The following is a conversation with Peter Norvig.
He's the director of research at Google and the co-author with Stuart Russell of the book
Artificial Intelligence and Modern Approach, that educated and inspired a whole generation
of researchers, including myself, to get into the field of artificial intelligence.
This is the Artificial Intelligence Podcast.
If you enjoy it, subscribe on YouTube, give it five stars on iTunes.
Support on Patreon or simply connect with me on Twitter.
Alex Friedman, spelled F-R-I-D-M-A-N.
And now, here's my conversation with Peter Norvig.
Most researchers in the AI community, including myself, own all three editions,
red, green, and blue, of the Artificial Intelligence and Modern Approach.
The field-defining textbook has made people aware that you wrote with Stuart Russell.
How has the book changed and how have you changed in relation to it,
from the first edition to the second to the third, and now fourth edition, as you work on it?
Yeah, so it's been a lot of years, a lot of changes.
One of the things changing from the first to maybe the second or third
was just the rise of computing power, right?
So I think in the in the first edition, we said, here's predicate logic.
But that only goes so far, because pretty soon you have millions of short little
predicate expressions, and they can possibly fit in memory.
So we're going to use first order logic that's more concise.
And then we quickly realized, oh, predicate logic is pretty nice
because there are really fast SAT solvers and other things.
And look, there's only millions of expressions, and that fits easily into memory,
or maybe even billions fit into memory now.
So that was a change of the type of technology we needed just because the hardware expanded.
Even to the second edition, the resource constraints were loosened significantly for the second.
And that was the early 2000s second edition.
Right, so 95 was the first, and then 2000, 2001 or so.
And then moving on from there, I think we're starting to see that again
with the GPUs and then more specific type of machinery, like the TPUs,
and we're seeing custom ASICs and so on for deep learning.
So we're seeing another advance in terms of the hardware.
Then I think another thing that we especially noticed this time around
is in all three of the first editions, we kind of said,
well, we're going to find AI as maximizing expected utility.
And you tell me your utility function, and now we've got 27 chapters
with the cool techniques for how to optimize that.
I think in this edition, we're saying more, you know what?
Maybe that optimization part is the easy part,
and the hard part is deciding what is my utility function?
What do I want?
And if I'm a collection of agents or a society, what do we want as a whole?
So you touch that topic in this edition,
you get a little bit more into utility.
Yeah.
That's really interesting.
On a technical level, we're almost pushing the philosophical.
I guess it is philosophical, right?
So we've always had a philosophy chapter, which I was glad that we were supporting.
And now it's less kind of the Chinese room type argument
and more of these ethical and societal type issues.
So we get into the issues of fairness and bias,
and just the issue of aggregating utilities.
So how do you encode human values into a utility function?
Is this something that you can do purely through data in a learned way,
or is there some systematic?
Obviously, there's no good answers yet.
There's just beginnings to this, to even opening the door to these questions.
Right. So there is no one answer.
Yes, there are techniques to try to learn that.
So we talk about inverse reinforcement learning, right?
So reinforcement learning, you take some actions, you get some rewards,
and you figure out what actions you should take.
And inverse reinforcement learning, you observe somebody taking actions,
and you figure out, well, this must be what they were trying to do.
If they did this action, it must be because they want it.
Of course, there's restrictions to that, right?
So lots of people take actions that are self-destructive,
or they're suboptimal in a certain way.
So you don't want to learn that.
You want to somehow learn the perfect actions,
rather than the ones they actually take.
So that's a challenge for that field.
Then another big part of it is just kind of theoretical
of saying, what can we accomplish?
And so you look at this work on the programs to predict recidivism
and decide who should get parole or who should get bail or whatever.
And how are you going to evaluate that?
And one of the big issues is fairness across protected classes,
protected classes being things like sex and race and so on.
And so two things you want is you want to say, well,
if I get a score of, say, six out of 10,
then I want that to mean the same, whether, no matter what race I'm on.
Yes.
Right?
So I want to have a 60% chance of reoccurring regardless.
And one of the makers of a commercial program to do that says,
that's what we're trying to optimize.
And look, we achieved that.
We've reached that kind of balance.
And then on the other side, you also want to say,
well, if it makes mistakes, I want that to affect
both sides of the protected class equally.
And it turns out they don't do that, right?
So they're twice as likely to make a mistake that would harm a black person over a white person.
So that seems unfair.
So you'd like to say, well, I want to achieve both those goals.
And then it turns out you do the analysis and it's theoretically impossible to achieve both those goals.
So you have to trade them off one against the other.
So that analysis is really helpful to know what you can aim for and how much you can get.
You can't have everything.
But the analysis certainly can't tell you where should we make that tradeoff point.
