Lex Fridman Podcast - #135 – Charles Isbell: Computing, Interactive AI, and Race in America

Episode Date: November 2, 2020

Charles Isbell is the Dean of the College of Computing at Georgia Tech. Please support this podcast by checking out our sponsors: - Neuro: https://www.getneuro.com and use code LEX to get 15% off - Decoding Digital: https://appdirect.com/decoding-digital - MasterClass: https://masterclass.com/lex to get 15% off annual sub - Cash App: https://cash.app/ and use code LexPodcast to get $10 EPISODE LINKS: Charles's Twitter: https://twitter.com/isbellHFh Charles's Website: https://www.cc.gatech.edu/~isbell/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Medium: https://medium.com/@lexfridman OUTLINE: Here's the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) - Introduction (07:16) - Top 3 movies of all time (13:26) - People are easily predictable (19:08) - Breaking out of our bubbles (30:54) - Interactive AI (37:26) - Lifelong machine learning (45:53) - Faculty hiring (53:27) - University rankings (1:00:55) - Science communicators (1:10:20) - Hip hop (1:19:20) - Funk (1:20:44) - Computing (1:36:35) - Race (1:52:40) - Cop story (2:01:01) - Racial tensions (2:10:23) - MLK vs Malcolm X (2:13:44) - Will human civilization destroy itself? (2:18:14) - Fear of death and the passing of time

The following is a conversation with Charles Isbel, Dean of the College of Computing at Georgia Tech, a researcher, an educator, and the field of artificial intelligence, and someone who deeply thinks about what exactly is the field of computing and how do we teach it. He also has a fascinatingly varied set of interests, including music, books, movies, sports, and history, they make him especially fun to talk with. When I first saw him speak, his charisma immediately took over the room, and I had a stupid excited smile on my face, and I knew I had to eventually talk to him on this podcast. Quick mention of each sponsor, followed by some thoughts related to the episode. First is Nuro, the maker of functional sugar free gum and mints that I used to give my

brain a quick caffeine boost. Second is the coding digital, a podcast on tech and entrepreneurship that I listen to and enjoy. Third is Masterclass, online courses that I watch from some of the most amazing humans in history, and finally Cash App, the app I use to send money to friends for food and drinks. Please check out these sponsors in the description to get a discount and to support this podcast. As a side note, let me say that I'm trying to make it so that the conversations with Charles, Eric Weinstein and Dan Carlin will be

published before American's vote for President on November 3rd. There's nothing explicitly political in these conversations, but they do touch on something in human nature that I hope can bring context to our difficult time, and maybe for a moment allow us to empathize with people we disagree with. With Eric, we talk about the nature of evil, with Charles, besides AI and music, we talk a bit about race in America and how we can bring more love and empathy to our online communication. And with Dan Carlin, well, we talk about Alexander the Great, Jengus Khan, Hitler, Stalin, and all the complicated parts of human history in between, with a hopeful eye toward a brighter future for our humble little civilization

here on earth. The conversation with Dan will hopefully be posted tomorrow on Monday and November 2. If you enjoy this thing, subscribe on YouTube, review it with 5 stars and not a podcast, follow on Spotify, support on Patreon, and connect with me on Twitter, and Lex Friedman. As usual, I'll do a few minutes of As Now, and no ads in the middle. I try to make these interesting, but I do give you time stamps, so if you skip, please still check out the sponsors by clicking the links in the description. It's the best way to support this podcast.

This show is sponsored by Nuro, a company that makes functional gum and mints that supercharge your mind with a sugar-free blend of caffeine, althianian, and B6B12 vitamins. It's loved by Olympians and engineers alike. I personally love the mint gum. It helps me focus during times when I can use a boost. My favorite use case is in the morning at the start of a deep work session. For me, it's really important to get that first 10-20 minutes off to great start. That's when the desire to think about and check on the stresses of the previous days strongest, but it's also when it's most important to call on the mind and focus on the task at hand. Anyway, two pieces of NeuroGum has one couple coffees worth of caffeine. Neuro is offering 15% off when you use code Lex at checkout, so go to getneuro.com and

use code Lex. This show is sponsored by the coding digital podcast that is hosted by app direct, co-CEO, Dan Sacks. It's a relatively new show I started listening to where every episode is an interview with an entrepreneur or expert on a particular topic in the tech space. I like the recent interview with Michelle Zatlin of CloudFlair about security.

I've been progressively getting more interested in hacking culture, both on the attack and the defense side. So this conversation was a fun educational 45 minutes to listen to. I think this podcast has a nice balance between tech and business that many of you might enjoy, especially if you're thinking of starting a business yourself or working at a startup. I'm thinking through this process myself, trying to find the balance between careful planning and throwing caution to the wind and just going with it. Anyway, check out the coding digital on Apple Podcasts or wherever you get your podcasts.

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Finally, this show is presented by CashApp, the number one finance app in the App Store. When you get it, use code Lex Podcast. CashApp lets you send money to friends by Bitcoin and invest in the stock market with as little as $1. I'm thinking of doing more conversations with folks who work in and around the cryptocurrency space, similar to AI, unfortunately, but even more so, I think there's a lot of charlatans in the space. But there's also a lot of free thinkers and technical geniuses that are worth exploring in depth and with care.

If I make mistakes in guest selection and details in conversation, technical geniuses that are worth exploring in depth and with care. If I make mistakes in guest selection and details in conversation, I'll keep trying to improve correct where I can and also keep following my curiosity wherever it takes me. So again, if you get cash out from the App Store Google Play and use code Lex Podcast, you get $10 and cash out will also donate $10 the first an organization that is helping to advance robotics and STEM education for young people around the world and now here's my conversation with Charles Isbel You've mentioned that you love movies and TV shows.

Let's ask an easy question, but you have to be definitively objectively conclusive. What's your top three movies of all time? So you're asking me to be definitive and to be conclusive. That's a little hard. I'm gonna tell you why. It's very simple. It's because movies is too broad of a category. I got to pick sub genres, but I will tell you that of those genres. I'll pick one or two from from each of the genres. I'll get us to three so I'm gonna cheat so my favorite Comedy of all times which probably my favorite movie of all time is his girlfriend Which is probably a movie that you've not ever heard of but it's based on a play called the front page from I don't know early

1900s and the movie is a fantastic film. What's the story? What's the independent film? No, no, no, no. What are we talking about? This is one of the movies that would have been very pop as a screwball comedy. You ever see moon lighting?

The TV show? You know what I'm talking about? So you've seen the 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. Yeah. Banner, banner, Bander, Bander.

Yeah. 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, 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's hilarious. It's mostly conversation. Not entirely, but mostly, mostly, just a lot of back and forth. There's a story there

someone's on death row and their newspaper men including her they're all newspaper men. They were divorced the editor publisher, I guess and the reporter they were divorced But you know they clearly he's, 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. Yeah, it's just the way the conversation is played.

It's fantastic, and I just love everything about the conversation, because it ended today sort of narrative and conversation, or sort of things that drive me. And so I really, I really like that movie for that reason. Similarly, I'm now gonna cheat, and I'm gonna give you two movies as one.

And they're Crouching Tiger Hidden Dragon and John Wick, both relatively modern, John Wick of course. One, two, and three. One. It gets increasing. I love them all for different reasons. And you 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 with the same movie, which is both of them create a world that you're coming in the middle of and they don't explain it to you. But the story is 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 and explain it to you, but you figure it out. Crouching Tiger Hidden Drag is a lot like that. You get the feeling that this is chapter 9 of a 10-part story and you've missed the first eight chapters and they're're not gonna explain it to you, but there's this sort of rich world behind you. So you get pulled in anyway, like in a video. You get pulled in anyway.

So it's just excellent story telling, in both cases, and very, very different. And also you like the outfit, I assume? The John Wick outfit. Oh yeah, of course, of course. Yes, I think John Wick outfit. All right, and so that's number two. And then-

But sorry to pause on the martial arts. You have a long list of hobbies, like it scrolls off the page, but I didn't see martial arts. You have a long list of hobbies like it scrolls off the page But I didn't see martial arts is one of them. I do not do martial arts, but I certainly watch it Oh, I appreciate it very much. Oh, we could talk about every Jackie Chan movie ever made Okay, and I would be I would be on board with that shower to like that kind of the comedy of it cop. Yes. Yes. By the way my favorite Jackie Chan movie would be Drunken master two No, in the states usually my favorite Jackie Chan movie would be Drunken Master 2

No, in the States usually is Legend of the Drunken Master Actually Drunken Master the first one is the first Kung Fu movie I ever saw but I did not know that the first Jackie Chan movie No, first one ever that I saw I remember but I had no idea What is this that's what it was? I didn't know that was Jackie Chan. That was like his first major movie. Yeah. Um, I was a kid. It's done in the 70s I only later Rediscovered that that was actually and he creates his own martial art by by drink was he actually drinking or was

Or was he played drinking? You mean as an actor or no? I'm sure it's an actor. No, he was a 70s or whatever. He was definitely drinking. And in the end, he drinks industrial grade alcohol. Yeah. Yeah. And has a fint, one of the most fantastic fights ever in that sub-journer. Anyway, that's my favorite one of his movies. But I'll tell you the last movie. There's actually a movie called Nothing But A Man, which is in the 1960s, start 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.

Godfather Part One. Part One. How does it end? I don't think you can, you need to worry about spoilers. If you haven't seen the Godfather, the spoiler alert, it ends with the wife coming to Michael. And he says, just this one saw that you asked me 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 she walks out the door and You see her you see him as she's going you see him go she's here 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. Do you see parallels between that and your position as Dean and Georgia Tech Chrome? Just kidding. Trick question. Sometimes the door gets closed. Okay, that was a rhetorical question. You've also mentioned that you, I think, enjoy all kinds of experiments, including on yourself. But I saw a video where you said you didn't experiment where you tracked all kinds of information about yourself. And a few others, sort of, wiring up your home. And this little idea that you mentioned in that video, which is quite an interesting

that you thought that two days worth of data is enough to capture majority of the behavior of the human being. First, can you describe what the heck you did to collect all the data? Because it's fascinating, just like little details of how you collect that data, and also what you're intuition behind the two days is. First of all, 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 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 called 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. What you know, what I'm about to put, what look in 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 is, I mean, it's a bunch of computer systems. It's really easy to capture the, if you know what you're looking for, a Georgia Tech long before I got there, we had this thing called the Aware Home, where everything was wired up and 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, like a weekend, but it turns out not only can you predict what someone's going to do next at the level of what button they're going to press next on a remote control, but you can do it with something really, really simple, like a, you don't even need a hidden mark off model. It's like a mark just

simply, I press this, this is my prediction of the next thing. It turns out you can get 93% thing really, really simple. You don't even need a hidden mark off model. Just simply press this. This is my prediction of the next thing. It turns out you can get 93% accuracy just with by doing something very simple and stupid and just counting statistics. But what's 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 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, 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 number to each other and exactly what 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 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. 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. Yeah. What about, let me play devil's advocate and philosophically speaking. Is it possible to say that what defines humans is the outlier? So even though 90 some large percentage of our behaviors, whatever the signal we measure is the same and it would cluster nicely, but maybe it's the special moments of when we break out of the routine. Is the definitive things and the way we break out of the routine is the definitive

things. And the way we break out of that routine for each one of us might be different. It's possible. I would say that I would say it a little differently, I think. I would make two things. One is a, 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%. So, in fact, even if you do this kind of clustering of people, that'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. It's not even that it's difficult. It's just that you know that the outcome is going to be highly so optimal for you. And I do think that that's a reasonable place to start

for the question of what makes this human. So before we talk about sort of explore the different ideas underlying interactive artificial intelligence, which we're working on, let me just go on along this thread and just skip to kind of our world of social media, which is something that at least on the artificial intelligence side, you think about,

there's a popular narrative, I don't know if it's true, but that we have these silos in social media, and we have these clustering as you're kind of mentioning. And the idea is that, you know, along that narrative is that, you know, we want to break each other out of those silos so we can be empathetic to other people. If you're a Democrat, you're empathetic to the Republican, if you're Republican, you're empathetic, Democrat. Those are just two silly bins that we seem to be very excited about, but there's other binnings that we can think about. Is there from an artificial intelligence perspective? Because you're just saying we cluster along the data, but then interactive artificial intelligence is referring to throwing agents into that mix, AI systems into that mix, helping us interacting with us humans and maybe getting us out of those silos. Is that something that you think is possible? Do you see a hopeful possibility for artificial

intelligence systems in these large networks of people to get us out of side of our habits in at least the idea space? To where we can sort of be empathetic to other people's lived experiences, other people's points of view, and all that kind of stuff. Yes, and I actually don't think it's that hard. Well, it's not hard in this sense. So imagine that you can, let's make life simple for a minute. Let's assume that you can do a kind of partial ordering over ideas or clustering 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? This is kind of, 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. 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, the edges and sort of the strengths on them, and you kind of have some semantic meaning for them.

