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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727456 | [S1] Uh, because if you train it with real data, uh, and then it will automatically, you know, self-generate into this, uh, in, in bloom into this particular activity. [S2] Cool. Um- [S1] So very exciting. [S2] Yeah, this could be- [S1] Uh, that the brain can organize itself for something that's, uh, that's useful just... | 21.64 | 3.116639 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727456.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727457 | [S1] Is it this paper? [S2] Oh, that's, this as well. I think, like, uh, I, I think that there's been a lot of movement in this subfield. And by the way, I want to tell your viewers, 'cause I know a lot of you viewers are coming from a machine learning background versus, uh, a, a neuroscience background. And, uh, you k... | 29.88 | 3.06095 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727457.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727458 | [S1] ... jump onto and, uh, and have positive reception. So, um, there's, there's still a, a bunch of, uh, a bunch of questions, so grab your nearest neuroscientist and go write a paper. Uh, encourage everybody to do it. [S2] Yep. Definitely. How to, how to hack, how to hack the right publications. There you go. [S1] U... | 29 | 3.416152 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727458.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727459 | [S1] Um, and, uh, so in this particular instance, they, uh, they presented different kinds of, uh, of concepts and images. And one of the cells that they found had this, like, amazing property that if you just show the words Jennifer Aniston, it would respond. If you showed the face of Jennifer Aniston- [S2] Mm-hmm. [S... | 19.32 | 3.379604 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727459.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727461 | [S1] Um, so, uh, ever since then, uh, people have been, like, fascinated by this idea, although it's a, it's a much older idea, you know, this idea that you have, like, a cell in your hippocampus that responds- [S2] Yeah. [S1] ... to your grandmother. It's the grandmother cell idea. But, um, um, one thing that was very... | 23.52 | 3.075386 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727461.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727462 | [S1] Um, so it sounds very hippocampus-like to me. And so in this particular paper, they, uh, they actually looked at, um, at this problem and found that out of all the different models that, uh, that they could look, um, they, they, they found that, uh, Clip could explain, uh, the most, uh, hippocampal data, which is ... | 28.28 | 3.343319 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727462.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727463 | [S1] But it's Clip specifically. [S2] Yeah. Yeah. [S1] 'Cause there's a lot of other unsupervised models and, uh, somehow Clip is the best and we still don't understand why this is. I mean, it's like the delta between it and the, uh, the, the second best model is- [S2] Yeah. [S1] ... is huge. But why? Uh, I, I think no... | 21.24 | 3.251334 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727463.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727464 | [S1] Yeah. And, uh, and one thing that's, uh, that's interesting, in particular for babies, you know, if, uh, if you ever interacted with babies, they really like to have toys- [S2] Yeah. [S1] ... which make lots of noise, which drives parents crazy. And, but I think that there's a reason for that, right? Like, why wou... | 29.88 | 3.239234 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727464.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727465 | [S1] ... Because you could like- [S2] ... on that neuron. [S1] So you, you could rotate the entire space, it would still make sense, right? So there's no, there's no reason why an individual neuron should align with just like one axis in, in that particular subspace. | 14.96 | 3.321738 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727465.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727466 | [S1] Mm-hmm. [S2] Yeah, exactly. Um, so, but, uh, neuroscientists really like, uh, labeled axes. [S1] Yeah. [S2] [LAUGHS] That's, that's one thing that, uh, that they're very fond of. Um, so, you know, you can imagine that you have, like, an axis. I don't know if you're in, in Unity or in Unreal. [S1] Yeah. [S2] You kn... | 25.2 | 2.970898 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727466.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727467 | [S1] Yeah, it's, uh, it's too bad. So I still print out papers because there's been research that shows that you retain more when you print something out, rather, when you read it in a, um, on a printed document rather than- [S2] Yeah. [S1] ... reading it on the, but it's just becoming so, so inconvenient that I think ... | 26.88 | 3.338636 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727467.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727468 | [S1] Yes, yes, exactly. And that might be because, uh, you know, neuroscientists like to name things, and if something is not nameable, they'll say it's mixed selectivity or whatever, and then they'll just forget about it. [S2] Yeah. [S1] [LAUGHS] That's also a very good assumption. Um, so both of these things can be h... | 29.8 | 2.950998 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727468.