The following is a conversation with Charles Isbell and Michael Litman.

Charles is the Dean of the College of Computing at Georgia Tech,

and Michael is a computer science professor at Brown University.

I've spoken with each of them individually on this podcast,

and since they are good friends in real life, we all thought it would be fun

to have a conversation together. Quick mention of each sponsor,

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for a while. In particular, because down the road I

would like to occasionally be a kind of moderator for debates

between people that may disagree in some interesting ways.

If you have suggestions for who you would like to see debate on this podcast,

let me know. As with all experiments of this kind,

it is a learning process. Both the video and the audio might need

improvement. I realized I think I should probably do

three or more cameras next time as opposed to just two

and also try different ways to mount the microphone for the

third person. Also, after recording this intro,

I'm going to have to go figure out the thumbnail for the video version of the

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and now there's two heads and two names

to try to fit into the thumbnail. It's a kind of

bin packing problem, which in theoretical computer science

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And now here's my conversation with Charles Isbell

and Michael Litman. You'll probably disagree

about this question, but what is your biggest, would you say, disagreement

about either something profound and very important or something completely not

important at all? I don't think you have any disagreements at all.

I'm not sure that's true. We walked into that one, didn't we?

So one thing that you sometimes mention is that, and we did this one on

air too, as it were, whether or not machine learning is

computational statistics. It's not. But it is.

Well, it's not. And in particular, and more importantly,

it is not just computational statistics. So what's missing in the picture?

All the rest of it. What's missing? That which is missing.

Oh, you can't be wrong now. Well, it's not just the statistics.

He doesn't even believe this. We've had this conversation before.

If it were just the statistics, then we would be happy with where we are.

But it's not just the statistics. That's why it's computational

statistics. Or if it were just the computational.

I agree that machine learning is not just statistics.

It is not just the statistics. We can agree on that.

Nor is it just computational statistics. It's computational statistics.

It is computational. What is the computational and

computational statistics? Does this take us into the realm of computing?

It does, but I think perhaps the way I can get him to admit that he's wrong

is that it's about rules. It's about rules.

It's about symbols. It's about all these other things.

But the statistics is not about rules? I'm going to say statistics is about rules.

But it's not just the statistics, right? It's not just a random variable that you

choose and you have a probability. I think you have a narrow view of statistics.

Okay. Well, then what would be the broad view of statistics that would still allow it to be

statistics and not say history that would make computational statistics okay?

Well, okay. So I had my first sort of research mentor, a guy named Tom Landauer,

who taught me to do some statistics, right? And I was annoyed all the time because the

statistics would say that what I was doing was not statistically significant.

And I was like, but, and basically what he said to me is,

statistics is how you're going to keep from lying to yourself,

which I thought was really deep. It is a way to keep yourself honest in a

particular way. I agree with that. Yeah. And so you're trying to find rules.

I'm just going to bring it back to rules. Wait, wait, wait. Could you possibly try to

define rules? Even regular statisticians, non computational statisticians, do spend some

of their time evaluating rules, right? Applying statistics to try to understand,

is this, you know, is this, does this rule capture this? Does this not capture that?

You mean like hypothesis testing kind of thing? Sure. Or like confidence intervals?

Like, like, I think more like hypothesis. Like I feel like the word

statistic literally means like a summary, like a number that summarizes other numbers.

Right. But I think the field of statistics actually applies that idea to, to things like

rules to understand whether or not a rule is valid. So software engineering statistics?

No. Programming languages statistics? No. Because I think there's a very, it's useful

to think about a lot of what AI and machine learning is or certain it should be as software

engineering as programming languages. Just if to put it in language that you might understand,

the hyperparameters beyond the problem. The hyperparameters is too many syllables

for me to understand. The hyperparameters. That's better.

That goes around it, right? It's the decisions you choose to make. It's the metrics you choose to

use. It's the loss function. You want to say the practice of machine learning is different

than the practice of statistics. Like the things you have to worry about and how you worry about

them are different. Therefore, they're different. Right. At a very little, at the very least,

it's that much is true. It doesn't mean that statistics computational or otherwise aren't

important. I think they are. I mean, I do a lot of that, for example. But I think it goes beyond

that. I think that we could think about game theory in terms of statistics, but I don't think

it's very as useful to do. I mean, the way I would think about it or a way I would think about it

is this way. Chemistry is just physics. But I don't think it's as useful to think about chemistry

as being just physics. It's useful to think about it as chemistry. The level of abstraction

really matters here. So I think there are contexts in which it is useful. I think it's that way,

right? So finding that connection is actually helpful. And I think that's when I emphasize

the computational statistics thing. I think I want to befriend statistics and not absorb them.

Here's the a way to think about it beyond what I just said. So what would you say,

and I want you to think back to a conversation I had a very long time ago, what would you say

is the difference between, say, the early 2000s, ICML and what we used to call NIPS, NURPS. Is

there a difference? A lot of it, particularly on the machine learning that was done there?

ICML was around that long. Oh, yeah. So I clear as the new conference, newish.

Yeah, I guess so. And ICML was around the 2000. Oh, ICML predates that. I think my most cited

ICML papers from 94. Michael knows this better than me because, of course, he's significantly

older than I am. But the point is, what is the difference between ICML and NURPS in the late

90s, early 2000s? I don't know what everyone else's perspective would be, but I had a particular

perspective at that time, which is I felt like ICML was more of a computer science place and that

NURPS, NURPS was more of an engineering place, like the kind of math that happened at the two

places. As a computer scientist, I felt more comfortable with the ICML math. And the NURPS

people would say that that's because I'm dumb. And that's such an engineering thing to say.

I agree with that part of it, but I do a little differently. I actually had a nice conversation

with Tom Dietrich about this on Twitter just a couple days ago. I put it a little differently,

which is that ICML was machine learning done by computer scientists. And NURPS was machine

learning done by computer scientists trying to impress statisticians, which was weird because

it was the same people, at least by the time I started paying attention. But it just felt very,

very different. And I think that that perspective of whether you're trying to impress the statisticians

or you're trying to impress the programmers is actually very different and has real impact on

what you choose to worry about and what kind of outcomes you come to. So I think it really

matters. I think computer statistics is a means to an end. It is not an end in some sense. And I

think that really matters here in the same way that I don't think computer science is just

engineering or just science or just math or whatever. Okay. So I'd have to now agree that now we

agree on everything. Yes. Yes. The important thing here is that my opinions may have changed,

but not the fact that I'm right, I think is what we just came to. Right. And my opinions may have

changed and not the fact that I'm wrong. That's right. I've lost me. I think I lost myself there

too. But anyway, we're back. We're back. This happens sometimes. We're sorry. How does neural

networks change this? Just leave and linger on this topic, change this idea of statistics,

how big of a pie statistics is within the machine learning thing? Like, because it sounds like

hyperparameters and also just the role of data. You know, this people are starting to use the

terminology of software 2.0, which is like the act of programming as a, as a, like you're a designer

in the hyperparameter space of neural networks, and you're also the collector and the organizer and

the cleaner of the data. And that's part of the programming. So how did, on the NeurIPS versus

ICML topic, what's the role of neural networks in redefining the size and the role of machine

learning? I can't wait to hear what Michael thinks about this, but I would add one. But you will.

But that's true. I will force myself to. I think the, the, there's one other thing I would add to

your description, which is the kind of software engineering part is what does it mean to debug,

for example. But this is a difference between the kind of computational statistics view of

machine learning and the computational view of machine learning, which is, I think one is worried

about the equation as it were. And by the way, this is not a value judgment. I just think it's

about perspective. But the kind of questions you would ask, we start asking yourself, well,

what does it mean to program and develop and build the system is a very computer

sciencey view of the problem. I mean, when, if you get on data science Twitter and econ Twitter,

you actually hear this a lot with the, you know, the economist and the data scientist complaining

about the machine learning people was, you know, it's just statistics. And I don't know why they

don't don't see this, but they're not even asking the same questions. They're not thinking about it

as a kind of programming problem. And I think that that really matters just asking this question.

