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bilibili_data_18951476_BV16c411A7W6_p7_BV16c411A7W6_p7_m4-dialogue_0129842 | [S1] Seven, target practice. [S2] Dad, do you think you can hit the target? [S1] Just watch and learn, my boy. [S2] Dad, it's all right. I have an idea. | 25 | 3.127463 | 24,000 | audio/en/bilibili_data_18951476_BV16c411A7W6_p7_BV16c411A7W6_p7_m4-dialogue_0129842.mp3 | [
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bilibili_data_18951476_BV16c411A7W6_p80_BV16c411A7W6_p80_m4-dialogue_0818344 | [S1] Ouch! [S2] Dad, you should let the bee outside again. [S1] No, I am going to kill it. | 18.32 | 3.149149 | 24,000 | audio/en/bilibili_data_18951476_BV16c411A7W6_p80_BV16c411A7W6_p80_m4-dialogue_0818344.mp3 | [
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bilibili_data_18951476_BV16c411A7W6_p83_BV16c411A7W6_p83_m4-dialogue_0652774 | [S1] I think here is okay, Dad. [S2] How am I supposed to reach that, my boy? [S1] I'll carry you up. | 17.28 | 3.352176 | 24,000 | audio/en/bilibili_data_18951476_BV16c411A7W6_p83_BV16c411A7W6_p83_m4-dialogue_0652774.mp3 | [
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bilibili_data_18951476_BV16c411A7W6_p84_BV16c411A7W6_p84_m4-dialogue_0198037 | [S1] I just wanted to show you my trophy, Dad. [S2] Don't worry, my boy. [S1] Dad, what are you doing? [S2] You see, my boy, your trophy helped me clean my pipe. | 27.44 | 3.13692 | 24,000 | audio/en/bilibili_data_18951476_BV16c411A7W6_p84_BV16c411A7W6_p84_m4-dialogue_0198037.mp3 | [
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bilibili_data_18951476_BV16c411A7W6_p92_BV16c411A7W6_p92_m4-dialogue_0096385 | [S1] 96. Family photo. [S2] I like these costumes, Dad. You think this will work? [S1] Of course, my boy. Just play nice with the rabbits. There you go. That is the perfect shot. | 28.28 | 2.858459 | 24,000 | audio/en/bilibili_data_18951476_BV16c411A7W6_p92_BV16c411A7W6_p92_m4-dialogue_0096385.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891758 | [S1] ... different years. [S2] Why don't you just go around and say your name and, um, uh, tell me, uh, what year you're in and what you were hoping, you know, to do. [S1] I'm Mika Schperling. I'm, um, from Germany. [S2] Yeah. [S1] Originally, I came here last year. Yeah. But I, um, because you mentioned photojournalis... | 25.32 | 3.275029 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891758.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891759 | [S1] ... a headache for photojournalists. [S2] Yeah, exactly. [S1] It's like, it's such a great moment for photojournalism. I mean, it is like, to me, it's the most powerful work being done right now, is photojournalism. I, I, I, I run to the door to get my New York Times in the morning 'cause I still look at the, at t... | 29.88 | 3.082098 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891759.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891760 | [S1] It's not as satisfying as, as looking, you know, at the paper. I don't know if, if, if many of you know this, but I, uh, you know, while still going to the school, to school here at the Art Institute, I started working for Rolling Stone magazine. [S2] Mm-hmm. [S1] And I was, you know, at that time, Life magazine w... | 26.48 | 3.30772 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891760.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891761 | [S1] I liked having a point of view. And I, and I thought that, you know, you're supposed to stay relatively objective, you know, in, in, in your, in your journalism. [S2] And where's the line? I mean, I just, I don't- [S1] That, that would be a whole chapter. I'm gonna write that down. Where... Actually, I do want to ... | 23.08 | 3.222945 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891761.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891762 | [S1] ... direct a lot of how, how you photograph. The other thing is, when you work, let's say you work for a newspaper, you don't, may not necessarily have control over what, what gets used. [S2] You, you will eventually. [S1] I've been doing this long enough to know | 22.56 | 3.250135 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891762.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891763 | [S1] I mean, I, I think that's an elusive, uh, answer. I mean, an elusive question, answer about what makes a great photograph. I mean, I, I think, like taking good photographs, I mean, I, I think it, it does come with experience. I mean, y- you may take a good photograph, but you may not even know you took it. [S2] [L... | 26.48 | 3.321959 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p1_BV1Gt4y1K7We_p1_m4-dialogue_0891763.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p4_BV1Gt4y1K7We_p4_m4-dialogue_0891765 | [S1] Let me take a look at both of those for the, for the wing shot. And then we might do them in a little longer shot, like the... [S2] Yeah. [S1] 35 is going to be too short for what I need to do. I have to make the wings really feel big. Yeah, 20. [S2] I'll, I'll feel you, and then... [S1] Let's try the 20. Well, ha... | 19.2 | 3.085595 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p4_BV1Gt4y1K7We_p4_m4-dialogue_0891765.mp3 | [
