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Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | even how to get out of the house or how to make breakfast. You show this presentation of the WTF, | https://karpathy.ai/lexicap/0015-large.html#00:29:25.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | where's the fork of robot looking at a sink. And can you describe how we plan in this world | https://karpathy.ai/lexicap/0015-large.html#00:29:31.920 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | of this idea of hierarchical planning we've mentioned? So yeah, how can a robot hope to | https://karpathy.ai/lexicap/0015-large.html#00:29:40.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | plan about something with such a long horizon where the goal is quite far away? | https://karpathy.ai/lexicap/0015-large.html#00:29:47.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | People since probably reasoning began have thought about hierarchical reasoning, | https://karpathy.ai/lexicap/0015-large.html#00:29:54.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the temporal hierarchy in particular. Well, there's spatial hierarchy, but let's talk | https://karpathy.ai/lexicap/0015-large.html#00:29:59.840 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | about temporal hierarchy. So you might say, oh, I have this long execution I have to do, | https://karpathy.ai/lexicap/0015-large.html#00:30:03.040 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | but I can divide it into some segments abstractly, right? So maybe you have to get out of the house, | https://karpathy.ai/lexicap/0015-large.html#00:30:08.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | I have to get in the car, I have to drive and so on. And so you can plan if you can build | https://karpathy.ai/lexicap/0015-large.html#00:30:15.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | abstractions. So this we started out by talking about abstractions. And we're back to that now, | https://karpathy.ai/lexicap/0015-large.html#00:30:22.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | if you can build abstractions in your state space, and abstractions sort of temporal abstractions, | https://karpathy.ai/lexicap/0015-large.html#00:30:26.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | then you can make plans at a high level. And you can say, I'm going to go to town and then I'll | https://karpathy.ai/lexicap/0015-large.html#00:30:34.560 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | have to get gas and then I can go here and I can do this other thing. And you can reason about the | https://karpathy.ai/lexicap/0015-large.html#00:30:39.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | dependencies and constraints among these actions, again, without thinking about the complete | https://karpathy.ai/lexicap/0015-large.html#00:30:43.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | details. What we do in our hierarchical planning work is then say, all right, I make a plan at a | https://karpathy.ai/lexicap/0015-large.html#00:30:50.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | high level of abstraction, I have to have some reason to think that it's feasible without working | https://karpathy.ai/lexicap/0015-large.html#00:30:56.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | it out in complete detail. And that's actually the interesting step. I always like to talk about | https://karpathy.ai/lexicap/0015-large.html#00:31:03.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | walking through an airport, like you can plan to go to New York and arrive at the airport, and then | https://karpathy.ai/lexicap/0015-large.html#00:31:08.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | find yourself an office building later. You can't even tell me in advance what your plan is for | https://karpathy.ai/lexicap/0015-large.html#00:31:15.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | walking through the airport, partly because you're too lazy to think about it, maybe, but partly | https://karpathy.ai/lexicap/0015-large.html#00:31:20.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | also because you just don't have the information, you don't know what gate you're landing in, or | https://karpathy.ai/lexicap/0015-large.html#00:31:24.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | what people are going to be in front of you or anything. So there's no point in planning in | https://karpathy.ai/lexicap/0015-large.html#00:31:28.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | detail, but you have to have, you have to make a leap of faith that you can figure it out once you | https://karpathy.ai/lexicap/0015-large.html#00:31:34.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | get there. And it's really interesting to me how you arrive at that. How do you, so you have learned | https://karpathy.ai/lexicap/0015-large.html#00:31:40.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | over your lifetime to be able to make some kinds of predictions about how hard it is to achieve some | https://karpathy.ai/lexicap/0015-large.html#00:31:50.000 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | kinds of sub goals. And that's critical. Like you would never plan to fly somewhere if you couldn't, | https://karpathy.ai/lexicap/0015-large.html#00:31:54.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | didn't have a model of how hard it was to do some of the intermediate steps. So one of the things | https://karpathy.ai/lexicap/0015-large.html#00:32:00.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | we're thinking about now is how do you do this kind of very aggressive generalization to situations | https://karpathy.ai/lexicap/0015-large.html#00:32:04.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that you haven't been in and so on to predict how long will it take to walk through the Kuala Lumpur | https://karpathy.ai/lexicap/0015-large.html#00:32:12.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | airport. Like you could give me an estimate and it wouldn't be crazy. And you have to have an | https://karpathy.ai/lexicap/0015-large.html#00:32:16.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | estimate of that in order to make plans that involve walking through the Kuala Lumpur airport, | https://karpathy.ai/lexicap/0015-large.html#00:32:22.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | even if you don't need to know it in detail. So I'm really interested in these kinds of abstract | https://karpathy.ai/lexicap/0015-large.html#00:32:27.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | models and how do we acquire them. But once we have them, we can use them to do hierarchical | https://karpathy.ai/lexicap/0015-large.html#00:32:32.