But no less than we can, as humans, deliberate where that tradeoff should be.
Yeah.
So at least we now we're arguing in an informed way.
We're not asking for something impossible.
We're saying, here's where we are and here's what we aim for.
And this strategy is better than that strategy.
So that's, I would argue, is a really powerful and really important first step.
But it's a doable one, sort of removing undesirable degrees of bias in systems
in terms of protected classes.
And then there's something, I listened to your commencement speech,
or there's some fuzzier things like you mentioned angry birds.
You mentioned angry birds.
Do you want to create systems that feed the dopamine enjoyment,
that feed, that optimize for you returning to the system,
enjoying the moment of playing the game, of getting likes or whatever,
this kind of thing, or some kind of long-term improvement?
Are you even thinking about that?
That's really going to the philosophical area.
No, I think that's a really important issue too.
It's early thinking about that.
I don't think about that as an AI issue as much.
But as you say, the point is we've built this society and this infrastructure
where we say we have a marketplace for attention.
And we've decided as a society that we like things that are free.
And so we want all the apps on our phone to be free.
And that means they're all competing for your attention.
And then eventually they make some money some way through ads or in game sales or whatever.
But they can only win by defeating all the other apps by instilling your attention.
We build a marketplace where it seems like they're working against you rather than working with you.
And I'd like to find a way where we can change the playing field so that you feel more like,
well, these things are on my side.
Yes, they're letting me have some fun in the short term,
but they're also helping me in the long term rather than competing against me.
And those aren't necessarily conflicting objectives.
They're just the incentives, the direct current incentives.
As we try to figure out this whole new world seem to be on the easier part of that,
the easier part of that, which is feeding the dopamine, the rush.
So maybe take a quick step back at the beginning of the artificial intelligence
and modern approach book of writing.
So here you are in the 90s.
When you first sat down with Stuart to write the book to cover an entire field,
which is one of the only books that's successfully done that for AI.
And actually in a lot of other computer science fields,
it's a huge undertaking.
So it must have been quite daunting.
What was that process like?
Did you envision that you would be trying to cover the entire field?
Was there a systematic approach to it that was more step by step?
How did it feel?
So I guess it came about, I'd go to lunch with the other AI faculty at Berkeley,
and we'd say the field is changing.
Seems like the current books are a little bit behind.
Nobody's come out with a new book recently.
We should do that.
And everybody said, yeah, yeah, that's a great thing to do.
And we never did anything.
Right.
And then I ended up heading off to industry.
I went to Sun Labs.
So I thought, well, that's the end of my possible academic publishing career.
But I met Stuart again at a conference like a year later
and said, you know, that book we were always talking about,
you guys must be half done with it by now, right?
And he said, well, we keep talking.
We never do anything.
So I said, well, you know, we should do it.
And I think the reason is that we all felt it was a time where the field was changing.
And that was in two ways.
So, you know, the good old fashioned AI was based primarily on Boolean logic.
And you had a few tricks to deal with uncertainty.
And it was based primarily on knowledge engineering,
that the way you got something done is you went out and you interviewed an expert
and you wrote down by hand everything they knew.
And we saw in 95 that the field was changing in two ways.
One, we're moving more towards probability rather than Boolean logic.
And we're moving more towards machine learning rather than knowledge engineering.
And the other books hadn't caught that way if they were still in the
more in the in the old school, although certainly they had part of that on the way.
But we said, if we start now completely taking that point of view,
we can have a different kind of book and we were able to put that together.
And what was literally the process?
If you remember, did you start writing a chapter?
Did you outline?
Yeah, I guess we did an outline and then we sort of assigned chapters to each person.
At the time, I had moved to Boston and Stuart was in Berkeley.
So basically, we did it over the internet and that wasn't the same as doing it today.
It meant dial up lines and telnetting in and you telnet it into one shell and you type cat file name.
At the other end, and certainly you're not sending images and figures back and forth.
Right, right. That didn't work.
But did you anticipate where the field would go from that day, from the 90s?
Did you see the growth into learning based methods, into data driven methods
that followed in the future decades?
We certainly thought that learning was important.
I guess we missed it as being as important as it is today.
We missed this idea of big data.
We missed that the idea of deep learning hadn't been invented yet.
We could have taken the book from a complete machine learning point of view right from the start.
We chose to do it more from a point of view of we're going to first develop
the different types of representations and we're going to talk about different types of environments.
Is it fully observable or partially observable?
And is it deterministic or stochastic and so on?
And we made it more complex along those axes rather than focusing on the machine learning axis first.
Do you think there's some sense in which the deep learning craze
is extremely successful for a particular set of problems?
And eventually it's going to, in the general case, hit challenges.
