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.
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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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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 sub-genres.
But I will tell you that of those genres, I'll pick one or two from
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 pop. 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.
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. So it's most of 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 you know, 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. It'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,
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 courses. One, two, or three.
One. It gets increasingly, I love them all for different reasons and increasingly more ridiculous.
Kind of like loving alien and aliens, despite the fact they're two completely different movies.
But the reason I put 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 explaining 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 is perfect.
And so that's number two. And then. But sorry to pause on the martial arts. You have a long
list of hobbies, like it scrolls off the page, but I didn't see martial arts 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. And I would be on board with that.
So like The Shower 2, like that kind of the comedy of its 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, I remember,
but I had no idea. What is this? That's what it was. And I didn't know that was Jackie Chan.
That was like his first major movie. Yeah. I was a kid. I was done in the 70s. I only later
rediscovered that it was actually. And he creates his own martial art by
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 scenes, I'm cheating. 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 in 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? Just kidding. 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 he did an experiment where
you tracked all kinds of information about yourself. And a few others, the 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 the 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 of all, it has to be the right two days. But I was thinking of
a very specific experiment. There's actually a suite of them that I've been a part of. And other
people have done this, of course, I just 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. But you know, what I'm about to look at a movie, I'm about to turn on the TV or whatever
and just see what I could predict from it. It was kind of surprising, it shouldn't have been.
But that's all very easy to do, by the way, just capturing all the little stuff. I mean,
it's a bunch of computer systems. It's really easy to capture the 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. And it turns out you can get 93% accuracy,
just by doing something very simple and stupid and just counting statistics.
But what's actually more interesting is that you could use that information. This comes up again
and again in my work. If you try to represent people or objects by the things they do, the things
you can measure about them that have to do with action in the world, so distribution over actions,
and you try to represent them by the distribution of actions that are done on them, then you do
a pretty good job of sort of understanding how people are and they cluster remarkably well.
In fact, irritatingly so. And so by clustering people this way, you can, maybe, you know,
I got the 93% accuracy of what's the next button you're going to press, but I can get 99% accuracy
or somewhere there's about on the collections of things you might press. And it turns out the
things that you might press are all related to number to each other in exactly the way that
you would expect. So for example, all the key, 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 just 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 90 some large percentage of our behaviors, whatever the signal we measure is
the same, and it would cluster nicely, but maybe it's the special moments of when we break out
of the routine is the definitive things. And the way we break out of that routine for each one
of us might be different. It's possible. I would say that that I would say it a little
differently. I think I would make two things. One is a, I'm going to disagree with the premise,
I think, but that's fine. I think the way I would put it is there are people who are very
different from lots of other people, but they're not 0%. They're closer to 10%. 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,
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, 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, 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're 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
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? There's 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 learn, you want to merge what they've done together,
it can be extremely hard because they don't, they basically don't speak the same language anymore.
Like when you create these problems and you dive into them, you create your own language.
So the example this one person gave me, which I found kind of interesting because we were in
the middle of that at the time, was they're sitting over there and they're talking about this,
these rooms that you can see, but you're seeing them from different vantage points,
depending upon which side of the room you're on. 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. And 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 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 nother 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, to experience, 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 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 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 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 must, the only way that you
can, to understand someone means you must agree with everything that they do, which isn't 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,
you know, the, if I think of this as distribution, right? Then, you know, I can take the cosine of
the angle between you. And if it's, you know, if it's zero, you've got nothing in common,
if it's one, you're completely the same person. Well, you know, you're probably not one,
you're almost certainly not zero. If 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, that step of empathy. It's not,
it's not impossible to do, although I wonder if it requires that everyone involved is at least
interested in asking the question. So maybe the hard part is just getting them interested in
asking the question. In fact, maybe if you can get them to ask the question, how are we more
like, then we are different, they'll solve it themselves. Maybe that's the problem that AI
should be working on, not telling you how you're similar or different, but just getting you to
decide that it's worthwhile asking the question. So it feels like an economist's answer, actually.
Well, people, okay, first of all, people like it 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, 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 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. I, you know, my experience is
it requires two things. It requires, in fact, maybe this is really at the end what you're
saying. And 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 the 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 on 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 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, you know, 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.
