The following is a conversation with Charles Isbell,

Dean of the College of Computing at Georgia Tech,

a researcher and educator in 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.

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that I use to give my brain a quick caffeine boost.

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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 Americans 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, Jengas 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, November 2nd.

If you enjoy this thing, subscribe on YouTube,

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support on Patreon, and connect with me on Twitter at Lex Freedman.

And now, here's my conversation with Charles Isbell.

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 going to tell you why. It's very simple. It's because

movies is too broad of a category. I got to pick subgenres, but I will tell you

that of those genres, I'll pick one or two for me to the genres.

I'll get us to three, sometimes I'm going to cheat. So, my favorite

comedy of all times, which probably my favorite movie of all time,

is His Girl Friday, 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. What are we talking about? This is one of the movies that would

have been very popular. It's a screwball comedy. Have you ever

seen Moonlighting, the TV show? You know what I'm talking about?

So, you've seen these shows where there's a man and a woman,

and they clearly are in love with one another, and they're constantly fighting

and always talking over each other. Yeah.

Banter, banter, banter, banter, banter. This was the movie that started all that,

as far as I'm concerned. It's very much of its time, so it's,

I don't know, it must have come out sometime between 1934 and 1939. I'm not sure exactly when

the movie itself came out. It's black and white. It's just a fantastic

film. It is hilarious. It's just most of the conversation.

Not entirely, but mostly, mostly. Just a lot of back and forth. There's a story there.

Someone's on death row, and they're newspaper men, including her. They're all newspaper men.

They were divorced. The editor, the publisher, I guess, and the reporter, they were divorced,

but they clearly, he's thinking, trying to get back together, and there's this whole other

thing that's going on, but none of that matters. The plot doesn't matter.

Yeah. There's just total play in conversation.

It's fantastic, and I just love everything about the conversation, because at the end of the day,

sort of narrative and conversation are the sort of things that drive me, and so I really like

that movie for that reason. Similarly, I'm now going to cheat, and I'm going to give you two

movies as one, and they're Crouching Tiger, Hidden Dragon, and John Wick, both relatively modern.

John Wick, of course. One, two, or three.

One. I love them all for different reasons, and they're increasingly more ridiculous,

kind of like loving alien and aliens, despite the fact they're two completely different movies.

But the reason I put Crouching Tiger, Hidden Dragon, and John Wick together is because I

actually think they're the same movie, or what I like about them, 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 Dragon is a lot like that. You get the feeling

that this is chapter nine of a 10 part story, and you've missed the first eight chapters,

and they're not going to explain it to you, but there's this sort of rich world behind you.

You get pulled in anyway, like immediately.

You get pulled in anyway. So it's just excellent storytelling in both cases,

and very, very different.

And also you like the outfit, I assume, the John Wick outfit?

Oh yeah, of course. Well, of course. Yes. I think John Wick outfit first.

And so that's number two. And then...

But sorry to pause on that, Martial Arts. You have a long list of hobbies,

like it scrolls off the page, but I didn't see Martial Arts as one of them.

I do not do Martial Arts, but I certainly watch Martial Arts.

Oh, I appreciate it very much. Oh, we could talk about every Jackie Chan movie I've ever made.

Okay.

And I would be on board with that.

The Couch Hour 2, like that kind of comedy of a cop?

Yes, yes. By the way, my favorite Jackie Chan movie would be

Drunken Master 2, known in the States usually as 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 on Remember.

But I had no idea that that's what it was, and I didn't know that was Jackie Chan.

That was like his first major movie. I was a kid. It was done in the 70s.

I only later rediscovered that it was actually...

And he creates his own martial art by drinking.

Was he actually drinking or was he played drinking?

You mean as an actor or?

No, I'm sure as an actor.

No, he was 70s or whatever.

He was definitely drinking, and in the end, he drinks industrial grade alcohol.

Yeah.

Yeah, and has one of the most fantastic fights ever in that subgenre.

Anyway, that's my favorite one of his movies, but I'll tell you the last movie

is actually a movie called Nothing but a Man, which is 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 scene, so 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 need to worry about spoilers if you haven't seen The Godfather.

Spoiler alert.

It ends with the wife coming to Michael, and he says,

just this once, I'll let you ask me my business.

And she asks him if he did this terrible thing, and he looks her in the eye and he lies,

and he says, no.

And she says, thank you.

And she walks out the door, and you see him, you see her 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 at Georgia Tech Chrome?

I'm just kidding, a trick question.

Sometimes, certainly, the door gets closed on me every once in a while.

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 did an 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 kind of 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 that data?

Because it's fascinating, just like little details of how you collect that data,

and also what your intuition behind the two days is.

So first off, it has to be the right two days, but I was thinking of a very specific

experiment. There's actually a suite of them that I've been a part of, and other people

have done this. Of course, I just sort of dabbled in that part of the world.

But to be very clear, the specific thing that I was talking about had to do with recording

all the IR going on in my, infrared going on in my house.

So this is a long time ago.

So this is everything's being curled by pressing buttons on remote controls as opposed to speaking

to Alexa or Siri or someone like that.

And I was just trying to figure out if you could get enough data on people to figure out

what they were going to do with their TVs or their lights.

My house was completely wired up at the time, which, you know, what I'm about to look at

a movie or I'm about to turn on the TV or whatever and just see what I could predict

from it. It was kind of surprising.

It shouldn't have been, but that's all very easy to do, by the way, just capturing all

the little stuff. I mean, it's a bunch of computer systems.