So in terms of the difference between perception systems and robots that have to act in the world,
do you think we're going to return to AI, a modern approach, type breadth in addition five
and six in future decades?
Do you think deep learning will take its place as a chapter in this bigger view of AI?
Yeah, I think we don't know yet how it's all going to play out.
So in the new edition, we have a chapter on deep learning.
We got Ian Goodfellow to be the guest author for that chapter.
So he said he could condense his whole deep learning book into one chapter.
I think he did a great job.
We were also encouraged that he's, you know, we gave him the old neural net chapter and said,
have fun with it.
Modernize that.
And he said, you know, half of that was okay.
That certainly there's lots of new things that have been developed,
but some of the core was still the same.
So I think we'll gain a better understanding of what you can do there.
I think we'll need to incorporate all the things we can do with the other technologies, right?
So deep learning started out, convolutional networks, and very close to perception.
And it's since moved to be able to do more with actions and some degree of longer term planning.
But we need to do a better job with representation and reasoning.
And one shot learning and so on.
And I think we don't know yet how that's going to play out.
So do you think looking at the some success, but certainly eventual demise or partial demise
of experts to symbolic systems in the 80s, do you think there is kernels of wisdom in the work
that was done there with logic and reasoning and so on that will rise again in your view?
So certainly I think the idea of representation and reasoning is crucial that,
you know, sometimes you just don't have enough data about the world to learn de novo.
So you've got to have some idea of representation, whether that was programmed in or told or
whatever, and then be able to take steps of reasoning.
I think the problem with, you know, the good old fashioned AI was one,
we tried to base everything on these symbols that were atomic.
And that's great if you're like trying to define the properties of a triangle,
right? Because they have necessary and sufficient conditions.
But things in the real world don't real world is is messy and doesn't have sharp
edges and atomic symbols do. So that was a poor match.
And then the other aspect was that the reasoning was universal and applied anywhere,
which in some sense is good, but it also means there's no guidance as to where to apply.
And so you, you know, you started getting these paradoxes like, well, if I have a mountain and
I remove one grain of sand, then it's still a mountain. And but if I do that repeatedly,
at some point, it's not right. And with logic, you know, there's nothing to stop you from applying
things repeatedly. But maybe with something like deep learning, and I don't really know what the
right name for it is, we could separate out those ideas. So one, we could say, you know,
mountain isn't just an atomic notion. It's some sort of something like a word embedding that
has a more complex representation. And secondly, we could somehow learn, yeah,
there's this rule that you can remove one grain of sand, and you can do that a bunch of times,
but you can't do it a near infinite amount of times. But on the other hand, when you're doing
induction on the integer, sure, then it's fine to do it an infinite number of times.
And if we could, somehow we have to learn when these strategies are applicable,
rather than having the strategies be completely neutral and available everywhere.
Anytime you use neural networks, anytime you learn from data, form representation from data in an
automated way, it's not very explainable as to, or it's not introspective to us humans,
in terms of how this neural network sees the world, where why does it succeed so brilliantly
on so many in so many cases, and fail so miserably in surprising ways and small. So what do you think
is the future there? Can simply more data, better data, more organized data solve that problem?
Or is there elements of symbolic systems that need to be brought in, which are a little bit more
explainable? Yeah. So I prefer to talk about trust and validation and verification rather than just
about explainability. And then I think explanations are one tool that you use towards those goals.
And I think it is an important issue that we don't want to use these systems unless we trust
them and we want to understand where they work and where they don't work. And an explanation
can be part of that, right? So I apply for loan and I get denied. I want some explanation of why.
And you have in Europe, we have the GDPR that says you're required to be able to get that.
But on the other hand, an explanation alone is not enough, right? So,
you know, we're used to dealing with people and with organizations and corporations and so on,
and they can give you an explanation and you have no guarantee that that explanation relates to
reality, right? So the bank can tell me, well, you didn't get the loan because you didn't have
enough collateral. And that may be true or it may be true that they just didn't like my religion
or something else. I can't tell from the explanation. And that's true whether the decision
was made by a computer or by a person. So I want more. I do want to have the explanations and I
want to be able to have a conversation to go back and forth and said, well, you gave this
explanation, but what about this? And what would have happened if this had happened? And what would
I need to change that? So I think a conversation is a better way to think about it than just an
explanation as a single output. And I think we need testing of various kinds, right? So in order to know
was the decision really based on my collateral or was it based on my religion or skin color or
whatever, I can't tell if I'm only looking at my case. But if I look across all the cases, then I can
detect a pattern. Right? So you want to have that kind of capability. You want to have these
adversarial testing, right? So we thought we were doing pretty good at object recognition in images.
