episode stringlengths 45 100 | text stringlengths 1 528 | timestamp_link stringlengths 56 56 |
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Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | dozens of examples, these things will need millions for very, very, very simple tasks. | https://karpathy.ai/lexicap/0004-large.html#00:12:03.440 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | And so I think there's an opportunity for academics who don't have the kind of computing | https://karpathy.ai/lexicap/0004-large.html#00:12:10.000 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | power that, say, Google has to do really important and exciting research to advance | https://karpathy.ai/lexicap/0004-large.html#00:12:16.640 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | the state of the art in training frameworks, learning models, agent learning in even simple | https://karpathy.ai/lexicap/0004-large.html#00:12:23.440 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | environments that are synthetic, that seem trivial, but yet current machine learning fails on. | https://karpathy.ai/lexicap/0004-large.html#00:12:30.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | We talked about priors and common sense knowledge. It seems like | https://karpathy.ai/lexicap/0004-large.html#00:12:38.240 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | we humans take a lot of knowledge for granted. So what's your view of these priors of forming | https://karpathy.ai/lexicap/0004-large.html#00:12:43.760 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | this broad view of the world, this accumulation of information and how we can teach neural networks | https://karpathy.ai/lexicap/0004-large.html#00:12:52.160 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | or learning systems to pick that knowledge up? So knowledge, for a while, the artificial | https://karpathy.ai/lexicap/0004-large.html#00:12:58.880 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | intelligence was maybe in the 80s, like there's a time where knowledge representation, knowledge, | https://karpathy.ai/lexicap/0004-large.html#00:13:05.520 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | acquisition, expert systems, I mean, the symbolic AI was a view, was an interesting problem set to | https://karpathy.ai/lexicap/0004-large.html#00:13:14.320 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | solve and it was kind of put on hold a little bit, it seems like. Because it doesn't work. | https://karpathy.ai/lexicap/0004-large.html#00:13:22.240 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | It doesn't work. That's right. But that's right. But the goals of that remain important. | https://karpathy.ai/lexicap/0004-large.html#00:13:27.680 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Yes. Remain important. And how do you think those goals can be addressed? | https://karpathy.ai/lexicap/0004-large.html#00:13:34.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Right. So first of all, I believe that one reason why the classical expert systems approach failed | https://karpathy.ai/lexicap/0004-large.html#00:13:39.760 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | is because a lot of the knowledge we have, so you talked about common sense intuition, | https://karpathy.ai/lexicap/0004-large.html#00:13:48.400 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | there's a lot of knowledge like this, which is not consciously accessible. | https://karpathy.ai/lexicap/0004-large.html#00:13:56.320 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | There are lots of decisions we're taking that we can't really explain, even if sometimes we make | https://karpathy.ai/lexicap/0004-large.html#00:14:01.680 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | up a story. And that knowledge is also necessary for machines to take good decisions. And that | https://karpathy.ai/lexicap/0004-large.html#00:14:05.440 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | knowledge is hard to codify in expert systems, rule based systems and classical AI formalism. | https://karpathy.ai/lexicap/0004-large.html#00:14:15.600 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | And there are other issues, of course, with the old AI, like not really good ways of handling | https://karpathy.ai/lexicap/0004-large.html#00:14:22.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | uncertainty, I would say something more subtle, which we understand better now, but I think still | https://karpathy.ai/lexicap/0004-large.html#00:14:29.520 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | isn't enough in the minds of people. There's something really powerful that comes from | https://karpathy.ai/lexicap/0004-large.html#00:14:37.040 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | distributed representations, the thing that really makes neural nets work so well. | https://karpathy.ai/lexicap/0004-large.html#00:14:43.920 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | And it's hard to replicate that kind of power in a symbolic world. The knowledge in expert systems | https://karpathy.ai/lexicap/0004-large.html#00:14:49.280 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | and so on is nicely decomposed into like a bunch of rules. Whereas if you think about a neural net, | https://karpathy.ai/lexicap/0004-large.html#00:14:58.640 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | it's the opposite. You have this big blob of parameters which work intensely together to | https://karpathy.ai/lexicap/0004-large.html#00:15:04.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | represent everything the network knows. And it's not sufficiently factorized. It's not | https://karpathy.ai/lexicap/0004-large.html#00:15:10.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | sufficiently factorized. And so I think this is one of the weaknesses of current neural nets, | https://karpathy.ai/lexicap/0004-large.html#00:15:16.