Charles Isbell: Computing, Interactive AI, and Race in America
嘉宾：Charles Isbell
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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
that make him especially fun to talk with.
When I first saw him speak, his charisma
immediately took over the room,
and I had a stupid excited smile on my face,
and I knew I had to eventually talk to him on this podcast.
Quick mention of each sponsor,
followed by some thoughts related to the episode.
First is Neuro, the maker of functional sugar-free gum
and mints that I use to give my brain a quick caffeine boost.
Second is Decoding Digital,
a podcast on tech and entrepreneurship
that I listen to and enjoy.
Third is Masterclass, online courses that I watch
from some of the most amazing humans in history.
And finally, Cash App, the app I use to send money
to friends for food and drinks.
Please check out these sponsors in the description
to get a discount and to support this podcast.
As a side note, let me say that I'm trying to make it
so that the conversations with Charles, Eric Weinstein,
and Dan Carlin will be published
before 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, Genghis 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,
review it with Five Stars on Apple Podcasts,
follow on Spotify, support on Patreon,
or connect with me on Twitter @LexFriedman.
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 gonna tell you why.
It's very simple.
It's because movies is too broad of a category.
I gotta pick sub-genres.
But I will tell you that of those genres,
I'll pick one or two from each of the genres.
I'll get us to three, so I'm not gonna cheat.
So my favorite comedy of all times,
which is probably my favorite movie of all time,
is "His Girl Friday," which is probably a movie
that you've not ever heard of,
but it's based on a play called "The Front Page"
from, I don't know, early 1900s.
And the movie is a fantastic film.
- What's the story?
What's the independent film?
- No, no, no. - What are we talking about?
- This is one of the movies
that would have been very popular.
It's a screwball comedy.
You ever see "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,
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 mostly conversation?
- Not entirely, but mostly, mostly.
Just a lot of back and forth.
There's a story there.
Someone's on death row, and they're newspapermen,
including her.
They're all newspapermen.
They were divorced, the editor, the publisher, I guess,
and the reporter, they were divorced.
But they clearly, he's thinking,
trying to get back together,
and there's this whole other thing that's going on.
But none of that matters.
The plot doesn't matter.
- Yeah, it's just a little play in conversation.
- It's fantastic, and I just love everything
about the conversation, because at the end of the day,
sort of narrative and conversation
are the sort of things that drive me,
and so I really like that movie for that reason.
Similarly, I'm now gonna cheat,
and I'm gonna give you two movies as one,
and they're Crouching Tiger, Hidden Dragon, and John Wick.
Both relatively modern.
John Wick, of course, is--
- 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 'cause I actually think
they're the same movie, or what I like about them,
the same movie, which is both of them create a world
that you're coming in the middle of,
and they don't explain it to you.
But the story is done so well that you pick it up.
So anyone who's seen John Wick, you know,
you have these little coins, and they're headed out,
and there are these rules, and apparently,
every single person in New York City is an assassin.
There's like two people who come through who aren't,
but otherwise they are.
But there's this complicated world,
and everyone knows each other.
They don't sit down and explain it to you,
but you figure it out.
Crouching Tiger, Hidden Dragon's 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 gonna
explain it to you, but there's this sort of
rich world behind you.
- But 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, of course, yes.
I think John Wick outfit's perfect.
And so that's number two, and then--
- But sorry to pause on that, martial arts,
you have a long list of hobbies.
Like it scrolls off the page, but I didn't see
martial arts as one of them.
- I do not do martial arts, but I certainly--
- Watch it. - Watch martial arts.
Oh, I appreciate it very much.
- Oh, we could talk about every Jackie Chan movie
ever made, and I would be on board with that.
- The Couch Hour 2, like that kind of,
the comedy of it, 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.
- First Jackie Chan movie?
- No, first one ever that I saw and remember,
but I had no idea--
- What is this?
- I didn't know what it was, and I didn't know
that was Jackie Chan.
That was like his first major movie.
I was a kid, it was done in the '70s.
I only later rediscovered that that was actually--
- And he creates his own martial art by,
was he actually drinking, or was he play drinking?
- You mean as an actor, or--
- No. (laughing)
I'm sure as an actor he was--
- No, he was-- - It was the '70s,
or whatever.
- He was definitely drinking, and in the end,
he drinks industrial-grade alcohol.
- Ah, yeah.
- Yeah, and has one of the most fantastic fights ever
in that sub-genre.
Anyway, that's my favorite one of his movies,
but I'll tell you the last movie.
It's actually a movie called Nothing But a Man,
which is a 1960s, starred Ivan Dixon,
who you'll know from Hogan's Heroes, and Abbie Lincoln.
It's just a really small little drama.
§02Transcript · 07:34–15:00
It's a beautiful story, but my favorite scenes,
I'm cheating, my favorite,
one of my favorite movies just for the ending
is The Godfather.
I think the last scene of that is just fantastic.
It's the whole movie all summarized in just eight,
nine seconds.
- Godfather Part One?
- Part One.
- How does it end?
I don't think you can, you need to worry about spoilers
if you haven't seen The Godfather.
- Spoiler alert.
It ends with the wife coming to Michael,
and he says, "Just this once,
"I'll let you ask me my business."
And she asks him if he did this terrible thing,
and he looks her in the eye, and he lies,
and he says, "No," and she says, "Thank you,"
and she walks out the door,
and you see him, you see her going out of the door,
and all these people are coming in,
and they're kissing Michael's hands, and Godfather.
And then the camera switches perspectives,
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, Carl?
- Ha!
Just kidding, trick question.
(laughing)
Sometimes, certainly if the door gets closed
on me every once in a while.
- Okay, that was a rhetorical question.
You've also mentioned that you, I think,
enjoy all kinds of experiments, including on yourself,
but I saw a video where you said you did an experiment
where you tracked all kinds of information about yourself,
and a few others, sort of wiring up your home,
and this little idea that you mentioned in that video,
which is kind of interesting,
that you thought that two days' worth of data
is enough to capture majority of the behavior
of the human being.
First, can you describe what the heck you did
to collect all that data, 'cause it's fascinating,
just like little details of how you collect that data,
and also what your intuition behind the two days is.
- So, first off, it has to be the right two days,
but I was thinking of a very specific experiment.
There's actually a suite of them that I've been a part of,
and other people have done this, of course.
I just sort of dabbled in that part of the world.
But to be very clear, the specific thing
that I was talking about had to do with recording
all the IR going on in my, infrared going on in my house.
So this is a long time ago, so everything's being controlled
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 gonna 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,
or I'm about to turn on the TV, or whatever,
and just see what I could predict from it.
It was kind of surprising, it shouldn't have been.
But that's all very easy to do, by the way,
just capturing all the little stuff.
I mean, it's a bunch of computer systems.
It's really easy to capture,
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 saw, you captured everything that was going on.
Nothing even difficult, not with video,
or anything like that, just the way that the system
was just capturing everything.
So it turns out that, and I did this with myself,
and then I had students, and they worked with
many other people, and it turns out at the end of the day,
people do the same things over and over and over again.
So it has to be the right two days, 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 gonna 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 Markov model,
it's like a Mark, just simply, I press this,
this is my prediction of the next thing.
And it turns out, you get 93% accuracy,
just by doing something very simple and stupid,
and just counting statistics.
But what was actually more interesting,
is that you could use that information.
This comes up again and again in my work.
If you try to represent people or objects
by the things they do, the things you can measure about them
that have to do with action in the world,
so 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 gonna press,
but I can get 99% accuracy, or somewhere there's about,
on the collections of things you might press.
And it turns out, the things that you might press
are all related to each other in exactly
the way that you would expect.
So, for example, all the numbers on a keypad,
it turns out, all have the same behavior,
with respect to you as a human being.
And so, you would naturally cluster them together,
and you discover that numbers are all related
to one another in some way, and all these other things.
And then, and here's the part that I think's 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.
- What about, let me play devil's advocate,
and philosophically speaking, is it possible to say
that what defines humans is the outlier?
So, even though some large percentage of our behaviors,
whatever the signal we measure is the same,
and it would cluster nicely,
but maybe it's the special moments
of when we break out of the routine,
is the definitive things,
and the way we break out of that routine
for each one of us might be different?
- It's possible.
I would say it a little differently, I think.
I would make two things.
One is, I'm gonna 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
this small number of people.
They all behave like each other,
even if they individually behave very differently
from everyone else.
So, I think that's kind of important.