It's really easy to capture the day, if you know what you're looking for at 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 markoff 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% accuracy just by doing something very simple and stupid

and just counting statistics.

What was actually more interesting is that you could use that information.

This comes up again and again in my work.

If you try to represent people or objects by the things they do, the things you can

measure about them that have to do with action in the world, so 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

where 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

in exactly the way that you would expect.

So, for example, all the numbers on the keypad, it turns out all have the same behavior with

respect to you as a human being.

And so you would naturally cluster them together and you discover that numbers are all related

to one another in some way and all these other things.

And then, and here's the part that I think is important.

I mean, you can see this in all kinds of things.

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.

And 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 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 that routine

for each one of us might be different.

It's possible.

I would say it a little differently, I think.

I would make two things.

One is I'm going to disagree with the premise, I think.

But that's fine.

I think the way I would put it is there are people who are very different from lots of

other people, but they're not 0%.

They're closer to 10%, right?

So, in fact, even if you do this kind of clustering of people,

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 suboptimal for you.

And I do think that that's a reasonable place to start for the question of what makes us human.

So, before we talk about sort of exploring the different ideas underlying interactive

artificial intelligence, which we are working on, let me just go along this thread

to 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

clusterings 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.

To if you're a Democrat, you'd be empathetic to the Republican.

If you're a 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 in 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 outside 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, you know, all that kind of stuff?

Yes, 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 clusterings of behavior.

It doesn't even matter what I mean here.

So long as there's some way that this is a cluster, this is a cluster,

there's some edge between them, right?

And 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, right? And I think that machines are actually very good at that sort of thing

once we kind of define the problem, either in terms of behavior or ideas or words or whatever.

So it's easy in the sense that if you already have the network and you know the relationships,

you know, the edges and sort of the strengths on them, and you kind of have some semantic

meaning for them, the machine doesn't have to, you do as the 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, it's roughly, it roughly goes like this. You take two sets of people

from the same backgrounds and you want them to solve a problem. So you separate them up,

which we do all the time, right? Oh, you know, we're going to break out in the,

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 you're going to 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 learned, you want to merge what they've

done together, it can be extremely hard because they don't, they basically don't speak the same

language anymore. Like when you create these problems and you dive into them, you create

your own language. So the example this one person gave me, which I found kind of interesting because

we were in the middle of that at the time, was they're sitting over there and they're talking

about this, these rooms that you can see, but you're seeing them from different vantage points,

depending upon what side of the room you're on. 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, you know, not in their line of sight or whatever. So they

end up referring to it by some other way. When they get back together and they're talking about

things, they're referring to the same room and they don't even realize they're referring to the

same room. In fact, this group doesn't even see that there's a clock there and this group doesn't

see whatever, the clock on the wall is the thing that stuck with me. So if you create these different

silos, the problem isn't that the ideologies disagree. It's that you're using the same words

and they mean radically different things. The hard part is just getting them to agree on the,

well, maybe we'd say the axioms in our world, right? But, you know, just get them to agree on

some basic definitions. Because right now, they're talking past each other, just completely

talking past each other. That's the hard part. Getting them to meet, getting them to interact,

that may not be that difficult. Getting them to see where their language is leading them to

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

worldview. 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 another discussion, that's psychology, that's sociology,

so on. But I wonder if AI systems can help somehow and 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, the, to experience, you know, the, even just to think

about what was there upbringing like, 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, you know, the word understand there's doing a lot of work, right? So, 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, in 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, right? It doesn't mean that the only way, because if that isn't the case, then what

it requires is that you, you must, the only way that you can, to understand someone means you must

agree with everything that they do, which is it right, right? And if the only way I can feel for

someone is to completely understand them and make them like me in some way, well, then we're lost,

right? Because we're not all exactly like each other. I don't have to understand everything

that you've gone through. It helps clearly, but they're separable ideas, right? Even though they

get clearly, 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 as kind of, I understand how

you interact, the words that you use, 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 alike than we are different,

they'll solve it themselves. Maybe that's the problem that AI should be working on,

not telling you how you're similar or different, but just getting you to decide that it's

worth while asking the question. It feels like an economist to answer actually.

Well, first of all, people 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 that an AI system would

show the commonality is incredible. That's a really good starting point. I would say if you

should, 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 over 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 feel more and more compassionate towards them. I think for majority of the

population, that's true. But that might be, that's a hypothesis.

Yeah. I mean, it's an empirical question, right? You'd have to figure it out. I mean,

I want to believe you're right. And so I'm going to say that I think you're right.

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. But I don't know. My experience is,

it requires two things. In fact, maybe this is really at the end what you're saying. And I do

agree with this for sure. So it's hard to hold on to that kind of anger or to hold on to just a

desire to humiliate someone for that long. It's just difficult to do. It takes a toll on you.

But more importantly, we know this, both from people having done studies on it, but also from

our own experiences, that it is much easier to be dismissive of a person if they're not in front

of you, if they're not real, right? So much of the history of the world is about making people

other, right? So if you're 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 then you're one, 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 end.

So I think bringing people together is really a kind of substitute for forcing them to see the

humanity in another person and to not be able to treat them as bits. It's hard to troll someone

when you're looking them in the eye. 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 up front that if you look at, certainly my early

work, but even if you look at most of it, I'm a machine learning guy, right? I do machine

learning. First paper I ever published was a NIPS. Back then, it was NIPS. Now, it's NURPS.

