|
WEBVTT |
|
|
|
00:00.000 --> 00:03.720 |
|
The following is a conversation with Francois Chollet. |
|
|
|
00:03.720 --> 00:05.760 |
|
He's the creator of Keras, |
|
|
|
00:05.760 --> 00:08.080 |
|
which is an open source deep learning library |
|
|
|
00:08.080 --> 00:11.480 |
|
that is designed to enable fast, user friendly experimentation |
|
|
|
00:11.480 --> 00:13.600 |
|
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 --> 00:24.080 |
|
a while ago. |
|
|
|
00:24.080 --> 00:27.000 |
|
Meaning, if you want to create, train, |
|
|
|
00:27.000 --> 00:28.640 |
|
and use neural networks, |
|
|
|
00:28.640 --> 00:31.040 |
|
probably the easiest and most popular option |
|
|
|
00:31.040 --> 00:33.840 |
|
is to use Keras inside TensorFlow. |
|
|
|
00:34.840 --> 00:37.240 |
|
Aside from creating an exceptionally useful |
|
|
|
00:37.240 --> 00:38.680 |
|
and popular library, |
|
|
|
00:38.680 --> 00:41.920 |
|
Francois is also a world class AI researcher |
|
|
|
00:41.920 --> 00:43.680 |
|
and software engineer at Google. |
|
|
|
00:44.560 --> 00:46.960 |
|
And he's definitely an outspoken, |
|
|
|
00:46.960 --> 00:50.560 |
|
if not controversial personality in the AI world, |
|
|
|
00:50.560 --> 00:52.920 |
|
especially in the realm of ideas |
|
|
|
00:52.920 --> 00:55.920 |
|
around the future 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 --> 01:02.760 |
|
give it five stars on iTunes, |
|
|
|
01:02.760 --> 01:04.160 |
|
support it on Patreon, |
|
|
|
01:04.160 --> 01:06.120 |
|
or simply connect with me on Twitter |
|
|
|
01:06.120 --> 01:09.960 |
|
at Lex Friedman, spelled F R I D M A N. |
|
|
|
01:09.960 --> 01:13.840 |
|
And now, here's my conversation with Francois Chollet. |
|
|
|
01:14.880 --> 01:17.320 |
|
You're known for not sugarcoating your opinions |
|
|
|
01:17.320 --> 01:19.160 |
|
and speaking your mind about ideas in AI, |
|
|
|
01:19.160 --> 01:21.160 |
|
especially on Twitter. |
|
|
|
01:21.160 --> 01:22.760 |
|
It's one of my favorite Twitter accounts. |
|
|
|
01:22.760 --> 01:26.320 |
|
So what's one of the more controversial ideas |
|
|
|
01:26.320 --> 01:29.360 |
|
you've expressed online and gotten some heat for? |
|
|
|
01:30.440 --> 01:31.360 |
|
How do you pick? |
|
|
|
01:33.080 --> 01:33.920 |
|
How do I pick? |
|
|
|
01:33.920 --> 01:36.880 |
|
Yeah, no, I think if you go through the trouble |
|
|
|
01:36.880 --> 01:39.640 |
|
of maintaining a Twitter account, |
|
|
|
01:39.640 --> 01:41.840 |
|
you might as well speak your mind, you know? |
|
|
|
01:41.840 --> 01:44.600 |
|
Otherwise, what's even the point of having a Twitter account? |
|
|
|
01:44.600 --> 01:45.480 |
|
It's like having a nice car |
|
|
|
01:45.480 --> 01:47.560 |
|
and just leaving it in the garage. |
|
|
|
01:48.600 --> 01:50.840 |
|
Yeah, so what's one thing for which I got |
|
|
|
01:50.840 --> 01:53.600 |
|
a lot of pushback? |
|
|
|
01:53.600 --> 01:56.640 |
|
Perhaps, you know, that time I wrote something |
|
|
|
01:56.640 --> 02:00.920 |
|
about the idea of intelligence explosion, |
|
|
|
02:00.920 --> 02:04.520 |
|
and I was questioning the idea |
|
|
|
02:04.520 --> 02:06.840 |
|
and the reasoning behind this idea. |
|
|
|
02:06.840 --> 02:09.640 |
|
And I got a lot of pushback on that. |
|
|
|
02:09.640 --> 02:11.840 |
|
I got a lot of flak for it. |
|
|
|
02:11.840 --> 02:13.600 |
|
So yeah, so intelligence explosion, |
|
|
|
02:13.600 --> 02:14.960 |
|
I'm sure you're familiar with the idea, |
|
|
|
02:14.960 --> 02:18.800 |
|
but it's the idea that if you were to build |
|
|
|
02:18.800 --> 02:22.920 |
|
general AI problem solving algorithms, |
|
|
|
02:22.920 --> 02:26.000 |
|
well, the problem of building such an AI, |
|
|
|
02:27.480 --> 02:30.520 |
|
that itself is a problem that could be solved by your AI, |
|
|
|
02:30.520 --> 02:31.880 |
|
and maybe it could be solved better |
|
|
|
02:31.880 --> 02:33.760 |
|
than what humans can do. |
|
|
|
02:33.760 --> 02:36.840 |
|
So your AI could start tweaking its own algorithm, |
|
|
|
02:36.840 --> 02:39.520 |
|
could start making a better version of itself, |
|
|
|
02:39.520 --> 02:43.240 |
|
and so on iteratively in a recursive fashion. |
|
|
|
02:43.240 --> 02:47.320 |
|
And so you would end up with an AI |
|
|
|
02:47.320 --> 02:50.080 |
|
with exponentially increasing intelligence. |
|
|
|
02:50.080 --> 02:50.920 |
|
That's right. |
|
|
|
02:50.920 --> 02:55.880 |
|
And I was basically questioning this idea, |
|
|
|
02:55.880 --> 02:59.040 |
|
first of all, because the notion of intelligence explosion |
|
|
|
02:59.040 --> 03:02.200 |
|
uses an implicit definition of intelligence |
|
|
|
03:02.200 --> 03:05.360 |
|
that doesn't sound quite right to me. |
|
|
|
03:05.360 --> 03:10.360 |
|
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 --> 03:25.240 |
|
between a brain, a body, |
|
|
|
03:25.240 --> 03:28.320 |
|
like embodied intelligence, and an environment. |
|
|
|
03:28.320 --> 03:30.720 |
|
And if you're missing one of these pieces, |
|
|
|
03:30.720 --> 03:33.800 |
|
then you cannot really define intelligence anymore. |
|
|
|
03:33.800 --> 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 --> 03:39.960 |
|
So first of all, |
|
|
|
03:39.960 --> 03:43.000 |
|
you're crushing the dreams of many people, right? |
|
|
|
03:43.000 --> 03:46.000 |
|
So there's a, let's look at like Sam Harris. |
|
|
|
03:46.000 --> 03:48.680 |
|
Actually, a lot of physicists, Max Tegmark, |
|
|
|
03:48.680 --> 03:52.120 |
|
people who think the universe |
|
|
|
03:52.120 --> 03:54.640 |
|
is an information processing system, |
|
|
|
03:54.640 --> 03:57.680 |
|
our brain is kind of an information processing system. |
|
|
|
03:57.680 --> 03:59.400 |
|
So what's the theoretical limit? |
|
|
|
03:59.400 --> 04:03.160 |
|
Like, it doesn't make sense that there should be some, |
|
|
|
04:04.800 --> 04:07.520 |
|
it seems naive to think that our own brain |
|
|
|
04:07.520 --> 04:10.000 |
|
is somehow the limit of the capabilities |
|
|
|
04:10.000 --> 04:11.600 |
|
of this information system. |
|
|
|
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 --> 04:17.760 |
|
And then if you just scale it, |
|
|
|
04:17.760 --> 04:19.360 |
|
if you're able to build something |
|
|
|
04:19.360 --> 04:20.920 |
|
that's on par with the brain, |
|
|
|
04:20.920 --> 04:24.040 |
|
you just, the process that builds it just continues |
|
|
|
04:24.040 --> 04:26.400 |
|
and it'll improve exponentially. |
|
|
|
04:26.400 --> 04:30.160 |
|
So that's the logic that's used actually |
|
|
|
04:30.160 --> 04:32.560 |
|
by almost everybody |
|
|
|
04:32.560 --> 04:36.920 |
|
that is worried about super human intelligence. |
|
|
|
04:36.920 --> 04:39.120 |
|
So you're trying to make, |
|
|
|
04:39.120 --> 04:40.960 |
|
so most people who are skeptical of that |
|
|
|
04:40.960 --> 04:43.000 |
|
are kind of like, this doesn't, |
|
|
|
04:43.000 --> 04:46.520 |
|
their thought process, this doesn't feel right. |
|
|
|
04:46.520 --> 04:47.680 |
|
Like that's for me as well. |
|
|
|
04:47.680 --> 04:49.760 |
|
So I'm more like, it doesn't, |
|
|
|
04:51.440 --> 04:52.800 |
|
the whole thing is shrouded in mystery |
|
|
|
04:52.800 --> 04:55.840 |
|
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 --> 05:00.640 |
|
This doesn't feel like that's how the brain works. |
|
|
|
05:00.640 --> 05:02.400 |
|
And you're trying to with your blog posts |
|
|
|
05:02.400 --> 05:05.680 |
|
and now making it a little more explicit. |
|
|
|
05:05.680 --> 05:10.680 |
|
So one idea is that the brain isn't exist alone. |
|
|
|
05:10.680 --> 05:13.200 |
|
It exists within the environment. |
|
|
|
05:13.200 --> 05:15.680 |
|
So you can't exponentially, |
|
|
|
05:15.680 --> 05:18.000 |
|
you would have to somehow exponentially improve |
|
|
|
05:18.000 --> 05:20.920 |
|
the environment and the brain together almost. |
|
|
|
05:20.920 --> 05:25.920 |
|
Yeah, in order to create something that's much smarter |
|
|
|
05:25.960 --> 05:27.840 |
|
in some kind of, |
|
|
|
05:27.840 --> 05:29.960 |
|
of course we don't have a definition of intelligence. |
|
|
|
05:29.960 --> 05:31.280 |
|
That's correct, that's correct. |
|
|
|
05:31.280 --> 05:34.280 |
|
I don't think, you should look at very smart people today, |
|
|
|
05:34.280 --> 05:37.280 |
|
even humans, not even talking about AIs. |
|
|
|
05:37.280 --> 05:38.640 |
|
I don't think their brain |
|
|
|
05:38.640 --> 05:41.960 |
|
and the performance of their brain is the bottleneck |
|
|
|
05:41.960 --> 05:45.200 |
|
to their expressed intelligence, to their achievements. |
|
|
|
05:46.600 --> 05:49.960 |
|
You cannot just tweak one part of this system, |
|
|
|
05:49.960 --> 05:52.840 |
|
like of this brain, body, environment system |
|
|
|
05:52.840 --> 05:55.960 |
|
and expect that capabilities like what emerges |
|
|
|
05:55.960 --> 06:00.280 |
|
out of this system to just explode exponentially. |
|
|
|
06:00.280 --> 06:04.200 |
|
Because anytime you improve one part of a system |
|
|
|
06:04.200 --> 06:06.760 |
|
with many interdependencies like this, |
|
|
|
06:06.760 --> 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 --> 06:15.000 |
|
their brain is not the bottleneck |
|
|
|
06:15.000 --> 06:17.560 |
|
to the sort of problems they can solve, right? |
|
|
|
06:17.560 --> 06:19.800 |
|
In fact, many very smart people today, |
|
|
|
06:20.760 --> 06:22.520 |
|
you know, they are not actually solving |
|
|
|
06:22.520 --> 06:24.800 |
|
any big scientific problems, they're not Einstein. |
|
|
|
06:24.800 --> 06:28.280 |
|
They're like Einstein, but you know, the patent clerk days. |
|
|
|
06:29.800 --> 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 would have 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 --> 06:55.640 |
|
Wow, that's brilliant. |
|
|
|
06:55.640 --> 06:58.520 |
|
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 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 --> 07:24.480 |
|
Exactly, intelligence is the meeting |
|
|
|
07:24.480 --> 07:27.480 |
|
of great problem solving capabilities |
|
|
|
07:27.480 --> 07:28.760 |
|
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 any intelligence. |
|
|
|
07:32.280 --> 07:34.760 |
|
All you're left with is potential intelligence, |
|
|
|
07:34.760 --> 07:36.240 |
|
like the performance of your brain |
|
|
|
07:36.240 --> 07:38.680 |
|
or how high your IQ is, |
|
|
|
07:38.680 --> 07:42.080 |
|
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 --> 07:51.800 |
|
What do you think of as problem solving capacity? |
|
|
|
07:51.800 --> 07:55.160 |
|
Can you try to define intelligence? |
|
|
|
07:56.640 --> 08:00.000 |
|
Like what does it mean to be more or less intelligent? |
|
|
|
08:00.000 --> 08:03.000 |
|
Is it completely coupled to a particular problem |
|
|
|
08:03.000 --> 08:05.720 |
|
or is there something a little bit more universal? |
|
|
|
08:05.720 --> 08:07.440 |
|
Yeah, I do believe all intelligence |
|
|
|
08:07.440 --> 08:09.080 |
|
is specialized intelligence. |
|
|
|
08:09.080 --> 08:12.200 |
|
Even human intelligence has some degree of generality. |
|
|
|
08:12.200 --> 08:15.320 |
|
Well, all intelligent systems have some degree of generality |
|
|
|
08:15.320 --> 08:19.400 |
|
but they're always specialized in one category of problems. |
|
|
|
08:19.400 --> 08:21.880 |
|
So the human intelligence is specialized |
|
|
|
08:21.880 --> 08:23.560 |
|
in the human experience. |
|
|
|
08:23.560 --> 08:25.560 |
|
And that shows at various levels, |
|
|
|
08:25.560 --> 08:30.200 |
|
that shows in some prior knowledge that's innate |
|
|
|
08:30.200 --> 08:32.040 |
|
that we have at birth. |
|
|
|
08:32.040 --> 08:35.360 |
|
Knowledge about things like agents, |
|
|
|
08:35.360 --> 08:38.080 |
|
goal driven behavior, visual priors |
|
|
|
08:38.080 --> 08:43.080 |
|
about what makes an object, priors about time and so on. |
|
|
|
08:43.520 --> 08:45.360 |
|
That shows also in the way we learn. |
|
|
|
08:45.360 --> 08:47.160 |
|
For instance, it's very, very easy for us |
|
|
|
08:47.160 --> 08:48.600 |
|
to pick up language. |
|
|
|
08:49.560 --> 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 problem |
|
|
|
08:58.280 --> 08:59.720 |
|
and we are quite useless |
|
|
|
08:59.720 --> 09:01.440 |
|
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 --> 09:12.880 |
|
We have no capability of seeing the very long term. |
|
|
|
09:12.880 --> 09:16.880 |
|
We don't have very much working memory. |
|
|
|
09:18.000 --> 09:20.080 |
|
So how do you think about long term? |
|
|
|
09:20.080 --> 09:21.360 |
|
Do you think long term planning, |
|
|
|
09:21.360 --> 09:24.880 |
|
are we talking about scale of years, millennia? |
|
|
|
09:24.880 --> 09:26.400 |
|
What do you mean by long term? |
|
|
|
09:26.400 --> 09:28.120 |
|
We're not very good. |
|
|
|
09:28.120 --> 09:29.760 |
|
Well, human intelligence is specialized |
|
|
|
09:29.760 --> 09:30.720 |
|
in the human experience. |
|
|
|
09:30.720 --> 09:32.800 |
|
And human experience is very short. |
|
|
|
09:32.800 --> 09:34.240 |
|
One lifetime is short. |
|
|
|
09:34.240 --> 09:35.880 |
|
Even within one lifetime, |
|
|
|
09:35.880 --> 09:40.000 |
|
we have a very hard time envisioning things |
|
|
|
09:40.000 --> 09:41.360 |
|
on a scale of years. |
|
|
|
09:41.360 --> 09:43.240 |
|
It's very difficult to project yourself |
|
|
|
09:43.240 --> 09:46.960 |
|
at a scale of five years, at a scale of 10 years and so on. |
|
|
|
09:46.960 --> 09:50.000 |
|
We can solve only fairly narrowly scoped problems. |
|
|
|
09:50.000 --> 09:52.320 |
|
So when it comes to solving bigger problems, |
|
|
|
09:52.320 --> 09:53.760 |
|
larger scale problems, |
|
|
|
09:53.760 --> 09:56.360 |
|
we are not actually doing it on an individual level. |
|
|
|
09:56.360 --> 09:59.280 |
|
So it's not actually our brain doing it. |
|
|
|
09:59.280 --> 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 artificially intelligent system, right? |
|
|
|
10:10.000 --> 10:12.120 |
|
And it's not running on one brain, |
|
|
|
10:12.120 --> 10:14.080 |
|
it's running on a network of brains. |
|
|
|
10:14.080 --> 10:15.640 |
|
In fact, it's running on much more |
|
|
|
10:15.640 --> 10:16.760 |
|
than a network of brains. |
|
|
|
10:16.760 --> 10:20.080 |
|
It's running on a lot of infrastructure, |
|
|
|
10:20.080 --> 10:23.040 |
|
like books and computers and the internet |
|
|
|
10:23.040 --> 10:25.800 |
|
and human institutions and so on. |
|
|
|
10:25.800 --> 10:30.240 |
|
And that is capable of handling problems |
|
|
|
10:30.240 --> 10:33.760 |
|
on a much greater scale than any individual human. |
|
|
|
10:33.760 --> 10:37.600 |
|
If you look at computer science, for instance, |
|
|
|
10:37.600 --> 10:39.840 |
|
that's an institution that solves problems |
|
|
|
10:39.840 --> 10:42.560 |
|
and it is superhuman, right? |
|
|
|
10:42.560 --> 10:44.200 |
|
It operates on a greater scale. |
|
|
|
10:44.200 --> 10:46.880 |
|
It can solve much bigger problems |
|
|
|
10:46.880 --> 10:49.080 |
|
than an individual human could. |
|
|
|
10:49.080 --> 10:52.160 |
|
And science itself, science as a system, as an institution, |
|
|
|
10:52.160 --> 10:57.120 |
|
is a kind of artificially intelligent problem solving |
|
|
|
10:57.120 --> 10:59.360 |
|
algorithm that is superhuman. |
|
|
|
10:59.360 --> 11:02.800 |
|
Yeah, it's, at least computer science |
|
|
|
11:02.800 --> 11:07.720 |
|
is like a theorem prover at a scale of thousands, |
|
|
|
11:07.720 --> 11:10.400 |
|
maybe hundreds of thousands of human beings. |
|
|
|
11:10.400 --> 11:14.680 |
|
At that scale, what do you think is an intelligent agent? |
|
|
|
11:14.680 --> 11:18.280 |
|
So there's us humans at the individual level, |
|
|
|
11:18.280 --> 11:22.400 |
|
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 --> 11:29.160 |
|
You can even go to the particle level |
|
|
|
11:29.160 --> 11:31.000 |
|
as systems that behave, |
|
|
|
11:31.840 --> 11:34.360 |
|
you can say intelligently in some ways. |
|
|
|
11:35.440 --> 11:37.840 |
|
And then you can look at the earth as a single organism, |
|
|
|
11:37.840 --> 11:39.200 |
|
you can look at our galaxy |
|
|
|
11:39.200 --> 11:42.160 |
|
and even the universe as a single organism. |
|
|
|
11:42.160 --> 11:44.680 |
|
Do you think, how do you think about scale |
|
|
|
11:44.680 --> 11:46.280 |
|
in defining intelligent systems? |
|
|
|
11:46.280 --> 11:50.440 |
|
And we're here at Google, there is millions of devices |
|
|
|
11:50.440 --> 11:53.360 |
|
doing computation just in a distributed way. |
|
|
|
11:53.360 --> 11:55.880 |
|
How do you think about intelligence versus scale? |
|
|
|
11:55.880 --> 11:59.400 |
|
You can always characterize anything as a system. |
|
|
|
12:00.640 --> 12:03.600 |
|
I think people who talk about things |
|
|
|
12:03.600 --> 12:05.320 |
|
like intelligence explosion, |
|
|
|
12:05.320 --> 12:08.760 |
|
tend to focus on one agent is basically one brain, |
|
|
|
12:08.760 --> 12:10.960 |
|
like one brain considered in isolation, |
|
|
|
12:10.960 --> 12:13.200 |
|
like a brain, a jaw that's controlling a body |
|
|
|
12:13.200 --> 12:16.280 |
|
in a very like 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 --> 12:22.880 |
|
You have the brain at the top of the pyramid, |
|
|
|
12:22.880 --> 12:25.960 |
|
then you have the body just plainly receiving orders. |
|
|
|
12:25.960 --> 12:27.640 |
|
And then the body is manipulating objects |
|
|
|
12:27.640 --> 12:28.920 |
|
in the environment and so on. |
|
|
|
12:28.920 --> 12:32.920 |
|
So everything is subordinate to this one thing, |
|
|
|
12:32.920 --> 12:34.720 |
|
this epicenter, which is the brain. |
|
|
|
12:34.720 --> 12:37.120 |
|
But in real life, intelligent agents |
|
|
|
12:37.120 --> 12:39.240 |
|
don't really work like this, right? |
|
|
|
12:39.240 --> 12:40.920 |
|
There is no strong delimitation |
|
|
|
12:40.920 --> 12:43.400 |
|
between the brain and the body to start with. |
|
|
|
12:43.400 --> 12:45.000 |
|
You have to look not just at the brain, |
|
|
|
12:45.000 --> 12:46.560 |
|
but at the nervous system. |
|
|
|
12:46.560 --> 12:48.840 |
|
But then the nervous system and the body |
|
|
|
12:48.840 --> 12:50.760 |
|
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 --> 12:57.000 |
|
But then you start realizing as you observe an animal |
|
|
|
12:57.000 --> 13:00.200 |
|
over any length of time, |
|
|
|
13:00.200 --> 13:03.160 |
|
that a lot of the intelligence of an animal |
|
|
|
13:03.160 --> 13:04.600 |
|
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 --> 13:10.360 |
|
When you write down some notes, |
|
|
|
13:10.360 --> 13:11.960 |
|
that is externalized intelligence. |
|
|
|
13:11.960 --> 13:14.000 |
|
When you write a computer program, |
|
|
|
13:14.000 --> 13:16.000 |
|
you are externalizing cognition. |
|
|
|
13:16.000 --> 13:19.720 |
|
So it's externalizing books, it's externalized in computers, |
|
|
|
13:19.720 --> 13:21.520 |
|
the internet, in other humans. |
|
|
|
13:23.080 --> 13:25.400 |
|
It's externalizing language and so on. |
|
|
|
13:25.400 --> 13:30.400 |
|
So there is no hard delimitation |
|
|
|
13:30.480 --> 13:32.640 |
|
of what makes an intelligent agent. |
|
|
|
13:32.640 --> 13:33.880 |
|
It's all about context. |
|
|
|
13:34.960 --> 13:38.800 |
|
Okay, but AlphaGo is better at Go |
|
|
|
13:38.800 --> 13:40.200 |
|
than the best human player. |
|
|
|
13:42.520 --> 13:45.000 |
|
There's levels of skill here. |
|
|
|
13:45.000 --> 13:48.600 |
|
So do you think there's such a ability, |
|
|
|
13:48.600 --> 13:52.800 |
|
such a concept as intelligence explosion |
|
|
|
13:52.800 --> 13:54.760 |
|
in a specific task? |
|
|
|
13:54.760 --> 13:57.360 |
|
And then, well, yeah. |
|
|
|
13:57.360 --> 14:00.120 |
|
Do you think it's possible to have a category of tasks |
|
|
|
14:00.120 --> 14:02.080 |
|
on which you do have something |
|
|
|
14:02.080 --> 14:05.040 |
|
like an exponential growth of ability |
|
|
|
14:05.040 --> 14:07.440 |
|
to solve that particular problem? |
|
|
|
14:07.440 --> 14:10.320 |
|
I think if you consider a specific vertical, |
|
|
|
14:10.320 --> 14:13.720 |
|
it's probably possible to some extent. |
|
|
|
14:15.320 --> 14:18.320 |
|
I also don't think we have to speculate about it |
|
|
|
14:18.320 --> 14:22.280 |
|
because we have real world examples |
|
|
|
14:22.280 --> 14:26.920 |
|
of recursively self improving intelligent systems, right? |
|
|
|
14:26.920 --> 14:30.920 |
|
So for instance, science is a problem solving system, |
|
|
|
14:30.920 --> 14:32.600 |
|
a knowledge generation system, |
|
|
|
14:32.600 --> 14:36.240 |
|
like a system that experiences the world in some sense |
|
|
|
14:36.240 --> 14:40.160 |
|
and then gradually understands it and can act on it. |
|
|
|
14:40.160 --> 14:42.120 |
|
And that system is superhuman |
|
|
|
14:42.120 --> 14:45.600 |
|
and it is clearly recursively self improving |
|
|
|
14:45.600 --> 14:47.560 |
|
because science feeds into technology. |
|
|
|
14:47.560 --> 14:50.200 |
|
Technology can be used to build better tools, |
|
|
|
14:50.200 --> 14:52.880 |
|
better computers, better instrumentation and so on, |
|
|
|
14:52.880 --> 14:56.720 |
|
which in turn can make science faster, right? |
|
|
|
14:56.720 --> 15:00.560 |
|
So science is probably the closest thing we have today |
|
|
|
15:00.560 --> 15:04.760 |
|
to a recursively self improving superhuman AI. |
|
|
|
15:04.760 --> 15:08.520 |
|
And you can just observe is science, |
|
|
|
15:08.520 --> 15:10.320 |
|
is scientific progress to the exploding, |
|
|
|
15:10.320 --> 15:12.800 |
|
which itself is an interesting question. |
|
|
|
15:12.800 --> 15:15.560 |
|
You can use that as a basis to try to understand |
|
|
|
15:15.560 --> 15:17.920 |
|
what will happen with a superhuman AI |
|
|
|
15:17.920 --> 15:21.000 |
|
that has a science like behavior. |
|
|
|
15:21.000 --> 15:23.320 |
|
Let me linger on it a little bit more. |
|
|
|
15:23.320 --> 15:27.600 |
|
What is your intuition why an intelligence explosion |
|
|
|
15:27.600 --> 15:28.560 |
|
is not possible? |
|
|
|
15:28.560 --> 15:30.920 |
|
Like taking the scientific, |
|
|
|
15:30.920 --> 15:33.240 |
|
all the semi scientific revolutions, |
|
|
|
15:33.240 --> 15:38.080 |
|
why can't we slightly accelerate that process? |
|
|
|
15:38.080 --> 15:41.200 |
|
So you can absolutely accelerate |
|
|
|
15:41.200 --> 15:43.120 |
|
any problem solving process. |
|
|
|
15:43.120 --> 15:46.720 |
|
So a recursively self improvement |
|
|
|
15:46.720 --> 15:48.640 |
|
is absolutely a real thing. |
|
|
|
15:48.640 --> 15:51.880 |
|
But what happens with a recursively self improving system |
|
|
|
15:51.880 --> 15:53.680 |
|
is typically not explosion |
|
|
|
15:53.680 --> 15:56.520 |
|
because no system exists in isolation. |
|
|
|
15:56.520 --> 15:58.640 |
|
And so tweaking one part of the system |
|
|
|
15:58.640 --> 16:00.880 |
|
means that suddenly another part of the system |
|
|
|
16:00.880 --> 16:02.200 |
|
becomes a bottleneck. |