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | reasoning, which is, I think is very important. Yeah. There's this notion of goal regression and | https://karpathy.ai/lexicap/0015-large.html#00:32:37.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | preimage backchaining, this idea of starting at the goal and just forming these big clouds of | https://karpathy.ai/lexicap/0015-large.html#00:32:43.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | states. I mean, it's almost like saying to the airport, you know, once you show up to the airport | https://karpathy.ai/lexicap/0015-large.html#00:32:50.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that you're like a few steps away from the goal. So like thinking of it this way, it's kind of | https://karpathy.ai/lexicap/0015-large.html#00:33:01.920 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | interesting. I don't know if you have sort of further comments on that of starting at the goal. | https://karpathy.ai/lexicap/0015-large.html#00:33:08.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | Yeah. I mean, it's interesting that Simon, Herb Simon back in the early days of AI talked a lot | https://karpathy.ai/lexicap/0015-large.html#00:33:14.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | about means ends reasoning and reasoning back from the goal. There's a kind of an intuition that | https://karpathy.ai/lexicap/0015-large.html#00:33:22.000 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | people have that the number of that state space is big. The number of actions you could take is | https://karpathy.ai/lexicap/0015-large.html#00:33:26.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | really big. So if you say, here I sit and I want to search forward from where I am, what are all | https://karpathy.ai/lexicap/0015-large.html#00:33:34.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the things I could do? That's just overwhelming. If you say, if you can reason at this other level | https://karpathy.ai/lexicap/0015-large.html#00:33:39.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | and say, here's what I'm hoping to achieve, what could I do to make that true? That somehow the | https://karpathy.ai/lexicap/0015-large.html#00:33:44.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | branching is smaller. Now what's interesting is that like in the AI planning community, | https://karpathy.ai/lexicap/0015-large.html#00:33:49.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that hasn't worked out in the class of problems that they look at and the methods that they tend | https://karpathy.ai/lexicap/0015-large.html#00:33:54.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | to use. It hasn't turned out that it's better to go backward. It's still kind of my intuition that | https://karpathy.ai/lexicap/0015-large.html#00:33:59.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | it is, but I can't prove that to you right now. Right. I share your intuition, at least for us | https://karpathy.ai/lexicap/0015-large.html#00:34:04.560 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | mere humans. Speaking of which, when you maybe now we take a little step into that philosophy circle. | https://karpathy.ai/lexicap/0015-large.html#00:34:10.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | How hard would it, when you think about human life, you give those examples often. How hard do | https://karpathy.ai/lexicap/0015-large.html#00:34:22.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | you think it is to formulate human life as a planning problem or aspects of human life? So | https://karpathy.ai/lexicap/0015-large.html#00:34:28.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | when you look at robots, you're often trying to think about object manipulation, | https://karpathy.ai/lexicap/0015-large.html#00:34:33.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | tasks about moving a thing. When you take a slight step outside the room, let the robot | https://karpathy.ai/lexicap/0015-large.html#00:34:38.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | leave and go get lunch, or maybe try to pursue more fuzzy goals. How hard do you think is that | https://karpathy.ai/lexicap/0015-large.html#00:34:46.240 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | problem? If you were to try to maybe put another way, try to formulate human life as a planning | https://karpathy.ai/lexicap/0015-large.html#00:34:54.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | problem. Well, that would be a mistake. I mean, it's not all a planning problem, right? I think | https://karpathy.ai/lexicap/0015-large.html#00:35:00.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | it's really, really important that we understand that you have to put together pieces and parts | https://karpathy.ai/lexicap/0015-large.html#00:35:05.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that have different styles of reasoning and representation and learning. I think it seems | https://karpathy.ai/lexicap/0015-large.html#00:35:11.920 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | probably clear to anybody that it can't all be this or all be that. Brains aren't all like this | https://karpathy.ai/lexicap/0015-large.html#00:35:18.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | or all like that, right? They have different pieces and parts and substructure and so on. | https://karpathy.ai/lexicap/0015-large.html#00:35:25.680 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | So I don't think that there's any good reason to think that there's going to be like one true | https://karpathy.ai/lexicap/0015-large.html#00:35:30.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | algorithmic thing that's going to do the whole job. So it's a bunch of pieces together designed | https://karpathy.ai/lexicap/0015-large.html#00:35:34.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | to solve a bunch of specific problems. Or maybe styles of problems. I mean, there's probably some | https://karpathy.ai/lexicap/0015-large.html#00:35:40.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | reasoning that needs to go on in image space. I think, again, there's this model based versus | https://karpathy.ai/lexicap/0015-large.html#00:35:49.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | model free idea, right? So in reinforcement learning, people talk about, oh, should I learn, | https://karpathy.ai/lexicap/0015-large.html#00:35:57.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | I could learn a policy, just straight up a way of behaving. I could learn it's popular | https://karpathy.ai/lexicap/0015-large.html#00:36:02.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | on a value function. That's some kind of weird intermediate ground. Or I could learn a transition | https://karpathy.ai/lexicap/0015-large.html#00:36:08.