> 33:17.840
+ Python Lasagne was like a Theano based very early library that was, I think, developed.
+
+33:17.840 --> 33:18.840
+ I don't remember exactly.
+
+33:18.840 --> 33:19.840
+ Probably late 2014.
+
+33:19.840 --> 33:20.840
+ It's Python as well.
+
+33:20.840 --> 33:21.840
+ It's Python as well.
+
+33:21.840 --> 33:25.040
+ It was like on top of Theano.
+
+33:25.040 --> 33:32.760
+ And so I started working on something and the value proposition at the time was that not
+
+33:32.760 --> 33:40.920
+ only that what I think was the first reusable open source implementation of LSTM, you could
+
+33:40.920 --> 33:47.080
+ combine RNNs and covenants with the same library, which is not really possible before.
+
+33:47.080 --> 33:50.760
+ Like Cafe was only doing covenants.
+
+33:50.760 --> 33:52.880
+ And it was kind of easy to use.
+
+33:52.880 --> 33:55.760
+ Because so before I was using Theano, I was actually using Psykitlin.
+
+33:55.760 --> 33:58.480
+ And I loved Psykitlin for its usability.
+
+33:58.480 --> 34:02.440
+ So I drew a lot of inspiration from Psykitlin when I met Keras.
+
+34:02.440 --> 34:05.680
+ It's almost like Psykitlin for neural networks.
+
+34:05.680 --> 34:06.680
+ The fit function.
+
+34:06.680 --> 34:07.680
+ Exactly.
+
+34:07.680 --> 34:08.680
+ The fit function.
+
+34:08.680 --> 34:13.000
+ Like reducing a complex string loop to a single function call.
+
+34:13.000 --> 34:17.480
+ And of course, some people will say, this is hiding a lot of details, but that's exactly
+
+34:17.480 --> 34:18.480
+ the point.
+
+34:18.480 --> 34:20.360
+ The magic is the point.
+
+34:20.360 --> 34:25.280
+ So it's magical, but in a good way, it's magical in the sense that it's delightful.
+
+34:25.280 --> 34:27.600
+ I'm actually quite surprised.
+
+34:27.600 --> 34:31.920
+ I didn't know that it was born out of desire to implement RNNs and LSTMs.
+
+34:31.920 --> 34:32.920
+ It was.
+
+34:32.920 --> 34:33.920
+ That's fascinating.
+
+34:33.920 --> 34:39.160
+ So you were actually one of the first people to really try to attempt to get the major
+
+34:39.160 --> 34:41.160
+ architecture together.
+
+34:41.160 --> 34:45.160
+ And it's also interesting, I mean, you realize that that was a design decision at all is
+
+34:45.160 --> 34:47.480
+ defining the model and code.
+
+34:47.480 --> 34:52.320
+ Just I'm putting myself in your shoes, whether the YAML, especially if Cafe was the most
+
+34:52.320 --> 34:53.320
+ popular.
+
+34:53.320 --> 34:54.760
+ It was the most popular by far.
+
+34:54.760 --> 35:01.880
+ If I was if I were, yeah, I don't, I didn't like the YAML thing, but it makes more sense
+
+35:01.880 --> 35:05.760
+ that you will put in a configuration file, the definition of a model.
+
+35:05.760 --> 35:10.160
+ That's an interesting gutsy move to stick with defining it in code.
+
+35:10.160 --> 35:14.800
+ Just if you look back, other libraries, we're doing it as well, but it was definitely the
+
+35:14.800 --> 35:16.200
+ more niche option.
+
+35:16.200 --> 35:17.200
+ Yeah.
+
+35:17.200 --> 35:18.200
+ Okay.
+
+35:18.200 --> 35:19.200
+ Keras and then Keras.
+
+35:19.200 --> 35:24.220
+ So I released Keras in March, 2015, and it got users pretty much from the start.
+
+35:24.220 --> 35:27.480
+ So the deep learning community was very, very small at the time.
+
+35:27.480 --> 35:30.640
+ Lots of people were starting to be interested in LSTMs.
+
+35:30.640 --> 35:34.760
+ So it was going to release at the right time because it was offering an easy to use LSTM
+
+35:34.760 --> 35:35.760
+ implementation.
+
+35:35.760 --> 35:40.840
+ Exactly at the time where lots of you started to be intrigued by the capabilities of RNN,
+
+35:40.840 --> 35:42.340
+ RNNs 1LP.
+
+35:42.340 --> 35:47.000
+ So it grew from there.
+
+35:47.000 --> 35:53.760
+ Then I joined Google about six months later, and that was actually completely unrelated
+
+35:53.760 --> 35:54.760
+ to Keras.
+
+35:54.760 --> 36:00.720
+ Keras actually joined a research team working on image classification mostly like computer
+
+36:00.720 --> 36:01.720
+ vision.
+
+36:01.720 --> 36:03.840
+ So I was doing computer vision research at Google initially.
+
+36:03.840 --> 36:11.440
+ And immediately when I joined Google, I was exposed to the early internal version of TensorFlow.
+
+36:11.440 --> 36:15.400
+ And the way it appeared to me at the time, and it was definitely the way it was at the
+
+36:15.400 --> 36:20.880
+ time, is that this was an improved version of Tiano.
+
+36:20.880 --> 36:27.040
+ So I immediately knew I had to port Keras to this new TensorFlow thing.
+
+36:27.040 --> 36:31.760
+ And I was actually very busy as a new Googler.
+
+36:31.760 --> 36:34.600
+ So I had not time to work on that.
+
+36:34.600 --> 36:41.360
+ But then in November, I think it was November 2015, TensorFlow got released.
+
+36:41.360 --> 36:47.440
+ And it was kind of like my wake up call that, hey, I had to actually go and make it happen.
+
+36:47.440 --> 36:53.360
+ So in December, I ported Keras to run on TensorFlow, but it was not exactly a port.
+
+36:53.360 --> 36:59.360
+ It was more like a refactoring where I was abstracting away all the backend functionality
+
+36:59.360 --> 37:05.200
+ into one module so that the same code base could run on top of multiple backends.
+
+37:05.200 --> 37:07.560
+ So on top of TensorFlow or Tiano.
+
+37:07.560 --> 37:21.000
+ And for the next year, Tiano stayed as the default option, it was easier to use, it was
+
+37:21.000 --> 37:23.440
+ much faster, especially when it came to on it.
+
+37:23.440 --> 37:27.560
+ But eventually, TensorFlow overtook it.
+
+37:27.560 --> 37:34.000
+ And TensorFlow, the early TensorFlow has similar architectural decisions as Tiano.
+
+37:34.000 --> 37:38.360
+ So it was a natural transition.
+
+37:38.360 --> 37:45.360
+ So what, I mean, that still carries as a side, almost one project, right?
+
+37:45.360 --> 37:50.280
+ Yeah, so it was not my job assignment, it was not.
+
+37:50.280 --> 37:52.360
+ I was doing it on the side.
+
+37:52.360 --> 37:57.840
+ And even though it grew to have a lot of uses for deep learning library at the time, like
+
+37:57.840 --> 38:02.560
+ Stroud 2016, but I wasn't doing it as my main job.
+
+38:02.560 --> 38:10.680
+ So things started changing in, I think it must have been maybe October 2016, so one year
+
+38:10.680 --> 38:11.680
+ later.
+
+38:11.680 --> 38:18.440
+ So Rajat, who has the lead in TensorFlow, basically showed up one day in our building
+
+38:18.440 --> 38:23.040
+ where I was doing like, so I was doing research and things like, so I did a lot of computer
+
+38:23.040 --> 38:29.040
+ vision research, also collaborations with Christian Zegedi and Deep Learning for Theraim
+
+38:29.040 --> 38:34.720
+ Proving, that was a really interesting research topic.
+
+38:34.720 --> 38:42.600
+ And so Rajat was saying, hey, we saw Keras, we like it, we saw that you had Google, why
+
+38:42.600 --> 38:46.960
+ don't you come over for like a quarter and work with us?
+
+38:46.960 --> 38:50.560
+ And I was like, yeah, that sounds like a great opportunity, let's do it.
+
+38:50.560 --> 38:57.520
+ And so I started working on integrating the Keras API into TensorFlow more tightly.
+
+38:57.520 --> 39:06.000
+ So what followed up is a sort of temporary TensorFlow only version of Keras that was
+
+39:06.000 --> 39:12.560
+ in TensorFlow.contrib for a while, and finally moved to TensorFlow Core.
+
+39:12.560 --> 39:17.320
+ And I've never actually gotten back to my old team doing research.
+
+39:17.320 --> 39:27.360
+ Well, it's kind of funny that somebody like you who dreams of or at least sees the power
+
+39:27.360 --> 39:33.800
+ of AI systems that reason and Theraim Proving will talk about has also created a system
+
+39:33.800 --> 39:41.600
+ that makes the most basic kind of Lego building that is deep learning, super accessible, super
+
+39:41.600 --> 39:43.840
+ easy, so beautifully so.
+
+39:43.840 --> 39:50.280
+ It's a funny irony that you're both, you're responsible for both things.
+
+39:50.280 --> 39:55.360
+ So TensorFlow 2.0 is kind of, there's a sprint, I don't know how long it'll take, but there's
+
+39:55.360 --> 39:57.080
+ a sprint towards the finish.
+
+39:57.080 --> 40:01.120
+ What do you look, what are you working on these days?
+
+40:01.120 --> 40:02.120
+ What are you excited about?
+
+40:02.120 --> 40:05.040
+ What are you excited about in 2.0?
+
+40:05.040 --> 40:09.880
+ Eager execution, there's so many things that just make it a lot easier to work.
+
+40:09.880 --> 40:11.640
+ What are you excited about?
+
+40:11.640 --> 40:13.800
+ And what's also really hard?
+
+40:13.800 --> 40:15.880
+ What are the problems you have to kind of solve?
+
+40:15.880 --> 40:22.880
+ So I've spent the past year and a half working on TensorFlow 2.0 and it's been a long journey.
+
+40:22.880 --> 40:25.040
+ I'm actually extremely excited about it.
+
+40:25.040 --> 40:26.560
+ I think it's a great product.
+
+40:26.560 --> 40:29.440
+ It's a delightful product compared to TensorFlow 1.0.
+
+40:29.440 --> 40:32.800
+ We've made huge progress.
+
+40:32.800 --> 40:40.640
+ So on the Keras side, what I'm really excited about is that, so previously Keras has been
+
+40:40.640 --> 40:50.880
+ this very easy to use high level interface to do deep learning, but if you wanted to,
+
+40:50.880 --> 40:57.760
+ if you wanted a lot of flexibility, the Keras framework was probably not the optimal way
+
+40:57.760 --> 41:02.160
+ to do things compared to just writing everything from scratch.
+
+41:02.160 --> 41:05.040
+ So in some way, the framework was getting in the way.
+
+41:05.040 --> 41:08.280
+ And in TensorFlow 2.0, you don't have this at all, actually.
+
+41:08.280 --> 41:13.600
+ You have the usability of the high level interface, but you have the flexibility of this lower
+
+41:13.600 --> 41:20.520
+ level interface, and you have this spectrum of workflows where you can get more or less
+
+41:20.520 --> 41:26.960
+ usability and flexibility, the tradeoffs, depending on your needs.
+
+41:26.960 --> 41:33.800
+ You can write everything from scratch and you get a lot of help doing so by subclassing
+
+41:33.800 --> 41:38.520
+ models and writing some train loops using eager execution.
+
+41:38.520 --> 41:39.520
+ It's very flexible.
+
+41:39.520 --> 41:40.520
+ It's very easy to debug.
+
+41:40.520 --> 41:42.400
+ It's very powerful.
+
+41:42.400 --> 41:48.600
+ But all of this integrates seamlessly with higher level features up to the classic Keras
+
+41:48.600 --> 41:56.440
+ workflows, which are very psychedelic and ideal for a data scientist, machine learning
+
+41:56.440 --> 41:58.320
+ engineer type of profile.
+
+41:58.320 --> 42:04.320
+ So now you can have the same framework offering the same set of APIs that enable a spectrum
+
+42:04.320 --> 42:11.000
+ of workflows that are lower level, more or less high level, that are suitable for profiles
+
+42:11.000 --> 42:15.400
+ ranging from researchers to data scientists and everything in between.
+
+42:15.400 --> 42:16.400
+ Yeah.
+
+42:16.400 --> 42:17.400
+ So that's super exciting.
+
+42:17.400 --> 42:18.600
+ I mean, it's not just that.
+
+42:18.600 --> 42:21.560
+ It's connected to all kinds of tooling.
+
+42:21.560 --> 42:26.760
+ You can go on mobile, you can go with TensorFlow Lite, you can go in the cloud or serving
+
+42:26.760 --> 42:29.240
+ and so on, it all is connected together.
+
+42:29.240 --> 42:37.440
+ Some of the best software written ever is often done by one person, sometimes two.
+
+42:37.440 --> 42:42.920
+ So with a Google, you're now seeing sort of Keras having to be integrated in TensorFlow.
+
+42:42.920 --> 42:46.520
+ I'm sure it has a ton of engineers working on.
+
+42:46.520 --> 42:52.320
+ So I'm sure there are a lot of tricky design decisions to be made.
+
+42:52.320 --> 42:54.600
+ How does that process usually happen?
+
+42:54.600 --> 43:00.800
+ At least your perspective, what are the debates like?
+
+43:00.800 --> 43:07.160
+ Is there a lot of thinking considering different options and so on?
+
+43:07.160 --> 43:08.160
+ Yes.
+
+43:08.160 --> 43:17.920
+ So a lot of the time I spend at Google is actually discussing design discussions, writing design
+
+43:17.920 --> 43:22.200
+ docs, participating in design review meetings and so on.
+
+43:22.200 --> 43:25.520
+ This is as important as actually writing a code.
+
+43:25.520 --> 43:34.080
+ So there's a lot of thought and a lot of care that is taken in coming up with these decisions
+
+43:34.080 --> 43:39.920
+ and taking into account all of our users because TensorFlow has this extremely diverse user
+
+43:39.920 --> 43:40.920
+ base.
+
+43:40.920 --> 43:45.560
+ It's not like just one user segment where everyone has the same needs.
+
+43:45.560 --> 43:49.640
+ We have small scale production users, large scale production users.
+
+43:49.640 --> 43:56.520
+ We have startups, we have researchers, it's all over the place, and we have to cater to
+
+43:56.520 --> 43:57.520
+ all of their needs.
+
+43:57.520 --> 44:04.160
+ If I just look at the standard debates of C++ or Python, there's some heated debates.
+
+44:04.160 --> 44:05.680
+ Do you have those at Google?
+
+44:05.680 --> 44:10.560
+ I mean, they're not heated in terms of emotionally, but there's probably multiple ways to do it,
+
+44:10.560 --> 44:11.560
+ right?
+
+44:11.560 --> 44:16.080
+ So how do you arrive through those design meetings at the best way to do it, especially in deep
+
+44:16.080 --> 44:21.960
+ learning where the field is evolving as you're doing it?