The machine isn't have to. You do as a designer. Then yeah, I think you can kind of move people along and sort of expand them. 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 I, 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. I 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 basically don't speak the same language anymore. 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 about these rooms that you can see, but you're seeing them from different vantage points depending on what's inside the room.

They can see a clock very easily, and so they start referring to the room as the one with the clock. This group over here looking at the same room, they can see the clock, but it's not in their line of side 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 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 is a 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 talk 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 be past one another, that's the hard part. It's a really interesting question to me. It could be on the layer of language, but it feels like there's multiple layers to this. Like, it could be world view.

It could be, I mean, all boils down to empathy, being able to put yourself in the shoes of the other person, to learn the language, to learn, like, visually how they see the world, to learn like the, I mean, I experienced this now with trolls, the degree of humor in that world. For example, I talk about love a lot. I'm very lucky to have this amazing community of loving people, but whenever I encounter trolls, they always roll their eyes at the idea of love because it's so quote unquote cringe. So they show love by like, derision, I would say. And I think about, on the human level, that's a whole other discussion, that's psychology, that's a sociologist, so on. But I wonder if AI systems can help somehow.

And to bridge the gap of what is this person's life like? Encourage me to just ask that question, to put myself in their shoes, to experience the agitations, the fears, the hopes they have, to experience, you know, the... Even just to think about what was there upbringing like having a single parent home, or a shitty education, or all those kinds of things,

just to put myself in that mind space. It feels like that's really important for us to bring those clusters together to find that similar language, but it's unclear how AI can help that because it seems like AI systems need to understand both parties first. So, the word understand there's doing a lot of work, right? Yes. Do you have to understand it? Or do you just simply have to note that there is something similar as a point to touch,

right? So, you 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, but let's separate it from sympathy, right? So, you know, 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. Right. Right. Right. And 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 in 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, I understand how you interact. The words that you use, the, you know, I'm being quite fanciful, as it were. But if you think of these as kind of,

I understand how you interact, the words that you use, the, 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 this 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, if it's one, you're completely the same person. Well, 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 that way and make it easier for you to take that step of empathy. It's not impossible to do, although I wonder if 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 then we are different, they'll solve it themselves. Maybe that's the problem that I 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. So it feels like an economist answer actually. Well, people, okay, first of all, people like you would disagree. So let me disagree slightly, which is,

I think everything you said is brilliant, but I tend to believe philosophically speaking that people are interested underneath it all. And I would say that AI, the possibility of an AI system which show the commonality is incredible. That's a really good starting point. I would say if you, if on social media,

I could discover the common things deep or shallow between me and a person who there's tension with. I think that my basic human nature would take off from there and I think enjoy that commonality and like there's something sticky about that that my mind will linger on and That person in my mind will become like warmer and warmer and like I'll start to give a few more and more compassionate towards them I think for majority the population that's true, but that might be that's a hypothesis. Yeah, I mean, it's an empirical question Right, you didn't have to figure it out. I mean, I want to believe you're right And so I'm gonna say that I think you're right. Of course, some people come to those things for the Purpose of trolling right and it doesn't matter. They're playing a different game

Yeah, but I don't know I 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 and I do agree with this for sure so You it's hard to hold onto that kind of anger or to hold onto 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 in 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 in your want, you're forced to deal with their humanity because it's in front of you.

The other is, of course, that they might punch you in the face if you go too far, so both of those things kind of work together, I think, to the right hand. So I think bringing people together is really a substitute for forcing them to see the humanity and 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. This is very difficult to do. Agreed. Your broad set of research interests fall under interactive AI, as I mentioned, which is a fascinating set of ideas, and you have some concrete things that you're particularly

interested in. But maybe could you talk about how you think about the field of interactive artificial intelligence? Sure. So let me say upfront that if you look at certainly my early work, but even if you look at most of it, I'm a machine learning guy. Right, I do machine learning. First paper, I've republished it was a NIPs. Back then it was NIPs, now it's in NERPs. It's a long story there. Anyway, that's another thing. But so, so I'm a machine learning. I believe in data, I believe in statistics

and all those kind of things. And the reason I'm bringing that up is even though I'm a newfangled statistical machine learning guy and I'll end up in for a very long time, the problem I really care about is AI. Right, 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 own way. So that's the deep underlying love and dream is the bigger AI. Yes, the bigger 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 what most people think of themselves.

Machine learning people, that's how they think. I think it's a Pat Langley might disagree, or at least 1980s Pat Langley, my disagree with what it takes to do machine learning. But I care about the AI problem, which is why it's interactive AI and 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. Can we take a tiny tangent? Are my interrupting, which is, is there something you can say concrete about the mysterious gap between the subset ML and the bigger AI? What's missing? What do you think? I mean, obviously, it's totally unknown, not totally, but in part unknown at this time, but is it something like with Pat Langley's, is it knowledge, like, expert system reasoning type of kind of thing?

So AI is bigger than ML, but ML is bigger than AI. This is kind of the real problem here, is that they're really overlapping things that are really interested in slightly different problems. I tend to think of ML, and there are many people out there are going to be very upset at me about this, but I tend to think of ML being much more are many people out there are going to be very upset at me about this, but I tend to think of them out being much more concerned with the engineering of solving a problem. And AI about this sort of more philosophical goal of true intelligence, and that's the thing that motivates me even if I end up finding myself living in 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, you're sort of goals of where you're trying to be, or somewhat different. But to me, AI is about trying to build that intelligent thing. 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. Oh, there's, of course, a disagree. So on that note, so with the interactive AI, do you tend to in your mind visualize AI as a singular system or is it a collective, huge amount of systems interactively each other? Like is the social interaction of us humans

and of AI systems, the fundamental to intelligence? I think, well, 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 hardwired to like find intention, even whether there is no intention, why do we anthropomorphize everything? We, I think anyway, we, I think the interactive AI part is being intelligent and in and of myself

in an isolation is in meaningless act in some sense. 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 import from, you know, 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 in 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. So I mean, this is why people project, right? You project your own intentions and your own reasons for doing things on the others as a way of understanding them so that you 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 gals 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. I can push this analogy way too far. The chicken and broccoli. I don't know what's wrong with those people. I don't know what's wrong with them either. We have all had our trauma. So they get their chicken and broccoli and their egg drop soup or whatever.

We got to communicate and it's going to change, right? So it's not interactive AI. It's 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 who will themselves change. This is what we mean about things like adaptable models, right? That you have to have a model that models gonna 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 gonna drift, hopefully not too many discontinuities, but you're gonna drift over time. And I have to have some mechanism for adapting to that as you and an individual over time

and across individuals over time. On the topic of adaptive modeling and you talk about lifelong learning, which is, I think a topic that's understudied, or maybe because nobody knows what to do with it. But like, you know, if you look at Alexa or most of our artificial intelligence systems that are primarily machine learning based systems or dialogue systems, all those kinds of things, they know very little about you in the sense of the life long learning sense that we learn as humans, we learn a lot about each other not in the quantity effects, but like the temporally rich side of information that seems to like pick up the crumbs along the way that somehow seems to capture a person pretty well Mm-hmm. Do you have any ideas how to how to do lifelong learning? Because it seems like most of the machine learning community does not.

No, well, by the way, not only does the 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, you 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 optimal. 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 hard thing about AI is it keeps changing on us, right? You know what is AI? Yeah, 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 And good but but beyond that and they keep coming up with new movies. Yes, and they just, right, exactly.

We are driven by this kind of need to 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 derivatives and you divide it 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 on over engineer. 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 problems in front of myself, including 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 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 is set up to make progress very slow. It's fine. It's just the way it is, but it does make this gap seem really big, which is a my long way of saying, I don't have a great answer to it, except that stop doing in equals one. At least try to get in equal two and maybe in equal seven so that you can say, I'm going to, or maybe T is a better variable here. I'm going to not just solve this problem,

I'm going to solve this problem and another problem. I'm not going to learn just on you. I'm going to keep living out there in the world and just seeing what happens and that we'll learn something as designers and our machine learning algorithms and our AI algorithms can learn as well. But unless you're willing to build a system, which you're going to have live for months at a time

in an environment that is messy and chaotic, you cannot control, then you're never going to have live for months at a time in an environment that is messy and chaotic you cannot control Then you're never going to make progress in that direction. So I guess my answer to you is yes my idea is that you should it's not no It's yes, you should be Deploying these things and making them live for months at a time and be okay with the fact that it's going to take you five years to do this not Rerunning the same experiment over and over again and refining the machine so it's slightly better at whatever, but actually having it out there and living in the chaos of

the world and seeing what it's learning algorithm say, you can learn, what data structure it can build and how it can go from there. Without that, you're going to be stock-old about it. What do you think about the possibility of N equals one growing? Is it probably crude approximation, but growing like if you look at language models like GPT-3, if you just make it big enough, it'll swallow the world, meaning like it'll solve all your T-10 affinity by just growing in size of this, taking the small, over-engineered solution and just pumping it full of steroids in terms of compute, in terms of size of training data, and the Yanlequin style self-supervised

or the OpenAI self-supervised, just throw all of YouTube at it and it will learn how to reason, how to paint, how to create music, how to love all that by watching YouTube videos. I mean, I can't think of a more terrifying world to live in than a world that's based on YouTube videos, but yeah, I think the answer that I just kind of don't think that'll quite... Well, it won't work that easily. You will get somewhere and you will learn something, which means it's probably worth it, but you won't get there. You won't solve the, you know, here's a thing. We build these things and we say we want them to learn. But what actually happens is, and let's say they do learn.

I mean, certainly every paper I've gotten published the things learned. I don't know about anyone else. But they actually change us, right? We react to it differently, right? So we keep redefining what it means to be successful, both in the negative and the indicates, but also in the positive and that, oh, this is an accomplishment. I'll give you an example, which is like the one you just described with G2. Let's get completely out of machine learning.

Well, not completely, but mostly out of machine learning. Think about Google. People were trying to solve information retrieval to ad hoc information retrieval problem forever. I mean, first major book I ever read about it was what, 71. I think it was when it came out. Anyway, it's, you know, we'll treat everything as a vector and we'll do these vector space models and whatever. And that was all great.

And we made very little progress. I mean, we made some progress. And then Google comes and makes the ad hoc problem seem pretty easy. I mean, it's not. There's lots of computers and databases involved, but you know, and there's some brilliant algorithmic stuff behind it too and some systems building. But the problem changed, right? If you've got a world that's that connected so that you have, you know, there are 10 million answers quite literally to the question that you're asking. Then the problem wasn't, give me the things that are relevant.

The problem is don't give me anything that's irrelevant, at least in the first page because nothing else matters. So Google is not solving the information retrieval problem, at least not on this web page. Google is minimizing false positives, which is not the same thing as getting an answer. It turns out it's good enough for what it is we want to use Google for, but it also changes what the problem was we thought we were trying to solve in the first place. You thought you were trying to find an answer, but you're not worried you're trying to find

the answer, but it turns out you're just trying to find an answer. Now, yes, it is true, it's also very good at finding you exactly that web page. Of course, you trained yourself to figure out what the keywords were to get you that webpage. But in the end, by having that much data, you've just changed the problem into something else. You haven't actually learned what you set out to learn. Now, the counter to that would be,

maybe we're not doing that either. We just think we are, because we're in our own heads. Maybe we're learning the wrong problem in the first place. But I don't think that matters. I think the point is, is that Google has not solved information retrieval. Google has done amazing service. I have nothing bad to say about what they've done.

Lord knows my entire life is better because Google exists and foreign for Google maps. I don't think I've ever found this place. Where is this? 95? I see 110 and I see, but where's 95? I'm grateful 95 go. So I'm very grateful for Google, but they just have to make certain the first five things

are right. And everything after that is wrong. Look, we're going off in a totally different time here, but think about the way we hire faculty. It's exactly the same thing. I get in controversial. I get in controversial. It's exactly the same thing. I get in controversial. I don't get in controversial. It's exactly the same problem, right?

It's minimizing false positives. We say things like we want to find the best person to be an assistant professor at MIT in the new College of Computing, which I will point out was founded 30 years after the College of Computing. I'm a part of both of my alma mater, both of my fighting words. I'm just saying, I appreciate all that they did

and all that they're doing. Anyway, so we're gonna try to hire the best professor. That's what we say, the best person of this job. But that's not what we do at all, right? Do you know which percentage of faculty in the top four earn their PhDs from the top four? Say in 2017, which is the most recent year for which I've dated. Maybe a large percentage.