We said, look, we're at sort of pretty close to human level performance on ImageNet and so on.
And then you start seeing these adversarial images and you say, wait a minute,
that part is nothing like human performance. You can mess with it really easily. You can mess with
it really easily, right? And yeah, you can do that to humans too, right? In a different way,
perhaps. Humans don't know what color the dress was. Right. And so they're vulnerable to certain
attacks that are different than the attacks on the machines. But the attacks on the machines
are so striking, they really change the way you think about what we've done, right? And the way I
think about it is I think part of the problem is we're seduced by our low dimensional metaphors,
right? Yeah. So you know, you look in a textbook and you say, okay, now we've mapped out the space
and you know, cat is here and dog is here. And maybe there's a tiny little spot in the middle
where you can't tell the difference. But mostly we've got it all covered. And if you believe
that metaphor, then you say, well, we're nearly there. And, you know, there's only going to be
a couple adversarial images. But I think that's the wrong metaphor. And what you should really say
is it's not a 2D flat space that we've got mostly covered. It's a million dimension space. And cat
is this string that goes out in this crazy path. And if you step a little bit off the path in any
direction, you're in nowhere's land, and you don't know what's going to happen. And so I think
that's where we are. And now we've got to deal with that. So it wasn't so much an explanation,
but it was an understanding of what the models are and what they're doing. And now we can start
exploring how do you fix that? Yeah, validating the robustness of the system so on. But take you
back to the, this, this word trust. Do you think we're a little too hard on our robots in terms of
the standards we apply? So, you know, of there's a dance, there's a there's a there's a dance in
nonverbal and verbal communication between humans. You know, if we apply the same kind of standard
in terms of humans, you know, we trust each other pretty quickly. You know, you and I haven't met
before. And there's some degree of trust, right, that nothing's going to go crazy wrong. And yet
to AI, when we look at AI systems, where we seem to approach the skepticism always always. And it's
like they have to prove through a lot of hard work that they're even worthy of even inkling of our
trust. What do you what do you think about that? How do we break that barrier, close that gap?
I think that's right. I think that's a big issue. Just listening, my friend,
Mark Moffat is a naturalist. And he says, the most amazing thing about humans is that you can
walk into a coffee shop or a busy street in a city. And there's lots of people around you
that you've never met before. And you don't kill each other. Yeah, he says chimpanzees cannot do
that. Yeah, right. Right. If a chimpanzee is in a situation where here's some that aren't from my
tribe, bad things happen. Especially in a coffee shop, there's delicious food around, you know.
Yeah, yeah. But we humans have figured that out. Right. And, you know, for the most part,
for the most part, we still go to war, we still do terrible things. But for the most part, we've
learned to trust each other and live together. So that's going to be important for our AI systems
as well. And also, I think a lot of the emphasis is on AI. But in many cases, AI is part of the
technology, but isn't really the main thing. So a lot of what we've seen is more due to
communications technology than AI technology. Yeah, you want to make these good decisions.
But the reason we're able to have any kind of system at all is we've got the communications
so that we're collecting the data and so that we can reach lots of people around the world.
I think that's a bigger change that we're dealing with.
Speaking of reaching a lot of people around the world, on the side of education, you've
one of the many things in terms of education you've done, you've taught the Intro to
Artificial Intelligence course that signed up 160,000 students. It was one of the first successful
examples of a MOOC, massive open online course. What did you learn from that experience?
What do you think is the future of MOOCs, of education online?
Yeah, it was a great fun doing it, particularly being right at the start,
just because it was exciting and new. But it also meant that we had less competition.
Right. So one of the things you hear about, well, the problem with MOOCs is the completion rates
are so low, so there must be a failure. And I got to admit, I'm a prime contributor.
I probably started 50 different courses that I haven't finished, but I got exactly what I wanted
out of them because I had never intended to finish them. I just wanted to dabble in a little bit,
either to see the topic matter or just to see the pedagogy of how are they doing this class.
So I guess the main thing I learned is when I came in, I thought the challenge was information,
saying, if I'm just take the stuff I want you to know, and I'm very clear and explain it well,
then my job is done and good things are going to happen.
And then in doing the course, I learned, well, yeah, you got to have the information,
but really the motivation is the most important thing that if students don't stick with it,
then it doesn't matter how good the content is. And I think being one of the first classes,
we were helped by sort of exterior motivation. So we tried to do a good job of making it enticing
and setting up ways for the community to work with each other to make it more motivating.