I actually think it's a little different from programming and hyperparameters space and sort

of collecting the data. But I do think that that immersion really matters. So I'll give you a quick,

a quick example of the way I think about this. So I teach machine learning, Michael and I have

co taught a machine learning class, which has now reached, I don't know, 10,000 people at least

over the last several years, or somewhere there's abouts. And my machine learning assignments are

of this form. So the super the first one is something like implement these five algorithms,

you know, K and N and S, you know, SVMs and boosting and decision trees and neural networks.

And maybe that's it. I can't remember. And when I say implement, I mean, steal the code. I'm

completely uninterested. You get zero points for getting the thing to work. And I want you

spending your time worrying about getting the corner case right of, you know, what happens

when you are trying to normalize distances and the points on the thing. And so you divide by

zero. I'm not interested in that, right? Steal the code. However, you're going to run those

algorithms on two data sets. The data sets have to be interesting. What does it mean to be interesting?

Well, data sets interesting if it reveals differences between algorithms, which presumably

are all the same, because they can represent whatever they can represent. And two data sets

are interesting together if they show different differences as it were. And you have to analyze

them. You have to justify their interestingness and you have to analyze them in a whole bunch of

ways. But all I care about is the data in your analysis, not the programming. And I occasionally

end up in these long discussions with students. Well, I don't really, I copy and paste the things

that I've said the other 15,000 times it's come up, which is, they go, but the only way to learn

really understand is to code them up, which is a very programmer software engineering view

of the world. If you don't program it, you don't understand it, which is, by the way,

I think is wrong in a very specific way. But it is a way that you come to understand because then

you have to wrestle with the algorithm. But the thing about machine learning is not just sorting

numbers. Where in some sense, the data doesn't matter. What matters is, well, does the algorithm

work on these abstract things, one to less than the other. In machine learning, the data matters.

It matters more than almost anything. And not everything, but almost anything. And so as a

result, you have to live with the data and don't get distracted by the algorithm per se. And I

think that that focus on the data and what it can tell you and what question it's actually answering

for you, as opposed to the question you thought you were asking, is a key and important thing

about machine learning and is a way that computationalists, as opposed to statisticians,

bring a particular view about how to think about the process. The statisticians, by contrast,

bring, I think I'd be willing to say, a better view about the kind of formal math that's behind

it and what an actual number ultimately is saying about the data. And those are both important,

but they're also different. I didn't really think of it this way is to build intuition about

the role of data, the different characteristics of data by having two data sets that are different

and they reveal the differences and the differences. That's a really fascinating,

that's a really interesting educational approach. The students love it, but not right away.

They love it later. They love it at the end. Not at the beginning. Not even immediately after.

I feel like there's a deep, profound lesson about education there. Yeah. That you can't listen to

students about whether what you're doing is the right or the wrong thing.

Well, as a wise, Michael Lippmann once said to me about children, which I think applies to teaching,

is you have to give them what they need without bending to their will. And students are like

that. You have to figure out what they need. You're a curator. Your whole job is to curate

and to present because on their own, they're not going to necessarily know where to search.

So you're providing pushes in some direction and learn space and you have to give them what they

need in a way that keeps them engaged enough so that they eventually discover what they want

and they get the tools they need to go and learn other things. What's your view? Let me put on

my Russian hat, which believes that life is... I like Russian hats, by the way. If you have one,

I would like this. Those are ridiculous. Yes. But in a delightful way, but sure.

What do you think is the role of... We talked about balance a little bit. What do you think is

the role of hardship in education? I think the biggest things I've learned, what made me fall

in love with math, for example, is by being bad at it until I got good at it. So struggling with

a problem, which increased the level of joy I felt when I finally figured it out. It always felt

with me, with teachers, especially modern discussions of education, how can we make

education more fun, more engaging, more all those things? Well, from my perspective, it's like

you may be missing the point that education, that life is suffering. Education is supposed to be hard

and that actually what increases the joy you feel when you actually learn something. Is that

ridiculous? Do you like to see your students suffer? Okay. So this may be a point where we differ.

I suspect not. Let me do go on. Well, what would your answer be? I want to hear you first. Okay. Well,

I was going to not answer the question. You don't want the students to know you're going to suffer?

No, no, no. I was going to say that there's... I think there's a distinction that you can make

in the kind of suffering. So I think you can be in a mode where you're suffering in a hopeless way

versus you're suffering in a hopeful way, where you're like, you can see that you can still imagine

getting to the end. And as long as people are in that mindset where they're struggling, but it's

not a hopeless kind of struggling, that's productive. I think that's really helpful.

But it's struggling. If you break their will, if you leave them hopeless, no, that don't... Sure,

some people are going to whatever lift themselves up by their bootstraps, but mostly you give up

and certainly it takes the joy out of it. And you're not going to spend a lot of time on something

that brings you no joy. So it is a bit of a delicate balance. You have to thwart people

in a way that they still believe that there's a way through.

Right. So that's a... We strongly agree, actually. So I think... Well, first off, struggling and

suffering aren't the same thing, right? Yeah, it's being poetic.

Oh, no. I actually appreciate the poetry. And one of the reasons I appreciate it is that they are

often the same thing and often quite different, right? So you can struggle without suffering.

You can certainly suffer pretty easily. You don't necessarily have to struggle to suffer.

So I think that you want people to struggle, but that hope matters. You have to understand

that they're going to get through it on the other side. And it's very easy to confuse the two.

I actually think Brown University has a very... Just philosophically has a very different take

on the relationship with their students, particularly undergrads from, say, a place like

Georgia Tech, which is... Which university is better? Well, I have my opinions on that.

I mean, remember, Charles said, it doesn't matter what the facts are, I'm always right.

The correct answer is that it doesn't matter. They're different. But they're clearly answers.

He went to a school like the school where he is as an undergrad. I went to a school,

specifically the same school, though it was changed a bit in the intervening years.

Brown or Georgia Tech.

No, I was talking about Georgia Tech.

Georgia Tech is changed.

Yeah. And I went to an undergrad place that's a lot like the place where I work now. And so it

does seem like we're more familiar with these models.

There's a similarity between Brown and Yale.

Yeah. I think they're quite similar.

Yeah.

And Duke.

Duke has some similarities too, but it's got a little Southern draw.

You've kind of worked your... You sort of worked at universities that are like the places where

you learned. And the same would be true for me.

Are you uncomfortable venturing outside the box? Is that what you're saying?

Joining out?

That's what I'm saying.

Yeah, Charles is definitely... He only goes to places that have Institute in the name, right?

It has worked out that way. Well, academic places anyway.

Well, no, I was a visiting scientist at UPIN or visiting something at UPIN.

Oh, wow. I just understood your joke.

Which one?

Five minutes later.

I like to set these sort of time bombs.

The Institute is in the...

That Charles only goes to places that have Institute in the name.

So I guess Georgia... I forget that Georgia Tech is Georgia Institute of Technology.

The number of people who refer to it as Georgia Tech University is large and incredibly year

old. It's one of the few things that genuinely gets under my skin.

But like schools like Georgia Tech and MIT have as part of the ethos.

Like there is... I want to say there's an abbreviation that someone taught me.

Like IHTFP, something like that.

Like there's an expression which is basically, I hate being here.

Which they say so proudly. And that is definitely not the ethos at Brown.

Like Brown is... There's a little more pampering and empowerment and stuff.

And it's not like we're going to crush you and you're going to love it.

So yeah, I think there's a... I think the ethoses are different.

That's interesting, yeah.

We had Drownproofer.

What's that? Drownproofer?

In order to graduate from Georgia Tech, this is a true thing.

Feel free to look it up.

If you...

A lot of schools have this, by the way.

No. Actually, Georgia Tech was barely the first.

Brandeis has it.

Had it.

I feel like Georgia Tech was the first in a lot of things.

It was the first in a lot of things.

Had the first Master's degree in Georgia.

Stop that.

First Masters in Computer Science, actually.

Right. Online Masters.

Well, that too, but way back in the 60s.

NSF, right?

Yeah, yeah.

Feel the first information in Computer Science Master's degree in the country.

But the Georgia Tech, it used to be the case that I would graduate from Georgia Tech.

You had to take a Drownproofing class.

Where effectively, they threw you in the water to hide you up.