But what you're really asking, I think,
and I think this is really a question,
is what do you do when you're faced
with a 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?
You react to that very differently,
and that is the thing that makes you human.
I would agree with that, at least at a philosophical level,
that it's the times when you are faced
with something difficult, a decision that you have to make,
where the answer isn't easy,
even if you know what the right answer is,
that's sort of what defines you as the individual,
and I think what defines people broadly.
It's the hard problem, it's not the easy problem.
It's the thing that's gonna 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 explore
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, along that narrative,
is that we wanna break each other out of those silos
§03Transcript · 15:05–22:46
so we can be empathetic to other people.
If you're a Democrat, you'd be empathetic to the Republican.
If you're a Republican, you're empathetic to a 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,
'cause you're just saying we cluster along the data,
but then interactive artificial intelligence
is referring to throwing agents into that mix,
AI systems in that mix,
helping us, interacting with us humans,
and maybe getting us out of those silos.
Is that something that you think is possible?
Do you see a hopeful possibility
for artificial intelligence systems
in these large networks of people
to get us outside of our habits,
in at least the idea space,
to where we can sort of be empathetic
to other people's lived experiences,
other people's points of view, all that kind of stuff?
- Yes, and I actually don't think it's that hard.
Well, it's not hard in this sense.
So imagine that you can,
now 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?
They don't quite touch even, or maybe they come very close.
If you can imagine that conceptually,
then the way you get from here to here
is not by going from here to here.
The way you get from here to here is you find the edge
and you move slowly together, right?
And I think that machines are actually very good
at that sort of thing, once we can kind of define the problem
either in terms of behavior or ideas or words or whatever.
So it's easy in the sense that
if you already have the network
and you know the relationships,
the edges and sort of the strings on them,
and you kind of have some semantic meaning for them,
the machine doesn't have to, you do as 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 gonna tell you a story
that someone else told me, and I don't,
I may get some of the details a little bit wrong,
but it's roughly, it roughly goes like this.
You take two sets of people from the same backgrounds
and you want them to solve a problem.
So you separate them up, which we do all the time,
right, oh, you know, we're gonna break out in the,
we're gonna break out groups,
you're gonna go over there and you're gonna talk about this,
you're gonna go over there and you're gonna talk about this.
And then you have them sort of in this big room,
but far apart from one another,
and you have them sort of interact with one another.
When they come back to talk about what they learned,
you wanna 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
'cause we were in the middle of that at the time,
was they're sitting over there
and they're talking about these rooms that you can see,
but you're seeing them from different vantage points,
depending upon what side of the room you're on.
They can see a clock very easily.
And so they start referring to the room
as the one with the clock.
This group over here, looking at the same room,
they can see the clock,
but it's 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 it is.
The clock on the wall is the thing that stuck with me.
So if you create these different silos,
the problem isn't that the ideologies disagree,
it's that you're using the same words
and they mean radically different things.
The hard part is just getting them to agree on the,
well, maybe we'd say the axioms in our world, right?
But just get them to agree on some basic definitions.
Because right now they're talking past each other,
just completely talking past each other.
That's the hard part, getting them to meet,
getting them to interact, that may not be that difficult.
Getting them to see where their language
is leading them to lead past one another,
that's the hard part.
- 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, it all boils down to empathy,
being able to put yourself in the shoes of the other person,
to learn the language, to learn visually
how they see the world, to learn the,
I mean, I experience 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.
- Yeah.
- So they show love by 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 their 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 understander is doing a lot of work, right?
- Yeah, yes.
- Do you have to understand it
or do you just simply have to note
that there is something similar as a point to touch, right?
So you use the word empathy,
and I like that word for a lot of reasons.
I think you're right in the way that you're using
and the way that you're describing it,
but let's separate it from sympathy, right?
So 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, is it right, right?
And if the only way I can feel for someone
is to completely understand them
and make them like me in some way,
well, then we're lost, right?
Because we're not all exactly like each other.
I don't have to understand everything
that you've gone through.
It helps, clearly.
But they're separable ideas, right?
Even though they get clearly tangled up in one another.
So what I think AI could help you do, actually,
is if, and I'm being quite fanciful, as it were,
but if you think of these as kind of,
I understand how you interact,
the words that you use, the actions you take,
I have some way of doing this,
let's not worry about what that is,
but I can see you as a kind of distribution
of experiences and actions taken upon you,
things you've done, and so on.
And I can do this with someone else,
and I can find the places where there's
some kind of commonality, a mapping, as it were,
even if it's not total.
If I think of it as distribution, right,
§04Transcript · 22:48–30:00
then I can take the cosine of the angle between you,
and if it's zero, you've got nothing in common.
If it's one, you're completely the same person.
Well, you're probably not one,
you're almost certainly not zero.
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 empathy.
It's not impossible to do,
although I wonder if it requires that everyone involved
is at least interested in asking the question.
So maybe the hard part is just getting them interested
in asking the question.
In fact, maybe if you can get them to ask the question,
how are we more alike than we are different,
they'll solve it themselves.
Maybe that's the problem that AI should be working on,
not telling you how you're similar or different,
but just getting you to decide
that it's worthwhile asking the question.
- So, no.
- It feels like an economist's answer, actually.
- Well, people, okay, first of all,
people like it when I 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 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 there's something sticky about that
that my mind will linger on,
and that person in my mind will become warmer and warmer,
and I'll start to feel more and more compassion towards them.
I think for majority of the population, that's true,
but that's a hypothesis.
- Yeah, I mean, it's an empirical question, right?
You'd have to figure it out.
I mean, I want to believe you're right,
and so I'm gonna say that I think you're right.
Of course, some people come to those things
for the purpose of trolling, right?
And it doesn't matter.
They're playing a different game.
- Yeah.
- But I don't know.
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 onto that kind of anger
or to hold onto 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 on 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, 'cause then you're,
one, you're forced to deal with their humanity
'cause it's in front of you.
The other is, of course, that they might punch you
in the face if you go too far,
so both of those things kind of work together,
I think, to the right end.
So I think bringing people together
is really a kind of substitute for forcing them
to see the humanity in another person
and to not be able to treat them as bits.
It's hard to troll someone
when you're looking them in the eye.
This is very difficult to do.
- Agreed.
Your broad set of research interests
fall under interactive AI, as I mentioned,
which is a fascinating set of ideas,
and you have some concrete things
that you're particularly interested in,
but maybe could you talk about how you think
about the field of interactive artificial intelligence?
- Sure.
So let me say up front that if you look at,
certainly my early work,
but even if you look at most of it,
I'm a machine learning guy.
Right, I do machine learning.
First paper I ever published was in NIPS.
Back then it was NIPS, now it's NIRPS.
It's a long story there.
Anyway, that's another thing.
So I'm a machine learning guy.
I believe in data, I believe in statistics
and all those kind of things.
- Yes.
- 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 in their own way.
- So that's the deep underlying love and dream
is the bigger AI.
- Yes.
- It's the bigger, whatever the heck that is.
- Yeah, the machine learning in some ways
is a means to the end.
It is not the end.
And I don't understand how one could be intelligent
without learning, so therefore I gotta 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.
I think it's what 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?
- Sure.
- 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 what Pat Langley's,
is it knowledge, like expert system reasoning
type of kind of thing?
- So AI is bigger than ML, but ML is bigger than AI.
This is kind of the real problem here,
is that they're really overlapping things
that are really interested in slightly different problems.
I tend to think of ML, and there are many people out there
are gonna 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.
I'm an 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 you know, it's, to me, they just feel very different.
You're just measuring them differently.
Your sort of goals of where you're trying to be
are somewhat different, but to me, AI is about
trying to build that intelligent thing.
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 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 gonna sound simple,
but I don't care whether a tree makes a sound
when it falls and there's no one around,
§05Transcript · 30:01–36:58
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 find intention,
even whether there is no intention.
Why, I mean, anthropomorphize everything.
I think, anyway.
I think the interactive AI part is being intelligent
in and of myself in isolation is a meaningless act,
in some sense.
The correct answer is you have to be intelligent
in the way that you interact with others.
That's also efficient, because it allows you
to learn faster, because you can import from past history.
It also allows you to be efficient
in the transmission of that.
So we ask ourselves about me.
Am I intelligent?
Clearly, I think so.
But I'm also intelligent as a part of a larger species
and group of people, and we're trying to move
the species forward as well.