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727470 | [S1] How the, what the distribution of responses is over, uh, a stimulus ensemble is very important, uh, for how efficient, uh, the code is. Because remember, neurons are super noisy, right? [S2] Yeah. [S1] Uh, so you want them, you, you wanna have like a nice exponential distribution of, uh, of responses, uh, in order... | 28.16 | 3.28328 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727470.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727471 | [S1] ... that, you know, the, the, the result of there being eyebrows is that they look a certain way. [S2] Yeah. [S1] And then it would make sense, again, that they are encoded, like the structural prior is encoded in one space, and then simply the manifestation of that is the picture we see. [S2] Yeah, yeah, yeah. Ma... | 23.04 | 3.405407 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727471.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727472 | [S1] Um, but I think that what I mean, uh, by this is a, is a forward model for how, like, one individual- [S2] Yeah, exactly. [S1] Uh, so you, you can think of, uh, you, you can think of a, uh, of a directed acyclic graph in which, you know, there's a bunch of different factors. One of them is whether or not I wake up... | 29.8 | 3.431349 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727472.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727473 | [S1] ... I turn on and off the switch and generate different, uh, different, uh- [S2] Yeah. [S1] ... faces. So that's, uh, I think, like, the underlying naive model is the Mr. Potato Head, uh, model, right? In which you just, like, switch out the different, uh, the, the different components. Uh, and of course, there ar... | 26.32 | 3.357564 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727473.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727474 | [S1] I, I guess, like, the question is, like, are these factors in this, uh, this factor graph, are they, like, can you put labels on them and they correspond to one thing that we would identify as something that is independently changeable? So, for instance, like, we understand that age and lighting, for instance, lik... | 25.2 | 3.505393 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727474.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727475 | [S1] So, uh, so the question is, are they, are they different factors or are you rotated like one is square root of two, like one over square root of two times age minus, uh, one over square root of two times, uh, uh, lighting and, and so on and so forth. And it looks like they're really aligned towards, um, towards th... | 28.72 | 2.839022 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727475.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727476 | [S1] correlated with, uh, with each other. Uh, [LAUGHS] but- [S2] Yeah. Yeah, I see what, I see what you mean, yeah. So, um, we're, we're, we're, we've been, we've been going through this a little bit. There's all, I mean, there's a lot of, there's other papers which, which are definitely also interesting, like the, th... | 24.84 | 3.262572 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727476.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727477 | [S1] ... particularly? [S2] Well, I wanted to give, uh, for, you know, readers, uh, that are coming slightly outside of this field and moving into this, like, very rapidly moving field, kind of an overview of what are the questions that people are interested in. [S1] Yeah. [S2] Like, what are kind of the some of the in... | 28.08 | 3.158593 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727477.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727478 | [S1] Um, so, uh, one of the things that I think is going to be interesting for, uh, for the future is, like, really taking evolution seriously. So we saw the, uh, uh, actually, maybe if you can scroll to, uh, where I show, um, Jess's, uh, Jess Thompson's, um, diagram of the different types of, um, of models and, and ho... | 24.2 | 3.237674 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727478.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727479 | [S1] We really got into this accounts for neural activity, uh, part of, uh, so, you know, we can find models that can both perform a task which is biologically relevant and accounts for neural activity. [S2] Mm-hmm. [S1] I think this year was a big year for biological plausibility, and I don't want to say this is the l... | 29.88 | 3.441719 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727479.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727480 | [S1] ... evolution, uh, component to it. [S2] Yeah. [S1] So I think we're gonna start to see, and, and we can actually, like, see the process by which the brain can bootstrap itself into existence. [S2] Mm-hmm. [S1] Which I think is gonna teach us something about what it is to be human. And I'm sure there'll be TED tal... | 21.08 | 3.103858 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727480.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727482 | [S1] I, I think that, uh, uh, one, uh, one thing that we, uh, that we're, haven't, like, really taken seriously so far is the role of weak supervision, uh, from a parental perspective. [S2] Mm-hmm. [S1] But if you think of, like, uh, a parent and their baby, they're gonna point at things, they're gonna say, "This is th... | 29.88 | 3.419116 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727482.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727483 | [S1] ... that, um, uh, uh, uh, uh, sign language preceded the appearance of, uh, of voiced, uh, speech. [S2] Yeah. [S1] Uh, so that we probably have somewhere in our noggins, uh, some areas which are highly selective for hand gestures and which are used for a kind of weak supervision, uh, that's important for, uh, | 24.96 | 3.327386 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727483.