It's about 60%. 60% of the faculty in the top four earn their PhDs in the top, or this is computer science. For which there is no top five, there's only a top four, right? Cause they're all tied for one. For people who don't know, by the way, that will be MIT staff for Berkeley CMU. Yep Georgia tech number eight number eight. Mm-hmm. You keep in track. Oh, yes. It's a large part of my job number five is Illinois number six is a tie with

Udov and Cornell and Princeton and Georgia Tech are tied for eight and UT Austin is number 10 Michigan's number 11 by the way, So if you look at the top 10, you know a percentage of faculty in the top 10 are in their PhDs from the top 10? 65, roughly, 65%. If you look at the top 55 ranked departments, 50% of the faculty earn their PhDs from the top 10. There is no universe in which all the best faculty, even just for R1 universities, the majority of them come from 10 places. There's just no way that's true, especially when you consider how small some of those universities are in terms of the number of PhDs they produce. Now, that's not a negative. I mean, it is a negative.

It also has a habit of entrically certain historical inequities and accents. But what it tells you is, well, ask yourself the question. Why is it like that? Well, because it's easier. If we go all the way back to the 1980s, you know, there was a saying that, you know, nobody ever lost his job buying a computer for my VM. And it was true. And nobody ever lost their job hiring a PhD from MIT, right? If the person turned out to be terrible, well, you know, they came from MIT, what did you expect me to know? However, that same person coming from pick

whichever is your least favorite place that produces PhDs and say computer science, well, you took a risk, right? So all the incentives, particularly because you're only gonna hire one this year, well, now we're hiring 10, but you know, you're only gonna have one or two or three this year, and by the way, when they come in, you're stuck with them for at least seven years in most places, because that's before you know whether you're getting tenure or not,

and if they get tenure, you're stuck with them for the good 30 years unless they decide to leave. That means the pressure to get this right is very high. So what are you gonna do?? You're going to minimize false positives. You don't care about saying no inappropriately. You only care about saying yes inappropriately. So all the pressure drives you into that particular direction. Google, not to put too fine a point on it, was in exactly the same situation with their search. It turns out you just don't want to give people the wrong page in the first three or four

pages. And if there's 10 million right answers and 100 bazillion wrong answers, just make certain the wrong answers don't get up there. And who cares if you, the right answer was actually the 13th page, a right answer, a satisfying answer is number one, two, three or four. So who cares? Or an answer that will make you discover something beautiful, profound to your question. Well, that's a different problem, right? But isn't that the problem? Can we linger on this topic without sort of walking with grace? How do we get for hiring

faculty? How do we get that 13th page with the truly special person? Like, there's, I mean, it depends on the department. Computer science probably has those departments, those kinds of people. Like, you have the Russian guy, Gregory Proman, like, just these awkward, strange minds that don't know how to play the little game of etiquette minds that don't know how to play the little game of etiquette that that faculty have all agreed somehow like converged over the decades how to play with each other and also is not you know on top of that is not from the top four top whatever numbers the schools and and maybe actually just says a few of you

wants in a while to the to the traditions of old within the computer science community maybe talks trash about machine learning is a total waste of time and that's there on the resume. So like how do you allow the system to give those folks a chance? Well, you have to be willing to take a certain kind of without taking a particular position on any particular person. You'd have to take, you have to be willing to take a certain kind of without taking a particular position on any particular person You'd have to take you have to be willing to take risk Right a small amount of it. I mean if we were treating this as a

Well as a machine learning problem, right as a search problem Which is what it is a search problem if we were treating it that way you would say oh well the main thing is you You know you've got a prior you want some data cuz I'm Asian if you don't want to do it that way We'll just inject some randomness in and it'll be okay Problem is that feels very very want to do it that way, we'll just inject some randomness in. And it'll be okay. The problem is that feels very, very hard to do with people. All the incentives are wrong there. But it turns out, and let's say, let's say that's the right answer. Let's just give for the sake of argument that injecting randomness in the system at that

level for who your hire is just not worth doing because the price is too high or the cost is too high. We had infinite resources, but we don't. And also, you've got to teach people. So, you know, you're ruining other people's lives if you get it too wrong. But we've taken that principle even if I granted and pushed it all the way back, right? So we could have a better pool than we have of people we look at and give an opportunity to. If we do that, then we have a better chance of finding that. Of course, that just pushes the problem back another level, but let me tell you something else.

I did a study, I called it a study, I called it a study, I called it a paid of my friends and asked them for all of their data for graduate admissions. But then someone else followed up and did an actual study. It turns out that I can tell you how everybody gets into grad school more or less, more or less. You basically admit everyone from places higher ranked than you. You've been most people from places ranked around you. And you've been almost no one from places ranked below you with the exception of the smaller, lower-large colleges that aren't ranked at all like Harvey Mutt because they don't have PhDs with their ranked. This is all CS. Which means the decision of whether you become a professor

at Cornell was determined when you were 17, right? But what you knew to go to undergrad to do it out, right? So if we can push these things back a little bit and just make the pull a little bit bigger, at least you raise the probability that you will be able to see someone interesting and take the risk. The other answer to that question, by the way, which you could argue is the same as you either adjust the pool so the probabilities go up, that's a way of injecting a little bit of uniform noise in the system as it were. As you change your loss function, you just let yourself be measured by something other than

whatever it is that we're measuring ourselves by now. I mean, US News and World Report, every time they change their formulas for determining rankings, move entire universities to behave differently because rankings matter. Can you talk trash about those rankings for a second? Not, I'm joking about talking trash. I actually, it's so funny how, from my perspective, from very shallow perspective,

how dogmatic, like how much I trust those rankings. They're almost ingrained in my head. I mean, at MIT, everybody kind of, it's a propagated, mutually agreed upon, like idea that those rankings matter. And I don't think anyone knows what they're like most people don't know what they're based on. And what are they exactly based on? And what are the flaws in that? Well, so it depends on which rankings you're talking about. Do you want to talk about computer science? We're talking about universities. Computer science,

US news is not the main one. The, the only one that matters is US News. Nothing matters. Sorry, CS rankings, nothing else matters, but US News. So US News has formula that it uses for many things, but not for computer science, because computer science is considered a science, which is absurd. So the rankings for computer science is 100% reputation. So two people at each department, that's not really department, but whatever, at each department basically rank everybody. It's like they were complicated and that, but whatever, they rank everyone. And then those things are put

together and somehow, so that means how do you improve reputation? How do you move up and down the space of reputation? Yes, that's exactly the question. What are you? It can help. I can tell you how Georgia Tech did it, or at least how I think Georgia Tech did, because Georgia Tech is actually the case to look at, not just because I'm at Georgia Tech, but because Georgia Tech is the only computing unit that was not in the top 20 that has made it into the top 10. It's also the only one in the last

two decades, I think that moved up in the top 10 as opposed to having someone else move down. So we used to be number 10 and then we became number nine because UT Austin went down slightly and now we were tied for ninth because that's how rankings work and we moved from nine to eight because our raw score moved up. So some about your technical leaders, I think it's because we have shown leadership at every crisis level. So we created college, first public university, second university, after CMU is number one. I also think it's no accident that CMU is the largest and we're depending upon how you count and depending

exactly where MIT ends up with its final college of computing, second or third largest. I don't think that's an accident. We've been doing this for a long time. But in the 2000s, when there was a crisis about undergraduate education, Georgia Tech took a big risk and succeeded at rethinking undergrad education and computing. I think we created these schools at a time when most public universities in a way were afraid to do it. We did the online masters. And that mattered because people were trying to figure out what to do with MOOCs and so on. I think it's about being observed by your peers and having an impact.

So, I mean, that is what reputation is, right? So, the way you move up in the reputation rankings is by doing something that makes people turn and look at you and say, that's good, they're better than I thought. Yeah, beyond that, it's just a nurse, I'm just a huge history system, the system, right? Like, I mean, there was these, I can't remember this, this is maybe apocryphal, but there's a major or department that like MIT was ranked number one in and they

didn't have it. It's just about what you, I don't know if that's true, but someone said that to me anyway. But it's a thing, right? It's all about reputation. Of course, MIT is great, because MIT is great. It's always been great. By the way, because MIT is great, the best students come, which keeps it being great. I mean, it's just a positive feedback loop, but limit, not surprising. I don't think it's wrong. Yeah, but it's almost like a narrative.

Like, it doesn't actually have to be backed by reality. And it's a, you know, not the same thing about MIT, but like, it, it does feel like we're playing in the space of narratives, not the space of some, something grounded in like one of the surprising things when I showed up at MIT and just all the students I've worked with and all the research I've done is it like they're the same people as I've met other places. I mean, what MIT is going for it? Well, MIT has many things going for it. One thing MIT is going for it is.

Nice logo. It's a nice logo. It's a lot better than it was when I was here. Nice colors too. Terrible, terrible name for it mascot. But the thing that MIT has going for it is it really does get the best students. It just doesn't get all of the best students. There are many more best students out there, right? And the best students want to be here because it's the best place to be or one of the best places to be. And it's just kind of, it's a sort of positive event. But you said something earlier, which I think is worth examining for a moment, right? You said it's, I forget the words you as you said, we're living in the space of narrative as opposed to something objective. Narrative is objective. I mean, one could argue that the

only thing that we do as humans is narrative, we just build stories to explain what we do. Someone wants to ask me, but wait, there's nothing objective. No, it's completely an objective measure. It's an objective measure of the opinions of everybody else. Now, is that physics? I don't know, but you know, what, I mean, tell me something you think is actually objective and measurable in a way that makes sense. Like, cameras. They don't, do you know that, I mean, you're getting me off of something. But do you know that cameras, which are just reflecting light and putting them on film, like did not work for dark skin people until like the 1970s?

You know why? Because you were building cameras for the people who were gonna buy cameras, who all, at least in the United States and Western Europe were relatively light skin. Turns out, it took terrible pictures of people who look like me. That got fixed with better film and whole processes. Do you know why?

Because furniture manufacturers wanted to be able to take pictures of Mahogany furniture, right? Because furniture manufacturers wanted to be able to take pictures of Mahogany furniture, right? Because candy manufacturers wanted to be able to take pictures of chocolate. Now, the reason I bring that up is because you might think that cameras are objective. They're objective. They're just capturing light. No, they're made. They are doing the things that they're doing based upon decisions by real human beings to privilege, if I may use that word, some physics over others, because it's an engineering problem, they're trade-offs, right? So I can either worry about

this part of the spectrum or this part of the spectrum. This costs more, that costs less, this costs the same, but I have more people paying money over here, right? And it turns out that, you know, if a Jack, you know, if an elaborate, wants you demands that you do something different, and it's going to involve all kinds of money for you, suddenly the trade-offs change, right? And so there you go. I actually don't know how I ended up there. Oh, it's because of this notion of objectiveness, right? So, so even the objective is an objective, because at the end you've got to tell a story, you've got to make decisions, you've got to make trade-off, or wills is engineering other than that. So I think that the rankings capture something.

They just don't necessarily capture what people assume they capture. You know, just to linger on this idea, why is there not more people who just like play with whatever that narrative is? I fun with it. I have like excite the world, whether it's in the Carl Sagan style of like that calm sexy boys, have explaining the stars and

all the romantic stuff or the Elon Musk, dare I even say Donald Trump, where you're like trolling and shaking up the system and just saying controversial things. I talked to Lisa Ferman Barrett, who's a neuroscientist, who just enjoys playing the controversy. He finds the counterintuitive ideas in the particular science and throws them out there and sees how they play in the public discourse. Like why don't we see more of that? What does an academia track that can heal a musk type? Well, tenure is a powerful thing that allows you to do it wherever you want, but getting tenure typically requires you

to be relatively narrow, right? Because people are judging you. Well, I think the answer is we, we have told ourselves a story, a narrative that that is vulgar, which we just described as vulgar. It's certainly unscientific, right? And it is easy to convince yourself that in some ways you're the mathematician, right? The fewer there are in your major, the more that proves your purity, right? Yeah. So once you tell yourself that story, then it is beneath you to do that kind of thing, right? I think that's wrong. I think that, and by the way, everyone doesn't have to do so, everyone's

not good at it, and everyone even if they would be good at would enjoy it. So it's fine. But I do think you need some diversity in the way that people choose to relate to the world as academics, because I think the great universities are ones that engage with the rest of the world. It is a home for public intellectuals. And in 2020, being a public intellectual probably means being on Twitter, whereas of course, that wasn't true 20 years ago, because Twitter wasn't around and 20 years ago. And if it wasn't around in a meaningful way, I don't actually know how long Twitter has been around. As I get older, I find that my notes just have gotten worse and worse.

Like Google really has been around that long. Anyway, the point is that I think that we sometimes forget that a part of our job is to impact the people who aren't in the world that we're in and that that's the point of being at a great place and being a great person, frankly. There's an interesting force in terms of public intellectuals. If we get Twitter, we can look at just online courses that are public facing in some part. Like, there is a kind of force that pulls you back. I would, let me just call it off

because I don't give a damn at this point. There's a little bit of all of us have this, but certainly faculty have this, which is jealousy. It's whoever's popular at being a good communicator exciting the world with their science. And of course, when you excite the world with their science. And of course, when you excite the world with the science, it's not peer reviewed, clean. It's it's it's all sounds like bullshit. It's like a TED talk. And people roll their eyes

and they they hate that a TED talk gets millions of views or something like that. And then everybody pulls each other back. There's this force that just kind of, it's hard to stand out unless you like win a Nobel Prize or whatever. It's only when you like get senior enough, we just stop giving a damn. But just like you said, even when you get tenure, that was always the surprising thing to me. I have many colleagues and friends who have gotten tenure. But there's not a switch. You know, there's not an FU money switch, or you're like, you know what? Now I'm going to be more bold. It doesn't, I don't see it. Well, there's a reason for that. Tenure isn't a test. It's a training process. It teaches you to behave in a certain way, to think in a certain way, to accept certain values and to react accordingly. And the better you are at the more likely you are to earn tenure. And by the way, this is not a bad thing. Most things are like that.