And so I think that notion of being intelligent
with others is kind of the key thing,
because otherwise you come and you go,
and then it doesn't matter.
And so that's why I care about that aspect of it.
And it has lots of other implications.
One is not just building something intelligent with others,
but understanding that you can't always communicate
with those others.
They have been in a room where there's a clock on the wall
that you haven't seen, which means you have to spend
an enormous amount of time communicating
with one another constantly in order to figure out
what each other wants.
So, I mean, this is why people project, right?
You project your own intentions and your own reasons
for doing things onto others as a way of understanding them
so that you know how to behave.
But by the way, you, completely predictable person,
I don't know how you're predictable,
I don't know you well enough, but you probably eat
the same five things over and over again,
or whatever it is that you do, right?
I know I do.
If I'm going to a new Chinese restaurant,
I will get General Gao's chicken,
because that's the thing that's easy to get.
I will get hot and sour soup.
People do the things that they do,
but other people get the chicken and broccoli.
I think I can push this analogy way too far.
The chicken and broccoli--
- I don't know what's wrong with those people.
- I don't know what's wrong with them either.
- 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's going to change.
And by the way, it's not just the case
that you're different from that person,
but you're different from the person you were 15 minutes ago,
or certainly 15 years ago,
and I have to assume that you're at least going to drift.
Hopefully not too many discontinuities,
but you're going to drift over time.
And I have to have some mechanism for adapting to that
as you and an individual over time,
and across individuals over time.
- On the topic of adaptive modeling,
and you talk about lifelong learning,
which is, I think, a topic that's understudied,
or maybe because nobody knows what to do with it.
But 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 the temporally rich set of information
that seems to pick up the crumbs along the way
that somehow seems to capture a person pretty well.
Do you have any ideas how to do lifelong learning?
Because it seems like most of the machine learning community
does not.
- No, well, by the way,
not only does the machine learning community
not 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 over-fitting 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 gonna learn on one task,
then learn the other task.
You still end up creating the task.
You know, it's like looking for your keys
where the light is, 'cause that's where the light is, right?
It's not because the keys have to be there.
I mean, one could argue that we tend to do this in general.
We tend to kind of do it as a group.
We tend to hill climb and get stuck in local optima.
And I think we do this in the small as well.
I think it's very hard to do.
Because, so, look, here's the hard thing about AI, right?
The hard thing about AI is it keeps changing on us, right?
You know, what is AI?
AI is the art and science of making computers act
the way they do in the movies, right?
That's what it is, right?
(laughing)
- That's a good definition.
- But beyond that, it's--
- And they keep coming out with new movies.
- Yes, and they just, right, exactly.
We are driven by this kind of need
to the sort of ineffable quality of who we are.
Which means that the moment you understand something
is no longer AI, right?
Well, we understand this.
That's just, you take the derivative and you divide by two
and then you average it out over time in the window.
So therefore, that's no longer AI.
So the problem is unsolvable
because it keeps kind of going away.
This creates a kind of illusion,
which I don't think is an entire illusion,
of either there's very simple task-based things
you can do very well and 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 problem
that's in front of us, myself included.
I'm not gonna 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
'cause you have to get the next paper out,
you have to get the next product out,
you have to solve this problem,
and it's very sort of naturally incremental.
And none of the incentives are set up
to allow you to take a huge risk
unless you're already so well-established
you can take that big risk.
And if you're that well-established
that you can take that big risk,
then you've probably spent much of your career
taking these little risks, relatively speaking.
And so you have got a lifetime of experience
telling you not to take that particular big risk, right?
So the whole system's set up to make progress very slow.
That's fine.
It's just the way it is.
But it does make this gap seem really big,
which is my long way of saying
I don't have a great answer to it
except that stop doing n equals one.
At least try to get n equal two and maybe n equal seven
so that you can say I'm gonna,
or maybe T is a better variable here.
I'm gonna not just solve this problem,
I'm gonna solve this problem and another problem.
I'm not gonna learn just on you.
I'm gonna 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 gonna 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
§06Transcript · 36:59–44:36
that it's gonna take you five years to do this.
Not rerunning the same experiment over and over again
and refining the machine
so it's slightly better at whatever,
but actually having it out there
and living in the chaos of the world
and seeing what it's learning algorithm, say, can learn,
what data structure it can build
and how it can go from there.
Without that, you're gonna be stuck ultimately.
- What do you think about the possibility of N equals one
growing, it's probably a crude approximation,
but growing like if we look at language models like GPT-3,
if you just make it big enough, it'll swallow the world.
Meaning like it'll solve all your T to infinity
by just growing in size of this.
Taking the small over-engineered solution
and just pumping it full of steroids in terms of compute,
in terms of size of training data
and the Yann LeCun 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,
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 webpage.
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 wanna use Google for, but it also changes
what the problem was we thought we were trying to solve
in the first place.
You thought you were trying to find an answer,
but you're not, we're trying to find the answer,
but it turns out you're just trying to find an answer.
Now, yes, it is true, it's also very good
at finding you exactly that webpage.
Of course, you trained yourself to figure out
what the keywords were to get you that webpage.
But in the end, by having that much data,
you've just changed the problem into something else.
You haven't actually learned what you set out to learn.
Now, the counter to that would be
maybe we're not doing that either, we just think we are,
because we're in our own heads, maybe we're learning
the wrong problem in the first place,
but I don't think that matters.
I think the point is is that Google has not solved
information retrieval, Google has done amazing service.
I have nothing bad to say about what they've done.
Lord knows my entire life is better because Google exists,
in foreign for Google Maps, I don't think I've ever
found this place.
(laughing)
- Where is this?
- Like 95, I see 110 and I see, but where did 95 go?
So I'm very grateful for Google, but they just have
to make certain the first five things are right.
And everything after that is wrong.
Look, we're going off on a totally different topic here,
but think about the way we hire faculty.
It's exactly the same thing.
- Are you getting controversial?
- I'm not getting controversial.
It's exactly the same problem, right?
It's minimizing false positives.
We say things like we want to find the best person
to be an assistant professor at MIT
in the new College of Computing,
which I will point out was founded 30 years after
the College of Computing I'm a part of.
Both of my alma mater--
- No more fighting words.
- I'm just saying I appreciate all that they did
and all that they're doing.
Anyway, so we're gonna try to hire the best person
to be 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.
- About 60%.
- 60.
- 60% 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?
'Cause 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%.
If you look at the top 55 ranked departments,
50% of the faculty earn their PhDs from the top 10.
There's 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,
you know, there was a saying that, you know,
nobody ever lost his job buying a computer from IBM.
And it was true.
And nobody ever lost their job hiring a PhD from MIT, right?
If the person turned out to be terrible, well, you know,
they came from MIT, what did you expect me to know?
However, that same person coming from,
pick whichever is your least favorite place
that produces PhDs in say, computer science,
well, you took a risk, right?
So all the incentives,
particularly because you're only gonna hire one this year,
well, now we're hiring 10, but you know,
you're only gonna 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 they're getting tenure or not.
And if they get tenure, you're stuck with them
for a good 30 years, unless they decide to leave.
That means the pressure to get this right is very high.
So what are you gonna do?
You're gonna 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,
§07Transcript · 44:40–52:16
was in exactly the same situation with their search.
It turns out you just don't wanna give people
the wrong page in the first three or four pages.
And if there's 10 million right answers
and a hundred bazillion wrong answers,
just make certain the wrong answers don't get up there.
And who cares if the right answer
was actually the 13th page?
A right answer, a satisficing answer,
is number one, two, three, or four.
So who cares?
- Or an answer that will make you discover
something beautiful, profound to your question.
- Well, that's a different problem, right?
- But isn't that the problem?
Can we linger on this topic
without sort of walking with grace?
How do we get, for hiring faculty,
how do we get that 13th page with a truly special person?
I mean, it depends on the department.
Computer science probably has those kinds of people.
Like you have the Russian guy, Grigori Perelman.
Just these awkward, strange minds
that don't know how to play the little game of etiquette
that faculty have all agreed somehow converged
over the decades how to play with each other.
And also is not, on top of that,
is not from the top four, top whatever numbers, the schools.
And maybe actually just says a few every once in a while
to the traditions of old
within the computer science community.
Maybe talks trash about machine learning
is a total waste of time.
And that's there on their resume.
So 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.
A small amount of risk.
I mean, if we were treating this as a,
well, as a machine learning problem,
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 'cause I'm Bayesian.