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bilibili_data_1897325101_BV1Gt4y1K7We_p4_BV1Gt4y1K7We_p4_m4-dialogue_0891767 | [S1] I'll try. [S2] I mean, you're, everyone else is pretty passive. [S1] Yeah. [S2] So it really, you, you know, everything, all their anger is in you. [S1] Yeah. [S2] Amanda, can you turn more to the side for us? I mean, yeah, that, to your right. Yes. Okay. And then, and then bring your upper body back. | 27.16 | 2.811218 | 24,000 | audio/en/bilibili_data_1897325101_BV1Gt4y1K7We_p4_BV1Gt4y1K7We_p4_m4-dialogue_0891767.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199947 | [S1] Uh, cooking. It could be travel. It could be, um, selling mortgages. But it's something, something that resonates with you. [S2] Okay. [S1] Now, when, when we get started, all I'm gonna do is the two skills I'm just gonna use. First is just gonna be a label, which is, it sounds like, it seems like, or you sound, o... | 28.24 | 3.4252 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199947.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199948 | [S1] I'm going to repeat the last three words of what you just said. [S2] Okay. [S1] And when I do that, if you feel like talking some more, then talk some more. If you don't, then don't. [S2] Okay. [S1] But whatever I say, just react how it makes you feel. So have you thought of something you're passionate about? [S2]... | 25.6 | 3.381657 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199948.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199949 | [S1] What is it about escape room games that makes you passionate? [S2] They are so fun to do with friends and they're immersive. It's like such an immersive experience and it, um, challenges your mind. [S1] It challenges your mind? [S2] Yeah. [S1] [LAUGHS] | 22.76 | 3.072667 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199949.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199950 | [S1] ... there, because when you go in, you know, if you know what an escape room is, you have 60 minutes to get out, right? And so, uh, you know, there's a series of different puzzles that, uh, you have to solve in order to get out. [S2] Sounds like you like mental challenge. [S1] [LAUGHS] Yeah, I do. Um, I do, when i... | 26.84 | 3.345611 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199950.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199951 | [S1] You know, that it's like puzzles and fun, and then you're kind of cooperating and doing them together. And you're also in this, like, immersive experience. So it's like, as if you're in the mystery movie, or if you're in the, uh, you know, the play. You're, like, part of the play. [S2] Sounds like you like drama. ... | 21.08 | 3.330788 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199951.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199952 | [S1] Maybe I do like drama. I guess I like the, uh, the suspense of it. I guess maybe I do like drama. [S2] It also sounds like you really like people. [S1] I do, I do. I like to spend time with my friends and I like working together with them. | 14.96 | 3.165317 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199952.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199953 | [S1] In escape rooms. And the other thing that's kind of interesting about them is that if you have someone who's never done one before and then they come with your group, you're like, "This is gonna be so fun for you," and you try to make it the best experience for that person so that they, they enjoy them too. [S2] Y... | 27.16 | 3.324755 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199953.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199954 | [S1] Like to have, and I don't- [S2] Sounds like- [S1] Not like I'm a terrible person who's so selfish, but, uh- [S2] You sound like a really loyal person too. [S1] Oh. | 9.52 | 3.164593 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199954.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199955 | [S1] I hope so. And hopefully good people are drawn to me too. [S2] So it sounds like you just really enjoy bringing out the best in others. [S1] Mm, I like to think, yeah, that is fun. [S2] All right, so we just engaged in a practice session. [S1] Right. [S2] Where you knew that I was using negotiation skills. [S1] Ri... | 24.24 | 3.368421 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199955.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199956 | [S1] Well, I felt like I was like, oh, kind of, as you were asking me something, I would be like, "Oh, I guess I've never thought of it like that. Oh, maybe that is what that means." So I was kind of discovering things, I guess, about myself. But I felt like you, it made me think that you were like listening to me and ... | 26.4 | 3.346943 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199956.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199957 | [S1] Nothing. [S2] Or how I feel about people. [S1] Yeah, nothing. [S2] Or whether or not I have family. [S1] Yeah. [S2] Or whether or not I've ever done anything like it. So I shared with you no common ground whatsoever. [S1] Right. [S2] And you talked about everything anyway. [S1] Right. [S2] So it felt comfortable f... | 17.92 | 3.401535 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199957.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199958 | [S1] comfortable for me. [S2] So as of right now, I know that you like intellectual mental stimulation. You like challenges. You like people. You like collaborating with people. My guess is you've got some real strong feelings about how people should collaborate and that they should help each other. [S1] Mm-hmm. | 21.96 | 3.540054 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199958.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199959 | [S1] And that's why I took a shot at this loyal thing. Because I start to add all this up. Like if I, if I want you, and now I know whether or not I want you to, for a friend, and you got all these great qualities. [S2] Right. [S1] You'd be an awesome friend. I'd probably call you and, and I'd say, "Hey, I'm in jail." ... | 16.96 | 2.925796 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199959.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199960 | [S1] So in that period of time, I learned an awful lot about whether or not I want to be friends with you and whether or not I want to work with you. [S2] Yeah. [S1] And I never asked you one question other than, you know, what are you passionate about? What makes you passionate? [S2] That's true. You weren't asking me... | 27.92 | 3.387052 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199960.mp3 | [