But so I'm a machine learning guy. I believe in data. I believe in statistics and all those
kind of things. And the reason I'm bringing that up is even though I'm a newfangled,
statistical machine learning guy and have been for a very long time, the problem I really care
about is AI. I care about artificial intelligence. I care about building some kind of intelligent
artifact. However that gets expressed, that would be at least as intelligent as humans
and as interesting as humans, perhaps on their sort of 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 long-term goal here, which I will probably
never live to see. But I would love to have been a part of, which is building something truly
intelligent outside of ourselves. Can we take a tiny tangent 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 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? 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, 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 engineering-ish 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 or somewhat different.
But to me, AI is about trying to build that intelligent thing. And typically, but not always,
for the purpose of understanding ourselves a little bit better. Machine learning is, I think,
trying to solve the problem, whatever that problem is. Now, that's my take. Others, of course, would
disagree. So on that note, so with the interactive AI, do you tend to, in your mind, visualize AI
as a singular system, or is it as a collective huge amount of systems interacting with each other?
Like, is the social interaction of us humans and of AI systems the fundamental to intelligence?
I think, well, it's certainly fundamental to our kind of intelligence, right? And I actually
think it matters quite a bit. So the reason the interactive AI part matters to me is because I
don't, this is going to sound simple, but I don't care whether a tree makes a sound when it falls
and there's no one around because I don't think it matters, right? If there's no observer in some
sense. And I think what's interesting about the way that we're intelligent is we're intelligent
with other people, right? Or other things anyway. And we go out of our way to make other things
intelligent. We're hardwired to, like, find intention even whether there is no intention
why we anthropomorphize everything. We, I think anyway, we, I think the interactive AI part is
being intelligent in and of myself in an isolation isn't 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 to get. 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 that 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.
That's not good. 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 is going to change. And by the way, it's not just
the case that you're different from that person, but you're different from the person you were
15 minutes ago or certainly 15 years ago. And I have to assume that you're at least going to drift,
hopefully not too many discontinuities, but you're going to drift over time. And
I have to have some mechanism for adapting to that as you and 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, by the way, not only does the machine learning community not spend a lot of time
on lifelong learning. I don't think they spend a lot of time on learning period in the sense
that they tend to be very task-focused. Everybody is overfitting to whatever problem is they
happen to have. They're over-engineering their solutions to the task. Even the people, and I
think these people do, are trying to solve a hard problem of transfer learning, right? I'm going to
learn on one task and learn the other task. You still end up creating the task. 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 do it as a group. We tend to hill climb and get stuck in local optima. I think
we do this in the small as well. I think it's very hard to do. Here's the hard thing about AI.
The hard thing about AI is it keeps changing on us, right? What is AI? AI is 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, and they just write 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 derivative and you divide by two, and then you
average it out over time in the window. 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 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. So the whole system
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 equal seven
so that you can say, 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 crude approximation,
but growing, like if you look at language models like GPT three, if you just make it big enough,
it'll swallow the world, meaning like it'll solve all your tea 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, you know, 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, right? We react to it differently, right? 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 was when it came out. Anyway, it's, you know,
we'll treat everything as a vector and we'll do these vector space models and whatever,
and that was all great. And we made very little progress. I mean, we made some progress.
And then Google comes and makes the ad hoc problem seem pretty easy. I mean, it's not,
there's lots of computers and databases involved, but, you know, and there's some brilliant
algorithmic stuff behind it too, and some systems building. But the problem changed, right?
If you've got a world that's that connected so that you have, you know,
there are 10 million answers quite literally to the question that you're asking, then the problem
wasn't give me the things that are relevant. The problem is don't give me anything that's
irrelevant, at least in the first page, because nothing else matters. So Google is not solving
the information retrieval problem, at least not on this web page. Google is minimizing false
positives, which is not the same thing as getting an answer. It turns out it's good enough for what
it is we want to use Google for. But it also changes what the problem was we thought we were trying
to solve in the first place. You thought you were trying to find an answer, but you're not,
or you're trying to find the answer. But it turns out you're just trying to find an answer.
Now, yes, it is true. It was 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 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 way.
Where is this?
Like in 95, I see 110 and I see, but where did 95 go? I'm very grateful for Google,
but they just have to make certain the first five things are right.
And everything after that is wrong. Look, we're going off into totally different
topic here, but think about the way we hire faculty. It's exactly the same thing.