If you don't wanna 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 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 gotta 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
another level, but let me tell you something else.
You know, I did a sort of study, I call it a study.
I called up eight of my friends and asked them
for all of their data for graduate admissions,
but then someone else followed up and did an actual study.
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,
'cause they don't, they don't have PhDs,
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 what you knew to go to undergrad to do whatever, right?
So if we can push these things back a little bit
and just make the pool a little bit bigger,
at least you raise the probability
that you will be able to see someone interesting
and take the risk.
The other answer to that question, by the way,
which you could argue is the same as,
you either adjust the pool so the probabilities go up,
that's a way of injecting a little bit of uniformity.
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, US News and World Report,
every time they change their formula
for determining rankings,
move entire universities to behave differently,
because rankings matter.
- Can you talk trash about those rankings for a second?
No, I'm joking about talking trash.
I actually, it's so funny how, from my perspective,
from a very shallow perspective,
how dogmatic, like how much I trust those rankings.
They're almost ingrained in my head.
I mean, at MIT, everybody kind of,
it's a propagated, mutually agreed upon idea
that those rankings matter.
And I don't think anyone knows what they're,
like most people don't know what they're based on.
And what are they exactly based on
and what are the flaws in that?
- Well, so it depends on which rankings you're talking about.
Do you wanna talk about computer science
or you wanna talk about universities?
- Computer science, US News, isn't that the main one?
- Yeah, it's US News.
The only one that matters is US News.
Nothing else matters.
Sorry, csrankings.org, but nothing else matters
but US News.
So US News has formula that it uses for many things,
but not for computer science,
because computer science is considered a science,
which is absurd.
So the rankings for computer science is 100% reputation.
So two people at each department,
it's not really a department, whatever,
at each department basically rank everybody.
Slightly more complicated than that.
But whatever, they rank everyone.
And then those things are put together
and somehow-- - Oh no.
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?
(Dave laughs)
- 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 were tied for ninth,
'cause that's how rankings work.
And we moved from nine to eight,
because our raw score moved up a point.
So Georgia, something about Georgia Tech,
computer science, or computing anyway.
I think it's because we have shown leadership
at every crisis level, right?
So we created a college, first public university to do it,
second college, second university to do it
after CMU is number one.
I also think it's no accident that CMU is the largest,
and we're, depending upon how you count
and depending on exactly where MIT ends up
with its final college of computing,
second or third largest.
I don't think that's an accident.
We've been doing this for a long time.
But in the 2000s,
when there was a crisis about undergraduate education,
Georgia Tech took a big risk
and succeeded at rethinking undergrad education
and computing.
I think we created these schools at a time
when most public universities 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.
So, I mean, that is what reputation is, right?
So the way you move up in the reputation rankings
is by doing something that makes people turn
and look at you and say, "That's good.
They're better than I thought."
- Yeah. - Beyond that,
it's just inertia.
And there's huge hysteria in the system, right?
Like, I mean, there was these,
§08Transcript · 52:18–1:00:13
I can't remember this, this may be apocryphal,
but there's a major or a department
that like MIT was ranked number one in,
and they didn't have it.
Right, it's just about what you,
I don't know if that's true,
but someone said that to me anyway.
But it's a thing, right?
It's all about reputation.
Of course MIT is great because MIT is great.
It's always been great.
By the way, because MIT is great,
the best students come, which keeps it being great.
I mean, it's just a positive feedback loop.
It's not surprising.
I don't think it's wrong.
- Yeah, but it's almost like a narrative.
Like, it doesn't actually have to be backed by reality.
And it's, you know, not to say anything about MIT,
but like, it does feel like we're playing
in the space of narratives,
not the space of something grounded in,
like one of the surprising things when I showed up at MIT
and just all the students I've worked with
and all the research I've done is it like,
they're the same people as I've met other places.
- I mean, what MIT is going for,
well, MIT has many things going for it,
but one of the things MIT is going for is--
- Nice logo.
- It has 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
'cause it's the best place to be
or one of the best places to be.
And it just kind of, it's a sort of positive feedback.
But you said something earlier,
which I think is worth examining for a moment, right?
You said it's, I forget the word you used.
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, you know, what, I mean, tell me something
you think is actually objective and measurable
in a way that makes sense.
Like cameras, they don't, do you know that,
I mean, you're getting me off on something here,
but do you know that cameras,
which are just reflecting light and putting them on film,
like did not work for dark-skinned people
until like the 1970s?
You know why?
Because you were building cameras
for the people who were gonna buy cameras,
who all, at least in the United States
and Western Europe, were relatively light-skinned.
Turns out it took terrible pictures
of people who look like me.
That got fixed with better film and whole processes.
Do you know why?
Because furniture manufacturers
wanted to be able to take pictures of mahogany furniture.
Right, because 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.
- Are 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,
there are 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 giant conglomerate
wants, demands that you do something different
and it's gonna involve all kinds of money for you,
suddenly the trade-offs change, right?
And so there you go.
I actually don't know how I ended up there.
Oh, it's because of this notion of objectiveness, right?
So even the objective isn't objective
because at the end you've gotta tell a story,
you've gotta make decisions,
you've gotta make trade-off,
and 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.
- 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 like excite the world,
whether it's in the Carl Sagan style
of 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 Feldman Barrett,
who's a neuroscientist who just enjoys
playing the controversy,
thinks 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 doesn't 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 you just described as vulgar.
It's certainly unscientific, right?
And it is easy to convince yourself
that in some ways you're the mathematician, right?
The fewer there are in your major,
the more that proves your purity, right?
- Yeah.
- So once you tell yourself that story,
then it is beneath you to do that kind of thing, right?
I think that's wrong.
I think that, and by the way, everyone doesn't have to do 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,
'cause 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
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.
Forget Twitter, we could look at just online courses
that are public-facing in some part.
Like there is a kind of force that pulls you back.
I would, let me just call it out
'cause 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 just kind of,
it's hard to stand out
unless you like win a Nobel Prize or whatever.
Like it's only when you like get senior enough
where you 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 F-you money switch
where you're like, you know what?
Now I'm going to be more bold.
§09Transcript · 1:00:15–1:07:35
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, 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?
- Yes, sir, yes.
- 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.
(laughing)
- I don't know how it explains.
I love Rod.
But I also love my other advisor, Paul.
Paul, if you're listening, I love you too.
Both very, very different people.
- Paul Viola.
- Paul Viola, 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,
'cause I was a student at the time, it was amazing.
This guy was in a movie, being very much himself.
Actually, the movie version of him
is a little bit more Rod than Rod.
I mean, I think they edited it appropriately for him.
But it was very much Rod, and he did all this
while doing great work.
I mean, he was running, was he running the iLab
at that point or not?
I don't know.
I think the iLab 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 it.
I mean, he did 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 and 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 PFUNK.
P stands for probabilistic.
And what does FUNK stand for?
- So a lot of my life is about making acronyms.
So if I have one quirk, it's that people will say words,
and I see if they make acronyms.
And if they do, then I'm happy,
and then if they don't, I try to change it
so that they make acronyms.
It's just a thing that I do.
So PFUNK 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 FUNK 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 PFUNK era,
and we take a picture,
and I hand them a pair of fuzzy dice,
which they get to keep.
- So there's a sense to it which is not an acronym,
like literally FUNK.
You have a dark, mysterious past.
Oh, it's not dark, it's just fun,
as in hip hop and funk.
- Yep.
- So can you educate a Soviet-born Russian
about this thing called hip hop?
Like if you were to give me,
like 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 funk,
what records or artists would you introduce me to?
Would you tell me about,
or maybe what influenced you in your journey,
or what you just love?
Like when the family's gone and you just sit back
and just blast some stuff these days,
what do you listen to?
- Well, so I listen to a lot,
but I will tell you, well, first off,
all great music was made when I was 14,
and that statement is true for all people,
no matter how old they are or where they live.
But for me, the first thing that's worth pointing out
is that hip hop and rap aren't the same thing,
so depending on who 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, I take that.
- 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,
'cause they get very angry about it.
But hip hop is-- - Tagging 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, or a music part.
- A music part, yeah.
I mean, you wouldn't call the stuff that DJs do
the (imitates scratching) scratching.
That's not rap, right?
But it's a part of hip hop, right?
So given that we understand that hip hop is this whole thing,
what are the rap albums that best touch that for me?
Well, if I were gonna educate you,
I would try to figure out what you liked,
and then I would work you there.