If you didn't drown, you got to graduate.

Hide you up?

I believe so.

No.

You basically... There were certainly versions of it.

But I mean, luckily, they ended it just before I had to graduate

because otherwise I would have never graduated.

That wasn't going to happen.

I want to say 84, 83, someone around them, they ended it.

But yeah, it used to have to prove you could tread water

for some ridiculous amount of time or you couldn't graduate.

No, it was more than two minutes.

I bet it was two minutes.

Okay, well, we'll look.

And it was in a bathtub.

It was in a pool.

But it was a real thing.

But that idea that pushed you...

Fully clothed.

Yeah, fully clothed.

I don't think... I bet it was that and not tied up.

Because who needs to learn how to swim when you're tied?

Nobody.

But who needs to learn to swim when you're actually falling

into the water dressed?

That's a real thing.

I think your facts are getting in the way with a good story.

Oh, that's fair.

That's fair.

I didn't mean to...

All right.

So they tie you up.

Sometimes the narrative matters.

But whatever it was, you had to...

It was called drownproofing for a reason.

The point of the story, Michael, is that...

Struggle.

It's...

Well, no, but that's good.

It doesn't bring it back to struggle.

That's a part of what Georgia Tech has always been.

And we struggle with that, by the way,

about what we want to be, particularly as things go.

But you sort of...

How much can you be pushed without breaking?

And you come out of the other end stronger, right?

There's this saying we used to have when I was an undergrad.

There was Georgia Tech, building tomorrow the night before.

And it was just kind of idea that, you know,

give me something impossible to do,

and I'll do it in a couple of days,

because that's what I just spent the last four or five or six years with.

That ethos definitely stuck to you.

Having now done a number of projects with you,

you definitely will do it the night before.

That's not entirely true.

There's nothing wrong with waiting until the last minute.

The secret is knowing when the last minute is.

Right.

That's brilliantly put.

Yeah, that is a definite Charles statement

that I am trying not to embrace.

Well, and I appreciate that because you helped move my last minute.

That's a social construct where you converge together

with the definitional last minute is.

And we figure that all together.

In fact, MIT, you know, I'm sure a lot of universities have this,

but MIT has like MIT time that everyone has always agreed together

that there is such a concept,

and everyone just keeps showing up like 10 to 15 to 20,

depending on the department, late to everything.

So there's like a weird drift that happens.

It's kind of fascinating.

Yeah, we're five minutes.

We're five minutes.

In fact, the classes will say, you know,

well, this is no longer true, actually,

but it used to be a class that started eight,

but actually it started eight or five.

It ends at nine, actually it ends at eight, 55.

Everything's five minutes off,

and nobody expects anything to start until five minutes

after the half hour, whatever it is.

It still exists.

It hurts my head.

Well, let's rewind the clock back to the 50s and 60s

when you guys met.

How did you, I'm just kidding, I don't know.

But what, can you tell the story of how you met?

So you've, like the internet and the world

kind of knows you as connected in some ways

in terms of education, of teaching the world.

That's like the public facing thing,

but how did you as human beings

and as collaborators meet?

I think there's two stories.

One is how we met,

and the other is how we got to know each other.

I'm not gonna say follow, I'm not gonna say follow,

I'm gonna say that we came to understand that we...

Had some common something, yeah.

It's funny, because on the surface,

I think we're different in a lot of ways,

but there's something that's just consonant.

Yeah, I mean, now we complete each other's...

That's just consonant.

There you go.

Afternoons.

So I will tell the story of how we met,

and I'll let Michael tell the story of how we met.

Okay, all right.

Okay, so here's how we met.

I was already at that point was AT&T Labs.

There's a long, interesting story there,

but anyway, I was there,

and Michael was coming to interview.

He was a professor at Duke at the time,

but decided for reasons that he wanted to be in New Jersey.

And so that would mean Bell Labs slash AT&T Labs.

And we were doing interview,

interviews are very much like academic interviews,

and so I had to be there.

We all had to meet with him afterwards

and so on, one on one.

But it was obvious to me that he was going to be hired,

like no matter what, because everyone loved him.

They were just talking about all the great stuff he did.

Oh, he did this great thing,

and you had just won something at AAAI, I think,

or maybe you got 18 papers in AAAI that year.

I got the best paper award at AAAI for the crossword stuff.

Right, exactly.

So that had all happened,

and everyone was going on and on and on

about it actually.

So Tinder was saying incredibly nice things about you.

Really?

Yes.

So...

He can be very grumpy.

Yes.

So that's very...

That's nice to hear.

He was grumpily saying very nice things.

Oh, that makes sense.

Yeah, it does make sense.

So, you know, so it was going to come.

So why were we...

Why was I meeting him?

I had something else I had to do.

I can't remember what it was.

Yeah.

Probably involved comic books.

So he remembers meeting me as inconveniencing his afternoon.

So he came...

So I eventually came to my office.

I was in the middle of trying to do something.

I can't remember what, and he came and he sat down.

And for reasons that are purely accidental,

despite what Michael thinks,

my desk at the time was set up in such a way

that had sort of an L shape,

and the chair on the outside was always lower

than the chair that I was in.

And, you know, the kind of point was to...

The only reason I think that it was on purpose

is because you told me it was on purpose.

I don't remember that.

Anyway, the thing is that, you know, it kind of gives...

His guest chair was really low

so that he could look down at everybody.

The idea was just to simply create a nice environment

that you were asking for a mortgage,

and I was going to say no.

That was the point.

It was a very simple idea here.

Anyway, so we sat there and we just talked for a little while,

and I think he got the impression that I didn't like him.

It wasn't true.

Strongly got that impression.

The talk was really good.

And he pulled the assignment.

The talk, by the way, was terrible.

And right after the talk, I said to my host,

Michael Kearns, who ultimately was my boss.

I'm a huge fan.

I'm a friend and a huge fan of Michael, yeah.

Yeah.

He is a remarkable person.

After my talk, I went into the...

He went into the basketball.

I went...

Racquetball.

He's good at everything.

No, basketball.

No, but basketball and racquetball, too.

Squash.

He's a squash.

Squash, squash, not racquetball.

Yeah, squash, which is not...

Racquetball, yes.

Squash, no.

And I hope you hear that, Michael.

You mean as a game, not his skill level,

because I'm pretty sure he's...

All right.

There's some competitiveness there,

but the point is that it was like the middle of the day.

I had full day of interviews.

Like, I've met with people,

but then in the middle of the day, I gave a job talk.

And then there was going to be more interviews,

but I pulled Michael aside and I said,

I think it's in both of our best interests

if I just leave now, because that was so bad

that it'd just be embarrassing

if I have to talk to any more people.

Like, you look bad for having invited me.

Like, it's just, let's just forget this ever happened.

So I don't think the talk went well.

That's one of the most Michael Lipman

set of sentences I think I've ever heard.

He did great.

Or at least everyone knew he was great,

so maybe it didn't matter.

I was there.

I remember the talk,

and I remember him being very much

the way I remember him now in any given week.

So it was good.

And we met and we talked about stuff.

He thinks I didn't like him, but...

Because he was so grumpy.

Must have been the chair thing.

The chair thing and the low voice, I think.

Like, he obviously...

And that slight skeptical look.

Yes.

I have no idea what you're talking about.

Well, I probably didn't have any idea

what you were talking about.

Anyway, I liked him.

He asked me questions.

I answered questions.

I felt bad about myself.

It was a normal day.

What's a normal day?

And then he left.

And then he left.

And that's how we met.

Can we take a...

And then I got hired and I was in the group.

Can we take a slight tangent on this topic of...

It sounds like...

Maybe you could speak through the bigger picture.

It sounds like you're quite self critical.

Who, Charles?

No, you.

Oh.

I think I can do better.

I can do better.

I'll try me again.

I'll do better.

Yeah, that was like a three out of 10 response.

So let's try to work it up to five and six.

I remember Marvin Minsky said on a video interview

something that the key to success in academic research

is to hate everything you do.

For some reason...

I think I followed that

because I hate everything he's done.

That's a good line.

That's a success.

Maybe that's a keeper.

But do you find that resonance with you at all

in how you think about talks and so on?