It's a long story there. Anyway, that's another thing. So I'm a machine learning guy. I believe

in data. I believe in statistics and all those kinds of things. And the reason I'm bringing it up

is even though I'm a newfangled statistical machine learning guy and have been for a very long time,

the problem I really care about is AI. I care about artificial intelligence. I care about

building some kind of intelligent artifact. However, that gets expressed, that would be

the, at least as intelligent as humans and as interesting as humans, perhaps on their,

on their, they're sort of in their own way. So that's the deep underlying love and dream is the

bigger AI. Yes. 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, right? 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. Thinking that Pat Langley might disagree or at

least 1980s, Pat Langley might disagree with what it takes to do machine learning. But I care about

the AI problem, which is why it's interactive AI, not just interactive ML. I think it's important

to understand that there's a longterm 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,

outside of ourselves. Can we take a tiny tangent or am I 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's 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 concerned with the engineering of solving a problem and AI about

the sort of more philosophical goal of true intelligence. And that's the thing that motivates

me, even if I end up finding myself living in this kind of engineeringish space. I've now made

Michael Jordan upset. But 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 are 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. Others,

of course, would disagree. So on that note, with the interactive AI, do you tend to in your mind

visualize AI as a singular system, or is it as a collective huge amount of systems interacting

with each other? Is the social interaction of us humans and of AI systems the fundamental

to intelligence? Well, it's certainly fundamental to our kind of intelligence,

right? And I actually think it matters quite a bit. So the reason the interactive AI part matters

to me is because I don't, this is going to sound simple, but I don't care whether a tree makes a

sound when it falls and there's no one around because I don't think it matters, right? If there's

no observer in some sense. And I think what's interesting about the way that we're intelligent

is we're intelligent with other people, right, or other things anyway. And we go out of our way to

make other things intelligent. We're hardwired to like find intention, even whether there is no

intention to why we anthropomorphize everything. We, I think anyway, we, we, I think the interactive

AI part is being intelligent in and of myself in an isolation is a meaningless act in some sense.

The correct answer is you have to be intelligent in the way that you interact others. It'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 and group of people and we're trying to move the species forward as well. And so I think

that notion of being intelligent with others is kind of the key thing because otherwise you,

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, you know, 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 the other, what each other wants, right? 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, 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 Gal's chicken

because that's the thing that's easy. I will get hot and sour soup. You know, the 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. They, 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 is not just about learning to solve a problem or a task. It's about

having to adapt that over time, over a very long period of time and interacting with other people

who will themselves change. This is what we mean about things like adaptable models, right?

That you have to have a model that model's going to change. And by the way, it's not just the case

that you're different from that person, but you're different from the person you were 15 minutes ago

or certainly 15 years ago. And I have to assume that you're at least going to drift, hopefully not

too many discontinuities, but you're going to, you're going to drift over time. And I have to

have some mechanism for adapting to that as you an individual over time and across individuals

over time. On the topic of adaptive modeling and you talk about lifelong learning, which is a,

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 lifelong 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. Do you have any ideas 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,

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 overengineering 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.

It's, you still end up creating the task. You know, it's like looking for your keys where the

light is because that's where the light is, right? It's not because the keys have to be there.

I mean, one could argue that we tend to do this in general. We tend to kind of do it as a group.

We tend to hill climb and get stuck in local optima. And I think we do this in the small,

the small as well. I think it's very hard to do because, so, look, here's the hard thing about

AI, right? The hard thing about AI is it keeps changing on us, right? You know, what is AI?

AI is the, you know, the art and science of making computers act the way they do in the movies,

right? That's what it is, right? That's a good definition.

But beyond that, it's... And they keep coming up with new movies.

Yes. 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 by two and then you

average it out over time in the window. So, therefore, that's no longer AI.

So, the problem is unsolvable because it keeps kind of going away. This creates a kind of

illusion, which I don't think is an entire illusion, of either there's very simple task

based things you can do very well than over engineer. 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 it, myself included. I'm not going to

pretend that I'm better at this than anyone else. And of course, all the incentives in academia

and in industry are set to make that very hard because you have to get the next paper out.

You have to get the next product out. You have to solve this problem and it's very sort of

naturally incremental. And none of the incentives are set up to allow you to take a huge risk

unless you're already so well established you can take that big risk. And if you're that well

established that you can take that big risk, then you've probably spent much of your career

taking these little risks, relatively speaking. And so, you have got a lifetime of experience

telling you not to take that particular big risk, right? So, the whole system's set up to make

progress very slow. That's fine. It's just the way it is. But it does make this gap seem really big,

which is my long way of saying, I don't have a great answer to it, except that stop doing

n equals one. At least try to get n equal two and maybe n equals 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

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

its learning algorithm say can learn, what data structure it can build and how it can go from

there. Without that, you're going to be stuck all the moment. What do you think about the

possibility of n equals one growing is probably a crude approximation, but growing like if you

look at language models like GPT3, if you just make it big enough, it'll swallow the world,

meaning like it'll solve all your t to infinity by just growing in size of this, taking the small

overengineered solution and just pumping it full of steroids in terms of compute, in terms of

size of training data and the Yanlacoon style self supervised or open AI 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 of that by watching YouTube videos. I mean, I can't think of a more terrifying

world to live in than a world that is 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 problem. Here's the thing. We build these things and we say we want

them to learn, but what actually happens, and let's say they do learn, I mean, certainly every

paper I've gotten published, the things learn, I don't know about anyone else, but they actually

change us. We react to it differently, so we keep redefining what it means to be successful,

both in the negative in the case, but also in the positive in that, oh, well, this is an accomplishment.