But really a lot of it was, hey, this is a new thing, and I'm really excited to be part of a
new thing. And so the students brought their own motivation. And so I think this is great,
because there's lots of people around the world who have never had this before,
would never have the opportunity to go to Stanford and take a class or go to MIT or go to one of
the other schools. But now we can bring that to them. And if they bring their own motivation,
they can be successful in a way they couldn't before. But that's really just the top tier
of people that are ready to do that. The rest of the people just don't see or don't have their
motivation and don't see how if they push through and we're able to do it, what advantage that would
get them. So I think we got a long way to go before we're able to do that. And I think it'll be,
some of it is based on technology, but more of it's based on the idea of community. You got to
actually get people together. Some of the getting together can be done online. I think some of it
really has to be done in person in order to build that type of community and trust.
You know, there's an intentional mechanism that we've developed a short attention span,
especially younger people, because sort of short and short of videos online,
there's a whatever the way the brain is developing now, and with people that have grown up with the
internet, they have quite a short attention span. So, and I would say I had the same when I was
growing up too, probably for different reasons. So I probably wouldn't have learned as much as I have
if I wasn't forced to sit in a physical classroom, sort of bored, sometimes fall asleep, but sort
of forcing myself through that process to sometimes extremely difficult computer science courses.
What's the difference in your view between in-person education experience, which you,
first of all, yourself had and you yourself taught and online education? And how do we close that
gap if it's even possible? Yeah. So I think there's two issues. One is whether it's in person or
online. So it's sort of the physical location. And then the other is kind of the affiliation, right?
So you stuck with it in part because you were in the classroom and you saw everybody else was
suffering the same way you were, but also because you were enrolled, you had paid tuition, sort of
everybody was expecting you to stick with it. Society, parents, peers. And so those are two
separate things. I mean, you could certainly imagine, I pay a huge amount of tuition and
everybody signed up and says, yes, you're doing this. But then I'm in my room and my classmates
are in different rooms, right? We could have things set up that way. So it's not just the
online versus offline. I think what's more important is the commitment that you've made.
And certainly it is important to have that kind of informal, you know, I meet people outside
of class. We talk together because we're all in it together. I think that's really important,
both in keeping your motivation and also that's where some of the most important learning goes
on. So you want to have that. Maybe, you know, especially now we start getting into higher
bandwidths and augmented reality and virtual reality, you might be able to get that without
being in the same physical place. Do you think it's possible we'll see a course at Stanford,
for example, that for students enrolled students is only online in the near future,
or literally sort of it's part of the curriculum and there is no. Yeah, so you're starting to see
that. I know Georgia Tech has a master's that's done that way. Oftentimes it's sort of they're
creeping in in terms of a master's program or sort of further education considering the constraints
of students and so on. But I mean, literally, is it possible that we just, you know, Stanford,
MIT, Berkeley, all these places go online only in the next few decades?
Yeah, probably not because, you know, they've got a big commitment to physical campus.
Sure. There's a momentum that's both financial and culturally.
Right. And then there are certain things that's just hard to do virtually. Right. So,
you know, we're in a field where if you have your own computer and your own paper and so on,
you can do the work anywhere. But if you're in a biology lab or something,
you know, you don't have all the right stuff at home.
Right. So our field programming, you've also done a lot of, you've done a lot of programming yourself.
In 2001, you wrote a great article about programming called Teach Yourself Programming
in 10 Years, sort of response to all the books that say Teach Yourself Programming in 21 Days.
So if you're giving advice to someone getting into programming today, this is a few years
since you've written that article. What's the best way to undertake that journey?
I think there's lots of different ways and I think programming means more things now.
And I guess, you know, when I wrote that article, I was thinking more about
becoming a professional software engineer. And I thought that's a, you know,
a sort of a career long field of study. But I think there's lots of things now that people can do
where programming is a part of solving what they want to solve without achieving that
professional level status. Right. So I'm not going to be going and writing a million lines of code,
but, you know, I'm a biologist or a physicist or something or even a historian. And I've got
some data and I want to ask a question of that data. And I think for that, you don't need 10 years.
Right. So there are many shortcuts to being able to answer those kinds of questions.
And, you know, you see today a lot of emphasis on learning to code, teaching kids how to code.
I think that's great. But I wish they would change the message a little bit. Right. So
I think code isn't the main thing. I don't really care if you know the syntax of JavaScript or if
you can connect these blocks together in this visual language. But what I do care about is that
you can analyze a problem. You can think of a solution. You can carry out, you know, make a model,
run that model, test the model, see the results, verify that they're reasonable, ask questions
and answer them. Right. So it's more modeling and problem solving. And you use coding in order to
do that. But it's not just learning coding for its own sake. That's really interesting. So it's
actually almost, in many cases, it's learning to work with data to extract something useful out of
data. So when you say problem solving, you really mean taking some kind of, maybe collecting some
kind of data set, cleaning it up and saying something interesting about it, which is useful
in all kinds of domains. And, you know, and I see myself being stuck sometimes in kind of the old
ways. Right. So, you know, be working on a project, maybe with a younger employee. And we say, oh,
well, here's this new package that could help solve this problem. And I'll go and I'll start reading
the manuals. And, you know, I'll be two hours into reading the manuals. And then my colleague comes
back and says, I'm done. You know, I downloaded the package, I installed it, I tried calling some
things. The first one didn't work. The second one didn't work. Now I'm done. And I say, but I have
under questions about how does this work? And how does that work? And they say, who cares? Right.
