YouSaid · the spoken record

Charles Isbell

lines on the record: 192
first: 2020-11-02
most recent: 2020-11-02
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“There's all of AI, and there's like nothing in the middle. Like, it's very hard to get from here to here, and it's very hard to see how to get from here to here. And I don't think that we've done a very good job of it because we get stuck trying to solve the small problem that's in front of myself included. I'm not going to pretend that I'm better at this than anyone else. And of course, all the incentives in academia and in industry are set to make that very hard because you have to get the next paper out. You have to get the next product out. You have to solve this problem. And it's very sort of naturally incremental. And none of the incentives are set up to allow you to take a huge risk unless you're already so well established you can take that big risk. And if you're that well established that you can take that big risk, then you've probably spent much of your career taking these little risks, relatively speaking. And so you have got a lifetime of experience telling you not to take that particular big risk, right? So the whole system set up to make progress very slow. That's fine. It's just the way.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, and they just. Right, exactly. We are driven by this kind of need. The sort of ineffable quality of who we are, which means that the moment you understand something is no longer AI, right? Well, we understand this. That's just you take the derivative and you divide by two, and then you average it out over time in the window. So therefore, that's no longer AI. So the problem is unsolvable because it keeps kind of going away. This creates a kind of illusion, which I don't think is an entire illusion, of either there's very simple task-based things you can do very well than over engineer.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Not only is this machine learning community, not spend a lot of time on lifelong learning. I don't think they spend a lot of time on learning period in the sense that they tend to be very task focused. Everybody is overfitting to whatever problem is they happen to have. They're over-engineering their solutions to the task. Even the people, and I think these people do, are trying to solve a hard problem of transfer learning, right? I'm going to learn on one task and learn the other task. You still end up creating the task. It's like looking for your keys where the light is because that's where the light is, right? It's not because the keys have to be there. I mean, one could argue that we tend to do this in general. We tend to kind of do it as a group. We tend to hill climb and get stuck in local optima. And I think we do this in the small as well. I think it's very hard to do because look, here's the hard thing about AI, right? The hard thing about AI is it keeps changing on us, right? You know, what is AI? AI is the, you know, the art and science of making computers act the way they do in the movies, right? That's what it is, right?”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“This is what we mean by things like adaptable models, right? That you have to have a model that models going to change. And by the way, it's not just the case that you're different from that person, but you're different from the person you were 15 minutes ago or certainly 15 years ago. And I have to assume that you're at least going to drift. Hopefully not too many discontinuities, but you're going to drift over time. And I have to have some mechanism for adapting to that as you an individual over time and across individuals over time.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“It's not good. We have all had our trauma. So they get their chicken and broccoli and their egg dropped super, whatever. We got to communicate and it's going to change, right? So it's not interactive AI is not just about learning to solve a problem or a task. It's about having to adapt that over time, over a very long period of time and interacting with other people.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Know how to behave. But by the way, you completely predictable person, I don't know how you're predictable, I don't know you well enough, but you probably eat the same five things over and over again or whatever it is that you do, right? I know I do. If I'm going to a new Chinese restaurant, I will get General Gal's chicken because that's the thing that's easy to get. I will get hot and sour soup. You know, people do the things that they do, but other people get the chicken and broccoli”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Import from past history. It also allows you to be efficient in the transmission of that. So we ask ourselves about me. Am I intelligent? Clearly, I think so, but I'm also intelligent as a part of a larger species and group of people. And we're trying to move the species forward as well. And so I think that notion of being intelligent with others is kind of the key thing because otherwise you come and you go and then it doesn't matter. And so that's why I care about that aspect of it. And it has lots of other implications. One is not just building something intelligent with others, but understanding that you can't always communicate with those others. They have been in a room where there's a clock on the wall that you haven't seen, which means you have to spend an enormous amount of time communicating with one another constantly in order to figure out what each other wants, right? So, I mean, this is why people project, right? You project your own intentions in your own reasons for doing things on the others as a way of understanding them so that you know.