+
+44:21.960 --> 44:23.440
+ Is there some magic to it?
+
+44:23.440 --> 44:25.240
+ Is there some magic to the process?
+
+44:25.240 --> 44:30.800
+ I don't know if there's magic to the process, but there definitely is a process.
+
+44:30.800 --> 44:37.240
+ So making design decisions is about satisfying a set of constraints, but also trying to do
+
+44:37.240 --> 44:42.720
+ so in the simplest way possible because this is what can be maintained, this is what can
+
+44:42.720 --> 44:45.080
+ be expanded in the future.
+
+44:45.080 --> 44:51.200
+ So you don't want to naively satisfy the constraints by just, you know, for each capability you
+
+44:51.200 --> 44:54.760
+ need available, you're going to come up with one argument in your API and so on.
+
+44:54.760 --> 45:03.920
+ You want to design APIs that are modular and hierarchical so that they have an API surface
+
+45:03.920 --> 45:07.520
+ that is as small as possible, right?
+
+45:07.520 --> 45:14.800
+ And you want this modular hierarchical architecture to reflect the way that domain experts think
+
+45:14.800 --> 45:19.960
+ about the problem because as a domain expert, when you're reading about a new API, you're
+
+45:19.960 --> 45:27.120
+ reading a tutorial or some docs, pages, you already have a way that you're thinking about
+
+45:27.120 --> 45:28.120
+ the problem.
+
+45:28.120 --> 45:35.600
+ You already have certain concepts in mind and you're thinking about how they relate together
+
+45:35.600 --> 45:41.280
+ and when you're reading docs, you're trying to build as quickly as possible a mapping
+
+45:41.280 --> 45:47.240
+ between the concepts featured in your API and the concepts in your mind so you're trying
+
+45:47.240 --> 45:53.720
+ to map your mental model as a domain expert to the way things work in the API.
+
+45:53.720 --> 45:59.320
+ So you need an API and an underlying implementation that are reflecting the way people think about
+
+45:59.320 --> 46:00.320
+ these things.
+
+46:00.320 --> 46:02.960
+ So in minimizing the time it takes to do the mapping?
+
+46:02.960 --> 46:03.960
+ Yes.
+
+46:03.960 --> 46:10.000
+ Minimizing the time, the cognitive load there is in ingesting this new knowledge about your
+
+46:10.000 --> 46:11.000
+ API.
+
+46:11.000 --> 46:16.080
+ An API should not be self referential or referring to implementation details, it should only
+
+46:16.080 --> 46:22.360
+ be referring to domain specific concepts that people already understand.
+
+46:22.360 --> 46:24.560
+ Brilliant.
+
+46:24.560 --> 46:27.640
+ So what's the future of Keras and TensorFlow look like?
+
+46:27.640 --> 46:30.680
+ What does TensorFlow 3.0 look like?
+
+46:30.680 --> 46:36.440
+ So that's kind of too far in the future for me to answer, especially since I'm not even
+
+46:36.440 --> 46:39.480
+ the one making these decisions.
+
+46:39.480 --> 46:44.840
+ But so from my perspective, which is just one perspective among many different perspectives
+
+46:44.840 --> 46:52.600
+ on the TensorFlow team, I'm really excited by developing even higher level APIs, higher
+
+46:52.600 --> 46:53.600
+ level than Keras.
+
+46:53.600 --> 47:01.040
+ I'm really excited by hyperparameter tuning, by automated machine learning, AutoML.
+
+47:01.040 --> 47:07.480
+ I think the future is not just defining a model like you were assembling Lego blocks
+
+47:07.480 --> 47:14.280
+ and then colleague fit on it, it's more like an automagical model that would just look
+
+47:14.280 --> 47:19.120
+ at your data and optimize the objective you're after.
+
+47:19.120 --> 47:22.440
+ So that's what I'm looking into.
+
+47:22.440 --> 47:23.440
+ Yes.
+
+47:23.440 --> 47:30.120
+ So you put the baby into a room with the problem and come back a few hours later with a fully
+
+47:30.120 --> 47:31.120
+ solved problem.
+
+47:31.120 --> 47:32.120
+ Exactly.
+
+47:32.120 --> 47:36.520
+ It's not like a box of Legos, it's more like the combination of a kid that's really good
+
+47:36.520 --> 47:41.560
+ at Legos, and a box of Legos, and just building the thing on the song.
+
+47:41.560 --> 47:42.760
+ Very nice.
+
+47:42.760 --> 47:44.080
+ So that's an exciting feature.
+
+47:44.080 --> 47:50.680
+ I think there's a huge amount of applications and revolutions to be had under the constraints
+
+47:50.680 --> 47:52.800
+ of the discussion we previously had.
+
+47:52.800 --> 47:57.520
+ But what do you think are the current limits of deep learning?
+
+47:57.520 --> 48:05.200
+ If we look specifically at these function approximators that tries to generalize from
+
+48:05.200 --> 48:06.200
+ data?
+
+48:06.200 --> 48:11.800
+ If you've talked about local versus extreme generalization, you mentioned that neural
+
+48:11.800 --> 48:17.840
+ networks don't generalize well and humans do, so there's this gap.
+
+48:17.840 --> 48:22.840
+ And you've also mentioned that extreme generalization requires something like reasoning to fill those
+
+48:22.840 --> 48:24.040
+ gaps.
+
+48:24.040 --> 48:27.120
+ So how can we start trying to build systems like that?
+
+48:27.120 --> 48:28.120
+ Right.
+
+48:28.120 --> 48:29.120
+ Yes.
+
+48:29.120 --> 48:30.640
+ So this is by design, right?
+
+48:30.640 --> 48:39.600
+ And deep learning models are huge, parametric models, differentiable, so continuous, that
+
+48:39.600 --> 48:42.840
+ go from an input space to an output space.
+
+48:42.840 --> 48:46.560
+ And they're trained with gradient descent, so they're trained pretty much point by point.
+
+48:46.560 --> 48:53.560
+ They're learning a continuous geometric morphing from an input vector space to an output vector
+
+48:53.560 --> 48:55.640
+ space, right?
+
+48:55.640 --> 49:02.920
+ And because this is done point by point, a deep neural network can only make sense of
+
+49:02.920 --> 49:08.160
+ points in experience space that are very close to things that it has already seen in string
+
+49:08.160 --> 49:09.160
+ data.
+
+49:09.160 --> 49:14.040
+ At best, it can do interpolation across points.
+
+49:14.040 --> 49:20.560
+ But that means in order to train your network, you need a dense sampling of the input cross
+
+49:20.560 --> 49:27.040
+ output space, almost a point by point sampling, which can be very expensive if you're dealing
+
+49:27.040 --> 49:33.760
+ with complex real world problems like autonomous driving, for instance, or robotics.
+
+49:33.760 --> 49:37.240
+ It's doable if you're looking at the subset of the visual space.
+
+49:37.240 --> 49:41.200
+ But even then, it's still fairly expensive, you still need millions of examples.
+
+49:41.200 --> 49:45.600
+ And it's only going to be able to make sense of things that are very close to ways that's
+
+49:45.600 --> 49:47.000
+ seen before.
+
+49:47.000 --> 49:50.720
+ And in contrast to that, well, of course, you have human intelligence, but even if you're
+
+49:50.720 --> 49:56.840
+ not looking at human intelligence, you can look at very simple rules, algorithms.
+
+49:56.840 --> 50:03.080
+ If you have a symbolic rule, it can actually apply to a very, very large set of inputs
+
+50:03.080 --> 50:04.920
+ because it is abstract.
+
+50:04.920 --> 50:10.760
+ It is not obtained by doing a point by point mapping, right?
+
+50:10.760 --> 50:15.640
+ For instance, if you try to learn a sorting algorithm using a deep neural network, well,
+
+50:15.640 --> 50:21.800
+ you're very much limited to learning point by point what the sorted representation of
+
+50:21.800 --> 50:24.520
+ this specific list is like.
+
+50:24.520 --> 50:32.120
+ But instead, you could have a very, very simple sorting algorithm written in a few lines.
+
+50:32.120 --> 50:35.720
+ Maybe it's just two nested loops.
+
+50:35.720 --> 50:42.320
+ And it can process any list at all because it is abstract, because it is a set of rules.
+
+50:42.320 --> 50:47.440
+ So deep learning is really like point by point geometric morphings, morphings trained with
+
+50:47.440 --> 50:48.880
+ God and Descent.
+
+50:48.880 --> 50:54.200
+ And meanwhile, abstract rules can generalize much better.
+
+50:54.200 --> 50:56.400
+ And I think the future is really to combine the two.
+
+50:56.400 --> 50:59.720
+ So how do we, do you think, combine the two?
+
+50:59.720 --> 51:08.040
+ How do we combine good point by point functions with programs, which is what the symbolic AI
+
+51:08.040 --> 51:09.040
+ type systems?
+
+51:09.040 --> 51:10.040
+ Yeah.
+
+51:10.040 --> 51:11.600
+ At which levels the combination happened.
+
+51:11.600 --> 51:17.480
+ I mean, obviously, we're jumping into the realm of where there's no good answers.
+
+51:17.480 --> 51:20.120
+ It's just kind of ideas and intuitions and so on.
+
+51:20.120 --> 51:21.120
+ Yeah.
+
+51:21.120 --> 51:25.200
+ Well, if you look at the really successful AI systems today, I think there are already
+
+51:25.200 --> 51:29.600
+ hybrid systems that are combining symbolic AI with deep learning.
+
+51:29.600 --> 51:36.120
+ For instance, successful robotics systems are already mostly model based, rule based
+
+51:36.120 --> 51:39.560
+ things like planning algorithms and so on.
+
+51:39.560 --> 51:44.320
+ At the same time, they're using deep learning as perception modules.
+
+51:44.320 --> 51:49.120
+ Sometimes they're using deep learning as a way to inject fuzzy intuition into a rule
+
+51:49.120 --> 51:51.000
+ based process.
+
+51:51.000 --> 51:56.720
+ If you look at a system like a self driving car, it's not just one big end to end neural
+
+51:56.720 --> 52:00.920
+ network that wouldn't work at all, precisely because in order to train that, you would
+
+52:00.920 --> 52:06.960
+ need a dense sampling of experience space when it comes to driving, which is completely
+
+52:06.960 --> 52:08.480
+ unrealistic, obviously.
+
+52:08.480 --> 52:18.560
+ Instead, the self driving car is mostly symbolic, it's software, it's programmed by hand.
+
+52:18.560 --> 52:25.760
+ It's mostly based on explicit models, in this case, mostly 3D models of the environment
+
+52:25.760 --> 52:31.600
+ around the car, but it's interfacing with the real world, using deep learning modules.
+
+52:31.600 --> 52:36.480
+ The deep learning there serves as a way to convert the raw sensory information to something
+
+52:36.480 --> 52:38.600
+ usable by symbolic systems.
+
+52:38.600 --> 52:42.440
+ Okay, well, let's linger on that a little more.
+
+52:42.440 --> 52:48.400
+ So dense sampling from input to output, you said it's obviously very difficult.
+
+52:48.400 --> 52:49.400
+ Is it possible?
+
+52:49.400 --> 52:51.960
+ In the case of self driving, you mean?
+
+52:51.960 --> 52:53.240
+ Let's say self driving, right?
+
+52:53.240 --> 52:57.760
+ Self driving for many people.
+
+52:57.760 --> 53:03.320
+ Let's not even talk about self driving, let's talk about steering, so staying inside the
+
+53:03.320 --> 53:05.320
+ lane.
+
+53:05.320 --> 53:09.200
+ It's definitely a problem you can solve with an end to end deep learning model, but that's
+
+53:09.200 --> 53:10.200
+ like one small subset.
+
+53:10.200 --> 53:14.600
+ Hold on a second, I don't know how you're jumping from the extreme so easily, because
+
+53:14.600 --> 53:17.800
+ I disagree with you on that.
+
+53:17.800 --> 53:23.240
+ I think, well, it's not obvious to me that you can solve lane following.
+
+53:23.240 --> 53:25.720
+ No, it's not obvious, I think it's doable.
+
+53:25.720 --> 53:33.800
+ I think in general, there is no hard limitations to what you can learn with a deep neural network,
+
+53:33.800 --> 53:42.160
+ as long as the search space is rich enough, is flexible enough, and as long as you have
+
+53:42.160 --> 53:47.640
+ this dense sampling of the input cross output space, the problem is that this dense sampling
+
+53:47.640 --> 53:52.920
+ could mean anything from 10,000 examples to trillions and trillions.
+
+53:52.920 --> 53:54.440
+ So that's my question.
+
+53:54.440 --> 53:56.360
+ So what's your intuition?
+
+53:56.360 --> 54:01.800
+ And if you could just give it a chance and think what kind of problems can be solved
+
+54:01.800 --> 54:08.080
+ by getting a huge amounts of data and thereby creating a dense mapping.
+
+54:08.080 --> 54:14.040
+ So let's think about natural language dialogue, the Turing test.
+
+54:14.040 --> 54:20.080
+ Do you think the Turing test can be solved with a neural network alone?
+
+54:20.080 --> 54:26.480
+ Well, the Turing test is all about tricking people into believing they're talking to a
+
+54:26.480 --> 54:27.480
+ human.
+
+54:27.480 --> 54:35.720
+ It's actually very difficult because it's more about exploiting human perception and
+
+54:35.720 --> 54:37.680
+ not so much about intelligence.
+
+54:37.680 --> 54:41.520
+ There's a big difference between mimicking into Asian behavior and actually into Asian
+
+54:41.520 --> 54:42.520
+ behavior.
+
+54:42.520 --> 54:46.680
+ So, okay, let's look at maybe the Alexa prize and so on, the different formulations of the
+
+54:46.680 --> 54:51.720
+ natural language conversation that are less about mimicking and more about maintaining
+
+54:51.720 --> 54:54.920
+ a fun conversation that lasts for 20 minutes.
+
+54:54.920 --> 54:59.240
+ It's a little less about mimicking and that's more about, I mean, it's still mimicking,
+
+54:59.240 --> 55:03.200
+ but it's more about being able to carry forward a conversation with all the tangents that
+
+55:03.200 --> 55:05.120
+ happen in dialogue and so on.
+
+55:05.120 --> 55:12.480
+ Do you think that problem is learnable with this kind of neural network that does the
+
+55:12.480 --> 55:14.600
+ point to point mapping?
+
+55:14.600 --> 55:17.800
+ So I think it would be very, very challenging to do this with deep learning.
+
+55:17.800 --> 55:21.480
+ I don't think it's out of the question either.
+
+55:21.480 --> 55:23.440
+ I wouldn't read it out.
+
+55:23.440 --> 55:27.080
+ The space of problems that can be solved with a large neural network.
+
+55:27.080 --> 55:31.280
+ What's your sense about the space of those problems?
+
+55:31.280 --> 55:32.680
+ Useful problems for us.
+
+55:32.680 --> 55:33.960
+ In theory, it's infinite.
+
+55:33.960 --> 55:36.320
+ You can solve any problem.
