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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276431 | [S1] ... forward predicts. For example, you could have optimized the predictions themselves at runtime- [S2] Yes. [S1] ... uh, to make both of them happy. You could have, um, I, I don't know, you could have, uh, just learned it as, as one thing and not even bothered with runtime optimization. Why did you | 19.64 | 3.340468 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276431.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276432 | [S1] and this is going to lead me to, uh, good predictions. But, uh, this is only happens, you only can look at the effect at the very end of training, and then you're going to use that on validation. [S2] Mm-hmm. [S1] And so, uh, you could do that, and I think there's papers that do that using implicit gradients, um, ... | 29.68 | 2.82559 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276432.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276433 | [S1] ... the ways to change the output conditioned on the input that, uh, kind of still do not, um, deviate too much from what it has learned. [S2] Mm-hmm. [S1] Uh, so theta captures the dynamics and says, "Okay, I probably got it a bit wrong because I'm not conserving G." Uh, so, but- [S2] Mm-hmm. [S1] ... but I don't... | 25.2 | 3.105202 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276433.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276434 | [S1] It, it's, it's, I think there's something like what you said that, that, that, that going to be, uh, there. Uh, in particular, it, it, I, I think it, G has a feeling like, uh, like this adversarial discriminator because it's telling you, "Oh, if you're not satisfying G conservation- [S2] Yeah. [S1] ... then most l... | 25.12 | 2.912322 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276434.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276435 | [S1] ... interested in, uh, going forward, and, and I think that, that could be a, a venue of, of many future works, is that we focused a lot on when we were trying to make predictions on kind of generative, uh, networks. So the fact that you, sorry, generative not in the sense of self-supervised learning, but- [S2] Ye... | 24.44 | 3.111304 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276435.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276436 | [S1] ... and code and, and construct maybe architecturally different from, from the F-networks. [S2] Mm-hmm. [S1] Uh, and maybe combining this, uh, proposal networks with this, uh, checking networks, uh, may, may make a different architecture classes that could be useful. | 14 | 3.190112 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276436.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276437 | [S1] Yeah, I wanted to get a little bit more into, so you have, you have experimental results where you compare to various baselines, like, you know, without, um, and, and, and obviously, obviously you're better than them, which is what we've come to expect from machine learning papers. [S2] [LAUGHS] [S1] I wanna, I wa... | 26.16 | 3.419096 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276437.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276438 | [S1] We don't know. And the sixth one, we, uh, we found that it was following, uh, blue objects very closely. So here, of course, we only show, uh, one example over time. [S2] Mm-hmm. [S1] So this is a time sequence as we track the object. On, on the appendix, we, we show that they're, it basically didn't matter. The e... | 20.56 | 3.229515 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276438.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276439 | [S1] Everything is physics. If you're in the real world, um, like cars or people moving around. But, but they also, like, they also have some intrinsic mov- movement that not, doesn't follow. Passive physics loss, but, um, there's other- [S2] Do you have, do you have, like, something in mind? Like, except, except cuts ... | 29.24 | 3.006374 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276439.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276441 | [S1] Uh, so, go ahead. [S2] One, one easy example of something that would fail is you have a video and you, uh, often have things that entered the video that were not in the video. [S1] Yeah. [S2] Um, then here you get into trouble because there's a, something that was not observed. It's the same thing that we were tal... | 29.24 | 3.159605 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276441.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276442 | [S1] Yes, yes, exactly. So, yeah, things, and one other thing, I think, conversely, it could be that there's a lot of work that will need to be done if the camera is, uh, is, uh, moving a lot, um, because- [S2] And then- [S1] ... all of these objects will for sure appear that were not there, because you're looking at s... | 21.72 | 3.152182 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276442.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276443 | [S1] But it's, I mean, just, just out of intuition, it seems more likely that the network detects something like, you know, there's, there's a blue bunch of pixels and, and, uh, an orange bunch of pixels, and these pixels sort of move together as objects. [S2] Yeah. [S1] Rather than the network from video somehow deter... | 28.48 | 3.357961 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276443.