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727484 | [S1] Uh, for parents. [S2] Yeah. [S1] So understanding what happens with that peri- per- personal space and what, what happens as, uh, um, as we use tools, uh, is clearly important from, like, just this, that curiosity of how, uh, you know, we went from, uh, Australopithecus to, uh, to modern humans. And I think it's g... | 25.48 | 3.452587 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727484.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727485 | [S1] Awesome. Um, last question from my side. With, you're clearly interested in how the brain works, right? And, and, and see- [S2] Yeah. [S1] ... and seeing, you know, can we, can we make parallels between AI models, like deep models and, and brain areas and so on. Do you think that it is a necessity that we sort of | 21.4 | 2.994642 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727485.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727486 | [S1] ... and certainly suboptimal. So how the retina is wired up is the classic example. It's wired up the wrong way around. Octopuses have, have it the right way around and it doesn't seem to bother them. [S2] [LAUGHS] [S1] So, uh, that's a, that's a clear example. Um, but maybe there's some thing that we can, | 19.64 | 3.104032 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727486.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727487 | [S1] ... that we can identify with, uh, with brains and that is gonna unlock the next generation of machine learning. Maybe it's spiking neural networks, for instance. [S2] Yeah. [S1] You know, people are demonstrating, like, you could get something which is, uh, like a thousand times or 10,000 times more energy effici... | 22.64 | 3.166213 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727487.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727488 | [S1] Yeah, and that would, I mean, a thousand times, 10,000 times, that is sort of the orders of magnitude you spoke about before when it came to, to data. [S2] Well, those are, so here I'm thinking about the, the energy efficiency. [S1] Sure. [S2] Uh, so I think like one recurrent, not, not super- | 17.2 | 3.114332 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727488.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727490 | [S1] ... machine. So, again, I encourage your, uh, your, your viewers to, uh, to get into this because you can still do things with one- [S2] Yeah. [S1] ... GTX 1080. [S2] That's awesome. [S1] [LAUGHS] | 9.08 | 3.011373 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727490.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727491 | [S1] I don't think that naturally if you just train a neural network to solve a task, it's going to do it the same way that the brain does because I don't think that that's, that's really pointed out. I don't think that, uh, GPT-3 does things the same way that a human does in any sort of meaningful way. No way. [S2] Ye... | 25.48 | 3.37314 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727491.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727492 | [S1] Well, if you ask Gary Marcus, he'll say that there's no way. It'll never happen. [S2] Never. [S1] Uh, Neuro-symbolic AI all the way. Yeah. [S2] All right. Cool. Yeah. Um, for ev- to everyone, uh, follow Patrick, uh, many, he's mi- he's written papers, lots of papers. You're also the CTO of Neuromatch Academy, is t... | 21.56 | 3.153773 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727492.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727493 | [S1] ... year. Uh, I'm sure the announcement actually for, um, for the next, uh, version of, uh, Neuromatch Academy will happen pretty soon. [S2] Okay. [S1] Uh, so, uh, if you have people in the, in, in your, um, audience that are interested in that, I highly, uh, recommend to them to, uh, to do that. It's a great occa... | 29.84 | 3.118103 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p165_BV1pP4y1k7Jr_p165_m4-dialogue_0727493.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238369 | [S1] Really fun project for me as well. [S2] It just, it, it sounds like fun. [S1] Yeah, just, just trying to see, uh, whether you can output something like really, uh, realistic and, and really fun. Yeah. | 11.8 | 3.108595 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238369.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238370 | [S1] Google Scholar and then search for, uh, appropriate environments for this. And we found this virtual home environment and we, we really liked it because it actually, um, can model any, uh, any tasks if you can express them, um, in terms of this, like, uh, textual, uh, language plan. [S2] Mm-hmm. [S1] Um, like a, l... | 29.84 | 3.403565 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238370.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238371 | [S1] Of course, yeah. So, um, the, um, the way that virtual home, uh, expresses this actions are, uh, via like this specific format where you put a square bracket, um, like, uh, for the action, atomic action, like grab, put open. [S2] Mm-hmm. [S1] And then you put, uh, I think it's a parenthesis, uh, or, or, yeah, some... | 28.32 | 2.874084 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238371.