+
+55:36.320 --> 55:45.400
+ In practice, while deep learning is a great fit for perception problems, in general, any
+
+55:45.400 --> 55:52.120
+ problem which is naturally amenable to explicit handcrafted rules or rules that you can generate
+
+55:52.120 --> 55:56.160
+ by exhaustive search over some program space.
+
+55:56.160 --> 56:03.400
+ So perception, artificial intuition, as long as you have a sufficient training data set.
+
+56:03.400 --> 56:04.400
+ And that's the question.
+
+56:04.400 --> 56:08.800
+ I mean, perception, there's interpretation and understanding of the scene, which seems
+
+56:08.800 --> 56:13.040
+ to be outside the reach of current perception systems.
+
+56:13.040 --> 56:19.240
+ So do you think larger networks will be able to start to understand the physics and the
+
+56:19.240 --> 56:23.960
+ physics of the scene, the three dimensional structure and relationships of objects in
+
+56:23.960 --> 56:25.720
+ the scene, and so on?
+
+56:25.720 --> 56:28.880
+ Or really, that's where symbolic at has to step in?
+
+56:28.880 --> 56:37.680
+ Well, it's always possible to solve these problems with deep learning is just extremely
+
+56:37.680 --> 56:38.680
+ inefficient.
+
+56:38.680 --> 56:45.240
+ A model would be an explicit rule based abstract model would be a far better, more compressed
+
+56:45.240 --> 56:50.280
+ representation of physics than learning just this mapping between in this situation, this
+
+56:50.280 --> 56:51.280
+ thing happens.
+
+56:51.280 --> 56:54.520
+ If you change the situation slightly, then this other thing happens and so on.
+
+56:54.520 --> 57:00.840
+ Do you think it's possible to automatically generate the programs that would require that
+
+57:00.840 --> 57:01.840
+ kind of reasoning?
+
+57:01.840 --> 57:07.120
+ Or does it have to, so where expert systems fail, there's so many facts about the world
+
+57:07.120 --> 57:08.640
+ had to be hand coded in.
+
+57:08.640 --> 57:15.360
+ Do you think it's possible to learn those logical statements that are true about the
+
+57:15.360 --> 57:17.120
+ world and their relationships?
+
+57:17.120 --> 57:22.640
+ I mean, that's kind of what they're improving at a basic level is trying to do, right?
+
+57:22.640 --> 57:28.360
+ Yeah, except it's much harder to formulate statements about the world compared to fermenting
+
+57:28.360 --> 57:30.680
+ mathematical statements.
+
+57:30.680 --> 57:34.320
+ Statements about the world tend to be subjective.
+
+57:34.320 --> 57:39.320
+ So can you learn rule based models?
+
+57:39.320 --> 57:40.320
+ Yes.
+
+57:40.320 --> 57:41.320
+ Yes, definitely.
+
+57:41.320 --> 57:43.720
+ That's the field of program synthesis.
+
+57:43.720 --> 57:48.080
+ However, today we just don't really know how to do it.
+
+57:48.080 --> 57:52.640
+ So it's very much a grass search or tree search problem.
+
+57:52.640 --> 57:58.080
+ And so we are limited to the sort of a tree session grass search algorithms that we have
+
+57:58.080 --> 57:59.080
+ today.
+
+57:59.080 --> 58:02.080
+ Personally, I think genetic algorithms are very promising.
+
+58:02.080 --> 58:04.640
+ So it's almost like genetic programming.
+
+58:04.640 --> 58:05.760
+ Genetic programming, exactly.
+
+58:05.760 --> 58:12.200
+ Can you discuss the field of program synthesis, like what, how many people are working and
+
+58:12.200 --> 58:13.840
+ thinking about it?
+
+58:13.840 --> 58:20.360
+ What, where we are in the history of program synthesis and what are your hopes for it?
+
+58:20.360 --> 58:24.760
+ Well, if it were deep learning, this is like the 90s.
+
+58:24.760 --> 58:29.320
+ So meaning that we already have existing solutions.
+
+58:29.320 --> 58:35.720
+ We are starting to have some basic understanding of what this is about.
+
+58:35.720 --> 58:38.120
+ But it's still a field that is in its infancy.
+
+58:38.120 --> 58:40.560
+ There are very few people working on it.
+
+58:40.560 --> 58:44.520
+ There are very few real world applications.
+
+58:44.520 --> 58:51.960
+ So the one real world application I'm aware of is Flash Fill in Excel.
+
+58:51.960 --> 58:58.240
+ It's a way to automatically learn very simple programs to format cells in an Excel spreadsheet
+
+58:58.240 --> 58:59.840
+ from a few examples.
+
+58:59.840 --> 59:02.840
+ For instance, learning a way to format a date, things like that.
+
+59:02.840 --> 59:03.840
+ Oh, that's fascinating.
+
+59:03.840 --> 59:04.840
+ Yeah.
+
+59:04.840 --> 59:06.280
+ You know, okay, that's that's fascinating topic.
+
+59:06.280 --> 59:12.880
+ I was wondering when I provide a few samples to Excel, what it's able to figure out, like
+
+59:12.880 --> 59:18.280
+ just giving it a few dates, what are you able to figure out from the pattern I just gave
+
+59:18.280 --> 59:19.280
+ you?
+
+59:19.280 --> 59:20.280
+ That's a fascinating question.
+
+59:20.280 --> 59:24.240
+ It's fascinating whether that's learnable patterns and you're saying they're working
+
+59:24.240 --> 59:25.240
+ on that.
+
+59:25.240 --> 59:26.240
+ Yeah.
+
+59:26.240 --> 59:27.240
+ How big is the toolbox currently?
+
+59:27.240 --> 59:28.240
+ Yeah.
+
+59:28.240 --> 59:29.240
+ Are we completely in the dark?
+
+59:29.240 --> 59:30.240
+ So if you set the 90s.
+
+59:30.240 --> 59:32.240
+ In terms of program synthesis?
+
+59:32.240 --> 59:33.240
+ No.
+
+59:33.240 --> 59:40.520
+ So I would say, so maybe 90s is even too optimistic because by the 90s, you know, we already understood
+
+59:40.520 --> 59:41.520
+ backprop.
+
+59:41.520 --> 59:44.720
+ We already understood, you know, the engine of deep learning, even though we couldn't
+
+59:44.720 --> 59:50.440
+ really see its potential quite today, I don't think we found the engine of program synthesis.
+
+59:50.440 --> 59:52.960
+ So we're in the winter before backprop.
+
+59:52.960 --> 59:53.960
+ Yeah.
+
+59:53.960 --> 59:55.760
+ In a way, yes.
+
+59:55.760 --> 1:00:02.400
+ So I do believe program synthesis, in general, discrete search over rule based models is going
+
+1:00:02.400 --> 1:00:06.960
+ to be a cornerstone of AI research in the next century, right?
+
+1:00:06.960 --> 1:00:10.240
+ And that doesn't mean we're going to drop deep learning.
+
+1:00:10.240 --> 1:00:11.960
+ Deep learning is immensely useful.
+
+1:00:11.960 --> 1:00:19.480
+ Like being able to learn this is a very flexible, adaptable, parametric models, that's actually
+
+1:00:19.480 --> 1:00:20.480
+ immensely useful.
+
+1:00:20.480 --> 1:00:24.960
+ Like all it's doing, it's pattern cognition, but being good at pattern cognition, given
+
+1:00:24.960 --> 1:00:27.880
+ lots of data is just extremely powerful.
+
+1:00:27.880 --> 1:00:31.000
+ So we are still going to be working on deep learning and we're going to be working on
+
+1:00:31.000 --> 1:00:32.000
+ program synthesis.
+
+1:00:32.000 --> 1:00:36.520
+ We're going to be combining the two in increasingly automated ways.
+
+1:00:36.520 --> 1:00:38.640
+ So let's talk a little bit about data.
+
+1:00:38.640 --> 1:00:46.120
+ You've tweeted about 10,000 deep learning papers have been written about hard coding
+
+1:00:46.120 --> 1:00:50.280
+ priors, about a specific task in a neural network architecture, it works better than
+
+1:00:50.280 --> 1:00:52.760
+ a lack of a prior.
+
+1:00:52.760 --> 1:00:57.480
+ By summarizing all these efforts, they put a name to an architecture, but really what
+
+1:00:57.480 --> 1:01:01.680
+ they're doing is hard coding some priors that improve the performance of the system.
+
+1:01:01.680 --> 1:01:07.000
+ But we get straight to the point, it's probably true.
+
+1:01:07.000 --> 1:01:12.080
+ So you say that you can always buy performance, buy in quotes performance by either training
+
+1:01:12.080 --> 1:01:17.520
+ on more data, better data, or by injecting task information to the architecture of the
+
+1:01:17.520 --> 1:01:18.520
+ preprocessing.
+
+1:01:18.520 --> 1:01:22.720
+ However, this is informative about the generalization power the techniques use, the fundamentals
+
+1:01:22.720 --> 1:01:23.720
+ of ability to generalize.
+
+1:01:23.720 --> 1:01:30.040
+ Do you think we can go far by coming up with better methods for this kind of cheating,
+
+1:01:30.040 --> 1:01:35.320
+ for better methods of large scale annotation of data, so building better priors?
+
+1:01:35.320 --> 1:01:37.400
+ If you've made it, it's not cheating anymore.
+
+1:01:37.400 --> 1:01:38.400
+ Right.
+
+1:01:38.400 --> 1:01:46.480
+ I'm joking about the cheating, but large scale, so basically I'm asking about something
+
+1:01:46.480 --> 1:01:54.300
+ that hasn't, from my perspective, been researched too much is exponential improvement in annotation
+
+1:01:54.300 --> 1:01:56.800
+ of data.
+
+1:01:56.800 --> 1:01:58.120
+ You often think about...
+
+1:01:58.120 --> 1:02:00.880
+ I think it's actually been researched quite a bit.
+
+1:02:00.880 --> 1:02:06.120
+ You just don't see publications about it, because people who publish papers are going
+
+1:02:06.120 --> 1:02:10.000
+ to publish about known benchmarks, sometimes they're going to read a new benchmark.
+
+1:02:10.000 --> 1:02:14.360
+ People who actually have real world large scale defining problems, they're going to spend
+
+1:02:14.360 --> 1:02:18.800
+ a lot of resources into data annotation and good data annotation pipelines, but you don't
+
+1:02:18.800 --> 1:02:19.800
+ see any papers about it.
+
+1:02:19.800 --> 1:02:20.800
+ That's interesting.
+
+1:02:20.800 --> 1:02:24.600
+ Do you think there are certain resources, but do you think there's innovation happening?
+
+1:02:24.600 --> 1:02:25.920
+ Oh, yeah.
+
+1:02:25.920 --> 1:02:33.960
+ To clarify at the point in the twist, machine learning in general is the science of generalization.
+
+1:02:33.960 --> 1:02:41.080
+ You want to generate knowledge that can be reused across different datasets, across different
+
+1:02:41.080 --> 1:02:42.680
+ tasks.
+
+1:02:42.680 --> 1:02:49.320
+ If instead you're looking at one dataset, and then you are hard coding knowledge about
+
+1:02:49.320 --> 1:02:55.920
+ this task into your architecture, this is no more useful than training a network and
+
+1:02:55.920 --> 1:03:03.160
+ then saying, oh, I found these weight values perform well.
+
+1:03:03.160 --> 1:03:08.720
+ David Ha, I don't know if you know David, he had a paper the other day about weight
+
+1:03:08.720 --> 1:03:13.840
+ agnostic neural networks, and this is very interesting paper because it really illustrates
+
+1:03:13.840 --> 1:03:20.800
+ the fact that an architecture, even without weight, an architecture is a knowledge about
+
+1:03:20.800 --> 1:03:21.800
+ a task.
+
+1:03:21.800 --> 1:03:24.280
+ It encodes knowledge.
+
+1:03:24.280 --> 1:03:31.560
+ When it comes to architectures that are uncrafted by researchers, in some cases, it is very,
+
+1:03:31.560 --> 1:03:39.400
+ very clear that all they are doing is artificially reencoding the template that corresponds
+
+1:03:39.400 --> 1:03:45.240
+ to the proper way to solve the task and coding in a given dataset.
+
+1:03:45.240 --> 1:03:52.120
+ For instance, if you've looked at the baby dataset, which is about natural language
+
+1:03:52.120 --> 1:03:55.800
+ question answering, it is generated by an algorithm.
+
+1:03:55.800 --> 1:03:59.320
+ This is a question under pairs that are generated by an algorithm.
+
+1:03:59.320 --> 1:04:01.680
+ The algorithm is solving a certain template.
+
+1:04:01.680 --> 1:04:06.760
+ Turns out, if you craft a network that literally encodes this template, you can solve this
+
+1:04:06.760 --> 1:04:13.160
+ dataset with nearly 100% accuracy, but that doesn't actually tell you anything about how
+
+1:04:13.160 --> 1:04:17.760
+ to solve question answering in general, which is the point.
+
+1:04:17.760 --> 1:04:21.560
+ The question is just the linger on it, whether it's from the data side or from the size of
+
+1:04:21.560 --> 1:04:22.560
+ the network.
+
+1:04:22.560 --> 1:04:27.960
+ I don't know if you've read the blog post by Ray Sutton, the bitter lesson, where he
+
+1:04:27.960 --> 1:04:33.480
+ says the biggest lesson that we can read from 70 years of AI research is that general methods
+
+1:04:33.480 --> 1:04:38.120
+ that leverage computation are ultimately the most effective.
+
+1:04:38.120 --> 1:04:45.520
+ As opposed to figuring out methods that can generalize effectively, do you think we can
+
+1:04:45.520 --> 1:04:50.720
+ get pretty far by just having something that leverages computation and the improvement of
+
+1:04:50.720 --> 1:04:51.720
+ computation?
+
+1:04:51.720 --> 1:04:52.720
+ Yes.
+
+1:04:52.720 --> 1:04:56.880
+ I think Rich is making a very good point, which is that a lot of these papers, which
+
+1:04:56.880 --> 1:05:03.760
+ are actually all about manually hard coding prior knowledge about a task into some system,
+
+1:05:03.760 --> 1:05:08.720
+ doesn't have to be deeply architected into some system, right?
+
+1:05:08.720 --> 1:05:11.560
+ These papers are not actually making any impact.
+
+1:05:11.560 --> 1:05:18.680
+ Instead, what's making really long term impact is very simple, very general systems that
+
+1:05:18.680 --> 1:05:23.560
+ are really agnostic to all these tricks, because these tricks do not generalize.
+
+1:05:23.560 --> 1:05:31.680
+ And of course, the one general and simple thing that you should focus on is that which
+
+1:05:31.680 --> 1:05:37.360
+ leverages computation, because computation, the availability of large scale computation
+
+1:05:37.360 --> 1:05:40.720
+ has been increasing exponentially, following Morse law.
+
+1:05:40.720 --> 1:05:46.160
+ So if your algorithm is all about exploiting this, then your algorithm is suddenly exponentially
+
+1:05:46.160 --> 1:05:47.640
+ improving, right?