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | that we have to take lessons from classical AI in order to bring in another kind of compositionality, | https://karpathy.ai/lexicap/0004-large.html#00:15:24.240 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | which is common in language, for example, and in these rules, but that isn't so native to neural | https://karpathy.ai/lexicap/0004-large.html#00:15:32.320 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | nets. And on that line of thinking, disentangled representations. Yes. So let me connect with | https://karpathy.ai/lexicap/0004-large.html#00:15:38.800 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | disentangled representations, if you might, if you don't mind. So for many years, I've thought, | https://karpathy.ai/lexicap/0004-large.html#00:15:48.400 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | and I still believe that it's really important that we come up with learning algorithms, | https://karpathy.ai/lexicap/0004-large.html#00:15:55.280 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | either unsupervised or supervised, but reinforcement, whatever, that build representations | https://karpathy.ai/lexicap/0004-large.html#00:16:00.560 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | in which the important factors, hopefully causal factors are nicely separated and easy to pick up | https://karpathy.ai/lexicap/0004-large.html#00:16:06.400 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | from the representation. So that's the idea of disentangled representations. It says transform | https://karpathy.ai/lexicap/0004-large.html#00:16:13.360 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | the data into a space where everything becomes easy. We can maybe just learn with linear models | https://karpathy.ai/lexicap/0004-large.html#00:16:18.480 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | about the things we care about. And I still think this is important, but I think this is missing out | https://karpathy.ai/lexicap/0004-large.html#00:16:25.120 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | on a very important ingredient, which classical AI systems can remind us of. | https://karpathy.ai/lexicap/0004-large.html#00:16:30.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | So let's say we have these disentangled representations. You still need to learn about | https://karpathy.ai/lexicap/0004-large.html#00:16:38.080 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | the relationships between the variables, those high level semantic variables. They're not going | https://karpathy.ai/lexicap/0004-large.html#00:16:43.440 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | to be independent. I mean, this is like too much of an assumption. They're going to have some | https://karpathy.ai/lexicap/0004-large.html#00:16:47.200 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | interesting relationships that allow to predict things in the future, to explain what happened | https://karpathy.ai/lexicap/0004-large.html#00:16:52.000 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | in the past. The kind of knowledge about those relationships in a classical AI system | https://karpathy.ai/lexicap/0004-large.html#00:16:56.320 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | is encoded in the rules. Like a rule is just like a little piece of knowledge that says, | https://karpathy.ai/lexicap/0004-large.html#00:17:01.600 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | oh, I have these two, three, four variables that are linked in this interesting way, | https://karpathy.ai/lexicap/0004-large.html#00:17:06.000 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | then I can say something about one or two of them given a couple of others, right? | https://karpathy.ai/lexicap/0004-large.html#00:17:10.960 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | In addition to disentangling the elements of the representation, which are like the variables | https://karpathy.ai/lexicap/0004-large.html#00:17:14.800 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | in a rule based system, you also need to disentangle the mechanisms that relate those | https://karpathy.ai/lexicap/0004-large.html#00:17:22.160 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | variables to each other. So like the rules. So the rules are neatly separated. Like each rule is, | https://karpathy.ai/lexicap/0004-large.html#00:17:31.840 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | you know, living on its own. And when I change a rule because I'm learning, it doesn't need to | https://karpathy.ai/lexicap/0004-large.html#00:17:37.200 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | break other rules. Whereas current neural nets, for example, are very sensitive to what's called | https://karpathy.ai/lexicap/0004-large.html#00:17:43.360 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | catastrophic forgetting, where after I've learned some things and then I learn new things, | https://karpathy.ai/lexicap/0004-large.html#00:17:48.720 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | they can destroy the old things that I had learned, right? If the knowledge was better | https://karpathy.ai/lexicap/0004-large.html#00:17:54.080 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | factorized and separated, disentangled, then you would avoid a lot of that. | https://karpathy.ai/lexicap/0004-large.html#00:17:59.280 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Now, you can't do this in the sensory domain. | https://karpathy.ai/lexicap/0004-large.html#00:18:06.560 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | What do you mean by sensory domain? | https://karpathy.ai/lexicap/0004-large.html#00:18:10.320 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Like in pixel space. But my idea is that when you project the data in the right semantic space, | https://karpathy.ai/lexicap/0004-large.html#00:18:13.120 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | it becomes possible to now represent this extra knowledge beyond the transformation from inputs | https://karpathy.ai/lexicap/0004-large.html#00:18:18.640 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | to representations, which is how representations act on each other and predict the future and so on | https://karpathy.ai/lexicap/0004-large.html#00:18:25.040 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | in a way that can be neatly disentangled. So now it's the rules that are disentangled from each | https://karpathy.ai/lexicap/0004-large.html#00:18:31.120 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | other and not just the variables that are disentangled from each other. | https://karpathy.ai/lexicap/0004-large.html#00:18:37.680 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | And you draw a distinction between semantic space and pixel, like does there need to be | https://karpathy.ai/lexicap/0004-large.html#00:18:40.400 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | an architectural