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bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199961 | [S1] If I saw you in a coffee shop after this. [S2] Yeah, if you saw me a couple of days, a couple of days from now, yeah. [S1] And I would say, "Hi." [S2] Yeah. [S1] Right? [S2] Yeah. [S1] [LAUGHS] Can I say hi to you when I see you in the coffee shop? [LAUGHS] [S2] Exactly. | 12.4 | 2.847087 | 24,000 | audio/en/bilibili_data_1897325101_BV1RM411m72a_p4_BV1RM411m72a_p4_m4-dialogue_0199961.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0931999 | [S1] for a race. So let's see how this goes. You ready, Drake? [S2] Yes. [S1] Okay. Hey, Drake. [S2] How you doing? [S1] Uh, you know what? I'm busy and beleaguered like every restaurant owner in the world. [S2] Uh, I see. This won't, this won't take long. [S1] Okay, good. [S2] Uh, I- | 11.8 | 3.428161 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0931999.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932000 | [S1] I've just been here at the company for five years. Uh, I have positive reviews with our customers. [S2] Oh, yeah, I know that. [S1] Um, and I took it upon myself, um, while the manager was gone last month, to do the scheduling for some of our, uh, staff workers. [S2] Okay. [S1] And increase the attendance 85%. I j... | 20.52 | 3.493325 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932000.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932001 | [S1] Hard for me to, hard for me to say. I mean, we would have to, you know, maybe we can revisit this later on, but, but for now, it's just really not in the cards. [S2] Okay. [S1] Uh, we don't have the budget for that. [S2] I understand. [S1] Okay. So, Drake. [S2] That didn't go all that well for you. | 15.6 | 3.389444 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932001.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932002 | [S1] ... you did it. [S2] Uh, not, not at all. [S1] Okay, so, and, and what I did is, is kind of hustle you out relatively quickly. So, let's talk about some ways that you could have done things a little bit differently. So, one of the most important things to do in managing up is start with and concentrate not on your... | 23.2 | 3.48567 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932002.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932003 | [S1] What's good for me. [S2] Uh, it makes sense. [S1] Okay? So, what I would've had you do is go in and say why it's in my interest to give you a raise. And what that could be is | 13.4 | 3.483137 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932003.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932004 | [S1] ... turnover because I'm not sure I can afford to stay here for that much longer. [S2] Mm-hmm. [S1] So the most important thing in managing up is don't worry about my emotions so, so much. Don't worry about, you know, am I angry, am I annoyed? Ask yourself, what's in it for me? | 16.6 | 3.453377 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932004.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932005 | [S1] An appeal to fairness. Um, fairness meaning how much everyone else is getting paid, how much other waiters in the area are getting paid. So you might want to do a little work ahead of time and say, what's the going rate for waiters and are you being paid fairly? Appeals to fairness can be very effective. [S2] Mm-h... | 27.32 | 3.413177 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932005.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932007 | [S1] Hey, what's up, Drake? [S2] I think I found a way to save you some money. [S1] Whoa. [S2] Um, last month- [S1] Yeah. [S2] ... we did not have the manager here. Um, I did our scheduling for us. [S1] Okay. [S2] Um, and what I noticed is that our attendance, as far as staff- [S1] Uh-huh. [S2] ... was substantially lo... | 21.56 | 3.269495 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932007.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932008 | [S1] Exactly. [S2] Whoa, okay. So how are you going to save me money on that? [S1] Well, I changed the scheduling around. [S2] Uh-huh. [S1] Now, it increased the attendance to 85%. [S2] Oh, wow. [S1] So it's a higher, higher rate than it was before. [S2] Right. [S1] Um, and the wages that which the, uh, workers are mak... | 16.68 | 3.360303 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932008.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932009 | [S1] ... the restaurants around the area is. [S2] Do you think, you think that's why people aren't showing up? [S1] There could be a reason. [S2] Uh-huh. [S1] But with the new scheduling, you were able to have a higher increase of attendance- [S2] Uh-huh. [S1] ... and have the substantially lower range of wage- [S2] Ok... | 28.2 | 3.431855 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932009.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932010 | [S1] Okay, okay. So you're want to, so you want a raise. And so what happens if I don't give you a raise? Are you going to leave me? [S2] That's, that's a possibility. [S1] Oh, that'll break my heart. [S2] Uh, yeah, possibly. [S1] If I were to give you a raise, would you take on the side project of looking for ways to ... | 18.08 | 3.209788 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932010.mp3 | [