+
+1:05:47.640 --> 1:05:51.800
+ So I think Rich is definitely right.
+
+1:05:51.800 --> 1:05:59.520
+ However, he's right about the past 70 years, he's like assessing the past 70 years.
+
+1:05:59.520 --> 1:06:05.440
+ I am not sure that this assessment will still hold true for the next 70 years.
+
+1:06:05.440 --> 1:06:12.040
+ It might, to some extent, I suspect it will not, because the truth of his assessment is
+
+1:06:12.040 --> 1:06:17.040
+ a function of the context, right, in which this research took place.
+
+1:06:17.040 --> 1:06:22.560
+ And the context is changing, like Morse law might not be applicable anymore, for instance,
+
+1:06:22.560 --> 1:06:24.080
+ in the future.
+
+1:06:24.080 --> 1:06:32.320
+ And I do believe that when you tweak one aspect of a system, when you exploit one aspect
+
+1:06:32.320 --> 1:06:36.680
+ of a system, some other aspect starts becoming the bottleneck.
+
+1:06:36.680 --> 1:06:41.640
+ Let's say you have unlimited computation, well, then data is the bottleneck.
+
+1:06:41.640 --> 1:06:46.560
+ And I think we are already starting to be in a regime where our systems are so large
+
+1:06:46.560 --> 1:06:50.960
+ in scale and so data ingrained, the data today, and the quality of data, and the scale of
+
+1:06:50.960 --> 1:06:53.280
+ data is the bottleneck.
+
+1:06:53.280 --> 1:07:00.960
+ And in this environment, the beta lesson from Rich is not going to be true anymore, right?
+
+1:07:00.960 --> 1:07:08.000
+ So I think we are going to move from a focus on a scale of a competition scale to focus
+
+1:07:08.000 --> 1:07:10.080
+ on data efficiency.
+
+1:07:10.080 --> 1:07:11.080
+ Data efficiency.
+
+1:07:11.080 --> 1:07:13.240
+ So that's getting to the question of symbolic AI.
+
+1:07:13.240 --> 1:07:19.120
+ But the linger on the deep learning approaches, do you have hope for either unsupervised learning
+
+1:07:19.120 --> 1:07:28.280
+ or reinforcement learning, which are ways of being more data efficient in terms of the
+
+1:07:28.280 --> 1:07:31.720
+ amount of data they need that require human annotation?
+
+1:07:31.720 --> 1:07:36.320
+ So unsupervised learning and reinforcement learning are frameworks for learning, but
+
+1:07:36.320 --> 1:07:39.080
+ they are not like any specific technique.
+
+1:07:39.080 --> 1:07:42.800
+ So usually when people say reinforcement learning, what they really mean is deep reinforcement
+
+1:07:42.800 --> 1:07:47.440
+ learning, which is like one approach which is actually very questionable.
+
+1:07:47.440 --> 1:07:53.440
+ The question I was asking was unsupervised learning with deep neural networks and deeper
+
+1:07:53.440 --> 1:07:54.440
+ reinforcement learning.
+
+1:07:54.440 --> 1:07:58.840
+ Well, these are not really data efficient because you're still leveraging these huge
+
+1:07:58.840 --> 1:08:03.760
+ parametric models, point by point with gradient descent.
+
+1:08:03.760 --> 1:08:09.000
+ It is more efficient in terms of the number of annotations, the density of annotations
+
+1:08:09.000 --> 1:08:10.000
+ you need.
+
+1:08:10.000 --> 1:08:16.680
+ The idea being to learn the latent space around which the data is organized and then map the
+
+1:08:16.680 --> 1:08:18.960
+ sparse annotations into it.
+
+1:08:18.960 --> 1:08:23.640
+ And sure, I mean, that's clearly a very good idea.
+
+1:08:23.640 --> 1:08:27.960
+ It's not really a topic I would be working on, but it's clearly a good idea.
+
+1:08:27.960 --> 1:08:32.040
+ So it would get us to solve some problems that...
+
+1:08:32.040 --> 1:08:38.280
+ It will get us to incremental improvements in labeled data efficiency.
+
+1:08:38.280 --> 1:08:46.640
+ Do you have concerns about short term or long term threats from AI, from artificial intelligence?
+
+1:08:46.640 --> 1:08:50.720
+ Yes, definitely to some extent.
+
+1:08:50.720 --> 1:08:52.360
+ And what's the shape of those concerns?
+
+1:08:52.360 --> 1:08:57.200
+ This is actually something I've briefly written about.
+
+1:08:57.200 --> 1:09:06.160
+ But the capabilities of deep learning technology can be used in many ways that are concerning
+
+1:09:06.160 --> 1:09:13.920
+ from mass surveillance with things like facial recognition, in general, tracking lots of
+
+1:09:13.920 --> 1:09:20.040
+ data about everyone and then being able to making sense of this data, to do identification,
+
+1:09:20.040 --> 1:09:22.520
+ to do prediction.
+
+1:09:22.520 --> 1:09:23.520
+ That's concerning.
+
+1:09:23.520 --> 1:09:31.680
+ That's something that's being very aggressively pursued by totalitarian states like China.
+
+1:09:31.680 --> 1:09:40.760
+ One thing I am very much concerned about is that our lives are increasingly online, are
+
+1:09:40.760 --> 1:09:45.960
+ increasingly digital, made of information, made of information consumption and information
+
+1:09:45.960 --> 1:09:52.160
+ production or digital footprint, I would say.
+
+1:09:52.160 --> 1:10:01.200
+ And if you absorb all of this data and you are in control of where you consume information,
+
+1:10:01.200 --> 1:10:10.160
+ social networks and so on, recommendation engines, then you can build a sort of reinforcement
+
+1:10:10.160 --> 1:10:13.920
+ loop for human behavior.
+
+1:10:13.920 --> 1:10:18.440
+ You can observe the state of your mind at time t.
+
+1:10:18.440 --> 1:10:25.040
+ You can predict how you would react to different pieces of content, how to get you to move
+
+1:10:25.040 --> 1:10:33.280
+ your mind in a certain direction, then you can feed the specific piece of content that
+
+1:10:33.280 --> 1:10:35.920
+ would move you in a specific direction.
+
+1:10:35.920 --> 1:10:45.000
+ And you can do this at scale in terms of doing it continuously in real time.
+
+1:10:45.000 --> 1:10:50.560
+ You can also do it at scale in terms of scaling this to many, many people, to entire populations.
+
+1:10:50.560 --> 1:10:57.800
+ So potentially, artificial intelligence, even in its current state, if you combine it with
+
+1:10:57.800 --> 1:11:04.120
+ the internet, with the fact that we have all of our lives are moving to digital devices
+
+1:11:04.120 --> 1:11:11.800
+ and digital information consumption and creation, what you get is the possibility to achieve
+
+1:11:11.800 --> 1:11:16.960
+ mass manipulation of behavior and mass psychological control.
+
+1:11:16.960 --> 1:11:18.360
+ And this is a very real possibility.
+
+1:11:18.360 --> 1:11:22.240
+ Yeah, so you're talking about any kind of recommender system.
+
+1:11:22.240 --> 1:11:28.160
+ Let's look at the YouTube algorithm, Facebook, anything that recommends content you should
+
+1:11:28.160 --> 1:11:35.480
+ watch next, and it's fascinating to think that there's some aspects of human behavior
+
+1:11:35.480 --> 1:11:45.520
+ that you can say a problem of, is this person hold Republican beliefs or Democratic beliefs?
+
+1:11:45.520 --> 1:11:52.720
+ And it's a trivial, that's an objective function, and you can optimize and you can measure and
+
+1:11:52.720 --> 1:11:55.720
+ you can turn everybody into a Republican or everybody into a Democrat.
+
+1:11:55.720 --> 1:11:56.720
+ Absolutely, yeah.
+
+1:11:56.720 --> 1:11:57.960
+ I do believe it's true.
+
+1:11:57.960 --> 1:12:02.520
+ So the human mind is very...
+
+1:12:02.520 --> 1:12:06.760
+ If you look at the human mind as a kind of computer program, it has a very large exploit
+
+1:12:06.760 --> 1:12:07.760
+ surface, right?
+
+1:12:07.760 --> 1:12:08.760
+ It has many, many vulnerabilities.
+
+1:12:08.760 --> 1:12:09.760
+ Exploit surfaces, yeah.
+
+1:12:09.760 --> 1:12:16.920
+ Where you can control it, for instance, when it comes to your political beliefs, this is
+
+1:12:16.920 --> 1:12:19.360
+ very much tied to your identity.
+
+1:12:19.360 --> 1:12:26.080
+ So for instance, if I'm in control of your news feed on your favorite social media platforms,
+
+1:12:26.080 --> 1:12:29.680
+ this is actually where you're getting your news from.
+
+1:12:29.680 --> 1:12:35.560
+ And of course, I can choose to only show you news that will make you see the world in a
+
+1:12:35.560 --> 1:12:37.200
+ specific way, right?
+
+1:12:37.200 --> 1:12:44.720
+ But I can also create incentives for you to post about some political beliefs.
+
+1:12:44.720 --> 1:12:52.720
+ And then when I get you to express a statement, if it's a statement that me as a controller,
+
+1:12:52.720 --> 1:12:53.720
+ I want to reinforce.
+
+1:12:53.720 --> 1:12:57.080
+ I can just show it to people who will agree and they will like it.
+
+1:12:57.080 --> 1:12:59.400
+ And that will reinforce the statement in your mind.
+
+1:12:59.400 --> 1:13:06.280
+ If this is a statement I want you to, this is a belief I want you to abandon, I can,
+
+1:13:06.280 --> 1:13:10.800
+ on the other hand, show it to opponents, right, will attack you.
+
+1:13:10.800 --> 1:13:16.440
+ And because they attack you at the very least, next time you will think twice about posting
+
+1:13:16.440 --> 1:13:17.440
+ it.
+
+1:13:17.440 --> 1:13:22.920
+ But maybe you will even, you know, stop believing this because you got pushed back, right?
+
+1:13:22.920 --> 1:13:30.560
+ So there are many ways in which social media platforms can potentially control your opinions.
+
+1:13:30.560 --> 1:13:38.320
+ And today, the, so all of these things are already being controlled by algorithms.
+
+1:13:38.320 --> 1:13:43.080
+ These algorithms do not have any explicit political goal today.
+
+1:13:43.080 --> 1:13:50.960
+ Well, potentially they could, like if some totalitarian government takes over, you know,
+
+1:13:50.960 --> 1:13:55.280
+ social media platforms and decides that, you know, now we're going to use this not just
+
+1:13:55.280 --> 1:13:59.960
+ for my surveillance, but also for my opinion control and behavior control, very bad things
+
+1:13:59.960 --> 1:14:02.000
+ could happen.
+
+1:14:02.000 --> 1:14:08.680
+ But what's really fascinating and actually quite concerning is that even without an
+
+1:14:08.680 --> 1:14:15.480
+ explicit intent to manipulate, you're already seeing very dangerous dynamics in terms of
+
+1:14:15.480 --> 1:14:19.960
+ how this content recommendation algorithms behave.
+
+1:14:19.960 --> 1:14:26.920
+ Because right now, the goal, the objective function of these algorithms is to maximize
+
+1:14:26.920 --> 1:14:32.600
+ engagement, right, which seems fairly innocuous at first, right?
+
+1:14:32.600 --> 1:14:40.400
+ However, it is not because content that will maximally engage people, you know, get people
+
+1:14:40.400 --> 1:14:44.480
+ to react in an emotional way, get people to click on something.
+
+1:14:44.480 --> 1:14:54.480
+ It is very often content that, you know, is not healthy to the public discourse.
+
+1:14:54.480 --> 1:15:01.560
+ For instance, fake news are far more likely to get you to click on them than real news,
+
+1:15:01.560 --> 1:15:07.080
+ simply because they are not constrained to reality.
+
+1:15:07.080 --> 1:15:14.120
+ So they can be as outrageous, as surprising as good stories as you want, because they
+
+1:15:14.120 --> 1:15:15.120
+ are artificial, right?
+
+1:15:15.120 --> 1:15:16.120
+ Yeah.
+
+1:15:16.120 --> 1:15:19.640
+ To me, that's an exciting world because so much good can come.
+
+1:15:19.640 --> 1:15:24.680
+ So there's an opportunity to educate people.
+
+1:15:24.680 --> 1:15:31.200
+ You can balance people's worldview with other ideas.
+
+1:15:31.200 --> 1:15:33.880
+ So there's so many objective functions.
+
+1:15:33.880 --> 1:15:41.080
+ The space of objective functions that create better civilizations is large, arguably infinite.
+
+1:15:41.080 --> 1:15:51.720
+ But there's also a large space that creates division and destruction, civil war, a lot
+
+1:15:51.720 --> 1:15:53.360
+ of bad stuff.
+
+1:15:53.360 --> 1:15:59.480
+ And the worry is, naturally, probably that space is bigger, first of all.
+
+1:15:59.480 --> 1:16:06.920
+ And if we don't explicitly think about what kind of effects are going to be observed from
+
+1:16:06.920 --> 1:16:10.280
+ different objective functions, then we're going to get into trouble.
+
+1:16:10.280 --> 1:16:16.400
+ Because the question is, how do we get into rooms and have discussions?
+
+1:16:16.400 --> 1:16:22.200
+ So inside Google, inside Facebook, inside Twitter, and think about, okay, how can we
+
+1:16:22.200 --> 1:16:28.240
+ drive up engagement and at the same time create a good society?
+
+1:16:28.240 --> 1:16:31.760
+ Is it even possible to have that kind of philosophical discussion?
+
+1:16:31.760 --> 1:16:33.200
+ I think you can definitely try.
+
+1:16:33.200 --> 1:16:40.160
+ So from my perspective, I would feel rather uncomfortable with companies that are in control
+
+1:16:40.160 --> 1:16:49.760
+ of these new algorithms, with them making explicit decisions to manipulate people's opinions
+
+1:16:49.760 --> 1:16:55.360
+ or behaviors, even if the intent is good, because that's a very totalitarian mindset.
+
+1:16:55.360 --> 1:16:59.840
+ So instead, what I would like to see is probably never going to happen, because it's not super
+
+1:16:59.840 --> 1:17:02.560
+ realistic, but that's actually something I really care about.
+
+1:17:02.560 --> 1:17:10.680
+ I would like all these algorithms to present configuration settings to their users, so
+
+1:17:10.680 --> 1:17:17.960
+ that the users can actually make the decision about how they want to be impacted by these
+
+1:17:17.960 --> 1:17:22.080
+ information recommendation, content recommendation algorithms.
+
+1:17:22.080 --> 1:17:27.120
+ For instance, as a user of something like YouTube or Twitter, maybe I want to maximize
+
+1:17:27.120 --> 1:17:30.480
+ learning about a specific topic.
+
+1:17:30.480 --> 1:17:38.720
+ So I want the algorithm to feed my curiosity, which is in itself a very interesting problem.