Oh, you're getting controversial.
I'm 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 found at 30 years after
the college of computing I'm a part of. Both of my alma mater, both of them 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, which is the most recent year for which I have data?
Maybe a large percentage.
It's about 60 percent.
60 percent of the faculty in the top four earn their PhDs in the 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 Stanford, 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 percent.
If you look at the top 55 ranked departments, 50 percent of the faculty earn their PhDs from
the top 10. There is no universe in which all the best faculty, even just for R1 universities,
the majority of them come from 10 places.
There's just no way that's true, especially when you consider how small
some of those universities are in terms of the number of PhDs they produce.
Now, that's not a negative. I mean, it is a negative.
It also has a habit of 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. 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, you're 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 100 bazillion wrong answers, just make certain
the wrong answers don't get up there. And who cares if you, the right answer was actually the
13th page. A right answer, a satisfying answer is number one, two, three or four. So who cares?
Or an answer that will make you discover something beautiful, profound to your question.
Well, that's a different problem, right? But isn't that the problem? Can we 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 a truly special person? Like there's, I mean, it depends on the department.
Computer science probably has those department, those kinds of people like you have the Russian
guy, Grigori Perlman, like just these awkward, strange minds that don't know how to play the
little game of etiquette that, that faculty have all agreed somehow like converged over the decades,
how to play with each other. And also is not, you know, on top of that is not from the top four,
top whatever numbers, the schools and, and, and maybe actually just says a few every once in a
while to the, to the traditions of old within the computer science community, maybe talks trash
about machine learning is a total waste of time. And that's there on the resume. So like,
how do you allow the system to give those folks a chance?
Well, you have to be willing to take a certain kind of, without taking a particular position
on any particular person, you'd have to take, you have to be willing to take 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 want, 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 pools so
the probabilities go up, that's a way of injecting a little bit of uniform, uniform noise in the
system as it were, is 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, U.S. 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 idea that those rankings matter. I don't think anyone knows what they're,
most people don't know what they're based on. What are they exactly based on and what are
the flaws in that? 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, U.S. News is not
the main one. Yeah, it's U.S. News. The only one that matters is U.S. News. Nothing that matters.
Sorry, CSRankings.org, but nothing else matters, but U.S. News. U.S. News has formula that it uses
for many things, but not for computer science, because computer science is considered a science,
which is absurd. The rankings for computer science is 100% reputation. Two people
at each department, it's not really department, but 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.
So that means how do you improve reputation? How do you move up and down the space of reputation?
Yes, that's exactly the question. 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 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 college, first public university to do it, 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 anyway 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. That is what
reputation is. 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. There's huge hysteresis in the system. I can't remember
this as maybe apocryphal, but there's a major or department that MIT was ranked number one in,
and they didn't have it. It's just about what you... I don't know if that's true,
but someone said that to me anyway. But it's a thing. 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. 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. 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, they're the same people as I've met other places.
I mean, what MIT is going for... Well, MIT has many things going for it. One of the things MIT
has going for it is... Nice logo. It's a nice logo. It's a lot better than it was when I was here.
Nice colors too. Terrible, terrible name for 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 loop. 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 what we do. 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 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 the 1970s? Do 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 trade-offs, right?
So, I can either worry about this part of the spectrum or this part of the spectrum. This
costs more, that costs less, this costs the same, but I have more people paying money over here,
right? And it turns out that if a jack... 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 trade-offs
change, right? And so, there you go. I actually don't know how I ended up there. Oh, it's because
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 trade-off,
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 idea, why is there not more people who just play with whatever
that narrative is, have fun with it, have 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, the film, and Barrett, who's a neuroscientist who
just enjoys playing the controversy, things like 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's 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 like get senior enough, we just stop
giving a damn. But just like you said, even when you get tenure, that was always the surprising
thing to me. I have many colleagues and friends who have gotten tenure, but there's not a switch.
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, 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 Vio. Paul Vio. Both very
interesting people, very different in many ways. But I don't know what Rod would say to you
about what the reaction was. I know that for the students at the time, 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 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 was running the iLab or
would be soon. He was 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
the 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 star glasses 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 as 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 is 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 how you talk to
about this, and there are people who feel very strongly about this, much more strongly than
I do. You're offending everybody in this conversation. So this is great. Let's keep going.