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238372 | [S1] expect language models to, to handle this because, um, I, I mean, even if we put an example in front, maybe they can do it, but it's definitely not the, the way that usually humans, um- [S2] Yeah. [S1] ... produce language. So, and after all, this language models are trained on human text, so we decide, like, mayb... | 29.84 | 3.393424 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238372.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238373 | [S1] I tried briefly, but it's definitely not, uh, thoroughly investigated. Uh, and- [S2] Yeah. [S1] Like, it, like, in- intuition-wise, I think it's definitely- [S2] Sure, yeah. [S1] ... accurate to, [LAUGHS] to, to use, like, natural language. But we, we did adopt for the, um, the most basic approach that we can, we ... | 27.2 | 2.858623 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238373.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238374 | [S1] Yeah. [S2] Like this. [S1] And it's interesting because when the humans raid it, the humans would also skip a bunch of steps, right? If you, if you tell a human, "Go to the fridge and grab a glass of milk," the human will go like, "Oh, yeah, of course." Right? Which is, which is one of my, um, maybe this is jumpin... | 22.72 | 3.374381 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238374.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238375 | [S1] Yeah, so that's a really good question. Actually, um, this granularity actually comes from the data set, uh, or mainly the vir- the virtual environment itself. [S2] Yeah. [S1] Because the way, um, because we essentially follow the format of, uh, virtual home environment and also this data set that you collected fo... | 22.96 | 3.20712 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238375.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238376 | [S1] And it's after that they build this environment based on the verbs used by, by those humans. So you can think of like this environment is really built on top of, uh, what humans says. Now, now- [S2] Yeah. [S1] ... the developers, um, who, uh, who just say like, okay, we want this granularity, we want this like wal... | 22.8 | 3.400307 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238376.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238377 | [S1] Uh, yeah, it's, I mean, you would, you would imagine that sort of the human-ness of how the environment was built would also be present a little bit in these language models, which makes, which makes sense. I don't have a better idea, like, of how to build an environment like this, so- [S2] Yeah, I think it's- [S1... | 19.08 | 3.26221 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238377.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238378 | [S1] I think, uh, this is also what makes, like, language models particularly useful for- [S2] Yeah. [S1] ... for this task because these are basically just human tasks and language models are really good at, like, mimicking humans. Yeah. | 13.52 | 3.361327 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238378.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238379 | [S1] ... thing, right? And that comes in- [S2] Yeah. [S1] ... in a, in a second. And correctness is a thing that is rated by human annotators. They look at the plan that was produced and they just, from their own intuition are like, "Well, is this a good plan to make breakfast, yes or no?" | 15 | 3.323744 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238379.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238380 | [S1] Yeah, so, uh, there were actually two questions, uh, that, like, uh, I think you, you raised. One is, uh, why this, like, smaller models, like, um, like when I say smaller, it's actually still pretty large. The large- [S2] Yeah. [S1] ... GPT, uh, GPT-2 model. So why do they produce, like, more executable plants? U... | 29.68 | 3.092567 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238380.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238381 | [S1] Uh, so that's why, like, th- this smaller models are now really good at scalability. [S2] Mm-hmm. [S1] And, and the second question that you ask is why, um, this, like, larger models, um, are actually better than humans. So, uh, we, I- actually, this is not a completely fair comparison if you just look at one axis... | 21.28 | 3.339043 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238381.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238382 | [S1] ... cross source from, from, from Amazon, Turkish. So, um, this plans, they make sure that, like, these are executable plans. So, which means, um, that they're, they have- [S2] What, like, here. [S1] Yeah. [S2] They'd be over here. | 16.64 | 3.324272 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238382.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238383 | [S1] Yeah, but, but we, we don't want to put a spot right there, uh- [S2] Yeah. [S1] ... on the right because, uh, it's, it's hard to see because humans are, are a big, um, baseline and reference here. Uh, it's not a baseline that we're trying to beat. Of course, like, 253- [S2] Yeah. [S1] ... is not there yet in terms... | 20.92 | 3.166041 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238383.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238384 | [S1] is a very general problem, has many applications. And if we could solve that bridge, that technically is, you know, is, is a big gain. That's exactly what you do. Uh, so how, how did you go about this? [S2] Yeah, so, uh, actually, uh, I just want to make sure that, uh, actually this paper just present a, a really,... | 28 | 3.36083 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238384.