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bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932011 | [S1] Okay, well, if giving you a raise is a way to actually save more money in the long haul, then I am, I'm absolutely open. Let me check with my accountant and I'll get back to you tomorrow. [S2] Sounds good. [S1] Okay, so how did that feel to you? [S2] Uh, it felt good. [S1] The key lesson here, and I love that you ... | 20.16 | 3.364155 | 24,000 | audio/en/bilibili_data_1897325101_BV1p14y1s7MS_p11_BV1p14y1s7MS_p11_m4-dialogue_0932011.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276358 | [S1] ... through the paper. So, Martin, uh, welcome. Uh, thank you very much for being here. [S2] Hey, I'm happy to be here. [S1] Um, did, was, was it a sort of a good description of what I said so far about Player of Games? [S2] Oh, yes, very, very, very much so. | 16.92 | 3.153081 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276358.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276359 | [S1] ... that, uh, we will see in this paper, which is it introduced this notion of, uh, of local search in, uh, in poker and these value functions. [S2] Mm-hmm. [S1] And Play of Games is really just putting together AlphaZero in deep step into a single big unified, uh, algorithm. So the, the, the, let's maybe start wi... | 25.28 | 2.834349 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276359.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276361 | [S1] It's, it makes, uh, little to no sense to say what's a value, what's a value of a sub-game or sub-problem in a poker where, where I hold a pair of, of aces. That's pretty much ill-defined, uh, ill-defined sub-game. [S2] Mm-hmm. [S1] But what we, what you need to do is given a, given a public, uh, state, which is, ... | 29.92 | 2.913961 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276361.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276363 | [S1] Yeah, I see. Okay. And you have this network and you train it, uh, in some way via, via self play. And now we get to the part where you generalize this search procedure, right? And- [S2] Mm-hmm. [S1] ... let me see. Oh, this is here. [S2] Yes. [S1] So this search procedure, as we said, in, in alpha, uh, again, in ... | 22.2 | 3.346411 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276363.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276364 | [S1] You would, you would sort of, you would make many iterations, let's say 50 iterations or something like this. In every iteration, you'd go down the tree and you find a node that you haven't expanded yet and you'd expand that node, right? [S2] [LAUGHS] [S1] In, in, in player of games, this is quite a bit more intri... | 21.56 | 3.133394 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276364.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276365 | [S1] and improve the policy of both players, right? And it just does this for many, many iterations. It improves here, here, here, everywhere in the game tree until the whole game tree is, uh, approximately optimal. [S2] [LAUGHS] [S1] And the, the biggest game that has been solved so far, if you describe this in the pa... | 26.4 | 3.335737 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276365.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276368 | [S1] Get the entire tree in order again. So you expand the new node and then you have to do the whole update of the whole tree for a bunch of iterations before you can expand another one, such that everything, like, stays consistent. [S2] Mm-hmm. [S1] Yeah. Okay. That's, I mean, this, this, it gives a bit of an impress... | 21.88 | 3.239764 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276368.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276369 | [S1] Mm-hmm. So you collect the train. It's similar to Alpha Zero, as you say. You collect the training set as you go. So the training set for the next iteration is whatever the network had to do during this iteration. So it's not just a random sample of states. [S2] Yeah. [S1] And you train in the same manner as Alpha... | 28.2 | 3.279196 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276369.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276370 | [S1] ... like one or two or three steps in the future, you actually win or lose the game, you can train on your reward of the game. But Alpha Zero also, if it doesn't win or lose the game in the next step or so, it tries to predict its own output. So it, it tries to improve that way using TD Lambda. Um, and you here ha... | 28.2 | 3.442012 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276370.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276372 | [S1] ... small solver where we also substitute the full solver with a small search tree. [S2] Mm-hmm. [S1] So rather than fully solving a game, we use the same method to basically do a search. [S2] Mm-hmm. [S1] And the outcome of the search, basically a small solver, is what the, the, what is the target. | 17.8 | 2.924421 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276372.