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276444 | [S1] Sure. Uh, I didn't know exactly how. And then, um, the, Ross DeDray gave a talk at MIT, uh, it's online on the YouTube, uh, seminar. Uh, and he was talk- telling us how, um, it's very hard to encode inductive biases in neural networks. And in their case, basically, they were predicting how a robot was pushing a bu... | 27 | 3.271325 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276444.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276445 | [S1] Cool. Is there anything you, else you want to say about the, the experimental results? We touched on sort of upping the inner steps and the, and the, uh, the grad chem, but is there anything- [S2] We- [S1] ... special you want to say about sort of your, your tests on, uh, for example, the pendulums or- | 17.52 | 3.381934 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276445.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276446 | [S1] Yeah, I think some of the experiments, uh, depends on the, how much time we have, but on the, on the pendulum, there was a symbolic component, so the, the G doesn't have to be fully neural. [S2] Yeah. [S1] Uh, so in, in the origin, in the first, I think those are the first experiment, the G is kind of a program wi... | 26.16 | 3.166793 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276446.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276447 | [S1] And there you, we search over formulas, um, and then there's some parameters as well that get trained over, uh, with gray in the center. [S2] Yeah. [S1] And there we saw that, okay, we, we are able to recover the true formulas of the energy and it leads to better prediction than a vanilla MLP that does not learn a... | 29.88 | 3.230198 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276447.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276448 | [S1] Uh, this is changing H and N conserve quantity, uh, which is what they believe is, is, uh, they predict it's going to be some more, the energy. You can see the baseline neural network, which is just the, uh, the F, basically, just F. [S2] Mm-hmm. [S1] Uh, quickly loses energy, and therefore, this is going to lead ... | 26.84 | 2.926327 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276448.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276449 | [S1] And then, uh, we also, uh, had Professor Josh Tenenbaum from, uh, MIT Cognitive Science and Kenji Kawaguchi, uh, from the University of Singapore. [S2] Cool. Excellent. Well, Ferran, thank you so much for being here with us, uh, today. [S1] Thank you. [S2] And, and all the, all the best. I hope you have great, gre... | 19.84 | 3.421408 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276449.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238310 | [S1] Welcome everyone. Uh, today I have with me right here, Stefan Dascholi, who is the first author of the paper, Deep Symbolic Regression for Recurrent Sequences. Stefan, welcome. Thank you very much for being here. [S2] Yeah, pleasure. Bad time to have COVID, but I'll try my best to- [S1] Yeah. | 17.16 | 3.195547 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238310.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238311 | [S1] ... taking a, a, a matrix and then, uh, outputting its inverse or stuff like that. And so, uh, a natural continuation of this was to start from numeric data and go to a symbolic formula, and that's basically, uh, symbolic regression, which means you take a function, uh, you only see its values and you have to try ... | 26 | 3.017724 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238311.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238312 | [S1] Yeah. [S2] Yeah, the sum of integers. [S1] Okay. Yeah. Um, okay. And, and from that, we just want the final digit. So this, the sequence here is 0, 1, 3, 6, 0, 5, 1, 8, 6, 5. That is, it is, it is, I would, I would call it pretty complicated if you just gave me this as a human, but there is some kind of a rule beh... | 27.24 | 2.934804 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238312.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238313 | [S1] This one is actually a good example. It's kind of hard to recognize for us. And if you look at the formula that the model gave us, uh, you can actually figure out why, uh, it predicted that formula. It's UN minus one plus N. Uh, and the reason for that is that NN plus one divided by two is the formula for the sum ... | 29.88 | 2.977886 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238313.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238314 | [S1] ... realized that that was pretty easy. Uh, pretty quickly we managed to get a model working on integer sequences. And, uh, so we then started to think about, can we do the same thing for float sequences, which are a bit more challenging because you have more freedom in the expressions you can build, you have more... | 29.88 | 2.804121 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238314.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238315 | [S1] our input numbers as embeddings. And that's complicated because of course integers, just like reals, are an infinite set. So you have to sometime, uh, somehow find them, find a way to encode them as a fixed vocabulary. [S2] Mm-hmm. [S1] And so this is where we really have to distinguish our two setups. We basicall... | 18 | 3.000752 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238315.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238316 | [S1] Um, actually, it turns out that it's better to use a, a long, um, a larger base because if you lose a, use a larger base, well, you're gonna have a bigger vocabulary, but you're gonna have shorter sequences. [S2] Mm-hmm. [S1] And typically, you know, transformers have a quadratic complexity. They struggle a bit wi... | 21.44 | 2.828549 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238316.