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“I think, well, it's certainly fundamental to our kind of intelligence, right? And I actually think it matters quite a bit. So the reason the interactive AI part matters to me is because I don't, this is going to sound simple, but I don't care whether a tree makes a sound when it falls and there's no one around because I don't think it matters, right? If there's no observer in some sense. And I think what's interesting about the way that we're intelligent is we're intelligent with other people, right? or other things anyway and we go out of our way to make other things intelligent we're we're hardwired to like find intention even where there is no intention why we anthropomorphize everything we i think anyway we we i think the interactive ai part is being intelligent in and of myself in a nice in isolation is a meaningless act in some sense uh the correct answer is you have to be intelligent in the way that you interact others that's also efficient because it allows you to learn faster because you can”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“And typically, but not always, for the purpose of understanding ourselves a little bit better. Machine learning is, I think, trying to solve the problem, whatever that problem is. Now, that's my take. Others, of course, would disagree.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“So AI is bigger than ML, but ML is bigger than AI. This is kind of the real problem here is the really overlapping things that are really interested in slightly different problems. I tend to think of ML, and there are many people out there who are going to be very upset at me about this, but I tend to think of ML being much more concerned with the engineering of solving a problem and AI about the sort of more philosophical goal of true intelligence. And that's the thing that motivates me, even if I end up finding myself living this kind of engineering-ish space. I've now made Michael Jordan upset. But, you know, it's, to me, they just feel very different. You're just measuring them differently. Your sort of goals of where you're trying to be are somewhat different. But to me, AI is about trying to build that intelligent thing.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Whatever the heck that is. Yeah, the machine learning in some ways is a means to the end. It is not the end. And I don't understand how one could be intelligent without learning. So therefore I got to figure out how to do that. So that's important. But machine learning, by the way, is also a tool. I said statistical because that's when most people think of themselves as machine learning people. That's how they think of Pat Langley might disagree, or at least 1980s Pat Langley might disagree with what it takes to do machine learning. But I care about the AI problem, which is why it's interactive AI, not just interactive ML. I think it's important to understand that there's a long-term goal here, which I will probably never live to see, but I would love to have been a part of, which is building something truly intelligent outside of ourselves.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“And the reason I'm bringing that up is even though I'm a newfangled statistical machine learning guy and have been for a very long time, the problem I really care about is AI. I care about artificial intelligence. I care about building some kind of intelligent artifact, however that gets expressed, that would be At least as intelligent as humans and as interesting as humans, perhaps on their sort of way.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“So let me say up front that if you look at certainly my early work, but even if you look at most of it, I'm a machine learning guy I do machine learning. First paper ever published was a NIPS. Back then, it was NIPS. Now it's a NIRIPS. It's a long story there. Anyway, that's another thing. So I'm a machine learning. I believe in data. I believe in statistics and all those kind of things.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Bringing people together. Is really a kind of substitute for forcing them to see the humanity in another person and to not be able to treat them as bits. It's hard to troll someone when you're looking them in the eye. It's very difficult to do.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't know. You know, my experience is it requires two things. It requires, in fact, maybe this is really at the end what you're saying. And I, and I do agree with this for sure. Hold on to that kind of anger or to hold on to just a desire to humiliate someone for that long. It's just difficult to do. It takes a toll on you. But more importantly, we know this, both from people having done studies on it, but also from our own experiences, that it is much easier to be dismissive of a person if they're not in front of you, if they're not real, right? So much of the history of the world is about making people other, right? So if you're on social media, if you're on the web, if you're doing whatever in the internet, being forced to deal with someone as a person, some equivalent to being in the same room makes a huge difference because you're forced to deal with their humanity.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, it's an empirical question, right? You'd have to figure it out. I mean, I want to believe you're right. And so I'm going to say that I think you're right. Course, some people come to those things for the purpose of trolling, right? And it doesn't matter. They're playing a different game.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“If it's one, you're completely the same person. Well, you know, you're probably not one, you're almost certainly not zero. I can find the place where there's the overlap, then I might be able to introduce you on that basis or connect you in you in that way and make it easier for you to take that step of empathy. It's not impossible to do, although I wonder. It requires that everyone involved is at least interested in asking the question. So maybe the hard part is just getting them interested in asking the question. In fact, maybe if you can get them to ask the question, how are we more like than we are different? They'll solve it themselves. Maybe that's the problem that AI should be working on, not telling you how you're similar or different, but just getting you to decide that it's worthwhile asking the question.