And I think most of my colleagues are interested in doing great work and they're just having impact in the way that they want to have impact. I do think that as a field, not just as a field, as a profession, we have a habit of belittling those who are popular, as it were, as if the word itself is a kind of scarlet A. I think it's easy to convince yourself and no one is immune to this, that the people who are better known are better known for bad reasons. The people who are out there dumbing it down are not being pure to whatever the values in Anithas is for your field. And it's just very easy to do.

Now, having said that, I think that ultimately, people who are able to be popular and out there in a touching the world and making a difference, our colleagues do in fact appreciate that in the long run. It's just, you know, you have to be very good at it or you have to be very interested in pursuing it and once you get past a certain level,

I think people accept that for who it is. I mean, I don't know. I'd be very interested in pursuing it. And once you get past a certain level, I think people accept that for who it is. I mean, I don't know. I'd be really interested in how Rod Brooks felt about how people were interacting with him. We needed fast cheap and out of control. Way, way, way back when. What's fast cheap and out of control? It was a documentary that involved four people.

And I remember nothing about it other than Rod Brooks was in it and something about naked mole rats. Can't remember what the other two things were. It was robots, naked mole rats and then two other. By the way, Rod Brooks used to be the head of the artificial telogen laboratory at MIT and then launched, I think, I robot and then think robotics, rethink robotics. Yes. Think is in the word and and also is a little bit of a rock star personality in the AI world, a very opinionated, very intelligent. Anyway, sorry, Malratz and Naked. Naked Malratz. Also, he was one of my two advisors for my PhD. So I have this explains a lot. I love rock. But I also love my other advisor, Paul. Paul, if you're listening, I love you too.

Both very, very different people. Paul Vio. Both very interesting people, very different in many ways. But I don't know what Rod would say to you about what the reaction was. I know that for the students at the time, from a student at the time, it was amazing, right? This guy was on in a movie being very much himself.

Actually, the movie version of him is a little bit more rod than rod. I mean, I think they edited it appropriately for him, but it was very much rod. And he did all this while doing great work to me. Was you running the I lab at that point or not? I don't know, but he was running the I lab or would be soon. He's a giant in the field. He did amazing things, made a lot of his bones by doing the kind of

counterintuitive science, right, and saying, no, you're doing this all wrong. Representation is crazy. The world is your own representation. You just react to it. I mean, this is amazing things and continues to do those those sorts of things as he's moved on. I have, I think he might tell you, I don't know if he would tell you, it was good or bad, but I know that for everyone else out there in the world it was a good thing And certainly he continued to be respected so it's not as if it destroyed his career by being popular All right, let's go into a topic where I'm a thin ice because I grew up in the Soviet Union Russia my my knowledge of music This American thing you guys do is the American thing you guys do is quite foreign. So your research group is called, as we've

talked about the LAP for Interactive Artificial Intelligence, but also there's just a bunch of mystery around this. My research fails me. Also called P-Funk. P stands for probabilistic and what does funk stand for? So a lot of my life is about making acronyms. So if I have one quirk it's that people will say words and I see if they make acronyms. And if they do, then I'm happy. And then if they don't, I try to change it so that they make acronyms. It's just a thing that I do. So P-Funk is an acronym. It has three or four different meetings, but finally I decided that the P stands for probabilistic because at the end of the day, it's machine learning and it's randomness and it's uncertainty, which is the important thing here.

And the FUNC can be lots of different things, but I decided I should leave it up to the individual to figure out exactly what it is. But I will tell you that when my students graduate, out exactly what it is. But I will tell you that when my students graduate, when they get out, so we say, at tech, I hand them, they put on a hat and star glasses and I'm a dallion from the p-func era, and we take a picture, and I hand them a pair of fuzzy dice, which they get to keep. So there's a sense to it, which is not an acronym, like literally funk. There's a, you have a dark mysterious past. Mm-hmm.

It's not dark, it's just fun. It's in hip hop and funk. Yep. So can you educate Soviet-born Russian about this thing called hip-hop like if you were to Give me like you know, went on a journey together and you were trying to educate me about especially That you know the past couple of decades in the 90s about hip-hop or funk what records are artists would you? Would you introduce me to would you Well would you introduce me to? Would you tell me about,

or maybe what influenced you in your journey, or what you just love. Like when the family's gone and you just sit back and just blast some stuff these days, what are you listening to? Well, so I listen to a lot, but I will tell you, well, first off, all great music was made when I was 14 And that statement is true for all people in the matter how old they are where they live, but

For me the first thing that's worth pointing out is that hit hip-hop and rap aren't the same thing So depending on you talk to about this and their people who feel Very strongly about this much more stronger than you're defending everybody in this conversation. So this is great Hip- is a culture. Yeah, a whole set of things, of which rap is a part. So tagging is a part of hip hop. I don't know why that's true, but people tell me it's true, and I'm willing to go along with it

because they get very angry about it. But hip hop is like graffiti. Tagging is like graffiti. And there's all these, including the popping and the locking and all the dancing and all those things. That's all a part of hip hop. It's a way of life. Which I think is true.

And then there's rap, which is this particular. It's the music part. Yes, there are a music part. It's a way of life. Which I think is true. And then there's rap, which is this particular music part. Yes, there are a music part. I mean, you wouldn't call the stuff that DJs do, the scratching. That's not rap, right? But it's a part of hip hop, right? So given that we understand that hip hop is this whole thing, what are the rap albums that best touch that for me? Well, if I were going to educate you, I would try to figure out what you'd like and then

I would work you there. I'm a skinner. Oh my god. Yes. I would probably start with. That's that. There's a fascinating ex. No, it's okay. There's a fascinating exercise. One can do by watching old episodes of I love the 70s. I love the 80s. I love the 90s with a bunch of friends and just see where people come in and out of pop culture. So if you're talking about those people, then I would actually start you with,

where I would hope to start you with anyway, which is public enemy. Particularly it takes a nation of millions to hold us back, which is clearly the best album ever produced. And certainly the best hip-hop album ever produced, in part because it was so much of what was great about the time. Fantastic lyrics, excuse me, it's all about the lyrics. Amazing music that was coming from Rick Rubin was the producer of that, and he did a lot

very kind of heavy metal-ish, at least in the 80s, at the time, and it was focused on politics in the 1980s, which was what made hip-hop so great. I would start you there, then I would move you up through things that have been happening more recently. I'd probably get you to someone like a most deaf. I would give you a history lesson, basically, most deaf, the math. He hosted a poetry jam thing on HBO or something like that. Probably, I don't know if you've seen it, but I wouldn't be surprised.

Spoken poetry, that's it. Yes, he's amazing. Yes, he's amazing. He's amazing. And then after I got you there, I'd work you back to EPMD, and eventually I would take you back to the last poets, and particularly the first album, The Last Poets, which was 1970, to give you a sense of history, and that it actually has been building up over a very, very long time. So we would start there because that's where your music

aligns and then we would cycle out and I'd move you to the present and then I take you back to the past and because I think a large part of people who are kind of confused about any kind of music, you know the truth is this is the same thing we've always been talking about right it's about narrative and being a part of something and being immersed in something so you understand it right? It's about narrative and being a part of something and being immersed in some things you understand it, right? Jazz, which I also like is one of the things that's cool about jazz is that you come and you meet someone who's talking to you about jazz and you have no idea what they're talking about. And then one day it all clicks and you've been so immersed in it, you go, oh yeah, that's a Charlie Parker. You start using words

and nobody else understands, right? And it becomes a part of hip-hop the same way, everything is the same way. They're all cultural artifact. But I would help you to see that there's a history of it, and how it connects to other genres of music that you might like to bring you in, so that you could kind of see how it connects to what you already like, including some of the good work that's been done with fusions of hip-hop and bluegrass.

Oh, no. Yes. Some of it's even good. Not all of it, but somegrass. Oh no. Yes. So I'm up it's even good. Not all of it. But some of it is good. But I'd start you with it takes a nation and I can hold this back. There's an interesting tradition and like more modern hip hop of integrating almost like classic rock songs or whatever like integrating into their music, into the beat, into the whatever.

It's kind of interesting. It gives a whole new, not just class of crock, but what is it? Kanye Gold digger, the whole R&B. It's taken in pulling old R&B, right? Well, that's been true since the beginning. In fact, that's in some ways. That's why the DJ used to get top billing. Because it was the DJ that brought all the records together and made it worth so that people could dance. You go back to those days, mostly in New York, not exclusively, but mostly in New York

where it sort of came out of, you know, it was the DJ that brought all the music together in the beats and showed that basically music is itself an instrument. Very meta. And you can bring it together and then you sort of wrap over it and so on. And it sort of, it moved that way. So that's going way, way back. Now, in the period of time where I grew up, when I became really into it, which was mostly the 80s, it was more funk,

was the back for a lot of the stuff, publicating enemy at that time, notwithstanding. And so, which is very nice, because it tied into what my parents listened to and what I vaguely remember listening to when I was very small. So, and by the way, complete revival of George Clinton and Parliament, it's like a delicate and all of those things, to bring it sort of back into the 80s and into the 90s.

And as we go on, you're gonna see, you know, the last decade and the decade before that being brought in. And when you don't think that you're hearing something you've heard, it's probably because it's being sampled by someone who, referring to something they remembered when they were young, and perhaps from somewhere else altogether. And you just didn't realize what it was, because it was in a popular song where you happen to grow up.

So this stuff's been going on for a long time. It's one of the things that I think is beautiful. Rundy MC, J.M.A.S.J. used to play piano. He would record himself playing piano and then sample that to make it a part of what was going on rather than play the piano. That's how his mind can think. Well, it's pieces. You're putting pieces together, you're putting pieces of music together to create new music, right? Now that doesn't mean that the roots are doing their own thing. Yeah. Right. Those are, that's a whole, yeah. But still, it's the right attitude. That, you know, and what else is jazz, right? Jazz is about putting pieces together and then putting your own spin on, right? It's all the same. It's all the same thing. It's all the same thing. You know, because you mentioned lyrics, it does make me sad, this is me talking trash about modern hip-hop.

Like, I haven't, you know, invested in, I'm sure people correct me that there's a lot of great artists That's part of the reason I'm saying it is they'll leave it in the comments You should listen to this person is the lyrics went away from Talking about maybe not just politics but life and so on like you you know the kind of like protest songs Even if you look like protest songs, even if you look at like a Bob Marley, where you said, public anime or rage against the machine more on the rock side, there's that's the place where we go to those lyrics. Like classic rock is all about like, my woman left me or, or I'm really happy that she's still with me,

or the flip side is like love songs of different kinds. It's all love, but it's less political, like less interesting, I would say, in terms of like deep profound knowledge. And it seems like rap is the place where you would find that. And it's sad that for the most part, what I see, like you look like mumble rap or whatever, they're moving away from lyrics and more towards the beat

and the musicality of it. I've always been a fan of the lyrics. In fact, if you go back and you read my reviews, which I recently was rereading, man, it's like I wrote my last review the month I graduated. I got my PhD, which says something about something. I'm not sure what though. I always would, I didn't always, but often would start with it's all about the lyrics. For me, it's all, it's about the lyrics. Someone has already written in the comments before I've even finished having this conversation that, you know, neither of us knows what we're talking about.

And it's all in the underground hip hop. And here's who you should go listen to. And that is true. Every time I despair for popular rap, I get someone points me to or I discover some underground hip hop song and I am, I am made happy and whole again. So I know it's out there I don't listen to as much as I used to because I'm listening to podcasts and old music from the 1980s Kind of rap. No beat. It's a kind of no beat at all But you know, there's a little bit of sampling here and there. I'm sure

By the way, James Brown is funk or no. Yes. And so is junior wells, by the way. Who's that? Oh, junior wells, Chicago Blues. He was James Brown before James Brown was. It's hard to imagine somebody being James Brown. Go look up Houdouman Blues, junior wells. And just listen to Snatch it back and hold it. And you'll see it.