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238317 | [S1] Yeah. So this is, this would be base 30, and obviously in base 10,000, I think it's important to note that every single number from zero to 9,999 is its own token, right? [S2] Exactly. [S1] The model has no inherent knowledge of, you know, three comes after two and four comes after three and so on. All of this has... | 28.96 | 3.232126 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238317.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238318 | [S1] ... to make the model learn essentially er, the entire ordering of 10,000 numbers rather than, you know, providing that as some sort of a, just to make the sequence a bit shorter, right? [S2] It's funny. [S1] Did you ever think of going with continuous values, right? Because the first, my first intuition would be ... | 29.64 | 2.889043 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238318.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238319 | [S1] Yeah. [S2] Yeah. [S1] And the float embeddings are, are very similar, right? In that you encode them as like a, a, a, a sign, a mantissa, and an exponent. And again, the mantissa, if I understand correctly, same deal, that you have a token per number between zero and, and 10,000. [S2] Mm-hmm. [S1] And the man- and... | 29.28 | 3.157107 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238319.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238320 | [S1] ... token for the mantissa. We don't have, like, a base-B representation. [S2] Yeah. [S1] Which means that we do lose some information in, in the discretization process. And then, indeed, to represent the scale of the, um, of the number, we use, uh, an exponent embedding. [S2] Mm-hmm. [S1] And, and that, indeed, g... | 29.88 | 2.864972 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238320.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238321 | [S1] ... big, big model. We, we, we've embedding dimension 512. [S2] Mm-hmm. [S1] Actually, when we were using a smaller model, uh, with a smaller embedding dimension, we saw a really neat pattern, um, which was basically the fact that it, the model was learning the, uh, arithmetic properties of integers. So it was bas... | 29.88 | 2.887318 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238321.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238322 | [S1] Uh, actually not those ones. The- [S2] Oh, the- [S1] Yeah, the- [S2] Sorry. Correlation. [S1] Yeah, those ones, exactly. [S2] Yeah. [S1] Like if you zoom in the lots on the left lot, you kind of see these, these diagonal lines which are spaced out every six and every 12. [S2] Mm-hmm. [S1] Uh, showing that basicall... | 24.48 | 2.801846 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238322.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238323 | [S1] So these plots, just to, to make it clear, these are the cosine similarities between each of the tokens. So the tokens would be distributed on the, on the axis here. [S2] Exactly. [S1] These are tokens and these are tokens, and then we plot the, uh, the cosine similarities between every two tokens. So naturally, o... | 22.08 | 3.162743 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238323.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238324 | [S1] One thing also that's hard to see in this big model book, which was much clearer in the small model, is like you could see, for example, the perfect squares would lie, would be complete outliers. You, you would get like, uh, 9, 16, 25, 49, which would completely stand apart due to their, like, sort of special prop... | 25.44 | 2.925161 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238324.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238326 | [S1] Oh, sorry, no, they're, they're repeated in part, but also, um, there are more in the float formulas. And then you just generate in, um, reverse Polish notation, is that correct? [S2] Exactly. [S1] So you generate reverse Polish notation formulas given these, these things, and you can also have integer prefactors,... | 29.6 | 3.062823 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238326.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238328 | [S1] Rather different things in the two setups. Really in the integer setup, we're focusing on sort of arithmetics and arithmetic properties of numbers. Whereas in the float setup, we're really interested in a, let's say a more classic, uh, symbolic regression problem with, with complex operators. [S2] Yeah. [S1] And y... | 26.04 | 2.89688 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238328.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238329 | [S1] Indeed, we fill in the nodes of these trees, uh, either with operators. Uh, so the nodes are filled in with operators, uh, either binary or, or unary. And then the leaves of the tree, indeed, as you said, um, can be either variables or, or constants. [S2] Mm-hmm. [S1] And as you said, uh, the, the, | 19.72 | 2.896241 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238329.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238331 | [S1] Yeah, probably we could have, like, tuned these parameters somehow, but here we really wanted to have the, the simplest choice possible, uh, on the rationale that basically our, our dataset is so huge, uh, that it's, eventually we're gonna see all possible, uh, formulas at some point. [S2] Yeah. [S1] Uh, it doesn'... | 24.24 | 2.915899 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238331.