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276373 | [S1] Maybe this has battery again. So during the inference, you, you, you make, you do these queries, you store them in this, in this buffer, and these now act as the root nodes for yet another search, which is exactly the same as the previous search, right? And so you, you sort of rely on the fact that this search pro... | 27.56 | 3.140431 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276373.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276374 | [S1] That's- [S2] That's, that's exactly- [S1] One would hope, though, that after a while, you know, if I do this again and again and again, at the end, I wouldn't even have to ask the neural network anymore. I, sorry, I wouldn't even have to do search anymore during inference. Is that something you- | 18.68 | 3.288989 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276374.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276376 | [S1] Um, yeah, so I think we're, we're, we're almost getting already to the sort of results, uh, weight. Would you, would you maybe summarize the results a little bit? I think if people are super interested, they, they may go into, uh- [S2] Okay. [S1] ... into the paper and into the tables, but maybe you can just summa... | 24.16 | 3.287836 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276376.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276380 | [S1] Do, do, do you mean for a Slumbot or for Scotland Yard? Are we, are we still talking about poker? [S2] Oh, sorry. Yeah. Let's, let's talk about poker for a while. So the, the, the player of games here gains, what is this, seven millibit blinds per, per hand. [S1] Yeah. [S2] Over Slumbot. | 17.28 | 3.149203 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276380.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276382 | [S1] Yeah. Okay. So, and here on the right side, you have a Pym Bot, which is, uh, the, a Scotland Yard-specific bot. For people, maybe people don't, does anyone not know what Scotland Yard is? Um, maybe you can describe 10 seconds what Scotland Yard even is as a game. It's somewhere, right? [S2] Yeah, there's a figure... | 26.56 | 3.34405 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276382.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276383 | [S1] Yeah, so this is, this would be, I guess the final win rate is here, uh, like at 55% or something like this, and that is with a huge number of, of iterations for, for PIMBot. [S2] Yes, and the player games is using only like four, 400, uh, iterations on our side. [S1] Yeah. [S2] So, yeah, as you can see, as you ca... | 24.16 | 3.404915 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276383.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276384 | [S1] ... that we, we can get you an optimal policy. Uh, other methods that are based on MCTS, they, they are not guaranteed to converge even on small games. [S2] Mm-hmm. [S1] So there's, there's, there's, there's also the limit of the, of the fact that these methods are not sound. | 16.48 | 3.088853 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276384.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276385 | [S1] I see. So I think the easiest to, for us, was, uh, poker. [S2] Mm-hmm. [S1] That, like, people probably can train on a few, few GPUs. Uh, the, the by far the hardest, uh, is, uh, is Go, where we used, uh, a, a lot of, uh, a lot of TPUs, but that was simply because we, we had them, uh, available. | 24.8 | 2.994558 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276385.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276387 | [S1] You know, no human, no, like, no, not all humans together even will ever match Alpha Zero in, in any of these games, which is crazy. [S2] Yeah, you, you would not win a single game out of a thousand. [S1] Yeah, exactly. Um, do you, you have a bit of a demonstration ready, you told me, of, uh- [S2] Right. [S1] ... ... | 27.16 | 3.141121 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276387.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276388 | [S1] ... color, details. So right now, I'm in here, and say I want to go to 49, and I want to use taxi to get there. So, yeah, hopefully, like we have been talking for a while, so maybe, maybe it's not, uh, alive, uh, anymore. But, uh, yeah, probably, probably it, uh, it died, uh, it died. [S2] You have scaled to zero,... | 26.52 | 3.007138 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276388.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276389 | [S1] Uh, we did. It's, I don't think it's, it's turn on right now, but it's exactly what we tried to do at some, at some point. [S2] Yeah. And did you see, did you observe this? Does the longer they didn't see Mr. X, the more kind of spread out, the more unsure they become? Is that something you can clearly observe or ... | 28.44 | 3.01169 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276389.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276390 | [S1] alleviated this, uh, by sort of going to the latent space state and training everything in latent space. Is this something I could do with player of games? [S2] Uh, no, but that's, uh, that's arguably the limitation, uh, number two. I think the biggest- [S1] Yeah. [S2] ... being, uh, the biggest thing is, uh, righ... | 28.44 | 3.378782 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276390.