I don't need to understand the whole thing. I answered my question. It's a big complicated
package. I don't understand the rest of it. But I got the right answer. And I'm just, it's hard for
me to get into that mindset. I want to understand the whole thing. And, you know, if they wrote a
manual, I should probably read it. And but that's not necessarily the right way. And I think I have
to get used to dealing with more, being more comfortable with uncertainty and not knowing
everything. Yeah. So I struggle with the same instead of the the spectrum between Donald
and Donald Knuth. Yeah. It's kind of the very, you know, before he can say anything about a
problem, he really has to get down to the machine code to assembly. Yeah. Versus exactly what you
said. I have several students in my group that, you know, 20 years old, and they can solve almost
any problem within a few hours that would take me probably weeks because I would try to, as you
said, read the manual. So do you think the nature of mastery, you're mentioning biology,
sort of outside disciplines, applying programming, but computer scientists. So over time,
there's higher and higher levels of abstraction available now. So with this week, there's the
TensorFlow Summit, right? So if you're, if you're not particularly into deep learning,
but you're still a computer scientist, you can accomplish an incredible amount with TensorFlow
without really knowing any fundamental internals of machine learning. Do you think the nature of
mastery is changing even for computer scientists, like what it means to be an expert programmer?
Yeah, I think that's true. You know, we never really should have focused on programmers, right?
Because it's still, it's the skill and what we really want to focus on is the result. So we built
this ecosystem where the way you can get stuff done is by programming it yourself. At least when
I started it, you know, library functions meant you had square root, and that was about it, right?
Everything else you built from scratch. And then we built up an ecosystem where a lot of times,
well, you can download a lot of stuff that does a big part of what you need. And so now it's
more a question of assembly rather than manufacturing. And that's a different way of looking at problems.
From another perspective in terms of mastery and looking at programmers or people that reason
about problems in a computational way. So Google, you know, from the hiring perspective,
from the perspective of hiring or building a team of programmers, how do you determine if
someone's a good programmer? Or if somebody, again, so I want to deviate from, I want to move away
from the word programmer, but somebody who can solve problems of large scale data and so on,
what's, what's, how do you build a team like that through the interviewing process?
Yeah, and I, and I think as a company grows, you get more expansive in the types of people
you're looking for, right? So I think, you know, in the early days, we'd interview people and the
question we were trying to ask is, how close are they to Jeff Dean? And most people were pretty
far away, but we take the ones that were, you know, not that far away. And so we got kind of a
homogeneous group of people who are really great programmers. Then as a company grows, you say,
well, we don't want everybody to be the same, to have the same skill set. And so now we're hiring
biologists in our health areas, and we're hiring physicists, we're hiring mechanical engineers,
we're hiring, you know, social scientists and ethnographers and people with different backgrounds
who bring different skills. So you have mentioned that you still may partake in code reviews.
Given that you have a wealth of experiences, they've also mentioned it. What errors do you
often see and tend to highlight in the code of junior developers of people coming up now,
given your background from Wisp to a couple of decades of programming?
Yeah, that's a great question. You know, sometimes I try to look at flexibility
of the design of, yes, you know, this API solves this problem, but where's it going to go in the
future? Who else is going to want to call this? And, you know, are you making it easier for them
to do that? That's a matter of design. Is it documentation? Is it sort of an amorphous thing
you can't really put into words? It's just how it feels. If you put yourself in the shoes of a
developer, would you use this kind of thing? I think it is how you feel, right? And so, yeah,
documentation is good, but it's more a design question, right? If you get the design right,
then people will figure it out whether the documentation is good or not. And if the design
is wrong, then it'll be harder to use. How have you yourself changed as a programmer over the years
in a way? You already started to say sort of you want to read the manual, you want to understand
the core of the syntax to the how the language is supposed to be used and so on. But what's
the evolution been like from the 80s, 90s to today? I guess one thing is you don't have to
worry about the small details of efficiency as much as you used to, right? So, like, I remember
I did my list book in the 90s. And one of the things I wanted to do was say, here's how you do
an object system. And basically, we're going to make it so each object is a hash table and you
look up the methods and here's how it works. And then I said, of course, the real common list
object system is much more complicated. It's got all these efficiency type issues. And this is just
a toy, nobody would do this in real life. And it turns out Python pretty much did exactly
what I said and said, objects are just dictionaries. And yeah, they have a few little tricks as well.