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238336 | [S1] even numbers you divide by two. That's a rule which is possible to express with a, a mathematical expression. Essentially what you do is say, is write it as N modulus two times what you do if it's, uh, even plus- [S2] Yeah. [S1] ... one minus- [S2] N modulus one minus that, yeah. [S1] But that's- | 17.32 | 2.927948 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238336.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238338 | [S1] Or is there like a property of math that says, that says, well, if you, actually, if you look for the simplest sequence, it is kinda defined, even though there are infinite possibilities. Like, you, you, do you know a little bit what I mean? Is it more like- [S2] Yeah, yeah, yeah. [S1] ... a property of humanity o... | 26 | 3.182672 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238338.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238340 | [S1] ... presentation of the world. [S2] Of course, yeah. [S1] I could, you know, be, be, do much more powerful planning. Is there, are you thinking of applications like these when you develop this, right? Beyond- [S2] Definitely. [S1] ... number sequences or is there any- [S2] Yeah. [S1] ... interesting ones that, you... | 17.16 | 3.134973 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238340.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238341 | [S1] Um, you can have the two criterions. The criterion you, we choose in the papers, we want the, uh, the evaluations to be the same. [S2] Mm-hmm. [S1] Mm-hmm. [S2] So even if it comes up with, like, a different formula, it's, it's fine as long as, like, the, the ones you tested on, uh, match. | 15.48 | 3.030262 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238341.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238344 | [S1] Euler's constant, okay. So, N times the, s- the sine of gamma squared. So, the entire thing on the right-hand side is a, oh, sorry, is a constant, right? So, it's essentially N times a constant. [S2] Yeah. [S1] Uh, so the, the model, what it has to do is it has to somehow figure out the expression for the constant... | 26.72 | 3.113494 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238344.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238345 | [S1] ... that the model could, you know, figure it out from the data points it has. By the way, the, the green background, that's the input, right? The blue background- [S2] Exactly. [S1] ... that's, that's the, what it has to predict. [S2] Yeah. [S1] So the next one I find particularly interesting. It is, the formula ... | 22.16 | 3.065812 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238345.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238350 | [S1] ... your inputs. [S2] Yeah. [S1] However, there is one thing where symbolic regression is better than, uh, numeric regression, is that once it does find the correct formula, then it's gonna get, you know, perfect precision on all, all the, the, the subsequent numbers you're gonna give it for you. [S2] Mm-hmm. [S1]... | 21.48 | 2.800172 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238350.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238366 | [S1] Maybe it's, it's forbidden knowledge, but this might be like a field of deep learning where there's, you know- [S2] Where things actually work. [S1] You, you, you, you can get, you can get like results. It, it kind of, it works maybe, or maybe let's say you get started with something that works pretty quickly. [S2... | 25.4 | 2.872891 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238366.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238367 | [S1] So, yeah, it will, it will... Okay, that, that's, that is fairly regular if I look at the plot. [S2] [LAUGHS] [S1] Um, but, yeah, I invite people to go and, and challenge, challenge your model a little bit. Right here you can also choose, uh, sequences of this, uh, O, OEIS database and, um, yeah, check out the mod... | 22 | 2.845669 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238367.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238368 | [S1] All right. So I think this, this, is there anything you wanna like special that we haven't come to you, you wanna mention about the paper itself? [S2] No, that was, that was great for me. Thanks for your questions. [S1] I think that was great for me as well. I, I'm always happy if I can ask like all my, all my dum... | 29.6 | 3.0329 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p164_BV1pP4y1k7Jr_p164_m4-dialogue_0238368.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727399 | [S1] Hello, everyone. Today, here with me, I have Patrick Mino, who is a neuroscientist, uh, slash blogger, slash anything else that you might imagine in between, uh, deep learning and the human brain. Uh, welcome, Patrick, to the channel, uh, for this bit of a, a special episode, I guess. [S2] [LAUGHS] Thanks. Uh, it'... | 25.4 | 3.337104 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727399.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727401 | [S1] Uh, presenting deep learning to the world and saying like, "This is ready. This is a big deal," was ImageNet 2012. [S2] Mm-hmm. [S1] Right? Um, as you know. So that was, uh, during my PhD. So at the, uh, the very start of my, um, um, | 14.04 | 3.326112 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727401.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727402 | [S1] Well, that, it, it seems like it was an exciting time. I do remember Theano as well, so I'm definitely dated, dated the same. Um, so you, the dorsal stream, just to make clear, that's part of, sort of the visual, the visual stream, uh, into the brain. Is that correct or- [S2] Yeah, yeah, yeah. [S1] ... [S2] So- | 19.64 | 3.309498 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727402.