|
|
|
16:02.200 --> 16:03.800 |
|
And if you look at science, for instance, |
|
|
|
16:03.800 --> 16:06.800 |
|
which is clearly a recursively self improving, |
|
|
|
16:06.800 --> 16:09.040 |
|
clearly a problem solving system, |
|
|
|
16:09.040 --> 16:12.000 |
|
scientific progress is not actually exploding. |
|
|
|
16:12.000 --> 16:13.520 |
|
If you look at science, |
|
|
|
16:13.520 --> 16:16.480 |
|
what you see is the picture of a system |
|
|
|
16:16.480 --> 16:19.240 |
|
that is consuming an exponentially increasing |
|
|
|
16:19.240 --> 16:20.520 |
|
amount of resources, |
|
|
|
16:20.520 --> 16:23.960 |
|
but it's having a linear output |
|
|
|
16:23.960 --> 16:26.000 |
|
in terms of scientific progress. |
|
|
|
16:26.000 --> 16:28.960 |
|
And maybe that will seem like a very strong claim. |
|
|
|
16:28.960 --> 16:31.160 |
|
Many people are actually saying that, |
|
|
|
16:31.160 --> 16:34.560 |
|
scientific progress is exponential, |
|
|
|
16:34.560 --> 16:36.120 |
|
but when they're claiming this, |
|
|
|
16:36.120 --> 16:38.400 |
|
they're actually looking at indicators |
|
|
|
16:38.400 --> 16:43.080 |
|
of resource consumption by science. |
|
|
|
16:43.080 --> 16:46.680 |
|
For instance, the number of papers being published, |
|
|
|
16:47.560 --> 16:49.960 |
|
the number of patents being filed and so on, |
|
|
|
16:49.960 --> 16:53.600 |
|
which are just completely correlated |
|
|
|
16:53.600 --> 16:58.480 |
|
with how many people are working on science today. |
|
|
|
16:58.480 --> 17:00.640 |
|
So it's actually an indicator of resource consumption, |
|
|
|
17:00.640 --> 17:03.200 |
|
but what you should look at is the output, |
|
|
|
17:03.200 --> 17:06.680 |
|
is progress in terms of the knowledge |
|
|
|
17:06.680 --> 17:08.040 |
|
that science generates, |
|
|
|
17:08.040 --> 17:10.640 |
|
in terms of the scope and significance |
|
|
|
17:10.640 --> 17:12.520 |
|
of the problems that we solve. |
|
|
|
17:12.520 --> 17:16.720 |
|
And some people have actually been trying to measure that. |
|
|
|
17:16.720 --> 17:20.160 |
|
Like Michael Nielsen, for instance, |
|
|
|
17:20.160 --> 17:21.920 |
|
he had a very nice paper, |
|
|
|
17:21.920 --> 17:23.720 |
|
I think that was last year about it. |
|
|
|
17:25.200 --> 17:28.360 |
|
So his approach to measure scientific progress |
|
|
|
17:28.360 --> 17:33.360 |
|
was to look at the timeline of scientific discoveries |
|
|
|
17:33.720 --> 17:37.160 |
|
over the past, you know, 100, 150 years. |
|
|
|
17:37.160 --> 17:41.360 |
|
And for each major discovery, |
|
|
|
17:41.360 --> 17:44.360 |
|
ask a panel of experts to rate |
|
|
|
17:44.360 --> 17:46.760 |
|
the significance of the discovery. |
|
|
|
17:46.760 --> 17:49.600 |
|
And if the output of science as an institution |
|
|
|
17:49.600 --> 17:50.440 |
|
were exponential, |
|
|
|
17:50.440 --> 17:55.440 |
|
you would expect the temporal density of significance |
|
|
|
17:56.600 --> 17:58.160 |
|
to go up exponentially. |
|
|
|
17:58.160 --> 18:00.960 |
|
Maybe because there's a faster rate of discoveries, |
|
|
|
18:00.960 --> 18:02.960 |
|
maybe because the discoveries are, you know, |
|
|
|
18:02.960 --> 18:04.920 |
|
increasingly more important. |
|
|
|
18:04.920 --> 18:06.800 |
|
And what actually happens |
|
|
|
18:06.800 --> 18:10.040 |
|
if you plot this temporal density of significance |
|
|
|
18:10.040 --> 18:11.320 |
|
measured in this way, |
|
|
|
18:11.320 --> 18:14.520 |
|
is that you see very much a flat graph. |
|
|
|
18:14.520 --> 18:16.600 |
|
You see a flat graph across all disciplines, |
|
|
|
18:16.600 --> 18:19.720 |
|
across physics, biology, medicine, and so on. |
|
|
|
18:19.720 --> 18:22.480 |
|
And it actually makes a lot of sense |
|
|
|
18:22.480 --> 18:23.320 |
|
if you think about it, |
|
|
|
18:23.320 --> 18:26.000 |
|
because think about the progress of physics |
|
|
|
18:26.000 --> 18:28.000 |
|
110 years ago, right? |
|
|
|
18:28.000 --> 18:30.080 |
|
It was a time of crazy change. |
|
|
|
18:30.080 --> 18:31.960 |
|
Think about the progress of technology, |
|
|
|
18:31.960 --> 18:34.360 |
|
you know, 170 years ago, |
|
|
|
18:34.360 --> 18:35.400 |
|
when we started having, you know, |
|
|
|
18:35.400 --> 18:37.560 |
|
replacing horses with cars, |
|
|
|
18:37.560 --> 18:40.000 |
|
when we started having electricity and so on. |
|
|
|
18:40.000 --> 18:41.520 |
|
It was a time of incredible change. |
|
|
|
18:41.520 --> 18:44.600 |
|
And today is also a time of very, very fast change, |
|
|
|
18:44.600 --> 18:48.040 |
|
but it would be an unfair characterization |
|
|
|
18:48.040 --> 18:50.560 |
|
to say that today technology and science |
|
|
|
18:50.560 --> 18:52.920 |
|
are moving way faster than they did 50 years ago |
|
|
|
18:52.920 --> 18:54.360 |
|
or 100 years ago. |
|
|
|
18:54.360 --> 18:59.360 |
|
And if you do try to rigorously plot |
|
|
|
18:59.520 --> 19:04.520 |
|
the temporal density of the significance, |
|
|
|
19:04.880 --> 19:07.320 |
|
yeah, of significance, sorry, |
|
|
|
19:07.320 --> 19:09.720 |
|
you do see very flat curves. |
|
|
|
19:09.720 --> 19:12.040 |
|
And you can check out the paper |
|
|
|
19:12.040 --> 19:16.000 |
|
that Michael Nielsen had about this idea. |
|
|
|
19:16.000 --> 19:20.000 |
|
And so the way I interpret it is, |
|
|
|
19:20.000 --> 19:24.160 |
|
as you make progress in a given field, |
|
|
|
19:24.160 --> 19:26.120 |
|
or in a given subfield of science, |
|
|
|
19:26.120 --> 19:28.680 |
|
it becomes exponentially more difficult |
|
|
|
19:28.680 --> 19:30.440 |
|
to make further progress. |
|
|
|
19:30.440 --> 19:35.000 |
|
Like the very first person to work on information theory. |
|
|
|
19:35.000 --> 19:36.440 |
|
If you enter a new field, |
|
|
|
19:36.440 --> 19:37.920 |
|
and it's still the very early years, |
|
|
|
19:37.920 --> 19:41.160 |
|
there's a lot of low hanging fruit you can pick. |
|
|
|
19:41.160 --> 19:42.000 |
|
That's right, yeah. |
|
|
|
19:42.000 --> 19:43.960 |
|
But the next generation of researchers |
|
|
|
19:43.960 --> 19:48.160 |
|
is gonna have to dig much harder, actually, |
|
|
|
19:48.160 --> 19:50.640 |
|
to make smaller discoveries, |
|
|
|
19:50.640 --> 19:52.640 |
|
probably larger number of smaller discoveries, |
|
|
|
19:52.640 --> 19:54.640 |
|
and to achieve the same amount of impact, |
|
|
|
19:54.640 --> 19:57.480 |
|
you're gonna need a much greater head count. |
|
|
|
19:57.480 --> 20:00.040 |
|
And that's exactly the picture you're seeing with science, |
|
|
|
20:00.040 --> 20:03.760 |
|
that the number of scientists and engineers |
|
|
|
20:03.760 --> 20:06.520 |
|
is in fact increasing exponentially. |
|
|
|
20:06.520 --> 20:08.400 |
|
The amount of computational resources |
|
|
|
20:08.400 --> 20:10.040 |
|
that are available to science |
|
|
|
20:10.040 --> 20:11.880 |
|
is increasing exponentially and so on. |
|
|
|
20:11.880 --> 20:15.560 |
|
So the resource consumption of science is exponential, |
|
|
|
20:15.560 --> 20:18.200 |
|
but the output in terms of progress, |
|
|
|
20:18.200 --> 20:21.000 |
|
in terms of significance, is linear. |
|
|
|
20:21.000 --> 20:23.120 |
|
And the reason why is because, |
|
|
|
20:23.120 --> 20:26.000 |
|
and even though science is regressively self improving, |
|
|
|
20:26.000 --> 20:28.440 |
|
meaning that scientific progress |
|
|
|
20:28.440 --> 20:30.240 |
|
turns into technological progress, |
|
|
|
20:30.240 --> 20:32.960 |
|
which in turn helps science. |
|
|
|
20:32.960 --> 20:35.280 |
|
If you look at computers, for instance, |
|
|
|
20:35.280 --> 20:38.480 |
|
our products of science and computers |
|
|
|
20:38.480 --> 20:41.560 |
|
are tremendously useful in speeding up science. |
|
|
|
20:41.560 --> 20:43.840 |
|
The internet, same thing, the internet is a technology |
|
|
|
20:43.840 --> 20:47.480 |
|
that's made possible by very recent scientific advances. |
|
|
|
20:47.480 --> 20:52.400 |
|
And itself, because it enables scientists to network, |
|
|
|
20:52.400 --> 20:55.520 |
|
to communicate, to exchange papers and ideas much faster, |
|
|
|
20:55.520 --> 20:57.440 |
|
it is a way to speed up scientific progress. |
|
|
|
20:57.440 --> 20:58.440 |
|
So even though you're looking |
|
|
|
20:58.440 --> 21:01.440 |
|
at a regressively self improving system, |
|
|
|
21:01.440 --> 21:04.080 |
|
it is consuming exponentially more resources |
|
|
|
21:04.080 --> 21:09.080 |
|
to produce the same amount of problem solving, very much. |
|
|
|
21:09.200 --> 21:11.080 |
|
So that's a fascinating way to paint it, |
|
|
|
21:11.080 --> 21:14.960 |
|
and certainly that holds for the deep learning community. |
|
|
|
21:14.960 --> 21:18.120 |
|
If you look at the temporal, what did you call it, |
|
|
|
21:18.120 --> 21:21.240 |
|
the temporal density of significant ideas, |
|
|
|
21:21.240 --> 21:23.920 |
|
if you look at in deep learning, |
|
|
|
21:24.840 --> 21:26.960 |
|
I think, I'd have to think about that, |
|
|
|
21:26.960 --> 21:29.040 |
|
but if you really look at significant ideas |
|
|
|
21:29.040 --> 21:32.400 |
|
in deep learning, they might even be decreasing. |
|
|
|
21:32.400 --> 21:37.400 |
|
So I do believe the per paper significance is decreasing, |
|
|
|
21:39.600 --> 21:41.240 |
|
but the amount of papers |
|
|
|
21:41.240 --> 21:43.440 |
|
is still today exponentially increasing. |
|
|
|
21:43.440 --> 21:45.880 |
|
So I think if you look at an aggregate, |
|
|
|
21:45.880 --> 21:48.840 |
|
my guess is that you would see a linear progress. |
|
|
|
21:48.840 --> 21:53.840 |
|
If you were to sum the significance of all papers, |
|
|
|
21:56.120 --> 21:58.640 |
|
you would see roughly in your progress. |
|
|
|
21:58.640 --> 22:03.640 |
|
And in my opinion, it is not a coincidence |
|
|
|
22:03.880 --> 22:05.800 |
|
that you're seeing linear progress in science |
|
|
|
22:05.800 --> 22:07.720 |
|
despite exponential resource consumption. |
|
|
|
22:07.720 --> 22:10.280 |
|
I think the resource consumption |
|
|
|
22:10.280 --> 22:15.280 |
|
is dynamically adjusting itself to maintain linear progress |
|
|
|
22:15.880 --> 22:18.560 |
|
because we as a community expect linear progress, |
|
|
|
22:18.560 --> 22:21.240 |
|
meaning that if we start investing less |
|
|
|
22:21.240 --> 22:23.600 |
|
and seeing less progress, it means that suddenly |
|
|
|
22:23.600 --> 22:26.800 |
|
there are some lower hanging fruits that become available |
|
|
|
22:26.800 --> 22:31.280 |
|
and someone's gonna step up and pick them, right? |
|
|
|
22:31.280 --> 22:36.280 |
|
So it's very much like a market for discoveries and ideas. |
|
|
|
22:36.920 --> 22:38.720 |
|
But there's another fundamental part |
|
|
|
22:38.720 --> 22:41.800 |
|
which you're highlighting, which as a hypothesis |
|
|
|
22:41.800 --> 22:45.160 |
|
as science or like the space of ideas, |
|
|
|
22:45.160 --> 22:48.160 |
|
any one path you travel down, |
|
|
|
22:48.160 --> 22:51.080 |
|
it gets exponentially more difficult |
|
|
|
22:51.080 --> 22:54.720 |
|
to get a new way to develop new ideas. |
|
|
|
22:54.720 --> 22:57.640 |
|
And your sense is that's gonna hold |
|
|
|
22:57.640 --> 23:01.520 |
|
across our mysterious universe. |
|
|
|
23:01.520 --> 23:03.360 |
|
Yes, well, exponential progress |
|
|
|
23:03.360 --> 23:05.480 |
|
triggers exponential friction. |
|
|
|
23:05.480 --> 23:07.440 |
|
So that if you tweak one part of the system, |
|
|
|
23:07.440 --> 23:10.680 |
|
suddenly some other part becomes a bottleneck, right? |
|
|
|
23:10.680 --> 23:14.880 |
|
For instance, let's say you develop some device |
|
|
|
23:14.880 --> 23:17.160 |
|
that measures its own acceleration |
|
|
|
23:17.160 --> 23:18.720 |
|
and then it has some engine |
|
|
|
23:18.720 --> 23:20.800 |
|
and it outputs even more acceleration |
|
|
|
23:20.800 --> 23:22.360 |
|
in proportion of its own acceleration |
|
|
|
23:22.360 --> 23:23.320 |
|
and you drop it somewhere, |
|
|
|
23:23.320 --> 23:25.240 |
|
it's not gonna reach infinite speed |
|
|
|
23:25.240 --> 23:27.880 |
|
because it exists in a certain context. |
|
|
|
23:29.080 --> 23:31.000 |
|
So the air around it is gonna generate friction |
|
|
|
23:31.000 --> 23:34.320 |
|
and it's gonna block it at some top speed. |
|
|
|
23:34.320 --> 23:37.480 |
|
And even if you were to consider the broader context |
|
|
|
23:37.480 --> 23:39.840 |
|
and lift the bottleneck there, |
|
|
|
23:39.840 --> 23:42.240 |
|
like the bottleneck of friction, |
|
|
|
23:43.120 --> 23:45.120 |
|
then some other part of the system |
|
|
|
23:45.120 --> 23:48.120 |
|
would start stepping in and creating exponential friction, |
|
|
|
23:48.120 --> 23:49.920 |
|
maybe the speed of flight or whatever. |
|
|
|
23:49.920 --> 23:51.920 |
|
And this definitely holds true |
|
|
|
23:51.920 --> 23:54.960 |
|
when you look at the problem solving algorithm |
|
|
|
23:54.960 --> 23:58.160 |
|
that is being run by science as an institution, |
|
|
|
23:58.160 --> 23:59.720 |
|
science as a system. |
|
|
|
23:59.720 --> 24:01.720 |
|
As you make more and more progress, |
|
|
|
24:01.720 --> 24:05.800 |
|
despite having this recursive self improvement component, |
|
|
|
24:06.760 --> 24:09.840 |
|
you are encountering exponential friction. |
|
|
|
24:09.840 --> 24:13.480 |
|
The more researchers you have working on different ideas, |
|
|
|
24:13.480 --> 24:14.880 |
|
the more overhead you have |
|
|
|
24:14.880 --> 24:18.040 |
|
in terms of communication across researchers. |
|
|
|
24:18.040 --> 24:22.920 |
|
If you look at, you were mentioning quantum mechanics, right? |
|
|
|
24:22.920 --> 24:26.880 |
|
Well, if you want to start making significant discoveries |
|
|
|
24:26.880 --> 24:29.680 |
|
today, significant progress in quantum mechanics, |
|
|
|
24:29.680 --> 24:33.000 |
|
there is an amount of knowledge you have to ingest, |
|
|
|
24:33.000 --> 24:34.080 |
|
which is huge. |
|
|
|
24:34.080 --> 24:36.520 |
|
So there's a very large overhead |
|
|
|
24:36.520 --> 24:39.240 |
|
to even start to contribute. |
|
|
|
24:39.240 --> 24:40.680 |
|
There's a large amount of overhead |
|
|
|
24:40.680 --> 24:44.040 |
|
to synchronize across researchers and so on. |
|
|
|
24:44.040 --> 24:47.440 |
|
And of course, the significant practical experiments |
|
|
|
24:48.600 --> 24:52.160 |
|
are going to require exponentially expensive equipment |
|
|
|
24:52.160 --> 24:56.480 |
|
because the easier ones have already been run, right? |
|
|
|
24:56.480 --> 25:00.480 |
|
So in your senses, there's no way escaping, |
|
|
|
25:00.480 --> 25:04.480 |
|
there's no way of escaping this kind of friction |
|
|
|
25:04.480 --> 25:08.600 |
|
with artificial intelligence systems. |
|
|
|
25:08.600 --> 25:11.520 |
|
Yeah, no, I think science is a very good way |
|
|
|
25:11.520 --> 25:14.280 |
|
to model what would happen with a superhuman |
|
|
|
25:14.280 --> 25:16.440 |
|
recursive research improving AI. |
|
|
|
25:16.440 --> 25:18.240 |
|
That's your sense, I mean, the... |
|
|
|
25:18.240 --> 25:19.680 |
|
That's my intuition. |
|
|
|
25:19.680 --> 25:23.400 |
|
It's not like a mathematical proof of anything. |
|
|
|
25:23.400 --> 25:24.400 |
|
That's not my point. |
|
|
|
25:24.400 --> 25:26.600 |
|
Like, I'm not trying to prove anything. |
|
|
|
25:26.600 --> 25:27.920 |
|
I'm just trying to make an argument |
|
|
|
25:27.920 --> 25:31.160 |
|
to question the narrative of intelligence explosion, |
|
|
|
25:31.160 --> 25:32.880 |
|
which is quite a dominant narrative. |
|
|
|
25:32.880 --> 25:35.840 |
|
And you do get a lot of pushback if you go against it. |
|
|
|
25:35.840 --> 25:39.320 |
|
Because, so for many people, right, |
|
|
|
25:39.320 --> 25:42.200 |
|
AI is not just a subfield of computer science. |
|
|
|
25:42.200 --> 25:44.120 |
|
It's more like a belief system. |
|
|
|
25:44.120 --> 25:48.640 |
|
Like this belief that the world is headed towards an event, |
|
|
|
25:48.640 --> 25:55.040 |
|
the singularity, past which, you know, AI will become... |
|
|
|
25:55.040 --> 25:57.080 |
|
will go exponential very much, |
|
|
|
25:57.080 --> 25:58.600 |
|
and the world will be transformed, |
|
|
|
25:58.600 --> 26:00.840 |
|
and humans will become obsolete. |
|
|
|
26:00.840 --> 26:03.880 |
|
And if you go against this narrative, |
|
|
|
26:03.880 --> 26:06.920 |
|
because it is not really a scientific argument, |
|
|
|
26:06.920 --> 26:08.880 |
|
but more of a belief system, |
|
|
|
26:08.880 --> 26:11.240 |
|
it is part of the identity of many people. |
|
|
|
26:11.240 --> 26:12.600 |
|
If you go against this narrative, |
|
|
|
26:12.600 --> 26:14.400 |
|
it's like you're attacking the identity |
|
|
|
26:14.400 --> 26:15.560 |
|
of people who believe in it. |
|
|
|
26:15.560 --> 26:17.640 |
|
It's almost like saying God doesn't exist, |
|
|
|
26:17.640 --> 26:19.000 |
|
or something. |
|
|
|
26:19.000 --> 26:21.880 |
|
So you do get a lot of pushback |
|
|
|
26:21.880 --> 26:24.040 |
|
if you try to question these ideas. |
|
|
|
26:24.040 --> 26:26.520 |
|
First of all, I believe most people, |
|
|
|
26:26.520 --> 26:29.240 |
|
they might not be as eloquent or explicit as you're being, |
|
|
|
26:29.240 --> 26:30.920 |
|
but most people in computer science |
|
|
|
26:30.920 --> 26:33.000 |
|
are most people who actually have built |
|
|
|
26:33.000 --> 26:36.360 |
|
anything that you could call AI, quote, unquote, |
|
|
|
26:36.360 --> 26:38.080 |
|
would agree with you. |
|
|
|
26:38.080 --> 26:40.560 |
|
They might not be describing in the same kind of way. |
|
|
|
26:40.560 --> 26:43.960 |
|
It's more, so the pushback you're getting |
|
|
|
26:43.960 --> 26:48.080 |
|
is from people who get attached to the narrative |
|
|
|
26:48.080 --> 26:51.000 |
|
from, not from a place of science, |
|
|
|
26:51.000 --> 26:53.400 |
|
but from a place of imagination. |
|
|
|
26:53.400 --> 26:54.760 |
|
That's correct, that's correct. |
|
|
|
26:54.760 --> 26:56.920 |
|
So why do you think that's so appealing? |
|
|
|
26:56.920 --> 27:01.920 |
|
Because the usual dreams that people have |
|
|
|
27:02.120 --> 27:03.960 |
|
when you create a superintelligence system |
|
|
|
27:03.960 --> 27:05.120 |
|
past the singularity, |
|
|
|
27:05.120 --> 27:08.600 |
|
that what people imagine is somehow always destructive. |
|
|
|
27:09.440 --> 27:12.240 |
|
Do you have, if you were put on your psychology hat, |
|
|
|
27:12.240 --> 27:17.240 |
|
what's, why is it so appealing to imagine |
|
|
|
27:17.400 --> 27:20.760 |
|
the ways that all of human civilization will be destroyed? |
|
|
|
27:20.760 --> 27:22.080 |
|
I think it's a good story. |
|
|
|
27:22.080 --> 27:23.120 |
|
You know, it's a good story. |
|
|
|
27:23.120 --> 27:28.120 |
|
And very interestingly, it mirrors a religious stories, |
|
|
|
27:28.160 --> 27:30.560 |
|
right, religious mythology. |
|
|
|
27:30.560 --> 27:34.360 |
|
If you look at the mythology of most civilizations, |
|
|
|
27:34.360 --> 27:38.280 |
|
it's about the world being headed towards some final events |
|
|
|
27:38.280 --> 27:40.480 |
|
in which the world will be destroyed |
|
|
|
27:40.480 --> 27:42.800 |
|
and some new world order will arise |
|
|
|
27:42.800 --> 27:44.920 |
|
that will be mostly spiritual, |
|
|
|
27:44.920 --> 27:49.400 |
|
like the apocalypse followed by a paradise probably, right? |
|
|
|
27:49.400 --> 27:52.600 |
|
It's a very appealing story on a fundamental level. |
|
|
|
27:52.600 --> 27:54.560 |
|
And we all need stories. |
|
|
|
27:54.560 --> 27:58.160 |
|
We all need stories to structure the way we see the world, |
|
|
|
27:58.160 --> 27:59.960 |
|
especially at timescales |
|
|
|
27:59.960 --> 28:04.520 |
|
that are beyond our ability to make predictions, right? |
|
|
|
28:04.520 --> 28:08.840 |
|
So on a more serious non exponential explosion, |
|
|
|
28:08.840 --> 28:13.840 |
|
question, do you think there will be a time |
|
|
|
28:15.000 --> 28:19.800 |
|
when we'll create something like human level intelligence |
|
|
|
28:19.800 --> 28:23.800 |
|
or intelligent systems that will make you sit back |
|
|
|
28:23.800 --> 28:28.520 |
|
and be just surprised at damn how smart this thing is? |
|
|
|
28:28.520 --> 28:30.160 |
|
That doesn't require exponential growth |
|
|
|
28:30.160 --> 28:32.120 |
|
or an exponential improvement, |
|
|
|
28:32.120 --> 28:35.600 |
|
but what's your sense of the timeline and so on |
|
|
|
28:35.600 --> 28:40.600 |
|
that you'll be really surprised at certain capabilities? |
|
|
|
28:41.080 --> 28:42.560 |
|
And we'll talk about limitations and deep learning. |
|
|
|
28:42.560 --> 28:44.480 |
|
So do you think in your lifetime, |
|
|
|
28:44.480 --> 28:46.600 |
|
you'll be really damn surprised? |
|
|
|
28:46.600 --> 28:51.440 |
|
Around 2013, 2014, I was many times surprised |
|
|
|
28:51.440 --> 28:53.960 |
|
by the capabilities of deep learning actually. |
|
|
|
28:53.960 --> 28:55.920 |
|
That was before we had assessed exactly |
|
|
|
28:55.920 --> 28:57.880 |
|
what deep learning could do and could not do. |
|
|
|
28:57.880 --> 29:00.600 |
|
And it felt like a time of immense potential. |
|
|
|
29:00.600 --> 29:03.080 |
|
And then we started narrowing it down, |
|
|
|
29:03.080 --> 29:04.360 |
|
but I was very surprised. |
|
|
|
29:04.360 --> 29:07.120 |
|
I would say it has already happened. |
|
|
|
29:07.120 --> 29:10.800 |
|
Was there a moment, there must've been a day in there |
|
|
|
29:10.800 --> 29:14.360 |
|
where your surprise was almost bordering |
|
|
|
29:14.360 --> 29:19.360 |
|
on the belief of the narrative that we just discussed. |
|
|
|
29:19.440 --> 29:20.800 |
|
Was there a moment, |
|
|
|
29:20.800 --> 29:22.400 |
|
because you've written quite eloquently |
|
|
|
29:22.400 --> 29:23.960 |
|
about the limits of deep learning, |
|
|
|
29:23.960 --> 29:25.760 |
|
was there a moment that you thought |
|
|
|
29:25.760 --> 29:27.720 |
|
that maybe deep learning is limitless? |
|
|
|
29:30.000 --> 29:32.400 |
|
No, I don't think I've ever believed this. |
|
|
|
29:32.400 --> 29:35.560 |
|
What was really shocking is that it worked. |
|
|
|
29:35.560 --> 29:37.640 |
|
It worked at all, yeah. |
|
|
|
29:37.640 --> 29:40.520 |
|
But there's a big jump between being able |
|
|
|
29:40.520 --> 29:43.400 |
|
to do really good computer vision |
|
|
|
29:43.400 --> 29:44.920 |
|
and human level intelligence. |
|
|
|
29:44.920 --> 29:49.520 |
|
So I don't think at any point I wasn't under the impression |
|
|
|
29:49.520 --> 29:51.280 |
|
that the results we got in computer vision |
|
|
|
29:51.280 --> 29:54.080 |
|
meant that we were very close to human level intelligence. |
|
|
|
29:54.080 --> 29:56.040 |
|
I don't think we're very close to human level intelligence. |
|
|
|
29:56.040 --> 29:58.520 |
|
I do believe that there's no reason |
|
|
|
29:58.520 --> 30:01.760 |
|
why we won't achieve it at some point. |
|
|
|
30:01.760 --> 30:06.400 |
|
I also believe that it's the problem |
|
|
|
30:06.400 --> 30:08.560 |
|
with talking about human level intelligence |
|
|
|
30:08.560 --> 30:11.240 |
|