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“And if the only way I can feel for someone is to completely understand them and make them like me in some way, well then we're lost, right? Because we're not all exactly like each other. I don't have to understand everything that you've gone through. It helps clearly. But there's separable ideas, right? Even though they get clearly tangled up by one another. So what I think AI could help you do, actually, is if, and I'm being quite fanciful, as it were, but if you think of these as kind of, I understand how you interact with the words that you use, the district, you know, the actions you take. I have some way of doing this. Let's not worry about what that is. But I can see you as a kind of distribution of experiences and actions taken upon you, things you've done and so on. And I can do this with someone else. And I can find the places where there's some kind of commonality, a mapping, as it were, even if it's not total. If I think of it as distribution, right, then I can take the cosine of the angle between you. And if it's zero, you've got nothing in common.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Sympathy is feeling sort of for someone, empathy is kind of understanding where they're coming from and how they feel, right? And for most people, those things go hand in hand. For some people, some are very good at empathy and very bad at sympathy. Some people cannot experience, well, my observation would be I'm not a psychologist. My observation would be that some people seem incapable of feeling sympathy unless they feel empathy first. You can understand someone, understand where they're coming from, and still think, no, I can't support that. It doesn't mean that the only way, because if that isn't the case, then what it requires is that you must, the only way that you can to understand someone means you must agree with everything that they do. Which isn't right, right?”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Have to understand it, or do you just simply have to note that there is something similar and as a point to touch, right? Know you use the word empathy, and I like that word for a lot of reasons. I think you're right in the way that you're using and the way that you're describing it, but let's separate it from sympathy, right? So”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Group over here looking at the same room, they can see the clock, but it's not in their line of sight or whatever, so they end up referring to it by some other way. When they get back together and they're talking about things, they're referring to the same room and they don't even realize they're referring to the same room. In fact, this group doesn't even see that there's a clock there and this group doesn't see whatever the clock on the wall was the thing that stuck with me. So if you create these different silos, the problem isn't that the ideologies disagree. It's that you're using the same words and they mean radically different things. The hard part is just getting them to agree on the, well, maybe we'd say the axioms in our world, right? But, you know, just get them to agree on some basic definitions. Because right now they're talking past each other. Just completely talking past each other. That's the hard part. Getting them to meet, getting them to interact, that may not be that difficult. Getting them to see where their language is leading them to lead past one another. That's the hard part.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“About these rooms that you can see, but you're seeing them from different vantage points depending upon which side of the room you're on. Can see a clock very easily. And so they start referring to the room as the one with the clock.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“But it's harder than that. And the reason it's harder than that, or sort of coming up with the network structure itself is hard, is because I'm going to tell you a story that someone else told me and I don't, I may get some of the details a little bit wrong, but it's roughly, it roughly goes like this. You take two sets of people from the same backgrounds and you want them to solve a problem. So you separate them up, which we do all the time, right? Oh, you know, we're going to break out groups. You're going to go over there and you're going to talk about this. You're going to go over there and talk about this. And then you have them sort of in this big room, but far apart from one another and you have them sort of interact with one another. When they come back to talk about what they learn, you want to merge what they've done together, it can be extremely hard because they don't, they basically don't speak the same language anymore. Like when you create these problems and you dive into them, you create your own language. So the example this one person gave me, which I found kind of interesting because we were in the middle of that at the time, was they're sitting over there and they're talking.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes. And I actually don't think it's that hard. Well, it's not hard in this sense. So imagine that you can't make life simple for a minute. Let's assume that you can do a kind of partial ordering over ideas or clusterings of behavior. It doesn't even matter what I mean here. So long as there's some way that this is a cluster, this is a cluster, there's some edge between them, right? They don't quite touch even, or maybe they come very close. If you can imagine that conceptually, then the way you get from here to here is not by going from here to here. The way you get from here to here is you find the edge and you move slowly together, right? And I think that machines are actually very good at that sort of thing once we can kind of define the problem, either in terms of behavior or ideas or words or whatever. So it's easy in the sense that if you already have the network and you know the relationships, you know, the edges and sort of the strings on them and you kind of have some semantic meaning for them. The machine doesn't have to. You do as a designer, then yeah, I think you can kind of move people along and sort of expand them.