They were contemporaries. Where do you put like little Richard or all that kind of stuff like Rachel's, like when they get like hit the road Jack and don't you come back? Isn't that like there's a funkiness in it? Oh, that's definitely a funkiness in it. I mean, it's all, I mean, it's all, it's all the line. I mean, it's all, there's all the line that carries it all together. You know, it's, I guess I have an answer to your question, Pity Fow on whether I'm thinking about it in 2020 or I'm thinking about it in 1960. I'd probably

give a different answer. I'm just thinking in terms of, you know, that was rock, but when you look back on it, it was funky, but we didn't use those words, or maybe we did, I wasn't around, but, you know, I don't think we used the word 1960 funk. Certainly not the way we used it in the 70s and the 80s. Do you reject disco? I do not reject disco. I appreciate all the mistakes that we have made together. Actually some of the discos actually really, really good. John Travolta. Oh boy. He regrets it probably. Maybe not. Well, like it's the mistake thing. Yeah. And it got him the way he's going. Where he is. Oh, well, thank you for taking that detour.

You've talked about computing, we've already talked about computing a little bit. But can you try to describe how you think about the world of computing, where it fits into the sets of different disciplines? We mentioned college of computing. How should they think about computing, especially from an educational perspective of like, what is the perfect curriculum that defines for a young mind what computing is? So, I don't know about a perfect curriculum, although that's an important question, because at the end of the day, without the curriculum, you don't get anywhere. Curriculum, to me, is the fundamental data structure. It's not even the classroom. It's the world.

To me is the fundamental data structure. It's not even the classroom. It's just a lot of that. The world is, right? So I think the curriculum is where I like to play. So I spent a lot of time thinking about this. But I will tell you, I'll answer your question by the answer. It's a slightly different question first than getting back to this, which is, you know, you talked about disciplines and what does it mean to be a discipline? The truth is what we really educate people in from the beginning,

but certainly through college, you sort of failed if you don't think about it this way, I think, is the world, people often think about tools and tool sets, and when you're really trying to be good, you think about skills and skill sets, but disciplines are about mindsets, right? They're about fundamental ways of thinking,

not just the hammer that you pick up, whatever that is to hit the nail, not just the hammer that you pick up, whatever that is to hit the nail, not just the skill of learning how to hammer well or whatever, it's the mindset of like, what's the fundamental way to think about, to think about the world, right? And disciplines, different disciplines give you different mindsets, give you different ways of sort of thinking through. So with that in mind, I think that computing, to even ask the question whether it's a discipline, as you have to decide, does it have a mindset,

does it have a way of thinking about the world that is different from the scientist who is doing discover and using the scientific method as a way of doing it, or the mathematician who builds abstractions and tries to find sort of steady state truths about the abstractions that may be artificial, but whatever, or is it the engineer who's all about building demonstrably superior technology with respect to some notion of trade-offs, whatever that means?

That's sort of the world that you live in. What is computing? How is computing different? So I've thought about this for a long time, and I've come to a view about what computing actually is, what the mindset is, and it's a little abstract, but that would be appropriate for computing. I think that what distinguishes the computationalist from others is that here she understands that models, languages, and machines are equivalent. They're the same thing. And because it's not just a model, but it's a machine that is an executable thing that

can be described as a language, that means that it's dynamic. So it is mathematical in some sense, in the sense of abstraction, but it is fundamentally dynamic and executable. The mathematician is not necessarily worried about either the dynamic part. In fact, whenever I tried to write something for mathematicians, they invariably demand that I make it static. And that's not a bad thing. It's just, it's a way of viewing the world

that truth is a thing, right? It's not a process that continually runs, right? So that dynamic thing matters, that self-reflection of the system itself matters. And that is what computing brought us. So it is a science because it fun the models fundamentally represent Truths in the world information is a scientific thing to discover right not just a mathematical conceit that that gets created but of course it's engineering because you're actually dealing with constraints in the world and trying to execute machines That that actually run but it's also a math because you're actually worrying about these languages that describe what

describe what's happening. But the fact that regular expressions and finite state of time, one of which feels like a machine, or at least an abstraction machine, the other is the language that they're actually the equivalent thing. I mean, that is not a small thing. And it permeates everything that we do, even when we're just trying to figure out how to duty-bugging. So that idea, I think, is fundamental.

And we would do better if we made that more explicit. How my life has changed and my thinking about this in the 10 or 15 years, it's been since I tried to put that to paper with some colleagues, is the realization realization which comes to a question you actually asked me earlier, which has to do with trees falling down and whether it matters, is this sort of triangle of equality? It only matters because there's a person inside the triangle, right? That what's changed about computing, computer science, whatever you want to call it, is we now have so much data and so much computational power. We're able to do really, really interesting promising things. But the interesting and the promising kind of only matters with respect to human beings in their relationship to it.

So, the triangle exists that is fundamentally computing. What makes it worthwhile and interesting and potentially world species changing is that there are human beings inside of it and intelligence that has to interact with, that the changes the data, the information that makes sense and gives meaning to the models, the languages and the machines. So if the curriculum can convey that while conveying the tools and the skills that you need in order to succeed, then it is a big win. That's what I think you have to do.

Do you pull psychology, like these human things into that, into the idea, into this framework of computing, do you pull in psychology and neuroscience, like parts of psychology, parts of neuroscience, parts of sociology, what about philosophy, like studies of human nature from different perspectives? Absolutely. And by the way, it works both ways. So let's take biology for a moment. It turns out a cell as basically a bunch of if-then statements. If you look at it the right way, which is nice because I understand if-then statements. I never really enjoyed biology, but I do understand if-then statements. And if you tell the biologists that and they begin to understand that, it actually helps them to think about a bunch of really

cool things. There'll still be biology involved, but whatever. On the other hand, the fact of biology is, in fact, a bunch of the cell is a bunch of if-then statements or whatever allows the computationalists to think differently about the language and the way that we, well, certainly the way we would do AI machine learning, but it's just even the way that we think about computation. So the important thing to me is, as you know, my engineering colleagues who are not in computer science worry about computer science eating up engineering to colleges where computer science is trapped.

It's not a worry. You shouldn't worry about that at all. Computing is computer science computing. It's not, it's central, but it's not the most. You shouldn't worry about that at all. Computing is computer science computing. It's not it's central But it's not the most important thing in the world. It's not more important It is just key to helping others do other cool things are going to do you're not going to be a historian in 2030 and I'm going to repeat it in history without understanding some data science and computing because the way you're going to get history done in part And I say done the way you're going to get it done is you're going to look at data, and you're going to let you're going to have the system that's going to help you analyze things,

to help you to think about a better way to describe history and to understand what's going on, and what it tells us about where we might be going, the same truth or psychology, the same truth or all of these things. The reason I brought that up is because the philosopher has a lot to say about computing. The psychologist has a lot to say about the way humans interact with computing, right? And certainly a lot about intelligence, which, for me, ultimately is kind of the goal of building these computational devices is to build something intelligent. Did you think computing will eat everything in some certain sense or almost like disappear because it's part of everything? It's so funny to say this. I want to say it's going to be a task-size, but there's kind of two ways that fields

destroy themselves. One is they become super narrow, and I think we can think of fields that might be that way. They become pure. And we have that instinct. We have that impulse. I'm sure you can think of several people who want computer science to be this pure thing. The other way is you become everywhere and you become everything and nothing. And so everyone says, you know, I'm going to teach for a trend for

engineers or whatever I'm going to do this. And then you lose the thing that makes it worth studying in and of itself. The thing about computing, and this is not unique to computing, though at this point in time, it is distinctive about computing, where we happen to be in 2020 is we are both a thriving major. In fact, the thriving major almost every place. And we're a service unit because people need to know the things we need to know. And our job, much as the mathematicians job is to help, you know, this person over here to think like a mathematician much the way the point is the point of you taking chemistry as a freshman is not to learn chemistry. It's over here to think like a mathematician much the way the point is it the point of you taking chemistry as a freshman is not to learn chemistry It's to learn to think like a scientist, right? Our job is to help them to think think like a computationalist

And we have to take both of those things very seriously and I'm not sure that as a field We have historically certainly taken the second thing that our job is to help them to think a certain way people aren't gonna be I mean, I don't think we've taken that very seriously at all. I don't know if you know who Dan Carlin is. He has this podcast called Heartcore History. Yes. I've just did an amazing four hour conversation with him, mostly a butt Hitler. But I bring him up because he talks about this idea that it's possible that history as a field will become like currently most people study history a little bit kind of our aware of it we

have a conversation about it different parts of it I mean there's a lot of criticism to say that some parts of history are being ignored blah blah blah so on but most people are able to to have a curiosity and able to learn it. His thought is it's possible given the way social media works, the current way we communicate, the history becomes a niche field where literally most people just ignore, because everything is happening so fast, that the history starts losing its meaning and then it starts being a thing that only, like the theoretical computer science, part of computer science,

it becomes a niche thing that only like the rare holders of the world wars and all the history, the founding of the United States, all those kinds of things, the civil wars. And it's a kind of profound thing to think about how these, how we can lose track, how we can lose these fields when their best, like in the case of history, is best for that to be a pervasive thing that everybody learns and thinks about and so on. Now, let's say computing is quite obviously similar to history.

In a sense that it seems like it should be a part of everybody's life, to some degree, especially like as we move into the later parts of the 21st century. And it's not obvious that that's the way it'll go. It might be in the hands of the few still. Like depending if it's machine learning, you know, it's unclear that it'll computing will win out. It's currently very successful, but I would say that's something you're at the leadership level of this. You're defining the future.

So it's in your hands. No pressure. But I feel like there's multiple ways this can go. And there's this kind of conversation of everybody should learn to code, right? The changing nature of jobs and so on. Do you have a sense of what your role in education of computing is here?

Like what's the hopeful path forward? There's a lot there. I will say that, well, first off, it would be an absolute shame if no one studied history On the other hand, it's T approaches infinity the amount of history is presumably also growing at least laterally and so It's you you have to forget more and more of history, but history needs always be there I mean, I can imagine a world where you you know, if you think of your brains as being you know outside of your head that you can kind of learn the history you need to know when you need to know it, that seems fanciful, but it's a it's a kind of way of, you know, is there sufficient statistic of history? No, and there's certainly, but there may be for the particular thing you have to care about, but you know, the

who's you do not know? It's our objective camera discussion, right? Yeah, it's. Right. And, you know, we've already lost lots of history. And of course, you have your own history that some of which will be our, it's even lost to you, right? You don't even remember whatever it was you were doing 17 years ago. All the ex-girlfriends. Yeah. Never gone.

Exactly. So, you know, history is being lost anyway, but the big lessons of history shouldn't be. And I think, you know, to take it to the question of computing and sort of education, the point is you have to get across those lessons, you have to get across the way of thinking. And you have to be able to go back and, you know, you don't want to lose the data, even if, you know, you don't necessarily have the information that you're fingertips. With computing, I think it's somewhat different. Everyone doesn't have to learn how to code, but everyone needs to learn how to think in the way that you can be precise.

And I mean precise in the sense of repeatable, not just, you know, in the sense of not resolution in the sense of get the right number of bits. In saying what it is you want the machine to do, I mean, you'll be able to describe a problem in such a way that it is executable, which we are not. Even beings are not very good at that. In fact, I think we spend much of our time talking back and forth just to kind of vaguely understand what the other person means and hope we get a good enough that we can act accordingly. You can't do that with machines at least not yet. And so, you know, having to think that precisely about things is quite important. And that's somewhat different from coding. Coding is a crude means to an end.

On the other hand, the idea of coding, what that means, that it's a programming language, and it has these sort of things that you fiddle with in these ways that you express. That is an incredibly important point. In fact, I would argue that one of the big holes in machine learning right now, in NAI, is that we forget that we are basically doing software engineering.

We forget that we are using programming. We're using language just to express what we're doing. We get to so all caught up in the deep network, or we get all caught up in whatever that we forget that we're making decisions based upon a set of parameters that we made up. And if we did slightly different parameters, we'd have completely different outcomes. And so the lesson of computing, computer science education is to be able to think like that and to be aware of it when you're doing it. Basically, it's a way of surfacing your assumptions. I mean, we call them parameters or

you know, we call them if then you know, we we call them if then statements or whatever, but you're forced to surface those those assumptions. That's the key, the key thing that you should get out of a computing education, that and that the models, the languages and the machines are equivalent, but it actually follows from that that you have to be explicit about about what it is you're trying to do because the model your building is something you will one day run. So you better get it right or at least understand it and be able to express roughly what you want to express. So I think it is key that we figure out how to educate everyone to think that way because

at the end, it would not only make them better at whatever it is that they are doing, and I emphasize doing, it'll also make them better citizens. It'll help them to understand what others are doing to them so that they can react accordingly. Because you're not going to solve the problem of social media in so far as you think of social media as a problem by just making slightly better code, right? It only works if people react to it appropriately and know what's happening. And therefore, take control over what they're doing. I mean, that's that's my take on. Okay, let me try to proceed awkwardly into the topic of race. Okay. One is because it's a fascinating part of your story and you're just eloquent and fun about it

And then the second is because we're living through a pretty tense time in terms of race tensions and discussions and ideas in in this time in America You grew up in Atlanta not born in Atlanta. You're, is some Southern state, somewhere at Tennessee, something like that? Nice. Okay. But that you, early on you moved, you basically, you,

identifies and that went native. Yeah. And you've mentioned that you grew up in a predominantly black neighborhood. By the way, black, African American personal color. Is it black? Black.