But mostly, you know, the thing that would have been 100 times too slow in the 80s is now
plenty fast for most everything. So you had to as a programmer let go of perhaps an obsession
that I remember coming up with of trying to write efficient code. Yeah, to say, you know,
what really matters is the total time it takes to get the project done. And most of that's
going to be the programmer time. So if you're a little bit less efficient, but it makes it easier
to understand and modify, then that's the right trade off. So you've written quite a bit about
Lisp, your book on programming is in Lisp, you have a lot of code out there that's in Lisp.
So myself and people who don't know what Lisp is should look it up. It's my favorite language for
many AI researchers. It is a favorite language. The favorite language they never use these days.
So what part of the list do you find most beautiful and powerful? So I think the beautiful part is
the simplicity that in half a page, you can define the whole language. And other languages don't
have that. So you feel like you can hold everything in your head. And then, you know, a lot of people
say, well, then that's too simple, you know, here's all these things I want to do. And, you know,
my Java or Python or whatever has 100 or 200 or 300 different syntax rules. And don't I need all
those? And Lisp's answer was no, we're only going to give you eight or so syntax rules. But we're
going to allow you to define your own. And so that was a very powerful idea. And I think this idea
of saying, I can start with my problem and with my data. And then I can build the language I want
for that problem and for that data. And then I can make Lisp define that language. So you're
sort of mixing levels and saying, I'm simultaneously a programmer in a language and a language
designer. And that allows a better match between your problem and your eventual code. And I think
Lisp had done that better than other languages. Yeah, it's a very elegant implementation of
functional programming. But why do you think Lisp has not had the mass adoption and success of
languages like Python? Is it the parentheses? Is it all the parentheses? Yeah. So I think a couple
things. So one was, I think it was designed for a single programmer or a small team. And a skilled
programmer who had the good taste to say, well, I am doing language design. And I have to make
good choices. And if you make good choices, that's great. If you make bad choices, you can hurt
yourself. And it can be hard for other people on the team to understand it. So I think there was a
limit to the scale of the size of a project in terms of number of people that Lisp was good for.
And as an industry, we kind of grew beyond that. I think it is in part the parentheses. You know,
one of the jokes is the acronym for Lisp is lots of irritating silly parentheses. My acronym was
Lisp is syntactically pure. Saying all you need is parentheses and atoms. But I remember, you know,
as we had the AI textbook, and because we did it in the 90s, we had, we had pseudocode in the book,
but then we said, well, we'll have Lisp online because that's the language of AI at the time.
And I remember some of the students complaining because they hadn't had Lisp before, and they
didn't quite understand what was going on. And I remember one student complained, I don't understand
how this pseudocode corresponds to this Lisp. And there was a one-to-one correspondence between the
symbols in the code and the pseudocode. And the only thing difference was the parentheses. So I
said, it must be that for some people, a certain number of left parentheses shuts off their brain.
Yeah, it's very, it's very possible in that sense. And Python just goes the other way.
And so that was the point at which I said, okay, can't have only Lisp as a language. Because I,
you know, I don't want to, you know, you only got 10 or 12 or 15 weeks or whatever it is to teach AI.
And I don't want to waste two weeks of that teaching Lisp. So I said, I got to have another
language Java was the most popular language at the time. I started doing that. And then I said,
it's really hard to have a one-to-one correspondence between the pseudocode and the Java because
Java is so verbose. So then I said, I'm going to do a survey and find the language that's most like
my pseudocode. And turned out Python basically was my pseudocode. Somehow I had channeled Guido
designed a pseudocode that was the same as Python, although I hadn't heard of Python
at that point. And from then on, that's what I've been using because it's been a good match.
So what's the story in Python behind Pytudes? You're a GitHub repository with puzzles and
exercises and Python is pretty fun. Yeah, just it seems like fun. You know, I like doing puzzles
and I like being an educator. I did a class with Udacity, Udacity 212, I think it was. It was
basically problem solving using Python and looking at different problems.
Does Pytudes feed that class in terms of the exercises? I was wondering what that
Yeah, so the class came first. Some of the stuff that's in Pytudes was write-ups of what was in
the class and then some of it was just continuing to work on new problems.
So what's the organizing madness of Pytudes? Is it just a collection of cool exercises?
Just whatever I thought was fun.
Okay, awesome. So you were the director of search quality at Google from 2001 to 2005
in the early days when there's just a few employees and when the company was growing like crazy,
right? So I mean, Google revolutionized the way we discover, share and aggregate knowledge.