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Even that it's difficult, it's just that you know that the outcome is going to be highly suboptimal for you. And I do think that that's a reasonable place to start for the question of what makes us human.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Possible. I would say that a little differently, I think. I would make two things. One is I'm going to disagree with the premise, I think, but that's fine. I think the way I would put it is... There are people who are very different from lots of other people, but they're not 0%. They're closer to 10%, right? So in fact, even if you do this kind of clustering of people, it'll turn out to be the small number of people. They all behave like each other, even if they individually behave very differently from everyone else. So I think that's kind of important. But what you're really asking, I think, and I think this is really a question, is what do you do when you're faced with the situation you've never seen before? What do you do when you're faced with an extraordinary situation? Maybe you've seen others do and you're actually forced to do something and you react to that very differently. And that is the thing that makes you human. I would agree with that, at least at a philosophical level, that it's the times when you are faced with something difficult, a decision that you have to make, where the answer isn't easy, even if you know what the right answer is. That's sort of what defines you as the individual. And I think what defines people broadly. It's the hard problem. It's not the easy problem. It's the thing that's going to hurt you. It's not the thing.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Every individual is different, but any given individual is remarkably predictable because you keep doing the same things over and over again. And the two things that I've learned in the long time that I've been thinking about this is people are easily predictable and people hate when you tell them that they're easily predictable. But they are. And there you go.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, I got the 93% accuracy of what's the next button you're going to press, but I can get 99% accuracy or somewhere there's about on the collections of things you might press. And it turns out the things that you might press are all related to each other in exactly the way that you would expect. So for example, All the numbers on a keypad, it turns out, all have the same behavior with respect to you as a human being. And so you would naturally cluster them together and you discover that numbers are all related to one another in some way. And all these other things. And then, and here's the part that I think is important. I mean, you can see this in all kinds of things.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Someone's going to do next at the level of what button they're going to press next on a remote control. You can do it with something really, really simple. You don't even need a hidden markoff mode. It's like a mark just simply press this. This is my prediction of the next thing. It turns out you can get 93% accuracy just by doing something very simple and stupid and just counting statistics. But what was actually more interesting is that you could use that information. This comes up again and again in my work. If you try to represent people or objects by the things they do, the things you can measure about them that have to do with action in the world. So a distribution over actions. And you try to represent them by the distribution of actions that are done on them. Then you do a pretty good job of sort of understanding how people are and they cluster remarkably well. In fact, irritatingly so. And so by clustering people this way, you can maybe.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Where everything was wired up, and you saw you captured everything that was going on. Nothing even difficult, not with video or anything like that, just the way that the system was just capturing everything. So it turns out that, and I did this with myself and then I had students and they worked with many other people. And it turns out at the end of the day, people do the same things over and over and over again. So it has to be the right two days.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“So, first off, it has to be the right two days, but I was thinking of a very specific experiment. There's actually a suite of them that I've been a part of, and other people have done this, of course. I just sort of dabbled in that part of the world. But to be very clear, the specific thing that I was talking about had to do with recording all the IR going on in my infrared going on in my house. So this is a long time ago. So this is everything's being crolled by pressing buttons on remote controls as opposed to speaking to Alexa or Siri or someone like that. And I was just trying to figure out if you could get enough data on people to figure out what they were going to do with their TVs or their lights. My house was completely wired up at the time, which, you know, what I'm about to look at a movie, I'm about to turn on the TV or whatever and just see what I could predict from it. It was kind of surprising. It shouldn't have been, but that's all very easy to do, by the way, just capturing all the little stuff. I mean, it's a bunch of computer systems. It's really easy to capture the if you know what you're looking for at Georgia Tech long before I got there. We had this thing called the Aware Home.