I'll give you an example, which is like the one you just described with YouTube. 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, the 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, we'll treat everything as a vector and we'll do these vector

space models and whatever, and that was all great. We made very little progress. I mean,

we made some progress. 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, 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. We'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 web page,

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, you know, 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 in form for Google Maps. I don't think I've ever found this place.

Where is this? Like in 95, I see 110 and I see, but where did 95 go? You know, so I'm very grateful

for Google, but you know, 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

topic here, but think about the way we hire faculty. Exactly the same thing.

How are you getting controversial? How are you getting 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 are my

alma mater. Both are fighting words. I'm just saying I appreciate all that they did and all that

they're doing. Anyway, so we're going to try to hire the best professor. That's what we say,

the best person for 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,

for which we have, which is the most recent year for which I have data.

Maybe a large percentage. It's about 60%. 60%. 60% of the faculty in the top four earn their

PhDs in top four. This is computer science for which there is no top five. There's only a top

four, right? Because they're all tied for one. For people who don't know, by the way, that would be

MIT stand for Berkeley CMU. Yep. Georgia Tech. Number eight. Number eight. You're keeping track.

Oh, yes. It's a large part of my job. Number five is Illinois. Number six is a tie with

UW 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 what percentage of faculty in the top 10 earn 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. Yeah. Now, that's not a negative. I mean, it is a negative. It also has

a habit of entrenching certain historical inequities and accidents. 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, there was a saying that nobody ever lost his job

buying a computer from IBM. It was true. Nobody ever lost their job hiring a PhD from MIT.

If the person turned out to be terrible, well, 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. So all the incentives, particularly because

you're only going to hire one this year, well, now we're hiring 10, but you're only going to hire 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, stuck with them for 30 years unless they decide to leave. That means

the pressure to get this right is very high. So what are you going to 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, wasn't 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 a hundred 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 link 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 of computer science probably has those department,

those kinds of people, like you have the Russian guy, Grigory Perlman,

like just these awkward, strange minds that don't know how to play the little game of etiquette 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 maybe actually just says a few every once in a while 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 risk, right? A small amount of, 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,

it's a search problem. If we were treating it that way, you would say, oh, well, the main thing is,

you know, you've got a prior, you want some data because I'm Bayesian, if you don't 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,

you know, injecting randomness in the system at that level for who you hire is just not,

not worth doing because the price is too high or the cost is too high. We had infinite resources,

sure, 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 grant it,

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, back another level. But let me tell you something

else, you know, I did a sort of study, I call it a study, I call the pay to my friends and ask

them for all of their data for graduate admissions. But then someone else followed up and did an

actual study. And 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 admit

most people from places ranked around you, and you admit almost no one from places ranked below

you, with the exception of the small liberal arts colleges that aren't ranked at all, like Harvey

Mudd, because they don't, they're a PhD, so they aren't ranked. This is all CS. Which means the

decision of whether, you know, you become a professor at Cornell was determined when you

were 17, right? By where, what you knew to go to undergrad to do whatever, right? So if we can push

these things back a little bit and just make the pool 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 formula for determining rankings, move entire universities

to behave differently, because rankings matter. Can you talk trash about those rankings for a

second? No, I'm joking about talking trash. I actually, it's so funny how from my perspective,

from a 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 or are we going to talk about university?

Computer science, US News is not the main one. The only one that matters is US News,

nothing that matters. Sorry, CSRankings.org, but 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, it's not really department, whatever, at each

department basically rank everybody, slightly more complicated than that, but whatever, they

rank everyone. And then those things are put together and then somehow rankings come up.

How do you improve reputation? How do you move up and down the space of reputation?

Yes, that's exactly the question. Twitter? It can help. I can tell you how Georgia Tech did it,

or at least how I think Georgia Tech did it, 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 retired for ninth because that's how rankings work. And we moved from nine to eight

because our raw score moved up a point. So, Georgia, something about Georgia Tech, computer science,

or computing, anyway. I think it's because we have shown leadership at every crisis level.

So, we created a college, first public university to do it, second college, second university to

do it 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 on 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. Beyond that, it's just inertia

and there's huge hysteresis in the system, right? 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, right? 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. It's not surprising.

I don't think it's wrong. Yeah, but it's almost like a narrative. It doesn't actually have to be

backed by reality and it's not to say anything about MIT, but it does feel like we're playing

in the space of narratives, not the space of something grounded. And 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 they're the same people as I've met other places.

I mean, what MIT has going for it? Well, MIT has many things going for it. One of the things MIT

has going for it is a nice logo. It's a lot better than it was when I was here. Nice colors too.

Terrible, terrible name for a 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 just kind of, it's a sort of positive feedback.

But you said something earlier, which I think is worth examining for a moment, right? And you said

it's, I forget the word G 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 why we do this. Someone

once asked 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

on something, but do you know that cameras, which are just reflecting light and putting them on film,

like did not work for dark skinned people until like the 1970s? You know why? Because you were

building cameras for the people who were going to buy cameras, who all, at least in the United

States and Western Europe, were relatively light skinned. Turns out, 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 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 are 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 tradeoffs, 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 agglomerate wants you,

demands that you do something different and it's going to involve all kinds of money for you,

suddenly the tradeoffs 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 even the objective

isn't objective because at the end you've got to tell a story, you've got to make decisions,

you've got to make tradeoff or what else 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, this idea, why is there not more people who just like play

with whatever that narrative is, have fun with it, have like excite the world,

whether it's in the Carl Sagan style of like that calm, sexy voice of 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 from a Barrett, who's

a neuroscientist who just enjoys playing the controversy, 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? And why does an academia attract an Elon Musk type?