- Leonard Skinner.
- Oh my God.
- Yes. (laughs)
- I would probably start with--
(Leonard laughs)
- Led Zeppelin.
- There's a fascinating, oh, it's okay.
There's a fascinating exercise one can do
§10Transcript · 1:07:37–1:14:50
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, 'cause to 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 Mos Def.
I would give you a history lesson, basically.
Mos Def's amazing.
- So 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, that guy.
- Yeah, he's amazing.
He's amazing.
And then, after I got you there,
I'd work you back to EPMD,
and eventually, I would take you back to "The Last Poets,"
and particularly their first album, "The Last Poets,"
which was 1970, to give you a sense of history,
and that it actually has been building up
over a very, very long time.
So we would start there,
because that's where your music aligns,
and then we would cycle out,
and I'd move you to the present,
and then I'd take you back to the past.
Because I think a large part of people
who are kind of confused about any kind of music,
you know, the truth is, this is the same thing
we've always been talking about, right?
It's about narrative and being a part of something
and being immersed in something,
so you understand it, right?
Jazz, which I also like,
is, one of the things that's cool about jazz
is that you come and you meet someone
who's talking to you about jazz,
and you have no idea what they're talking about.
And then one day it all clicks,
and you've been so immersed in it,
you go, "Oh yeah, that's a Charlie Parker,"
you start using words that nobody else understands, right?
And it becomes part of, hip-hop's the same way,
everything's the same way, they're all cultural artifacts.
But I would help you to see that there's a history of it,
and how it connects to other genres of music
that you might like, to bring you in,
so that you could kind of see how it connects
to what you already like,
including some of the good work that's been done
with fusions of hip-hop and bluegrass.
- Oh no. - Yes.
Some of it's even good.
Not all of it, but some of it is good.
But I'd start you with "It Takes a Nation to Make Us All"
as a back.
- There's an interesting tradition in 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, Kanye, Gold Digger, the--
- Mm-hmm, 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,
'cause it was the DJ that brought all the records together
and made it work 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,
it was the DJ that brought all the music together
and 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 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,
'cause 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 gonna 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 more.
- 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 root,
I mean, the roots are doing their own thing, right?
- Yeah, those are, that's a whole.
- Yeah, but still, it's the right attitude.
And what else is jazz, right?
Jazz is about putting pieces together
and then putting your own spin on it.
It's all the same, it's all the same thing.
It's all the same thing.
- 'Cause you mentioned lyrics.
It does make me sad, again,
this is me talking trash about modern hip hop.
I haven't investigated, I'm sure people will 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.
The kind of protest songs,
even if you look at a Bob Marley,
or you see Public Enemy, or Rage Against the Machine
more on the rock side,
that's the place where we go to those lyrics.
Classic rock is all about my woman left me,
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 deep, profound knowledge.
It seems like rap is the place where you would find that,
and it's sad that for the most part,
what I see, 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 rereading,
man, fuck, I wrote my last review
the month I graduated, when I got my PhD,
which says something about something.
I'm not sure what, though.
I always would, I don't always,
but I often would start with, it's all about the lyrics.
For me, it's about the lyrics.
Someone has already written in the comments
before I've even finished having this conversation
that 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 where I discover
some underground hip hop song,
and I am made happy and whole again.
So I know it's out there.
I don't listen to it as much as I used to,
because I'm listening to podcasts
and old music from the 1980s and '80s.
- Kind of rap, no beat, though.
- It's a kind of, no, no beat at all,
but there's a little bit of sampling here and there,
I'm sure. (laughing)
- 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.
§11Transcript · 1:14:52–1:22:25
- 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 Little Richard
or all that kind of stuff, like Ray Charles,
like when they get hit the road, jack,
and don't you come back?
Isn't that like, there's a funkiness in it.
- Oh, there's definitely a funkiness in it.
I mean, it's all, I mean, it's all a lie.
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
depending on whether I'm thinking about it in 2020
or I'm thinking about it in 1960.
I'd probably give a different answer.
I'm just thinking in terms of, you know, that was rock,
but when you look back on it, it was funky.
But we didn't use those words, or maybe we did,
I wasn't around, but you know,
I don't think we used the word 1960, funk.
Certainly not the way we used it in the '70s and the '80s.
- Do you reject disco?
- I do not reject disco.
I appreciate all the mistakes that we have made
to get to where we are 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 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 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.
- Data structure, I love it.
- Right?
So I think the curriculum is where I like to play.
So I spend a lot of time thinking about this.
But I will tell you, I'll answer your question
by answering a slightly different question first
and getting back to this, which is,
you talked about disciplines,
and what does it mean to be a discipline?
The truth is, what we really educate people in
from the beginning, but certainly through college,
you've sort of failed if you don't think about it this way,
I think, is the world,
people often think about tools and tool sets,
and when you're really trying to be good,
you think about skills and skill sets.
But disciplines are about mindsets, right?
They're about fundamental ways of thinking,
not just the hammer that you pick up,
whatever that is, to hit the nail,
not just the skill of learning how to hammer well,
or whatever, it's the mindset of, like,
what's the fundamental way to think about the world, right?
And disciplines, different disciplines,
give you different mindsets.
They give you different ways of sort of thinking through.
So, with that in mind, I think that computing,
to even ask the question whether it's a discipline,
you have to decide, does it have a mindset,
does it have a way of thinking about the world
that is different from the scientist who is doing discovery
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 truth
about the abstractions that may be artificial, but whatever.
Or is it the engineer who's all about
building demonstrably superior technology
with respect to some notion of trade-offs,
whatever that means, right?
That's sort of the world that you live in.
What is computing?
How is computing different?
So, I've thought about this for a long time,
and I've come to a view about what computing actually is,
what the mindset is, and it's a little abstract,
but that would be appropriate for computing.
I think that what distinguishes the computationalist
from others is that he or she understands
that models, languages, and machines are equivalent.
They're the same thing.
And because it's not just a model,
but it's a machine that is an executable thing
that can be described as a language,
that means that it's dynamic.
So, it is mathematical in some sense,
in the kind of sense of abstraction,
but it is fundamentally dynamic and executable.
The mathematician is not necessarily worried
about either the dynamic part.
In fact, whenever I tried to write something
for mathematicians, they invariably demand
that I make it static, and that's not a bad thing.
It's just, it's a way of viewing the world,
that truth is a thing, right?
It's not a process that continually runs, right?
So, that dynamic thing matters,
that self-reflection of the system itself matters,
and that is what computing brought us.
So, it is a science, because the models fundamentally
represent truths in the world.
Information is a scientific thing to discover, right?
Not just a mathematical conceit that gets created.
But of course, it's engineering,
because you're actually dealing with constraints
in the world and trying to execute machines
that actually run.
But it's also 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,
and the other is a language,
that they're actually the equivalent thing.
I mean, that is not a small thing,
and it permeates everything that we do,
even when we're just trying to figure out
how to do debugging.
So, that idea, I think, is fundamental,
and we would do better if we made that more explicit.
How my life has changed in my thinking about this
in the 10 or 15 years it's been
since I tried to put that to paper with some colleagues
is the realization, which comes to a question
you actually asked me earlier,
which has to do with trees falling down
and whether it matters,
is this sort of triangle of equality,
it only matters because there's a person
inside the triangle, right?
That what's changed about computing,
computer science, 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
that changes the data, the information that makes sense
and gives meaning to the models,
the languages, and the machines.
So, if the curriculum can convey that
while conveying the tools and the skills
that you need in order to succeed,
then it is a big win.
That's what I think you have to do.
- Do you pull psychology,
it's like these human things into that,
into the idea, into this framework of computing,
do you pull in psychology, neuroscience,
like parts of psychology, parts of neuroscience,
parts of sociology?
What about philosophy, like studies of human nature
from different perspectives?
- Absolutely.
And by the way, it works both ways.
So, let's take biology for a moment.
It turns out a cell is basically
a bunch of if-then statements,
if you look at it the right way,
which is nice because I understand if-then statements.
I never really enjoyed biology,
but I do understand if-then statements.
And if you tell the biologist 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
and the way that we, well,
certainly the way we would do AI and machine learning,
but there's just even the way that we think about,
we think about computation.
§12Transcript · 1:22:27–1:30:17
So, the important thing to me is,
as my engineering colleagues
who are not in computer science worry about computer science
eating up engineering 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 gonna do.