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Spoiler alert. Ends with the wife coming to Michael. And he says just this once, I'll let you ask me my business. And she asks him if he did this terrible thing. And he looks her in the eye and he lies. And he says, no. And she says, thank you. And she walks out the door. And you see him, as she's going, you see him going out of the door. And all these people are coming in and they're kissing Michael's hands. And Godfather. And then the camera switches perspective. So instead of looking at him, you're looking at her. And the door closes in her face, and that's the end of the movie. And that's the whole movie right there.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“No, it was the 70s or whatever. He was definitely drinking, and in the end, he drinks industrial grade alcohol. And has one of the most fantastic fights ever in that subgenre. Anyway, that's my favorite one of his movies. But I'll tell you the last movie. It's actually a movie called Nothing But a Man, which is the 1960s star Ivan Dixon, who you'll know from Hogan's Heroes. And Abby Lincoln. It's just a really small little drama. It's a beautiful story. But my favorite scenes I'm cheating, my favorite, one of my favorite movies just for the ending is The Godfather. I think the last scene of that is just fantastic. It's the whole movie all summarized in just eight, nine seconds.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“That's what it was, and I didn't know that was Jackie Chan. That was like his first major movie I was a kid. It was done in the 70s. I only later rediscovered that it was actually.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, yes. By the way, my favorite Jackie Chan movie would be Draunkid Master II, known in the States usually as Legend of the Drunken Mas Actually, Drunken Master, the first one, is the first Kung Fu movie I ever saw, but I did not know that”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“I do not do martial arts, but I certainly watch martial arts. Oh, I appreciate it very much. Oh, we could talk about every Jackie Chan movie ever made. And I would be on board with that.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“That you're coming in the middle of, and they don't explain it to you. The story has done so well that you pick it up. So, anyone who's seen John Wick, you know, you have these little coins and they're headed out and there are these rules and apparently every single person in New York City is an assassin. There's like two people who come through who aren't, but otherwise they are. But there's this complicated world and everyone knows each other. They don't sit down explaining to you, but you figure it out. Crouching tiger hidden drag is a lot like that. You get the feeling that this is chapter nine of a 10-part story and you miss the first eight chapters and they're not going to explain it to you, but there's this sort of rich world behind you.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Both relatively modern John Wicca courses. One, two, or three. One, it gets increasingly, I love them all for different reasons and increasingly more ridiculous. Kind of like loving alien and aliens despite the fact they're two completely different movies. But the reason I put couching Tiger Hidden Dragon and John Wick together is because I actually think they're the same movie. Or what I like about them is the same movie, which is both of them create a world.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“Not entirely, but mostly. Mostly. Just a lot of back and forth. There's a story there someone's on death row and they're newspaper men, including her. They're all newspaper men. They were divorced. The editor, the publisher, I guess, and the reporter, they were divorced. But they clearly, he's thinking, trying to get back together. And there's this whole other thing that's going on. But none of that matters. The plot doesn't matter. Just put it on play in conversation. It's fantastic. And I just love everything about the conversation. Because at the end of the day, sort of narrative and conversation are sort of things that drive me. And so I really, I really like that movie for that reason. Similarly, I'm now going to cheat and I'm going to give you two movies as one. And they're crouching tiger, hidden dragon, and John Wick.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“What I'm talking about, so you've seen these shows where there's a man and a woman, and they clearly are in love with one another, and they're constantly fighting and always talking over each other. Banter, banter, banter, banter, banter. This was the movie that started all that as far as I'm concerned. It's very much of its time. So it's, I don't know, it must have come out sometime between 1934 and 1939. I'm not sure exactly when the movie itself came out. It's black and white. It's just a fantastic film. It is hilarious.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“No, no, no. What are we talking about? This is one of the movies that would have been very popular. So Screwball Comedy. You ever see Moonlighting, the TV show?”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source
“You're asking me to be definitive and to be conclusive. That's a little hard. I'm going to tell you why. It's very simple. It's because movies is too broad of a category. I got to pick subgenres. But I will tell you that of those genres, I'll pick one or two from each of the genres. And I'll get us to three. So if I'm going to cheat. So my favorite comedy of all times, which probably my favorite movie of all time, is his girl Friday. Is probably a movie that you've not ever heard of, but it's based on a play called The Front Page.”
2020-11-02 · Lex Fridman Podcast · #135 – Charles Isbell: Computing, Interactive AI, and Race in America · IDENTIFIED FROM THE TRANSCRIPT · source