Well, tenure is a powerful thing that allows you to do whatever 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 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? 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 this. Everyone's not good at it. And everyone,

even if they would be good at it, 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 20 years ago,

and if it was, it wasn't around in a meaningful way. I don't actually know how long Twitter's

been around. As I get older, I find that my notion of time has 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. There is a kind of

force that pulls you back. 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 is popular at being a good communicator, exciting the world

with their science. And of course, when you excite the world with the science, it's not

peer reviewed, clean. It all sounds like bullshit. It's like a TED talk. And people roll their eyes

and 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's just kind of, it's hard to stand out unless you

like win a Nobel Prize or whatever. It's only when you 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.

There's not an FU money switch where 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 that, 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,

but we have a habit of belittling those who are popular, as it were, as if the word itself is a

kind of scarlet A, right? 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 and ethos 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 and are touching the world and making a difference,

our colleagues do, in fact, appreciate that in the long run. It's just 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 really interested

in how Rod Brooks felt about how people were interacting with him when he did 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. 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 Intelligence Laboratory at

MIT and then launched, I think, iRobot and then Think Robotics, Rethink Robotics.

Think is in the word and also is a little bit of a rock star personality in the AI world,

a very opinionated, very intelligent. Anyway, sorry, mole rats and naked.

Naked mole rats. Also, he was one of my two advisors for my PhD.

This explains a lot.

I love Rod. But I also live with my other advisor, Paul. Paul, if you're listening,

I love you too. Both very, very different people.

Paul Veol.

Paul Veol. 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, because I was a student at the time, it was amazing, right? This guy was 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 he running the iLab at that point or

not? I don't know. But anyway, he's running the iLab 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 me. These are amazing things.

And continues to do 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 on thin ice, because I grew up in the Soviet Union,

Russia, my knowledge of music. This American thing you guys do is quite foreign. So your

research group is called, as we've talked about, the Lab for Interactive Artificial Intelligence.

But also, there's just a bunch of mystery around this. My research fails me. Also called PFUNC.

Yep. P stands for probabilistic. And what does FUNC 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 PFUNC is an

acronym. It has three or four different meanings. 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, when they get out, as we say at Tech,

I hand them, they put on a hat and starglasses and a medallion from the PFUNC 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 FUNC. You have a dark mysterious past.

Oh, it's not dark. It's just fun. It's in hip hop and FUNC. So can you educate

a Soviet born Russian about this thing called hip hop? Like if you were to give me, you know,

if we went on a journey together and you were trying to educate me about, especially the past

couple of decades in the 90s about hip hop or FUNC, what records or artists 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 do you listen 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,

no matter how old they are or where they live. But for me, the first thing that's

worth pointing out is that hip hop and rap aren't the same thing. So depending on what you talk

to about this, and there are people who feel very strongly about this, much more strongly.

You're offending everybody in this conversation. So this is great. Let's keep going.

Hip hop is a culture. It's 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. 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 touched 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. Skinner. Oh my God. I would probably start with

a supplement. There's a fascinating exercise. 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 was the producer of that.

And he did a lot of very kind of heavy mental ish, at least in the 80s sense at the time.

And it was focused on politics in the 1980s, which was what made hip hop so great then.

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 is amazing. He hosted a poetry jam thing on HBO or something like that.

Probably. I don't think I've seen it, but I wouldn't be surprised. Yeah. Spoken poetry.

Spoken poetry. He's amazing. He's amazing. And then I would, 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'd take you back to the past. Because I think a large part of people who are kind of

confused about any kind of music, the truth is this is the same thing we've always been talking

about. It's about narrative and being a part of something and being immersed in something,

so 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 that nobody else

understands, right? And it becomes a part of hip hop's the same way. Everything's the same way.

They're all cultural artifacts. 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 some of it is good. But I'd start you with, it takes a nation to make as a whole as back.

There's an interesting tradition and like more modern hip hop of integrating almost like classic

croc 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 classic croc, but what is it,

the Kanye Gold Digger, the old R&B? Taking and pulling old R&B, right?

Well, that's been true since the beginning. I mean, 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. If you go back, you go back to those days,

mostly in New York, though not exclusively, but mostly in New York where it sort of came out of,

as the DJ that brought all the music together in the beats and showed that basically

music is itself an instrument, very meta. You can bring it together and then you sort of wrap

over it and so on. It moved that way. That's going way, way back. Now, in the period of time

where I grew up, when I became really into it, which was most of the 80s, it was more funk,

was the back for a lot of the stuff, public enemy at that time notwithstanding,

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. And by the way, complete revival of George Clinton and Parliament

and Funkadelic 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 going to see 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,

perhaps from somewhere else altogether. And you just didn't realize what it was because it wasn't

a popular song where you happened 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. Run DMC, Jam Master Jay used to play, he played

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. Those are, that's a whole.