Hip hop is a culture. Yeah. 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 that 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 liked and then I would work you there. Skinner.
Oh my God. Well, then I would probably start with...
Let's supplement.
There's a fascinating exercise. One can do by watching old episodes of I love the 70s,
I love the 80s, I love the 90s with a bunch of friends and just see where people come in
and out of pop culture. So if you're talking about those people, then I would actually start you with
where I would hope to start you with anyway, which is public enemy. Particularly it takes a
nation of millions to hold us back, which is clearly the best album ever produced and certainly
the best hip hop album ever produced in part because it was so much of what was great about
the time. Fantastic lyrics. Excuse me. It's all about the lyrics. Amazing music that was coming
from Rick Rubin was the producer of that and he did a lot, very kind of heavy metal-ish,
at least in the 80s 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.
Yeah. He's amazing. 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 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. 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. 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 and it becomes a part of hip-hop 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 more modern hip-hop of integrating almost like
classic rock songs or whatever, like integrating into their music, into the beat, into the whatever.
It's kind of interesting. It gives a whole new, not just classic rock, 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 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. And you can bring it together and then you sort of
wrap over it and so on. And it sort of, it moved that way. So that's going way, way back. Now,
in the period of time where I grew up, when I became really into it, which was most of the 80s,
it was more funk was the back for a lot of the stuff, public enemy at that time, notwithstanding.
And so, which is very nice because it tied into what my parents listened to and what I vaguely
remember listening to when I was very small. 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, I mean, the roots are doing their own
thing. Yeah. Those are, that's a whole. Yeah. But still, it's the right attitude that, you know,
and what else is jazz, right? Jazz is about putting pieces together and then putting your
own spin on, right? It's all the same. It's all the same thing. It's all the same thing.
You know, because you mentioned lyrics, it does make me sad. Again, this is me talking trash
about modern hip hop. I haven't, you know, 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 where you said public enemy or rage against the machine more on the
rock side. There's, that's the place where we go to those lyrics. Like classic rock is all about
like my woman left me or, or I'm really happy that she's still with me or the flip side. 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 and you read my reviews,
which I recently was reading, man, fuck, 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. And 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? Ah, Junior Wells, Chicago Blues. He was James Brown before James Brown was.
It's hard to imagine somebody being James Brown. Go look up Hoodoo 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 would answer your 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 rid of it now. 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. Yeah. 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. 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. It's not even the world.
Right. I, 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, 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 trade-offs, 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 are the same thing.
And because it's not just a model, but it's a machine that is an executable thing that can be
described as a language, that means that it's dynamic. So it is mathematical in some sense,
in the 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, 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
a 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 that what's changed
about computing, computer science, whatever you want to call it, is we now have so much data
and so much computational power. We're able to do really, really interesting, promising things.
But the interesting and the promising kind of only matters with respect to human beings
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, like these human things into that, into the idea, into this framework
of computing? Do you pull in psychology and neuroscience, like parts of psychology, parts of
neuroscience, parts of sociology? What about philosophy, like studies of human nature from
different perspectives? Absolutely. And by the way, it works both ways. So let's take biology for a
moment. It turns out a cell 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, the cell is a bunch of if-then statements or whatever allows the computationalists to
think differently about the language in the way that we, well, certainly the way we would do AI,
machine learning, but there'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, you know, 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, you know, this person over here to think like a mathematician, much the way the
point isn't, the point of you taking chemistry as a freshman is not to learn chemistry. It's to
learn to think like a scientist, right? Our job is to help them to think, think like a computational
ist. 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 major, 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, 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, it's like the theoretical computer
science part of computer science. It becomes a niche thing that only like the rare holders of the
world wars and all the history, the founding of the United States, all those kinds of things,
the civil wars. And it's a kind of profound thing to think about how 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.
Now 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 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, 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 doing,
we are using programming. Like, we're using languages to express what we're doing. Like,
it's just so 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 will also make them better citizens. It will 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 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. Black. Black.
With a capital B. With a capital. 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 what just in the video you talked about when you showed up to Georgia Tech for your
undergrad, you were 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.
And by the way, that story continues through MIT as well. In fact, it was quite a bit more stark
and 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. Didn't do it. 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'd
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.