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727404 | [S1] ... differences in luminance between like a center and a surround or differences in time. Um, so you can think of it as a camera with like a little bit of linear filtering. Um, and, uh, it then gets forwarded to, um, different areas of the brain. First to the lateral geniculate nucleus and then to the back of the ... | 29.96 | 3.300386 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727404.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727405 | [S1] ... Visual processing splits into two different substreams. Uh, there's the, uh, ventral visual stream, which is the object stream. Um, so if you think, like, what does a, you know, ResNet-50, that's trained on, uh, on ImageNet, do, maybe it's something similar to that, and we can get into that later. [S2] Mm-hmm.... | 29.96 | 3.348769 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727405.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727406 | [S1] ... these, uh, you, you know, you've, uh, for instance, you have increases in the size of receptive fields, you have increases in the size of, in the complexity of things that these neurons respond to. But this time, they don't care about form. They don't care whether, uh, they don't care about texture. Uh, what t... | 21.72 | 3.365446 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727406.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727407 | [S1] Uh, a neuron in, uh, let's say the middle temporal area, which is part of the dorsal stream, and 80 or 90% of the neurons will respond when you show them the right moving stimulus. [S2] Yeah. [S1] Uh, which is, which is, uh, remarkable. | 14.48 | 3.380368 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727407.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727408 | [S1] understanding how the brain does certain things. And the answer is- [S2] Absolutely. [S1] Right? The answer is a little bit yes and a little bit no. Like, there's still, there's still questions. But you point out a bunch of areas of where progress has been made in, uh, correlating, let's say, neural activities in ... | 22 | 3.423783 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727408.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727409 | [S1] ... they would be, uh, it, it wouldn't matter the, the precise location of, uh, of this line in question. And it wouldn't matter the, the contrast. So it could be white to black or it could be black to white. [S2] Yeah. [S1] It, it, uh, it wouldn't matter. And so their hunch was that, okay, well, you have this, th... | 21.04 | 3.294171 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727409.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727410 | [S1] ... that looked inside of these deep neural networks and found that, you know, the kinds of selectivity that you see inside the cells, they're very, very similar to what you would, to what a neurophysiologist would describe in areas like V1, V2, V4, inferotemporal cortex. Um, so the combination of the quantitative... | 29.84 | 3.291113 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727410.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727411 | [S1] Sure, just like the idea of, look, how, like, what, what do we, what do we measure? Like, you know, is it a number, is it a correlation, or is it, uh, am I training a regression model from one signal to the other signal? Like, how, how can I make the statement that- [S2] Yeah. So the- [S1] ... this neural- [S2] Oh... | 21.32 | 3.386295 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727411.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727412 | [S1] ... down sampling or whatever. Um, and then you measure the output of that deep neural network, uh, with respect to some stimulus ensemble. [S2] Mm-hmm. [S1] So, which gives you a big matrix, big X, uh, which has a bunch of rows for the different examples and a bunch of, of columns for the different features. | 18.48 | 3.319435 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727412.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727413 | [S1] and then you just regress that, uh, against neural data that's, uh, that's recorded with the same, um, im- with the same images. [S2] And, yeah, that- [S1] So it's just a regression, so you can add, like, a bunch of, of different spices into your, your basic recipe. So you can, uh, add some, uh, some sparseness pr... | 29.84 | 3.211614 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727413.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727414 | [S1] Definitely, yeah, uh, the, the regular regression will usually crash and burn. Neural data is, is very noisy. [S2] Yeah. [S1] That's, uh, something that people don't, uh, often appreciate. Um, and so it's a regression. Let's just put it that way. [S2] Yeah. [S1] Now- [S2] That would be so, sort of, so, for example... | 20.64 | 3.255055 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727414.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727415 | [S1] I think. [S2] [LAUGHS] We just say MEG. Um, and, or it could be a single neuron recordings or array recordings. [S1] Yeah. [S2] So those are taken inside the brain. [S1] Mm-hmm. [S2] Or it might be ECOG, which is just on the surface of the brain. So there's different kinds of, uh, of recordings. Now, it happens th... | 23.16 | 3.092908 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727415.