+
+1:17:38.720 --> 1:17:44.840
+ So instead of maximizing my engagement, it will maximize how fast and how much I'm learning,
+
+1:17:44.840 --> 1:17:50.880
+ and it will also take into account the accuracy, hopefully, of the information I'm learning.
+
+1:17:50.880 --> 1:17:57.800
+ So yeah, the user should be able to determine exactly how these algorithms are affecting
+
+1:17:57.800 --> 1:17:58.800
+ their lives.
+
+1:17:58.800 --> 1:18:08.240
+ I don't want actually any entity making decisions about in which direction they're going to
+
+1:18:08.240 --> 1:18:09.480
+ try to manipulate me.
+
+1:18:09.480 --> 1:18:11.840
+ I want technology.
+
+1:18:11.840 --> 1:18:18.520
+ So AI, these algorithms are increasingly going to be our interface to a world that is increasingly
+
+1:18:18.520 --> 1:18:20.280
+ made of information.
+
+1:18:20.280 --> 1:18:27.440
+ And I want everyone to be in control of this interface, to interface with the world on
+
+1:18:27.440 --> 1:18:29.160
+ their own terms.
+
+1:18:29.160 --> 1:18:38.040
+ So if someone wants these algorithms to serve their own personal growth goals, they should
+
+1:18:38.040 --> 1:18:41.920
+ be able to configure these algorithms in such a way.
+
+1:18:41.920 --> 1:18:50.400
+ Yeah, but so I know it's painful to have explicit decisions, but there is underlying explicit
+
+1:18:50.400 --> 1:18:57.240
+ decisions, which is some of the most beautiful fundamental philosophy that we have before
+
+1:18:57.240 --> 1:19:01.200
+ us, which is personal growth.
+
+1:19:01.200 --> 1:19:08.080
+ If I want to watch videos from which I can learn, what does that mean?
+
+1:19:08.080 --> 1:19:13.600
+ So if I have a checkbox that wants to emphasize learning, there's still an algorithm with
+
+1:19:13.600 --> 1:19:18.000
+ explicit decisions in it that would promote learning.
+
+1:19:18.000 --> 1:19:19.000
+ What does that mean for me?
+
+1:19:19.000 --> 1:19:25.440
+ Like, for example, I've watched a documentary on Flat Earth theory, I guess.
+
+1:19:25.440 --> 1:19:28.200
+ It was very, like, I learned a lot.
+
+1:19:28.200 --> 1:19:29.880
+ I'm really glad I watched it.
+
+1:19:29.880 --> 1:19:35.480
+ It was a friend recommended it to me, because I don't have such an allergic reaction to
+
+1:19:35.480 --> 1:19:37.800
+ crazy people as my fellow colleagues do.
+
+1:19:37.800 --> 1:19:42.320
+ But it was very eye opening, and for others, it might not be.
+
+1:19:42.320 --> 1:19:47.640
+ From others, they might just get turned off from the same with the Republican and Democrat.
+
+1:19:47.640 --> 1:19:50.480
+ And it's a non trivial problem.
+
+1:19:50.480 --> 1:19:56.440
+ And first of all, if it's done well, I don't think it's something that wouldn't happen
+
+1:19:56.440 --> 1:20:00.160
+ that the YouTube wouldn't be promoting or Twitter wouldn't be.
+
+1:20:00.160 --> 1:20:02.400
+ It's just a really difficult problem.
+
+1:20:02.400 --> 1:20:05.080
+ How do we do, how do give people control?
+
+1:20:05.080 --> 1:20:09.000
+ Well, it's mostly an interface design problem.
+
+1:20:09.000 --> 1:20:16.280
+ The way I see it, you want to create technology that's like a mentor or a coach or an assistant
+
+1:20:16.280 --> 1:20:22.680
+ so that it's not your boss, right, you are in control of it.
+
+1:20:22.680 --> 1:20:25.920
+ You are telling it what to do for you.
+
+1:20:25.920 --> 1:20:30.760
+ And if you feel like it's manipulating you, it's not actually, it's not actually doing
+
+1:20:30.760 --> 1:20:31.920
+ what you want.
+
+1:20:31.920 --> 1:20:35.040
+ You should be able to switch to a different algorithm, you know.
+
+1:20:35.040 --> 1:20:39.720
+ So that fine tune control, you kind of learn, you're trusting the human collaboration.
+
+1:20:39.720 --> 1:20:44.440
+ I mean, that's how I see autonomous vehicles, too, is giving as much information as possible
+
+1:20:44.440 --> 1:20:46.560
+ and you learn that dance yourself.
+
+1:20:46.560 --> 1:20:51.040
+ Yeah, Adobe, I don't know if you use Adobe product for like Photoshop.
+
+1:20:51.040 --> 1:20:56.600
+ Yeah, they're trying to see if they can inject YouTube into their interface, but basically
+
+1:20:56.600 --> 1:21:01.920
+ allow you to show you all these videos that, because everybody's confused about what to
+
+1:21:01.920 --> 1:21:03.360
+ do with features.
+
+1:21:03.360 --> 1:21:09.720
+ So basically teach people by linking to, in that way, it's an assistant that shows, uses
+
+1:21:09.720 --> 1:21:12.960
+ videos as a basic element of information.
+
+1:21:12.960 --> 1:21:23.080
+ Okay, so what practically should people do to try to, to try to fight against abuses of
+
+1:21:23.080 --> 1:21:26.880
+ these algorithms or algorithms that manipulate us?
+
+1:21:26.880 --> 1:21:31.080
+ Honestly, it's a very, very difficult problem because to start with, there is very little
+
+1:21:31.080 --> 1:21:34.120
+ public awareness of these issues.
+
+1:21:34.120 --> 1:21:39.960
+ Very few people would think that, you know, anything wrong with their new algorithm, even
+
+1:21:39.960 --> 1:21:44.440
+ though there is actually something wrong already, which is that it's trying to maximize engagement
+
+1:21:44.440 --> 1:21:50.000
+ most of the time, which has very negative side effects, right?
+
+1:21:50.000 --> 1:21:59.760
+ So ideally, so the very first thing is to stop trying to purely maximize engagement, try
+
+1:21:59.760 --> 1:22:11.000
+ to propagate content based on popularity, right, instead take into account the goals
+
+1:22:11.000 --> 1:22:13.640
+ and the profiles of each user.
+
+1:22:13.640 --> 1:22:20.200
+ So you will, you will be, one example is, for instance, when I look at topic recommendations
+
+1:22:20.200 --> 1:22:25.640
+ on Twitter, it's like, you know, they have this news tab with switch recommendations.
+
+1:22:25.640 --> 1:22:33.480
+ That's always the worst garbage because it's content that appeals to the smallest command
+
+1:22:33.480 --> 1:22:37.560
+ denominator to all Twitter users because they're trying to optimize, they're purely
+
+1:22:37.560 --> 1:22:41.680
+ trying to obtain us popularity, they're purely trying to optimize engagement, but that's
+
+1:22:41.680 --> 1:22:43.080
+ not what I want.
+
+1:22:43.080 --> 1:22:50.440
+ So they should put me in control of some setting so that I define what's the objective function
+
+1:22:50.440 --> 1:22:54.280
+ that Twitter is going to be following to show me this content.
+
+1:22:54.280 --> 1:22:59.320
+ And honestly, so this is all about interface design, and we are not, it's not realistic
+
+1:22:59.320 --> 1:23:04.760
+ to give users control of a bunch of knobs that define an algorithm, instead, we should
+
+1:23:04.760 --> 1:23:11.200
+ purely put them in charge of defining the objective function, like let the user tell
+
+1:23:11.200 --> 1:23:15.320
+ us what they want to achieve, how they want this algorithm to impact their lives.
+
+1:23:15.320 --> 1:23:20.200
+ So do you think it is that or do they provide individual article by article reward structure
+
+1:23:20.200 --> 1:23:24.760
+ where you give a signal, I'm glad I saw this or I'm glad I didn't?
+
+1:23:24.760 --> 1:23:31.520
+ So like a Spotify type feedback mechanism, it works to some extent, I'm kind of skeptical
+
+1:23:31.520 --> 1:23:38.920
+ about it because the only way the algorithm, the algorithm will attempt to relate your choices
+
+1:23:38.920 --> 1:23:45.040
+ with the choices of everyone else, which might, you know, if you have an average profile that
+
+1:23:45.040 --> 1:23:49.680
+ works fine, I'm sure Spotify accommodations work fine if you just like mainstream stuff.
+
+1:23:49.680 --> 1:23:54.040
+ But if you don't, it can be, it's not optimal at all, actually.
+
+1:23:54.040 --> 1:24:00.880
+ It'll be in an efficient search for the part of the Spotify world that represents you.
+
+1:24:00.880 --> 1:24:09.000
+ So it's a tough problem, but do note that even a feedback system like what Spotify has
+
+1:24:09.000 --> 1:24:15.680
+ does not give me control over why the algorithm is trying to optimize for.
+
+1:24:15.680 --> 1:24:21.440
+ Well, public awareness, which is what we're doing now, is a good place to start.
+
+1:24:21.440 --> 1:24:27.760
+ Do you have concerns about long term existential threats of artificial intelligence?
+
+1:24:27.760 --> 1:24:34.800
+ Well, as I was saying, our world is increasingly made of information, AI algorithms are increasingly
+
+1:24:34.800 --> 1:24:40.280
+ going to be our interface to this world of information, and somebody will be in control
+
+1:24:40.280 --> 1:24:46.000
+ of these algorithms, and that puts us in any kind of bad situation, right?
+
+1:24:46.000 --> 1:24:48.120
+ It has risks.
+
+1:24:48.120 --> 1:24:55.000
+ It has risks coming from potentially large companies wanting to optimize their own goals,
+
+1:24:55.000 --> 1:25:01.760
+ maybe profit, maybe something else, also from governments who might want to use these algorithms
+
+1:25:01.760 --> 1:25:04.720
+ as a means of control of the entire population.
+
+1:25:04.720 --> 1:25:07.560
+ Do you think there's existential threat that could arise from that?
+
+1:25:07.560 --> 1:25:15.840
+ So existential threat, so maybe you're referring to the singularity narrative where robots
+
+1:25:15.840 --> 1:25:16.840
+ just take over?
+
+1:25:16.840 --> 1:25:22.040
+ Well, I don't not terminate a robot, and I don't believe it has to be a singularity.
+
+1:25:22.040 --> 1:25:30.000
+ We're just talking to, just like you said, the algorithm controlling masses of populations,
+
+1:25:30.000 --> 1:25:37.840
+ the existential threat being hurt ourselves much like a nuclear war would hurt ourselves,
+
+1:25:37.840 --> 1:25:38.840
+ that kind of thing.
+
+1:25:38.840 --> 1:25:44.600
+ I don't think that requires a singularity, that requires a loss of control over AI algorithms.
+
+1:25:44.600 --> 1:25:47.920
+ So I do agree there are concerning trends.
+
+1:25:47.920 --> 1:25:53.600
+ Honestly, I wouldn't want to make any long term predictions.
+
+1:25:53.600 --> 1:25:59.560
+ I don't think today we really have the capability to see what the dangers of AI are going to
+
+1:25:59.560 --> 1:26:02.240
+ be in 50 years, in 100 years.
+
+1:26:02.240 --> 1:26:11.480
+ I do see that we are already faced with concrete and present dangers surrounding the negative
+
+1:26:11.480 --> 1:26:17.280
+ side effects of content recombination systems of new seed algorithms concerning algorithmic
+
+1:26:17.280 --> 1:26:19.520
+ bias as well.
+
+1:26:19.520 --> 1:26:26.000
+ So we are delegating more and more decision processes to algorithms.
+
+1:26:26.000 --> 1:26:30.160
+ Some of these algorithms are uncrafted, some are learned from data.
+
+1:26:30.160 --> 1:26:34.040
+ But we are delegating control.
+
+1:26:34.040 --> 1:26:37.240
+ Sometimes it's a good thing, sometimes not so much.
+
+1:26:37.240 --> 1:26:41.720
+ And there is in general very little supervision of this process.
+
+1:26:41.720 --> 1:26:50.160
+ So we are still in this period of very fast change, even chaos, where society is restructuring
+
+1:26:50.160 --> 1:26:56.160
+ itself, turning into an information society, which itself is turning into an increasingly
+
+1:26:56.160 --> 1:26:59.240
+ automated information processing society.
+
+1:26:59.240 --> 1:27:05.760
+ And well, yeah, I think the best we can do today is try to raise awareness around some
+
+1:27:05.760 --> 1:27:06.760
+ of these issues.
+
+1:27:06.760 --> 1:27:13.000
+ And I think we are actually making good progress if you look at algorithmic bias, for instance.
+
+1:27:13.000 --> 1:27:17.240
+ Three years ago, even two years ago, very, very few people were talking about it.
+
+1:27:17.240 --> 1:27:22.400
+ And now all the big companies are talking about it, often not in a very serious way,
+
+1:27:22.400 --> 1:27:24.600
+ but at least it is part of the public discourse.
+
+1:27:24.600 --> 1:27:27.360
+ You see people in Congress talking about it.
+
+1:27:27.360 --> 1:27:32.840
+ And it all started from raising awareness.
+
+1:27:32.840 --> 1:27:40.200
+ So in terms of alignment problem, trying to teach as we allow algorithms, just even recommend
+
+1:27:40.200 --> 1:27:50.280
+ their systems on Twitter, encoding human values and morals, decisions that touch on ethics.
+
+1:27:50.280 --> 1:27:52.640
+ How hard do you think that problem is?
+
+1:27:52.640 --> 1:27:59.800
+ How do we have lost functions in neural networks that have some component, some fuzzy components
+
+1:27:59.800 --> 1:28:01.280
+ of human morals?
+
+1:28:01.280 --> 1:28:07.400
+ Well, I think this is really all about objective function engineering, which is probably going
+
+1:28:07.400 --> 1:28:10.680
+ to be increasingly a topic of concern in the future.
+
+1:28:10.680 --> 1:28:16.160
+ Like for now, we are just using very naive loss functions because the hard part is not
+
+1:28:16.160 --> 1:28:19.240
+ actually what you're trying to minimize, it's everything else.
+
+1:28:19.240 --> 1:28:25.280
+ But as the everything else is going to be increasingly automated, we're going to be
+
+1:28:25.280 --> 1:28:30.920
+ focusing our human attention on increasingly high level components, like what's actually
+
+1:28:30.920 --> 1:28:34.040
+ driving the whole learning system, like the objective function.
+
+1:28:34.040 --> 1:28:38.360
+ So loss function engineering is going to be, loss function engineer is probably going to
+
+1:28:38.360 --> 1:28:40.760
+ be a job title in the future.
+
+1:28:40.760 --> 1:28:46.200
+ And then the tooling you're creating with Keras essentially takes care of all the details
+
+1:28:46.200 --> 1:28:52.960
+ underneath and basically the human expert is needed for exactly that.
+
+1:28:52.960 --> 1:28:59.240
+ Keras is the interface between the data you're collecting and the business goals.