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238385 | [S1] about the semantic, uh, meaning for, for that sentence. So what we do is- [S2] Yeah. [S1] ... that we take the sentence output by GPT-3 or codecs, and then, uh, we just compare that against all the possible, um, admissible actions, allowed actions by the environments. [S2] Mm-hmm. [S1] And then we found the most s... | 29.88 | 2.845086 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238385.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238386 | [S1] And that's my kind of translated action. So here we have an example that would, that would translate like squeeze out a glob of lotion into pour lotion into right hand. [S2] Yeah. [S1] So this, it would map, yeah, action into and pour, pour, it would be the verb, lotion, kind of the object, and right hand also one... | 26.96 | 3.049027 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238386.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238387 | [S1] ... to, like, the quick, quick options to respond, right? [S2] Yeah, yeah. [S1] And, and the, I think the first, I'm not sure how it is done now, but the first version of this, we were like, "Wow, this is, you know, you know, cool. It actually takes, you know, it takes into account the, the email message that was ... | 23.48 | 3.330367 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238387.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238388 | [S1] pretty difficult to map a compounded action. Like you said, like, um, like two actions in, in one sentence into one admissible action. But this is partly mitigated by how you tune the temperature, the sampling parameter, uh, just the temperature for the GPT-3 or Codex models. [S2] Yeah. [S1] Because we found that ... | 24.84 | 3.184094 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238388.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238389 | [S1] If you try, like, all this, like, different settings, we, we, we, in the end, we found, like, usually you want to use, like, uh, lower temperature than, than what people mostly use for language generation, for example. [S2] Yeah. [S1] So, so that, like, each action, uh, is, uh, like, small enough and succinct enou... | 27.12 | 3.322767 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238389.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238390 | [S1] How am I gonna do based on this like action already- [S2] Yeah. [S1] ... performed? Um, so, yeah, like you said, uh, like you pointed, this is the third, uh, sub-figure here. Um, | 11.36 | 3.430128 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238390.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238391 | [S1] If you were to let it generate all at once and then you translate each action individually, they almost like lose connection to each other, right? [S2] Exactly, yeah. [S1] It, this here might mitigate some of this, this stuff, right? If I have a compound action, like go to the fridge and grab a glass, and the clos... | 29.6 | 3.307522 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238391.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238392 | [S1] ... Shave, Task Shave is determined to be most similar. Uh, again, judged by this Roberto model using the same technique. Yeah. So, um, uh, I think that, that's the, that's the main motivation for using this, but we, we didn't thoroughly investigate it, like how you structure the prompt, whether you- [S2] Yeah. [S... | 25.44 | 3.413161 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238392.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238393 | [S1] Mm-hmm. I mean, it's, yeah. So, sorry, the, the, the recording has stopped on the screen side, but we can, we can still see it. [S2] Okay. [S1] Um, yeah, so, yeah, I was, I was, I was quite interested in, in that, in the sense of, of the prompt structuring because I know that can also, uh, make a big difference, b... | 28.08 | 3.222099 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238393.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238394 | [S1] Because- [S2] You can't restrict the vocabulary dynamically. [S1] Yes. So, uh, I've only done this on smaller model, uh, relatively smaller models like, uh, the GBT Neo, uh, and then I think I might have tried on GBTJ as well, which is a 6 billion per ampere model. And, uh, it actually turns out that they don't do... | 26.2 | 2.89417 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238394.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238395 | [S1] Uh, vocabulary can generate. But, uh, so my hypo- so this is now thoroughly tested because it's now invested on larger models as well. [S2] Mm-hmm. [S1] Um, but my intuition why it doesn't work so well is that this la- language models are really trained on human text, so it really- | 18.64 | 3.277086 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238395.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238396 | [S1] It's, it, they're really used to how humans, um, speak a certain language, uh, in this case, English. So, like, people don't speak things in this way. Step one, something. [S2] Yeah. [S1] Two, something. Step three, something. So, that's why if you really constrain the models this way, a lot of the, um, the, the, ... | 22.92 | 3.045261 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238396.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238397 | [S1] So- [S2] Yeah. [S1] It's, uh, it's actually imagining, like, people just, like, asking the docstring here. But instead of letting, keep generating the code, uh, we kinda just stop here. So, okay. [S2] Yeah. [S1] Finish the docstring for us. That's enough. So, um, so, yeah, so it's actually doing some, this kind of... | 26.6 | 3.246971 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238397.