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727416 | [S1] Yeah, so that's super exciting. [S2] Yeah. [S1] And the reason is that I, I think that everybody got very excited when they saw that these networks, which were trained for ImageNet, they could be aligned for, to the ventral stream, uh, to that object recognition stream. [S2] Yeah. [S1] Because now it's something t... | 25.72 | 3.256068 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727416.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727417 | [S1] Um, but there's different ways in which something can be a model of, of, of the brain. And some of these are, are a little bit more useful than others. [S2] Yeah. [S1] And, and one of the ways I, one of the big flaws I think for, uh, uh, for supervised learning | 14.88 | 3.292273 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727417.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727418 | [S1] Is that it's not like really a way, it's not really a model of how the brain would learn a task. [S2] Mm-hmm. [S1] Uh, because, you know, I'm not walking around as a baby and like, you know, my, uh, my parent, uh, just tells me like, "Dog, dog, dog, dog, dog." [S2] Mm-hmm. [S1] "Cat." | 18.6 | 3.008698 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727418.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727419 | [S1] So people generally like unsupervised learning and self-supervised learning better for that reason, because you don't have to, you know, come up with this like, uh, weird concept that- [S2] Yeah. [S1] ... dog, dog, dog, cat. Um, | 13.36 | 3.381906 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727419.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727420 | [S1] And, and, uh, but you do have to do the math to make sure that it actually does work out in practice and that, you know, the right, the, the kinds of, the, the quantity of examples that you feed into, um, into the model is similar to the kinds of, to the, the quantity of examples that you would feed into a human- ... | 19.08 | 3.290323 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727420.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727421 | [S1] I think you have, you have a- [S2] So, uh- [S1] ... in your conclusion, you have a little bit of an example that, uh, it would, like, the language models that we train, such as GPT-3, would be equivalent to, like, years and years and years of, of human- [S2] Of just constant- [S1] Yeah. [S2] ... constant- [S1] Yea... | 23.4 | 3.365662 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727421.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727422 | [S1] Uh, exactly. So, uh, so I think that there's still a, a, a big gap- [S2] Mm-hmm. [S1] ... there that comes from that. You still, I mean, we're off, I think I calculated that we're off by four orders of magnitude in terms of- [S2] Yeah. [S1] ... the efficiency. Um, but, y- you know, I'm, uh, th- th- to score everyb... | 29.28 | 3.169101 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727422.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727423 | [S1] ... trying to solve these, uh, uh, these problems. So, um, so for, for instance, there's a lot of work on, um, trying to fit the same kinds of unsupervised learning models, but with streams of data that look more like what a baby would- [S2] Yeah. [S1] ... would see in, uh, in their early years. Uh, in which the c... | 26.48 | 3.467023 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727423.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727424 | [S1] ... a lot of gesturing. [S2] But it's also, it's also, also, there it's special because the baby, with time, is able to move its head, right? And, and therefore, it's also not the same as just placing a camera somewhere because whatever captures attention will be actively looked at more. So it's, it's definitely l... | 25.52 | 3.440126 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727424.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727425 | [S1] So to close the, uh, the, the- [S2] Yeah. [S1] ... just, just that, uh, that one paper, 'cause we've been on it for, [LAUGHS] like 15 minutes. But super cool that you can have, uh, you can train a model in a unsupervised or self-supervised manner, and it turns out to be just as good at explaining, you know, V1, V4... | 28.72 | 3.336495 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727425.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727426 | [S1] Oh, yeah. So, uh, so I'll, I'll just go very rapidly with, uh, true that actually the second one is, uh, ventral stream. [S2] Oh, sorry. [S1] Uh, again. And so that's, uh, from, uh, Talia Kanko, um, and very, very, uh, consistent, uh, data. So they use fMRI rather than- [S2] Mm-hmm. [S1] ... than single neuron dat... | 20.44 | 3.261023 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727426.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727427 | [S1] actually is, um, I was going about this, uh, very naively, but I, I just looked into, like, the torch vision models. [S2] Yeah. [S1] You know, the, they have, like, some, uh, some model database and just downloaded all the models that were trained on, um, video recognition. Uh, so all the models that were trained ... | 21.96 | 2.911937 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727427.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727428 | [S1] I, I'm drawing a blank here. [S2] Yeah. [S1] Kinetics 400, uh, which is a task where you have to look at a video of somebody juggling and say, "Oh, it's juggling," rather than unicycling, rather than soccer or whatever. And so the special thing about these models is that they look at 3D data. Uh, by 3D, I mean spa... | 21.4 | 3.324812 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727428.