+
+1:28:59.240 --> 1:29:04.280
+ And your job as an engineer is going to be to express your business goals and your understanding
+
+1:29:04.280 --> 1:29:10.440
+ of your business or your product, your system as a kind of loss function or a kind of set
+
+1:29:10.440 --> 1:29:11.440
+ of constraints.
+
+1:29:11.440 --> 1:29:19.560
+ Does the possibility of creating an AGI system excite you or scare you or bore you?
+
+1:29:19.560 --> 1:29:23.600
+ So intelligence can never really be general, you know, at best it can have some degree
+
+1:29:23.600 --> 1:29:26.600
+ of generality, like human intelligence.
+
+1:29:26.600 --> 1:29:30.720
+ And it's also always as some specialization in the same way that human intelligence is
+
+1:29:30.720 --> 1:29:35.680
+ specialized in a certain category of problems, is specialized in the human experience.
+
+1:29:35.680 --> 1:29:41.440
+ And when people talk about AGI, I'm never quite sure if they're talking about very,
+
+1:29:41.440 --> 1:29:46.200
+ very smart AI, so smart that it's even smarter than humans, or they're talking about human
+
+1:29:46.200 --> 1:29:49.880
+ like intelligence, because these are different things.
+
+1:29:49.880 --> 1:29:54.840
+ Let's say, presumably I'm oppressing you today with my humanness.
+
+1:29:54.840 --> 1:29:59.400
+ So imagine that I was in fact a robot.
+
+1:29:59.400 --> 1:30:02.400
+ So what does that mean?
+
+1:30:02.400 --> 1:30:05.160
+ I'm oppressing you with natural language processing.
+
+1:30:05.160 --> 1:30:08.320
+ Maybe if you weren't able to see me, maybe this is a phone call.
+
+1:30:08.320 --> 1:30:09.320
+ That kind of system.
+
+1:30:09.320 --> 1:30:10.320
+ Okay.
+
+1:30:10.320 --> 1:30:11.320
+ So companion.
+
+1:30:11.320 --> 1:30:15.200
+ So that's very much about building human like AI.
+
+1:30:15.200 --> 1:30:18.200
+ And you're asking me, you know, is this an exciting perspective?
+
+1:30:18.200 --> 1:30:19.200
+ Yes.
+
+1:30:19.200 --> 1:30:21.960
+ I think so, yes.
+
+1:30:21.960 --> 1:30:29.640
+ Not so much because of what artificial human like intelligence could do, but, you know,
+
+1:30:29.640 --> 1:30:34.240
+ from an intellectual perspective, I think if you could build truly human like intelligence,
+
+1:30:34.240 --> 1:30:40.160
+ that means you could actually understand human intelligence, which is fascinating, right?
+
+1:30:40.160 --> 1:30:44.480
+ Human like intelligence is going to require emotions, it's going to require consciousness,
+
+1:30:44.480 --> 1:30:48.640
+ which is not things that would normally be required by an intelligent system.
+
+1:30:48.640 --> 1:30:55.560
+ If you look at, you know, we were mentioning earlier like science as a superhuman problem
+
+1:30:55.560 --> 1:31:02.240
+ solving agent or system, it does not have consciousness, it doesn't have emotions.
+
+1:31:02.240 --> 1:31:07.760
+ In general, so emotions, I see consciousness as being on the same spectrum as emotions.
+
+1:31:07.760 --> 1:31:17.560
+ It is a component of the subjective experience that is meant very much to guide behavior
+
+1:31:17.560 --> 1:31:20.880
+ generation, right, it's meant to guide your behavior.
+
+1:31:20.880 --> 1:31:27.080
+ In general, human intelligence and animal intelligence has evolved for the purpose of
+
+1:31:27.080 --> 1:31:30.760
+ behavior generation, right, including in a social context.
+
+1:31:30.760 --> 1:31:32.600
+ So that's why we actually need emotions.
+
+1:31:32.600 --> 1:31:35.080
+ That's why we need consciousness.
+
+1:31:35.080 --> 1:31:39.280
+ An artificial intelligence system developed in a different context may well never need
+
+1:31:39.280 --> 1:31:43.280
+ them, may well never be conscious like science.
+
+1:31:43.280 --> 1:31:50.160
+ But on that point, I would argue it's possible to imagine that there's echoes of consciousness
+
+1:31:50.160 --> 1:31:55.640
+ in science when viewed as an organism, that science is consciousness.
+
+1:31:55.640 --> 1:31:59.320
+ So I mean, how would you go about testing this hypothesis?
+
+1:31:59.320 --> 1:32:07.240
+ How do you probe the subjective experience of an abstract system like science?
+
+1:32:07.240 --> 1:32:12.280
+ Well the point of probing any subjective experience is impossible, because I'm not science, I'm
+
+1:32:12.280 --> 1:32:13.280
+ a science.
+
+1:32:13.280 --> 1:32:20.720
+ So I can't probe another entity's, another, it's no more than bacteria on my skin.
+
+1:32:20.720 --> 1:32:25.360
+ Your legs, I can ask you questions about your subjective experience and you can answer me.
+
+1:32:25.360 --> 1:32:27.720
+ And that's how I know you're conscious.
+
+1:32:27.720 --> 1:32:32.080
+ Yes, but that's because we speak the same language.
+
+1:32:32.080 --> 1:32:35.800
+ You perhaps, we have to speak the language of science and we have to ask it.
+
+1:32:35.800 --> 1:32:41.120
+ Honestly, I don't think consciousness, just like emotions of pain and pleasure, is not
+
+1:32:41.120 --> 1:32:47.120
+ something that inevitably arises from any sort of sufficiently intelligent information
+
+1:32:47.120 --> 1:32:48.120
+ processing.
+
+1:32:48.120 --> 1:32:54.080
+ It is a feature of the mind and if you've not implemented it explicitly, it is not there.
+
+1:32:54.080 --> 1:32:59.120
+ So you think it's an emergent feature of a particular architecture.
+
+1:32:59.120 --> 1:33:00.120
+ So do you think?
+
+1:33:00.120 --> 1:33:02.080
+ It's a feature in the same sense.
+
+1:33:02.080 --> 1:33:09.800
+ So again, the subjective experience is all about guiding behavior.
+
+1:33:09.800 --> 1:33:15.560
+ If the problems you're trying to solve don't really involve embedded agents, maybe in a
+
+1:33:15.560 --> 1:33:19.800
+ social context, generating behavior and pursuing goals like this.
+
+1:33:19.800 --> 1:33:23.280
+ And if you look at science, that's not really what's happening, even though it is, it is
+
+1:33:23.280 --> 1:33:29.600
+ a form of artificial air in this artificial intelligence in the sense that it is solving
+
+1:33:29.600 --> 1:33:35.240
+ problems, it is committing knowledge, committing solutions and so on.
+
+1:33:35.240 --> 1:33:41.120
+ So if you're not explicitly implementing a subjective experience, implementing certain
+
+1:33:41.120 --> 1:33:47.120
+ emotions and implementing consciousness, it's not going to just spontaneously emerge.
+
+1:33:47.120 --> 1:33:48.360
+ Yeah.
+
+1:33:48.360 --> 1:33:53.640
+ But so for a system like human like intelligent system that has consciousness, do you think
+
+1:33:53.640 --> 1:33:55.240
+ it needs to have a body?
+
+1:33:55.240 --> 1:33:56.240
+ Yes, definitely.
+
+1:33:56.240 --> 1:33:59.920
+ I mean, it doesn't have to be a physical body, right?
+
+1:33:59.920 --> 1:34:03.680
+ And there's not that much difference between a realistic simulation in the real world.
+
+1:34:03.680 --> 1:34:06.560
+ So there has to be something you have to preserve kind of thing.
+
+1:34:06.560 --> 1:34:07.560
+ Yes.
+
+1:34:07.560 --> 1:34:12.400
+ But human like intelligence can only arise in a human like context.
+
+1:34:12.400 --> 1:34:13.400
+ Intelligence needs to be tired.
+
+1:34:13.400 --> 1:34:20.480
+ You need other humans in order for you to demonstrate that you have human like intelligence, essentially.
+
+1:34:20.480 --> 1:34:29.240
+ So what kind of tests and demonstration would be sufficient for you to demonstrate human
+
+1:34:29.240 --> 1:34:30.480
+ like intelligence?
+
+1:34:30.480 --> 1:34:31.480
+ Yeah.
+
+1:34:31.480 --> 1:34:37.080
+ And just out of curiosity, you talked about in terms of theorem proving and program synthesis,
+
+1:34:37.080 --> 1:34:40.480
+ I think you've written about that there's no good benchmarks for this.
+
+1:34:40.480 --> 1:34:41.480
+ Yeah.
+
+1:34:41.480 --> 1:34:42.480
+ That's one of the problems.
+
+1:34:42.480 --> 1:34:46.560
+ So let's talk programs, program synthesis.
+
+1:34:46.560 --> 1:34:51.440
+ So what do you imagine is a good, I think it's related questions for human like intelligence
+
+1:34:51.440 --> 1:34:53.720
+ and for program synthesis.
+
+1:34:53.720 --> 1:34:56.160
+ What's a good benchmark for either or both?
+
+1:34:56.160 --> 1:34:57.160
+ Right.
+
+1:34:57.160 --> 1:34:59.400
+ So I mean, you're actually asking two questions.
+
+1:34:59.400 --> 1:35:06.520
+ Which is one is about quantifying intelligence and comparing the intelligence of an artificial
+
+1:35:06.520 --> 1:35:08.800
+ system to the intelligence for human.
+
+1:35:08.800 --> 1:35:13.520
+ And the other is about a degree to which this intelligence is human like.
+
+1:35:13.520 --> 1:35:16.800
+ It's actually two different questions.
+
+1:35:16.800 --> 1:35:19.320
+ So if you look, you mentioned earlier the Turing test.
+
+1:35:19.320 --> 1:35:20.320
+ Right.
+
+1:35:20.320 --> 1:35:24.080
+ Well, I actually don't like the Turing test because it's very lazy.
+
+1:35:24.080 --> 1:35:28.960
+ It's all about completely bypassing the problem of defining and measuring intelligence.
+
+1:35:28.960 --> 1:35:34.400
+ And instead delegating to a human judge or a panel of human judges.
+
+1:35:34.400 --> 1:35:38.400
+ So it's a total cobalt, right?
+
+1:35:38.400 --> 1:35:45.640
+ If you want to measure how human like an agent is, I think you have to make it interact
+
+1:35:45.640 --> 1:35:47.920
+ with other humans.
+
+1:35:47.920 --> 1:35:54.120
+ Maybe it's not necessarily a good idea to have these other humans be the judges.
+
+1:35:54.120 --> 1:36:00.800
+ Maybe you should just observe BFU and compare it to what the human would actually have done.
+
+1:36:00.800 --> 1:36:09.160
+ When it comes to measuring how smart, how clever an agent is and comparing that to the
+
+1:36:09.160 --> 1:36:11.240
+ degree of human intelligence.
+
+1:36:11.240 --> 1:36:13.680
+ So we're already talking about two things, right?
+
+1:36:13.680 --> 1:36:20.600
+ The degree, kind of like the magnitude of an intelligence and its direction, right?
+
+1:36:20.600 --> 1:36:23.560
+ Like the norm of a vector and its direction.
+
+1:36:23.560 --> 1:36:27.200
+ And the direction is like human likeness.
+
+1:36:27.200 --> 1:36:32.880
+ And the magnitude, the norm is intelligence.
+
+1:36:32.880 --> 1:36:34.280
+ You could call it intelligence, right?
+
+1:36:34.280 --> 1:36:42.440
+ So the direction, your sense, the space of directions that are human like is very narrow.
+
+1:36:42.440 --> 1:36:49.880
+ So the way you would measure the magnitude of intelligence in a system in a way that
+
+1:36:49.880 --> 1:36:54.960
+ also enables you to compare it to that of a human.
+
+1:36:54.960 --> 1:37:02.000
+ Well, if you look at different benchmarks for intelligence today, they're all too focused
+
+1:37:02.000 --> 1:37:04.480
+ on skill at a given task.
+
+1:37:04.480 --> 1:37:11.080
+ That's skill at playing chess, skill at playing Go, skill at playing Dota.
+
+1:37:11.080 --> 1:37:17.560
+ And I think that's not the right way to go about it because you can always be the human
+
+1:37:17.560 --> 1:37:20.240
+ at one specific task.
+
+1:37:20.240 --> 1:37:25.320
+ The reason why our skill at playing Go or at juggling or anything is impressive is because
+
+1:37:25.320 --> 1:37:29.480
+ we are expressing this skill within a certain set of constraints.
+
+1:37:29.480 --> 1:37:33.840
+ If you remove the constraints, the constraints that we have one lifetime, that we have this
+
+1:37:33.840 --> 1:37:40.120
+ body and so on, if you remove the context, if you have unlimited train data, if you
+
+1:37:40.120 --> 1:37:44.840
+ can have access to, you know, for instance, if you look at juggling, if you have no restriction
+
+1:37:44.840 --> 1:37:50.040
+ on the hardware, then achieving arbitrary levels of skill is not very interesting and
+
+1:37:50.040 --> 1:37:53.960
+ says nothing about the amount of intelligence you've achieved.
+
+1:37:53.960 --> 1:37:59.320
+ So if you want to measure intelligence, you need to rigorously define what intelligence
+
+1:37:59.320 --> 1:38:04.360
+ is, which in itself, you know, it's a very challenging problem.
+
+1:38:04.360 --> 1:38:05.960
+ And do you think that's possible?
+
+1:38:05.960 --> 1:38:06.960
+ To define intelligence?
+
+1:38:06.960 --> 1:38:07.960
+ Yes, absolutely.
+
+1:38:07.960 --> 1:38:11.680
+ I mean, you can provide, many people have provided, you know, some definition.
+
+1:38:11.680 --> 1:38:13.640
+ I have my own definition.
+
+1:38:13.640 --> 1:38:16.520
+ Where does your definition begin if it doesn't end?
+
+1:38:16.520 --> 1:38:25.560
+ Well, I think intelligence is essentially the efficiency with which you turn experience
+
+1:38:25.560 --> 1:38:29.960
+ into generalizable programs.
+
+1:38:29.960 --> 1:38:35.280
+ So what that means is it's the efficiency with which you turn a sampling of experience
+
+1:38:35.280 --> 1:38:46.200
+ space into the ability to process a larger chunk of experience space.
+
+1:38:46.200 --> 1:38:53.480
+ So measuring skill can be one proxy because many, many different tasks can be one proxy
+
+1:38:53.480 --> 1:38:54.680
+ for measure intelligence.
+
+1:38:54.680 --> 1:38:58.880
+ But if you want to only measure skill, you should control for two things.
+
+1:38:58.880 --> 1:39:07.920
+ You should control for the amount of experience that your system has and the priors that your
+
+1:39:07.920 --> 1:39:08.920
+ system has.