difference? | https://karpathy.ai/lexicap/0004-large.html#00:18:45.200 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Well, yeah. So there's the sensory space like pixels, which where everything is entangled. | https://karpathy.ai/lexicap/0004-large.html#00:18:46.560 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | The information, like the variables are completely interdependent in very complicated ways. | https://karpathy.ai/lexicap/0004-large.html#00:18:52.080 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | And also computation, like it's not just the variables, it's also how they are related to | https://karpathy.ai/lexicap/0004-large.html#00:18:58.160 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | each other is all intertwined. But I'm hypothesizing that in the right high level | https://karpathy.ai/lexicap/0004-large.html#00:19:03.520 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | representation space, both the variables and how they relate to each other can be | https://karpathy.ai/lexicap/0004-large.html#00:19:11.280 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | disentangled. And that will provide a lot of generalization power. | https://karpathy.ai/lexicap/0004-large.html#00:19:16.720 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Generalization power. | https://karpathy.ai/lexicap/0004-large.html#00:19:20.800 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Yes. | https://karpathy.ai/lexicap/0004-large.html#00:19:22.240 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Distribution of the test set is assumed to be the same as the distribution of the training set. | https://karpathy.ai/lexicap/0004-large.html#00:19:22.720 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Right. This is where current machine learning is too weak. It doesn't tell us anything, | https://karpathy.ai/lexicap/0004-large.html#00:19:29.280 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | is not able to tell us anything about how our neural nets, say, are going to generalize to | https://karpathy.ai/lexicap/0004-large.html#00:19:35.600 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | a new distribution. And, you know, people may think, well, but there's nothing we can say | https://karpathy.ai/lexicap/0004-large.html#00:19:40.080 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | if we don't know what the new distribution will be. The truth is humans are able to generalize | https://karpathy.ai/lexicap/0004-large.html#00:19:45.120 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | to new distributions. | https://karpathy.ai/lexicap/0004-large.html#00:19:50.880 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Yeah. How are we able to do that? | https://karpathy.ai/lexicap/0004-large.html#00:19:52.560 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Yeah. Because there is something, these new distributions, even though they could look | https://karpathy.ai/lexicap/0004-large.html#00:19:54.000 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | very different from the training distributions, they have things in common. So let me give you | https://karpathy.ai/lexicap/0004-large.html#00:19:57.920 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | a concrete example. You read a science fiction novel. The science fiction novel, maybe, you | https://karpathy.ai/lexicap/0004-large.html#00:20:02.240 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | know, brings you in some other planet where things look very different on the surface, | https://karpathy.ai/lexicap/0004-large.html#00:20:07.920 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | but it's still the same laws of physics. And so you can read the book and you understand | https://karpathy.ai/lexicap/0004-large.html#00:20:15.200 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | what's going on. So the distribution is very different. But because you can transport | https://karpathy.ai/lexicap/0004-large.html#00:20:20.000 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | a lot of the knowledge you had from Earth about the underlying cause and effect relationships | https://karpathy.ai/lexicap/0004-large.html#00:20:27.360 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | and physical mechanisms and all that, and maybe even social interactions, you can now | https://karpathy.ai/lexicap/0004-large.html#00:20:33.120 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | make sense of what is going on on this planet where, like, visually, for example, | https://karpathy.ai/lexicap/0004-large.html#00:20:38.720 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | things are totally different. | https://karpathy.ai/lexicap/0004-large.html#00:20:42.160 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Taking that analogy further and distorting it, let's enter a science fiction world of, | https://karpathy.ai/lexicap/0004-large.html#00:20:45.280 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | say, Space Odyssey, 2001, with Hal. Or maybe, which is probably one of my favorite AI movies. | https://karpathy.ai/lexicap/0004-large.html#00:20:50.800 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Me too. | https://karpathy.ai/lexicap/0004-large.html#00:20:59.840 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | And then there's another one that a lot of people love that may be a little bit outside | https://karpathy.ai/lexicap/0004-large.html#00:21:00.480 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | of the AI community is Ex Machina. I don't know if you've seen it. | https://karpathy.ai/lexicap/0004-large.html#00:21:05.360 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Yes. Yes. | https://karpathy.ai/lexicap/0004-large.html#00:21:10.000 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | By the way, what are your views on that movie? Are you able to enjoy it? | https://karpathy.ai/lexicap/0004-large.html#00:21:11.600 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | Are there things I like and things I hate? | https://karpathy.ai/lexicap/0004-large.html#00:21:16.000 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | So you could talk about that in the context of a question I want to ask, which is, there's | https://karpathy.ai/lexicap/0004-large.html#00:21:21.120 |
Yoshua Bengio: Deep Learning | Lex Fridman Podcast #4 | quite a large community of people from different backgrounds, often outside of AI, who are concerned | https://karpathy.ai/lexicap/0004-large.html#00:21:26.800 |
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