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727429 | [S1] And so I looked at these models and I did, uh, the kinds of visualization tricks that, uh, Crisola and, uh, and Yang do at, uh, at OpenAI to, uh, look inside. 'Cause I was curious, you know, do they learn motion? Do they align with, uh, with the brain? And I found that they were actually, like, really terrible. [S... | 25.44 | 3.434117 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727429.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727430 | [S1] Yeah. So vertical is like kind of a, sorry. [S2] [LAUGHS] [S1] This is like a weird non-secular. But, um, vertical is kind of a, a funny thing, right? Because it's an inner ear problem. [S2] Yeah. [S1] Right? So you have your vestibule and it kind of, it basically tells you there's acceleration in ways that there ... | 21.12 | 2.862946 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727430.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727431 | [S1] But also gives you like these weird visual effects. [S2] Yeah. [S1] Right? Which is, uh, uh, which is strange. Or, you know, if you drink a little too much, you might have that, uh, that same kind of feeling. Um, so there's an area in the brain, which is called MST, which has these neurons which receive both visua... | 19.44 | 3.234434 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727431.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727432 | [S1] And the way that they receive, uh, visual input is, uh, they have a lot of selectivity for things like rotation and expansion and, uh, and wide field translation. [S2] Yeah. [S1] And so we think that they're really involved in navigation. So if you're going forward, uh, | 17.64 | 3.463415 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727432.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727433 | [S1] In a, in a line, uh, you have these neurons which receive both the vestibular input, so they know how you're accelerating and where gravity is, and they receive all this white field optic flow, which is, tells you, uh, where you're, uh, where you're heading. So we said, why don't we train a deep neural network to,... | 29.24 | 3.340395 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727433.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727434 | [S1] Uh, it's an environment for, uh, drone simulations. [S2] Yeah. [S1] It's called AirSim. And, uh, it's really fun. Uh, so it's an, uh, Unreal engine. [S2] Yeah. [S1] Uh, and you can, [LAUGHS] you can, uh, basically fly a drone in these suburban environments. [S2] Yeah. [S1] And, uh, back out these sequences of vide... | 29.24 | 3.307392 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727434.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727435 | [S1] ... for, uh, for translation and, um, translation, but they don't care about the pattern that underlies the, uh, the translation. And in particular, you see these cells, like the one that you're, that you're visualizing here, that like things like spirals, uh, and some of the higher level layers of, uh, of this ne... | 29.16 | 3.093223 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727435.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727436 | [S1] ... anything from a video that contains motion weren't, aren't, like, turns out these neural ne- sorry, the deep networks, uh, I have to stop saying neural networks here because it's ambiguous. Um, [S2] Ah, yes, yes, yes. [S1] ... the deep networks that, that train on vi- any kind of video data, they're not super ... | 29.88 | 3.432907 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727436.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727438 | [S1] One, um, one big question that, uh, came up during the review is that, you know, we claimed originally this was, uh, unsupervised or self-supervised. [S2] Yeah. [S1] In the abstract. And then the reviewers came back and said, "Well, it's not really unsupervised or self-supervised. It's a supervised network because... | 21.96 | 3.342162 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727438.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727439 | [S1] My feeling is that it is, uh, self-supervised in the sense of when you embody this in an agent. So when I'm, when I'm a baby, [LAUGHS] let's, let's imagine that I'm a baby and I'm walking around the world. I have some control over where I'm heading. [S2] Yeah. [S1] Right? So I can say, like, I'm gonna turn this wa... | 29.88 | 2.815602 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727439.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727440 | [S1] ... control the motion that comes into my eyes. [S2] Yeah. [S1] Uh, because the vast majority of motion that we see in the world comes from, uh, from our self-motion. [S2] Mm-hmm. [S1] And so I can correlate my motor plans with what I see in the world. And that means that, uh, it's a, it's a much easier kind of pr... | 24.04 | 3.3302 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727440.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727441 | [S1] Here's found data. [S2] Yeah. [S1] Which is the, the case of ImageNet, and figure out something to, to, to model with this. [S2] Yeah, exactly. [S1] Right? [S2] Yeah. | 7.84 | 3.285768 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727441.