You're not gonna be a historian in 2030.
You're not gonna get a PhD in history
without understanding some data science and computing
because the way you're gonna get history done, in part,
and I say done,
the way you're gonna get it done
is you're going to look at data
and you're gonna let,
you're gonna have the system
that's gonna 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 is 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, at least 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 wouldn't say it's gonna metastasize,
but there's kind of two ways that fields destroy themselves.
One is they become super narrow,
and I think we can think of fields that might be that way.
They become pure.
And we have that instinct, we have that impulse.
I'm sure you can think of several people
who want computer science to be this pure thing.
The other way is you become everywhere
and you become everything and nothing.
And so everyone says,
I'm gonna teach Fortran for engineers or whatever,
I'm gonna do this.
And then you lose the thing
that makes it worth studying in and of itself.
The thing about computing,
and this is not unique to computing,
though at this point in time,
it is distinctive about computing,
where we happen to be in 2020,
is we are both a thriving major.
In fact, the thriving major, almost every place.
And we're a service unit,
because people need to know the things we need to know.
And our job, much as the mathematician's job,
is to help this person over here
to think like a mathematician,
much the way the point of you taking chemistry as a freshman
is not to learn chemistry,
it's to learn to think like a scientist, right?
Our job is to help them to think like a computationalist,
and we have to take both of those things very seriously.
And I'm not sure that as a field,
we have historically certainly taken the second thing,
that our job is to help them to think a certain way.
People who aren't gonna be our 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,
'cause everything is happening so fast
that the history starts losing its meaning
and then it starts being a thing that only,
like the theoretical computer science part
of computer science, it becomes a niche thing
that only 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,
it's best for that to be a pervasive thing
that everybody learns and thinks about and so on.
And I would say computing is quite obviously
similar to history in the sense that it seems
like it should be a part of everybody's life to some degree,
especially 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.
Depending if it's machine learning,
it's unclear that 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 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 you have to forget
more and more of history, but history
needs to always be there.
I mean, I can imagine a world where,
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 kind of way of,
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 those who do not remember.
- For our objective camera discussion, right?
- Yeah, right, and we've already lost lots of history,
and of course, you have your own history,
that some of which will be, it's even lost to you.
You don't even remember whatever it was
you were doing 17 years ago.
- All the ex-girlfriends.
- Yeah.
- Gone.
- Exactly, so history's being lost anyway,
but the big lessons of history shouldn't be,
and I think 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 don't wanna lose the data,
even if 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 in the sense of,
not resolution in the sense of get the right number of bits.
In saying what it is you want the machine to do,
and being able to describe a problem in such a way
that it is executable, which we are not,
human beings are not very good at that.
In fact, I think we spend much of our time
talking back and forth just to kind of vaguely understand
what the other person means, and hope we get it good enough
that we can act accordingly.
You can't do that with machines, at least not yet.
And so 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 and 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.
We get just so all caught up in the deep network,
or we get all caught up in whatever,
§13Transcript · 1:30:18–1:38:26
that we forget that we're making decisions
based upon a set of parameters that we made up.
And if we did slightly different parameters,
we'd have completely different outcomes.
And so the lesson of computing, computer science education,
is to be able to think like that,
and to be aware of it when you're doing it.
Basically, at the end of the day,
it's a way of surfacing your assumptions.
I mean, we call them parameters,
or we call them if-then statements or whatever,
but you're forced to surface those assumptions.
That's the key thing that you should get out
of a computing education, that and that the models,
the languages, and the machines are equivalent.
But it actually follows from that,
that you have to be explicit about
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 will not only make them better
at whatever it is that they are doing,
and I emphasize doing,
it'll also make them better citizens.
It'll help them to understand what others are doing to them
so that they can react accordingly,
'cause you're not gonna 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, 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.
- I prefer black. - Black.
- With a capital B. - With a capital B.
The other letters are-- - The rest of them,
no matter, it's a capital B.
(Lex laughing)
- Okay, so predominantly black neighborhood,
and so you didn't almost see race.
Maybe you can correct me on that.
And then 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?
- All right, that's easy.
And by the way, that story continues through MIT as well.
In fact, it was quite a bit more stark at MIT and Boston.
- So maybe just a quick pause,
Georgia Tech was undergrad, MIT was graduate school.
- Mm-hmm, mm-hmm, 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.
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--
- You miss MIT?
- Oh, I loved MIT.
I don't miss Boston at all, but I loved MIT.
- And then Miss Fighting Warrants, okay.
- Oh, so let's back up to this.
So as you said, I was born in Chattanooga, Tennessee.
My earliest memory is arriving in Atlanta
in a moving truck at the age of three and a half,
so I think of myself as being from Atlanta.
I have 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's 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, this 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, or your parents?
Did your parents know about this?
- Yes, but being a girlfriend and boyfriend in first grade
just basically meant that you spent
slightly more time together during recess.
It had no, I think we Eskimo kissed once.
It doesn't mean, it didn't mean anything.
It was, at the time, it felt very scandalous
'cause 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.
That would come during puberty,
when I wrote lots and lots of poetry.
Anyway, so I was aware of it.
I didn't think too much about it, but I was aware of it.
But I was surrounded.
It wasn't that I wasn't aware of race,
it's that I wasn't aware that I was a minority.
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,
'cause there were only three, four, five channels, right?
And I saw the news, which my mother might make me watch.
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
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 much of the rest of my career at Georgia Tech.
Of course I found lots of other students.
And I met people, 'cause 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 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.
I just didn't know any better, right?
No one told me.
When I went to grad school, I applied to three places,
Georgia Tech, because that's where I was, MIT, and CMU.
When I got in to MIT, I got into CMU,
but I had a friend who went to CMU.
And so I asked him what he thought about it.
He spent his time explaining to me about Pittsburgh,
much less about CMU, but more about Pittsburgh,
of 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.
§14Transcript · 1:38:29–1:46:58
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 dads told me why.
And the stories were remarkably the same
for the two clusters.
And I came up here, and I could see it immediately,
why people would love it and why people would not.
- Why people tell you about the nice coffee shops.
- Well, it wasn't 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 wasn't the slightest bit interested in.
The outdoors is for the bugs, it's not for humans.
And the-- (Lyle laughs)
- 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 wanna be caught in
after dark, and I heard all, but that was nothing new.
So I decided that MIT was a great place to be
as a university, and I believed it then, I believe it now.
And that whatever it is I wanted to do,
I thought I knew what I wanted to do,
but what if I was wrong?
Someone there would know how to do it.
Of course, then I would pick the one topic
that nobody was working on at the time, but that's okay.
It was great, and so I thought that I would be fine.
And I'd only be there for like four or five years.
I told myself, 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 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.
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 a certain age.
But the idea is you could be in a major metropolitan area
and there wasn't a single black radio station,
by which I mean a radio station
that played 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.
And where you had concentrations of black Bostonians,
they tended to be poor.
It was very different from where I grew up.
I grew up on the poor side of town, sure,
but then in high school, well, in ninth grade,
we didn't have middle school.
I went to an eighth grade school
where there was a lot of, let's just say,
we had a riot the year that I was there.
There was at least one major fight every week.
It was an amazing experience.
But when I went to ninth grade, I went to academy.
- Math and-- - Math and 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 school, high school,
I think, in the state of Georgia
to sweep the state math and science fairs.
It was great.
It had 385 students, all but four of whom were black.
I went to school with the daughter
of the former mayor of Atlanta, Michael Jackson's cousin.
I mean, you know, it was an upper middle class--
- Dropping names. - Whatever.
I just dropped names occasionally.
You know, I dropped the mic, dropped some names,
just to let you know I used to hang out
with Michael Jackson's cousin.
12th cousin, nine times removed, I don't know.
The point is, we had a parking problem
'cause the kids had cars.
I did not come from a place where you had cars.
I had my first car when I came to MIT, actually.
So it was just a very different experience for me.
But I'd been to places where, whether you were rich
or whether you were poor, you could be black and rich
or black and poor, and it was there,
and there were places, and they were segregated
by class as well as by race, but that existed.
Here, at least when I was here,
it didn't feel that way at all,
and it felt like a bunch of,
a really interesting contradiction.
It felt like it was the interracial dating capital
of the country.
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.
- Subtle racism or explicit racism?
- Both, both.
- In terms of within the institutions,
did you feel, 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 a part of,
you know, Georgia Tech or MIT?