that implicitly you're considering |
|
|
|
30:11.240 --> 30:14.360 |
|
like an axis of intelligence with different levels, |
|
|
|
30:14.360 --> 30:16.720 |
|
but that's not really how intelligence works. |
|
|
|
30:16.720 --> 30:19.480 |
|
Intelligence is very multi dimensional. |
|
|
|
30:19.480 --> 30:22.480 |
|
And so there's the question of capabilities, |
|
|
|
30:22.480 --> 30:25.560 |
|
but there's also the question of being human like, |
|
|
|
30:25.560 --> 30:27.040 |
|
and it's two very different things. |
|
|
|
30:27.040 --> 30:28.280 |
|
Like you can build potentially |
|
|
|
30:28.280 --> 30:30.640 |
|
very advanced intelligent agents |
|
|
|
30:30.640 --> 30:32.640 |
|
that are not human like at all. |
|
|
|
30:32.640 --> 30:35.240 |
|
And you can also build very human like agents. |
|
|
|
30:35.240 --> 30:37.840 |
|
And these are two very different things, right? |
|
|
|
30:37.840 --> 30:38.760 |
|
Right. |
|
|
|
30:38.760 --> 30:42.240 |
|
Let's go from the philosophical to the practical. |
|
|
|
30:42.240 --> 30:44.240 |
|
Can you give me a history of Keras |
|
|
|
30:44.240 --> 30:46.440 |
|
and all the major deep learning frameworks |
|
|
|
30:46.440 --> 30:48.480 |
|
that you kind of remember in relation to Keras |
|
|
|
30:48.480 --> 30:52.040 |
|
and in general, TensorFlow, Theano, the old days. |
|
|
|
30:52.040 --> 30:55.400 |
|
Can you give a brief overview Wikipedia style history |
|
|
|
30:55.400 --> 30:59.120 |
|
and your role in it before we return to AGI discussions? |
|
|
|
30:59.120 --> 31:00.720 |
|
Yeah, that's a broad topic. |
|
|
|
31:00.720 --> 31:04.040 |
|
So I started working on Keras. |
|
|
|
31:04.920 --> 31:06.240 |
|
It was the name Keras at the time. |
|
|
|
31:06.240 --> 31:08.320 |
|
I actually picked the name like |
|
|
|
31:08.320 --> 31:10.200 |
|
just the day I was going to release it. |
|
|
|
31:10.200 --> 31:14.800 |
|
So I started working on it in February, 2015. |
|
|
|
31:14.800 --> 31:17.240 |
|
And so at the time there weren't too many people |
|
|
|
31:17.240 --> 31:20.320 |
|
working on deep learning, maybe like fewer than 10,000. |
|
|
|
31:20.320 --> 31:22.840 |
|
The software tooling was not really developed. |
|
|
|
31:25.320 --> 31:28.800 |
|
So the main deep learning library was Cafe, |
|
|
|
31:28.800 --> 31:30.840 |
|
which was mostly C++. |
|
|
|
31:30.840 --> 31:32.760 |
|
Why do you say Cafe was the main one? |
|
|
|
31:32.760 --> 31:36.000 |
|
Cafe was vastly more popular than Theano |
|
|
|
31:36.000 --> 31:38.920 |
|
in late 2014, early 2015. |
|
|
|
31:38.920 --> 31:42.400 |
|
Cafe was the one library that everyone was using |
|
|
|
31:42.400 --> 31:43.400 |
|
for computer vision. |
|
|
|
31:43.400 --> 31:46.120 |
|
And computer vision was the most popular problem |
|
|
|
31:46.120 --> 31:46.960 |
|
in deep learning at the time. |
|
|
|
31:46.960 --> 31:47.800 |
|
Absolutely. |
|
|
|
31:47.800 --> 31:50.440 |
|
Like ConvNets was like the subfield of deep learning |
|
|
|
31:50.440 --> 31:53.160 |
|
that everyone was working on. |
|
|
|
31:53.160 --> 31:57.680 |
|
So myself, so in late 2014, |
|
|
|
31:57.680 --> 32:00.600 |
|
I was actually interested in RNNs, |
|
|
|
32:00.600 --> 32:01.760 |
|
in recurrent neural networks, |
|
|
|
32:01.760 --> 32:05.800 |
|
which was a very niche topic at the time, right? |
|
|
|
32:05.800 --> 32:08.640 |
|
It really took off around 2016. |
|
|
|
32:08.640 --> 32:11.080 |
|
And so I was looking for good tools. |
|
|
|
32:11.080 --> 32:14.800 |
|
I had used Torch 7, I had used Theano, |
|
|
|
32:14.800 --> 32:17.640 |
|
used Theano a lot in Kaggle competitions. |
|
|
|
32:19.320 --> 32:20.840 |
|
I had used Cafe. |
|
|
|
32:20.840 --> 32:25.840 |
|
And there was no like good solution for RNNs at the time. |
|
|
|
32:25.840 --> 32:28.640 |
|
Like there was no reusable open source implementation |
|
|
|
32:28.640 --> 32:30.000 |
|
of an LSTM, for instance. |
|
|
|
32:30.000 --> 32:32.920 |
|
So I decided to build my own. |
|
|
|
32:32.920 --> 32:35.440 |
|
And at first, the pitch for that was, |
|
|
|
32:35.440 --> 32:39.960 |
|
it was gonna be mostly around LSTM recurrent neural networks. |
|
|
|
32:39.960 --> 32:41.360 |
|
It was gonna be in Python. |
|
|
|
32:42.280 --> 32:44.280 |
|
An important decision at the time |
|
|
|
32:44.280 --> 32:45.440 |
|
that was kind of not obvious |
|
|
|
32:45.440 --> 32:50.360 |
|
is that the models would be defined via Python code, |
|
|
|
32:50.360 --> 32:54.400 |
|
which was kind of like going against the mainstream |
|
|
|
32:54.400 --> 32:58.000 |
|
at the time because Cafe, Pylon 2, and so on, |
|
|
|
32:58.000 --> 33:00.600 |
|
like all the big libraries were actually going |
|
|
|
33:00.600 --> 33:03.520 |
|
with the approach of setting configuration files |
|
|
|
33:03.520 --> 33:05.560 |
|
in YAML to define models. |
|
|
|
33:05.560 --> 33:08.840 |
|
So some libraries were using code to define models, |
|
|
|
33:08.840 --> 33:12.280 |
|
like Torch 7, obviously, but that was not Python. |
|
|
|
33:12.280 --> 33:16.680 |
|
Lasagne was like a Theano based very early library |
|
|
|
33:16.680 --> 33:18.640 |
|
that was, I think, developed, I don't remember exactly, |
|
|
|
33:18.640 --> 33:20.240 |
|
probably late 2014. |
|
|
|
33:20.240 --> 33:21.200 |
|
It's Python as well. |
|
|
|
33:21.200 --> 33:22.040 |
|
It's Python as well. |
|
|
|
33:22.040 --> 33:24.320 |
|
It was like on top of Theano. |
|
|
|
33:24.320 --> 33:28.320 |
|
And so I started working on something |
|
|
|
33:29.480 --> 33:32.520 |
|
and the value proposition at the time was that |
|
|
|
33:32.520 --> 33:36.240 |
|
not only what I think was the first |
|
|
|
33:36.240 --> 33:38.800 |
|
reusable open source implementation of LSTM, |
|
|
|
33:40.400 --> 33:44.440 |
|
you could combine RNNs and covenants |
|
|
|
33:44.440 --> 33:45.440 |
|
with the same library, |
|
|
|
33:45.440 --> 33:46.920 |
|
which is not really possible before, |
|
|
|
33:46.920 --> 33:49.080 |
|
like Cafe was only doing covenants. |
|
|
|
33:50.440 --> 33:52.560 |
|
And it was kind of easy to use |
|
|
|
33:52.560 --> 33:54.440 |
|
because, so before I was using Theano, |
|
|
|
33:54.440 --> 33:55.680 |
|
I was actually using scikitlin |
|
|
|
33:55.680 --> 33:58.320 |
|
and I loved scikitlin for its usability. |
|
|
|
33:58.320 --> 34:01.560 |
|
So I drew a lot of inspiration from scikitlin |
|
|
|
34:01.560 --> 34:02.400 |
|
when I made Keras. |
|
|
|
34:02.400 --> 34:05.600 |
|
It's almost like scikitlin for neural networks. |
|
|
|
34:05.600 --> 34:06.680 |
|
The fit function. |
|
|
|
34:06.680 --> 34:07.960 |
|
Exactly, the fit function, |
|
|
|
34:07.960 --> 34:10.800 |
|
like reducing a complex string loop |
|
|
|
34:10.800 --> 34:12.880 |
|
to a single function call, right? |
|
|
|
34:12.880 --> 34:14.880 |
|
And of course, some people will say, |
|
|
|
34:14.880 --> 34:16.320 |
|
this is hiding a lot of details, |
|
|
|
34:16.320 --> 34:18.680 |
|
but that's exactly the point, right? |
|
|
|
34:18.680 --> 34:20.280 |
|
The magic is the point. |
|
|
|
34:20.280 --> 34:22.680 |
|
So it's magical, but in a good way. |
|
|
|
34:22.680 --> 34:24.960 |
|
It's magical in the sense that it's delightful. |
|
|
|
34:24.960 --> 34:26.160 |
|
Yeah, yeah. |
|
|
|
34:26.160 --> 34:27.640 |
|
I'm actually quite surprised. |
|
|
|
34:27.640 --> 34:29.600 |
|
I didn't know that it was born out of desire |
|
|
|
34:29.600 --> 34:32.480 |
|
to implement RNNs and LSTMs. |
|
|
|
34:32.480 --> 34:33.320 |
|
It was. |
|
|
|
34:33.320 --> 34:34.160 |
|
That's fascinating. |
|
|
|
34:34.160 --> 34:36.040 |
|
So you were actually one of the first people |
|
|
|
34:36.040 --> 34:37.960 |
|
to really try to attempt |
|
|
|
34:37.960 --> 34:41.000 |
|
to get the major architectures together. |
|
|
|
34:41.000 --> 34:42.760 |
|
And it's also interesting. |
|
|
|
34:42.760 --> 34:45.160 |
|
You made me realize that that was a design decision at all |
|
|
|
34:45.160 --> 34:47.360 |
|
is defining the model and code. |
|
|
|
34:47.360 --> 34:49.920 |
|
Just, I'm putting myself in your shoes, |
|
|
|
34:49.920 --> 34:53.200 |
|
whether the YAML, especially if cafe was the most popular. |
|
|
|
34:53.200 --> 34:54.720 |
|
It was the most popular by far. |
|
|
|
34:54.720 --> 34:58.480 |
|
If I was, if I were, yeah, I don't, |
|
|
|
34:58.480 --> 34:59.560 |
|
I didn't like the YAML thing, |
|
|
|
34:59.560 --> 35:02.840 |
|
but it makes more sense that you will put |
|
|
|
35:02.840 --> 35:05.720 |
|
in a configuration file, the definition of a model. |
|
|
|
35:05.720 --> 35:07.200 |
|
That's an interesting gutsy move |
|
|
|
35:07.200 --> 35:10.040 |
|
to stick with defining it in code. |
|
|
|
35:10.040 --> 35:11.600 |
|
Just if you look back. |
|
|
|
35:11.600 --> 35:13.480 |
|
Other libraries were doing it as well, |
|
|
|
35:13.480 --> 35:16.320 |
|
but it was definitely the more niche option. |
|
|
|
35:16.320 --> 35:17.160 |
|
Yeah. |
|
|
|
35:17.160 --> 35:18.360 |
|
Okay, Keras and then. |
|
|
|
35:18.360 --> 35:21.520 |
|
So I released Keras in March, 2015, |
|
|
|
35:21.520 --> 35:24.160 |
|
and it got users pretty much from the start. |
|
|
|
35:24.160 --> 35:25.800 |
|
So the deep learning community was very, very small |
|
|
|
35:25.800 --> 35:27.240 |
|
at the time. |
|
|
|
35:27.240 --> 35:30.600 |
|
Lots of people were starting to be interested in LSTM. |
|
|
|
35:30.600 --> 35:32.440 |
|
So it was gonna release it at the right time |
|
|
|
35:32.440 --> 35:35.560 |
|
because it was offering an easy to use LSTM implementation. |
|
|
|
35:35.560 --> 35:37.680 |
|
Exactly at the time where lots of people started |
|
|
|
35:37.680 --> 35:42.280 |
|
to be intrigued by the capabilities of RNN, RNNs for NLP. |
|
|
|
35:42.280 --> 35:43.920 |
|
So it grew from there. |
|
|
|
35:43.920 --> 35:48.920 |
|
Then I joined Google about six months later, |
|
|
|
35:51.480 --> 35:54.920 |
|
and that was actually completely unrelated to Keras. |
|
|
|
35:54.920 --> 35:57.080 |
|
So I actually joined a research team |
|
|
|
35:57.080 --> 35:59.520 |
|
working on image classification, |
|
|
|
35:59.520 --> 36:00.680 |
|
mostly like computer vision. |
|
|
|
36:00.680 --> 36:02.320 |
|
So I was doing computer vision research |
|
|
|
36:02.320 --> 36:03.640 |
|
at Google initially. |
|
|
|
36:03.640 --> 36:05.520 |
|
And immediately when I joined Google, |
|
|
|
36:05.520 --> 36:10.520 |
|
I was exposed to the early internal version of TensorFlow. |
|
|
|
36:10.520 --> 36:13.920 |
|
And the way it appeared to me at the time, |
|
|
|
36:13.920 --> 36:15.720 |
|
and it was definitely the way it was at the time |
|
|
|
36:15.720 --> 36:20.760 |
|
is that this was an improved version of Theano. |
|
|
|
36:20.760 --> 36:24.720 |
|
So I immediately knew I had to port Keras |
|
|
|
36:24.720 --> 36:26.800 |
|
to this new TensorFlow thing. |
|
|
|
36:26.800 --> 36:29.800 |
|
And I was actually very busy as a noobler, |
|
|
|
36:29.800 --> 36:30.720 |
|
as a new Googler. |
|
|
|
36:31.600 --> 36:34.520 |
|
So I had not time to work on that. |
|
|
|
36:34.520 --> 36:38.680 |
|
But then in November, I think it was November, 2015, |
|
|
|
36:38.680 --> 36:41.240 |
|
TensorFlow got released. |
|
|
|
36:41.240 --> 36:44.560 |
|
And it was kind of like my wake up call |
|
|
|
36:44.560 --> 36:47.320 |
|
that, hey, I had to actually go and make it happen. |
|
|
|
36:47.320 --> 36:52.200 |
|
So in December, I ported Keras to run on top of TensorFlow, |
|
|
|
36:52.200 --> 36:53.320 |
|
but it was not exactly a port. |
|
|
|
36:53.320 --> 36:55.280 |
|
It was more like a refactoring |
|
|
|
36:55.280 --> 36:57.920 |
|
where I was abstracting away |
|
|
|
36:57.920 --> 37:00.480 |
|
all the backend functionality into one module |
|
|
|
37:00.480 --> 37:02.320 |
|
so that the same code base |
|
|
|
37:02.320 --> 37:05.080 |
|
could run on top of multiple backends. |
|
|
|
37:05.080 --> 37:07.440 |
|
So on top of TensorFlow or Theano. |
|
|
|
37:07.440 --> 37:09.760 |
|
And for the next year, |
|
|
|
37:09.760 --> 37:14.760 |
|
Theano stayed as the default option. |
|
|
|
37:15.400 --> 37:20.400 |
|
It was easier to use, somewhat less buggy. |
|
|
|
37:20.640 --> 37:23.360 |
|
It was much faster, especially when it came to audience. |
|
|
|
37:23.360 --> 37:26.360 |
|
But eventually, TensorFlow overtook it. |
|
|
|
37:27.480 --> 37:30.200 |
|
And TensorFlow, the early TensorFlow, |
|
|
|
37:30.200 --> 37:33.960 |
|
has similar architectural decisions as Theano, right? |
|
|
|
37:33.960 --> 37:37.440 |
|
So it was a natural transition. |
|
|
|
37:37.440 --> 37:38.320 |
|
Yeah, absolutely. |
|
|
|
37:38.320 --> 37:42.960 |
|
So what, I mean, that still Keras is a side, |
|
|
|
37:42.960 --> 37:45.280 |
|
almost fun project, right? |
|
|
|
37:45.280 --> 37:49.040 |
|
Yeah, so it was not my job assignment. |
|
|
|
37:49.040 --> 37:50.360 |
|
It was not. |
|
|
|
37:50.360 --> 37:52.240 |
|
I was doing it on the side. |
|
|
|
37:52.240 --> 37:55.840 |
|
And even though it grew to have a lot of users |
|
|
|
37:55.840 --> 37:59.600 |
|
for a deep learning library at the time, like Stroud 2016, |
|
|
|
37:59.600 --> 38:02.480 |
|
but I wasn't doing it as my main job. |
|
|
|
38:02.480 --> 38:04.760 |
|
So things started changing in, |
|
|
|
38:04.760 --> 38:09.760 |
|
I think it must have been maybe October, 2016. |
|
|
|
38:10.200 --> 38:11.320 |
|
So one year later. |
|
|
|
38:12.360 --> 38:15.240 |
|
So Rajat, who was the lead on TensorFlow, |
|
|
|
38:15.240 --> 38:19.240 |
|
basically showed up one day in our building |
|
|
|
38:19.240 --> 38:20.080 |
|
where I was doing like, |
|
|
|
38:20.080 --> 38:21.640 |
|
so I was doing research and things like, |
|
|
|
38:21.640 --> 38:24.640 |
|
so I did a lot of computer vision research, |
|
|
|
38:24.640 --> 38:27.560 |
|
also collaborations with Christian Zighetti |
|
|
|
38:27.560 --> 38:29.640 |
|
and deep learning for theorem proving. |
|
|
|
38:29.640 --> 38:32.920 |
|
It was a really interesting research topic. |
|
|
|
38:34.520 --> 38:37.640 |
|
And so Rajat was saying, |
|
|
|
38:37.640 --> 38:41.040 |
|
hey, we saw Keras, we like it. |
|
|
|
38:41.040 --> 38:42.440 |
|
We saw that you're at Google. |
|
|
|
38:42.440 --> 38:45.280 |
|
Why don't you come over for like a quarter |
|
|
|
38:45.280 --> 38:47.280 |
|
and work with us? |
|
|
|
38:47.280 --> 38:49.240 |
|
And I was like, yeah, that sounds like a great opportunity. |
|
|
|
38:49.240 --> 38:50.400 |
|
Let's do it. |
|
|
|
38:50.400 --> 38:55.400 |
|
And so I started working on integrating the Keras API |
|
|
|
38:55.720 --> 38:57.320 |
|
into TensorFlow more tightly. |
|
|
|
38:57.320 --> 39:02.320 |
|
So what followed up is a sort of like temporary |
|
|
|
39:02.640 --> 39:05.480 |
|
TensorFlow only version of Keras |
|
|
|
39:05.480 --> 39:09.320 |
|
that was in TensorFlow.com Trib for a while. |
|
|
|
39:09.320 --> 39:12.200 |
|
And finally moved to TensorFlow Core. |
|
|
|
39:12.200 --> 39:15.360 |
|
And I've never actually gotten back |
|
|
|
39:15.360 --> 39:17.600 |
|
to my old team doing research. |
|
|
|
39:17.600 --> 39:22.320 |
|
Well, it's kind of funny that somebody like you |
|
|
|
39:22.320 --> 39:27.320 |
|
who dreams of, or at least sees the power of AI systems |
|
|
|
39:28.960 --> 39:31.680 |
|
that reason and theorem proving we'll talk about |
|
|
|
39:31.680 --> 39:36.520 |
|
has also created a system that makes the most basic |
|
|
|
39:36.520 --> 39:40.400 |
|
kind of Lego building that is deep learning |
|
|
|
39:40.400 --> 39:42.640 |
|
super accessible, super easy. |
|
|
|
39:42.640 --> 39:43.800 |
|
So beautifully so. |
|
|
|
39:43.800 --> 39:47.720 |
|
It's a funny irony that you're both, |
|
|
|
39:47.720 --> 39:49.120 |
|
you're responsible for both things, |
|
|
|
39:49.120 --> 39:54.000 |
|
but so TensorFlow 2.0 is kind of, there's a sprint. |
|
|
|
39:54.000 --> 39:55.080 |
|
I don't know how long it'll take, |
|
|
|
39:55.080 --> 39:56.960 |
|
but there's a sprint towards the finish. |
|
|
|
39:56.960 --> 40:01.040 |
|
What do you look, what are you working on these days? |
|
|
|
40:01.040 --> 40:02.160 |
|
What are you excited about? |
|
|
|
40:02.160 --> 40:04.280 |
|
What are you excited about in 2.0? |
|
|
|
40:04.280 --> 40:05.760 |
|
I mean, eager execution. |
|
|
|
40:05.760 --> 40:08.440 |
|
There's so many things that just make it a lot easier |
|
|
|
40:08.440 --> 40:09.760 |
|
to work. |
|
|
|
40:09.760 --> 40:13.640 |
|
What are you excited about and what's also really hard? |
|
|
|
40:13.640 --> 40:15.800 |
|
What are the problems you have to kind of solve? |
|
|
|
40:15.800 --> 40:19.080 |
|
So I've spent the past year and a half working on |
|
|
|
40:19.080 --> 40:22.920 |
|
TensorFlow 2.0 and it's been a long journey. |
|
|
|
40:22.920 --> 40:25.080 |
|
I'm actually extremely excited about it. |
|
|
|
40:25.080 --> 40:26.440 |
|
I think it's a great product. |
|
|
|
40:26.440 --> 40:29.360 |
|
It's a delightful product compared to TensorFlow 1.0. |
|
|
|
40:29.360 --> 40:31.440 |
|
We've made huge progress. |
|
|
|
40:32.640 --> 40:37.400 |
|
So on the Keras side, what I'm really excited about is that, |
|
|
|
40:37.400 --> 40:42.400 |
|
so previously Keras has been this very easy to use |
|
|
|
40:42.400 --> 40:45.840 |
|
high level interface to do deep learning. |
|
|
|
40:45.840 --> 40:47.280 |
|
But if you wanted to, |
|
|
|
40:50.520 --> 40:53.040 |
|
if you wanted a lot of flexibility, |
|
|
|
40:53.040 --> 40:57.520 |
|
the Keras framework was probably not the optimal way |
|
|
|
40:57.520 --> 40:59.760 |
|
to do things compared to just writing everything |
|
|
|
40:59.760 --> 41:00.600 |
|
from scratch. |
|
|
|
41:01.800 --> 41:04.680 |
|
So in some way, the framework was getting in the way. |
|
|
|
41:04.680 --> 41:07.960 |
|
And in TensorFlow 2.0, you don't have this at all, actually. |
|
|
|
41:07.960 --> 41:11.040 |
|
You have the usability of the high level interface, |
|
|
|
41:11.040 --> 41:14.480 |
|
but you have the flexibility of this lower level interface. |
|
|
|
41:14.480 --> 41:16.800 |
|
And you have this spectrum of workflows |
|
|
|
41:16.800 --> 41:21.560 |
|
where you can get more or less usability |
|
|
|
41:21.560 --> 41:26.560 |
|
and flexibility trade offs depending on your needs, right? |
|
|
|
41:26.640 --> 41:29.680 |
|
You can write everything from scratch |
|
|
|
41:29.680 --> 41:32.320 |
|
and you get a lot of help doing so |
|
|
|
41:32.320 --> 41:36.400 |
|
by subclassing models and writing some train loops |
|
|
|
41:36.400 --> 41:38.200 |
|
using ego execution. |
|
|
|
41:38.200 --> 41:40.160 |
|
It's very flexible, it's very easy to debug, |
|
|
|
41:40.160 --> 41:41.400 |
|
it's very powerful. |
|
|
|
41:42.280 --> 41:45.000 |
|
But all of this integrates seamlessly |
|
|
|
41:45.000 --> 41:49.440 |
|
with higher level features up to the classic Keras workflows, |
|
|
|
41:49.440 --> 41:51.560 |
|
which are very scikit learn like |
|
|
|
41:51.560 --> 41:56.040 |
|
and are ideal for a data scientist, |
|
|
|
41:56.040 --> 41:58.240 |
|
machine learning engineer type of profile. |
|
|
|
41:58.240 --> 42:00.840 |
|
So now you can have the same framework |
|
|
|
42:00.840 --> 42:02.880 |
|
offering the same set of APIs |
|
|
|
42:02.880 --> 42:05.000 |
|
that enable a spectrum of workflows |
|
|
|
42:05.000 --> 42:08.560 |
|
that are more or less low level, more or less high level |
|
|
|
42:08.560 --> 42:13.520 |
|
that are suitable for profiles ranging from researchers |
|
|
|
42:13.520 --> 42:15.560 |
|
to data scientists and everything in between. |
|
|
|
42:15.560 --> 42:16.960 |
|
Yeah, so that's super exciting. |
|
|
|
42:16.960 --> 42:18.400 |
|
I mean, it's not just that, |
|
|
|
42:18.400 --> 42:21.680 |
|
it's connected to all kinds of tooling. |
|
|
|
42:21.680 --> 42:24.520 |
|
You can go on mobile, you can go with TensorFlow Lite, |
|
|
|
42:24.520 --> 42:27.240 |
|
you can go in the cloud or serving and so on. |
|
|
|
42:27.240 --> 42:28.960 |
|
It all is connected together. |
|
|
|
42:28.960 --> 42:31.880 |
|
Now some of the best software written ever |
|
|
|
42:31.880 --> 42:36.880 |
|
is often done by one person, sometimes two. |
|
|
|
42:36.880 --> 42:40.800 |
|
So with a Google, you're now seeing sort of Keras |
|
|
|
42:40.800 --> 42:42.840 |
|
having to be integrated in TensorFlow, |
|
|
|
42:42.840 --> 42:46.800 |
|
I'm sure has a ton of engineers working on. |
|
|
|
42:46.800 --> 42:51.040 |
|
And there's, I'm sure a lot of tricky design decisions |
|
|
|
42:51.040 --> 42:52.200 |
|
to be made. |
|
|
|
42:52.200 --> 42:54.440 |
|
How does that process usually happen |
|
|
|
42:54.440 --> 42:56.800 |
|
from at least your perspective? |
|
|
|
42:56.800 --> 42:59.800 |
|
What are the debates like? |
|
|
|
43:00.720 --> 43:04.200 |
|
Is there a lot of thinking, |
|
|
|
43:04.200 --> 43:06.880 |
|
considering different options and so on? |
|
|
|
43:06.880 --> 43:08.160 |
|
Yes. |
|
|
|
43:08.160 --> 43:12.640 |
|
So a lot of the time I spend at Google |
|
|
|
43:12.640 --> 43:17.280 |
|
is actually discussing design discussions, right? |
|
|
|
43:17.280 --> 43:20.480 |
|
Writing design docs, participating in design review meetings |
|
|
|
43:20.480 --> 43:22.080 |
|
and so on. |
|
|
|
43:22.080 --> 43:25.240 |
|
This is as important as actually writing a code. |
|
|
|
43:25.240 --> 43:26.080 |
|
Right. |
|
|
|
43:26.080 --> 43:28.120 |
|
So there's a lot of thought, there's a lot of thought |
|
|
|
43:28.120 --> 43:32.280 |
|
and a lot of care that is taken |
|
|
|
43:32.280 --> 43:34.160 |
|
in coming up with these decisions |
|
|
|
43:34.160 --> 43:37.160 |
|
and taking into account all of our users |
|
|
|
43:37.160 --> 43:40.680 |
|
because TensorFlow has this extremely diverse user base, |
|
|
|
43:40.680 --> 43:41.520 |
|
right? |
|
|
|
43:41.520 --> 43:43.120 |
|
It's not like just one user segment |
|
|
|
43:43.120 --> 43:45.480 |
|
where everyone has the same needs. |
|
|
|
43:45.480 --> 43:47.640 |
|
We have small scale production users, |
|
|
|
43:47.640 --> 43:49.520 |
|
large scale production users. |
|
|
|
43:49.520 --> 43:52.800 |
|
We have startups, we have researchers, |
|
|
|
43:53.720 --> 43:55.080 |
|
you know, it's all over the place. |
|
|
|
43:55.080 --> 43:57.560 |
|
And we have to cater to all of their needs. |
|
|
|
43:57.560 --> 44:00.040 |
|
If I just look at the standard debates |
|
|
|
44:00.040 --> 44:04.000 |
|
of C++ or Python, there's some heated debates. |
|
|
|
44:04.000 --> 44:06.000 |
|
Do you have those at Google? |
|
|
|
44:06.000 --> 44:08.080 |
|
I mean, they're not heated in terms of emotionally, |
|
|
|
44:08.080 --> 44:10.800 |
|
but there's probably multiple ways to do it, right? |
|
|
|
44:10.800 --> 44:14.040 |
|
So how do you arrive through those design meetings |
|
|
|
44:14.040 --> 44:15.440 |
|
at the best way to do it? |
|
|
|
44:15.440 --> 44:19.280 |
|
Especially in deep learning where the field is evolving |
|
|
|
44:19.280 --> 44:20.880 |
|