Where the cap will be? The cap. The other letters are the rest of the matter. Okay, so the predominantly black neighborhood, and so you didn't almost see race. Maybe you can correct me on that. And then what just in the video you talked about when you showed up to Georgia Tech for your undergrad, you're one of the only black folks there. And that was like, oh, that was a new experience.

So can you take me from just a human perspective, but also from a race perspective, your journey growing up in Atlanta and then showing up at Georgia Tech. Okay, that's easy. And by the way, that story continues through MIT as well. In fact, it was quite a bit more stark at MIT and Boston. So maybe just a quick pause, Georgia Tech was undergrad, MIT was graduate school. And I went directly to grad school from undergrad. So I had no, I had no distractions in between my bachelors and my masters in PhD.

You didn't go out on backpacking trip in Europe. Didn't do any of that. The new effect, I literally went to IBM for three months, got in a car and drove straight to Boston with my mother or Cambridge. Yeah. Moved into an apartment I've never seen over the Royal East.

Anyway, that's another story. So let me tell you a little bit about- You miss MIT? Oh, I love MIT. I know him is Boston at all, but I love MIT. That was fighting war. So let's back up to this. So as you said, I was born in Chattanooga, Tennessee. My earliest memories are arriving at Atlanta and I'm moving truck at the edge of three and a half. So I think of myself as being from Atlanta. Very distinct memory of that. So I grew up in Atlanta and it's only place I ever knew as a kid.

I loved it like much of the country. And certainly, much of Atlanta in the 70s and 80s, it was deeply highly segregated, though not in a way that I think was obvious to you, unless you were looking at it, or were old enough to have noticed it. But you could divide up Atlanta, and Atlanta is hardly unique in this way by highway. And you could get race in class that way. So I grew up not only in a predominantly black area,

to say the very least, I grew up on the poor side of that. But I was very much aware of race for a bunch of reasons, one that people made certain that I was, my family did, but also that it would come up. So in first grade, I had a girlfriend. I say I had a girlfriend. I didn't have a girlfriend. I wasn't even entirely sure what girls were in the first grade, but I do remember she decided I was her girlfriend, the white girl named Heather. And we had a long discussion about how it was okay for us to be boyfriend and girlfriend, despite the fact that she was white and I was black. Between the two of you?

Yeah, between the two. Between the two of you? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two?

Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two?

Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? Between the two? very school list because everyone was watching. I was like, ah, my life is, now my life is changed in first grade. No one told me elementary school would be like this. Did you write poetry or not in first grade? That would come later. I would come during puberty when I wrote lots and lots of poetry. Anyway, so I was aware of it. I didn't think too much about it, but I was aware of it, but I was surrounded. It wasn't that I wasn't aware of race. It said I wasn't aware that I was at minority.

It's different. And it's because I wasn't as far as my world was concerned. I mean, I'm six years old, five years old, and first grade. The world is the seven people I see every day, right? So it didn't feel that way at all. And by the way, this being Atlanta, home to the Civil Rights Movement, and all the rest, it meant that when I looked at TV which back then one did because there were only three four or five channels, right? And I saw the news which my mother might make me watch, you know, the Monica Kaufman was was on TV on telling these news and they were all black and the mayor was black and always been black and so it just never occurred to me. When I went to Georgia Tech, I remember the first day walking across campus from West Campus to East Campus and realizing along the way that of the hundreds and hundreds and hundreds

of students that I was seeing, I was the only black one. That was enlightening and very off-putting because that occurred to me. And then of course it continued that way for the rest of my, for much of the rest of my, for much of the rest of my career, or detect, of course I found lots of other students and I met people because in Atlanta you're either black or you're white, there was nothing else. So I began to meet students of Asian descent and I met students who we would call Hispanic and so on and so forth. And you know, so my world, this is what college is supposed to do, right? It's supposed to open you up to people and it did. But it was a very strange thing to be in the minority.

When I came to Boston, I will tell you a story. I applied to one place as an undergrad Georgia Tech because I was stupid and I didn't know any better. I just didn't know any better, right? No one told me. When I went to grad school, I applied to three places, Georgia Tech because that's where it was, MIT and CMU. When I got in to MIT,

I got in to CMU, but I had a friend who went to CMU. And so I asked him what he thought about it. He spent his time explaining to me about Pittsburgh, much less about CMU, but more about Pittsburgh, in which I developed a strong opinion based upon his strong opinion, something about the sun coming out two days out of the year.

I couldn't have a chance to go there because the timing was wrong. I think it was because the timing was wrong. In MIT, I asked 20 people I knew, either when I visited or I had already known for a variety of reasons, whether they liked Boston. And 10 of them loved it and 10 of them hated it. The 10 who loved it were all white. The 10 who hated it were all black.

And they explained to me very much why that was the case. Both deaths told me why, why it is. And the stories were remarkably the same for the two clusters. And I came up here and I could see it immediately why people would love it and why people would not. And people tell you about the nice coffee shops and when coffee shops it was CDU CD places but yeah it was that kind of a thing. Nice shops. Oh there's all these students here Harvard Square is beautiful. You can do all these things and you can walk in

something about the outdoors which I was inled. It's been interesting. The outdoors is for the bugs, it's not for humans. And that's should be a t-shirt. I mean, it's the way I feel about it. And the black folk told me a completely different story about which part of town you did not want to be caught in after dark. And I heard all, but that was nothing new. So I decided that MIT was a great place to be as a university and I believed it then I believe it now. And that whatever it is I wanted to do. I thought I knew what I wanted to do, but what if I was wrong. Someone there would know how to do it. Of course, then I would pick the one topic that nobody was working on at the time, but that's okay. It was great. And so I thought that I would be fine. And I'd only be there for like four or five years. I told myself, we'd turn out not to be true at all. But I enjoyed my time. I enjoyed my time there. But I did see a lot of, I ran across a lot of things that were driven

by what I looked like. While I was here, I got asked a lot of questions. I ran into a lot of cops. I did a, I saw a lot about the city, but at the time, I mean, I've been here a of questions. I ran into a lot of cops. I did a I saw a lot about this city but at the time I mean I've been here a long time. These are the things that I remember. So this is 1990. There is not a single black radio station. Now this is 1990. There are, I don't know if there are any radio stations anymore. I'm sure there are, but you know I don't listen to the radio anymore. Almost no one does. At least if you're under certain age. But the idea should be in a major metropolitan area and there wasn't a single black radio station by which I mean a radio station to play what we would call black music then. Was absurd, but somehow

captured kind of everything about about the city. I grew up in Atlanta and you know, you've heard me tell you about Atlanta. the city. I grew up in Atlanta and you know you've heard me tell you about Atlanta. Boston had no economically viable or socially cohesive black middle class. Insofar as it existed, it was uniformly distributed throughout large parts, not all parts, with large parts of the city. And where you had concentrations of black Bostonians, they tended to be poor. It was very different from where I grew up. I grew up on the poor side of town, sure, but then in high school, well, in ninth grade, we didn't have middle school. I went to an eighth grade school where there was a lot of,

let's just say we had a riot the year that I was there. There was at least one major fight every week. It was, it was, it was, it was an amazing, it was an amazing it was an amazing experience But when I went to ninth grade I went to academy math and math and science academy at Maze High It was a public school. It's a magnet school. That's why I was able to go there It was the first school high school. I think in the state of Georgia to sweep the state math and science fairs It was great. It had 385 students all but four of them were black. I went to school with

the daughter of the former mayor of Atlanta, Michael Jackson's cousin. I mean, you know, there was, it was an upper middle class name. I just dropped names occasionally. You know, drop the mic, drop the names, just let you know, I used to hang out with Michael Jackson's cousin. 12, 9, 9 removed. I don't know. The point is, they just let you know I used to hang out with Michael Jackson's cousin 990 removed. I don't know the point is they had my we had a parking problem because the kids are cars I did not come from a place where where you had cars. I had my first car when I came to MIT actually so it was a It was just a It was just a very

Very different experience for me, but I'd been to places where whether you were rich or whether you were poor, you know, you could be black and richer, black and poor, and it was there and there were places and they were segregated by class as well as by race, but that existed. Here, at least when I was here, didn't feel that way at all, and it felt like a bunch of a really interesting contradiction. It felt like it was the interracial dating capital contradiction. It felt like it was the interracial dating capital of the country. You really felt that way. But it also felt like the most racist place I ever spitted, he talked. You couldn't go up the orange line at that time. I mean, again, that was 30 years ago. I don't know what it's like now. But there were places you couldn't go. And you knew it. Everybody knew it. And

there were places you couldn't live. And everybody knew that. And that was just the greater Boston area in 1992. Saddle racism or explicit racism both. So in terms of within the institutions, did you feel was, was there levels in which you were empowered to be first to what on the first black people in a particular discipline in some of these great institutions that you were part of, you know, Georgia Tech, RMIT, and was there part where it was felt limiting? I always felt empowered. Some of that was my own delusion, I think, but it worked out. So I never felt, in fact, quite the opposite.

Not only did I not feel as if no one was trying to stop me. I had this distinct impression that people wanted me to succeed. But people I met, the people in power. Not my fellow students, not they didn't want me to succeed, but I felt supported or at least that people were happy to see me succeed, at least as much as anyone else. But you know, 1990, you're dealing with a different set of problems, which you're very early, at least in computer science. You're very early in the sort of Jackie Robinson period. There's this thing called the Jackie Robinson syndrome, which is that you have to, the

first one has to be perfect or has to be sure to succeed because if that person fails, no one else comes after for a long time. It was kind of in everyone's best interest. But I think it came from a sincere place. I'm completely sure that people went out of their way to try to make certain that the environment would be good. Not just for me, but for the other people who of course were around. And I was hardly the only person in the AI lab, but I wasn't the only person in MIT by a long shot.

On the other hand, for what? At that point, we would have been what, less than 20 years away from the first black PhD to graduate from MIT, right? Shirley Jackson, right, 1971, something like that? Somewhere around then. So we weren't that far away from the first first, and we were still another eight years away from the first black PhD computer science, right?

So it was this sort of interesting time. But I did not feel as if the institutions of the university were against any of that. And furthermore, not feel as if the institutions of the university were against any of that, and furthermore, I felt as if there was enough of a critical mass across the institute from students and probably faculty, though I didn't know them, who wanted to make certain that the right thing happened, as very different from the institutions of the rest of the city, which I think were designed in such a way that they felt no need to be supported. Let me ask a touchy question on that. So you kind of said that

you didn't feel you felt empowered. Is there some lesson advice in the sense that no matter what, you should feel empowered? You should use the word I think illusion or delusion. Is there a sense from the individual perspective where you should always kind of ignore the, ignore your own eyes, ignore the little forces that you are able to observe around you. They're like trying to mess with you of whether it's jealousy, whether it's hatred in its pure form, whether it's just hatred in its like deluded form, all that kind of stuff, and just kind of see yourself as empowered and confident, all those kinds of things. I mean, it certainly helps, but there's a tradeoff, right? You have to be deluded enough to think that you can succeed.

I mean, you can't get a PhD unless you're crazy enough to think you can invent something that no one else has come up with. I mean, that kind of massive delusion is that. So you have to be deluded enough, to believe that you can succeed, despite whatever odds you see in front of you, but you can't be so deluded that you don't think that you need to step out of the way the oncoming train. Right. So it's all a tradeoff, right? You have to kind of believe in yourself.

It helps to have a support group around you in some way or another. I was able to find that. I've been able to find that wherever I've gone, even if it wasn't necessarily on the floor that I was in. I had lots of friends when I was years. Many of them still live here. And I've kept up with many of them. So I felt supported and certainly I had my mother and my family and those people back home. I could always lean back on even if it were a long distance call the cost money, which is not something that any of the kids today even know what I'm talking about. But back then it mattered calling my mom as an expensive proposition. But you have that and it's fine. I think it helps, but you cannot be so deluded that you miss the obvious because it makes

things slower and it makes you think you're doing better than you are and it will hurt you in the long run. You mentioned cops. You tell a story of being pulled over Perhaps it happened more than once more than once pressure One could you tell that story and in general can you give me a sense of what the world looks like when the When the law doesn't always look at you with The blank slate, with objective eyes, I don't know how to say it more poetically. Well, I guess the answer is it looks exactly the way it looks now, because this is the world that we

happen to live in. It's people clustering and doing the things that they do and making decisions based on and, you know, one or two bits of information they find relevant, which, by the way, are all positive feedback loops, which makes it easier for you to believe what you believed before because you'd behave in a certain way that makes it true when it circles on it and circles and cycles and cycles and cycles. So, it's just about being on edge. I do not, despite having made it over 50 now. Despite congratulations, mother. God, I have a few gray hairs here and there. He did pretty good. I think, you know, I don't imagine I will ever see a police officer and not get very, very tense.