I would say it differently.

It's not that...

No, not really.

That's such an MIT view of the world.

So I remember talking about this as a student.

You were basically told I will clean it up

for the purposes of the podcast.

My work is crap.

My work is crap.

My work is crap.

My work is crap.

Then you go to a conference or something

and you're like, everybody else's work is crap.

Everybody else's work is crap.

And you feel better and better about it,

relatively speaking.

And then you sort of keep working on it.

I don't hate my work.

That resonates with me.

Yes, I've never hated my work,

but I have been dissatisfied with it.

And I think being dissatisfied,

being okay with the fact that you've taken a positive step,

the derivatives positive,

maybe even the second derivatives positive,

that's important because that's a part of the hope, right?

But you have to...

But I haven't gotten there yet.

If that's not there, that I haven't gotten there yet,

then it's hard to move forward, I think.

So I buy that,

which is a little different from hating everything that you do.

Yeah.

I mean, there's things that I've done

that I like better than I like myself.

So it's separating me from the work, essentially.

So I think I am very critical of myself,

but sometimes the work I'm really excited about,

and sometimes I think it's kind of good.

It doesn't happen right away.

So I found the work that I've liked, that I've done,

most of it, I liked it in retrospect

more when I was far away from it in time.

I have to be fairly excited about it to get done.

No, excited at the time,

but then happy with the result or...

But years later, or even I might go back,

you know what, that actually turned out to matter.

That wasn't terrible, yeah, yeah.

That turned out to matter.

Or, oh gosh, it turns out I've been thinking

about that.

It's actually influenced all the work that I've done since

without realizing it.

Boy, that guy was smart.

Yeah, that guy had a future.

He's going places.

I think there's...

So yeah, so I think there's something to it.

I think there's something to the idea.

You've got to hate what you do,

but it's not quite hate.

It's just being unsatisfied.

And different people motivate themselves differently.

I don't happen to motivate myself with self loathing.

I happen to motivate myself with something else.

So you're able to sit back and be proud

of, in retrospect, of the work you've done.

Well, and it's easier when you can connect it

with other people, because then you can be proud of them.

Proud of the people, yeah.

And then the question is...

No, you can still safely hate yourself.

Yeah, that's right.

It's win, win, Michael, or at least win, lose,

which is what you're looking for.

Oh, wow.

There's so many brothers behind this.

Yeah, there's levels.

So how did you actually meet, meet?

Yeah, Michael.

So the way I think about it is,

because we didn't do much research together,

at AT&T, but then we all got laid off.

So that was...

By the way, sorry to interrupt,

but that was one of the most magical places,

historically speaking.

They did not appreciate what they had.

And how do we...

I feel like there's a profound lesson in there, too.

How do we get it...

Like, why was it so magical?

Was it just the coincidence of history,

or is there something special about...

There were some really good managers

and people who really believed in machine learning.

As this is going to be important,

let's get the people who are thinking about this

in creative and insightful ways

and put them in one place and stir.

Yeah, but even beyond that, right,

it was Bell Labs at its heyday.

And even when we were there,

which I think was past its heyday.

And to be clear, he's gotten to be at Bell Labs.

I never got to be at Bell Labs.

I joined after that.

Yeah, I should have been 91 as a grad student.

So I was there for a long time, every summer, except for...

So twice I worked for companies

that had just stopped being Bell Labs.

Bellcore and then AT&T Labs.

So Bell Labs was several locations

or for the research, or is it one...

Definitely several.

Jersey's involved somehow.

They're all in Jersey.

Yeah, they're all over the place.

But they were in a couple places in Jersey.

Murray Hill was the Bell Labs place.

So you had an office at Murray Hill

at one point in your career.

Yeah, and I played Ultimate Frisbee

on the cricket pitch at Bell Labs at Murray Hill.

And then it became AT&T Labs when it split off with Luce

during what we called Trivestiture.

Better than Michael Korn's at Ultimate Frisbee?

Yeah. Oh, yeah.

Okay.

But I think that one's not boasting.

I think Charles plays a lot of Ultimate,

and I don't think Michael does.

Yes, but that wasn't the point.

The point is, yes.

Sorry.

I have played on a championship winning Ultimate Frisbee team

or whatever, Ultimate team with Charles.

So I know how good he is.

He's really good.

How good I was anyway when I was younger.

But the thing is...

I know how young he was when he was younger.

That's true.

So much younger than now.

He's older now.

Yes, I know that.

Michael was a much better basketball player than I was.

Michael Korn's.

Yes, no, not Michael.

To be clear, I've not played basketball with you.

So you don't know how terrible I am,

but you have a probably pretty good guess.

And that you're not as good as Michael Korn's.

He's tall and athletic.

And he cared about it.

He's very athletic.

He's very good.

And probably competitive.

I love hanging out with Michael.

Anyway, but we were talking about something else,

although I no longer remember what it was.

What were we talking about?

Bell Labs.

But also Labs.

So this was kind of cool about what was magical about it.

The first thing you have to know is that Bell Labs was an arm of the government, right?

Because AT&T was an arm of the government.

It was a monopoly.

And, you know, every month you paid a little thing on your phone bill,

which turned out was a tax for like all the research that Bell Labs was doing.

And, you know, they invented transistors and the laser and whatever else they did.

The Big Bang or whatever the cosmic background radiation.

Yeah, they did all that stuff.

They had some amazing stuff with directional microphones, by the way.

I got to go in this room where they had all these panels and everything.

And we would talk at one another and he'd lose some panels around.

And then he'd have me step, two steps to the left.

And I couldn't hear a thing he was saying because nothing was bouncing off the walls.

And then he would shut it all down and you could hear your heartbeat,

which is deeply disturbing to hear your heartbeat.

You can feel it.

I mean, you can feel it now.

There's just so much other sort of noise around.

Anyway, Bell Labs is about pure research.

It was a university in some sense, the purest sense of a university,

but without students.

So it was all the faculty working with one another and students would come in to learn.

They would come in for three or four months, you know, during the summer and they would go away.

But it was just this kind of wonderful experience.

I could walk out my door.

In fact, I would often have to walk out my door and deal with Rich Sutton

and Michael Kerns yelling at each other about whatever it is they were yelling about

the proper way to prove something or another.

And I could just do that and Dave McAllister and even Peter Stone

and all of these other people, including Satinder and then eventually Michael.

And it was just a place where you could think thoughts and it was okay

because so long as once every 25 years or so somebody invented a transistor,

it paid for everything else.

You could afford to take the risk.

And then when that all went away, it became harder and harder and harder to justify it

as far as the folks who were very far away were concerned.

And there was such a fast turnaround among middle management on the AT&T side

that you never had a chance to really build a relationship.

At least people like us didn't have a chance to build a relationship.

So when the diaspora happened, it was amazing, right?

Everybody left.

And I think everybody ended up at a great place and made a huge,

continued to do really good work with machine learning.

But it was a wonderful place and people will ask me,

what's the best job you've ever had?

And as a professor, the answer that I would give is, well, probably Bell Labs

in some very real sense.

And I would never have a job like that again because Bell Labs doesn't exist anymore.

And, you know, Microsoft research is great and Google does good stuff

and you can pick IBM, you can tell everyone to, but Bell Labs was magical.

It was around for, it was an important time and represents a high water market

in basic research in the US.

Is there something you could say about the physical proximity and the chance collisions?

Like we live in this time of the pandemic where everyone is maybe trying to see

the silver lining and accepting the remote nature of things.

Is there one of the things that people like faculty that I talked to miss

is the procrastination.

Like the chance to make everything is about meetings that are supposed to be.

There's not a chance to just, you know, talk about comic book or whatever,

like go into discussion that's totally pointless.

So it's funny you say this because that's how we met.

Met was exactly that.

So I'll let Michael say that, but I'll just add one thing, which is just that, you know,

research is a social process and it helps to have random social interactions

even if they don't feel social at the time.

That's how you get things done.

One of the great things about the AI lab when I was there,

I don't quite know what it looks like now once they move buildings,

but we had entire walls that were whiteboards and people would just get up there

and they were just right and people would walk up and you'd have arguments

and you'd explain things to one another and you got so much out of the freedom to do that.