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 and I'm 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? 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 me,
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.
I would come during puberty when I wrote lots and lots of poetry. Anyway, so I was aware of it.
I didn't think too much about it, but I was aware of it. But I was surrounded. It wasn't
that I wasn't aware of race. It's that I wasn't aware that I was a minority. It was 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 than, because I 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, 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, why did. 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 CD, 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.
The outdoors is for the bugs. It's not for humans. And the... 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 I don't listen to the radio anymore and almost no one does, at least if you're under
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'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, 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 experience. But when I went to ninth grade, I went to academy.
Matt Mathen. Matten Science Academy, 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 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. It was an upper middle class, I just dropped names occasionally.
Dropped the mic, dropped some names, just to let you know, I used to hang out with Michael Jackson's
cousin. The 12th cousin, nine times removed. I don't know. The point is, 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. It was just a very different experience for me. But
I had been to places where whether you were rich or whether you were poor, you could be
black and rich or black and poor. 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. It felt like a bunch of a really interesting contradiction. It felt like it was
the interracial dating capital of the country. It really felt that way. But it also felt like
the most racist place I ever spent any time. You couldn't go up the orange line at that time.
I mean, again, that was 30 years ago. I don't know what it's like now. But there were places you
couldn't go. And you knew it. Everybody knew it. And there were places you couldn't live,
and everybody knew that. And that was just the greater Boston area in 1992.
Saddle racism or explicit racism? Both. So in terms of within the institutions, was there
levels in which you were empowered to be first or one of the first black people in a particular
discipline in some of these great institutions that you were part of? Georgia Tech or MIT?
And was there a part where it 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 it not feel as if no one was trying to stop me. I had the distinct impression that
people wanted me to succeed. My 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. You're very early, at least in computer science, you're very early in
the sort of Jackie Robinson period. There's this thing called the Jackie Robinson syndrome,
which is that you have to... The first one has to be perfect or has to be sure to succeed because
if that person fails, no one else comes after for a long time. So 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... I was the only person
in the iLab, but I wasn't the only person in MIT by a long shot. On the other hand, we're what?
At that point, we would have been what, less than 20 years away from the first Black PhD
to graduate from MIT, Shirley Jackson, 1971, something like that, somewhere around then.
So we weren't that far away from the first first. And we were still another eight years away from
the first Black PhD computer science. 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 your own
eyes, 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 and it's pure form, whether it's just hatred and
it's like deluded form, all that kind of stuff and just kind of see yourself as empowered and
confident and all those kinds of things. I mean, it certainly helps. But there's a trade-off,
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 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, right? So it's all a trade-off,
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. 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 the time when I was,
when I got pulled over that time, halfway between Boston and Wellesley actually,
I remember thinking, 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, then not only would we get away with it, but that I would be painted a villain. That was
probably big and scary and I probably moved too fast and when they had done what he said and da,
da, da, da, da, da, da, da, da, which is somehow worse, right? 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 anybody. He 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. And you're basically
like speeding or something like that? He said 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 a 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 an 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 carried around with you all the time.
Again, there's a big difference between the happy delusion that convinces you that you can
actually get out of bed and make it to work today without getting hit by a car. And the
sad delusion that means you can not worry about this car that is barreling towards you, 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 in all this because all things tie back into interactive AR.
Why? 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 non-zero 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 a perhaps a more knowledgeable and deeper perspective than me, but from my naive
perspective, this seems like a very strange time. There's a lot of anger and it has to do with
people – I mean, for many reasons. One, the thing that's not spoken about, I think,
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 in Boston,
not to draw anything. Really? When? Oh.
This is before – Going way – I mean, like the 1700s or whatever. 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 don't 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 – 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 what 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? 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 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 bright
was that it was too public to ignore. Now, we created other ways of ignoring it.
Lots of change happened in the South, but part of that delusion was that it wasn't going to
affect the West or the Northeast. Of course, it did, and that caused its own set of problems,
which went into the late 60s and into the 70s, and 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. Like, I distinctly remember watching
this as a kid. Actually, I guess I was in a kid. I was in college. I was in 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, right?