+
+1:39:08.920 --> 1:39:14.120
+ But if you control, if you look at two agents and you give them the same priors and you
+
+1:39:14.120 --> 1:39:21.480
+ give them the same amount of experience, there is one of the agents that is going to learn
+
+1:39:21.480 --> 1:39:27.720
+ programs, representation, something, a model that will perform well on the larger chunk
+
+1:39:27.720 --> 1:39:29.760
+ of experience space than the other.
+
+1:39:29.760 --> 1:39:31.920
+ And that is the smaller agent.
+
+1:39:31.920 --> 1:39:32.920
+ Yeah.
+
+1:39:32.920 --> 1:39:39.920
+ So if you fix the experience, which generate better programs, better meaning, more generalizable,
+
+1:39:39.920 --> 1:39:40.920
+ that's really interesting.
+
+1:39:40.920 --> 1:39:42.760
+ That's a very nice, clean definition of...
+
+1:39:42.760 --> 1:39:49.560
+ By the way, in this definition, it is already very obvious that intelligence has to be specialized
+
+1:39:49.560 --> 1:39:53.600
+ because you're talking about experience space and you're talking about segments of experience
+
+1:39:53.600 --> 1:39:54.600
+ space.
+
+1:39:54.600 --> 1:39:59.680
+ You're talking about priors and you're talking about experience, all of these things define
+
+1:39:59.680 --> 1:40:04.840
+ the context in which intelligence emerges.
+
+1:40:04.840 --> 1:40:10.040
+ And you can never look at the totality of experience space.
+
+1:40:10.040 --> 1:40:12.520
+ So intelligence has to be specialized.
+
+1:40:12.520 --> 1:40:16.760
+ But it can be sufficiently large, the experience space, even though specialized is a certain
+
+1:40:16.760 --> 1:40:22.200
+ point when the experience space is large enough to where it might as well be general.
+
+1:40:22.200 --> 1:40:23.200
+ It feels general.
+
+1:40:23.200 --> 1:40:24.200
+ It looks general.
+
+1:40:24.200 --> 1:40:25.200
+ I mean, it's very relative.
+
+1:40:25.200 --> 1:40:29.560
+ For instance, many people would say human intelligence is general.
+
+1:40:29.560 --> 1:40:32.960
+ In fact, it is quite specialized.
+
+1:40:32.960 --> 1:40:37.960
+ We can definitely build systems that start from the same innate priors as what humans
+
+1:40:37.960 --> 1:40:43.720
+ have at birth because we already understand fairly well what sort of priors we have as
+
+1:40:43.720 --> 1:40:44.720
+ humans.
+
+1:40:44.720 --> 1:40:50.680
+ Like many people have worked on this problem, most notably, Elzebeth Spelke from Harvard,
+
+1:40:50.680 --> 1:40:56.240
+ and if you know her, she's worked a lot on what she calls a core knowledge.
+
+1:40:56.240 --> 1:41:02.560
+ And it is very much about trying to determine and describe what priors we are born with.
+
+1:41:02.560 --> 1:41:06.080
+ Like language skills and so on and all that kind of stuff.
+
+1:41:06.080 --> 1:41:07.080
+ Exactly.
+
+1:41:07.080 --> 1:41:11.520
+ So we have some pretty good understanding of what priors we are born with.
+
+1:41:11.520 --> 1:41:13.960
+ So we could...
+
+1:41:13.960 --> 1:41:18.720
+ So I've actually been working on a benchmark for the past couple of years, on and off.
+
+1:41:18.720 --> 1:41:21.440
+ I hope to be able to release it at some point.
+
+1:41:21.440 --> 1:41:29.120
+ The idea is to measure the intelligence of systems by considering for priors, considering
+
+1:41:29.120 --> 1:41:34.840
+ for amount of experience, and by assuming the same priors as what humans are born with
+
+1:41:34.840 --> 1:41:40.160
+ so that you can actually compare these scores to human intelligence and you can actually
+
+1:41:40.160 --> 1:41:44.440
+ have humans pass the same test in a way that's fair.
+
+1:41:44.440 --> 1:41:54.720
+ And so importantly, such a benchmark should be such that any amount of practicing does
+
+1:41:54.720 --> 1:41:56.800
+ not increase your score.
+
+1:41:56.800 --> 1:42:04.120
+ So try to picture a game where no matter how much you play this game, it does not change
+
+1:42:04.120 --> 1:42:05.400
+ your skill at the game.
+
+1:42:05.400 --> 1:42:08.600
+ Can you picture that?
+
+1:42:08.600 --> 1:42:14.840
+ As a person who deeply appreciates practice, I cannot actually...
+
+1:42:14.840 --> 1:42:19.040
+ There's actually a very simple trick.
+
+1:42:19.040 --> 1:42:24.760
+ So in order to come up with a task, so the only thing you can measure is skill at a task.
+
+1:42:24.760 --> 1:42:28.280
+ All tasks are going to involve priors.
+
+1:42:28.280 --> 1:42:32.480
+ The trick is to know what they are and to describe that.
+
+1:42:32.480 --> 1:42:36.040
+ And then you make sure that this is the same set of priors as what humans start with.
+
+1:42:36.040 --> 1:42:41.080
+ So you create a task that assumes these priors, that exactly documents these priors, so that
+
+1:42:41.080 --> 1:42:44.720
+ the priors are made explicit and there are no other priors involved.
+
+1:42:44.720 --> 1:42:52.240
+ And then you generate a certain number of samples in experience space for this task.
+
+1:42:52.240 --> 1:42:59.480
+ And this, for one task, assuming that the task is new for the agent passing it, that's
+
+1:42:59.480 --> 1:43:07.560
+ one test of this definition of intelligence that we set up.
+
+1:43:07.560 --> 1:43:12.360
+ And now you can scale that to many different tasks, that each task should be new to the
+
+1:43:12.360 --> 1:43:13.360
+ agent passing it.
+
+1:43:13.360 --> 1:43:18.680
+ And also should be human interpretable and understandable, so that you can actually have
+
+1:43:18.680 --> 1:43:21.960
+ a human pass the same test and then you can compare the score of your machine and the score
+
+1:43:21.960 --> 1:43:22.960
+ of your human.
+
+1:43:22.960 --> 1:43:23.960
+ Which could be a lot.
+
+1:43:23.960 --> 1:43:28.580
+ It could even start a task like MNIST, just as long as you start with the same set of
+
+1:43:28.580 --> 1:43:29.580
+ priors.
+
+1:43:29.580 --> 1:43:35.880
+ Yeah, so the problem with MNIST, humans are already trained to recognize digits.
+
+1:43:35.880 --> 1:43:44.240
+ But let's say we're considering objects that are not digits, some complete arbitrary patterns.
+
+1:43:44.240 --> 1:43:50.120
+ Well, humans already come with visual priors about how to process that.
+
+1:43:50.120 --> 1:43:55.760
+ So in order to make the game fair, you would have to isolate these priors and describe
+
+1:43:55.760 --> 1:43:58.720
+ them and then express them as computational rules.
+
+1:43:58.720 --> 1:44:03.760
+ Having worked a lot with vision science people has exceptionally difficult, a lot of progress
+
+1:44:03.760 --> 1:44:07.720
+ has been made, there's been a lot of good tests, and basically reducing all of human
+
+1:44:07.720 --> 1:44:09.360
+ vision into some good priors.
+
+1:44:09.360 --> 1:44:14.640
+ We're still probably far away from that perfectly, but as a start for a benchmark, that's an
+
+1:44:14.640 --> 1:44:15.640
+ exciting possibility.
+
+1:44:15.640 --> 1:44:25.320
+ Yeah, so Elisabeth Belke actually lists objectness as one of the core knowledge priors.
+
+1:44:25.320 --> 1:44:26.320
+ Objectness.
+
+1:44:26.320 --> 1:44:27.320
+ Cool.
+
+1:44:27.320 --> 1:44:28.320
+ Objectness.
+
+1:44:28.320 --> 1:44:29.320
+ Yeah.
+
+1:44:29.320 --> 1:44:33.000
+ So we have priors about objectness, like about the visual space, about time, about agents,
+
+1:44:33.000 --> 1:44:34.600
+ about goal oriented behavior.
+
+1:44:34.600 --> 1:44:42.680
+ We have many different priors, but what's interesting is that, sure, we have this pretty
+
+1:44:42.680 --> 1:44:48.520
+ diverse and rich set of priors, but it's also not that diverse, right?
+
+1:44:48.520 --> 1:44:52.560
+ We are not born into this world with a ton of knowledge about the world.
+
+1:44:52.560 --> 1:44:59.240
+ There is only a small set of core knowledge, right?
+
+1:44:59.240 --> 1:45:00.240
+ Yeah.
+
+1:45:00.240 --> 1:45:07.120
+ So do you have a sense of how it feels to us humans that that set is not that large,
+
+1:45:07.120 --> 1:45:11.920
+ but just even the nature of time that we kind of integrate pretty effectively through all
+
+1:45:11.920 --> 1:45:17.680
+ of our perception, all of our reasoning, maybe how, you know, do you have a sense of
+
+1:45:17.680 --> 1:45:19.880
+ how easy it is to encode those priors?
+
+1:45:19.880 --> 1:45:26.000
+ Maybe it requires building a universe, and then the human brain in order to encode those
+
+1:45:26.000 --> 1:45:27.000
+ priors.
+
+1:45:27.000 --> 1:45:30.680
+ Or do you have a hope that it can be listed like an XAMAT?
+
+1:45:30.680 --> 1:45:31.680
+ I don't think so.
+
+1:45:31.680 --> 1:45:36.480
+ So you have to keep in mind that any knowledge about the world that we are born with is something
+
+1:45:36.480 --> 1:45:43.280
+ that has to have been encoded into our DNA by evolution at some point.
+
+1:45:43.280 --> 1:45:50.720
+ And DNA is a very, very low bandwidth medium, like it's extremely long and expensive to
+
+1:45:50.720 --> 1:45:57.120
+ encode anything into DNA, because first of all, you need some sort of evolutionary pressure
+
+1:45:57.120 --> 1:45:59.400
+ to guide this writing process.
+
+1:45:59.400 --> 1:46:05.720
+ And then, you know, the higher level of information you're trying to write, the longer it's going
+
+1:46:05.720 --> 1:46:13.960
+ to be, and the thing in the environment that you're trying to encode knowledge about has
+
+1:46:13.960 --> 1:46:17.240
+ to be stable over this duration.
+
+1:46:17.240 --> 1:46:22.840
+ So you can only encode into DNA things that constitute an evolutionary advantage.
+
+1:46:22.840 --> 1:46:27.120
+ So this is actually a very small subset of all possible knowledge about the world.
+
+1:46:27.120 --> 1:46:33.360
+ You can only encode things that are stable, that are true over very, very long periods
+
+1:46:33.360 --> 1:46:35.480
+ of time, typically millions of years.
+
+1:46:35.480 --> 1:46:40.520
+ For instance, we might have some visual prior about the shape of snakes, right?
+
+1:46:40.520 --> 1:46:43.800
+ But what makes a face?
+
+1:46:43.800 --> 1:46:46.440
+ What's the difference between a face and a nonface?
+
+1:46:46.440 --> 1:46:49.840
+ But consider this interesting question.
+
+1:46:49.840 --> 1:46:57.800
+ Do we have any innate sense of the visual difference between a male face and a female
+
+1:46:57.800 --> 1:46:58.800
+ face?
+
+1:46:58.800 --> 1:46:59.800
+ What do you think?
+
+1:46:59.800 --> 1:47:01.320
+ For a human, I mean.
+
+1:47:01.320 --> 1:47:05.920
+ I would have to look back into evolutionary history when the genders emerged.
+
+1:47:05.920 --> 1:47:11.280
+ But yeah, most, I mean, the faces of humans are quite different from the faces of great
+
+1:47:11.280 --> 1:47:14.000
+ apes, great apes, right?
+
+1:47:14.000 --> 1:47:15.000
+ Yeah.
+
+1:47:15.000 --> 1:47:16.000
+ That's interesting.
+
+1:47:16.000 --> 1:47:17.000
+ But yeah.
+
+1:47:17.000 --> 1:47:23.200
+ You couldn't tell the face of a female chimpanzee from the face of a male chimpanzee, probably.
+
+1:47:23.200 --> 1:47:24.200
+ Yeah.
+
+1:47:24.200 --> 1:47:26.720
+ And I don't think most humans evolve all that ability.
+
+1:47:26.720 --> 1:47:33.160
+ We do have innate knowledge of what makes a face, but it's actually impossible for us
+
+1:47:33.160 --> 1:47:39.200
+ to have any DNA encoding knowledge of the difference between a female human face and
+
+1:47:39.200 --> 1:47:40.680
+ a male human face.
+
+1:47:40.680 --> 1:47:50.800
+ Because that knowledge, that information came up into the world actually very recently.
+
+1:47:50.800 --> 1:47:56.920
+ If you look at the slowness of the process of encoding knowledge into DNA.
+
+1:47:56.920 --> 1:47:57.920
+ Yeah.
+
+1:47:57.920 --> 1:47:58.920
+ So that's interesting.
+
+1:47:58.920 --> 1:48:01.640
+ That's a really powerful argument that DNA is a low bandwidth and it takes a long time
+
+1:48:01.640 --> 1:48:05.480
+ to encode that naturally creates a very efficient encoding.
+
+1:48:05.480 --> 1:48:12.400
+ But one important consequence of this is that, so yes, we are born into this world with a
+
+1:48:12.400 --> 1:48:17.440
+ bunch of knowledge, sometimes very high level knowledge about the world like the rough shape
+
+1:48:17.440 --> 1:48:20.800
+ of a snake, of the rough shape of a face.
+
+1:48:20.800 --> 1:48:27.040
+ But importantly, because this knowledge takes so long to write, almost all of this innate
+
+1:48:27.040 --> 1:48:33.360
+ knowledge is shared with our cousins, with great apes, right?
+
+1:48:33.360 --> 1:48:37.600
+ So it is not actually this innate knowledge that makes us special.
+
+1:48:37.600 --> 1:48:44.120
+ But to throw it right back at you from the earlier on in our discussion, that encoding
+
+1:48:44.120 --> 1:48:50.600
+ might also include the entirety of the environment of Earth.
+
+1:48:50.600 --> 1:48:56.520
+ To sum up, so it can include things that are important to survival and production.
+
+1:48:56.520 --> 1:49:01.840
+ So for which there is some evolutionary pressure and things that are stable, constant over
+
+1:49:01.840 --> 1:49:05.240
+ very, very, very long time periods.
+
+1:49:05.240 --> 1:49:07.440
+ And honestly, it's not that much information.
+
+1:49:07.440 --> 1:49:15.600
+ There's also, besides the bandwidths, constraints and constraints of the writing process, there's
+
+1:49:15.600 --> 1:49:22.600
+ also memory constraints like DNA, the part of DNA that deals with the human brain, it's
+
+1:49:22.600 --> 1:49:23.600
+ actually fairly small.
+
+1:49:23.600 --> 1:49:26.360
+ It's like, you know, on the order of megabytes, right?