You had to be okay with people challenging every freaking word you said,

which I would sometimes find deeply irritating, but most of the time it was quite useful.

But the sort of pointlessness and the interaction was in some sense the point, at least for me.

Yeah, I mean, you, I think offline yesterday I mentioned Josh Tannenbaum and he's very much,

he put, he's, man, he's such an inspiration in the childlike way that he pulls you in on any topic.

It doesn't even have to be about machine learning or the brain.

He'll just pull you into a closest writable surface, which is still, you can find whiteboards in MIT everywhere.

And just like basically cancel all meetings and talk for a couple hours about some aimless thing

and it feels like the whole world, the time space continuum kind of warps

and that becomes the most important thing.

And then it's just, it's definitely something worth missing in this world where everything's remote.

There's some magic to the physical presence.

Whenever I wonder myself whether MIT really is as great as I remember it, I just go talk to Josh.

Yeah, you know, that's funny.

There's a few people in this world that carry the best of what particular institutions stand for, right?

It's Josh.

I mean, I don't, my guess is he's unaware of this.

That's the point.

Yeah.

That the masters are not aware of their mastery.

So, I'll meet.

Yes, but first the tangent, no.

How did you meet me?

So I'm not sure what you were thinking, but when it started to dawn on me that maybe we had a longer term bond

was after we all got laid off and you had decided at that point that we were still paid.

We were given an opportunity to like do a job search and kind of make a transition, but it was clear that we were done.

And I would go to my office to work and you would go to my office to keep me from working.

That was my recollection of it.

You had decided that there was really no point in working for the company because our relationship with the company was done.

Yeah, but remember I felt that way beforehand.

It wasn't about the company.

It was about the set of people there doing really cool things and it always, always been that way.

But we were working on something together.

Oh, yeah, yeah, yeah, that's right.

Oh, so at the very end we all got laid off, but then our boss came to our boss's boss came to us because our boss was Michael Kearns

and he had jumped ship brilliantly, like perfect timing, like things like right before the ship was about to sink.

He was like, gotta go and landed perfectly because Michael Kearns and leaving the rest of us to go like, this is fine.

And then it was clear that wasn't fine and we were all toast.

So we had this sort of long period of time.

But then our boss figured out, okay, wait, maybe we can save a couple of these people if we can have them do something really useful.

And the useful thing was we were going to make basically an automated assistant that could help you with your calendar.

You could like tell it things and it would respond appropriately.

It would just kind of integrate across all sorts of your personal information.

And so me and Charles and Peter Stone were set up as the crack team to actually solve this problem.

Other people maybe were too theoretical that they thought and but we could actually get something done.

So we sat down to get something done and there wasn't time and it wouldn't have saved us anyway.

And so it all kind of went downhill.

But the interesting, I think, Coda to that is that our boss's boss is a guy named Ron Brockman.

And when he left AT&T, because we were all laid off, he went to DARPA, started up a program there that became Calo,

which is the program from which Siri sprung, which is a digital assistant that helps you with your calendar and a bunch of other things.

It really, you know, in some ways got its start with me and Charles and Peter trying to implement this vision that Ron Brockman had,

that he ultimately got implemented through his role at DARPA.

So when I'm trying to feel less bad about having been laid off from what is possibly the greatest job of all time,

I think about, well, we kind of help birth Siri.

That's something.

And then he did other things too, but we got to spend a lot of time in his office and talk about...

We got to spend a lot of time in my office.

Yeah.

Yeah, yeah.

And so then we went on our merry way.

Everyone went to different places.

Charles landed at Georgia Tech, which was what he always dreamed he would do.

And so that worked out well.

I came up with a saying at the time, which is luck favors the Charles.

It's kind of like luck favors the prepared.

But Charles, like, he wished something and then it would basically happen just the way he wanted.

It was inspirational to see things go that way.

Things worked out.

And we stayed in touch.

And then I think it really helped when you were working on...

I mean, you kept me in the loop for things like threads and the work that you were doing at Georgia Tech.

But then when they were starting their online master's program,

he knew that I was really excited about MOOCs and online teaching.

And he's like, I have a plan.

And I'm like, tell me your plan. He's like, I can't tell you the plan yet.

Because they were deep in negotiations between Georgia Tech and Udacity to make this happen.

And they didn't want it to leak.

So Charles would kept teasing me about it, but wouldn't tell me what was actually going on.

And eventually it was announced.

And he said, I would like you to teach the machine learning course with me.

I'm like, that can't possibly work.

But it was a great idea.

And it was super fun.

It was a lot of work to put together, but it was really great.

Was that the first time you thought about, first of all, was it the first time you got seriously into teaching?

I mean, you know, I was a professor.

This was already after you jumped to, so like, there's a little bit of jumping around in time.

Yeah, sorry about that.

There's a pretty big jump in time.

So like the MOOCs thing.

So Charles got to Georgia Tech and he, I mean, maybe Charles, maybe this is a Charles story.

I guess it was like 2002.

He got to Georgia Tech in 2002.

And worked on things like revamping the curriculum, the undergraduate curriculum, so that it had some kind of semblance of modular structure because computer science was at the time moving from a fairly narrow specific set of topics to touching a lot of other parts of intellectual life.

And the curriculum was supposed to reflect that.

And so Charles played a big role in kind of redesigning that.

And then for my labors, I ended up as associate dean.

Right.

He got to become associate dean in charge of educational stuff.

It should be a valuable lesson if you're good at something.

They will give you responsibility to do more of that thing.

Don't show competence.

Don't show competence if you don't have responsibility.

Here's what they say.

The reward for good work is more work.

The reward for bad work is less work, which I don't know, depending on what you're trying to do that week.

One of those is better than the other.

Well, one of the problems with the word work, sorry to interrupt, is that it seems to be an antonym in this particular language.

We have the opposite of happiness, but it seems like they're, they're like, that's one of, you know, we talked about balance.

It's always like work life balance always rub me the wrong way as a terminology.

I know it's just words.

Right.

The opposite of work is play, but ideally work is play.

Oh, I can't tell you how much time I'd spend.

Certainly I was at Bell Labs, except for a few very key moments.

As a professor, I would do this too.

I was just saying, I cannot believe they're paying me to do this because it's fun.

It's something that I would, I would do for a hobby if I could anyway.

So that's what it worked out.

Are you sure you want to be saying that when this is being recorded?

As a dean, that is not true at all.

I need a raise.

But, but I think here with this, that even though a lot of time passed, you know, Michael and I talked almost every, well, we texted almost every day during the period.

Charles at one point took me, there was the ICML conference, the machine learning conference was in Atlanta.

I was the chair, the general chair of the conference.

Charles was my publicity chair or something like that or fund raising chair.

Fund raising chair.

Yeah.

But he decided it'd be really funny if he didn't actually show up for the conference in his own home city.

So he didn't, but he did at one point pick me up at the conference in his Tesla and drove me to the Atlanta mall and forced me to buy an iPhone because he didn't like how it was to text with me and thought it would be better for him if I had an iPhone.

The text would be somehow smoother.

And it was.

And it was.

And it is.

Better.

And so, yeah, but, but it was.

Yeah.

Charles forced me to get an iPhone so that he could text me more efficiently.

I thought that was an interesting moment.

It works for me anyway.

So we kept talking the whole time and then eventually we did the, we did the teaching thing and it was great.

And there's a couple of reasons for that, by the way.

One is I really wanted to do something different.

Like you've got this medium here.

People claim it can change things.

What's a thing that you could do in this medium that you could not do otherwise besides edit, right?

I mean, what could you do?

And being able to do something with another person was that kind of thing.

It's very hard.

I mean, you can take turns, but teaching together, having conversations is very hard, right?

So that was a cool thing.

The second thing, give me an excuse to do more stuff with him.

Yeah.

I always thought he makes it sound brilliant.

And it is, I guess.

But it's at the time, it really felt like I've got a lot to do, Charles is saying.

And it would be great if Michael could teach the course and I could just hang out.

Yeah.

Just kind of coast on that.

Well, that's what the second class was more like that.

Because the second class was explicit.

The first class, it was at least half.