as you're doing it. |
|
|
|
44:21.880 --> 44:23.600 |
|
Is there some magic to it? |
|
|
|
44:23.600 --> 44:26.240 |
|
Is there some magic to the process? |
|
|
|
44:26.240 --> 44:28.280 |
|
I don't know if there's magic to the process, |
|
|
|
44:28.280 --> 44:30.640 |
|
but there definitely is a process. |
|
|
|
44:30.640 --> 44:33.760 |
|
So making design decisions |
|
|
|
44:33.760 --> 44:36.080 |
|
is about satisfying a set of constraints, |
|
|
|
44:36.080 --> 44:39.920 |
|
but also trying to do so in the simplest way possible, |
|
|
|
44:39.920 --> 44:42.240 |
|
because this is what can be maintained, |
|
|
|
44:42.240 --> 44:44.920 |
|
this is what can be expanded in the future. |
|
|
|
44:44.920 --> 44:49.120 |
|
So you don't want to naively satisfy the constraints |
|
|
|
44:49.120 --> 44:51.880 |
|
by just, you know, for each capability you need available, |
|
|
|
44:51.880 --> 44:53.960 |
|
you're gonna come up with one argument in your API |
|
|
|
44:53.960 --> 44:54.800 |
|
and so on. |
|
|
|
44:54.800 --> 44:59.800 |
|
You want to design APIs that are modular and hierarchical |
|
|
|
45:00.640 --> 45:04.080 |
|
so that they have an API surface |
|
|
|
45:04.080 --> 45:07.040 |
|
that is as small as possible, right? |
|
|
|
45:07.040 --> 45:11.640 |
|
And you want this modular hierarchical architecture |
|
|
|
45:11.640 --> 45:14.560 |
|
to reflect the way that domain experts |
|
|
|
45:14.560 --> 45:16.400 |
|
think about the problem. |
|
|
|
45:16.400 --> 45:17.880 |
|
Because as a domain expert, |
|
|
|
45:17.880 --> 45:19.840 |
|
when you are reading about a new API, |
|
|
|
45:19.840 --> 45:24.760 |
|
you're reading a tutorial or some docs pages, |
|
|
|
45:24.760 --> 45:28.200 |
|
you already have a way that you're thinking about the problem. |
|
|
|
45:28.200 --> 45:32.320 |
|
You already have like certain concepts in mind |
|
|
|
45:32.320 --> 45:35.680 |
|
and you're thinking about how they relate together. |
|
|
|
45:35.680 --> 45:37.200 |
|
And when you're reading docs, |
|
|
|
45:37.200 --> 45:40.280 |
|
you're trying to build as quickly as possible |
|
|
|
45:40.280 --> 45:45.280 |
|
a mapping between the concepts featured in your API |
|
|
|
45:45.280 --> 45:46.800 |
|
and the concepts in your mind. |
|
|
|
45:46.800 --> 45:48.880 |
|
So you're trying to map your mental model |
|
|
|
45:48.880 --> 45:53.600 |
|
as a domain expert to the way things work in the API. |
|
|
|
45:53.600 --> 45:57.040 |
|
So you need an API and an underlying implementation |
|
|
|
45:57.040 --> 46:00.120 |
|
that are reflecting the way people think about these things. |
|
|
|
46:00.120 --> 46:02.880 |
|
So in minimizing the time it takes to do the mapping. |
|
|
|
46:02.880 --> 46:04.680 |
|
Yes, minimizing the time, |
|
|
|
46:04.680 --> 46:06.560 |
|
the cognitive load there is |
|
|
|
46:06.560 --> 46:10.920 |
|
in ingesting this new knowledge about your API. |
|
|
|
46:10.920 --> 46:13.160 |
|
An API should not be self referential |
|
|
|
46:13.160 --> 46:15.520 |
|
or referring to implementation details. |
|
|
|
46:15.520 --> 46:19.160 |
|
It should only be referring to domain specific concepts |
|
|
|
46:19.160 --> 46:21.360 |
|
that people already understand. |
|
|
|
46:23.240 --> 46:24.480 |
|
Brilliant. |
|
|
|
46:24.480 --> 46:27.560 |
|
So what's the future of Keras and TensorFlow look like? |
|
|
|
46:27.560 --> 46:29.640 |
|
What does TensorFlow 3.0 look like? |
|
|
|
46:30.600 --> 46:33.720 |
|
So that's kind of too far in the future for me to answer, |
|
|
|
46:33.720 --> 46:37.800 |
|
especially since I'm not even the one making these decisions. |
|
|
|
46:37.800 --> 46:39.080 |
|
Okay. |
|
|
|
46:39.080 --> 46:41.240 |
|
But so from my perspective, |
|
|
|
46:41.240 --> 46:43.200 |
|
which is just one perspective |
|
|
|
46:43.200 --> 46:46.040 |
|
among many different perspectives on the TensorFlow team, |
|
|
|
46:47.200 --> 46:52.200 |
|
I'm really excited by developing even higher level APIs, |
|
|
|
46:52.360 --> 46:53.560 |
|
higher level than Keras. |
|
|
|
46:53.560 --> 46:56.480 |
|
I'm really excited by hyperparameter tuning, |
|
|
|
46:56.480 --> 46:59.240 |
|
by automated machine learning, AutoML. |
|
|
|
47:01.120 --> 47:03.200 |
|
I think the future is not just, you know, |
|
|
|
47:03.200 --> 47:07.600 |
|
defining a model like you were assembling Lego blocks |
|
|
|
47:07.600 --> 47:09.200 |
|
and then collect fit on it. |
|
|
|
47:09.200 --> 47:13.680 |
|
It's more like an automagical model |
|
|
|
47:13.680 --> 47:16.080 |
|
that would just look at your data |
|
|
|
47:16.080 --> 47:19.040 |
|
and optimize the objective you're after, right? |
|
|
|
47:19.040 --> 47:23.040 |
|
So that's what I'm looking into. |
|
|
|
47:23.040 --> 47:26.480 |
|
Yeah, so you put the baby into a room with the problem |
|
|
|
47:26.480 --> 47:28.760 |
|
and come back a few hours later |
|
|
|
47:28.760 --> 47:30.960 |
|
with a fully solved problem. |
|
|
|
47:30.960 --> 47:33.560 |
|
Exactly, it's not like a box of Legos. |
|
|
|
47:33.560 --> 47:35.920 |
|
It's more like the combination of a kid |
|
|
|
47:35.920 --> 47:38.800 |
|
that's really good at Legos and a box of Legos. |
|
|
|
47:38.800 --> 47:41.520 |
|
It's just building the thing on its own. |
|
|
|
47:41.520 --> 47:42.680 |
|
Very nice. |
|
|
|
47:42.680 --> 47:44.160 |
|
So that's an exciting future. |
|
|
|
47:44.160 --> 47:46.080 |
|
I think there's a huge amount of applications |
|
|
|
47:46.080 --> 47:48.560 |
|
and revolutions to be had |
|
|
|
47:49.920 --> 47:52.640 |
|
under the constraints of the discussion we previously had. |
|
|
|
47:52.640 --> 47:57.480 |
|
But what do you think of the current limits of deep learning? |
|
|
|
47:57.480 --> 48:02.480 |
|
If we look specifically at these function approximators |
|
|
|
48:03.840 --> 48:06.160 |
|
that tries to generalize from data. |
|
|
|
48:06.160 --> 48:10.160 |
|
You've talked about local versus extreme generalization. |
|
|
|
48:11.120 --> 48:13.280 |
|
You mentioned that neural networks don't generalize well |
|
|
|
48:13.280 --> 48:14.560 |
|
and humans do. |
|
|
|
48:14.560 --> 48:15.760 |
|
So there's this gap. |
|
|
|
48:17.640 --> 48:20.840 |
|
And you've also mentioned that extreme generalization |
|
|
|
48:20.840 --> 48:23.960 |
|
requires something like reasoning to fill those gaps. |
|
|
|
48:23.960 --> 48:27.560 |
|
So how can we start trying to build systems like that? |
|
|
|
48:27.560 --> 48:30.600 |
|
Right, yeah, so this is by design, right? |
|
|
|
48:30.600 --> 48:37.080 |
|
Deep learning models are like huge parametric models, |
|
|
|
48:37.080 --> 48:39.280 |
|
differentiable, so continuous, |
|
|
|
48:39.280 --> 48:42.680 |
|
that go from an input space to an output space. |
|
|
|
48:42.680 --> 48:44.120 |
|
And they're trained with gradient descent. |
|
|
|
48:44.120 --> 48:47.160 |
|
So they're trained pretty much point by point. |
|
|
|
48:47.160 --> 48:50.520 |
|
They are learning a continuous geometric morphing |
|
|
|
48:50.520 --> 48:55.320 |
|
from an input vector space to an output vector space. |
|
|
|
48:55.320 --> 48:58.960 |
|
And because this is done point by point, |
|
|
|
48:58.960 --> 49:02.200 |
|
a deep neural network can only make sense |
|
|
|
49:02.200 --> 49:05.880 |
|
of points in experience space that are very close |
|
|
|
49:05.880 --> 49:08.520 |
|
to things that it has already seen in string data. |
|
|
|
49:08.520 --> 49:12.520 |
|
At best, it can do interpolation across points. |
|
|
|
49:13.840 --> 49:17.360 |
|
But that means in order to train your network, |
|
|
|
49:17.360 --> 49:21.680 |
|
you need a dense sampling of the input cross output space, |
|
|
|
49:22.880 --> 49:25.240 |
|
almost a point by point sampling, |
|
|
|
49:25.240 --> 49:27.160 |
|
which can be very expensive if you're dealing |
|
|
|
49:27.160 --> 49:29.320 |
|
with complex real world problems, |
|
|
|
49:29.320 --> 49:33.240 |
|
like autonomous driving, for instance, or robotics. |
|
|
|
49:33.240 --> 49:36.000 |
|
It's doable if you're looking at the subset |
|
|
|
49:36.000 --> 49:37.120 |
|
of the visual space. |
|
|
|
49:37.120 --> 49:38.800 |
|
But even then, it's still fairly expensive. |
|
|
|
49:38.800 --> 49:40.920 |
|
You still need millions of examples. |
|
|
|
49:40.920 --> 49:44.240 |
|
And it's only going to be able to make sense of things |
|
|
|
49:44.240 --> 49:46.880 |
|
that are very close to what it has seen before. |
|
|
|
49:46.880 --> 49:49.160 |
|
And in contrast to that, well, of course, |
|
|
|
49:49.160 --> 49:50.160 |
|
you have human intelligence. |
|
|
|
49:50.160 --> 49:53.240 |
|
But even if you're not looking at human intelligence, |
|
|
|
49:53.240 --> 49:56.800 |
|
you can look at very simple rules, algorithms. |
|
|
|
49:56.800 --> 49:58.080 |
|
If you have a symbolic rule, |
|
|
|
49:58.080 --> 50:03.080 |
|
it can actually apply to a very, very large set of inputs |
|
|
|
50:03.120 --> 50:04.880 |
|
because it is abstract. |
|
|
|
50:04.880 --> 50:09.560 |
|
It is not obtained by doing a point by point mapping. |
|
|
|
50:10.720 --> 50:14.000 |
|
For instance, if you try to learn a sorting algorithm |
|
|
|
50:14.000 --> 50:15.520 |
|
using a deep neural network, |
|
|
|
50:15.520 --> 50:18.520 |
|
well, you're very much limited to learning point by point |
|
|
|
50:20.080 --> 50:24.360 |
|
what the sorted representation of this specific list is like. |
|
|
|
50:24.360 --> 50:29.360 |
|
But instead, you could have a very, very simple |
|
|
|
50:29.400 --> 50:31.920 |
|
sorting algorithm written in a few lines. |
|
|
|
50:31.920 --> 50:34.520 |
|
Maybe it's just two nested loops. |
|
|
|
50:35.560 --> 50:40.560 |
|
And it can process any list at all because it is abstract, |
|
|
|
50:41.040 --> 50:42.240 |
|
because it is a set of rules. |
|
|
|
50:42.240 --> 50:45.160 |
|
So deep learning is really like point by point |
|
|
|
50:45.160 --> 50:48.640 |
|
geometric morphings, train with good and decent. |
|
|
|
50:48.640 --> 50:53.640 |
|
And meanwhile, abstract rules can generalize much better. |
|
|
|
50:53.640 --> 50:56.160 |
|
And I think the future is we need to combine the two. |
|
|
|
50:56.160 --> 50:59.160 |
|
So how do we, do you think, combine the two? |
|
|
|
50:59.160 --> 51:03.040 |
|
How do we combine good point by point functions |
|
|
|
51:03.040 --> 51:08.040 |
|
with programs, which is what the symbolic AI type systems? |
|
|
|
51:08.920 --> 51:11.600 |
|
At which levels the combination happen? |
|
|
|
51:11.600 --> 51:14.680 |
|
I mean, obviously we're jumping into the realm |
|
|
|
51:14.680 --> 51:16.880 |
|
of where there's no good answers. |
|
|
|
51:16.880 --> 51:20.280 |
|
It's just kind of ideas and intuitions and so on. |
|
|
|
51:20.280 --> 51:23.080 |
|
Well, if you look at the really successful AI systems |
|
|
|
51:23.080 --> 51:26.320 |
|
today, I think they are already hybrid systems |
|
|
|
51:26.320 --> 51:29.520 |
|
that are combining symbolic AI with deep learning. |
|
|
|
51:29.520 --> 51:32.520 |
|
For instance, successful robotics systems |
|
|
|
51:32.520 --> 51:36.400 |
|
are already mostly model based, rule based, |
|
|
|
51:37.400 --> 51:39.400 |
|
things like planning algorithms and so on. |
|
|
|
51:39.400 --> 51:42.200 |
|
At the same time, they're using deep learning |
|
|
|
51:42.200 --> 51:43.840 |
|
as perception modules. |
|
|
|
51:43.840 --> 51:46.000 |
|
Sometimes they're using deep learning as a way |
|
|
|
51:46.000 --> 51:50.920 |
|
to inject fuzzy intuition into a rule based process. |
|
|
|
51:50.920 --> 51:54.560 |
|
If you look at the system like in a self driving car, |
|
|
|
51:54.560 --> 51:57.240 |
|
it's not just one big end to end neural network. |
|
|
|
51:57.240 --> 51:59.000 |
|
You know, that wouldn't work at all. |
|
|
|
51:59.000 --> 52:00.760 |
|
Precisely because in order to train that, |
|
|
|
52:00.760 --> 52:05.160 |
|
you would need a dense sampling of experience base |
|
|
|
52:05.160 --> 52:06.200 |
|
when it comes to driving, |
|
|
|
52:06.200 --> 52:08.880 |
|
which is completely unrealistic, obviously. |
|
|
|
52:08.880 --> 52:12.440 |
|
Instead, the self driving car is mostly |
|
|
|
52:13.920 --> 52:18.360 |
|
symbolic, you know, it's software, it's programmed by hand. |
|
|
|
52:18.360 --> 52:21.640 |
|
So it's mostly based on explicit models. |
|
|
|
52:21.640 --> 52:25.840 |
|
In this case, mostly 3D models of the environment |
|
|
|
52:25.840 --> 52:29.520 |
|
around the car, but it's interfacing with the real world |
|
|
|
52:29.520 --> 52:31.440 |
|
using deep learning modules, right? |
|
|
|
52:31.440 --> 52:33.440 |
|
So the deep learning there serves as a way |
|
|
|
52:33.440 --> 52:36.080 |
|
to convert the raw sensory information |
|
|
|
52:36.080 --> 52:38.320 |
|
to something usable by symbolic systems. |
|
|
|
52:39.760 --> 52:42.400 |
|
Okay, well, let's linger on that a little more. |
|
|
|
52:42.400 --> 52:45.440 |
|
So dense sampling from input to output. |
|
|
|
52:45.440 --> 52:48.240 |
|
You said it's obviously very difficult. |
|
|
|
52:48.240 --> 52:50.120 |
|
Is it possible? |
|
|
|
52:50.120 --> 52:51.800 |
|
In the case of self driving, you mean? |
|
|
|
52:51.800 --> 52:53.040 |
|
Let's say self driving, right? |
|
|
|
52:53.040 --> 52:55.760 |
|
Self driving for many people, |
|
|
|
52:57.560 --> 52:59.520 |
|
let's not even talk about self driving, |
|
|
|
52:59.520 --> 53:03.880 |
|
let's talk about steering, so staying inside the lane. |
|
|
|
53:05.040 --> 53:07.080 |
|
Lane following, yeah, it's definitely a problem |
|
|
|
53:07.080 --> 53:08.880 |
|
you can solve with an end to end deep learning model, |
|
|
|
53:08.880 --> 53:10.600 |
|
but that's like one small subset. |
|
|
|
53:10.600 --> 53:11.440 |
|
Hold on a second. |
|
|
|
53:11.440 --> 53:12.760 |
|
Yeah, I don't know why you're jumping |
|
|
|
53:12.760 --> 53:14.480 |
|
from the extreme so easily, |
|
|
|
53:14.480 --> 53:16.280 |
|
because I disagree with you on that. |
|
|
|
53:16.280 --> 53:21.000 |
|
I think, well, it's not obvious to me |
|
|
|
53:21.000 --> 53:23.400 |
|
that you can solve lane following. |
|
|
|
53:23.400 --> 53:25.840 |
|
No, it's not obvious, I think it's doable. |
|
|
|
53:25.840 --> 53:30.840 |
|
I think in general, there is no hard limitations |
|
|
|
53:31.200 --> 53:33.680 |
|
to what you can learn with a deep neural network, |
|
|
|
53:33.680 --> 53:38.680 |
|
as long as the search space is rich enough, |
|
|
|
53:40.320 --> 53:42.240 |
|
is flexible enough, and as long as you have |
|
|
|
53:42.240 --> 53:45.360 |
|
this dense sampling of the input cross output space. |
|
|
|
53:45.360 --> 53:47.720 |
|
The problem is that this dense sampling |
|
|
|
53:47.720 --> 53:51.120 |
|
could mean anything from 10,000 examples |
|
|
|
53:51.120 --> 53:52.840 |
|
to like trillions and trillions. |
|
|
|
53:52.840 --> 53:54.360 |
|
So that's my question. |
|
|
|
53:54.360 --> 53:56.200 |
|
So what's your intuition? |
|
|
|
53:56.200 --> 53:58.720 |
|
And if you could just give it a chance |
|
|
|
53:58.720 --> 54:01.880 |
|
and think what kind of problems can be solved |
|
|
|
54:01.880 --> 54:04.240 |
|
by getting a huge amounts of data |
|
|
|
54:04.240 --> 54:08.000 |
|
and thereby creating a dense mapping. |
|
|
|
54:08.000 --> 54:12.480 |
|
So let's think about natural language dialogue, |
|
|
|
54:12.480 --> 54:14.000 |
|
the Turing test. |
|
|
|
54:14.000 --> 54:17.000 |
|
Do you think the Turing test can be solved |
|
|
|
54:17.000 --> 54:21.120 |
|
with a neural network alone? |
|
|
|
54:21.120 --> 54:24.440 |
|
Well, the Turing test is all about tricking people |
|
|
|
54:24.440 --> 54:26.880 |
|
into believing they're talking to a human. |
|
|
|
54:26.880 --> 54:29.040 |
|
And I don't think that's actually very difficult |
|
|
|
54:29.040 --> 54:34.040 |
|
because it's more about exploiting human perception |
|
|
|
54:35.600 --> 54:37.520 |
|
and not so much about intelligence. |
|
|
|
54:37.520 --> 54:39.680 |
|
There's a big difference between mimicking |
|
|
|
54:39.680 --> 54:42.080 |
|
intelligent behavior and actual intelligent behavior. |
|
|
|
54:42.080 --> 54:45.360 |
|
So, okay, let's look at maybe the Alexa prize and so on. |
|
|
|
54:45.360 --> 54:47.480 |
|
The different formulations of the natural language |
|
|
|
54:47.480 --> 54:50.520 |
|
conversation that are less about mimicking |
|
|
|
54:50.520 --> 54:52.800 |
|
and more about maintaining a fun conversation |
|
|
|
54:52.800 --> 54:54.720 |
|
that lasts for 20 minutes. |
|
|
|
54:54.720 --> 54:56.200 |
|
That's a little less about mimicking |
|
|
|
54:56.200 --> 54:59.080 |
|
and that's more about, I mean, it's still mimicking, |
|
|
|
54:59.080 --> 55:01.440 |
|
but it's more about being able to carry forward |
|
|
|
55:01.440 --> 55:03.640 |
|
a conversation with all the tangents that happen |
|
|
|
55:03.640 --> 55:05.080 |
|
in dialogue and so on. |
|
|
|
55:05.080 --> 55:08.320 |
|
Do you think that problem is learnable |
|
|
|
55:08.320 --> 55:13.320 |
|
with a neural network that does the point to point mapping? |
|
|
|
55:14.520 --> 55:16.280 |
|
So I think it would be very, very challenging |
|
|
|
55:16.280 --> 55:17.800 |
|
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 --> 55:23.240 |
|
I wouldn't rule it out. |
|
|
|
55:23.240 --> 55:25.400 |
|
The space of problems that can be solved |
|
|
|
55:25.400 --> 55:26.920 |
|
with a large neural network. |
|
|
|
55:26.920 --> 55:30.080 |
|
What's your sense about the space of those problems? |
|
|
|
55:30.080 --> 55:32.560 |
|
So useful problems for us. |
|
|
|
55:32.560 --> 55:34.800 |
|
In theory, it's infinite, right? |
|
|
|
55:34.800 --> 55:36.200 |
|
You can solve any problem. |
|
|
|
55:36.200 --> 55:39.800 |
|
In practice, well, deep learning is a great fit |
|
|
|
55:39.800 --> 55:41.800 |
|
for perception problems. |
|
|
|
55:41.800 --> 55:46.800 |
|
In general, any problem which is naturally amenable |
|
|
|
55:47.640 --> 55:52.200 |
|
to explicit handcrafted rules or rules that you can generate |
|
|
|
55:52.200 --> 55:54.960 |
|
by exhaustive search over some program space. |
|
|
|
55:56.080 --> 55:59.320 |
|
So perception, artificial intuition, |
|
|
|
55:59.320 --> 56:03.240 |
|
as long as you have a sufficient training dataset. |
|
|
|
56:03.240 --> 56:05.360 |
|
And that's the question, I mean, perception, |
|
|
|
56:05.360 --> 56:08.400 |
|
there's interpretation and understanding of the scene, |
|
|
|
56:08.400 --> 56:10.280 |
|
which seems to be outside the reach |
|
|
|
56:10.280 --> 56:12.960 |
|
of current perception systems. |
|
|
|
56:12.960 --> 56:15.920 |
|
So do you think larger networks will be able |
|
|
|
56:15.920 --> 56:18.280 |
|
to start to understand the physics |
|
|
|
56:18.280 --> 56:21.080 |
|
and the physics of the scene, |
|
|
|
56:21.080 --> 56:23.400 |
|
the three dimensional structure and relationships |
|
|
|
56:23.400 --> 56:25.560 |
|
of objects in the scene and so on? |
|
|
|
56:25.560 --> 56:28.320 |
|
Or really that's where symbolic AI has to step in? |
|
|
|
56:28.320 --> 56:34.480 |
|
Well, it's always possible to solve these problems |
|
|
|
56:34.480 --> 56:36.800 |
|
with deep learning. |
|
|
|
56:36.800 --> 56:38.560 |
|
It's just extremely inefficient. |
|
|
|
56:38.560 --> 56:42.000 |
|
A model would be an explicit rule based abstract model |
|
|
|
56:42.000 --> 56:45.240 |
|
would be a far better, more compressed |
|
|
|
56:45.240 --> 56:46.840 |
|
representation of physics. |
|
|
|
56:46.840 --> 56:49.080 |
|
Then learning just this mapping between |
|
|
|
56:49.080 --> 56:50.960 |
|
in this situation, this thing happens. |
|
|
|
56:50.960 --> 56:52.720 |
|
If you change the situation slightly, |
|
|
|
56:52.720 --> 56:54.760 |
|
then this other thing happens and so on. |
|
|
|
56:54.760 --> 56:57.440 |
|
Do you think it's possible to automatically generate |
|
|
|
56:57.440 --> 57:02.200 |
|
the programs that would require that kind of reasoning? |
|
|
|
57:02.200 --> 57:05.360 |
|
Or does it have to, so the way the expert systems fail, |
|
|
|
57:05.360 --> 57:07.120 |
|
there's so many facts about the world |
|
|
|
57:07.120 --> 57:08.960 |
|
had to be hand coded in. |
|
|
|
57:08.960 --> 57:14.600 |
|
Do you think it's possible to learn those logical statements |
|
|
|
57:14.600 --> 57:18.200 |
|
that are true about the world and their relationships? |
|
|
|
57:18.200 --> 57:20.360 |
|
Do you think, I mean, that's kind of what theorem proving |
|
|
|
57:20.360 --> 57:22.680 |
|
at a basic level is trying to do, right? |
|
|
|
57:22.680 --> 57:26.160 |
|
Yeah, except it's much harder to formulate statements |
|
|
|
57:26.160 --> 57:28.480 |
|
about the world compared to formulating |
|
|
|
57:28.480 --> 57:30.320 |
|
mathematical statements. |
|
|
|
57:30.320 --> 57:34.200 |
|
Statements about the world tend to be subjective. |
|
|
|
57:34.200 --> 57:39.600 |
|
So can you learn rule based models? |
|
|
|
57:39.600 --> 57:40.920 |
|
Yes, definitely. |
|
|
|
57:40.920 --> 57:43.640 |
|
That's the field of program synthesis. |
|
|
|
57:43.640 --> 57:48.040 |
|
However, today we just don't really know how to do it. |
|
|
|
57:48.040 --> 57:52.400 |
|
So it's very much a grass search or tree search problem. |
|
|
|
57:52.400 --> 57:56.800 |
|
And so we are limited to the sort of tree session grass |
|
|
|
57:56.800 --> 57:58.560 |
|
search algorithms that we have today. |
|
|
|
57:58.560 --> 58:02.760 |
|
Personally, I think genetic algorithms are very promising. |
|
|
|
58:02.760 --> 58:04.360 |
|
So almost like genetic programming. |
|
|
|
58:04.360 --> 58:05.560 |
|
Genetic programming, exactly. |
|
|
|
58:05.560 --> 58:08.840 |
|
Can you discuss the field of program synthesis? |
|
|
|
58:08.840 --> 58:14.560 |
|
Like how many people are working and thinking about it? |
|
|
|
58:14.560 --> 58:17.960 |
|
Where we are in the history of program synthesis |
|
|
|
58:17.960 --> 58:20.720 |
|
and what are your hopes for it? |
|
|
|
58:20.720 --> 58:24.600 |
|
Well, if it were deep learning, this is like the 90s. |
|
|
|
58:24.600 --> 58:29.120 |
|
So meaning that we already have existing solutions. |
|
|
|
58:29.120 --> 58:34.280 |
|
We are starting to have some basic understanding |
|
|
|
58:34.280 --> 58:35.480 |
|
of what this is about. |
|
|
|
58:35.480 --> 58:38.000 |
|
But it's still a field that is in its infancy. |
|
|
|
58:38.000 --> 58:40.440 |
|
There are very few people working on it. |
|
|
|
58:40.440 --> 58:44.480 |
|
There are very few real world applications. |
|
|
|
58:44.480 --> 58:47.640 |
|
So the one real world application I'm aware of |
|
|
|
58:47.640 --> 58:51.680 |
|
is Flash Fill in Excel. |
|
|
|
58:51.680 --> 58:55.080 |
|
It's a way to automatically learn very simple programs |
|
|
|
58:55.080 --> 58:58.200 |
|
to format cells in an Excel spreadsheet |
|
|
|
58:58.200 --> 59:00.240 |
|
from a few examples. |
|
|
|
59:00.240 --> 59:02.800 |
|
For instance, learning a way to format a date, things like that. |
|
|
|