Yeah. But still, it's the right attitude. And what else is jazz, right? Jazz is about

putting pieces together and then putting your own spin on. It's all the same. It's all the same

thing. It's all the same thing. Because you mentioned lyrics. It does make me sad. Again,

this is me talking trash about modern hip hop. I haven't, invest again. 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 that 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 know, the kind of like

protest songs, even if you look at like a Bob Marley or you said public enemy or rage against

the machine more on the rock side. 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. It's 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 at 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 in, you read my reviews, which I recently was reading. Man, 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 don't always, but I 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, someone points me to or I discover

some underground hip hop song and I'm 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, 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 who do man blues, Junior Wells. And just listen to snatch it back and hold it.

And you'll see it. And they were contemporaries. Where do you put like Little Richard or all

that kind of stuff like Ray Charles, 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 a line.

I mean, it's all, there's all a line that carries it all together. You know, it's,

I guess I have an answer question. 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's, 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 use 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 to get there. Actually, some of the disco is actually really, really good.

John Travolta. Oh boy. He regrets it probably. Maybe not. Well, like it's the mistakes thing.

Yes. And it got him to where he's going, where he is. Oh, well, thank you for taking that detour.

You've, you've talked about computing where 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. What, what should people,

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. The world is, right? I,

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 answering a slightly different question first

than getting back to this, which is, you know, you talked about disciplines and what does it mean to,

to be a discipline. The truth is what we really educate people in from the beginning,

but certainly through college, you'd 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, the, the hammer that you pick up whatever

that is to hit the nail, not just the, 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 to give you different ways

of sort of thinking through. So with that in mind, I think that computing, even ask the question,

whether it's a discipline, is you have to decide, does it have a mindset? Does it have a way of

thinking about the world that is different from, you know, 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, you know, building demonstrably superior

technology with respect to some notion of tradeoffs, whatever that means, right? That's sort of the

world that you live in. What is computing? You know, 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, you know, it's a little abstract, but that would be appropriate for computing.

I think that what distinguishes the computationalists from others is that he or 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's not the, it is mathematical in some sense,

in the kind of 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 the models fundamentally represent truths in the world. Information is

a scientific thing to discover, right? Not just a mathematical conceit 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 actually run. But it's also math because you're actually

worrying about these languages that describe what's happening. But the fact that regular

expressions and finite state automata, one of which feels like a machine or at least an abstraction

machine, the other is a 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 do debugging. So that idea, I think, is fundamental. And we would do better if we made

that more explicit. How my life has changed in 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 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 or 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 and 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 it to change 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, these human things into that, into the idea, into this framework of computing?

Do you pull in psychology, 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 is 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 and machine learning, but it's just even the way that we

think about, 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 important thing in the world. It's not more important. It is just key

to helping others do other cool things they're going to do. You're not going to be a historian

in 2030. You're not going to get a PhD 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 a system that's going to help you to 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 is true for psychology, same true for 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 you say this. I want to say it's

going to metastasize, 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, I'm going to teach Fortran

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 mathematician's job is to help this person over here to think like a mathematician

much the way the point of you taking chemistry as a freshman is not to learn chemistry. It's to

learn to think like a scientist. Our job is to help them to 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 who aren't going to be on me, 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 Hardcore History. Yes. I've just did an amazing four hour conversation with him,

mostly about 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

are 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, blah, so on.

But most people are able 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, that 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,

you know, 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 the, you know, 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 we can lose track, how we can lose these fields when they're

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. And I would say computing is quite obviously similar to history

in the 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 it, depending if it's machine

learning, you know, it's unclear that it'll, computing will win out. It's currently very

successful, but it's not, I would say that's something, I mean, you're at the leadership

level of this, you're defining the future. So it's in your hands. No pressure. But like,

it feels 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, as T approaches infinity, the amount of history

is presumably also growing at least linearly. And so it's, you have to forget more and more

of history. But history needs to always be there. I mean, I can imagine a world where,

you know, if you think of your brains as being 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 a 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,

those who do not remember. It's for our objective camera discussion, right?

Yeah. 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, or 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. 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 at your 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,

and being able to describe a problem in such a way that it is executable, which we are not,

human 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

it 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 AI is

that we forget that we are basically doing software engineering. We forget that we are using programming.

We're using languages to express what we're doing. We get just all caught up in the deep

network or we get all caught up in whatever that we forget that, you know, 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, at the end of the day, it's a way of surfacing your assumptions.

I mean, we call them parameters or, you know, we call them if then statements or whatever,

but you're forced to surface those assumptions. That's the key. The key thing that you should

get out of a computing education, that and that the models and languages and the machines are

equivalent. But it actually follows from that that you have to be explicit about what it is

you're trying to do because the model you're 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 insofar 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

my take on it. 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 this time in America. You grew up in Atlanta, not born in Atlanta. Is some

southern state somewhere in Tennessee, something like that? Tennessee. Nice. Okay. But early on,

you moved, you're basically, you identify as an Atlanta native. Yeah. And you've mentioned that

you grew up in a predominantly black neighborhood. By the way, black African American personal color.

I prefer black. Black. With a capital B. With a capital B. The other letters are.

The rest of them. Okay. So the predominantly black neighborhood. And so you didn't almost see

race. Maybe you can correct me on that. And then 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. 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 distractions

in between my bachelor's and my master's and PhD. You didn't go on a backpacking trip in Europe?

Didn't do any of that. In fact, 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 this. You miss MIT? Oh, I loved MIT. I don't miss Boston at all, but I loved MIT.

And that was fighting war. So let's back up to this. So as you said, I was born in Chattanooga,

Tennessee. My earliest memory is arriving in Atlanta in a moving truck at the age of three

and a half. So I think of myself as being from Atlanta. I'm a very distinct memory of that.