Yeah, but I do all the stuff.

I think you're once again letting the facts get in the way.

A good story.

A good story.

I should just let Charles talk.

But that's the facts that he saw.

So that was kind of true for 7642, which is the reinforcement learning class.

Because that was really his class.

You started with reinforcement learning?

No, we started with machine learning.

We started with machine learning, 7641, which is supervised learning, unsupervised learning,

and reinforcement learning and decision making.

Cram all that in there.

The kind of assignments that we talked about earlier.

And then eventually, about a year later, we did a follow on 7642, which is reinforcement learning and decision making.

The first class was based on something I had been teaching at that point for well over a decade.

And the second class was based on something Michael had been teaching.

Actually, I learned quite a bit teaching that class with him.

But he drove most of that.

But the first one I drove most of it was all my material, although I had stolen that material originally from slides I found online from Michael,

who had originally stolen that material from, I guess, slides he found online, probably from Andrew Moore.

Because the jokes were the same anyway.

At least some of the, at least when I found the slide, some of the stuff.

Yes, every machine learning class taught in the early 2000s stole from Andrew Moore.

A particular joke or two.

At least the structure.

Now, I did, and he did actually a lot more with reinforcement learning and such and game theory and those kinds of things.

But, you know, we all sort of built.

You need a research world.

No, no, no.

I mean, in teaching that class.

The coverage was different than what other people started.

Most people were just doing supervised learning and maybe a little bit of, you know, clustering and whatnot.

But we took it all the way to.

A lot of it just comes from Tom Mitchell's book.

Oh, no.

Yeah, except, well, half of it comes from Tom Mitchell's book, right?

But the other half doesn't.

This is why it's all readings, right?

Because certain things weren't invented when Tom Mitchell's book.

Yeah, okay, that's true.

Right.

But it was, it was quite good.

But there's a reason for that besides, you know, just, I wanted to do it.

I wanted to do something new and I wanted to do something with him, which is a realization, which is, despite what you might believe, he's an introvert and I'm an introvert, or I'm on the edge of being an introvert anyway.

But both of us, I think, enjoy the energy of the crowd, right?

There's something about talking to people and bringing them into whatever we find interesting that is empowering, energizing or whatever.

And I found the idea of staring alone at a computer screen and then talking off of materials less inspiring than I wanted it to be.

And I had, in fact, done a MOOC for Udacity on algorithms.

And it was a week in a dark room talking at the screen, writing on the little pad.

I didn't know this was happening, but they had watched the crew had watched some of the videos while, you know, like in the middle of this and they're like, something's wrong.

You're sort of shutting down.

And I think a lot of it was, I'll make jokes and no one would laugh.

Yeah.

And I felt like the crowd hated me.

Now, of course, there was no crowd.

So, like, it wasn't rational.

Yeah.

Each time I tried it and I got no reaction, it just was taking the energy out of my performance out of my presentation.

Such a fantastic metaphor for grad school.

Anyway, by working together, we could play off each other and have it.

And keep the energy up because you can't let your guard down for a moment with Charles.

He'll just overpower you.

I have no idea what you're talking about.

But we would work really well together.

I thought and we knew each other, so I knew that we could sort of make it work.

Plus, I was the associate dean, so they had to do what I told them to do.

So we had to do that.

We had to make it work.

And so it worked out very well, I thought.

Well enough that we...

With great power comes great power.

That's right.

And we became smooth and curly.

And that's when we did the overfitting thriller video.

Yeah.

Yeah.

Yeah.

That's a thing.

So, can we just, like, smooth and curly?

Where did that come from?

Okay.

So it happened.

It was completely spontaneous.

These are nicknames you go by.

Yeah.

Or...

It's what the students call us.

Yeah.

He was lecturing.

So the way that we structured the lectures is one of us is the lecturer and one of us

is basically the student.

And so the...

He was lecturing on...

The lecturer prepares all the materials, comes up with the quizzes, and then the student

comes in not knowing anything.

So it was, you know, just like being on campus.

And I was doing game theory in particular, the prisoner's dilemma.

Yeah.

The prisoner's dilemma.

And so he needed to set up a little prisoner's dilemma grid, so he drew it and I could see

what he was drawing.

And the prisoner's dilemma consists of two players, two parties, so he decided he would

make little cartoons of the two of us.

And so there was two criminals, right, that were deciding whether or not to rat each other

out.

One of them, he drew as, you know, a circle with a smiley face and a kind of goatee thing,

smooth head.

And the other one with all sorts of curly hair.

And he said, this is smooth and curly.

I said smooth and curly.

He said, no, no, smooth with a V. It's very important that it have a V.

And then...

And then the students really took to that, like they found that relatable.

He started singing Smooth Criminal by Michael Jackson.

Yeah, yeah, yeah.

And those names stuck.

So we now have a video series, an episode, our kind of first actual episode should be

coming out today, smooth and curly on video, where the two of us discuss episodes of West

World.

We watched West World and we're like, huh, what does this say about computer science

and AI?

And we've never, we did not watch it.

I mean, I know it's on season three or whatever we have.

As of this recording, it's on season three.

And...

We watched now two episodes total.

Yeah.

I think I watched three.

What do you think about West World?

Two episodes in.

So I can tell you, so far, I'm just guessing what's going to happen next.

It seems like bad things are going to happen with the robots uprising.

It's a lot of...

Spoiler alert.

So I have not, I mean, you know, I vaguely remember a movie existing, so I assume it's

related to that.

But...

That was more my time than your time, Charles.

That's right, because you're much older than I am.

I think the important thing here is that it's narrative, right?

It's all about telling a story.

That's the whole driving thing.

But the idea that they would give these reveries, that they would make people...

Let them remember.

They would make them remember the awful things that happened.

How horrible things that happened.

Who could possibly think that was going to happen?

I got it.

I mean, I don't know.

I've only seen the first two episodes or maybe the third one.

I think I've only seen the third one.

You know what it was?

Do you know what the problem is?

What?

That the robots were actually designed by Hannibal Lecter.

That's true.

They were.

So like, what do you think is going to happen?

Bad things.

It's clear that things are happening and characters are being introduced and we don't

yet know anything.

But still, I was just struck by how it's all driven by narrative and story.

And there's all these implied things, like programming hap...

The programming interface is talking to them about what's going on in their heads, which

is both...

I mean, artistically, it's probably useful to film it that way.

But think about how it would work in real life.

That just seems very great.

But there was...

We saw in the second episode, there's a screen.

You could see things.

They were wearing like cool clothes.

It was quite interesting to just kind of ask this question.

So far.

I mean, I assume it veers often to never, never land at some point.

But...

So we don't know.

We can't answer that question.

I'm also a fan of a guy named Alex Garland.

He's a director of Ex Machina.

And he is the first...

I wonder if Kubrick was like this, actually.

Is he like studies what would it take to program in AI systems?

Like he's curious enough to go into that direction.

On the Westworld side, I felt there was more emphasis on the narratives than like actually

asking like computer science questions.

Like, how would you build this?

How would you...

And how would you debug it?

I still...

To me, that's the key issue.

They were terrible debuggers.

Yeah.

Well, they said specifically.

So we make a change and we put it out in the world and that's bad because something terrible

could happen.

Like, if you're putting things out in the world and you're not sure whether something terrible

is going to happen, your process is probably...

I just feel like there should have been someone whose sole job it was was to walk around and

poke his head at it and say, what could possibly go wrong just over and over again?

I would have loved if there was an...

I did watch a lot more and I'm not giving anything away.

I would have loved it if there was like an episode where like the new intern is like debugging

a new model or something and like it just keeps failing and they're like, all right.

And then more turns into like a episode of Silicon Valley or something like that versus

like this ominous AI systems that are constantly like threatening the fabric of this world that's

been created.

Yeah.

Yeah.

And you know, this reminds me of something that...

So I agree with that.

That should be very cool, at least for the small percentage of people who care about debugging

systems.

But the other thing is...

Debugging.

The series.

Yeah.

It falls into...

Think of the sequels.

Fear of the debugging.

Oh my gosh.

And anyway, so...

It's a nightmare show.

It's a horror movie.

I think that's where we lose people.