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238398 | [S1] consists of doctrine and, and, and, and the code. So it not only, not only can handle code really well, it can also generate really realistic doctrines. So, and, and people in doctrine, they don't write in, um, like- [S2] Yeah, they don't write a novel. [S1] Yeah, so- | 18.16 | 3.016163 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238398.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238399 | [S1] Yeah, so you write something really step by step and have more structure in it. So that's my intuition why, uh, actually does really well with this task. So you can really process this sequential, um, like logical reasoning better than- [S2] Yeah. [S1] ... the same, uh, size GPT- GPT-3 model. But of course, if you... | 25.48 | 3.388456 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238399.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238400 | [S1] came to my attention right here is this top row right here, which I found hilarious. So this is, the task is complete Amazon Turk surveys. [S2] [LAUGHS] [S1] So the, the, the four steps, apparently, that you need to do is walk to home office, sit on chair, switch on computer, | 20.28 | 3.222649 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238400.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238401 | [S1] Look at computer. Like, that's, is this the, like, is this the, is this the, this is the description of complete Amazon ter- It's a pretty accurate description maybe of what Amazon ter- [S2] [LAUGHS] [S1] ... workers do. [S2] Yeah, so, so- | 14.04 | 2.967966 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238401.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238402 | [S1] Yeah, so, so, like I said, this tests are generated by, um, by, by cross source, uh, from humans. [S2] Yeah. [S1] And this, uh, the humans here happen to be Amazon Turkers. So one of them decided that, okay, if you want me to generate, like, generate some tests, I will say, like, just complete surveys on Amazon, A... | 24.88 | 3.089366 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238402.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238403 | [S1] Like, like I said, so, so this language model, so they c- can't really handle, uh, anything that you wanted to, um, to- [S2] Yeah. [S1] ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...... | 22.84 | 3.251566 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238403.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238404 | [S1] So, uh, in terms of, uh, so a bit background here for, for the virtual environment. So it comes in two versions. One is the, I think the, they call the evolving graph version, which is a pure, like you said, a state machine, a, a Python, like written in Python. So it just goes in and then checks which, uh, whether... | 29.16 | 3.322927 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238404.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238406 | [S1] Uh, I think, uh, no matter what, what the granularity is, ideally it should be at the same granularity as, uh, of, of humans. So ideally it should be, uh, this models should be able to generate something, something sensible and reasonable. But definitely right now it's something that you definitely can't trust to,... | 22.64 | 3.258452 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238406.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238407 | [S1] And you know you can make Klip classify things by just evaluating a bunch of different- [S2] Yeah. [S1] ... strings with it. So I, like, try to, I try to evaluate the strings, "Go left, go up, go right, go down." Or like, "Pac-Man should go left, Pac-Man should go up." But it never worked out. Um, so if you can, i... | 29.84 | 3.326385 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238407.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238408 | [S1] Excellent. All right. Thank you. And I hope to see you again. [S2] [LAUGHS] Yeah, I'm like, I, it would be a honor to [LAUGHS] always to, to be here. Yeah. [S1] Excellent. All right. Bye-bye. [S2] Yeah. See ya. | 14.96 | 2.956261 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p166_BV1pP4y1k7Jr_p166_m4-dialogue_0238408.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764149 | [S1] Welcome to the show and, uh, thanks for being here. [S2] Thank you. Thank you for having me. I'm excited to be here. [S1] So the, the goal of today, it's a little bit different because I'm a total newb at security. Most of the audience of this channel is not, is into machine learning. Maybe some know about securit... | 21.68 | 3.258125 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764149.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764150 | [S1] ... the, the censorship apparatus that's in place around the world and what people do about it, I think most won't. So today I'll be asking mostly noob-ish questions and, um- [S2] Let's do it. [S1] ... we'll have you here to guide us through everything, to guide us through, like, what's happening in this world. So... | 22.76 | 3.376583 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764150.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764151 | [S1] ... still discovering all sorts of new ways you can apply these techniques. Um, and that was one of my motivations initially, actually, of bringing this censorship, 'cause this project was really a, the entire field of censorship's first foray into using AI and ML-like techniques. [S2] And if you, if you talk abou... | 22.24 | 3.025817 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764151.