And was there a part where it was, 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 I not feel as if no one was trying to stop me,
I had the distinct impression
that people wanted me to succeed.
By people, I meant the people in power.
Not my fellow students, not that they didn't want me
to succeed, but I felt supported,
or at least that people were happy to see me succeed
at least as much as anyone else.
But you know, 1990, you're dealing with a different set
of problems, right?
You're very early, at least in computer science,
you're very early in the sort of Jackie Robinson period.
You know, there's this thing called
Jackie Robinson syndrome, which is that you have to,
you know, the first one has to be perfect,
or has to be sure to succeed because if that person fails,
no one else comes after for a long time.
So, you know, it was kind of in everyone's best interest.
But I think it came from a sincere place.
I'm completely sure that people went out of their way
to try to make certain that the environment would be good.
Not just for me, but for the other people
who of course were around.
And I was hardly the only, I was the only person
in the AI lab, but I wasn't the only person
at 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, right?
Shirley Jackson, right?
1971, something like that, somewhere around then.
So we weren't that far away from the first first.
And we were still another eight years away
from the first black PhD computer science, right?
So we were in a, 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,
though 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 said, you used the word, I think, illusion or delusion.
Is there a sense from the individual perspective
where you should always kind of ignore, you know,
the, ignore your own eyes,
ignore the little forces that you are able
to observe around you that are like trying
to mess with you of whether it's jealousy,
whether it's hatred in its pure form,
whether it's just hatred in its like deluded form,
all that kind of stuff, and just kind of see yourself
as empowered and confident, all those kinds of things?
- I mean, it certainly helps, but there's a 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.
§15Transcript · 1:47:00–1:55:16
I mean, that kind of massive delusion is that.
So you have to be deluded enough to believe
that you can succeed despite whatever odds you see
in front of you, but you can't be so deluded
that you don't think that you need to step out
of the way of the oncoming train.
- Right. - 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 a 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 the objective eyes?
I don't know how to say it more poetically.
- Well, I guess the, 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 it cycles
and it cycles and it cycles.
So it's just about being on edge.
I do not, despite having made it over 50 now.
Despite-- - Congratulations, by the way.
- 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 gonna get a ticket or whatever, right?
I mean, the interesting thing about the law in general
is that most human beings' experience of it
is fundamentally negative, right?
You're only dealing with a lawyer
if you're in trouble,
except in a few very small circumstances, right?
But, so that's just, that's 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,
as he, when he pulled his gun on me,
that if he shot me right now, he'd get away with it.
That was the worst thing that I felt
about that particular moment,
is that if he shoots me now, he will get away with it.
It would be years later when I realized,
actually much worse than that,
is that he'd get away with it,
and if anyone, if it became a thing
that other people knew about,
odds were, would be, of course, that it wouldn't,
but if it became a thing that other people knew about,
if I was living in today's world
as opposed to the world 30 years ago,
that not only would he get away with it,
but that I would be painted a villain.
I was probably big and scary,
and I probably moved too fast,
and if only I'd done what he said,
and da, da, da, da, da, da,
which is somehow worse, right?
You know, that hurts not just you, you're dead,
but your family, and the way people look at you,
and look at your legacy or your history,
that's terrible, and it would work.
I absolutely believe it would've worked had he done it.
Now, he didn't.
I don't think he wanted to shoot me.
I don't think 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 were basically speeding or something like that?
- He said I ran a light, I think.
- Ran a light, sure.
- I don't think I ran a light,
but in fact, I may not have even gotten a ticket.
I may have just gotten a warning.
I think he was a little spooked, too.
- But he pulled a gun.
- Yeah, apparently I moved too fast or something.
Rolled my window down before I should've.
It was unclear.
I think he thought I was gonna 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, and I apologize.
Just talked to Dan Carlin about it, he left for four hours.
So sorry if I go dark here a little bit,
but is it easy for this experience
of just being careful with the fire
and avoiding it to turn to hatred?
- Yeah, of course.
And one might even argue that it is a illogical conclusion.
On the other hand, you've got to live in the world,
and I don't think it's helpful.
Hate is something one should,
I mean, 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.
If 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,
you know, being a police officer isn't even
in the top 10 most dangerous jobs in the United States,
you're much more likely to get killed in a taxi cab.
Half of police officers are actually killed by suicide.
But that means their lives are something,
something's going on there with them.
And I would more than happy to be empathetic
about what it is they go through
and how they see the world.
I think though that if we step back from what I feel,
and we step back from what an individual police officer
feels, you step up a level, and all this,
because all things tie back into interactive AI.
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, in a completely different household,
as a completely different person, if I had been a woman,
would I see the world differently?
Would I believe what that crazy person over there believes?
And the answer's probably yes,
§16Transcript · 1:55:19–2:03:44
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, is 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, right?
It's not, I mean, individual conversations matter a lot,
but you have to create structures that allow people
to have those individual conversations all the time
in a way that is relatively safe,
and that allows them to understand that other people
have had different experiences, but that ultimately
we're 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 wanna be a little optimistic,
and then recognize that the hard problem
is actually setting up the structures in the first place,
because it's in almost no one's interest
to change the infrastructure.
- Right, I tend to believe that leaders
have a big role to that, of selling that optimistic delusion
to everybody, and that eventually leads
to the building of the structures.
But that requires a leader that unites,
sort of unites everybody on 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 non-violent civil war.
This is one of the most divisive periods
of American history in recent,
you can speak to this from perhaps a more knowledgeable
and deeper perspective than me,
but from my naive perspective,
this seems like a very strange time.
There's a lot of anger, and it has to do with people,
I mean, for many reasons.
One, the thing that's not spoken about,
I think, much is the quiet economic pain of millions
that's growing because of COVID,
because of closed businesses, because of lost dreams.
So 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 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, right?
I mean, there was a massive one in New York.
I mean, I'm talking way, way, way back when.
So Boston used to be the hotbed of riots.
It's just what Boston was all about,
or 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 gotta get another generation,
old enough to be angry, but not so old
to remember what happened the last time, right?
And that's sort of what happens.
But you said like 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 upbuilt?
And the other has to do with a kind of,
sort of the economic reality of COVID.
So I'm actually, I want to separate those things,
because for example, you know,
this happened before COVID happened, right?
So let's separate these two things for a moment.
Now, let me preface all this by saying
that although I am interested in history,
one of my three minors as an undergrad was history,
specifically history of the 1960s.
- Interesting. - The other was Spanish.
- Okay, that's a mistake.
- Oh, I loved, I loved. - Okay.
- And history of, and Spanish history, actually,
but Spanish, and the other was
what we would now call cognitive science,
but at the time--
- Oh, that's fascinating, interesting.
- I minored in cocci here for grad school.
That was really, that was really fascinating.
It was a very different experience
from all the computer science classes I'd been taking,
even the cocci classes I was taking at an undergrad.
Anyway, I'm not, I am a, I'm interested in history,
but I'm hardly a historian, right?
So, you know, 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 the 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 v. the Board of Education
or the Montgomery Bus Boycott,
and that's when it happened.
These things have been building up forever
and go all the way back and all the way back
and all the way back, and, you know,
Harriet Tubman was not born in 1950, right?
So, you know, 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?
'Cause one was born 20 years after the other, whatever.
I think it turns out that, you know what 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 gonna take people and we are going to
non-violently put them in trucks,
and then we are going to take them
and put them in jails very far away from here.
And we're gonna do that, and we're not gonna,
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 Bull O'Connor, and you got to see on TV
little boys and girls being hit with fire hoses
and being knocked down, and there was outrage,
and things changed, right?
Part of the delusion is pretending that nothing bad
is happening that might force you to do something big
you don't want to do, but sometimes it gets put in your face
and then you kind of can't ignore it.
And a large part, in my view, of what happened, right,
was that it was too public to ignore.
Now, we created other ways of ignoring it.
Lots of change happened in the South,
but part of that delusion was that it wasn't gonna affect
the West or the Northeast, and of course it did,
and that caused its own set of problems,
which went into the late '60s into the '70s,
and in some ways we're living with that legacy now,
and so on.
So, why not, what's happening now?
Why it 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.
- It makes a huge difference.
It's fascinating to think that whatever the mechanisms
that put injustice in front of our face,
not 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.
§17Transcript · 2:03:46–2:11:52
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 gonna prove it
by having some news, some camera people follow him around.
And he says, "I'm gonna go into these towns
"and just follow me for a week,
"and you will see that I'll get harassed."