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276391 | [S1] So when you expand the search tree, do you need to expand once for every possible, let's say, flop combination there is? [S2] Yes. [S1] Okay. That, that is a lot of combinations, right? [S2] Or you can, or you can substitute, uh, like if you are smart about it, you, you can, again, it's a neural network. | 19.92 | 3.151267 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p160_BV1pP4y1k7Jr_p160_m4-dialogue_0276391.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276394 | [S1] I'm not a German either, but, but I, I think that, uh, the author was called, was called Noether. Yeah, she- [S2] Yeah, yeah. So you're pronouncing it more properly than him, I think. [S1] [LAUGHS] Maybe. But essentially, could you give us maybe just first an insight, where does the name, because the name is kind ... | 24.72 | 3.124485 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276394.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276395 | [S1] of the theorem, uh, to, uh, build a new, uh, machine learning model. And the intuition is that in machine learning, symmetries are one of the core ways in which we've improved, uh, data efficiency and, and, and model performance. And so it would be very cool if we could kind of automatically learn some of these sy... | 27.08 | 3.268367 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276395.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276396 | [S1] Yeah, we, we've heard in, I think in the recent past even, a lot of people attempting to get more out of symmetries out of neural network with, I'm thinking of, I'm thinking of like, uh, group convolutional neural networks and so on, that try to actively build in symmetries into neural networks. [S2] Yeah. [S1] Bu... | 23.8 | 3.387855 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276396.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276397 | [S1] ... the symmetry that will appear. They already know a molecule. It doesn't matter which way I look at it, right? So I can directly build that in. But your reasoning is that because assessing conserved quantities, um, is an easier task than assessing symmetries, it might be possible to learn the conserved quantiti... | 25.4 | 3.406004 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276397.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276398 | [S1] Yeah. [S2] Oh, right. [S1] But that, that's a, another thing that may be a bit different, uh, from our work than other works, which is that, um, some symmetries are only approximately conserved or conserved quantities are only approximately conserved. So for instance, you ha- if you have a, a dissipative system, l... | 26.8 | 3.236369 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276398.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276399 | [S1] And maybe I wanna, I wanna get to sort of a little bit of an example of where, so people can imagine this a little bit more. Now, I only have a mouse here 'cause I forgot the iPad 'cause I'm, I'm stupid, but maybe we can give the small example of a, a pendulum, right? [S2] Mm-hmm. [S1] So here's a pendulum. It han... | 20.52 | 3.413876 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276399.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276400 | [S1] ... the future, let's say. [S2] Yeah. [S1] Or at least from, from what I can tell. So what your model would be able to do is it would be able to predict the next time step right here, right? Then it's a bit here, here, uh, sorry. It's a, it's a little bit more up to the left, right? [S2] Mm-hmm. [S1] So it's a lit... | 23.96 | 3.397678 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276400.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276401 | [S1] It will not actually make use of the fact that something is conserved. [S2] Exactly. [S1] So you, you go ahead and you say, "Since this is a dynamical system, we know more about the system. We can impose additional constraints." And the additional constraints right here, if I see this correctly, essentially at eve... | 19 | 3.446182 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276401.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276402 | [S1] And it always needs to predict the same thing, right? Since, since it needs to, uh, figure out a quantity that is conserved. [S2] Exactly. [S1] And now it is, it is, if I just train a neural network to always predict the same number right here, I would just end up with a neural network that is predicting some, som... | 28.4 | 3.270911 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276402.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276403 | [S1] Um, so this is the physics 101 data set, um, from Josh Tenenbaum's group. I think Jesuine was the first author. And they have a collection of videos, and in this case it's a, they have a hand dropping an object passively, like it, it just lets it drop down, and the object falls down, and there's a second object at... | 26.48 | 3.312711 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276403.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276404 | [S1] And does, so that there are multiple videos- [S2] Yes. [S1] ... and it's always different objects or- [S2] Uh, like some objects could be, uh, common between, uh, videos, but- [S1] Mm-hmm. [S2] ... there's lots of objects, so, uh, it's not always the same object and that's- [S1] Yeah. [S2] ... can point to the fac... | 27.08 | 3.275151 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276404.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276405 | [S1] Yeah. So here, the, the, the diagram shows a little bit of, of what you're, what you, um, are trying to do, but also what you're trying to avoid. So the bottom path right here, if I see this correctly, that would be if I did nothing else except the bottom path, I would build this neural network to just predict sor... | 29.08 | 3.469985 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276405.