So I grew up in Atlanta. It's the 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 and 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's little 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? Between the two. Did your parents know about this? Yes,

but being a girlfriend and boyfriend in first grade just basically meant that you spent slightly

more time together during recess. It had no, I think we Eskimo kissed once. It doesn't mean,

it didn't mean anything. It was at the time, it felt very scandalous because everyone was watching.

I was like, ah, my life is, now my life has 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.

Okay. That 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's that I wasn't aware that I was a 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 in first grade. The world is the seven people I see every day. So it didn't feel that way at all.

And by the way, this being Atlanta, home of 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,

the Monica Kaufman was on TV telling me the 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 it occurred to me.

And then of course, it continued that way for, well, for the rest of my, for much of the rest

of my career at Georgia Tech. 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. I didn't know any better numbers. It 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 I was,

MIT and CMU. When I got in to MIT, I got into 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, which I developed a strong opinion based upon

his strong opinion, something about the sun coming out two days out of the year. And I

didn't get a chance to go there because the timing was wrong. I think it was because the timing was

wrong. At 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 stats told me why. 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 why people tell you about the nice coffee shops. Well, when coffee shops,

it was used 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 a bit interested in. The outdoors is for the bugs.

It's not for humans. That should be a t shirt. Yeah, I mean, it's the way I feel about it.

And the black folk told me completely different stories 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 not only be there for like four or five years, I told myself,

which turned 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 look 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 haven't been here a long time. These are

the things that I remember. So this is 1990. There was not a single black radio station.

Now this is 1990. There aren't, 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 and almost no one does.

At least if you're under a certain age. But the idea is you could 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 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, but large parts of the city. You know, where you had concentrated 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 an amazing, it was an amazing experience. But when I went to ninth grade, I

went to academy. Math and science academy at Mays High. It was a public school. It was 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 whom 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. Dropping names. You know, I just dropped names occasionally. You know,

drop the mic, drop some names. Just to let you know, I used to hang out with Michael Jackson's

cousin, 12 cousin, nine guys removed. I don't know. The point is, they had my, we had a parking

problem because the kids had cars. I did not come from a place where you had cars. I had my

first car when I came to MIT actually. So it was, it was just a, it was just a very, very

different experience for me. But I had been to places where whether you were rich or whether

you were poor, you know, you could be black and rich or 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 of the country.

Yeah.

You really felt that way. But it also felt like the most racist place I ever spent any time.

You know, 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.

Southern racism or explicit racism?

Both.

In terms of within the institutions, did you feel, was, okay, was there levels in which you

were empowered to be first or one of the first black people in a particular discipline in

in some of these great institutions that you were part of, Georgia Tech or MIT?

And was there a 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 the distinct impression that people wanted me to succeed.

By people, I meant the people in power. Not my fellow students. Not that 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 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,

you know, 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. So, you know, 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 iLab, but I wasn't the only person in MIT by a long shot. On the other hand, we were 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 to computer science, right? So, it was a sort of interesting time. But I

did 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 that I didn't know them, who wanted to make certain that the right thing happened.

That's 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 supportive.

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 little forces that you

are able to observe around you that are 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 deluded form, all that kind of

stuff, and just kind of see yourself as empowered and confident in 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 of the oncoming train. 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 here. 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 that I could always lean back on, even if it were a long distance call

that 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 was 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 the story of being pulled over. Perhaps it happened

more than once. More than once, for sure. One, could you tell that story? And in general, can you

give me a sense of what the world looks like 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 I don't

either. I guess the answer is it looks exactly the way it looks now because this is the world that

we happen to live in, right? It's people clustering and doing the things that they do and making

decisions based on 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 behave in a certain way that makes it true and it goes on 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.

Congratulations, brother. Thank you. God, I have a few gray hairs here and there.

You 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 going to 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? 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 was,

when I got pulled over that time, halfway between Boston and Wellesley, actually. I remember thinking

as he, when 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 as opposed to the world 30 years ago, that not only 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'd done what he said and da, 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 or your history, that's terrible. And it would work. I

absolutely believe it would have worked had he done it. Now, he didn't. I don't think he wanted to

shoot me when he felt like killing your body. Did 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 anything 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 seem. But and you're basically like speeding or 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. But he pulled

a gun. Yeah. Apparently I moved too fast or something. Rolled my window down before I should

have. It'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 after that experience?

Well, I don't remember that guy. But my views on police officers is 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 family over.

Which is fine. I don't roast marshmallows.

Okay. So let me go a little darker. I apologize. Just talked to Dan Carlin about it over four

hours. So sorry if I go dark here a little bit. But is it easy for 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 that takes a lot of energy.

So one should reserve it for when it is useful and not carry it 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, right?

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. People would ask me to empathize with the police officer.

I would quote back statistics saying that being a police officer isn't even in the top 10 most

dangerous jobs in the United States, you're much more likely to be killed in a taxicab.

Half of police officers are actually killed 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 see the world. I think, though, that if we step back from

what I feel and 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 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 a 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 with that crazy

person over their beliefs? 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?

It's all structural. 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 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 an 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 everybody on a vision as opposed to divides on a vision,

which is this particular moment in history feels like there's a nonzero probability

if we go to the P of something akin to a violent or a nonviolent civil war.