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764153 | [S1] I guess it's, it's a little bit like if everyone encrypts, like with HTTPS nowadays, everyone does it, so you can't conceivably block HTTPS, you know, just, just because you don't like some traffic. But if there's a new type of encryption, uh, you, you can probably- [S2] Exactly, correct. [S1] It's, it's probably ... | 23.72 | 3.461093 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764153.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764154 | [S1] ... the development of the field, like what has been tried before and what has been, let's say, thwarted, or what's the cat and mouse game looked like in the past? I imagine different things, like there's Tor, there is, you know, all, all kinds of things. There is probably things that everyone installs on their en... | 25.84 | 3.363115 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764154.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764155 | [S1] they try and block you out. Um, and there's been all sorts of techniques people have pro- people have proposed, like maybe I can disguise my traffic to look like Skype, and then the sensor's like, "Well, you didn't disguise it quite well enough." [S2] Mm-hmm. [S1] Blocked. Um, there's a whole interesting field of,... | 29.68 | 2.886504 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764155.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764158 | [S1] ... such as elections, things like this, uh, a lot of VPNs will just mysteriously stop working. [S2] Yeah. [S1] And then after the event, they'll mysteriously start working again. Um, and it kinda points to this broader idea that some of these countries may be sitting on more censorship capability than they deploy... | 21.44 | 2.898325 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764158.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764161 | [S1] Right? The, and the, this amount of state it has to maintain, it'll grow. And it's in the size of somewhere like China, it could grow pretty fast. [S2] Mm-hmm. [S1] So they have to be really careful about what they remember and the state they maintain. So you could imagine doing something like, let's, let's say we... | 28 | 2.870771 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764161.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764169 | [S1] Yeah, so in our case, you're exactly right. It, capture, capturing that can be difficult. Um, what we do to make it easier on ourselves is we obtain machines inside these censored countries and directly try to request forbidden content. [S2] Yeah. [S1] So Geneva trains directly against the censor and we know we go... | 28.32 | 2.876991 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764169.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764173 | [S1] Um, so we build out these building blocks and then allow it to compose these things together in trees. [S2] Yeah. So like syntax, like, uh, you, you give it a syntax and it can perform, it can assemble a little program out of this syntax. One, like one we see right here. [S1] That's exactly correct. [S2] Can you w... | 23.72 | 3.161157 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764173.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764178 | [S1] I see. [S2] So that's this idea of a strategy. [S1] So that connection in the, in the mind of the sensor is already established as here's a server, here's a client, and it, it kinda keeps that state for subsequent packages. [S2] More or less. [S1] Yeah. [S2] Yeah, that's exactly it. [S1] Mm-hmm. [S2] Yeah. So that... | 23.04 | 3.115058 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764178.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764179 | [S1] Yeah, we had this, we had this really cool result initially where we set out to try and, uh, we wanted to, when we first developed this tool, kind of benchmark it against the, the rest of the field. [S2] Mm-hmm. [S1] Um, and that, that's kind of challenging because sensors have continued to evolve. [S2] Yeah. [S1]... | 18 | 2.821146 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764179.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764180 | [S1] Our best guess as to what, our, our best, uh, implementation, I should say, as to what these sensors looked like based on what previous researchers found. [S2] Mm-hmm. [S1] And then trained Geneva against these mock sensors and also trained it against the great firewall and, and real sensors where we could. [S2] M... | 28.08 | 3.006595 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764180.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764181 | [S1] Yeah. So you have, you have, uh, let's, let's get back a little bit. You have this syntax, right? [S2] Sure. [S1] That you can build, uh, trees from, which are valid programs in Geneva. This will modify the traffic somehow. Now, to say that most of this traffic will just not even be traffic, probably. Like it will... | 22.64 | 3.274402 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764181.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764182 | [S1] And, I mean, even if, if, like, sorry to interrupt, but if the fitness- [S2] It's pretty good. [S1] Even, like, if, if the fit, I guess the fitness, is it anything else than zero, like, okay, maybe some connections don't even work to, like, the server next to you. You can discard those. But other than that, the fi... | 28.44 | 3.149542 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764182.