59:02.800 --> 59:03.680 |
|
Oh, that's fascinating. |
|
|
|
59:03.680 --> 59:04.560 |
|
Yeah. |
|
|
|
59:04.560 --> 59:06.280 |
|
You know, OK, that's a fascinating topic. |
|
|
|
59:06.280 --> 59:10.480 |
|
I always wonder when I provide a few samples to Excel, |
|
|
|
59:10.480 --> 59:12.600 |
|
what it's able to figure out. |
|
|
|
59:12.600 --> 59:15.960 |
|
Like just giving it a few dates, what |
|
|
|
59:15.960 --> 59:18.480 |
|
are you able to figure out from the pattern I just gave you? |
|
|
|
59:18.480 --> 59:19.760 |
|
That's a fascinating question. |
|
|
|
59:19.760 --> 59:23.320 |
|
And it's fascinating whether that's learnable patterns. |
|
|
|
59:23.320 --> 59:25.520 |
|
And you're saying they're working on that. |
|
|
|
59:25.520 --> 59:28.200 |
|
How big is the toolbox currently? |
|
|
|
59:28.200 --> 59:29.520 |
|
Are we completely in the dark? |
|
|
|
59:29.520 --> 59:30.440 |
|
So if you said the 90s. |
|
|
|
59:30.440 --> 59:31.720 |
|
In terms of program synthesis? |
|
|
|
59:31.720 --> 59:32.360 |
|
No. |
|
|
|
59:32.360 --> 59:37.720 |
|
So I would say, so maybe 90s is even too optimistic. |
|
|
|
59:37.720 --> 59:41.080 |
|
Because by the 90s, we already understood back prop. |
|
|
|
59:41.080 --> 59:43.960 |
|
We already understood the engine of deep learning, |
|
|
|
59:43.960 --> 59:47.280 |
|
even though we couldn't really see its potential quite. |
|
|
|
59:47.280 --> 59:48.520 |
|
Today, I don't think we have found |
|
|
|
59:48.520 --> 59:50.400 |
|
the engine of program synthesis. |
|
|
|
59:50.400 --> 59:52.880 |
|
So we're in the winter before back prop. |
|
|
|
59:52.880 --> 59:54.160 |
|
Yeah. |
|
|
|
59:54.160 --> 59:55.720 |
|
In a way, yes. |
|
|
|
59:55.720 --> 1:00:00.120 |
|
So I do believe program synthesis and general discrete search |
|
|
|
1:00:00.120 --> 1:00:02.760 |
|
over rule based models is going to be |
|
|
|
1:00:02.760 --> 1:00:06.640 |
|
a cornerstone of AI research in the next century. |
|
|
|
1:00:06.640 --> 1:00:10.200 |
|
And that doesn't mean we are going to drop deep learning. |
|
|
|
1:00:10.200 --> 1:00:11.880 |
|
Deep learning is immensely useful. |
|
|
|
1:00:11.880 --> 1:00:17.200 |
|
Like, being able to learn is a very flexible, adaptable, |
|
|
|
1:00:17.200 --> 1:00:18.120 |
|
parametric model. |
|
|
|
1:00:18.120 --> 1:00:20.720 |
|
So it's got to understand that's actually immensely useful. |
|
|
|
1:00:20.720 --> 1:00:23.040 |
|
All it's doing is pattern cognition. |
|
|
|
1:00:23.040 --> 1:00:25.640 |
|
But being good at pattern cognition, given lots of data, |
|
|
|
1:00:25.640 --> 1:00:27.920 |
|
is just extremely powerful. |
|
|
|
1:00:27.920 --> 1:00:30.320 |
|
So we are still going to be working on deep learning. |
|
|
|
1:00:30.320 --> 1:00:31.840 |
|
We are going to be working on program synthesis. |
|
|
|
1:00:31.840 --> 1:00:34.680 |
|
We are going to be combining the two in increasingly automated |
|
|
|
1:00:34.680 --> 1:00:36.400 |
|
ways. |
|
|
|
1:00:36.400 --> 1:00:38.520 |
|
So let's talk a little bit about data. |
|
|
|
1:00:38.520 --> 1:00:44.600 |
|
You've tweeted, about 10,000 deep learning papers |
|
|
|
1:00:44.600 --> 1:00:47.080 |
|
have been written about hard coding priors |
|
|
|
1:00:47.080 --> 1:00:49.600 |
|
about a specific task in a neural network architecture |
|
|
|
1:00:49.600 --> 1:00:52.440 |
|
works better than a lack of a prior. |
|
|
|
1:00:52.440 --> 1:00:55.120 |
|
Basically, summarizing all these efforts, |
|
|
|
1:00:55.120 --> 1:00:56.920 |
|
they put a name to an architecture. |
|
|
|
1:00:56.920 --> 1:00:59.280 |
|
But really, what they're doing is hard coding some priors |
|
|
|
1:00:59.280 --> 1:01:01.560 |
|
that improve the performance of the system. |
|
|
|
1:01:01.560 --> 1:01:06.880 |
|
But which gets straight to the point is probably true. |
|
|
|
1:01:06.880 --> 1:01:09.800 |
|
So you say that you can always buy performance by, |
|
|
|
1:01:09.800 --> 1:01:12.920 |
|
in quotes, performance by either training on more data, |
|
|
|
1:01:12.920 --> 1:01:15.480 |
|
better data, or by injecting task information |
|
|
|
1:01:15.480 --> 1:01:18.400 |
|
to the architecture of the preprocessing. |
|
|
|
1:01:18.400 --> 1:01:21.280 |
|
However, this isn't informative about the generalization power |
|
|
|
1:01:21.280 --> 1:01:23.080 |
|
the techniques use, the fundamental ability |
|
|
|
1:01:23.080 --> 1:01:24.200 |
|
to generalize. |
|
|
|
1:01:24.200 --> 1:01:26.800 |
|
Do you think we can go far by coming up |
|
|
|
1:01:26.800 --> 1:01:29.920 |
|
with better methods for this kind of cheating, |
|
|
|
1:01:29.920 --> 1:01:33.520 |
|
for better methods of large scale annotation of data? |
|
|
|
1:01:33.520 --> 1:01:34.960 |
|
So building better priors. |
|
|
|
1:01:34.960 --> 1:01:37.280 |
|
If you automate it, it's not cheating anymore. |
|
|
|
1:01:37.280 --> 1:01:38.360 |
|
Right. |
|
|
|
1:01:38.360 --> 1:01:41.600 |
|
I'm joking about the cheating, but large scale. |
|
|
|
1:01:41.600 --> 1:01:46.560 |
|
So basically, I'm asking about something |
|
|
|
1:01:46.560 --> 1:01:48.280 |
|
that hasn't, from my perspective, |
|
|
|
1:01:48.280 --> 1:01:53.360 |
|
been researched too much is exponential improvement |
|
|
|
1:01:53.360 --> 1:01:55.960 |
|
in annotation of data. |
|
|
|
1:01:55.960 --> 1:01:58.120 |
|
Do you often think about? |
|
|
|
1:01:58.120 --> 1:02:00.840 |
|
I think it's actually been researched quite a bit. |
|
|
|
1:02:00.840 --> 1:02:02.720 |
|
You just don't see publications about it. |
|
|
|
1:02:02.720 --> 1:02:05.840 |
|
Because people who publish papers |
|
|
|
1:02:05.840 --> 1:02:07.920 |
|
are going to publish about known benchmarks. |
|
|
|
1:02:07.920 --> 1:02:09.800 |
|
Sometimes they're going to read a new benchmark. |
|
|
|
1:02:09.800 --> 1:02:12.200 |
|
People who actually have real world large scale |
|
|
|
1:02:12.200 --> 1:02:13.880 |
|
depending on problems, they're going |
|
|
|
1:02:13.880 --> 1:02:16.960 |
|
to spend a lot of resources into data annotation |
|
|
|
1:02:16.960 --> 1:02:18.400 |
|
and good data annotation pipelines, |
|
|
|
1:02:18.400 --> 1:02:19.640 |
|
but you don't see any papers about it. |
|
|
|
1:02:19.640 --> 1:02:20.400 |
|
That's interesting. |
|
|
|
1:02:20.400 --> 1:02:22.720 |
|
So do you think, certainly resources, |
|
|
|
1:02:22.720 --> 1:02:24.840 |
|
but do you think there's innovation happening? |
|
|
|
1:02:24.840 --> 1:02:25.880 |
|
Oh, yeah. |
|
|
|
1:02:25.880 --> 1:02:28.880 |
|
To clarify the point in the tweet. |
|
|
|
1:02:28.880 --> 1:02:31.160 |
|
So machine learning in general is |
|
|
|
1:02:31.160 --> 1:02:33.840 |
|
the science of generalization. |
|
|
|
1:02:33.840 --> 1:02:37.800 |
|
You want to generate knowledge that |
|
|
|
1:02:37.800 --> 1:02:40.440 |
|
can be reused across different data sets, |
|
|
|
1:02:40.440 --> 1:02:42.000 |
|
across different tasks. |
|
|
|
1:02:42.000 --> 1:02:45.280 |
|
And if instead you're looking at one data set |
|
|
|
1:02:45.280 --> 1:02:50.000 |
|
and then you are hard coding knowledge about this task |
|
|
|
1:02:50.000 --> 1:02:54.040 |
|
into your architecture, this is no more useful |
|
|
|
1:02:54.040 --> 1:02:56.760 |
|
than training a network and then saying, oh, I |
|
|
|
1:02:56.760 --> 1:03:01.920 |
|
found these weight values perform well. |
|
|
|
1:03:01.920 --> 1:03:05.680 |
|
So David Ha, I don't know if you know David, |
|
|
|
1:03:05.680 --> 1:03:08.760 |
|
he had a paper the other day about weight |
|
|
|
1:03:08.760 --> 1:03:10.400 |
|
agnostic neural networks. |
|
|
|
1:03:10.400 --> 1:03:12.120 |
|
And this is a very interesting paper |
|
|
|
1:03:12.120 --> 1:03:14.400 |
|
because it really illustrates the fact |
|
|
|
1:03:14.400 --> 1:03:17.400 |
|
that an architecture, even without weights, |
|
|
|
1:03:17.400 --> 1:03:21.360 |
|
an architecture is knowledge about a task. |
|
|
|
1:03:21.360 --> 1:03:23.640 |
|
It encodes knowledge. |
|
|
|
1:03:23.640 --> 1:03:25.840 |
|
And when it comes to architectures |
|
|
|
1:03:25.840 --> 1:03:30.440 |
|
that are uncrafted by researchers, in some cases, |
|
|
|
1:03:30.440 --> 1:03:34.160 |
|
it is very, very clear that all they are doing |
|
|
|
1:03:34.160 --> 1:03:38.880 |
|
is artificially reencoding the template that |
|
|
|
1:03:38.880 --> 1:03:44.400 |
|
corresponds to the proper way to solve the task encoding |
|
|
|
1:03:44.400 --> 1:03:45.200 |
|
a given data set. |
|
|
|
1:03:45.200 --> 1:03:48.120 |
|
For instance, I know if you looked |
|
|
|
1:03:48.120 --> 1:03:52.280 |
|
at the baby data set, which is about natural language |
|
|
|
1:03:52.280 --> 1:03:55.520 |
|
question answering, it is generated by an algorithm. |
|
|
|
1:03:55.520 --> 1:03:57.680 |
|
So this is a question answer pairs |
|
|
|
1:03:57.680 --> 1:03:59.280 |
|
that are generated by an algorithm. |
|
|
|
1:03:59.280 --> 1:04:01.520 |
|
The algorithm is solving a certain template. |
|
|
|
1:04:01.520 --> 1:04:04.400 |
|
Turns out, if you craft a network that |
|
|
|
1:04:04.400 --> 1:04:06.360 |
|
literally encodes this template, you |
|
|
|
1:04:06.360 --> 1:04:09.640 |
|
can solve this data set with nearly 100% accuracy. |
|
|
|
1:04:09.640 --> 1:04:11.160 |
|
But that doesn't actually tell you |
|
|
|
1:04:11.160 --> 1:04:14.640 |
|
anything about how to solve question answering |
|
|
|
1:04:14.640 --> 1:04:17.680 |
|
in general, which is the point. |
|
|
|
1:04:17.680 --> 1:04:19.400 |
|
The question is just to linger on it, |
|
|
|
1:04:19.400 --> 1:04:21.560 |
|
whether it's from the data side or from the size |
|
|
|
1:04:21.560 --> 1:04:23.280 |
|
of the network. |
|
|
|
1:04:23.280 --> 1:04:25.920 |
|
I don't know if you've read the blog post by Rich Sutton, |
|
|
|
1:04:25.920 --> 1:04:28.400 |
|
The Bitter Lesson, where he says, |
|
|
|
1:04:28.400 --> 1:04:31.480 |
|
the biggest lesson that we can read from 70 years of AI |
|
|
|
1:04:31.480 --> 1:04:34.720 |
|
research is that general methods that leverage computation |
|
|
|
1:04:34.720 --> 1:04:37.160 |
|
are ultimately the most effective. |
|
|
|
1:04:37.160 --> 1:04:39.720 |
|
So as opposed to figuring out methods |
|
|
|
1:04:39.720 --> 1:04:41.840 |
|
that can generalize effectively, do you |
|
|
|
1:04:41.840 --> 1:04:47.720 |
|
think we can get pretty far by just having something |
|
|
|
1:04:47.720 --> 1:04:51.520 |
|
that leverages computation and the improvement of computation? |
|
|
|
1:04:51.520 --> 1:04:54.960 |
|
Yeah, so I think Rich is making a very good point, which |
|
|
|
1:04:54.960 --> 1:04:57.560 |
|
is that a lot of these papers, which are actually |
|
|
|
1:04:57.560 --> 1:05:02.800 |
|
all about manually hardcoding prior knowledge about a task |
|
|
|
1:05:02.800 --> 1:05:04.720 |
|
into some system, it doesn't have |
|
|
|
1:05:04.720 --> 1:05:08.600 |
|
to be deep learning architecture, but into some system. |
|
|
|
1:05:08.600 --> 1:05:11.920 |
|
These papers are not actually making any impact. |
|
|
|
1:05:11.920 --> 1:05:14.800 |
|
Instead, what's making really long term impact |
|
|
|
1:05:14.800 --> 1:05:18.520 |
|
is very simple, very general systems |
|
|
|
1:05:18.520 --> 1:05:21.280 |
|
that are really agnostic to all these tricks. |
|
|
|
1:05:21.280 --> 1:05:23.320 |
|
Because these tricks do not generalize. |
|
|
|
1:05:23.320 --> 1:05:27.480 |
|
And of course, the one general and simple thing |
|
|
|
1:05:27.480 --> 1:05:33.160 |
|
that you should focus on is that which leverages computation. |
|
|
|
1:05:33.160 --> 1:05:36.200 |
|
Because computation, the availability |
|
|
|
1:05:36.200 --> 1:05:39.400 |
|
of large scale computation has been increasing exponentially |
|
|
|
1:05:39.400 --> 1:05:40.560 |
|
following Moore's law. |
|
|
|
1:05:40.560 --> 1:05:44.080 |
|
So if your algorithm is all about exploiting this, |
|
|
|
1:05:44.080 --> 1:05:47.440 |
|
then your algorithm is suddenly exponentially improving. |
|
|
|
1:05:47.440 --> 1:05:52.400 |
|
So I think Rich is definitely right. |
|
|
|
1:05:52.400 --> 1:05:57.120 |
|
However, he's right about the past 70 years. |
|
|
|
1:05:57.120 --> 1:05:59.440 |
|
He's like assessing the past 70 years. |
|
|
|
1:05:59.440 --> 1:06:02.360 |
|
I am not sure that this assessment will still |
|
|
|
1:06:02.360 --> 1:06:04.880 |
|
hold true for the next 70 years. |
|
|
|
1:06:04.880 --> 1:06:07.160 |
|
It might to some extent. |
|
|
|
1:06:07.160 --> 1:06:08.560 |
|
I suspect it will not. |
|
|
|
1:06:08.560 --> 1:06:11.560 |
|
Because the truth of his assessment |
|
|
|
1:06:11.560 --> 1:06:16.800 |
|
is a function of the context in which this research took place. |
|
|
|
1:06:16.800 --> 1:06:18.600 |
|
And the context is changing. |
|
|
|
1:06:18.600 --> 1:06:21.440 |
|
Moore's law might not be applicable anymore, |
|
|
|
1:06:21.440 --> 1:06:23.760 |
|
for instance, in the future. |
|
|
|
1:06:23.760 --> 1:06:31.200 |
|
And I do believe that when you tweak one aspect of a system, |
|
|
|
1:06:31.200 --> 1:06:32.920 |
|
when you exploit one aspect of a system, |
|
|
|
1:06:32.920 --> 1:06:36.480 |
|
some other aspect starts becoming the bottleneck. |
|
|
|
1:06:36.480 --> 1:06:38.800 |
|
Let's say you have unlimited computation. |
|
|
|
1:06:38.800 --> 1:06:41.440 |
|
Well, then data is the bottleneck. |
|
|
|
1:06:41.440 --> 1:06:43.560 |
|
And I think we are already starting |
|
|
|
1:06:43.560 --> 1:06:45.720 |
|
to be in a regime where our systems are |
|
|
|
1:06:45.720 --> 1:06:48.120 |
|
so large in scale and so data ingrained |
|
|
|
1:06:48.120 --> 1:06:50.360 |
|
that data today and the quality of data |
|
|
|
1:06:50.360 --> 1:06:53.040 |
|
and the scale of data is the bottleneck. |
|
|
|
1:06:53.040 --> 1:06:58.160 |
|
And in this environment, the bitter lesson from Rich |
|
|
|
1:06:58.160 --> 1:07:00.800 |
|
is not going to be true anymore. |
|
|
|
1:07:00.800 --> 1:07:03.960 |
|
So I think we are going to move from a focus |
|
|
|
1:07:03.960 --> 1:07:09.840 |
|
on a computation scale to focus on data efficiency. |
|
|
|
1:07:09.840 --> 1:07:10.720 |
|
Data efficiency. |
|
|
|
1:07:10.720 --> 1:07:13.120 |
|
So that's getting to the question of symbolic AI. |
|
|
|
1:07:13.120 --> 1:07:16.280 |
|
But to linger on the deep learning approaches, |
|
|
|
1:07:16.280 --> 1:07:19.240 |
|
do you have hope for either unsupervised learning |
|
|
|
1:07:19.240 --> 1:07:23.280 |
|
or reinforcement learning, which are |
|
|
|
1:07:23.280 --> 1:07:28.120 |
|
ways of being more data efficient in terms |
|
|
|
1:07:28.120 --> 1:07:31.560 |
|
of the amount of data they need that required human annotation? |
|
|
|
1:07:31.560 --> 1:07:34.280 |
|
So unsupervised learning and reinforcement learning |
|
|
|
1:07:34.280 --> 1:07:36.640 |
|
are frameworks for learning, but they are not |
|
|
|
1:07:36.640 --> 1:07:39.000 |
|
like any specific technique. |
|
|
|
1:07:39.000 --> 1:07:41.200 |
|
So usually when people say reinforcement learning, |
|
|
|
1:07:41.200 --> 1:07:43.320 |
|
what they really mean is deep reinforcement learning, |
|
|
|
1:07:43.320 --> 1:07:47.440 |
|
which is like one approach which is actually very questionable. |
|
|
|
1:07:47.440 --> 1:07:50.920 |
|
The question I was asking was unsupervised learning |
|
|
|
1:07:50.920 --> 1:07:54.680 |
|
with deep neural networks and deep reinforcement learning. |
|
|
|
1:07:54.680 --> 1:07:56.840 |
|
Well, these are not really data efficient |
|
|
|
1:07:56.840 --> 1:08:00.520 |
|
because you're still leveraging these huge parametric models |
|
|
|
1:08:00.520 --> 1:08:03.720 |
|
point by point with gradient descent. |
|
|
|
1:08:03.720 --> 1:08:08.000 |
|
It is more efficient in terms of the number of annotations, |
|
|
|
1:08:08.000 --> 1:08:09.520 |
|
the density of annotations you need. |
|
|
|
1:08:09.520 --> 1:08:13.840 |
|
So the idea being to learn the latent space around which |
|
|
|
1:08:13.840 --> 1:08:17.960 |
|
the data is organized and then map the sparse annotations |
|
|
|
1:08:17.960 --> 1:08:18.760 |
|
into it. |
|
|
|
1:08:18.760 --> 1:08:23.560 |
|
And sure, I mean, that's clearly a very good idea. |
|
|
|
1:08:23.560 --> 1:08:26.080 |
|
It's not really a topic I would be working on, |
|
|
|
1:08:26.080 --> 1:08:28.040 |
|
but it's clearly a good idea. |
|
|
|
1:08:28.040 --> 1:08:31.760 |
|
So it would get us to solve some problems that? |
|
|
|
1:08:31.760 --> 1:08:34.880 |
|
It will get us to incremental improvements |
|
|
|
1:08:34.880 --> 1:08:38.240 |
|
in labeled data efficiency. |
|
|
|
1:08:38.240 --> 1:08:43.520 |
|
Do you have concerns about short term or long term threats |
|
|
|
1:08:43.520 --> 1:08:47.800 |
|
from AI, from artificial intelligence? |
|
|
|
1:08:47.800 --> 1:08:50.640 |
|
Yes, definitely to some extent. |
|
|
|
1:08:50.640 --> 1:08:52.800 |
|
And what's the shape of those concerns? |
|
|
|
1:08:52.800 --> 1:08:56.880 |
|
This is actually something I've briefly written about. |
|
|
|
1:08:56.880 --> 1:09:02.680 |
|
But the capabilities of deep learning technology |
|
|
|
1:09:02.680 --> 1:09:05.200 |
|
can be used in many ways that are |
|
|
|
1:09:05.200 --> 1:09:09.760 |
|
concerning from mass surveillance with things |
|
|
|
1:09:09.760 --> 1:09:11.880 |
|
like facial recognition. |
|
|
|
1:09:11.880 --> 1:09:15.440 |
|
In general, tracking lots of data about everyone |
|
|
|
1:09:15.440 --> 1:09:18.920 |
|
and then being able to making sense of this data |
|
|
|
1:09:18.920 --> 1:09:22.240 |
|
to do identification, to do prediction. |
|
|
|
1:09:22.240 --> 1:09:23.160 |
|
That's concerning. |
|
|
|
1:09:23.160 --> 1:09:26.560 |
|
That's something that's being very aggressively pursued |
|
|
|
1:09:26.560 --> 1:09:31.440 |
|
by totalitarian states like China. |
|
|
|
1:09:31.440 --> 1:09:34.000 |
|
One thing I am very much concerned about |
|
|
|
1:09:34.000 --> 1:09:40.640 |
|
is that our lives are increasingly online, |
|
|
|
1:09:40.640 --> 1:09:43.280 |
|
are increasingly digital, made of information, |
|
|
|
1:09:43.280 --> 1:09:48.080 |
|
made of information consumption and information production, |
|
|
|
1:09:48.080 --> 1:09:51.800 |
|
our digital footprint, I would say. |
|
|
|
1:09:51.800 --> 1:09:56.280 |
|
And if you absorb all of this data |
|
|
|
1:09:56.280 --> 1:10:01.440 |
|
and you are in control of where you consume information, |
|
|
|
1:10:01.440 --> 1:10:06.960 |
|
social networks and so on, recommendation engines, |
|
|
|
1:10:06.960 --> 1:10:10.200 |
|
then you can build a sort of reinforcement |
|
|
|
1:10:10.200 --> 1:10:13.760 |
|
loop for human behavior. |
|
|
|
1:10:13.760 --> 1:10:18.360 |
|
You can observe the state of your mind at time t. |
|
|
|
1:10:18.360 --> 1:10:21.080 |
|
You can predict how you would react |
|
|
|
1:10:21.080 --> 1:10:23.800 |
|
to different pieces of content, how |
|
|
|
1:10:23.800 --> 1:10:27.000 |
|
to get you to move your mind in a certain direction. |
|
|
|
1:10:27.000 --> 1:10:33.160 |
|
And then you can feed you the specific piece of content |
|
|
|
1:10:33.160 --> 1:10:35.680 |
|
that would move you in a specific direction. |
|
|
|
1:10:35.680 --> 1:10:41.800 |
|
And you can do this at scale in terms |
|
|
|
1:10:41.800 --> 1:10:44.960 |
|
of doing it continuously in real time. |
|
|
|
1:10:44.960 --> 1:10:46.440 |
|
You can also do it at scale in terms |
|
|
|
1:10:46.440 --> 1:10:50.480 |
|
of scaling this to many, many people, to entire populations. |
|
|
|
1:10:50.480 --> 1:10:53.840 |
|
So potentially, artificial intelligence, |
|
|
|
1:10:53.840 --> 1:10:57.440 |
|
even in its current state, if you combine it |
|
|
|
1:10:57.440 --> 1:11:01.760 |
|
with the internet, with the fact that all of our lives |
|
|
|
1:11:01.760 --> 1:11:05.120 |
|
are moving to digital devices and digital information |
|
|
|
1:11:05.120 --> 1:11:08.720 |
|
consumption and creation, what you get |
|
|
|
1:11:08.720 --> 1:11:14.480 |
|
is the possibility to achieve mass manipulation of behavior |
|
|
|
1:11:14.480 --> 1:11:16.840 |
|
and mass psychological control. |
|
|
|
1:11:16.840 --> 1:11:18.520 |
|
And this is a very real possibility. |
|
|
|
1:11:18.520 --> 1:11:22.080 |
|
Yeah, so you're talking about any kind of recommender system. |
|
|
|
1:11:22.080 --> 1:11:26.160 |
|
Let's look at the YouTube algorithm, Facebook, |
|
|
|
1:11:26.160 --> 1:11:29.720 |
|
anything that recommends content you should watch next. |
|
|
|
1:11:29.720 --> 1:11:32.960 |
|
And it's fascinating to think that there's |
|
|
|
1:11:32.960 --> 1:11:41.120 |
|
some aspects of human behavior that you can say a problem of, |
|
|
|
1:11:41.120 --> 1:11:45.400 |
|
is this person hold Republican beliefs or Democratic beliefs? |
|
|
|
1:11:45.400 --> 1:11:50.240 |
|
And this is a trivial, that's an objective function. |
|
|
|
1:11:50.240 --> 1:11:52.600 |
|
And you can optimize, and you can measure, |
|
|
|
1:11:52.600 --> 1:11:54.360 |
|
and you can turn everybody into a Republican |
|
|
|
1:11:54.360 --> 1:11:56.080 |
|
or everybody into a Democrat. |
|
|
|
1:11:56.080 --> 1:11:57.840 |
|
I do believe it's true. |
|
|
|
1:11:57.840 --> 1:12:03.680 |
|
So the human mind is very, if you look at the human mind |
|
|
|
1:12:03.680 --> 1:12:05.320 |
|
as a kind of computer program, it |
|
|
|
1:12:05.320 --> 1:12:07.560 |
|
has a very large exploit surface. |
|
|
|
1:12:07.560 --> 1:12:09.360 |
|
It has many, many vulnerabilities. |
|
|
|
1:12:09.360 --> 1:12:10.840 |
|
Exploit surfaces, yeah. |
|
|
|
1:12:10.840 --> 1:12:13.520 |
|
Ways you can control it. |
|
|
|
1:12:13.520 --> 1:12:16.680 |
|
For instance, when it comes to your political beliefs, |
|
|
|
1:12:16.680 --> 1:12:19.400 |
|
this is very much tied to your identity. |
|
|
|
1:12:19.400 --> 1:12:23.040 |
|
So for instance, if I'm in control of your news feed |
|
|
|
1:12:23.040 --> 1:12:26.000 |
|
on your favorite social media platforms, |
|
|
|
1:12:26.000 --> 1:12:29.360 |
|
this is actually where you're getting your news from. |
|
|
|
1:12:29.360 --> 1:12:32.960 |
|
And of course, I can choose to only show you |
|
|
|
1:12:32.960 --> 1:12:37.120 |
|
news that will make you see the world in a specific way. |
|
|
|
1:12:37.120 --> 1:12:41.920 |
|
But I can also create incentives for you |
|
|
|
1:12:41.920 --> 1:12:44.720 |
|
to post about some political beliefs. |
|
|
|
1:12:44.720 --> 1:12:47.960 |
|
And then when I get you to express a statement, |
|
|
|
1:12:47.960 --> 1:12:51.840 |
|
if it's a statement that me as the controller, |
|
|
|
1:12:51.840 --> 1:12:53.800 |
|
I want to reinforce. |
|
|
|
1:12:53.800 --> 1:12:55.560 |
|
I can just show it to people who will agree, |
|
|
|
1:12:55.560 --> 1:12:56.880 |
|
and they will like it. |
|
|
|
1:12:56.880 --> 1:12:59.280 |
|
And that will reinforce the statement in your mind. |
|
|
|
1:12:59.280 --> 1:13:02.760 |
|
If this is a statement I want you to, |
|
|
|
1:13:02.760 --> 1:13:05.320 |
|
this is a belief I want you to abandon, |
|
|
|
1:13:05.320 --> 1:13:09.600 |
|
I can, on the other hand, show it to opponents. |
|
|
|
1:13:09.600 --> 1:13:10.640 |
|
We'll attack you. |