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, man. 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. Yeah, cameras, body cams are running. 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-ed. 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,
but 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. You can say like 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, 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, right? That,
you know, 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, 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 engineering? I'm not worried about AGI doing it, but I am worried. I mean,
at any given moment, right? Also, 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
it'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 this week in Black
History Calendar of facts. There's like a million questions that can ask here. You said you're not
a historian, but let's start at the big history question of, is there somebody in history,
in Black history, that you draw a lot of philosophical or personal inspiration from,
or you just find interesting, or a moment in history you find interesting?
Well, I find the entirety of the 40s to the 60s and the civil rights movement that didn't happen
and did happen at the same time during then quite inspirational. I mean, I've read quite a bit of
the time period, at least I did in my younger days when I had more time to read as many things as I
wanted to. What was quirky about this week in 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. Like I'd take calendars, anything I could find, Google didn't exist,
right? 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, Gerard Morgan, Benjamin Bannaker, 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 there
were so many terrible things happening around them, and they went to the wrong state at the wrong
time. They 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 the Charles Richard Jews of the world, you know, you create
things that impact people. You don't necessarily get credit for them, and that's not right, but it's
also okay. You're okay with that? Up to a point here. I mean, look, in our world, all we really
have is credit. 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 with Jan. I talked to Jorian Schmidhuber, right? The touring award
given to three people for deep learning, and you could say that a lot of other people should be on
that list. It's the Nobel Prize question. Yeah, it's sad. It's sad, and people like talking about it,
but I feel like in the long arc of history, the only person who will be remembered is Einstein
Hitler, maybe Elon Musk, and the rest of us are just like... Well, you know, someone asked me about
immortality once, and I said, and I stole this from somebody else. I don't remember who, but it was,
you know, I asked them, what's your great grandfather's name? Any of them. Of course,
they don't know. Most of us do not know. I mean, I'm not entirely sure. I know my grandparents
name, all my grandparents' names. I know what I called them, right? I don't know any of their
middle names, for example. Didn't within living memories, so I could find out. Actually, my grandfather
didn't know when he was born. Had no idea how old he was, right? But I definitely don't know
any of my great-grandparents are. So in some sense, immortality is doing something preferably positive,
so that's your great-grandchildren know who you are, right? And that's kind of what you can hope
for, which is very depressing in some ways. You can... I could turn it into something uplifting
if you need me to, but... Yeah, can you do the work here? Yeah, it's simple, right? It doesn't
matter. I don't have to know what my great-grandfather was to know that I wouldn't be here without him.
Yeah. And I don't know who my great-grandchildren are, and certainly who my great-great-grandchildren
are, and I'll probably never meet them, although I would very much like to. But hopefully, I'll set
the world in motion in such a way that their lives will be better than they would have been if I
hadn't done that. Well, certainly they wouldn't have existed if I hadn't done the things that I did.
So I think that's a good positive thing. You live on through other people.
Are you afraid of death? I don't know if I'm afraid of death, but I don't like it.
That's another T-shirt. I mean, do you ponder it? Do you think about the...
The inevitability of oblivion? Yes.
I do occasionally. This feels like a very Russian conversation, actually.
This is very... I will tell you a story, a very... Something that happened to me recently.
If you look very carefully, you will see I have a scar, which, by the way, is an interesting
story of its own about why people have half of their thyroid taken out. Some people get scars,
and some don't. But anyway, I had half my thyroid taken out. The way I got there, by the way,
is its own interesting story, but I won't go into it. Just suffice it to say I did what I
keep telling people you should never do, which is never go to the doctor unless you have to,
because there's nothing good that's ever going to come out of a doctor's visit, right?
So I went to the doctor to look at one thing. It's a little bump I had on the side that I thought
might be something bad because my mother made me. And I went there and was like, oh, it's nothing.
But by the way, your thyroid is huge. Can you breathe? Yes, I can breathe. Are you sure?
Because it's pushing on your windpipe. You should be dead. Ah! Right. So I ended up going there,
and to get my... To look at my thyroid, it was growing. I have what's called a goiter.
And he said, we're going to have to take it out at some point. Win. Sometime before you're 85,
probably. But if you wait till you're 85, that'll be really bad because you don't want to have
surgery when you're 85 years old if you can help it. Certainly not the kind of surgery it takes to
take out your thyroid. So I went there and we decided, I would decide I would put it off until
December 19th because my birthday is December 18th. And I wanted to be able to say I made it to 49
or whatever. So I said, I'll wait till after my birthday. In the interview, in the first six
months of that, nothing changed. Apparently in the next three months, it had grown. I had noticed
this at all. I went and had surgery. They took out half of it. The other half is still there.