By the way, early on is the people who either decide either figure out debugging or think

debugging is terrible.

This is part of...

Oh, where we lose people in computer science.

This is part of the struggle versus suffering, right?

You get through it and you kind of get the skills of it or you're just like, this is

dumb.

This is a dumb way to do anything.

And I think that's when we lose people.

But well, I'll leave it at that.

But I think that there's something really, really neat about framing it that way.

But what I don't like about all of these things, and I love Tex Mockingham, by the way, I love

that the ending was very depressing.

One of the things I have to talk to Alex about, he says that the thing that nobody noticed

he put in is at the end, spoiler alert, the robot turns and looks at the camera and smiles

very briefly.

And to him, he thought that his definition of passing the general version of the touring

test or the consciousness test is smiling for no one.

It's like the Chinese room kind of experiment, it's not always trying to act for others,

but just on your own, being able to have a relationship with the actual experience and

just take it in.

I don't know.

He said nobody noticed the magic of it.

I have this vague feeling that I remember the smile, but now you've just put the memory

in my head, so probably not.

But I do think that that's interesting, although by looking at the camera, you are smiling

for the audience, right?

You're breaking the fourth wall.

It seems, I mean, well, that's a limitation in the medium, but I like that idea.

But here's the problem I have with all of those movies, all of them.

But I know why it's this way, and I enjoy those movies.

And Westworld is, it sets up the problem of AI as succeeding and then having something

we cannot control, but it's not the bad part of AI.

The bad part of AI is the stuff we're living through now, right?

It's using the data to make decisions that are terrible.

It's not the intelligence that's going to go out there and surpass us and take over

the world or lock us into a room to starve to death slowly over multiple days.

It's instead the tools that we're building that are allowing us to make the terrible

decisions we would have less efficiently made before, right?

Others are very good at making us more efficient, including being more efficient at doing terrible

things.

And that's the part of the AI we have to worry about.

It's not the true intelligence that we're going to build sometime in the future, probably

long after we're around.

But I just, I think that whole framing of it sort of misses the point, even though it

is inspiring.

And I was inspired by those ideas, right, that I got into this in part because I wanted

to build something like that, philosophical questions were interesting to me, but that's

not where the terror comes from, the terror comes from the everyday.

And you can construct the situation, it's in the subtlety of the interaction between

AI and the human, like with social networks, all the stuff you're doing with interactive

artificial intelligence.

But I feel like how 9,000 came a little bit closer to that in 2001 Space Odyssey because

it felt like a personal assistant.

It felt like closer to the AI systems we have today and the real things we might actually

encounter, which is over relying in some fundamental way on our like dumb assistants or on social

networks like over offloading too much of us onto things that require internet and power

and so on.

And thereby becoming powerless as a standalone entity.

And then when that thing starts to misbehave in some subtle way, it creates a lot of problems.

And those problems are dramatized when you're in space because you don't have a way to walk

away.

Well, as the man said, once we started making the decisions for you, it stopped being your

world, right?

That's the matrix, Michael, in case you don't remember.

But on the other hand, I could say, no, because isn't that what we do with people anyway?

The shared intelligence that is humanity is relying on other people constantly.

I mean, we hyper specialize as individuals.

We're still generally intelligent.

We make our own decisions in a lot of ways, but we leave most of this up to other people

and that's perfectly fine.

And by the way, everyone doesn't necessarily share our goals.

Sometimes they seem to be quite against us.

Sometimes we make decisions that others would see as against our own interests and yet we

somehow manage it, manage and survive.

I'm not entirely sure why an AI would actually make that worse or even different, really.

You mentioned the matrix.

Do you think we're living in a simulation?

It does feel like a thought game more than a real scientific question.

Well, I'll tell you why.

I think it's an interesting thought experiment.

See what you think from a computer science perspective.

It's a good experiment of how difficult would it be to create a sufficiently realistic world

that us humans would enjoy being in.

That's almost like a competition.

If we're living in a simulation, then I don't believe that we were put in the simulation.

I believe that it's just physics playing out and we came out of that.

I don't think...

So you think you have to build the universe and all the fun and work?

I think the universe itself, we can think of that as a simulation.

In fact, sometimes I try to understand what it's like for a computer to start to think

about the world.

I try to think about the world, things like quantum mechanics where it doesn't feel very

natural to me at all.

It really strikes me as I don't understand this thing that we're living in.

It has...

There's weird things happening in it that don't feel natural to me at all.

Now, if you want to call that as the result of a simulator, okay.

I'm fine with that.

Because there's the bugs in the simulation.

There's the bugs.

I mean, the interesting thing about simulation is that it might have bugs.

That's the thing that I...

But there would be bugs for the people in the simulation.

That's just reality.

They're not bugs.

Unless you were very enough to know that there was a bug.

But I think...

Back to the matrix.

Yeah.

The way you put the question...

I don't think that we live in a simulation created for us.

Okay.

I would say that.

I think that's interesting.

I've actually never thought about it that way.

I mean, the way you asked the question though is, could you create a world that is enough

for us humans?

It's an interestingly sort of self referential question, because the beings that created

the simulation probably have not created the simulation that's realistic for them.

But we're in the simulation, and so it's realistic for us.

So we could create a simulation that is fine for the people in the simulation, as it were,

that would not necessarily be fine for us as the creators of the simulation.

But while you can forget, I mean, when you go into the...

If you play video games of virtual reality, you can... some suspension of disbelief or

whatever.

Yeah.

It becomes a world.

It becomes a world, even like in brief moments, you forget that another world exists.

I mean, that's what good stories do.

They pull you in.

And the question is, is it possible to pull...

Our brains are limited.

Is it possible to pull the brain in to where we actually stay in that world longer and

longer and longer and longer?

And not only that, but we don't want to leave.

And so, especially, this is the key thing about the developing brain, is if we journey

into that world early on in life, often.

How would you even know?

Yeah.

Yeah.

But from a video game design perspective, from a Westworld perspective, I think it's

an important thing for even computer scientists to think about, because it's clear that video

games are getting much better.

And virtual reality, although it's been ups and downs, just like artificial intelligence,

it feels like virtual reality will be here in a very impressive form if we were to fast

forward 100 years into the future in a way that might change society fundamentally.

Like, if I were to...

I'm very limited in predicting the future, as all of us are.

But if I were to try to predict, like, in which way I'd be surprised to see the world

100 years from now, it'd be that... or impressed.

It'd be that we're all no longer living in this physical world, that we're all living

in a virtual world.

You really need to read Calculating God by Sawyer.

It's a...

He'll read it in a night.

It's a very easy read, but it's a...

Assuming you're that kind of reader, but it's a good story.

And it's kind of about this, but not in a way that it appears.

And I really enjoyed the thought experiment, and I think it's pretty sure it's Robert Sawyer.

But anyway, he's apparently Canadian's top science fiction writer, which is why the story

mostly takes place in Toronto.

But it's a very good...

It's a very good sort of story that sort of imagines this very different kind of simulation

hypothesis sort of thing from, say, the egg, for example, you know, I'm talking about the

short story by the guy who did the Martian, who wrote the Martian.

You know, I'm talking...

Matt Damon.

No.

The book.

So we had this whole discussion that Michael doesn't partake in this exercise of reading.

He doesn't seem to like it, which seems very strange to me, considering how much he has

to read.

Yeah.

I read all the time.

I used to read 10 books every week when I was in sixth grade, or whatever.

I was...

A lot of it's science fiction, a lot of it.

A lot of it's history.

I love to read.

But anyway, you should recalculate in God.

I think you'll...

It's very easy to read, like I said, and I think you'll enjoy sort of the ideas that

it presents.

Yeah.

I think the thought experiment is quite interesting.

One thing I've noticed about people growing up now, I mean, we'll talk about social media,

but video games is a much bigger, bigger and bigger and bigger part of their lives.

And the video games have become much more realistic.

I think it's possible that the three of us are not...

Maybe the two of you are not familiar exactly with the numbers we're talking about here.

The number of people...

It's bigger than movies, right?

It's huge.

I used to do a lot of the computational narrative stuff.