|
|
|
1:13:10.640 --> 1:13:12.840 |
|
And because they attack you, at the very least, |
|
|
|
1:13:12.840 --> 1:13:16.840 |
|
next time you will think twice about posting it. |
|
|
|
1:13:16.840 --> 1:13:20.280 |
|
But maybe you will even start believing this |
|
|
|
1:13:20.280 --> 1:13:22.840 |
|
because you got pushback. |
|
|
|
1:13:22.840 --> 1:13:28.440 |
|
So there are many ways in which social media platforms |
|
|
|
1:13:28.440 --> 1:13:30.520 |
|
can potentially control your opinions. |
|
|
|
1:13:30.520 --> 1:13:35.040 |
|
And today, so all of these things |
|
|
|
1:13:35.040 --> 1:13:38.240 |
|
are already being controlled by AI algorithms. |
|
|
|
1:13:38.240 --> 1:13:41.880 |
|
These algorithms do not have any explicit political goal |
|
|
|
1:13:41.880 --> 1:13:42.880 |
|
today. |
|
|
|
1:13:42.880 --> 1:13:48.680 |
|
Well, potentially they could, like if some totalitarian |
|
|
|
1:13:48.680 --> 1:13:52.720 |
|
government takes over social media platforms |
|
|
|
1:13:52.720 --> 1:13:55.360 |
|
and decides that now we are going to use this not just |
|
|
|
1:13:55.360 --> 1:13:58.040 |
|
for mass surveillance, but also for mass opinion control |
|
|
|
1:13:58.040 --> 1:13:59.360 |
|
and behavior control. |
|
|
|
1:13:59.360 --> 1:14:01.840 |
|
Very bad things could happen. |
|
|
|
1:14:01.840 --> 1:14:06.480 |
|
But what's really fascinating and actually quite concerning |
|
|
|
1:14:06.480 --> 1:14:11.280 |
|
is that even without an explicit intent to manipulate, |
|
|
|
1:14:11.280 --> 1:14:14.760 |
|
you're already seeing very dangerous dynamics |
|
|
|
1:14:14.760 --> 1:14:18.160 |
|
in terms of how these content recommendation |
|
|
|
1:14:18.160 --> 1:14:19.800 |
|
algorithms behave. |
|
|
|
1:14:19.800 --> 1:14:24.920 |
|
Because right now, the goal, the objective function |
|
|
|
1:14:24.920 --> 1:14:28.640 |
|
of these algorithms is to maximize engagement, |
|
|
|
1:14:28.640 --> 1:14:32.520 |
|
which seems fairly innocuous at first. |
|
|
|
1:14:32.520 --> 1:14:36.480 |
|
However, it is not because content |
|
|
|
1:14:36.480 --> 1:14:42.000 |
|
that will maximally engage people, get people to react |
|
|
|
1:14:42.000 --> 1:14:44.720 |
|
in an emotional way, get people to click on something. |
|
|
|
1:14:44.720 --> 1:14:52.200 |
|
It is very often content that is not |
|
|
|
1:14:52.200 --> 1:14:54.400 |
|
healthy to the public discourse. |
|
|
|
1:14:54.400 --> 1:14:58.200 |
|
For instance, fake news are far more |
|
|
|
1:14:58.200 --> 1:15:01.320 |
|
likely to get you to click on them than real news |
|
|
|
1:15:01.320 --> 1:15:06.960 |
|
simply because they are not constrained to reality. |
|
|
|
1:15:06.960 --> 1:15:11.360 |
|
So they can be as outrageous, as surprising, |
|
|
|
1:15:11.360 --> 1:15:15.880 |
|
as good stories as you want because they're artificial. |
|
|
|
1:15:15.880 --> 1:15:18.880 |
|
To me, that's an exciting world because so much good |
|
|
|
1:15:18.880 --> 1:15:19.560 |
|
can come. |
|
|
|
1:15:19.560 --> 1:15:24.520 |
|
So there's an opportunity to educate people. |
|
|
|
1:15:24.520 --> 1:15:31.200 |
|
You can balance people's worldview with other ideas. |
|
|
|
1:15:31.200 --> 1:15:33.800 |
|
So there's so many objective functions. |
|
|
|
1:15:33.800 --> 1:15:35.840 |
|
The space of objective functions that |
|
|
|
1:15:35.840 --> 1:15:40.720 |
|
create better civilizations is large, arguably infinite. |
|
|
|
1:15:40.720 --> 1:15:43.720 |
|
But there's also a large space that |
|
|
|
1:15:43.720 --> 1:15:51.480 |
|
creates division and destruction, civil war, |
|
|
|
1:15:51.480 --> 1:15:53.160 |
|
a lot of bad stuff. |
|
|
|
1:15:53.160 --> 1:15:56.920 |
|
And the worry is, naturally, probably that space |
|
|
|
1:15:56.920 --> 1:15:59.160 |
|
is bigger, first of all. |
|
|
|
1:15:59.160 --> 1:16:04.920 |
|
And if we don't explicitly think about what kind of effects |
|
|
|
1:16:04.920 --> 1:16:08.320 |
|
are going to be observed from different objective functions, |
|
|
|
1:16:08.320 --> 1:16:10.160 |
|
then we're going to get into trouble. |
|
|
|
1:16:10.160 --> 1:16:14.480 |
|
But the question is, how do we get into rooms |
|
|
|
1:16:14.480 --> 1:16:18.560 |
|
and have discussions, so inside Google, inside Facebook, |
|
|
|
1:16:18.560 --> 1:16:21.840 |
|
inside Twitter, and think about, OK, |
|
|
|
1:16:21.840 --> 1:16:24.840 |
|
how can we drive up engagement and, at the same time, |
|
|
|
1:16:24.840 --> 1:16:28.200 |
|
create a good society? |
|
|
|
1:16:28.200 --> 1:16:29.560 |
|
Is it even possible to have that kind |
|
|
|
1:16:29.560 --> 1:16:31.720 |
|
of philosophical discussion? |
|
|
|
1:16:31.720 --> 1:16:33.080 |
|
I think you can definitely try. |
|
|
|
1:16:33.080 --> 1:16:37.280 |
|
So from my perspective, I would feel rather uncomfortable |
|
|
|
1:16:37.280 --> 1:16:41.560 |
|
with companies that are uncomfortable with these new |
|
|
|
1:16:41.560 --> 1:16:47.120 |
|
student algorithms, with them making explicit decisions |
|
|
|
1:16:47.120 --> 1:16:50.440 |
|
to manipulate people's opinions or behaviors, |
|
|
|
1:16:50.440 --> 1:16:53.480 |
|
even if the intent is good, because that's |
|
|
|
1:16:53.480 --> 1:16:55.200 |
|
a very totalitarian mindset. |
|
|
|
1:16:55.200 --> 1:16:57.440 |
|
So instead, what I would like to see |
|
|
|
1:16:57.440 --> 1:16:58.880 |
|
is probably never going to happen, |
|
|
|
1:16:58.880 --> 1:17:00.360 |
|
because it's not super realistic, |
|
|
|
1:17:00.360 --> 1:17:02.520 |
|
but that's actually something I really care about. |
|
|
|
1:17:02.520 --> 1:17:06.280 |
|
I would like all these algorithms |
|
|
|
1:17:06.280 --> 1:17:10.560 |
|
to present configuration settings to their users, |
|
|
|
1:17:10.560 --> 1:17:14.600 |
|
so that the users can actually make the decision about how |
|
|
|
1:17:14.600 --> 1:17:19.000 |
|
they want to be impacted by these information |
|
|
|
1:17:19.000 --> 1:17:21.960 |
|
recommendation, content recommendation algorithms. |
|
|
|
1:17:21.960 --> 1:17:24.240 |
|
For instance, as a user of something |
|
|
|
1:17:24.240 --> 1:17:26.520 |
|
like YouTube or Twitter, maybe I want |
|
|
|
1:17:26.520 --> 1:17:30.280 |
|
to maximize learning about a specific topic. |
|
|
|
1:17:30.280 --> 1:17:36.800 |
|
So I want the algorithm to feed my curiosity, |
|
|
|
1:17:36.800 --> 1:17:38.760 |
|
which is in itself a very interesting problem. |
|
|
|
1:17:38.760 --> 1:17:41.200 |
|
So instead of maximizing my engagement, |
|
|
|
1:17:41.200 --> 1:17:44.600 |
|
it will maximize how fast and how much I'm learning. |
|
|
|
1:17:44.600 --> 1:17:47.360 |
|
And it will also take into account the accuracy, |
|
|
|
1:17:47.360 --> 1:17:50.680 |
|
hopefully, of the information I'm learning. |
|
|
|
1:17:50.680 --> 1:17:55.680 |
|
So yeah, the user should be able to determine exactly |
|
|
|
1:17:55.680 --> 1:17:58.560 |
|
how these algorithms are affecting their lives. |
|
|
|
1:17:58.560 --> 1:18:03.520 |
|
I don't want actually any entity making decisions |
|
|
|
1:18:03.520 --> 1:18:09.480 |
|
about in which direction they're going to try to manipulate me. |
|
|
|
1:18:09.480 --> 1:18:11.680 |
|
I want technology. |
|
|
|
1:18:11.680 --> 1:18:14.280 |
|
So AI, these algorithms are increasingly |
|
|
|
1:18:14.280 --> 1:18:18.560 |
|
going to be our interface to a world that is increasingly |
|
|
|
1:18:18.560 --> 1:18:19.960 |
|
made of information. |
|
|
|
1:18:19.960 --> 1:18:25.840 |
|
And I want everyone to be in control of this interface, |
|
|
|
1:18:25.840 --> 1:18:29.160 |
|
to interface with the world on their own terms. |
|
|
|
1:18:29.160 --> 1:18:32.840 |
|
So if someone wants these algorithms |
|
|
|
1:18:32.840 --> 1:18:37.640 |
|
to serve their own personal growth goals, |
|
|
|
1:18:37.640 --> 1:18:40.640 |
|
they should be able to configure these algorithms |
|
|
|
1:18:40.640 --> 1:18:41.800 |
|
in such a way. |
|
|
|
1:18:41.800 --> 1:18:46.680 |
|
Yeah, but so I know it's painful to have explicit decisions. |
|
|
|
1:18:46.680 --> 1:18:51.080 |
|
But there is underlying explicit decisions, |
|
|
|
1:18:51.080 --> 1:18:53.360 |
|
which is some of the most beautiful fundamental |
|
|
|
1:18:53.360 --> 1:18:57.400 |
|
philosophy that we have before us, |
|
|
|
1:18:57.400 --> 1:19:01.120 |
|
which is personal growth. |
|
|
|
1:19:01.120 --> 1:19:05.680 |
|
If I want to watch videos from which I can learn, |
|
|
|
1:19:05.680 --> 1:19:08.080 |
|
what does that mean? |
|
|
|
1:19:08.080 --> 1:19:11.800 |
|
So if I have a checkbox that wants to emphasize learning, |
|
|
|
1:19:11.800 --> 1:19:15.480 |
|
there's still an algorithm with explicit decisions in it |
|
|
|
1:19:15.480 --> 1:19:17.800 |
|
that would promote learning. |
|
|
|
1:19:17.800 --> 1:19:19.200 |
|
What does that mean for me? |
|
|
|
1:19:19.200 --> 1:19:22.800 |
|
For example, I've watched a documentary on flat Earth |
|
|
|
1:19:22.800 --> 1:19:23.640 |
|
theory, I guess. |
|
|
|
1:19:27.280 --> 1:19:28.240 |
|
I learned a lot. |
|
|
|
1:19:28.240 --> 1:19:29.800 |
|
I'm really glad I watched it. |
|
|
|
1:19:29.800 --> 1:19:32.560 |
|
It was a friend recommended it to me. |
|
|
|
1:19:32.560 --> 1:19:35.800 |
|
Because I don't have such an allergic reaction to crazy |
|
|
|
1:19:35.800 --> 1:19:37.640 |
|
people, as my fellow colleagues do. |
|
|
|
1:19:37.640 --> 1:19:40.360 |
|
But it was very eye opening. |
|
|
|
1:19:40.360 --> 1:19:42.120 |
|
And for others, it might not be. |
|
|
|
1:19:42.120 --> 1:19:45.560 |
|
From others, they might just get turned off from that, same |
|
|
|
1:19:45.560 --> 1:19:47.160 |
|
with Republican and Democrat. |
|
|
|
1:19:47.160 --> 1:19:50.200 |
|
And it's a non trivial problem. |
|
|
|
1:19:50.200 --> 1:19:52.880 |
|
And first of all, if it's done well, |
|
|
|
1:19:52.880 --> 1:19:56.560 |
|
I don't think it's something that wouldn't happen, |
|
|
|
1:19:56.560 --> 1:19:59.280 |
|
that YouTube wouldn't be promoting, |
|
|
|
1:19:59.280 --> 1:20:00.200 |
|
or Twitter wouldn't be. |
|
|
|
1:20:00.200 --> 1:20:02.280 |
|
It's just a really difficult problem, |
|
|
|
1:20:02.280 --> 1:20:05.520 |
|
how to give people control. |
|
|
|
1:20:05.520 --> 1:20:08.960 |
|
Well, it's mostly an interface design problem. |
|
|
|
1:20:08.960 --> 1:20:11.080 |
|
The way I see it, you want to create technology |
|
|
|
1:20:11.080 --> 1:20:16.400 |
|
that's like a mentor, or a coach, or an assistant, |
|
|
|
1:20:16.400 --> 1:20:20.520 |
|
so that it's not your boss. |
|
|
|
1:20:20.520 --> 1:20:22.560 |
|
You are in control of it. |
|
|
|
1:20:22.560 --> 1:20:25.760 |
|
You are telling it what to do for you. |
|
|
|
1:20:25.760 --> 1:20:27.840 |
|
And if you feel like it's manipulating you, |
|
|
|
1:20:27.840 --> 1:20:31.760 |
|
it's not actually doing what you want. |
|
|
|
1:20:31.760 --> 1:20:34.920 |
|
You should be able to switch to a different algorithm. |
|
|
|
1:20:34.920 --> 1:20:36.440 |
|
So that's fine tune control. |
|
|
|
1:20:36.440 --> 1:20:38.840 |
|
You kind of learn that you're trusting |
|
|
|
1:20:38.840 --> 1:20:40.080 |
|
the human collaboration. |
|
|
|
1:20:40.080 --> 1:20:41.920 |
|
I mean, that's how I see autonomous vehicles too, |
|
|
|
1:20:41.920 --> 1:20:44.480 |
|
is giving as much information as possible, |
|
|
|
1:20:44.480 --> 1:20:47.240 |
|
and you learn that dance yourself. |
|
|
|
1:20:47.240 --> 1:20:50.280 |
|
Yeah, Adobe, I don't know if you use Adobe product |
|
|
|
1:20:50.280 --> 1:20:52.280 |
|
for like Photoshop. |
|
|
|
1:20:52.280 --> 1:20:55.040 |
|
They're trying to see if they can inject YouTube |
|
|
|
1:20:55.040 --> 1:20:57.120 |
|
into their interface, but basically allow you |
|
|
|
1:20:57.120 --> 1:20:59.840 |
|
to show you all these videos, |
|
|
|
1:20:59.840 --> 1:21:03.320 |
|
that everybody's confused about what to do with features. |
|
|
|
1:21:03.320 --> 1:21:07.120 |
|
So basically teach people by linking to, |
|
|
|
1:21:07.120 --> 1:21:10.280 |
|
in that way, it's an assistant that uses videos |
|
|
|
1:21:10.280 --> 1:21:13.440 |
|
as a basic element of information. |
|
|
|
1:21:13.440 --> 1:21:18.240 |
|
Okay, so what practically should people do |
|
|
|
1:21:18.240 --> 1:21:24.000 |
|
to try to fight against abuses of these algorithms, |
|
|
|
1:21:24.000 --> 1:21:27.400 |
|
or algorithms that manipulate us? |
|
|
|
1:21:27.400 --> 1:21:29.280 |
|
Honestly, it's a very, very difficult problem, |
|
|
|
1:21:29.280 --> 1:21:32.800 |
|
because to start with, there is very little public awareness |
|
|
|
1:21:32.800 --> 1:21:35.040 |
|
of these issues. |
|
|
|
1:21:35.040 --> 1:21:38.520 |
|
Very few people would think there's anything wrong |
|
|
|
1:21:38.520 --> 1:21:39.720 |
|
with the unused algorithm, |
|
|
|
1:21:39.720 --> 1:21:42.040 |
|
even though there is actually something wrong already, |
|
|
|
1:21:42.040 --> 1:21:44.480 |
|
which is that it's trying to maximize engagement |
|
|
|
1:21:44.480 --> 1:21:49.880 |
|
most of the time, which has very negative side effects. |
|
|
|
1:21:49.880 --> 1:21:56.160 |
|
So ideally, so the very first thing is to stop |
|
|
|
1:21:56.160 --> 1:21:59.560 |
|
trying to purely maximize engagement, |
|
|
|
1:21:59.560 --> 1:22:06.560 |
|
try to propagate content based on popularity, right? |
|
|
|
1:22:06.560 --> 1:22:11.040 |
|
Instead, take into account the goals |
|
|
|
1:22:11.040 --> 1:22:13.560 |
|
and the profiles of each user. |
|
|
|
1:22:13.560 --> 1:22:16.920 |
|
So you will be, one example is, for instance, |
|
|
|
1:22:16.920 --> 1:22:20.800 |
|
when I look at topic recommendations on Twitter, |
|
|
|
1:22:20.800 --> 1:22:24.480 |
|
it's like, you know, they have this news tab |
|
|
|
1:22:24.480 --> 1:22:25.480 |
|
with switch recommendations. |
|
|
|
1:22:25.480 --> 1:22:28.480 |
|
It's always the worst coverage, |
|
|
|
1:22:28.480 --> 1:22:30.360 |
|
because it's content that appeals |
|
|
|
1:22:30.360 --> 1:22:34.080 |
|
to the smallest common denominator |
|
|
|
1:22:34.080 --> 1:22:37.080 |
|
to all Twitter users, because they're trying to optimize. |
|
|
|
1:22:37.080 --> 1:22:39.040 |
|
They're purely trying to optimize popularity. |
|
|
|
1:22:39.040 --> 1:22:41.320 |
|
They're purely trying to optimize engagement. |
|
|
|
1:22:41.320 --> 1:22:42.960 |
|
But that's not what I want. |
|
|
|
1:22:42.960 --> 1:22:46.080 |
|
So they should put me in control of some setting |
|
|
|
1:22:46.080 --> 1:22:50.360 |
|
so that I define what's the objective function |
|
|
|
1:22:50.360 --> 1:22:52.200 |
|
that Twitter is going to be following |
|
|
|
1:22:52.200 --> 1:22:54.120 |
|
to show me this content. |
|
|
|
1:22:54.120 --> 1:22:57.360 |
|
And honestly, so this is all about interface design. |
|
|
|
1:22:57.360 --> 1:22:59.440 |
|
And we are not, it's not realistic |
|
|
|
1:22:59.440 --> 1:23:01.760 |
|
to give users control of a bunch of knobs |
|
|
|
1:23:01.760 --> 1:23:03.400 |
|
that define algorithm. |
|
|
|
1:23:03.400 --> 1:23:06.760 |
|
Instead, we should purely put them in charge |
|
|
|
1:23:06.760 --> 1:23:09.400 |
|
of defining the objective function. |
|
|
|
1:23:09.400 --> 1:23:13.240 |
|
Like, let the user tell us what they want to achieve, |
|
|
|
1:23:13.240 --> 1:23:15.280 |
|
how they want this algorithm to impact their lives. |
|
|
|
1:23:15.280 --> 1:23:16.680 |
|
So do you think it is that, |
|
|
|
1:23:16.680 --> 1:23:19.360 |
|
or do they provide individual article by article |
|
|
|
1:23:19.360 --> 1:23:21.600 |
|
reward structure where you give a signal, |
|
|
|
1:23:21.600 --> 1:23:24.720 |
|
I'm glad I saw this, or I'm glad I didn't? |
|
|
|
1:23:24.720 --> 1:23:28.480 |
|
So like a Spotify type feedback mechanism, |
|
|
|
1:23:28.480 --> 1:23:30.680 |
|
it works to some extent. |
|
|
|
1:23:30.680 --> 1:23:32.000 |
|
I'm kind of skeptical about it |
|
|
|
1:23:32.000 --> 1:23:34.880 |
|
because the only way the algorithm, |
|
|
|
1:23:34.880 --> 1:23:39.120 |
|
the algorithm will attempt to relate your choices |
|
|
|
1:23:39.120 --> 1:23:41.040 |
|
with the choices of everyone else, |
|
|
|
1:23:41.040 --> 1:23:45.000 |
|
which might, you know, if you have an average profile |
|
|
|
1:23:45.000 --> 1:23:47.880 |
|
that works fine, I'm sure Spotify accommodations work fine |
|
|
|
1:23:47.880 --> 1:23:49.560 |
|
if you just like mainstream stuff. |
|
|
|
1:23:49.560 --> 1:23:53.960 |
|
If you don't, it can be, it's not optimal at all actually. |
|
|
|
1:23:53.960 --> 1:23:56.040 |
|
It'll be in an efficient search |
|
|
|
1:23:56.040 --> 1:24:00.800 |
|
for the part of the Spotify world that represents you. |
|
|
|
1:24:00.800 --> 1:24:02.960 |
|
So it's a tough problem, |
|
|
|
1:24:02.960 --> 1:24:07.960 |
|
but do note that even a feedback system |
|
|
|
1:24:07.960 --> 1:24:10.880 |
|
like what Spotify has does not give me control |
|
|
|
1:24:10.880 --> 1:24:15.000 |
|
over what the algorithm is trying to optimize for. |
|
|
|
1:24:16.320 --> 1:24:19.360 |
|
Well, public awareness, which is what we're doing now, |
|
|
|
1:24:19.360 --> 1:24:21.360 |
|
is a good place to start. |
|
|
|
1:24:21.360 --> 1:24:25.960 |
|
Do you have concerns about longterm existential threats |
|
|
|
1:24:25.960 --> 1:24:27.360 |
|
of artificial intelligence? |
|
|
|
1:24:28.280 --> 1:24:31.040 |
|
Well, as I was saying, |
|
|
|
1:24:31.040 --> 1:24:33.360 |
|
our world is increasingly made of information. |
|
|
|
1:24:33.360 --> 1:24:36.240 |
|
AI algorithms are increasingly going to be our interface |
|
|
|
1:24:36.240 --> 1:24:37.880 |
|
to this world of information, |
|
|
|
1:24:37.880 --> 1:24:41.480 |
|
and somebody will be in control of these algorithms. |
|
|
|
1:24:41.480 --> 1:24:45.920 |
|
And that puts us in any kind of a bad situation, right? |
|
|
|
1:24:45.920 --> 1:24:46.880 |
|
It has risks. |
|
|
|
1:24:46.880 --> 1:24:50.840 |
|
It has risks coming from potentially large companies |
|
|
|
1:24:50.840 --> 1:24:53.760 |
|
wanting to optimize their own goals, |
|
|
|
1:24:53.760 --> 1:24:55.960 |
|
maybe profit, maybe something else. |
|
|
|
1:24:55.960 --> 1:25:00.720 |
|
Also from governments who might want to use these algorithms |
|
|
|
1:25:00.720 --> 1:25:03.520 |
|
as a means of control of the population. |
|
|
|
1:25:03.520 --> 1:25:05.000 |
|
Do you think there's existential threat |
|
|
|
1:25:05.000 --> 1:25:06.320 |
|
that could arise from that? |
|
|
|
1:25:06.320 --> 1:25:09.120 |
|
So existential threat. |
|
|
|
1:25:09.120 --> 1:25:13.240 |
|
So maybe you're referring to the singularity narrative |
|
|
|
1:25:13.240 --> 1:25:15.560 |
|
where robots just take over. |
|
|
|
1:25:15.560 --> 1:25:18.320 |
|
Well, I don't, I'm not terminating robots, |
|
|
|
1:25:18.320 --> 1:25:21.000 |
|
and I don't believe it has to be a singularity. |
|
|
|
1:25:21.000 --> 1:25:24.800 |
|
We're just talking to, just like you said, |
|
|
|
1:25:24.800 --> 1:25:27.920 |
|
the algorithm controlling masses of populations. |
|
|
|
1:25:28.920 --> 1:25:31.120 |
|
The existential threat being, |
|
|
|
1:25:32.640 --> 1:25:36.760 |
|
hurt ourselves much like a nuclear war would hurt ourselves. |
|
|
|
1:25:36.760 --> 1:25:37.600 |
|
That kind of thing. |
|
|
|
1:25:37.600 --> 1:25:39.480 |
|
I don't think that requires a singularity. |
|
|
|
1:25:39.480 --> 1:25:42.560 |
|
That requires a loss of control over AI algorithm. |
|
|
|
1:25:42.560 --> 1:25:43.560 |
|
Yes. |
|
|
|
1:25:43.560 --> 1:25:47.000 |
|
So I do agree there are concerning trends. |
|
|
|
1:25:47.000 --> 1:25:52.000 |
|
Honestly, I wouldn't want to make any longterm predictions. |
|
|
|
1:25:52.960 --> 1:25:56.000 |
|
I don't think today we really have the capability |
|
|
|
1:25:56.000 --> 1:25:58.560 |
|
to see what the dangers of AI |
|
|
|
1:25:58.560 --> 1:26:01.360 |
|
are going to be in 50 years, in 100 years. |
|
|
|
1:26:01.360 --> 1:26:04.800 |
|
I do see that we are already faced |
|
|
|
1:26:04.800 --> 1:26:08.840 |
|
with concrete and present dangers |
|
|
|
1:26:08.840 --> 1:26:11.560 |
|
surrounding the negative side effects |
|
|
|
1:26:11.560 --> 1:26:14.960 |
|
of content recombination systems, of newsfeed algorithms |
|
|
|
1:26:14.960 --> 1:26:17.640 |
|
concerning algorithmic bias as well. |
|
|
|
1:26:18.640 --> 1:26:21.200 |
|
So we are delegating more and more |
|
|
|
1:26:22.240 --> 1:26:25.080 |
|
decision processes to algorithms. |
|
|
|
1:26:25.080 --> 1:26:26.760 |
|
Some of these algorithms are uncrafted, |
|
|
|
1:26:26.760 --> 1:26:29.360 |
|
some are learned from data, |
|
|
|
1:26:29.360 --> 1:26:31.920 |
|
but we are delegating control. |
|
|
|
1:26:32.920 --> 1:26:36.280 |
|
Sometimes it's a good thing, sometimes not so much. |
|
|
|
1:26:36.280 --> 1:26:39.480 |
|
And there is in general very little supervision |
|
|
|
1:26:39.480 --> 1:26:41.000 |
|
of this process, right? |
|
|
|
1:26:41.000 --> 1:26:45.400 |
|
So we are still in this period of very fast change, |
|
|
|
1:26:45.400 --> 1:26:50.400 |
|
even chaos, where society is restructuring itself, |
|
|
|
1:26:50.920 --> 1:26:53.160 |
|
turning into an information society, |
|
|
|
1:26:53.160 --> 1:26:54.520 |
|
which itself is turning into |
|
|
|
1:26:54.520 --> 1:26:58.360 |
|
an increasingly automated information passing society. |
|
|
|
1:26:58.360 --> 1:27:02.520 |
|
And well, yeah, I think the best we can do today |
|
|
|
1:27:02.520 --> 1:27:06.040 |
|
is try to raise awareness around some of these issues. |
|
|
|
1:27:06.040 --> 1:27:07.680 |
|
And I think we're actually making good progress. |
|
|
|
1:27:07.680 --> 1:27:11.720 |
|
If you look at algorithmic bias, for instance, |
|
|
|
1:27:12.760 --> 1:27:14.760 |
|
three years ago, even two years ago, |
|
|
|
1:27:14.760 --> 1:27:17.040 |
|
very, very few people were talking about it. |
|
|
|
1:27:17.040 --> 1:27:20.320 |
|
And now all the big companies are talking about it. |
|
|
|
1:27:20.320 --> 1:27:22.360 |
|
They are often not in a very serious way, |
|
|
|
1:27:22.360 --> 1:27:24.560 |
|
but at least it is part of the public discourse. |
|
|
|
1:27:24.560 --> 1:27:27.080 |
|
You see people in Congress talking about it. |
|
|
|
1:27:27.080 --> 1:27:31.960 |
|
And it all started from raising awareness. |
|
|
|
1:27:31.960 --> 1:27:32.800 |
|
Right. |
|
|
|
1:27:32.800 --> 1:27:36.080 |
|
So in terms of alignment problem, |
|
|
|
1:27:36.080 --> 1:27:39.400 |
|
trying to teach as we allow algorithms, |
|
|
|
1:27:39.400 --> 1:27:41.520 |
|
just even recommender systems on Twitter, |
|
|
|
1:27:43.640 --> 1:27:47.080 |
|
encoding human values and morals, |
|
|
|
1:27:48.280 --> 1:27:50.200 |
|
decisions that touch on ethics, |
|
|
|
1:27:50.200 --> 1:27:52.600 |
|
how hard do you think that problem is? |
|
|
|
1:27:52.600 --> 1:27:57.240 |
|
How do we have lost functions in neural networks |
|
|
|
1:27:57.240 --> 1:27:58.640 |
|
that have some component, |
|
|
|
1:27:58.640 --> 1:28:01.080 |
|
some fuzzy components of human morals? |
|
|
|
1:28:01.080 --> 1:28:06.080 |
|
Well, I think this is really all about objective function engineering, |