And working fine, by the way. I don't have to take a pill or anything like that. It's great.
I'm in the hospital room and the doctor comes in. I've got these things in my arm. They're
going to do whatever. They're talking to me. And the anesthesiologist says,
huh, your blood pressure is through the roof. Do you have high blood pressure? I said, no,
but I'm terrified if that helps you at all. And the anesthesist, who's the nurse who supports
the anesthesiologist, if I got that right, said, oh, don't worry about it. I just put some stuff
in your IV. You're going to be feeling pretty good in a couple of minutes. And I remember turning
and saying, well, I'm going to feel pretty good in a couple of minutes. Next thing I know, there's
this guy and he's moving my bed. And he's talking to me and I have this distinct
impression that I've met this guy and I should know what he's talking about. But I kind of just
don't remember what just happened. And I look up and I see the tiles going by and I'm like, oh,
it's just like in the movies where you see the tiles go by. And then I have this brief thought
that I'm in an infinitely long warehouse and there's someone sitting next to me. And I remember
thinking, oh, she's not talking to me. And then I'm back in the hospital bed. And in between the
time where the tiles were going by and I got in the hospital bed, something like five hours had
passed. Apparently it had grown so much that it was a four and a half hour procedure instead of an
hour long procedure. I lost a neck size and a half. It was pretty big. Apparently it was as big as
my heart. Why am I telling you this? I'm telling you this because...
It's a hell of a story already. Between the tiles going by and me waking up in my hospital bed,
no time passed. There was no sensation of time passed. When I go to sleep and I wake up in the
morning, I have this feeling that time has passed, this feeling that something has physically changed
about me. Nothing happened between the time they put the magic juice in me and the time that I woke
up. Nothing. By the way, my wife was there with me talking. Apparently I was also talking. I don't
remember any of this, but luckily I didn't say anything I wouldn't normally say. My memory of
it is I would talk to her and she would teleport around the room. And then I accused her of witch
craft and that was the end of that. But her point of view is I would start talking and then I would
fall asleep and then I would wake up and leave off where I was before. I had no notion of any
time passing. I kind of imagine that that's death. Is the lack of sensation of time passing.
And on the one hand, I am, I don't know, soothed by the idea that I won't notice.
On the other hand, I am very unhappy at the idea that I won't notice.
So I don't know if I'm afraid of death, but I am completely sure that I don't like it
and that I particularly would prefer to discover on my own whether immortality sucks
and be able to make a decision about it. That's what I would prefer.
You'd like to have a choice in the matter. I would like to have a choice in the matter.
Well, again, on the Russian thing, I think the finiteness of it is the thing that gives it
a little flavor, a little spice. Well, reinforcement learning, we believe that.
That's why we have discount factors. Otherwise, it doesn't matter what you do.
Amen. Well, let me one last question to sticking on the Russian theme. You
talked about your great grandparents, not remembering their name. What do you think is the,
in this kind of Markov chain that is life, what do you think is the meaning of it all?
What's the meaning of life?
Well, in a world where eventually you won't know who your great grandchildren are,
I am reminded of something I heard once or I read once that I really like, which is
it is well worth remembering that the entire universe, say for one trifling exception,
is composed entirely of others. I think that's the meaning of life.
Charles, this was one of the best conversations I've ever had and I get to see you tomorrow again
to hang out with who looks to be one of the most, how should I say, interesting personalities
that I'll ever get to meet with Michael Liebman. So I can't wait. I'm excited to have had this
opportunity. Thank you for traveling all the way here. It was amazing. I'm excited. I always
love Georgia Tech. I'm excited to see with you being involved there with what the future holds.
So thank you for talking to me. Thank you for having me. I enjoyed every minute of it.
Thanks for listening to this conversation with Charles Lisbeau and thank you to our sponsors,
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some poetic words from Martin Luther King Jr. There comes a time when people get tired of being
pushed out of the glittering sunlight of life's July and left standing amid the piercing chill
of an alpine November. Thank you for listening and hope to see you next time.