Now, everyone gets a little tense because it probably means you're being pulled over for speeding or something, or you're gonna get a ticket or whatever, right? I mean, the interesting thing about the law, in general, is that most human beings experience of it is fundamentally negative, right? You're only dealing with the lawyer if you're in trouble except in a few very small circumstances, right? You're only dealing with the lawyer if you're in trouble,

except in a few very small circumstances, right? But so that's just an underlying reality. Now imagine that that's also at the hands of the police officer. I remember at the time when I got pulled over that time, halfway between Boston and Walsley actually. I remember thinking, as he pulled his gun on me, that if he shot me right now, he'd get away with it. That was the worst thing that I felt about that particular moment, is that if he shoots me now, he will get away with it. It would be years later

when I realized actually much worse than that, is that he'd get away with it. And if anyone, if it became a thing that other people knew about, odds would be, of course, that it wouldn't. But if it became a thing that other people knew about, if I was living in today's world and supposed to, the world, 30 years ago, then not only would we get away with it, but that I would be painted a villain. I was probably big and scary, and I probably moved too fast,

and if only I had done what he said, and da da da da da da, which is somehow worse, right? You, you know, that hurts not just you, you're dead, but your family, and the way people look at you, and look at your legacy of your history, that's terrible, and it would work. I absolutely believe it would have worked.

Had he done it. He didn't, I don't think he wanted to shoot me from the feeling of killing him, That's terrible. And it would work. I absolutely believe it would have worked. Had he done it. He didn't. I don't think he wanted to shoot me. I feel like killing a body to not go out that night expecting to do that. Or planning on doing it. And I wouldn't be surprised if he never ever did that. Or ever even pulled his gun again.

I don't know the man's name. I don't remember the thing about him. I do remember the gun. Guns are very big when they're in your face. I can tell you this much. They're much larger than they think. And you're basically speeding something like that. He said I ran a light.

I ran a light. I don't think I ran a light, but you know, in fact, I may not have even gotten a ticket. I may have just gotten a warning. I think he was a little sped up. He told the gun. Yeah. Apparently I moved too fast or something. Rolled my window down before I should have.

He's unclear. I think he thought I was going to do something or at least that's how he behaved. So how if we can take a little walk around your brain, how do you feel about that guy and how do you feel about cops? Well, I don't, I don't remember that guy, but my views on police officers are the same view I have about lots of things. Fire is an important and necessary thing in the world, but you must respect fire because it will burn you. Fire is a necessary evil in the sense that it can burn you, necessary in the sense that, you know, heat and all the other things that we use fire for. So when I see a cop, I see a giant ball of flame and I just try to avoid it.

And then some people might see a nice place, a nice thing to roast marshmallows with a family over. Which is fine. I don't roast marshmallows. So let me go a little darker and I apologize. Just talk to Dan Carlin about it for a while or so. Sorry if I go a dark here a little bit, but is it easy for that this experience of just being careful with the fire and avoiding it to turn to hatred? Yeah, of course. And one might even argue that it is a a logical conclusion right on the other hand you've got to live in the world and I

don't think it's helpful Hate is something one should reserve. I mean hate hate is something that Takes a lot of energy so one should reserve it for when it is useful and Not carried around with you all the time. Again, there's a big difference between the happy delusion that convinces you that you can actually get out of bed and make it to work today without getting hit by a car. And the sad delusion that means you can not worry about this car that is barreling towards you.

So we all have to be a little deluded because otherwise we're paralyzed, right? But one should not be ridiculous. We go all the way back to something you said earlier about empathy. I think what I would ask other people to get out of this one of many, many, many stories is to recognize that it is real. stories is to recognize that it is real. People would ask me to empathize with the police office. I would quote back statistics saying that, you know, being a police officer, police officer isn't even in the top 10 most dangerous jobs in the United States. You're much more likely to kill in a taxicab. Half of police officers are actually killed in the, by suicide. But that means their lives are something, something's going on there with them. And I would more than

happy to be empathetic about what it is they go through and how they, they see the world. I think though that if we step back from what I feel, we step back from what an individual police officer feels, you step up a level and all this, because all things tie back into interactive AR. The real problem here is that we've built a narrative, we've built a big structure that has made it easy for people to put themselves into different pots in the different clusters and to basically forget that the people in the other clusters are ultimately like them. It is useful exercise to ask yourself sometimes, I think, that if I had grown up in a completely different house,

and a completely different household as a completely different person, if I had been a woman, would I see the world differently? Would I believe what that crazy person over there believes? And the answer is probably yes, because after all they believe it. And fundamentally, they're the same as you. So then what can you possibly do to fix it? How do you fix Twitter? If you think Twitter needs to be broken or Facebook, if you think Facebook is broken, how do you fix racism?

How do you fix any of these things? That's all structural, right? It's not in, I mean, individual conversations matter a lot, but you have to create structures that allow people to have those individual conversations all the time in a way that is relatively safe, and that allows them to understand that other people have had different experiences, but that ultimately were the same. Which sounds very, I don't even know what the right word is, I'm trying to avoid a word

like saccharine, but you know, it's, it feels very optimistic. But I think that's okay. I think that's a part of the delusion is you want to be a little optimistic and then recognize that the hard problem is actually setting up the structures in the first place because it's in no one's, it's in almost no one's interest to change the infrastructure. Right. I tend to believe that leaders have a big role to that of selling that optimistic delusion to everybody and that eventually leads to the building of the structures. But

that requires a leader that unites sort of unites everybody on the vision as opposed to divides on the vision. This particular moment in history feels like there's a non-zero probability if we go to the P of something akin to a violent or a non-violent civil war. This is one of the most divisive periods of American history in recent, you can speak to this from a perhaps a more knowledgeable and deeper perspective than me. But from my naive perspective, this seems like a very strange time. There's a lot of anger. And it has to do with people, I mean, for many reasons, one, the thing that's not spoken about, I think much is the quiet economic pain of millions that's like growing because of COVID, because of closed businesses, because of like lost dreams. So that's building, whatever that tension

is building. The other is, there is seems to be an elevated level of emotion. I'm not sure if you can psychoanalyze where that's coming from, but this sort of from which the protests and so on percolated. It's like, why now? Why this particular moment in history? Oh, because time and enough time has passed. Right. I mean, you know, the very first race riots were Boston, not to draw anything. Really? When? Oh, just before late. Going way, I mean, like the 1700s or whatever, right? I mean, there was a mask. I got York. I mean, I'm talking way, way, way back. So Boston used to be the hotbed of riots. It's just what Boston was all about. Or so I'm told from history class. There's an interesting one in New York. I remember when that was anyway, the point is, you know, basically you got to get another generation old enough

to be angry, but not so old to remember what happened the last time. And that's what happens. But you said two completely, you said two things there that I think were them packing. One has to do with this sort of moment in time. And you know, why is this sort of upbuild? The other has to do with a kind of, you sort of the economic reality of COVID.

So I'm actually, I want to separate those things because for example, you know, this happened before COVID happened, right? So let's separate these two things for a moment. Now, let me preface all this by saying that although I am interested in history, one of my three minors is an undergrad with history, specifically history of the 1960s. Interesting.

The other was Spanish. Okay, that's a mistake. I love that. Okay, just the same. And history of Spanish history, actually. But Spanish and the other was what we would now call cock and science, but at the time. Well, that's fascinating. Interesting.

Am I under the cox I hear for grad school? That was really fascinating. It was a very different experience from all the computer science classes I've been taking. Even the cox I classes I was taking at an undergrad. Anyway, I'm interested in history, but I'm hardly a historian. So forgive my, last of the audience, forgive my simplification.

But I think the question that's always worth asking is opposed to, it's the same question, but a little different. Not why now, but why not before? So why the 1950s, 60s civil rights movement as opposed to 1930s, 1940s? Well, first off, there wasn't civil rights movement. The 30s and 40s just wasn't of the same character or quite as well known, post-World War II. Lots of interesting things were happening.

It's not as if a switch was turned on and brown versus the Board of Education or the Montgomery bus boycott. And that's when it happened. These things have been building up forever and you can go all the way back and all the way back and all the way back. And you know, Harriet Tubman was not born in 1950.

Right, so you know, we can take these things. They could have easily happened after we'll, right after we'll or two. Yes, I think. And again, I am not a scholar. I think that big difference was TV. These things are visible. People can see them.

It's hard to avoid, right? The, you know, why not James Farmer? Why Martin Luther King? Because one was born 20 years after the other or whatever. I think it turns out that, you know, the King's biggest failure was in the early days, was in Georgia. You know, they were doing some doing the usual thing,

trying to integrate. And I forget the guy's name, but you can look this up, but he copied it as a sheriff, made a deal with the whole state of Georgia. We're going to take people and we are going to non-violently put them in trucks. And then we are going to take them and put them in jails very far away from here. And we're going to do that.

And there will be no reason for the press to hang around. And they did that. And it worked. And the press left. And nothing changed. So, next they went to Birmingham, Alabama and Bullock, and you got to see on TV little boys and girls being hit with firehoses and being knocked down. And there was outrage and things changed.

Part of the delusion is pretending that nothing bad is happening. That might force you to do something big you don't want to do. But sometimes it gets put in your face and then you kind of can't ignore it. And a large part, in my view of what happened bright, was that it was too public to ignore. Now we created other ways of ignoring it. Lots of change happened in the south, but part of that delusion was that it wasn't going to affect the west or the northeast. Of course it did, and that caused its own set of problems, which went into the late 60s

and into the 70s, and you know, some ways we're living with that legacy now. And so on. So why not, what's happening now? Why didn't happen 10 years ago? I think it's, people have more voices. There's not just more TV, there's social media. It's very easy for these things to kind of build on themselves. And things are just quite visible. And there's demographic change. I mean, the world is changing rapidly, right? And so it's very difficult. on themselves and things are just quite visible. And there's demographic change. I mean, the world is changing rapidly, right? And so it's very difficult.

You're now seeing people you could have avoided seeing most of your life growing up in a particular time. And it's happening, it's dispersing at a speed that is fast enough to cause concern for some people, but not so fast to cause massive negative reaction. So that's that. On the other hand, and again, that's a massive oversimplification, but I think there's something there anyway, at least something worth exploring. I'm happy to be yelled at by a real historian.

Oh, yeah. I mean, there's just the obvious thing, I guess you're implying, but not saying this. I mean, it seemed to have percolated the most with just a single video, for example, the George Floyd video difference makes makes it's fascinating to think that whatever the mechanisms that put injustice in front of our face, not like directly in in front of our face, those mechanisms are the mechanisms of change. Yeah, On the other hand, Rodney King. So no one remembers this. I seem to be the only person who remembers this. But sometime before the Rodney King incident, there was a guy who was a police officer

who was saying that things were really bad in Southern California. And he was going to prove it by having some news, some camera people follow him around. And he says, I'm'm gonna go into these towns and just follow me for a week and you will see that I'll get harassed. And like the first night, he goes out there and he crosses into the city, some cops pull him over and he's a police officer, remember?

They don't know that, of course. They like shove his face through a glass window. This was on the new, like I distinctly remember watching this as a kid. Actually, I guess I was in a kid. I was in college at the thousand grad school the time. So that's not enough. Like just, just, just. Well, it disappeared.

Like a day like it didn't go viral. Whatever that is, whatever that magic thing is. And whatever it was in 92, it was harder to go viral than 92, right? Or 91, actually it must have been 90 or 91. But that happened. And like two days later, it's like it never happened. Like nobody, again, nobody remembers this, but Like the only person, I think I must have dreamed

it. Anyway, Rodney King happens. It goes viral or the moral equivalent thereof at the time. And eventually we get April 29th, right? And I don't know what the difference was between the two things other than the one thing called and one thing didn't. Maybe what's happening now is two things are freezing under one another. One is more people are willing to believe. And the other is there's easier and easier ways to give evidence. Yeah, cameras, body cams, or whatever. But we're still finding ourselves telling the same story. It's the same thing over again. I would invite you to go back and read the op-eds from what people were saying about the violence is not the right answer after Rodney King. And then go back to 1980 and the big riots that were happening around then and read the same op- it's the same words over and over and over again. I mean there's your

remembering history right there. I mean like literally the same words like it could have just just caught and I'm surprised no one got flagged forged for pleasure. It's interesting if you have an opinion on the question of violence and the popular perhaps caricature of Malcolm X versus King Martin Luther King. You know Malcolm X was older than Martin Luther King? People kind of have it in their head that he's younger. Well, he died sooner, right? But only by a few years, right? People think of him, if him, okay, as the older statesman and they think of Malcolm X is the young angry whatever. But that's more of a narrative device. It's not true at all.

I don't, I just, I reject the choice. As I think it's a false choice. I think there are just things that happen. You just do, as I said, hatred is not, it takes a lot of energy. But everyone's way, you have to fight. One thing I will say, without taking a moral position, which I will not take on this matter, violence has worked.