|
|
|
1:28:06.080 --> 1:28:10.520 |
|
which is probably going to be increasingly a topic of concern in the future. |
|
|
|
1:28:10.520 --> 1:28:14.640 |
|
Like for now, we're just using very naive loss functions |
|
|
|
1:28:14.640 --> 1:28:17.760 |
|
because the hard part is not actually what you're trying to minimize. |
|
|
|
1:28:17.760 --> 1:28:19.040 |
|
It's everything else. |
|
|
|
1:28:19.040 --> 1:28:22.840 |
|
But as the everything else is going to be increasingly automated, |
|
|
|
1:28:22.840 --> 1:28:27.040 |
|
we're going to be focusing our human attention |
|
|
|
1:28:27.040 --> 1:28:30.240 |
|
on increasingly high level components, |
|
|
|
1:28:30.240 --> 1:28:32.680 |
|
like what's actually driving the whole learning system, |
|
|
|
1:28:32.680 --> 1:28:33.960 |
|
like the objective function. |
|
|
|
1:28:33.960 --> 1:28:36.920 |
|
So loss function engineering is going to be, |
|
|
|
1:28:36.920 --> 1:28:40.640 |
|
loss function engineer is probably going to be a job title in the future. |
|
|
|
1:28:40.640 --> 1:28:44.520 |
|
And then the tooling you're creating with Keras essentially |
|
|
|
1:28:44.520 --> 1:28:47.040 |
|
takes care of all the details underneath. |
|
|
|
1:28:47.040 --> 1:28:52.720 |
|
And basically the human expert is needed for exactly that. |
|
|
|
1:28:52.720 --> 1:28:53.920 |
|
That's the idea. |
|
|
|
1:28:53.920 --> 1:28:57.640 |
|
Keras is the interface between the data you're collecting |
|
|
|
1:28:57.640 --> 1:28:59.080 |
|
and the business goals. |
|
|
|
1:28:59.080 --> 1:29:03.480 |
|
And your job as an engineer is going to be to express your business goals |
|
|
|
1:29:03.480 --> 1:29:06.720 |
|
and your understanding of your business or your product, |
|
|
|
1:29:06.720 --> 1:29:11.840 |
|
your system as a kind of loss function or a kind of set of constraints. |
|
|
|
1:29:11.840 --> 1:29:19.480 |
|
Does the possibility of creating an AGI system excite you or scare you or bore you? |
|
|
|
1:29:19.480 --> 1:29:22.080 |
|
So intelligence can never really be general. |
|
|
|
1:29:22.080 --> 1:29:26.400 |
|
You know, at best it can have some degree of generality like human intelligence. |
|
|
|
1:29:26.400 --> 1:29:30.640 |
|
It also always has some specialization in the same way that human intelligence |
|
|
|
1:29:30.640 --> 1:29:33.440 |
|
is specialized in a certain category of problems, |
|
|
|
1:29:33.440 --> 1:29:35.440 |
|
is specialized in the human experience. |
|
|
|
1:29:35.440 --> 1:29:37.280 |
|
And when people talk about AGI, |
|
|
|
1:29:37.280 --> 1:29:42.520 |
|
I'm never quite sure if they're talking about very, very smart AI, |
|
|
|
1:29:42.520 --> 1:29:45.080 |
|
so smart that it's even smarter than humans, |
|
|
|
1:29:45.080 --> 1:29:48.000 |
|
or they're talking about human like intelligence, |
|
|
|
1:29:48.000 --> 1:29:49.680 |
|
because these are different things. |
|
|
|
1:29:49.680 --> 1:29:54.760 |
|
Let's say, presumably I'm oppressing you today with my humanness. |
|
|
|
1:29:54.760 --> 1:29:59.240 |
|
So imagine that I was in fact a robot. |
|
|
|
1:29:59.240 --> 1:30:01.920 |
|
So what does that mean? |
|
|
|
1:30:01.920 --> 1:30:04.920 |
|
That I'm impressing you with natural language processing. |
|
|
|
1:30:04.920 --> 1:30:07.840 |
|
Maybe if you weren't able to see me, maybe this is a phone call. |
|
|
|
1:30:07.840 --> 1:30:10.000 |
|
So that kind of system. |
|
|
|
1:30:10.000 --> 1:30:11.120 |
|
Companion. |
|
|
|
1:30:11.120 --> 1:30:15.040 |
|
So that's very much about building human like AI. |
|
|
|
1:30:15.040 --> 1:30:18.200 |
|
And you're asking me, you know, is this an exciting perspective? |
|
|
|
1:30:18.200 --> 1:30:19.440 |
|
Yes. |
|
|
|
1:30:19.440 --> 1:30:21.760 |
|
I think so, yes. |
|
|
|
1:30:21.760 --> 1:30:28.000 |
|
Not so much because of what artificial human like intelligence could do, |
|
|
|
1:30:28.000 --> 1:30:30.880 |
|
but, you know, from an intellectual perspective, |
|
|
|
1:30:30.880 --> 1:30:34.120 |
|
I think if you could build truly human like intelligence, |
|
|
|
1:30:34.120 --> 1:30:37.240 |
|
that means you could actually understand human intelligence, |
|
|
|
1:30:37.240 --> 1:30:39.880 |
|
which is fascinating, right? |
|
|
|
1:30:39.880 --> 1:30:42.680 |
|
Human like intelligence is going to require emotions. |
|
|
|
1:30:42.680 --> 1:30:44.400 |
|
It's going to require consciousness, |
|
|
|
1:30:44.400 --> 1:30:49.720 |
|
which is not things that would normally be required by an intelligent system. |
|
|
|
1:30:49.720 --> 1:30:53.160 |
|
If you look at, you know, we were mentioning earlier like science |
|
|
|
1:30:53.160 --> 1:30:59.600 |
|
as a superhuman problem solving agent or system, |
|
|
|
1:30:59.600 --> 1:31:02.120 |
|
it does not have consciousness, it doesn't have emotions. |
|
|
|
1:31:02.120 --> 1:31:04.320 |
|
In general, so emotions, |
|
|
|
1:31:04.320 --> 1:31:07.640 |
|
I see consciousness as being on the same spectrum as emotions. |
|
|
|
1:31:07.640 --> 1:31:12.280 |
|
It is a component of the subjective experience |
|
|
|
1:31:12.280 --> 1:31:18.800 |
|
that is meant very much to guide behavior generation, right? |
|
|
|
1:31:18.800 --> 1:31:20.800 |
|
It's meant to guide your behavior. |
|
|
|
1:31:20.800 --> 1:31:24.520 |
|
In general, human intelligence and animal intelligence |
|
|
|
1:31:24.520 --> 1:31:29.280 |
|
has evolved for the purpose of behavior generation, right? |
|
|
|
1:31:29.280 --> 1:31:30.680 |
|
Including in a social context. |
|
|
|
1:31:30.680 --> 1:31:32.480 |
|
So that's why we actually need emotions. |
|
|
|
1:31:32.480 --> 1:31:34.920 |
|
That's why we need consciousness. |
|
|
|
1:31:34.920 --> 1:31:38.360 |
|
An artificial intelligence system developed in a different context |
|
|
|
1:31:38.360 --> 1:31:42.800 |
|
may well never need them, may well never be conscious like science. |
|
|
|
1:31:42.800 --> 1:31:47.960 |
|
Well, on that point, I would argue it's possible to imagine |
|
|
|
1:31:47.960 --> 1:31:51.480 |
|
that there's echoes of consciousness in science |
|
|
|
1:31:51.480 --> 1:31:55.480 |
|
when viewed as an organism, that science is consciousness. |
|
|
|
1:31:55.480 --> 1:31:59.160 |
|
So, I mean, how would you go about testing this hypothesis? |
|
|
|
1:31:59.160 --> 1:32:07.000 |
|
How do you probe the subjective experience of an abstract system like science? |
|
|
|
1:32:07.000 --> 1:32:10.400 |
|
Well, the point of probing any subjective experience is impossible |
|
|
|
1:32:10.400 --> 1:32:13.200 |
|
because I'm not science, I'm Lex. |
|
|
|
1:32:13.200 --> 1:32:20.520 |
|
So I can't probe another entity, it's no more than bacteria on my skin. |
|
|
|
1:32:20.520 --> 1:32:24.160 |
|
You're Lex, I can ask you questions about your subjective experience |
|
|
|
1:32:24.160 --> 1:32:28.440 |
|
and you can answer me, and that's how I know you're conscious. |
|
|
|
1:32:28.440 --> 1:32:31.840 |
|
Yes, but that's because we speak the same language. |
|
|
|
1:32:31.840 --> 1:32:35.520 |
|
You perhaps, we have to speak the language of science in order to ask it. |
|
|
|
1:32:35.520 --> 1:32:40.320 |
|
Honestly, I don't think consciousness, just like emotions of pain and pleasure, |
|
|
|
1:32:40.320 --> 1:32:44.160 |
|
is not something that inevitably arises |
|
|
|
1:32:44.160 --> 1:32:47.920 |
|
from any sort of sufficiently intelligent information processing. |
|
|
|
1:32:47.920 --> 1:32:53.920 |
|
It is a feature of the mind, and if you've not implemented it explicitly, it is not there. |
|
|
|
1:32:53.920 --> 1:32:58.960 |
|
So you think it's an emergent feature of a particular architecture. |
|
|
|
1:32:58.960 --> 1:33:00.320 |
|
So do you think... |
|
|
|
1:33:00.320 --> 1:33:02.000 |
|
It's a feature in the same sense. |
|
|
|
1:33:02.000 --> 1:33:08.240 |
|
So, again, the subjective experience is all about guiding behavior. |
|
|
|
1:33:08.240 --> 1:33:15.120 |
|
If the problems you're trying to solve don't really involve an embodied agent, |
|
|
|
1:33:15.120 --> 1:33:19.520 |
|
maybe in a social context, generating behavior and pursuing goals like this. |
|
|
|
1:33:19.520 --> 1:33:22.160 |
|
And if you look at science, that's not really what's happening. |
|
|
|
1:33:22.160 --> 1:33:27.920 |
|
Even though it is, it is a form of artificial AI, artificial intelligence, |
|
|
|
1:33:27.920 --> 1:33:31.920 |
|
in the sense that it is solving problems, it is accumulating knowledge, |
|
|
|
1:33:31.920 --> 1:33:35.040 |
|
accumulating solutions and so on. |
|
|
|
1:33:35.040 --> 1:33:39.440 |
|
So if you're not explicitly implementing a subjective experience, |
|
|
|
1:33:39.440 --> 1:33:44.000 |
|
implementing certain emotions and implementing consciousness, |
|
|
|
1:33:44.000 --> 1:33:47.360 |
|
it's not going to just spontaneously emerge. |
|
|
|
1:33:47.360 --> 1:33:48.080 |
|
Yeah. |
|
|
|
1:33:48.080 --> 1:33:53.200 |
|
But so for a system like, human like intelligence system that has consciousness, |
|
|
|
1:33:53.200 --> 1:33:55.840 |
|
do you think it needs to have a body? |
|
|
|
1:33:55.840 --> 1:33:56.720 |
|
Yes, definitely. |
|
|
|
1:33:56.720 --> 1:33:59.600 |
|
I mean, it doesn't have to be a physical body, right? |
|
|
|
1:33:59.600 --> 1:34:03.440 |
|
And there's not that much difference between a realistic simulation in the real world. |
|
|
|
1:34:03.440 --> 1:34:06.400 |
|
So there has to be something you have to preserve kind of thing. |
|
|
|
1:34:06.400 --> 1:34:11.840 |
|
Yes, but human like intelligence can only arise in a human like context. |
|
|
|
1:34:11.840 --> 1:34:16.800 |
|
Intelligence needs other humans in order for you to demonstrate |
|
|
|
1:34:16.800 --> 1:34:19.040 |
|
that you have human like intelligence, essentially. |
|
|
|
1:34:19.040 --> 1:34:19.540 |
|
Yes. |
|
|
|
1:34:20.320 --> 1:34:28.080 |
|
So what kind of tests and demonstration would be sufficient for you |
|
|
|
1:34:28.080 --> 1:34:30.960 |
|
to demonstrate human like intelligence? |
|
|
|
1:34:30.960 --> 1:34:31.360 |
|
Yeah. |
|
|
|
1:34:31.360 --> 1:34:35.600 |
|
Just out of curiosity, you've talked about in terms of theorem proving |
|
|
|
1:34:35.600 --> 1:34:38.000 |
|
and program synthesis, I think you've written about |
|
|
|
1:34:38.000 --> 1:34:40.480 |
|
that there's no good benchmarks for this. |
|
|
|
1:34:40.480 --> 1:34:40.720 |
|
Yeah. |
|
|
|
1:34:40.720 --> 1:34:42.000 |
|
That's one of the problems. |
|
|
|
1:34:42.000 --> 1:34:46.320 |
|
So let's talk program synthesis. |
|
|
|
1:34:46.320 --> 1:34:47.760 |
|
So what do you imagine is a good... |
|
|
|
1:34:48.800 --> 1:34:51.360 |
|
I think it's related questions for human like intelligence |
|
|
|
1:34:51.360 --> 1:34:52.560 |
|
and for program synthesis. |
|
|
|
1:34:53.360 --> 1:34:56.080 |
|
What's a good benchmark for either or both? |
|
|
|
1:34:56.080 --> 1:34:56.480 |
|
Right. |
|
|
|
1:34:56.480 --> 1:34:59.200 |
|
So I mean, you're actually asking two questions, |
|
|
|
1:34:59.200 --> 1:35:02.480 |
|
which is one is about quantifying intelligence |
|
|
|
1:35:02.480 --> 1:35:06.880 |
|
and comparing the intelligence of an artificial system |
|
|
|
1:35:06.880 --> 1:35:08.480 |
|
to the intelligence for human. |
|
|
|
1:35:08.480 --> 1:35:13.440 |
|
And the other is about the degree to which this intelligence is human like. |
|
|
|
1:35:13.440 --> 1:35:15.120 |
|
It's actually two different questions. |
|
|
|
1:35:16.560 --> 1:35:18.960 |
|
So you mentioned earlier the Turing test. |
|
|
|
1:35:19.680 --> 1:35:23.200 |
|
Well, I actually don't like the Turing test because it's very lazy. |
|
|
|
1:35:23.200 --> 1:35:28.720 |
|
It's all about completely bypassing the problem of defining and measuring intelligence |
|
|
|
1:35:28.720 --> 1:35:34.160 |
|
and instead delegating to a human judge or a panel of human judges. |
|
|
|
1:35:34.160 --> 1:35:37.120 |
|
So it's a total copout, right? |
|
|
|
1:35:38.160 --> 1:35:43.200 |
|
If you want to measure how human like an agent is, |
|
|
|
1:35:43.760 --> 1:35:46.640 |
|
I think you have to make it interact with other humans. |
|
|
|
1:35:47.600 --> 1:35:53.760 |
|
Maybe it's not necessarily a good idea to have these other humans be the judges. |
|
|
|
1:35:53.760 --> 1:35:59.280 |
|
Maybe you should just observe behavior and compare it to what a human would actually have done. |
|
|
|
1:36:00.560 --> 1:36:05.120 |
|
When it comes to measuring how smart, how clever an agent is |
|
|
|
1:36:05.120 --> 1:36:11.120 |
|
and comparing that to the degree of human intelligence. |
|
|
|
1:36:11.120 --> 1:36:12.960 |
|
So we're already talking about two things, right? |
|
|
|
1:36:13.520 --> 1:36:20.320 |
|
The degree, kind of like the magnitude of an intelligence and its direction, right? |
|
|
|
1:36:20.320 --> 1:36:23.280 |
|
Like the norm of a vector and its direction. |
|
|
|
1:36:23.280 --> 1:36:32.000 |
|
And the direction is like human likeness and the magnitude, the norm is intelligence. |
|
|
|
1:36:32.720 --> 1:36:34.080 |
|
You could call it intelligence, right? |
|
|
|
1:36:34.080 --> 1:36:41.040 |
|
So the direction, your sense, the space of directions that are human like is very narrow. |
|
|
|
1:36:41.040 --> 1:36:41.200 |
|
Yeah. |
|
|
|
1:36:42.240 --> 1:36:48.880 |
|
So the way you would measure the magnitude of intelligence in a system |
|
|
|
1:36:48.880 --> 1:36:54.640 |
|
in a way that also enables you to compare it to that of a human. |
|
|
|
1:36:54.640 --> 1:36:59.200 |
|
Well, if you look at different benchmarks for intelligence today, |
|
|
|
1:36:59.200 --> 1:37:04.160 |
|
they're all too focused on skill at a given task. |
|
|
|
1:37:04.160 --> 1:37:08.720 |
|
Like skill at playing chess, skill at playing Go, skill at playing Dota. |
|
|
|
1:37:10.720 --> 1:37:15.600 |
|
And I think that's not the right way to go about it because you can always |
|
|
|
1:37:15.600 --> 1:37:18.240 |
|
beat a human at one specific task. |
|
|
|
1:37:19.200 --> 1:37:23.920 |
|
The reason why our skill at playing Go or juggling or anything is impressive |
|
|
|
1:37:23.920 --> 1:37:28.400 |
|
is because we are expressing this skill within a certain set of constraints. |
|
|
|
1:37:28.400 --> 1:37:32.320 |
|
If you remove the constraints, the constraints that we have one lifetime, |
|
|
|
1:37:32.320 --> 1:37:36.080 |
|
that we have this body and so on, if you remove the context, |
|
|
|
1:37:36.080 --> 1:37:40.480 |
|
if you have unlimited string data, if you can have access to, you know, |
|
|
|
1:37:40.480 --> 1:37:44.640 |
|
for instance, if you look at juggling, if you have no restriction on the hardware, |
|
|
|
1:37:44.640 --> 1:37:48.400 |
|
then achieving arbitrary levels of skill is not very interesting |
|
|
|
1:37:48.400 --> 1:37:52.400 |
|
and says nothing about the amount of intelligence you've achieved. |
|
|
|
1:37:52.400 --> 1:37:57.440 |
|
So if you want to measure intelligence, you need to rigorously define what |
|
|
|
1:37:57.440 --> 1:38:02.960 |
|
intelligence is, which in itself, you know, it's a very challenging problem. |
|
|
|
1:38:02.960 --> 1:38:04.320 |
|
And do you think that's possible? |
|
|
|
1:38:04.320 --> 1:38:06.000 |
|
To define intelligence? Yes, absolutely. |
|
|
|
1:38:06.000 --> 1:38:09.760 |
|
I mean, you can provide, many people have provided, you know, some definition. |
|
|
|
1:38:10.560 --> 1:38:12.000 |
|
I have my own definition. |
|
|
|
1:38:12.000 --> 1:38:13.440 |
|
Where does your definition begin? |
|
|
|
1:38:13.440 --> 1:38:16.240 |
|
Where does your definition begin if it doesn't end? |
|
|
|
1:38:16.240 --> 1:38:21.680 |
|
Well, I think intelligence is essentially the efficiency |
|
|
|
1:38:22.320 --> 1:38:29.760 |
|
with which you turn experience into generalizable programs. |
|
|
|
1:38:29.760 --> 1:38:32.800 |
|
So what that means is it's the efficiency with which |
|
|
|
1:38:32.800 --> 1:38:36.720 |
|
you turn a sampling of experience space into |
|
|
|
1:38:36.720 --> 1:38:46.000 |
|
the ability to process a larger chunk of experience space. |
|
|
|
1:38:46.000 --> 1:38:52.560 |
|
So measuring skill can be one proxy across many different tasks, |
|
|
|
1:38:52.560 --> 1:38:54.480 |
|
can be one proxy for measuring intelligence. |
|
|
|
1:38:54.480 --> 1:38:58.720 |
|
But if you want to only measure skill, you should control for two things. |
|
|
|
1:38:58.720 --> 1:39:04.960 |
|
You should control for the amount of experience that your system has |
|
|
|
1:39:04.960 --> 1:39:08.080 |
|
and the priors that your system has. |
|
|
|
1:39:08.080 --> 1:39:13.120 |
|
But if you look at two agents and you give them the same priors |
|
|
|
1:39:13.120 --> 1:39:16.160 |
|
and you give them the same amount of experience, |
|
|
|
1:39:16.160 --> 1:39:21.360 |
|
there is one of the agents that is going to learn programs, |
|
|
|
1:39:21.360 --> 1:39:25.440 |
|
representations, something, a model that will perform well |
|
|
|
1:39:25.440 --> 1:39:28.720 |
|
on the larger chunk of experience space than the other. |
|
|
|
1:39:28.720 --> 1:39:30.960 |
|
And that is the smaller agent. |
|
|
|
1:39:30.960 --> 1:39:36.960 |
|
Yeah. So if you fix the experience, which generate better programs, |
|
|
|
1:39:37.680 --> 1:39:39.520 |
|
better meaning more generalizable. |
|
|
|
1:39:39.520 --> 1:39:40.560 |
|
That's really interesting. |
|
|
|
1:39:40.560 --> 1:39:42.400 |
|
That's a very nice, clean definition of... |
|
|
|
1:39:42.400 --> 1:39:47.280 |
|
Oh, by the way, in this definition, it is already very obvious |
|
|
|
1:39:47.280 --> 1:39:49.440 |
|
that intelligence has to be specialized |
|
|
|
1:39:49.440 --> 1:39:51.680 |
|
because you're talking about experience space |
|
|
|
1:39:51.680 --> 1:39:54.080 |
|
and you're talking about segments of experience space. |
|
|
|
1:39:54.080 --> 1:39:57.200 |
|
You're talking about priors and you're talking about experience. |
|
|
|
1:39:57.200 --> 1:40:02.480 |
|
All of these things define the context in which intelligence emerges. |
|
|
|
1:40:04.480 --> 1:40:08.640 |
|
And you can never look at the totality of experience space, right? |
|
|
|
1:40:09.760 --> 1:40:12.160 |
|
So intelligence has to be specialized. |
|
|
|
1:40:12.160 --> 1:40:14.960 |
|
But it can be sufficiently large, the experience space, |
|
|
|
1:40:14.960 --> 1:40:16.080 |
|
even though it's specialized. |
|
|
|
1:40:16.080 --> 1:40:19.120 |
|
There's a certain point when the experience space is large enough |
|
|
|
1:40:19.120 --> 1:40:21.440 |
|
to where it might as well be general. |
|
|
|
1:40:22.000 --> 1:40:23.920 |
|
It feels general. It looks general. |
|
|
|
1:40:23.920 --> 1:40:25.680 |
|
Sure. I mean, it's very relative. |
|
|
|
1:40:25.680 --> 1:40:29.360 |
|
Like, for instance, many people would say human intelligence is general. |
|
|
|
1:40:29.360 --> 1:40:31.200 |
|
In fact, it is quite specialized. |
|
|
|
1:40:32.800 --> 1:40:37.120 |
|
We can definitely build systems that start from the same innate priors |
|
|
|
1:40:37.120 --> 1:40:39.120 |
|
as what humans have at birth. |
|
|
|
1:40:39.120 --> 1:40:42.320 |
|
Because we already understand fairly well |
|
|
|
1:40:42.320 --> 1:40:44.480 |
|
what sort of priors we have as humans. |
|
|
|
1:40:44.480 --> 1:40:46.080 |
|
Like many people have worked on this problem. |
|
|
|
1:40:46.800 --> 1:40:51.040 |
|
Most notably, Elisabeth Spelke from Harvard. |
|
|
|
1:40:51.040 --> 1:40:52.240 |
|
I don't know if you know her. |
|
|
|
1:40:52.240 --> 1:40:56.000 |
|
She's worked a lot on what she calls core knowledge. |
|
|
|
1:40:56.000 --> 1:41:00.640 |
|
And it is very much about trying to determine and describe |
|
|
|
1:41:00.640 --> 1:41:02.320 |
|
what priors we are born with. |
|
|
|
1:41:02.320 --> 1:41:04.720 |
|
Like language skills and so on, all that kind of stuff. |
|
|
|
1:41:04.720 --> 1:41:05.220 |
|
Exactly. |
|
|
|
1:41:06.880 --> 1:41:11.440 |
|
So we have some pretty good understanding of what priors we are born with. |
|
|
|
1:41:11.440 --> 1:41:12.560 |
|
So we could... |
|
|
|
1:41:13.760 --> 1:41:17.760 |
|
So I've actually been working on a benchmark for the past couple years, |
|
|
|
1:41:17.760 --> 1:41:18.640 |
|
you know, on and off. |
|
|
|
1:41:18.640 --> 1:41:20.480 |
|
I hope to be able to release it at some point. |
|
|
|
1:41:20.480 --> 1:41:21.760 |
|
That's exciting. |
|
|
|
1:41:21.760 --> 1:41:25.680 |
|
The idea is to measure the intelligence of systems |
|
|
|
1:41:26.800 --> 1:41:28.640 |
|
by countering for priors, |
|
|
|
1:41:28.640 --> 1:41:30.480 |
|
countering for amount of experience, |
|
|
|
1:41:30.480 --> 1:41:34.800 |
|
and by assuming the same priors as what humans are born with. |
|
|
|
1:41:34.800 --> 1:41:39.520 |
|
So that you can actually compare these scores to human intelligence. |
|
|
|
1:41:39.520 --> 1:41:43.280 |
|
You can actually have humans pass the same test in a way that's fair. |
|
|
|
1:41:43.280 --> 1:41:52.320 |
|
Yeah. And so importantly, such a benchmark should be such that any amount |
|
|
|
1:41:52.960 --> 1:41:55.920 |
|
of practicing does not increase your score. |
|
|
|
1:41:56.480 --> 1:42:00.560 |
|
So try to picture a game where no matter how much you play this game, |
|
|
|
1:42:01.600 --> 1:42:05.040 |
|
that does not change your skill at the game. |
|
|
|
1:42:05.040 --> 1:42:05.920 |
|
Can you picture that? |
|
|
|
1:42:05.920 --> 1:42:11.040 |
|
As a person who deeply appreciates practice, I cannot actually. |
|
|
|
1:42:11.040 --> 1:42:16.560 |
|
There's actually a very simple trick. |
|
|
|
1:42:16.560 --> 1:42:19.440 |
|
So in order to come up with a task, |
|
|
|
1:42:19.440 --> 1:42:21.760 |
|
so the only thing you can measure is skill at the task. |
|
|
|
1:42:21.760 --> 1:42:22.320 |
|
Yes. |
|
|
|
1:42:22.320 --> 1:42:24.800 |
|
All tasks are going to involve priors. |
|
|
|
1:42:24.800 --> 1:42:25.600 |
|
Yes. |
|
|
|
1:42:25.600 --> 1:42:29.920 |
|
The trick is to know what they are and to describe that. |
|
|
|
1:42:29.920 --> 1:42:33.760 |
|
And then you make sure that this is the same set of priors as what humans start with. |
|
|
|
1:42:33.760 --> 1:42:38.560 |
|
So you create a task that assumes these priors, that exactly documents these priors, |
|
|
|
1:42:38.560 --> 1:42:42.240 |
|
so that the priors are made explicit and there are no other priors involved. |
|
|
|
1:42:42.240 --> 1:42:48.960 |
|
And then you generate a certain number of samples in experience space for this task, right? |
|
|
|
1:42:49.840 --> 1:42:56.320 |
|
And this, for one task, assuming that the task is new for the agent passing it, |
|
|
|
1:42:56.320 --> 1:43:04.320 |
|
that's one test of this definition of intelligence that we set up. |
|
|
|
1:43:04.320 --> 1:43:06.880 |
|
And now you can scale that to many different tasks, |
|
|
|
1:43:06.880 --> 1:43:10.480 |
|
that each task should be new to the agent passing it, right? |
|
|
|
1:43:11.360 --> 1:43:14.480 |
|
And also it should be human interpretable and understandable |
|
|
|
1:43:14.480 --> 1:43:16.880 |
|
so that you can actually have a human pass the same test. |