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276406 | [S1] Um, mess, but it, it, it sort of, it sort of, all of a sudden there are, like, three objects instead of two, and th- the one is, the one is kind of gone or split up. [S2] Yeah. [S1] And it's a, it's a bit of a, a mess. And you attribute this to the fact that it's just a video prediction or- | 19 | 3.301306 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276406.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276407 | [S1] At the very few, like at the beginning of the frames, like the first few frames, there was not that much mistakes, but when you go very far into the future, then it's, it's much harder. Um, so- [S2] Yeah. [S1] ... those two problems, lack of data and the fact that you go a lot of, into the future. [S2] Your method... | 29.8 | 2.94873 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276407.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276408 | [S1] I wanna maybe go back to the diagram and let's go, let's walk through it once. Before we, before we, you know, take a look at the formulas and all, we can walk through it once. So the first thing that happens, if I understand correctly, is you take your first input and you do exactly what we just said. You run it ... | 27.72 | 3.344877 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276408.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276409 | [S1] So this has, this has a bit of a, of a default thing. But now you try to improve that. And this is all, this is the entire thing we're describing right now. That is one forward pass through your system. [S2] Mm-hmm. [S1] So ev- you would take every single prediction that you made and you would feed it through this... | 28.96 | 3.486659 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276409.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276410 | [S1] But it's not, it's not necessarily just outputting one number. It's outputting an entire vector. [S2] Mm-hmm, mm-hmm, yeah. [S1] So it's an, outputting an embedding vector and the, the goal obviously is that for all of these inputs, it should output the same embedding vector. | 15.8 | 3.217804 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276410.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276411 | [S1] Exactly. [S2] But all the same across the, across the, uh, video sequence, okay? So this is how we can imagine you train this G-network to sort of predict whatever is special about this particular data point, but inside of the data point, conserved among all the frames. [S1] Exactly. Because if it was the same A f... | 25.44 | 3.396624 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276411.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276412 | [S1] Yeah. Um- [S2] It, it restricts, it restricts what you can output, right? Because ideally, the F-network should only output whatever the G-network says is, is the same, right? [S1] Yeah. [S2] If the F-network can only output things that the G-network will embed to the same place in the embedding space or a similar... | 21.16 | 3.433089 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276412.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276413 | [S1] ... of this information that the G-network output out of the initial sequence again. And you do this in a very special way in that you actually take the parameters of F and you update them on the fly. [S2] Yes. [S1] Uh, you update them on the... So this is within a forward pass. You actually update the parameters | 19.36 | 3.43071 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276413.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276414 | [S1] into the direction of the gradient of G. [S2] Exactly. Yes. So- [S1] So, yeah. Sorry. This is, I think that, that, it takes it, yeah. So here you have this neutralos. [S2] Yes, exactly. [S1] Which you maybe want to talk about this briefly. | 17.72 | 3.271819 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276414.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276415 | [S1] and now we update theta, and theta are the parameters of F, right? [S2] Exactly. [S1] Theta are the parameters of F. We update these on the fly, and I, I, I suppose that we just do this, you know, in the moment and for the next data point we, we go back to the sort of original parameters. [S2] Yes. [S1] Uh, and do... | 25.92 | 3.239306 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276415.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276416 | [S1] Exactly, exactly. So this is, uh, some, uh, previous work of ours, uh, which we call tailoring. And the idea of- [S2] Mm-hmm. [S1] ... of tailoring is just because of what you said, that the fact that the adaptation is customized for each individual data point. [S2] Mm-hmm. [S1] Uh, and the idea there was, uh, a g... | 19.72 | 3.062619 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276416.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276417 | [S1] ... loss is in general, you say, for instance, one thing we could say is, "Oh, why not we add energy conservation when, when we train?" Sometimes, auxiliary losses would say, "Okay, I train for good predictions and I train for energy conservation at training time." But if you do that, you're not going to enforce e... | 21.08 | 3.318252 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276417.