Yeah, that's the annoying thing. That's the annoying thing. It seems like over- top anchor works, outrage works. So you can say like being calm and rational, just talking it out is going to lead to progress, but it seems like if you just look through history, being irrationally upset is the way you make progress. Well, certainly the way that you get someone to notice you. Yeah. And that's it.

And if they don't notice you, I mean, what's the difference between that and what I do? Again, without taking a moral position on this, I'm just trying to observe history here. If you maybe a television didn't exist, this civil rights movement doesn't happen or it takes longer or it takes a very different form, maybe if social media doesn't exist, a whole host of things, positive and negative don't happen, right? So, and what do any of those things do other than expose things to people? Violence is a way of shouting. I mean, many people far more talented and thoughtful than I have, have said this in one

form or another, right? That violence is the voice of the unheard, right? I mean, it's a thing that people do when they feel as if they have no other option. And sometimes we agree and sometimes we disagree. Sometimes we think they're justified. Sometimes we think they are not. But regardless, it is a way of shouting. And when you shout, people tend to hear you, even if they don't necessarily hear the words that you're saying. They hear that you are shouting. I see no way... So another way of putting it, which I think is less... Let us just say provocative, but I think is true, is that

but I think is true is that all change, particularly change, that impacts power requires struggle. The struggle doesn't have to be violent, you know, but it's a struggle nonetheless. The powerful don't give up power easily. I mean, why should they? But even so, you still have to be a struggle. and by the way, this isn't just about, you know, violent political, whatever, nonviolent political change, right? This is true for understanding calculus, right? I mean, everything requires a struggle, right? We're back to talking about faculty hiring.

At the end of the day, in the end of the day, it all comes down to faculty hiring. That's not what I'm seeing. All the metaphor, faculty hiring the metaphor for all of life. Let me ask you a strange question. Do you think everything is going to be okay in the next year? Do you have a hope that we're going to be okay? I tend to think that everything is going to be okay, because I just tend to think that everything is going to be okay. My mother says something to me a lot and always always has, and I find it quite comforting,

which is this two-shell pass. And this two, she'll pass. Now, this two-shell pass is not just this bad thing is going away. Everything passes. I mean, I have a 16-year-old daughter who's going to go to college, probably about 15 minutes,

given how fast she seems to be growing up. And, you know, I get to hang out with her now, but one day I won't she'll ignore me just as much as I ignored my parents when I was in college In winter grad school this too. She'll pass but I think that you know one day if we're all lucky You live long enough to look back on something that happened a while ago even if it was painful and mostly It's a memory So yes, I think I think it'll be okay. What about humans? Do you think we'll live into the 21st century? He's really upset. He worried about that. He worried that we might destroy ourselves when nuclear weapons with AGI

was engineering. I'm not worried about AGI doing it, but I am worried. I mean, in any given moment, right? Also, but you know, at any given moment, a comic, I mean, you know, whatever. I didn't think that outside of things completely beyond our control, we have a better chance than not of making it. You know, I talked to Alex Littlepanko from Berkeley. He was talking about comments and then they can come out of nowhere. And that was the realization to me. Wow, we're just watching this darkness and they can just enter and then we

have less than a month. And yet you make it from day to day. That one will show not pet. Well, maybe for Earth, though, past but not for humans. But I'm just choosing to believe that it's going to be okay. And we're not going to get hit by an asteroid, at least not while I'm around. And if we are, well, there's very little I can do about it. So I might as well assume it's not going to happen. It makes food taste better. It makes food taste better.

So you, out of the millions of things you've done, you're like, you've also began the this week in black history calendar of facts. There's like a million questions that can ask here. You said you're not a historian. But is there, let's start at the big history question of, is there somebody in history in black history that you draw a lot of philosophical or personal inspiration from are you just find interesting or a moment in history you find interesting. Well I find the the entirety of the 40s to the 60s and the civil rights movement that didn't happen and did happen at the same time during then quite inspirational. I mean, I've read quite a bit of the time period

at least I did in my younger days when I had more time to read as many things as I wanted to. What was quirky about this week in my history when I started in the 80s? Was how focused it was, and it was because of the sources I was stealing from. And I was very much stealing from sort of like I take calendars, anything I could find, Google didn't exist, right? Now, I just pulled as much as I could and just put it together in one place for other people. What ended up being quirky about it, and I started getting people

sending me information on it, was the inventors. People who, you know, Garrett Morgan, Benjamin Bannocker, right? People who were inventing things. At a time when, how in the world did they manage to invent anything? Like, all these other things were happened, mother necessity, right? All these other things were happening, and you know, there were so many terrible things happening around them, and you know, they went to the wrong state at the wrong time. I mean, never, never come back, but they were inventing things we use, right? And it was always inspiring to me that people would still create even under those circumstances. I got a lot out of that. I also learned a few lessons. I think, you know, the Charles Richard Jews of the world, you know, you, you, you create things that impact people.

You don't necessarily get credit for them. And that's not right, but it's also okay. You're okay with that? Up to a point here. I mean, look, in our world, all we really have is credit. That was always bothered by how much value credit is given. That's the only thing you got. I mean, if you're an academic in some sense,

well, it isn't the only thing you've got, but it feels that way sometimes. But you got the actual, we're all going to be dead soon. You got the joy of having created the, you know, the credit we'd want to talk to Yorians mid-Huber, right? The touring award given to three people for deep learning, and you could say that a lot of other people should be on that list. It's the Nobel Prize question. Yeah, it's sad.

It's sad and people like talking about it, but I feel like in the long arc of history, the only person who will be remembered is Einstein Hitler, maybe almost. And the rest of us are just like, well, you know, someone asked me about immortality once and I said, and I stole this from somebody else, I don't remember who, but it was, you know, I asked them, what's your great grandfather's name? Any of them? Of course, they don't know. Most of us do not know.

I mean, I'm not entirely sure I know my grandparents, all my grandparents names. I know what I called them. Right. I don't know any of their middle names, for example. Didn't live in living memory, so I could find out actually my grandfather didn't know when he was born. I know I know how old it was. But I definitely don't know any of my great grandparents are. So in some sense immortality is doing something preferably positive, so there's your great grandchildren to know who you are. Right, and that's kind of what you can hope for, which is very depressing in some ways. You can, I could turn it into something uplifting

if you need me to, but it can you do the work here? Yeah, it's simple, right? It doesn't matter. I don't know, I have to know my great grandfather was to know that I wouldn't be here without him. Yeah. And I don't know who my great-grandchildren are, certainly who my great-great-grandchildren are, and I'll probably never meet them, although I would very much like to. But hopefully I'll set the world in motion in such a way that their lives will be better than they would have been if I hadn't done that.

Well, certainly they wouldn't have existed if I hadn't done the things that I did. So I think that's a good positive thing. You live on through other people. Are you afraid of death? I don't know if I'm afraid of death, but I don't like it. That's another t-shirt. I mean, do you ponder it? Do you think about the, the, the inevitability of oblivion? I do occasionally

It feels like a very Russian conversation actually. It's very yeah, I will tell you a story a very Something happened to me. I am if you look very carefully. You'll see I was scar. Yes Which by the way is an interesting story of its own about why people have half of their thyroid taken out some people get scars and some don't but anyway, I had half my thyroid taken out. The way I got there, by the way, is it's own interesting story, but I won't go into it. Just suffice it to say, I did what I keep telling people you should never do, which is never go to the doctor unless you have to, because there's nothing good that's ever going to come out of a doctor's visit, right? So I went to the doctor to do, look at one thing. It's a little bump I had on the side that I thought might be something bad because my mother made me. And I went there and is like, oh, it's nothing, but by the way,

your thyroid is huge. Can you breathe? Yes, I can breathe. Are you sure? Because it's pushing on your windpipe. You should be dead. Right. So I ended up going there and to look at my thyroid, it was growing. I would call it a goiter. And you say, we're going to take it out at some point. When? Sometime before you're 85, probably. But but if you wait to your 85, that'll be really bad because you don't want to have surgery when you're 85 years old. If you can help it, certainly not the kind of surgery it takes to take out your thyroid. So I went there and we decided, I would decide I'd put it off until December 19th, because my birthday's December 18th. And I wanted Bill to say I made it to 49 or whatever.

So I said, I'll wait till after my birthday. In the first six months of that, nothing changed. Apparently in the next three months, it had grown, I had notices at all. I went and had surgery. It took about half of it. The other half is still there. And working fine, by the way.

I don't have to take a pill or anything like that. It's great. I'm in the hospital room. And the doctor comes in. I've got these things in my arm. They're gonna do whatever they're talking to me. And the anesthesiology says, huh, your blood pressure is through the roof.

Are you, do you have high blood pressure? I said no, but I'm terrified if that helps you at all. And the anesthesist who's the nurse who supports the anesthesiologist, if I got that right, said, oh, don't worry about it. I've just put some stuff in your IV. You're gonna be feeling pretty good in a couple of minutes. And I remember turning and saying,

well, I'm gonna feel pretty good in a couple of minutes. Next thing I know, there's this guy, and he's moving my bed. And he's talking to me, and I have this distinct impression that I've met this guy, and I should know what he's talking about, but I kind of just don't remember what just happened.

And I look up and I see the tiles going by, and I'm like, oh, it's just like in the movies, where you see the tiles go by. And then, I have this brief thought that I'm in an infinitely long warehouse and there's someone sitting next to me. And I remember thinking, oh, she's not talking to me. And then I'm back in the hospital bed. And in between the time where the tiles were going by and I got in the hospital bed, something

like five hours it passed. Apparently it had grown so much that it was a four and a half hour procedure instead of an hour long procedure. I lost a next size and a half. This is pretty big. Apparently this is big as my heart. Why am I telling you this? I'm telling you this because I have a story already. Sweet. Tiles going by and me waking up in my hospital bed. No time passed. There was no sensation of time pass. When I go to sleep and I wake up in the morning, I have this feeling that time has passed.

This feeling that something has physically changed about me. Nothing happened between the time they put the magic juice in me and the time that I woke up. Nothing. By the way, my wife was there with me talking. Apparently, I was also talking. I don't remember any of this, but luckily I didn't say anything I would normally say. My memory of it is I would talk to her and she would teleport around the room. And then I accused her of witchcraft, and that was the end of that. But she, her point of view is I would start talking and then I would fall asleep and then I would wake up and leave off where I was before.

I had no notion of any time passing. I kind of imagined that that's death. Yeah. Is the lack of sensation of time passing. And on the one hand, I am, I don't know, sued by the idea that I won't notice. On the other hand, I am very unhappy at the idea that I won't notice. So I don't know if I'm afraid of death, but I am completely sure that I don't like it, and that I particularly would prefer to discover on my own whether immortality sucks and be

able to make a decision about it. That's what I would prefer. You'd like to have a choice in the matter. I would like to have a choice in the matter. Well, again, on the Russian thing, I think the finiteness of it is the thing that gives it a little flavor, a little spice. Well, in the reinforcement learning, we believe that. That's why we have discount factors.

Otherwise, it doesn't matter what you do. Amen. That's why we have discount factors. Otherwise, it doesn't matter what you do. Hey, man. Well, let me one last question. It's sticking on the Russian theme. You talked about your great grandparents. Not remember their name. What do you think is the, in this kind of mark of chain that is life? What do you think is the meaning of it? All. What is the meaning of it?

All. What's the meaning of life? Well, in a world where eventually you won't know your great grandchildren or I am reminded of something I heard once that I really like, which is it is well worth remembering that the entire universe, say for one trifling exception, is composed entirely of others. I think that's the meaning of life. Charles, this was one of the best conversations I've ever had. And I get to see you tomorrow again to hang out with a, with a, with a, who looks to be one of the most, how should I say, interesting personalities that I'll ever get to me with my commitments.

So I can't wait. I'm excited to have had this opportunity. Thank you for traveling all the way here. It was amazing. I'm excited. I had this opportunity. Thank you for traveling all the way here. It was amazing. I'm excited. I always love Georgia Tech. I'm excited to see with you being involved there with the future holds.

So thank you for talking to me. Thank you for having me, Android, every minute of it. Thanks for listening to this conversation with Charles Isbo and thank you to our sponsors, Nuro, they make a functional sugar-free gum and mince that I used to give my brain a quick caffeine boost. Decoding digital, a podcast, I'm tech and entrepreneurship that I listen to and enjoy. Masterclass, online courses that I watch from some of the most amazing humans in history

and cash app. The app I used to send money to friends for food and drinks. Please check out these sponsors in the description to get a discount and to support this podcast. If you enjoyed this thing, subscribe on YouTube, review it with 5 stars and apple podcasts, follow on Spotify, support on Patreon, or connect with me on Twitter and Lex Friedman. And now let me leave you with some poetic words from Martin Luther King Jr. There comes a time when people get tired of being pushed out of the glittering sunlight of life's July and left standing amid the piercing chill of an Alpine November.

Thank you for listening and hope to see you next time. you