I understand that economists can actually see the impact of video games on the labor

market that there are fewer young men of a certain age participating in paying jobs

than you'd expect and that they trace it back to video games.

The problem with Star Trek was not warp drive or teleportation.

It was the holodeck.

If you have the holodeck, that's it.

That's it.

You go in the holodeck, you never come out.

Once I saw that, I thought, okay, well, this is the end of humanity, they've been in the

holodeck.

Because that feels like the singularity, not some AGI or whatever.

It's some possibility to go into another world that can be artificially made better than

this one.

And slowing it down so you live forever or speeding it up so you appear to live forever

or making the decision of when to die.

And then most of us will just be old people on the porch yelling at the kids these days

in their virtual reality worlds.

But they won't hear us because they've got headphones on.

So I mean, rewinding back to MOOCs, is there lessons that you've speaking to kids these

days?

There you go.

That was a transition.

All right.

I'll fix it in post.

That's Charles's favorite phrase.

Fix it in post.

Fix it in post.

Fix it in post.

I said, when we were recording all the time, whenever the editor didn't like something

or whatever, I would say, we'll fix it in post.

He hated that.

He hated that more than anything.

Because it was Charles's way of saying, I'm not going to do it again.

You're on your own for this one.

But it always got fixed in post.

Exactly.

So is there something you've learned about, I mean, it's interesting to talk about MOOCs,

is there something you've learned about the process of education, about thinking about

the present?

I think there's two lines of conversation to be had here is the future of education

in general that you've learned about.

And more pressurantly is the education in the times of COVID.

The second thing in some ways matters more than the first, for at least in my head, not

just because it's happening now, but because I think it's reminded us of a lot of things.

Coincidentally, today, there's an article out by a good friend of mine who's also a

professor at Georgia Tech, but more importantly, a writer and editor at The Atlantic, Ian Bogos.

And the title is something like, Americans will sacrifice anything for the college experience.

And it's about why we went back to college and why people wanted us to go back to college.

And it's not greedy presidents trying to get the last dollar from someone.

It's because they want to go to college.

And what they're paying for is not the classes.

What they're paying for is the college experience, it's not the education that's being there.

I've believed this for a long time that we continually make this mistake of people want

to go back to college as being people want to go back to class.

They don't.

They want to go back to campus.

They want to move away from home.

They want to do all those things that people experience.

It's a rite of passage.

It's an identity if I can steal some of Ian's words here.

And I think that's right.

And I think what we've learned through COVID is it has made it, the disaggregation was

not the disaggregation of the education from the university place and that you can get

the best anywhere you want to in terms of there's lots of reasons why that is not necessarily

true.

The disaggregation is having it shoved in our faces that the reason to go again, that

the reason to go to college is not necessarily to learn.

It's to have the college experience.

And that's very difficult for us to accept even though we behave that way, most of us,

when we were undergrads.

A lot of us didn't go to every single class.

We learned and we got it and we look back on it and we're happy we had the learning

experience as well, obviously, particularly us because this is the kind of thing that

we do.

And my guess is that's true of the vast majority of your audience.

But that doesn't mean the I'm standing in front of you telling you this is the thing

that people are excited about.

And that's why they want to be there, primarily why they want to be there.

So to me, that's what COVID has forced us to deal with, even though I think we're still

all in deep denial about it and hoping that it'll go back to that.

And I think about 85% of it will.

We'll be able to pretend that that's really the way it is again and we'll forget the

lessons of this.

But technically what will come out of it or technologically will come out of it is a way

of providing a more dispersed experience through online education and these kinds of remote

things that we've learned.

And we'll have to come up with new ways to engage them in the experience of college,

which includes not just the parties or the whatever kids do, but the learning part of

it so that they actually come out for five or six years later with having actually having

actually learned something.

So I think the world will be radically different afterwards.

And I think technology will matter for that, just not in the way that the people who were

building the technology originally imagined it would be.

And I think this would have been true even without COVID, but COVID has accelerated that

reality.

So it's happening in two or three years or five years as opposed to 10 or 15.

That was an amazing answer that I did not understand.

It was passionate and meaningful.

Shots fired.

But I don't know.

I just didn't know.

I'm not trying to criticize it.

I think I don't think I'm getting it.

So you mentioned disaggregation, so what's that?

So the power of technology that if you go on the West Coast and hang out long enough is

all about we're going to disaggregate these things together, the books from the bookstore,

that kind of a thing.

And then suddenly Amazon controls the universe and technology is a disruptor and people have

been predicting that for a higher education for a long time.

But certainly in the media.

So is this the sort of idea like students can aggregate on a campus someplace and then

take classes over the network anywhere?

Yeah, this is what people thought was going to happen, or at least people claimed it was

going to happen, right?

Because my daughter is essentially doing that now.

She's on one campus, but learning in a different campus.

Sure.

And COVID makes that possible, right?

Or COVID makes that all but avoidable, right?

But the idea originally was that you and I were going to create this machine learning

class and it was going to be great and then no one else would be the machine learning

class everyone changed, right?

That was never going to happen.

But something like that you can see happened.

But I feel you didn't address that.

So why?

Why?

Why is it that?

Why Q?

Why?

I don't think that will be the thing that happens.

So the college experience, maybe I missed what the college experience was.

I thought it was peers, like people hanging around.

A large part of it is peers.

Well, it's peers and independence.

Yeah.

But none of that is...

At least that's...

You can do classes online for all of that.

No.

No, no, no.

Because...

No, definitely.

We're social people, right?

So when we take the classes, that also has to be part of an experience.

It's in a context.

And the context is the university.

And by the way, it actually matters that Georgia Tech really is different from Brown.

I see.

Because then students can choose the kind of experience they think is going to be best

for them.

Okay.

I think we're giving too much agency to the students in making an informed decision.

Okay.

But the truth...

But yes, they will make choices and they will have different experiences.

And some of those choices will be made for them.

Some of them will be choices they're making because they think it's this that or the other.

I just don't want to say...

I don't want to give the idea...

It's not homogenous.

Yes.

It's certainly not homogenous.

Right?

I mean, Georgia Tech is different from Brown.

Brown is different from pick your favorite state school in Iowa.

Iowa State, okay?

Which I guess is my favorite state school in Iowa.

But these are all different.

They have different contexts.

And a lot of those contexts are they're about history, yes, but they're also about the location

of where you are.

They're about the larger group of people who around you, whether you're in Athens, Georgia

and you're basically the only thing that's there as a university, you're responsible

for all the jobs or whether you're at Georgia State University, which is an urban campus,

where you're surrounded by six million people and your campus, where it ends and begins

in the city, ends and begins, we don't know.

It actually matters whether you're a small campus or a large campus.

I mean, these things matter.

Why is it that if you go to Georgia Tech, you're forever proud of that?

Can you say that to people at dinner, like bars and whatever?

And if you get a degree at an online university somewhere, that's not a thing that comes up

at a bar.

Well, it's funny you say that.

So the students who take our online masters by several measures are more loyal than the

students who come on campus, certainly for the master's degree.

The reason for that, I think, and you'd have to ask them, but based on my conversations

with them, I feel comfortable saying this, is because this didn't exist before.

I mean, we talk about this online masters and that it's reaching 11,000 students and

that's an amazing thing.

And we're admitting everyone we believe who can succeed.

We've got a 60% acceptance rate.

It's amazing, right?

It's also a $6,600 degree.

The entire degree costs $6,600 or $7,000, depending on how long you take, a dollar degree as opposed

to $46,000 and cost you to come on campus.

So that feels, and I can do it while I'm working full time and I've got a family and a mortgage

and all these other things.

So it's an opportunity to do something you wanted to do, but you didn't think was possible

without giving up two years of your life as well as all the money and everything else,

the life that you had built.

So I think we created something that's had an impact, but importantly, we gave a set

of people opportunities they otherwise didn't feel they had.

So I think people feel very loyal about that.

And my biggest piece of evidence for that besides the surveys is that we have somewhere

north of 80 students, might be 100 at this point, who graduated, but come back in TA

for this class for basically minimum wage, even though they're working full time because

they believe in sort of having that opportunity and they want to be a part of something.

Now will generation three feel this way, 15 years from now, will people have that same

sense?