|
|
|
1:43:16.880 --> 1:43:19.760 |
|
And then you can compare the score of your machine and the score of your human. |
|
|
|
1:43:19.760 --> 1:43:20.720 |
|
Which could be a lot of stuff. |
|
|
|
1:43:20.720 --> 1:43:23.040 |
|
You could even start a task like MNIST. |
|
|
|
1:43:23.040 --> 1:43:28.800 |
|
Just as long as you start with the same set of priors. |
|
|
|
1:43:28.800 --> 1:43:34.080 |
|
So the problem with MNIST, humans are already trying to recognize digits, right? |
|
|
|
1:43:35.600 --> 1:43:40.960 |
|
But let's say we're considering objects that are not digits, |
|
|
|
1:43:42.400 --> 1:43:43.920 |
|
some completely arbitrary patterns. |
|
|
|
1:43:44.480 --> 1:43:48.880 |
|
Well, humans already come with visual priors about how to process that. |
|
|
|
1:43:48.880 --> 1:43:54.080 |
|
So in order to make the game fair, you would have to isolate these priors |
|
|
|
1:43:54.080 --> 1:43:57.280 |
|
and describe them and then express them as computational rules. |
|
|
|
1:43:57.280 --> 1:44:01.680 |
|
Having worked a lot with vision science people, that's exceptionally difficult. |
|
|
|
1:44:01.680 --> 1:44:03.120 |
|
A lot of progress has been made. |
|
|
|
1:44:03.120 --> 1:44:08.080 |
|
There's been a lot of good tests and basically reducing all of human vision into some good priors. |
|
|
|
1:44:08.640 --> 1:44:10.960 |
|
We're still probably far away from that perfectly, |
|
|
|
1:44:10.960 --> 1:44:14.640 |
|
but as a start for a benchmark, that's an exciting possibility. |
|
|
|
1:44:14.640 --> 1:44:24.240 |
|
Yeah, so Elisabeth Spelke actually lists objectness as one of the core knowledge priors. |
|
|
|
1:44:24.800 --> 1:44:25.920 |
|
Objectness, cool. |
|
|
|
1:44:25.920 --> 1:44:26.880 |
|
Objectness, yeah. |
|
|
|
1:44:27.440 --> 1:44:31.520 |
|
So we have priors about objectness, like about the visual space, about time, |
|
|
|
1:44:31.520 --> 1:44:34.240 |
|
about agents, about goal oriented behavior. |
|
|
|
1:44:35.280 --> 1:44:39.280 |
|
We have many different priors, but what's interesting is that, |
|
|
|
1:44:39.280 --> 1:44:43.920 |
|
sure, we have this pretty diverse and rich set of priors, |
|
|
|
1:44:43.920 --> 1:44:46.880 |
|
but it's also not that diverse, right? |
|
|
|
1:44:46.880 --> 1:44:50.800 |
|
We are not born into this world with a ton of knowledge about the world, |
|
|
|
1:44:50.800 --> 1:44:57.840 |
|
with only a small set of core knowledge. |
|
|
|
1:44:58.640 --> 1:45:05.040 |
|
Yeah, sorry, do you have a sense of how it feels to us humans that that set is not that large? |
|
|
|
1:45:05.040 --> 1:45:09.600 |
|
But just even the nature of time that we kind of integrate pretty effectively |
|
|
|
1:45:09.600 --> 1:45:11.600 |
|
through all of our perception, all of our reasoning, |
|
|
|
1:45:12.640 --> 1:45:17.680 |
|
maybe how, you know, do you have a sense of how easy it is to encode those priors? |
|
|
|
1:45:17.680 --> 1:45:24.560 |
|
Maybe it requires building a universe and then the human brain in order to encode those priors. |
|
|
|
1:45:25.440 --> 1:45:28.640 |
|
Or do you have a hope that it can be listed like an axiomatic? |
|
|
|
1:45:28.640 --> 1:45:29.280 |
|
I don't think so. |
|
|
|
1:45:29.280 --> 1:45:33.040 |
|
So you have to keep in mind that any knowledge about the world that we are |
|
|
|
1:45:33.040 --> 1:45:41.120 |
|
born with is something that has to have been encoded into our DNA by evolution at some point. |
|
|
|
1:45:41.120 --> 1:45:41.440 |
|
Right. |
|
|
|
1:45:41.440 --> 1:45:45.440 |
|
And DNA is a very, very low bandwidth medium. |
|
|
|
1:45:46.000 --> 1:45:51.200 |
|
Like it's extremely long and expensive to encode anything into DNA because first of all, |
|
|
|
1:45:52.560 --> 1:45:57.440 |
|
you need some sort of evolutionary pressure to guide this writing process. |
|
|
|
1:45:57.440 --> 1:46:03.440 |
|
And then, you know, the higher level of information you're trying to write, the longer it's going to take. |
|
|
|
1:46:04.480 --> 1:46:13.520 |
|
And the thing in the environment that you're trying to encode knowledge about has to be stable |
|
|
|
1:46:13.520 --> 1:46:15.280 |
|
over this duration. |
|
|
|
1:46:15.280 --> 1:46:20.960 |
|
So you can only encode into DNA things that constitute an evolutionary advantage. |
|
|
|
1:46:20.960 --> 1:46:25.280 |
|
So this is actually a very small subset of all possible knowledge about the world. |
|
|
|
1:46:25.280 --> 1:46:32.080 |
|
You can only encode things that are stable, that are true, over very, very long periods of time, |
|
|
|
1:46:32.080 --> 1:46:33.680 |
|
typically millions of years. |
|
|
|
1:46:33.680 --> 1:46:38.720 |
|
For instance, we might have some visual prior about the shape of snakes, right? |
|
|
|
1:46:38.720 --> 1:46:43.920 |
|
But what makes a face, what's the difference between a face and an art face? |
|
|
|
1:46:44.560 --> 1:46:48.080 |
|
But consider this interesting question. |
|
|
|
1:46:48.080 --> 1:46:56.640 |
|
Do we have any innate sense of the visual difference between a male face and a female face? |
|
|
|
1:46:56.640 --> 1:46:57.600 |
|
What do you think? |
|
|
|
1:46:58.640 --> 1:46:59.840 |
|
For a human, I mean. |
|
|
|
1:46:59.840 --> 1:47:04.000 |
|
I would have to look back into evolutionary history when the genders emerged. |
|
|
|
1:47:04.000 --> 1:47:06.240 |
|
But yeah, most... |
|
|
|
1:47:06.240 --> 1:47:09.840 |
|
I mean, the faces of humans are quite different from the faces of great apes. |
|
|
|
1:47:10.640 --> 1:47:11.600 |
|
Great apes, right? |
|
|
|
1:47:12.880 --> 1:47:13.600 |
|
Yeah. |
|
|
|
1:47:13.600 --> 1:47:14.800 |
|
That's interesting. |
|
|
|
1:47:14.800 --> 1:47:22.800 |
|
Yeah, you couldn't tell the face of a female chimpanzee from the face of a male chimpanzee, |
|
|
|
1:47:22.800 --> 1:47:23.440 |
|
probably. |
|
|
|
1:47:23.440 --> 1:47:26.160 |
|
Yeah, and I don't think most humans have all that ability. |
|
|
|
1:47:26.160 --> 1:47:33.280 |
|
So we do have innate knowledge of what makes a face, but it's actually impossible for us to |
|
|
|
1:47:33.280 --> 1:47:40.320 |
|
have any DNA encoded knowledge of the difference between a female human face and a male human face |
|
|
|
1:47:40.320 --> 1:47:50.560 |
|
because that knowledge, that information came up into the world actually very recently. |
|
|
|
1:47:50.560 --> 1:47:56.400 |
|
If you look at the slowness of the process of encoding knowledge into DNA. |
|
|
|
1:47:56.400 --> 1:47:57.360 |
|
Yeah, so that's interesting. |
|
|
|
1:47:57.360 --> 1:48:02.080 |
|
That's a really powerful argument that DNA is a low bandwidth and it takes a long time to encode. |
|
|
|
1:48:02.800 --> 1:48:05.200 |
|
That naturally creates a very efficient encoding. |
|
|
|
1:48:05.200 --> 1:48:12.800 |
|
But one important consequence of this is that, so yes, we are born into this world with a bunch of |
|
|
|
1:48:12.800 --> 1:48:17.600 |
|
knowledge, sometimes high level knowledge about the world, like the shape, the rough shape of a |
|
|
|
1:48:17.600 --> 1:48:19.520 |
|
snake, of the rough shape of a face. |
|
|
|
1:48:20.480 --> 1:48:26.960 |
|
But importantly, because this knowledge takes so long to write, almost all of this innate |
|
|
|
1:48:26.960 --> 1:48:32.080 |
|
knowledge is shared with our cousins, with great apes, right? |
|
|
|
1:48:32.080 --> 1:48:35.600 |
|
So it is not actually this innate knowledge that makes us special. |
|
|
|
1:48:36.320 --> 1:48:42.000 |
|
But to throw it right back at you from the earlier on in our discussion, it's that encoding |
|
|
|
1:48:42.960 --> 1:48:48.320 |
|
might also include the entirety of the environment of Earth. |
|
|
|
1:48:49.360 --> 1:48:49.920 |
|
To some extent. |
|
|
|
1:48:49.920 --> 1:48:56.480 |
|
So it can include things that are important to survival and production, so for which there is |
|
|
|
1:48:56.480 --> 1:49:02.880 |
|
some evolutionary pressure, and things that are stable, constant over very, very, very long time |
|
|
|
1:49:02.880 --> 1:49:03.380 |
|
periods. |
|
|
|
1:49:04.160 --> 1:49:06.320 |
|
And honestly, it's not that much information. |
|
|
|
1:49:06.320 --> 1:49:14.400 |
|
There's also, besides the bandwidths constraint and the constraints of the writing process, |
|
|
|
1:49:14.400 --> 1:49:21.440 |
|
there's also memory constraints, like DNA, the part of DNA that deals with the human brain, |
|
|
|
1:49:21.440 --> 1:49:22.640 |
|
it's actually fairly small. |
|
|
|
1:49:22.640 --> 1:49:25.520 |
|
It's like, you know, on the order of megabytes, right? |
|
|
|
1:49:25.520 --> 1:49:29.600 |
|
There's not that much high level knowledge about the world you can encode. |
|
|
|
1:49:31.600 --> 1:49:38.880 |
|
That's quite brilliant and hopeful for a benchmark that you're referring to of encoding |
|
|
|
1:49:38.880 --> 1:49:39.360 |
|
priors. |
|
|
|
1:49:39.360 --> 1:49:43.120 |
|
I actually look forward to, I'm skeptical whether you can do it in the next couple of |
|
|
|
1:49:43.120 --> 1:49:44.320 |
|
years, but hopefully. |
|
|
|
1:49:45.040 --> 1:49:45.760 |
|
I've been working. |
|
|
|
1:49:45.760 --> 1:49:49.920 |
|
So honestly, it's a very simple benchmark, and it's not like a big breakthrough or anything. |
|
|
|
1:49:49.920 --> 1:49:53.200 |
|
It's more like a fun side project, right? |
|
|
|
1:49:53.200 --> 1:49:55.680 |
|
But these fun, so is ImageNet. |
|
|
|
1:49:56.480 --> 1:50:04.080 |
|
These fun side projects could launch entire groups of efforts towards creating reasoning |
|
|
|
1:50:04.080 --> 1:50:04.960 |
|
systems and so on. |
|
|
|
1:50:04.960 --> 1:50:05.440 |
|
And I think... |
|
|
|
1:50:05.440 --> 1:50:06.160 |
|
Yeah, that's the goal. |
|
|
|
1:50:06.160 --> 1:50:12.080 |
|
It's trying to measure strong generalization, to measure the strength of abstraction in |
|
|
|
1:50:12.080 --> 1:50:16.960 |
|
our minds, well, in our minds and in artificial intelligence agencies. |
|
|
|
1:50:16.960 --> 1:50:24.800 |
|
And if there's anything true about this science organism is its individual cells love competition. |
|
|
|
1:50:24.800 --> 1:50:26.800 |
|
So and benchmarks encourage competition. |
|
|
|
1:50:26.800 --> 1:50:29.520 |
|
So that's an exciting possibility. |
|
|
|
1:50:29.520 --> 1:50:32.640 |
|
If you, do you think an AI winter is coming? |
|
|
|
1:50:33.520 --> 1:50:34.640 |
|
And how do we prevent it? |
|
|
|
1:50:35.440 --> 1:50:36.080 |
|
Not really. |
|
|
|
1:50:36.080 --> 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 --> 1:50:47.280 |
|
are selling the capabilities of AI and the actual capabilities of AI. |
|
|
|
1:50:47.280 --> 1:50:50.560 |
|
And today, some deep learning is creating a lot of value. |
|
|
|
1:50:50.560 --> 1:50:56.240 |
|
And it will keep creating a lot of value in the sense that these models are applicable |
|
|
|
1:50:56.240 --> 1:51:00.000 |
|
to a very wide range of problems that are relevant today. |
|
|
|
1:51:00.000 --> 1:51:05.120 |
|
And we are only just getting started with applying these algorithms to every problem |
|
|
|
1:51:05.120 --> 1:51:06.320 |
|
they could be solving. |
|
|
|
1:51:06.320 --> 1:51:10.160 |
|
So deep learning will keep creating a lot of value for the time being. |
|
|
|
1:51:10.160 --> 1:51:15.920 |
|
What's concerning, however, is that there's a lot of hype around deep learning and around |
|
|
|
1:51:15.920 --> 1:51:16.240 |
|
AI. |
|
|
|
1:51:16.240 --> 1:51:22.000 |
|
There are lots of people are overselling the capabilities of these systems, not just |
|
|
|
1:51:22.000 --> 1:51:27.760 |
|
the capabilities, but also overselling the fact that they might be more or less, you |
|
|
|
1:51:27.760 --> 1:51:36.640 |
|
know, brain like, like given the kind of a mystical aspect, these technologies and also |
|
|
|
1:51:36.640 --> 1:51:43.840 |
|
overselling the pace of progress, which, you know, it might look fast in the sense that |
|
|
|
1:51:43.840 --> 1:51:46.480 |
|
we have this exponentially increasing number of papers. |
|
|
|
1:51:47.760 --> 1:51:52.960 |
|
But again, that's just a simple consequence of the fact that we have ever more people |
|
|
|
1:51:52.960 --> 1:51:53.840 |
|
coming into the field. |
|
|
|
1:51:54.400 --> 1:51:57.440 |
|
It doesn't mean the progress is actually exponentially fast. |
|
|
|
1:51:58.640 --> 1:52:02.720 |
|
Let's say you're trying to raise money for your startup or your research lab. |
|
|
|
1:52:02.720 --> 1:52:09.120 |
|
You might want to tell, you know, a grandiose 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 --> 1:52:15.760 |
|
and robotics and so on. |
|
|
|
1:52:15.760 --> 1:52:19.440 |
|
And maybe you can tell them that the field is progressing so fast and we are going to |
|
|
|
1:52:19.440 --> 1:52:23.040 |
|
have AGI within 15 years or even 10 years. |
|
|
|
1:52:23.040 --> 1:52:25.920 |
|
And none of this is true. |
|
|
|
1:52:25.920 --> 1:52:32.800 |
|
And every time you're like saying these things and an investor or, you know, a decision maker |
|
|
|
1:52:32.800 --> 1:52:41.680 |
|
believes them, well, this is like the equivalent of taking on credit card debt, but for trust, |
|
|
|
1:52:41.680 --> 1:52:42.480 |
|
right? |
|
|
|
1:52:42.480 --> 1:52:50.160 |
|
And maybe this will, you know, this will be what enables you to raise a lot of money, |
|
|
|
1:52:50.160 --> 1:52:54.320 |
|
but ultimately you are creating damage, you are damaging the field. |
|
|
|
1:52:54.320 --> 1:53:00.160 |
|
So that's the concern is that that debt, that's what happens with the other AI winters is |
|
|
|
1:53:00.160 --> 1:53:04.160 |
|
the concern is you actually tweeted about this with autonomous vehicles, right? |
|
|
|
1:53:04.160 --> 1:53:08.960 |
|
There's almost every single company now have promised that they will have full autonomous |
|
|
|
1:53:08.960 --> 1:53:11.760 |
|
vehicles by 2021, 2022. |
|
|
|
1:53:11.760 --> 1:53:18.080 |
|
That's a good example of the consequences of over hyping the capabilities of AI and |
|
|
|
1:53:18.080 --> 1:53:19.280 |
|
the pace of progress. |
|
|
|
1:53:19.280 --> 1:53:25.200 |
|
So because I work especially a lot recently in this area, I have a deep concern of what |
|
|
|
1:53:25.200 --> 1:53:30.400 |
|
happens when all of these companies after I've invested billions have a meeting and |
|
|
|
1:53:30.400 --> 1:53:33.600 |
|
say, how much do we actually, first of all, do we have an autonomous vehicle? |
|
|
|
1:53:33.600 --> 1:53:35.280 |
|
The answer will definitely be no. |
|
|
|
1:53:35.840 --> 1:53:40.560 |
|
And second will be, wait a minute, we've invested one, two, three, four billion dollars |
|
|
|
1:53:40.560 --> 1:53:43.120 |
|
into this and we made no profit. |
|
|
|
1:53:43.120 --> 1:53:49.200 |
|
And the reaction to that may be going very hard in other directions that might impact |
|
|
|
1:53:49.200 --> 1:53:50.400 |
|
even other industries. |
|
|
|
1:53:50.400 --> 1:53:55.520 |
|
And that's what we call an AI winter is when there is backlash where no one believes any |
|
|
|
1:53:55.520 --> 1:53:59.360 |
|
of these promises anymore because they've turned that to be big lies the first time |
|
|
|
1:53:59.360 --> 1:54:00.240 |
|
around. |
|
|
|
1:54:00.240 --> 1:54:06.000 |
|
And this will definitely happen to some extent for autonomous vehicles because the public |
|
|
|
1:54:06.000 --> 1:54:13.360 |
|
and decision makers have been convinced that around 2015, they've been convinced by these |
|
|
|
1:54:13.360 --> 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 --> 1:54:22.880 |
|
in maybe 2016, maybe 2017, maybe 2018. |
|
|
|
1:54:22.880 --> 1:54:26.080 |
|
Now we're in 2019, we're still waiting for it. |
|
|
|
1:54:27.600 --> 1:54:32.800 |
|
And so I don't believe we are going to have a full on AI winter because we have these |
|
|
|
1:54:32.800 --> 1:54:36.640 |
|
technologies that are producing a tremendous amount of real value. |
|
|
|
1:54:37.680 --> 1:54:39.920 |
|
But there is also too much hype. |
|
|
|
1:54:39.920 --> 1:54:43.520 |
|
So there will be some backlash, especially there will be backlash. |
|
|
|
1:54:44.960 --> 1:54:53.040 |
|
So some startups are trying to sell the dream of AGI and the fact that AGI is going to create |
|
|
|
1:54:53.040 --> 1:54:53.760 |
|
infinite value. |
|
|
|
1:54:53.760 --> 1:54:55.680 |
|
Like AGI is like a free lunch. |
|
|
|
1:54:55.680 --> 1:55:02.800 |
|
Like if you can develop an AI system that passes a certain threshold of IQ or something, |
|
|
|
1:55:02.800 --> 1:55:04.400 |
|
then suddenly you have infinite value. |
|
|
|
1:55:04.400 --> 1:55:14.160 |
|
And well, there are actually lots of investors buying into this idea and they will wait maybe |
|
|
|
1:55:14.160 --> 1:55:17.760 |
|
10, 15 years and nothing will happen. |
|
|
|
1:55:17.760 --> 1:55:22.560 |
|
And the next time around, well, maybe there will be a new generation of investors. |
|
|
|
1:55:22.560 --> 1:55:23.360 |
|
No one will care. |
|
|
|
1:55:24.800 --> 1:55:27.280 |
|
Human memory is fairly short after all. |
|
|
|
1:55:27.280 --> 1:55:34.320 |
|
I don't know about you, but because I've spoken about AGI sometimes poetically, I get a lot |
|
|
|
1:55:34.320 --> 1:55:42.000 |
|
of emails from people giving me, they're usually like a large manifestos of they've, they say |
|
|
|
1:55:42.000 --> 1:55:47.200 |
|
to me that they have created an AGI system or they know how to do it. |
|
|
|
1:55:47.200 --> 1:55:48.880 |
|
And there's a long write up of how to do it. |
|
|
|
1:55:48.880 --> 1:55:50.560 |
|
I get a lot of these emails, yeah. |
|
|
|
1:55:50.560 --> 1:55:57.760 |
|
They're a little bit feel like it's generated by an AI system actually, but there's usually |
|
|
|
1:55:57.760 --> 1:56:06.640 |
|
no diagram, you have a transformer generating crank papers about AGI. |
|
|
|
1:56:06.640 --> 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 --> 1:56:16.720 |
|
papers, how do we know they're not onto something? |
|
|
|
1:56:16.720 --> 1:56:24.240 |
|
How do I, so when you start to talk about AGI or anything like the reasoning benchmarks |
|
|
|
1:56:24.240 --> 1:56:28.160 |
|
and so on, so something that doesn't have a benchmark, it's really difficult to know. |
|
|
|
1:56:29.120 --> 1:56:34.560 |
|
I mean, I talked to Jeff Hawkins, who's really looking at neuroscience approaches to how, |
|
|
|
1:56:35.200 --> 1:56:41.520 |
|
and there's some, there's echoes of really interesting ideas in at least Jeff's case, |
|
|
|
1:56:41.520 --> 1:56:42.320 |
|
which he's showing. |
|
|
|
1:56:43.280 --> 1:56:45.040 |
|
How do you usually think about this? |
|
|
|
1:56:46.640 --> 1:56:52.880 |
|
Like preventing yourself from being too narrow minded and elitist about deep learning, it |
|
|
|
1:56:52.880 --> 1:56:56.720 |
|
has to work on these particular benchmarks, otherwise it's trash. |
|
|
|
1:56:56.720 --> 1:57:05.280 |
|
Well, you know, the thing is, intelligence does not exist in the abstract. |
|
|
|
1:57:05.280 --> 1:57:07.200 |
|
Intelligence has to be applied. |
|
|
|
1:57:07.200 --> 1:57:11.040 |
|
So if you don't have a benchmark, if you have an improvement in some benchmark, maybe it's |
|
|
|
1:57:11.040 --> 1:57:12.400 |
|
a new benchmark, right? |
|
|
|
1:57:12.400 --> 1:57:16.640 |
|
Maybe it's not something we've been looking at before, but you do need a problem that |
|
|
|
1:57:16.640 --> 1:57:17.360 |
|
you're trying to solve. |
|
|
|
1:57:17.360 --> 1:57:20.000 |
|
You're not going to come up with a solution without a problem. |
|
|
|
1:57:20.000 --> 1:57:25.520 |
|
So you, general intelligence, I mean, you've clearly highlighted generalization. |
|
|
|
1:57:26.320 --> 1:57:31.200 |
|
If you want to claim that you have an intelligence system, it should come with a benchmark. |
|
|
|
1:57:31.200 --> 1:57:35.760 |
|
It should, yes, it should display capabilities of some kind. |
|
|
|
1:57:35.760 --> 1:57:41.840 |
|
It should show that it can create some form of value, even if it's a very artificial form |
|
|
|
1:57:41.840 --> 1:57:42.800 |
|
of value. |
|
|
|
1:57:42.800 --> 1:57:48.800 |
|
And that's also the reason why you don't actually need to care about telling which papers have |
|
|
|
1:57:48.800 --> 1:57:52.000 |
|
actually some hidden potential and which do not. |
|
|
|
1:57:53.120 --> 1:57:59.200 |
|
Because if there is a new technique that's actually creating value, this is going to |
|
|
|
1:57:59.200 --> 1:58:02.480 |
|
be brought to light very quickly because it's actually making a difference. |
|
|
|
1:58:02.480 --> 1:58:08.160 |
|
So it's the difference between something that is ineffectual and something that is actually |
|
|
|
1:58:08.160 --> 1:58:08.800 |
|
useful. |
|
|
|
1:58:08.800 --> 1:58:14.080 |
|
And ultimately usefulness is our guide, not just in this field, but if you look at science |
|
|
|
1:58:14.080 --> 1:58:18.720 |
|
in general, maybe there are many, many people over the years that have had some really interesting |
|
|
|
1:58:19.440 --> 1:58:22.800 |
|
theories of everything, but they were just completely useless. |
|
|
|
1:58:22.800 --> 1:58:27.280 |
|
And you don't actually need to tell the interesting theories from the useless theories. |
|
|
|
1:58:28.000 --> 1:58:34.080 |
|
All you need is to see, is this actually having an effect on something else? |
|
|
|
1:58:34.080 --> 1:58:35.360 |
|
Is this actually useful? |
|
|
|
1:58:35.360 --> 1:58:36.800 |
|
Is this making an impact or not? |
|
|
|
1:58:37.600 --> 1:58:38.640 |
|
That's beautifully put. |
|
|
|
1:58:38.640 --> 1:58:43.680 |
|
I mean, the same applies to quantum mechanics, to string theory, to the holographic principle. |
|
|
|
1:58:43.680 --> 1:58:45.280 |
|
We are doing deep learning because it works. |
|
|
|
1:58:46.960 --> 1:58:52.720 |
|
Before it started working, people considered people working on neural networks as cranks |
|
|
|
1:58:52.720 --> 1:58:53.120 |
|
very much. |
|
|
|
1:58:54.560 --> 1:58:56.320 |
|
No one was working on this anymore. |
|
|
|
1:58:56.320 --> 1:58:59.120 |
|
And now it's working, which is what makes it valuable. |
|
|
|
1:58:59.120 --> 1:59:00.320 |
|
It's not about being right. |
|
|
|
1:59:01.120 --> 1:59:02.560 |
|
It's about being effective. |
|
|
|
1:59:02.560 --> 1:59:08.080 |
|
And nevertheless, the individual entities of this scientific mechanism, just like Yoshua |
|
|
|
1:59:08.080 --> 1:59:12.480 |
|
Banjo or Jan Lekun, they, while being called cranks, stuck with it. |
|
|
|
1:59:12.480 --> 1:59:12.880 |
|
Right? |
|
|
|
1:59:12.880 --> 1:59:13.280 |
|
Yeah. |
|
|
|
1:59:13.280 --> 1:59:17.840 |
|
And so us individual agents, even if everyone's laughing at us, just stick with it. |
|
|
|
1:59:18.880 --> 1:59:21.840 |
|
If you believe you have something, you should stick with it and see it through. |
|
|
|
1:59:23.520 --> 1:59:25.920 |
|
That's a beautiful inspirational message to end on. |
|
|
|
1:59:25.920 --> 1:59:27.600 |
|
Francois, thank you so much for talking today. |
|
|
|
1:59:27.600 --> 1:59:28.640 |
|
That was amazing. |
|
|
|
1:59:28.640 --> 1:59:44.000 |
|
Thank you. |
|
|
|
|