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727442 | [S1] Yeah, absolutely. So, I think it looks more like the bottom part of this diagram, uh, that you see there, where you have these two things which are happening in the present, but one, uh, part is occluded and the other part is visible. [S2] Yeah. [S1] So, you're doing multimodal masking, in other words, right? So, ... | 26.48 | 3.100889 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727442.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727443 | [S1] You know, in a way, you're trying to predict, um, language from, uh, from vision. But it's really, uh, this, this kind of, uh, of masking, and it's a, I think it's a more general approach to solving, uh, this, uh, this type of problem. So, yeah, I agree with you, embodied agents, I'm 100% on board. [S2] Yeah. [S1]... | 29.88 | 3.343243 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727443.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727444 | [S1] When they have a visual task, I think like those are super interesting. Like what do you need to put in there- [S2] Yeah. [S1] ... uh, in order to get that, uh, that effect. [S2] Yeah, that, that con- | 7.96 | 3.024545 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727444.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727445 | [S1] ... this, this paper you're describing, it tackles the question- [S2] Oh, it's the same people. [S1] It is actually, it is actually, I just saw, I just saw in my notes, that is again one of, one of your papers. Yeah. [S2] Yeah. [S1] It is the question, why are there even two different of these visual streams in, i... | 28.32 | 2.851769 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727445.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727446 | [S1] I worked on, um, looking at what, what it would take to, to recreate both ventral and dorsal stream. [S2] Mm-hmm. [S1] And, uh, I think the remarkable thing that he found is if you train a, uh, a network like, uh, a CPC network, so a contrastive predictive coding network, which is one form of self-supervised learn... | 29.88 | 3.302848 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727446.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727448 | [S1] on just, uh, on just one GPU. And so what they decided arbitrarily is to split it up into two parts, especially at the, uh, uh, at the early part. [S2] Yeah. [S1] And then basically they, so they were independent, but they could re-communicate a little bit later on. [S2] Mm-hmm. [S1] Um, so, which was, | 17.2 | 3.374405 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727448.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727449 | [S1] ... a pretty unique feature, uh, back then. People didn't really do that. [S2] Yeah. [S1] Uh, but now it's, it's quite common to, you know, chop up the channels in different ways and all sorts of things. [S2] Mm-hmm. [S1] Um, but what they found is that there's this, this, uh, there's this very interesting self-or... | 18.12 | 3.324872 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727449.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727450 | [S1] All the, all the, uh, the, the filters on one GPU turned out to be color selective, and all the filters on the other GPU turned out to be, uh, to, to be black and white. [S2] Yeah. [S1] Which is, whoa, that's weird. [S2] Just, just by the fact of, of splitting up. | 15.8 | 3.206177 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727450.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727451 | [S1] ... extreme. [S2] Yeah. So in that, in that case, in the early Alex- [S1] Mm-hmm. [S2] ... that paper, actually both, uh, of the types of filters are different sub- types that you see in, uh, in V1. But they are, you know, functionally- [S1] Yeah. [S2] ... different, and they have different roles. But it was like ... | 27.36 | 3.250215 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727451.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727452 | [S1] pushing the network very, very slightly out of, of equilibrium. [S2] Yeah. [S1] And that's enough to self-organize into this thing. And so, uh, Shahab found a, a very similar phenomenon in the context of these networks which are trained in an unsupervised manner in CDC. [S2] Mm-hmm. [S1] And, um, so being trained ... | 20.24 | 3.366963 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727452.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727453 | [S1] Um, and was able to correlate that with some, um, some data that we have in, uh, in mouse where there's tons and tons of data on what's the relative selectivity of the, of these different things and found some, uh, some really nice correlations. [S2] Cool. [S1] Uh, so that means that you can, | 15.92 | 3.093197 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727453.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727454 | [S1] Uh, all you would need basically is a little bit of a nudge. [S2] Yeah. [S1] Right? And, uh, so, so which is, is, is this great idea. Like maybe you just initialize the network in a sl- so that, like, the two things are just very slightly asymmetric. [S2] Mm-hmm. [S1] Because one thing I should say is that, um, | 20.8 | 3.214868 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727454.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727455 | [S1] ... the, uh, the two networks don't always get the same label, right? So if you train the network twice, one time it's going to be dorsal-ventral, and the other time it's going to be ventral-dorsal. Whereas the brain, every time that you train it- [S2] It's the same, yeah. [S1] ... [LAUGHS] that we know of. [S2] S... | 22.64 | 3.198703 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727455.mp3 | [
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