And like the first night, he goes out there,
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 wasn't a kid,
I was in college at the time, I was in grad school at the time.
- So that's not enough, like just--
- Well, it disappeared.
Like a day later, it didn't go viral.
- Whatever that is, whatever that magic thing is.
- And whatever it was in '92,
it was harder to go viral in '92, right?
Or '91, actually it must have been '90 or '91.
But that happened, and like two days later,
it's like it never happened.
Like nobody, again, nobody remembers this,
but I'm like the only person.
Sometimes I think I must have dreamed it.
Anyway, Rodney King happens, it goes viral,
or the moral equivalent thereof at the time.
And eventually, we get April 29th, right?
And I don't know what the difference was
between the two things,
other than one thing caught on and one thing didn't.
Maybe what's happening now
is two things are feeding onto one another.
One is more people are willing to believe.
And the other is there's easier and easier ways
to give evidence.
Cameras, body cams, or whatever.
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 it,
and 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 Martin Luther King.
- You know Malcolm X was older than Martin Luther King?
People kind of have it in their head that he's younger.
Well, he died sooner, right?
But only by a few years, right?
People think of MLK 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 don't, I just, I reject the choice.
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 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.
- That's the annoying thing.
- It seems like over-the-top anger works.
Outrage works.
So you can say being calm and rational,
just talking it out is gonna 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 that's--
- 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, right?
So, and what do any of those things do
other than expose things to people?
Violence is a way of shouting.
I mean, many people far more talented and thoughtful
than I have have said this in one form or another, right?
That violence is the voice of the unheard, right?
I mean, it's a thing that people do
when they feel as if they have no other option.
And sometimes we agree, and sometimes we disagree.
Sometimes we think they're justified.
Sometimes we think they are not.
But regardless, it is a way of shouting.
And when you shout, people tend to hear you,
even if they don't necessarily hear
the words that you're saying.
They hear that you 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.
But it's a struggle nonetheless.
- The powerful don't give up power easily.
- I mean, why should they?
But even so, it still has to be a struggle.
And by the way, this isn't just about 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.
- And the godfather scene.
- All a metaphor.
Faculty hiring is a metaphor for all of life.
- Let me ask a strange question.
Do you think everything is gonna be okay in the next year?
Do you have a hope that we're gonna be okay?
- I tend to think that everything's gonna be okay,
because I just tend to think
that everything's gonna 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 in about 15 minutes,
given how fast she seems to be growing up.
And 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 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 about,
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, but you know, at any given moment, a comet could,
I mean, you know, whatever.
I tend to 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 Villepenco from Berkeley.
He was talking about comets
and that they can come out of nowhere,
and that was a realization to me.
Wow, we're just watching this darkness,
and they can just enter, and then we have less than a month.
- Yeah, 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 gonna get hit by an asteroid,
at least not while I'm around, and if we are,
well, there's very little I can do about it,
so I might as well assume it's not going to happen.
- It makes food taste better.
- It makes food taste better.
- So you, out of the millions of things
you've done in your life, you've also began
the This Week in Black History calendar of facts.
There's like a million questions I can ask here.
You said you're not a historian, but is there,
let's start at the big history question of,
is there somebody in history, in black history,
that you draw a lot of philosophical
or personal inspiration from, or you just find interesting,
or a moment in history you find interesting?
§18Transcript · 2:11:55–2:19:14
- Well, I find the entirety of the '40s and 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.
It was because of the sources I was stealing from,
and I was very much stealing from,
so I'd take calendars, anything I could find,
Google didn't exist, right, and I just pulled
as much as I could and just put it together
in one place for other people.
What ended up being quirky about it,
and I started getting people sending me information,
was the inventors, people who,
Garrett Morgan to Benjamin Banneker,
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 may never come back,
but they were inventing things we use, right?
And it was always inspiring to me
that people would still create,
even under those circumstances.
I got a lot out of that.
I also learned a few lessons, I think,
you know, the Charles Richard Drews 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, yeah.
I mean, look, in our world, all we really have is credit.
- I was always bothered by how much value credit is given.
- That's the only thing you got.
I mean, if you're an academic in some sense,
well, it isn't the only thing you've got,
but it feels that way sometimes.
- But you got the actual, we're all gonna be dead soon.
You got the joy of having created.
You know, the credit with Jan,
I've talked to Jorgen Schmidhuber, right?
The Turing Award given to three people for deep learning,
and you could say that a lot of other people
should be on that list.
It's the Nobel Prize question.
Yeah, it's sad.
It's sad, and people like talking about it,
but I feel like in the long arc of history,
the only person who'll be 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 him, "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' names,
all my grandparents' names.
I know what I called them, right?
I don't know their middle names, for example.
Didn't live in living memory, so I could find out.
Actually, my grandfather didn't know when he was born.
Had no idea how old he was, right?
But I definitely don't know
who any of my great-grandparents are.
So in some sense, immortality is doing something,
preferably positive, so that 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.
I could turn it into something uplifting
if you need me to, but it's--
- Yeah, can you do the work here?
- Yeah, it's simple, right?
It doesn't matter.
I don't have to know who my great-grandfather was
to know that I wouldn't be here without him.
- Yeah.
- And I don't know who my great-grandchildren are,
certainly who my great-great-grandchildren are,
and I'll probably never meet them,
although I would very much like to.
But hopefully I'll set the world in motion
in such a way that their lives will be better
than they would have been if I hadn't done that.
Well, certainly they wouldn't have existed
if I hadn't done the things that I did.
So I think that's a good positive thing.
You live on through other people.
- Are you afraid of death?
- I don't know if I'm afraid of death, but I don't like it.
(laughing)
- Another T-shirt.
(laughing)
I mean, do you ponder it?
Do you think about the--
- Yes, the inevitability of oblivion?
- Yes.
- I do occasionally.
This feels like a very Russian conversation, actually.
- It's very, yeah.
- 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.
- Yes.
- Which, by the way, is an interesting story of its own
about why people who 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 gonna come out of a doctor's visit, right?
So I went to the doctor to look at one thing,
this little bump I had on the side
that I thought might be something bad
because my mother made me, and I went there,
and he's like, "Oh, it's nothing,
"but by the way, your thyroid is huge.
"Can you breathe?"
"Yes, I can breathe."
"Are you sure?
"'Cause it's pushing on your windpipe.
"You should be dead."
"Ah!"
So I was sitting there, and to look at my thyroid,
it was growing.
I had what's called a goiter,
and he said, "We're gonna have to take it out
"at some point."
"When?"
"Sometime before you're 85, probably,
"but if you wait 'til you're 85, that'll be really bad
"because you don't wanna 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's December 18th,
and I wouldn't be able to say I made it to 49 or whatever,
so I said, "I'll wait 'til after my birthday."
In the first six months of that, nothing changed.
Apparently, in the next three months,
it had grown, I hadn't noticed this at all.
I went and had surgery.
They took out half of it.
The other half is still there,
and it's 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 on my arm.
They're gonna do whatever.
They're talking to me, and the anesthesiologist says,
"Huh, your blood pressure's through the roof.
"Do you have high blood pressure?"
I said, "No, but I'm terrified if that helps you at all."
And the anesthetist, 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 gonna be feeling pretty good in a couple minutes."
And I remember turning and saying,
"Well, I'm gonna feel pretty good in a couple minutes."
Next thing I know, there's this guy,
and he's moving my bed.
And he's talking to me.
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.
§19Transcript · 2:19:15–2:23:44
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, so--
- Between the tiles going by
and me waking up in my hospital bed, no time passed.
There was no sensation of time passing.
When I go to sleep and I wake up in the morning,
I have this feeling that time has passed,
or 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 witchcraft
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.
- Yeah.
- 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'm very unhappy
at the idea that I won't notice.
So I don't know if I'm afraid of death,
but I'm 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.
So, immortality.
- Well, in 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
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'm 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, save 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 Lipman.
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 loved Georgia Tech.
I'm excited to see with you being involved there
what the future holds.
So thank you for talking today.
- Thank you for having me.
I appreciate every minute of it.
- Thanks for listening to this conversation
with Charles Isbell, and thank you to our sponsors,
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And now let me leave you with some poetic words
from Martin Luther King Jr.
There comes a time when people get tired
of being pushed out of the glittering sunlight
of life's July and left standing amid the piercing chill
of an alpine November.
Thank you for listening, and hope to see you next time.
(upbeat music)
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