+
+1:49:26.360 --> 1:49:31.880
+ There's not that much high level knowledge about the world you can encode.
+
+1:49:31.880 --> 1:49:39.400
+ That's quite brilliant and hopeful for a benchmark that you're referring to of encoding priors.
+
+1:49:39.400 --> 1:49:43.680
+ I actually look forward to, I'm skeptical that you can do it in the next couple of years,
+
+1:49:43.680 --> 1:49:44.680
+ but hopefully...
+
+1:49:44.680 --> 1:49:45.960
+ I've been working on it.
+
+1:49:45.960 --> 1:49:50.120
+ So honestly, it's a very simple benchmark and it's not like a big breakthrough or anything.
+
+1:49:50.120 --> 1:49:53.920
+ It's more like a fun side project, right?
+
+1:49:53.920 --> 1:49:56.720
+ So is ImageNet.
+
+1:49:56.720 --> 1:50:04.120
+ These fun side projects could launch entire groups of efforts towards creating reasoning
+
+1:50:04.120 --> 1:50:05.120
+ systems and so on.
+
+1:50:05.120 --> 1:50:06.120
+ And I think...
+
+1:50:06.120 --> 1:50:07.120
+ Yeah, that's the goal.
+
+1:50:07.120 --> 1:50:12.160
+ It's trying to measure strong generalization, to measure the strength of abstraction in
+
+1:50:12.160 --> 1:50:17.160
+ our minds, well, in our minds and in an artificially intelligent agency.
+
+1:50:17.160 --> 1:50:24.960
+ And if there's anything true about this science organism, it's individual cells love competition.
+
+1:50:24.960 --> 1:50:27.000
+ So benchmarks encourage competition.
+
+1:50:27.000 --> 1:50:29.680
+ So that's an exciting possibility.
+
+1:50:29.680 --> 1:50:30.680
+ If you...
+
+1:50:30.680 --> 1:50:35.720
+ Do you think an AI winter is coming and how do we prevent it?
+
+1:50:35.720 --> 1:50:36.720
+ Not really.
+
+1:50:36.720 --> 1:50:42.160
+ So an AI winter is something that would occur when there's a big mismatch between how we
+
+1:50:42.160 --> 1:50:47.560
+ are selling the capabilities of AI and the actual capabilities of AI.
+
+1:50:47.560 --> 1:50:52.000
+ And today, deep learning is creating a lot of value and it will keep creating a lot of
+
+1:50:52.000 --> 1:50:59.360
+ value in the sense that these models are applicable to a very wide range of problems that are
+
+1:50:59.360 --> 1:51:00.360
+ even today.
+
+1:51:00.360 --> 1:51:05.320
+ And we are only just getting started with applying algorithms to every problem they
+
+1:51:05.320 --> 1:51:06.520
+ could be solving.
+
+1:51:06.520 --> 1:51:10.440
+ So deep learning will keep creating a lot of value for the time being.
+
+1:51:10.440 --> 1:51:16.000
+ What's concerning, however, is that there's a lot of hype around deep learning and around
+
+1:51:16.000 --> 1:51:17.000
+ AI.
+
+1:51:17.000 --> 1:51:22.840
+ A lot of people are overselling the capabilities of these systems, not just the capabilities
+
+1:51:22.840 --> 1:51:31.520
+ but also overselling the fact that they might be more or less brain like, like given a kind
+
+1:51:31.520 --> 1:51:40.480
+ of a mystical aspect, these technologies, and also overselling the pace of progress,
+
+1:51:40.480 --> 1:51:46.000
+ which it might look fast in the sense that we have this exponentially increasing number
+
+1:51:46.000 --> 1:51:48.080
+ of papers.
+
+1:51:48.080 --> 1:51:53.000
+ But again, that's just a simple consequence of the fact that we have ever more people
+
+1:51:53.000 --> 1:51:54.000
+ coming into the field.
+
+1:51:54.000 --> 1:51:58.000
+ It doesn't mean the progress is actually exponentially fast.
+
+1:51:58.000 --> 1:52:02.960
+ Like, let's say you're trying to raise money for your startup or your research lab.
+
+1:52:02.960 --> 1:52:09.120
+ You might want to tell, you know, a grand yos story to investors about how deep learning
+
+1:52:09.120 --> 1:52:14.240
+ is just like the brain and how it can solve all these incredible problems like self driving
+
+1:52:14.240 --> 1:52:19.040
+ and robotics and so on, and maybe you can tell them that the field is progressing so fast
+
+1:52:19.040 --> 1:52:27.000
+ and we're going to have AI within 15 years or even 10 years, and none of this is true.
+
+1:52:27.000 --> 1:52:33.320
+ And every time you're like saying these things and an investor or, you know, a decision maker
+
+1:52:33.320 --> 1:52:43.400
+ believes them, well, this is like the equivalent of taking on credit card debt, but for trust.
+
+1:52:43.400 --> 1:52:50.920
+ And maybe this will, you know, this will be what enables you to raise a lot of money,
+
+1:52:50.920 --> 1:52:55.160
+ but ultimately you are creating damage, you are damaging the field.
+
+1:52:55.160 --> 1:53:01.240
+ That's the concern is that debt, that's what happens with the other AI winters is the concern
+
+1:53:01.240 --> 1:53:04.440
+ is you actually tweeted about this with autonomous vehicles, right?
+
+1:53:04.440 --> 1:53:08.960
+ There's almost every single company now have promised that they will have full autonomous
+
+1:53:08.960 --> 1:53:12.000
+ vehicles by 2021, 2022.
+
+1:53:12.000 --> 1:53:18.280
+ That's a good example of the consequences of overhyping the capabilities of AI and the
+
+1:53:18.280 --> 1:53:19.280
+ pace of progress.
+
+1:53:19.280 --> 1:53:25.160
+ So because I work especially a lot recently in this area, I have a deep concern of what
+
+1:53:25.160 --> 1:53:30.480
+ happens when all of these companies after every invested billions have a meeting and
+
+1:53:30.480 --> 1:53:33.720
+ say, how much do we actually, first of all, do we have an autonomous vehicle?
+
+1:53:33.720 --> 1:53:36.360
+ The answer will definitely be no.
+
+1:53:36.360 --> 1:53:40.680
+ And second will be, wait a minute, we've invested one, two, three, four billion dollars
+
+1:53:40.680 --> 1:53:43.400
+ into this and we made no profit.
+
+1:53:43.400 --> 1:53:49.280
+ And the reaction to that may be going very hard in other directions that might impact
+
+1:53:49.280 --> 1:53:50.840
+ you that even other industries.
+
+1:53:50.840 --> 1:53:55.320
+ And that's what we call in the air winter is when there is backlash where no one believes
+
+1:53:55.320 --> 1:54:00.600
+ any of these promises anymore because they've turned out to be big lies the first time around.
+
+1:54:00.600 --> 1:54:06.120
+ And this will definitely happen to some extent for autonomous vehicles because the public
+
+1:54:06.120 --> 1:54:13.440
+ and decision makers have been convinced that around 2015, they've been convinced by these
+
+1:54:13.440 --> 1:54:19.600
+ people who are trying to raise money for their startups and so on, that L5 driving was coming
+
+1:54:19.600 --> 1:54:23.120
+ in maybe 2016, maybe 2017, May 2018.
+
+1:54:23.120 --> 1:54:28.040
+ Now in 2019, we're still waiting for it.
+
+1:54:28.040 --> 1:54:32.880
+ And so I don't believe we are going to have a full on AI winter because we have these
+
+1:54:32.880 --> 1:54:39.480
+ technologies that are producing a tremendous amount of real value, but there is also too
+
+1:54:39.480 --> 1:54:40.480
+ much hype.
+
+1:54:40.480 --> 1:54:45.240
+ So there will be some backlash, especially there will be backlash.
+
+1:54:45.240 --> 1:54:53.080
+ So some startups are trying to sell the dream of AGI and the fact that AGI is going to create
+
+1:54:53.080 --> 1:54:54.080
+ infinite value.
+
+1:54:54.080 --> 1:55:01.240
+ AGI is like a freelance, like if you can develop an AI system that passes a certain threshold
+
+1:55:01.240 --> 1:55:06.440
+ of IQ or something, then suddenly you have infinite value.
+
+1:55:06.440 --> 1:55:11.640
+ And well, there are actually lots of investors buying into this idea.
+
+1:55:11.640 --> 1:55:18.920
+ And they will wait maybe 10, 15 years and nothing will happen.
+
+1:55:18.920 --> 1:55:22.800
+ And the next time around, well, maybe there will be a new generation of investors, no
+
+1:55:22.800 --> 1:55:24.040
+ one will care.
+
+1:55:24.040 --> 1:55:27.160
+ Human memory is very short after all.
+
+1:55:27.160 --> 1:55:34.440
+ I don't know about you, but because I've spoken about AGI sometimes poetically, I get a lot
+
+1:55:34.440 --> 1:55:42.360
+ of emails from people giving me, they're usually like a large manifestos of, they say to me
+
+1:55:42.360 --> 1:55:48.320
+ that they have created an AGI system or they know how to do it and there's a long write
+
+1:55:48.320 --> 1:55:49.320
+ up of how to do it.
+
+1:55:49.320 --> 1:55:51.400
+ I get a lot of these emails.
+
+1:55:51.400 --> 1:55:57.840
+ They're a little bit feel like it's generated by an AI system actually, but there's usually
+
+1:55:57.840 --> 1:55:58.840
+ no backup.
+
+1:55:58.840 --> 1:56:04.920
+ Maybe that's recursively self improving AI, it's you have a transformer generating crank
+
+1:56:04.920 --> 1:56:06.880
+ papers about a GI.
+
+1:56:06.880 --> 1:56:12.160
+ So the question is about, because you've been such a good, you have a good radar for crank
+
+1:56:12.160 --> 1:56:16.960
+ papers, how do we know they're not onto something?
+
+1:56:16.960 --> 1:56:24.320
+ How do I, so when you start to talk about AGI or anything like the reasoning benchmarks
+
+1:56:24.320 --> 1:56:28.720
+ and so on, so something that doesn't have a benchmark, it's really difficult to know.
+
+1:56:28.720 --> 1:56:35.480
+ I mean, I talked to Jeff Hawkins who's really looking at neuroscience approaches to how,
+
+1:56:35.480 --> 1:56:41.800
+ and there's some, there's echoes of really interesting ideas in at least Jeff's case,
+
+1:56:41.800 --> 1:56:43.520
+ which he's showing.
+
+1:56:43.520 --> 1:56:45.840
+ How do you usually think about this?
+
+1:56:45.840 --> 1:56:52.920
+ Like preventing yourself from being too narrow minded and elitist about deep learning.
+
+1:56:52.920 --> 1:56:57.040
+ It has to work on these particular benchmarks, otherwise it's trash.
+
+1:56:57.040 --> 1:57:05.880
+ Well, the thing is intelligence does not exist in the abstract.
+
+1:57:05.880 --> 1:57:07.440
+ Intelligence has to be applied.
+
+1:57:07.440 --> 1:57:11.040
+ So if you don't have a benchmark, if you don't have an improvement on some benchmark, maybe
+
+1:57:11.040 --> 1:57:12.680
+ it's a new benchmark.
+
+1:57:12.680 --> 1:57:16.760
+ Maybe it's not something we've been looking at before, but you do need a problem that
+
+1:57:16.760 --> 1:57:17.760
+ you're trying to solve.
+
+1:57:17.760 --> 1:57:21.040
+ You're not going to come up with a solution without a problem.
+
+1:57:21.040 --> 1:57:26.760
+ So you, general intelligence, I mean, you've clearly highlighted generalization.
+
+1:57:26.760 --> 1:57:31.320
+ If you want to claim that you have an intelligence system, it should come with a benchmark.
+
+1:57:31.320 --> 1:57:35.960
+ It should, yes, it should display capabilities of some kind.
+
+1:57:35.960 --> 1:57:41.920
+ It should show that it can create some form of value, even if it's a very artificial form
+
+1:57:41.920 --> 1:57:43.160
+ of value.
+
+1:57:43.160 --> 1:57:48.840
+ And that's also the reason why you don't actually need to care about telling which papers have
+
+1:57:48.840 --> 1:57:53.520
+ actually some hidden potential and which do not.
+
+1:57:53.520 --> 1:57:58.880
+ Because if there is a new technique, it's actually creating value.
+
+1:57:58.880 --> 1:58:02.640
+ This is going to be brought to light very quickly because it's actually making a difference.
+
+1:58:02.640 --> 1:58:08.240
+ So it's a difference between something that is ineffectual and something that is actually
+
+1:58:08.240 --> 1:58:09.240
+ useful.
+
+1:58:09.240 --> 1:58:14.120
+ And ultimately, usefulness is our guide, not just in this field, but if you look at science
+
+1:58:14.120 --> 1:58:19.560
+ in general, maybe there are many, many people over the years that have had some really interesting
+
+1:58:19.560 --> 1:58:23.120
+ theories of everything, but they were just completely useless.
+
+1:58:23.120 --> 1:58:28.240
+ And you don't actually need to tell the interesting theories from the useless theories.
+
+1:58:28.240 --> 1:58:34.120
+ All you need is to see, you know, is this actually having an effect on something else?
+
+1:58:34.120 --> 1:58:35.600
+ You know, is this actually useful?
+
+1:58:35.600 --> 1:58:37.960
+ Is this making an impact or not?
+
+1:58:37.960 --> 1:58:42.480
+ As beautifully put, I mean, the same applies to quantum mechanics, to string theory, to
+
+1:58:42.480 --> 1:58:43.480
+ the holographic principle.
+
+1:58:43.480 --> 1:58:46.080
+ We are doing deep learning because it works.
+
+1:58:46.080 --> 1:58:52.720
+ Before it started working, people considered people working on neural networks as cranks
+
+1:58:52.720 --> 1:58:53.720
+ very much.
+
+1:58:53.720 --> 1:58:56.560
+ Like, you know, no one was working on this anymore.
+
+1:58:56.560 --> 1:58:59.400
+ And now it's working, which is what makes it valuable.
+
+1:58:59.400 --> 1:59:02.840
+ It's not about being right, it's about being effective.
+
+1:59:02.840 --> 1:59:08.120
+ And nevertheless, the individual entities of this scientific mechanism, just like Yoshio
+
+1:59:08.120 --> 1:59:13.160
+ Banjo or Yanlacun, they, while being called cranks, stuck with it, right?
+
+1:59:13.160 --> 1:59:19.080
+ And so, us individual agents, even if everyone's laughing at us, should stick with it.
+
+1:59:19.080 --> 1:59:23.840
+ If you believe you have something, you should stick with it and see it through.
+
+1:59:23.840 --> 1:59:25.920
+ That's a beautiful, inspirational message to end on.
+
+1:59:25.920 --> 1:59:27.800
+ Francois, thank you so much for talking today.
+
+1:59:27.800 --> 1:59:28.800
+ That was amazing.
+
+1:59:28.800 --> 1:59:35.800
+ Thank you.
+