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276418 | [S1] But- [S2] Yeah. [S1] Uh, energy, uh, conservation, or any type of conservation, or any auxiliary laws, can be checked before making the prediction at test time or at training time. Inside the prediction function, I can first make my prediction and see, okay, uh, do I like it? Does my auxiliary laws, does my unsupe... | 18.64 | 3.094048 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276418.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276419 | [S1] Yeah, maybe it's, it's also important to highlight that the, the, the parameter here, this theta that we start with, and also the parameters of G, those are the ones that will be learned during the training procedure across the entire training data set. And then the parameters here, those are always constructed in... | 24.2 | 3.378997 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276419.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276420 | [S1] ... As you say, tailor the inductive bias. And the inductive bias in this case would sort of be the, this entire term right here. [S2] Exactly. [S1] Essentially says, you know, what do, how do I need to change my predictor in order to conserve the particular thing that G decides is the common quantity for this dat... | 20.36 | 3.449466 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276420.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276421 | [S1] and the L task here, that would just be the, what do you call the task loss. This would be the video prediction loss. [S2] Exactly. [S1] Or something like this. Okay. So my, I have, I have, I have a lot of questions. Um, first, first of all, this, it seems, it seems quite, um, intricate, right? Because if I think,... | 28.16 | 3.311375 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276421.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276422 | [S1] How do we need to change theta? This depends on these predictions right here. These predictions right here have one forward pass using theta, then have a gradient with respect to theta right here inside of them, right? [S2] Yeah. [S1] And, and the, all of those come from this quantity, which is already a forward p... | 24.56 | 3.491502 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276422.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276423 | [S1] Uh, is stable, then it works fine. Um, but if it, if the overall thing is already unstable, then it's ex- it's extremely tricky to, to, uh, add things there. So for instance, uh, one thing we realized was that, um, because video prediction is very expensive, uh, and, and basically we couldn't fit that many example... | 26.84 | 3.245497 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276423.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276424 | [S1] If the grad- of the, the original gradients, because of the batch normalization, if you compute the batch statistic with a very small batch, it's already very crazy and unstable. [S2] Mm-hmm. [S1] And then we could have it. When the or- the other thing is already stable, then it seems, um, for us, it worked pretty... | 22.08 | 3.207426 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276424.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276425 | [S1] was when we were using batch normalization and we were wondering, "Oh, is- is our another network unstable?" Or- [S2] Yeah. [S1] And then we realized, okay, no, it's the- it's the- the vanilla network that was unstable. Um, but, uh, that was part of our concern because, uh, there is some papers that mention that w... | 27.24 | 3.025206 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276425.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276427 | [S1] That, that, that might also be something where stability or computational graph size. First of all, you just do a gradient step. Many things would be possible, right? You could do an Ad- an Ad-grad step, you could do an Adam step, you could do a line search or a Newton step or anything like this, but you have chos... | 26.32 | 3.392578 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276427.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276428 | [S1] Uh, when you force the conserve G, all these features say, okay, no, you should conserve G and, therefore, it's kind of, uh, projecting one dimension. And so, um, in particular for conserve quantities, applying the same laws over and over, uh, it's kind of stable because, uh, it, you will just keep going closer to... | 21.4 | 3.29015 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276428.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276429 | [S1] So there's no, no, let's say, danger of over, overdoing. I mean, there's a little bit, but it, as I said, it hits after like 100 steps, which- [S2] Yeah, yeah. [S1] ... which is quite a bit, right? Given that you train with one. | 12.2 | 3.534303 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276429.mp3 | [
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bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276430 | [S1] But because it's a neural network, then suddenly I think you've tra- you're going, uh, outside. It's kind of a distribution shift. You train G to be useful for one or two or three- [S2] Yeah. [S1] ... green steps. Now you're using it for 100. It doesn't make you any promises. Um- [S2] Yeah. | 14.44 | 3.254836 | 24,000 | audio/en/bilibili_data_1897938584_BV1pP4y1k7Jr_p162_BV1pP4y1k7Jr_p162_m4-dialogue_0276430.mp3 | [
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