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764183 | [S1] Yeah. So, and, and part of what makes Geneva work is we've kind of shoehorned our way into getting fitness between zero and one. [S2] Yeah. [S1] Um, and specifically what we do is, is, uh, rule out those strategies that break your own connection. Um, so that, that's kind of how we've gotten between zero and one. '... | 20.8 | 2.86121 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764183.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764189 | [S1] Um, but it, it sounds easy until you try and write it. [S2] Mm-hmm. [S1] Um, 'cause if you think about it, writing something to detect abnormality when you have no idea what that abnormality looks like. [S2] Sure, yeah. [S1] Um, especially in the space of just like just how random and crazy the internet is all the... | 13.4 | 3.044672 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764189.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764192 | [S1] Uh, and you could imagine a sensor that's a little more sophisticated than that. So something we've kept an eye on is, is at the end of the future if either the sensor starts rolling out AI/ML techniques, or if the sensor, um, starts hunting for traffic that looks very abnormal. [S2] Mm-hmm. [S1] And you could ima... | 29.8 | 2.827263 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764192.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764193 | [S1] ... it, but, you know, in malware, it's like just bits, and I flip, like, you know, very small number of bits. There's nothing like how the human sees it and how the machine sees it. It's- [S2] Totally. [S1] It's so weird. Um, but, yeah, I th- I think, I think it's, it's pretty cool, and you got some attention in ... | 23.96 | 3.396109 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764193.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764195 | [S1] Cool. And the, just, just saying the code for Geneva is available. It's on GitHub. Um, you know, anyone can, anyone can, I guess, look it up. Your builds fail right now. I'll, I'll just have to tell you. I'm sorry. Um, but, uh- [S2] Yeah, we're, we're switching between CI systems and having to finish the migration... | 17.96 | 3.283333 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764195.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764196 | [S1] There's definitely possibilities. If you think of something like, you know, AlphaGo or something like this, that's a, it's a discrete game, but also- [S2] Right. [S1] ... you know, they, they work with neural networks that, for example, uh, when you build your tree, your modifications that guide that somehow, that... | 29.12 | 3.40244 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764196.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764197 | [S1] Definitely, definitely. Our, our, when we first wrote Geneva, our goal was not to be the last AI approach to the space. It was to be the first and hopefully the worst. [S2] Yeah. [S1] Uh, it would be great if, uh, if viewers out there, hey, take a crack at this. Um, there's all sorts of new techniques out there ju... | 25.92 | 2.865277 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764197.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764202 | [S1] So that's like a simple fix to a limitation. But when you apply that fix, you open yourself up to the entire space of a tax of maybe I can sneak a letter in there that you think belongs halfway through the message, but it actually belongs in the beginning, or it actually belongs in the end, or it actually doesn't ... | 29.64 | 2.853198 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764202.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764206 | [S1] So as an attacker, this opens up the space of, "Hey, maybe I can use the sensor to watch an attack at somebody else by making a request for forbidden things, pretending to be someone else, and then letting them send that huge response at that other person." [S2] Mm-hmm. [S1] Um, and this is a, this is an idea of a... | 29.96 | 2.802523 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764206.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764208 | [S1] And the, so the, the, who does it hurt more, the sensors or the final recipients of the, the attack? [S2] Yeah, so in this case, the, the weight is beared by both, but the brunt of the impact will be felt by the victim. [S1] Yeah. [S2] Um, so this line of work, it, it mucks with the sensor, but really, really the,... | 24.16 | 3.138283 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764208.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764220 | [S1] Yeah, I'd be happy to. So Breakerspace is a lab at the University of Maryland. Uh, any UMD students watching, come check us out. Uh, the Breakerspace lab, the, the kind of defining feature of this lab is that undergraduate students are invited to join and participate in the lab. [S2] Mm-hmm. [S1] Um, so it's, it's... | 29.96 | 2.800919 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764220.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764222 | [S1] And, okay, not unrelated to them being undergrads or not, did you, like, how often does it happen that you get into, like, hot waters, like, you know, that there- [S2] [LAUGHS] [S1] ... You know, in security research, there are implicate, there are n- national defense implications, there are legal implications and... | 26.2 | 3.307454 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p168_BV1pP4y1k7Jr_p168_m4-dialogue_0764222.mp3 | [
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