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | model, which tells me something about the dynamics of the world. If I take it, imagine that I learned | https://karpathy.ai/lexicap/0015-large.html#00:36:14.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | a transition model and I couple it with a planner and I draw a box around that, I have a policy | https://karpathy.ai/lexicap/0015-large.html#00:36:20.240 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | again. It's just stored a different way, right? But it's just as much of a policy as the other | https://karpathy.ai/lexicap/0015-large.html#00:36:25.600 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | policy. It's just I've made, I think the way I see it is it's a time space trade off in computation, | https://karpathy.ai/lexicap/0015-large.html#00:36:32.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | right? A more overt policy representation. Maybe it takes more space, but maybe I can | https://karpathy.ai/lexicap/0015-large.html#00:36:40.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | compute quickly what action I should take. On the other hand, maybe a very compact model of | https://karpathy.ai/lexicap/0015-large.html#00:36:46.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the world dynamics plus a planner lets me compute what action to take to just more slowly. There's | https://karpathy.ai/lexicap/0015-large.html#00:36:51.200 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | no, I don't, I mean, I don't think there's no argument to be had. It's just like a question of | https://karpathy.ai/lexicap/0015-large.html#00:36:57.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | what form of computation is best for us for the various sub problems. Right. So, and, and so like | https://karpathy.ai/lexicap/0015-large.html#00:37:02.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | learning to do algebra manipulations for some reason is, I mean, that's probably gonna want | https://karpathy.ai/lexicap/0015-large.html#00:37:10.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | naturally a sort of a different representation than writing a unicycle at the time constraints | https://karpathy.ai/lexicap/0015-large.html#00:37:16.000 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | on the unicycle are serious. The space is maybe smaller. I don't know, but so I could be the more | https://karpathy.ai/lexicap/0015-large.html#00:37:21.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | human size of falling in love, having a relationship that might be another, another style of how to | https://karpathy.ai/lexicap/0015-large.html#00:37:27.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | model that. Yeah. Let's first solve the algebra and the object manipulation. What do you think | https://karpathy.ai/lexicap/0015-large.html#00:37:36.000 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | is harder perception or planning perception? That's why understanding that's why. So what do you think | https://karpathy.ai/lexicap/0015-large.html#00:37:43.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | is so hard about perception by understanding the world around you? Well, I mean, I think the big | https://karpathy.ai/lexicap/0015-large.html#00:37:52.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | question is representational. Hugely the question is representation. So perception has made great | https://karpathy.ai/lexicap/0015-large.html#00:37:56.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | strides lately, right? And we can classify images and we can play certain kinds of games and predict | https://karpathy.ai/lexicap/0015-large.html#00:38:08.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | how to steer the car and all this sort of stuff. Um, I don't think we have a very good idea of | https://karpathy.ai/lexicap/0015-large.html#00:38:15.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | what perception should deliver, right? So if you, if you believe in modularity, okay, there's, | https://karpathy.ai/lexicap/0015-large.html#00:38:24.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | there's a very strong view which says we shouldn't build in any modularity. We should make a giant | https://karpathy.ai/lexicap/0015-large.html#00:38:29.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | gigantic neural network, train it end to end to do the thing. And that's the best way forward. | https://karpathy.ai/lexicap/0015-large.html#00:38:38.000 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | And it's hard to argue with that except on a sample complexity basis, right? So you might say, | https://karpathy.ai/lexicap/0015-large.html#00:38:44.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | Oh, well if I want to do end to end reinforcement learning on this giant, giant neural network, | https://karpathy.ai/lexicap/0015-large.html#00:38:51.440 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | it's going to take a lot of data and a lot of like broken robots and stuff. So then the only answer | https://karpathy.ai/lexicap/0015-large.html#00:38:55.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | is to say, okay, we have to build something in, build in some structure or some bias. We know | https://karpathy.ai/lexicap/0015-large.html#00:39:05.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | from theory of machine learning, the only way to cut down the sample complexity is to kind of cut | https://karpathy.ai/lexicap/0015-large.html#00:39:11.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | down, somehow cut down the hypothesis space. You can do that by building in bias. There's all kinds | https://karpathy.ai/lexicap/0015-large.html#00:39:15.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | of reasons to think that nature built bias into humans. Um, convolution is a bias, right? It's a | https://karpathy.ai/lexicap/0015-large.html#00:39:22.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | very strong bias and it's a very critical bias. So my own view is that we should look for more | https://karpathy.ai/lexicap/0015-large.html#00:39:30.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | things that are like convolution, but the address other aspects of reasoning, right? So convolution | https://karpathy.ai/lexicap/0015-large.html#00:39:37.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | helps us a lot with a certain kind of spatial reasoning. That's quite close to the imaging. | https://karpathy.ai/lexicap/0015-large.html#00:39:42.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | I think there's other ideas like that. Maybe some amount of forward search, maybe some notions of | https://karpathy.ai/lexicap/0015-large.html#00:39:48.320 |
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