This is one of the most divisive periods of American history in recent – you can speak to

this from 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,

I think, much is the quiet economic pain of millions that's growing because of COVID,

because of closed businesses, because of lost dreams. That's building, whatever that tension

is building. The other is there 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 – enough time has passed. I mean, the very first race riots were Boston,

not to draw anything. Really? When? Oh.

This is before – Going way – I mean, like the 1700s or whatever, right? I mean, there was a

massive one in New York. I mean, I'm talking way, way, way back when. So, Boston used to be the

hotbed of riots. It's just what Boston was all about. So, I'm told from history class.

There's an interesting one in New York. I remember when that was.

Anyway, the point is, basically, you got to get another generation, old enough to be angry,

but not so old to remember what happened the last time, right? Yeah.

And that's sort of what happens. But you said two completely – you said two things there that

I think are worth unpacking. One has to do with this sort of moment in time and why? Why is this

sort of up built? And the other has to do with the kind of – you sort of the economic reality

of COVID. So, I'm actually – I want to separate those things, because, for example,

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 and –

Okay, that's a mistake. Oh, I loved that one.

Okay. And history of Spanish. And Spanish history, actually. But Spanish and the other was what we

would now call cognitive science at the time. Oh, that's fascinating. Interesting.

I minored in COGSI here for grad school. That was really – that was really fascinating.

It was a very different experience from all the computer science classes I've been taking,

even the COGSI classes I was taking at an undergrad. But anyway, I'm not – I am a –

I'm interested in history, but I'm hardly historian, right?

Yes.

So, forgive my – I will ask the audience to forgive my simplification. But

I think the question that's always worth asking as opposed to – it's the same question,

but a little different. Not why now, but why not before, right? So, why the 1950s,

60s civil rights movement as opposed to 1930s, 1940s? Well, first off, there was a civil rights

movement in the 30s and 40s. It 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 go all the way back

and all the way back and all the way back. And Harriet Tubman was not born in 1950, right?

So, we can take these things –

It could have easily happened right after World War II.

Yes. I think – and again, I am not a scholar – I think that the big difference was TV.

These things are visible. People can see them. It's hard to avoid, right? The – 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, King's biggest failure was in the early days? It 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 – a cop, he was a sheriff, made a deal with the

whole state of Georgia. We're going to take people and we're going to nonviolently put them in trucks

and then we're going to take them and put them in jails very far away from here.

And we're going to do that. And we're not going to – there'll 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 Bullo Connor. And you got to see on TV little boys

and girls being hit with fire hoses and being knocked down. And there was outrage and things

changed, right? 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 right 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.

And of course, it did. And that caused its own set of problems, which went into the late 60s,

into the 70s. And in 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. 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 mean, 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.

A few difference. It makes it – it's fascinating to think that whatever the mechanisms that put

injustice in front of our face, not like – like directly 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 going to 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 news. I distinctly

remember watching this as a kid. Actually, I guess I wasn't a kid. I was in college at

2000 grad school at the time. So, that's not enough. Like just – just –

Well, it disappeared. Like a day like – it didn't go viral.

Yeah, whatever that is, whatever that magic thing is.

And whatever it was in 92. It was harder to go viral in 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 I'm like the only person. Sometimes

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 one thing caught and one thing didn't.

Maybe what's happening now is two things are feeding on one another. One is more people are

willing to believe. And the other is there's easier and easier ways to give evidence.

Cameras, body cams. But we're still finding ourselves telling the same story. It's the

same thing over and 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 eds. It's the same words,

over and over and over again. I mean, there's your remembering history right there. I mean,

it's like literally the same words. Like you could have just caught. I'm surprised no one got

flagged for plagiarism. 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. But only by a few years. People think of him as the older

statesman and they think of Malcolm X as the young, angry, whatever. But that's more of a

narrative device. It's not true at all. I reject the choice as I think it's a false choice. I

think they're just things that happen. You just do, as I said, hatred is not, it takes a lot of

energy. But you know, every once in a while, 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. It seems like over the top anchor works,

outrage works. So you can say being calm and rational and 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, it's certainly the way that you get someone to notice you.

Yeah. And if they don't notice you, I mean, what's the difference between that and what

a... Again, without taking a moral position on this, I'm just trying to observe history here. If you

maybe if television didn't exist, the 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. 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 said this in one form or another, 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 were 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 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, it has 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.

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 is all a metaphor. Faculty hiring is a metaphor

for all of life. Let me ask 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's going to be okay, because I just tend to think that everything's

going to be okay. My mother says something to me a lot and always has, and I find it quite

comforting, which is, this too shall pass. And this too shall pass. Now, this too shall 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 at 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 and went to grad school. This too shall 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 it'll be okay. What about humans? Do you think we'll live into the 21st century?

I certainly hope so. Are you worried that we might destroy ourselves with nuclear weapons,

with AGI, with the engineer? I'm not worried about AGI doing it. But I am worried. I mean,

at 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 Filipenko from Berkeley.

He was talking about comics 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 shall not pass. Well, maybe for Earth

they'll pass 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 in your life, you've also began the this week in black history calendar effects.

There's, 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, in history,

in black history that you draw a lot of philosophical or personal inspiration from?

Or 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,

I've, 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 black 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 a calendar, anything I could find,

Google didn't exist, right? And 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

Bannaker, 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 happening, 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, yeah. I mean, look, in our world, all we really

have is credit. I 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, no, 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 credit, you know, the credit with Jan. I talked to Jorian Schmidhuber, right?

The Turing 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'll be