So this is one of the fundamental aspects of civilization, right? Is information
being shared and there's different mechanisms throughout history, but Google is just 10x
improve that, right? And you're part of that, right? People discovering that information.
So what were some of the challenges on the philosophical or the technical level in those
early days? It definitely was an exciting time and as you say, we were doubling in size every year
and the challenges were we wanted to get the right answers, right? And we had to figure out
what that meant. We had to implement that and we had to make it all efficient and
we had to keep on testing and seeing if we were delivering good answers.
And now when you say good answers, it means whatever people are typing in in terms of
keywords in terms of that kind of thing that the results to get are ordered by the desirability
for them of those results. Like they're like the first thing they click on will likely be the thing
that they were actually looking for. Right. One of the metrics we had was focused on the first thing.
Some of it was focused on the whole page. Some of it was focused on top three or so.
So we looked at a lot of different metrics for how well we were doing and we broke it down into
subclasses of, you know, maybe here's a type of query that we're not doing well on, then we try
to fix that. Early on, we started to realize that we were in an adversarial position, right? So we
started thinking, well, we're kind of like the card catalog in the library, right? So the books
are here and we're off to the side and we're just reflecting what's there. And then we realized
every time we make a change, the webmasters make a change. And it's game theoretic. And so we had
to think not only is this the right move for us to make now, but also if we make this move,
what's the counter move going to be? Is that going to get us into a worse place? In which case,
we won't make that move. We'll make a different move. And did you find, I mean, I assume with the
popularity and the growth of the internet that people were creating new content. So you're almost
helping guide the creation. Yeah, so that's certainly true, right? So we knew we definitely
changed the structure of the network, right? So if you think back, you know, in the very early days,
Larry and Sergey had the page rank paper, and John Kleinberg had this hubs and authorities model,
which says the web is made out of these hubs, which will be my page of cool links about dogs
or whatever. And people would just list links. And then there'd be authorities, which were the
ones that page about dogs that most people linked to. That doesn't happen anymore. People don't
bother to say my page of cool links, because we took over that function, right? So we changed
the way that worked. Did you imagine back then that the internet would be as massively vibrant as
it is today? I mean, it was already growing quickly, but it's just another, I don't know if you've
ever, today, if you sit back and just look at the internet with wonder, the amount of content
that's just constantly being created, constantly being shared, employed. Yeah, it's always been
surprising to me. I guess I'm not very good at predicting the future in the future. Okay. And
I remember, you know, being a graduate student in 1980 or so, and, you know, we had the ARPANET,
and then there was this proposal to commercialize it and have this internet and this
crazy Senator Gore thought that might be a good idea. And I remember thinking, oh, come on,
you can't, you can't expect a commercial company to understand this technology. They'll never be
able to do it. Yeah, okay, we can have this dot com domain, but it won't go anywhere. So I was wrong,
Al Gore was right. At the same time, the nature of what it means to be a commercial company has
changed too. So Google anyways, is at its founding is different than a, you know, what companies
were before, I think. Right. So there's all these business models that are so different than what
was possible back then. So in terms of predicting the future, what do you think it takes to build
a system that approaches human level intelligence? You've talked about, of course, that, you know,
we shouldn't be so obsessed about creating human level intelligence, just create systems that are
very useful for humans. But what do you think it takes to, uh, to, uh, yeah, approach that level?
Right. So certainly, I don't think human level intelligence is one thing. Right. So I think
there's lots of different tasks, lots of different capabilities. I also don't think that should be
the goal. Right. So I, you know, I wouldn't want to create a calculator that could do multiplication
at human level. Right. That would be a step backwards. And so for many things, we should be
aiming far beyond human level. For other things, maybe human level is a good level to aim at.
And for others, we'd say, well, let's not bother doing this because we already have humans can
take on those tasks. So as you say, I like to focus on, uh, what, what's a useful tool?
And, and in some cases being on human level is an important part of crossing that threshold to,
to make the tool useful. So we see in, in things like these, uh, uh, personal assistance now that
you get either on your phone or on a speaker that sits on the table, uh, you want to be able to have
a conversation with those. And, and I think as an industry, we haven't quite figured out what the
right model is for what these things can do. Uh, and we're aiming towards, well, you just have a
conversation with them the way you can with a person. Right. Uh, but we haven't delivered on
that model yet. Right. So you can ask it, what's the weather? You can ask it, play some nice songs,
and, uh, you know, five or six other things. And then you run out of stuff that it can do.
In terms of, uh, deep, meaningful connection. So you've mentioned the movie,
HER, as one of your favorite AI movies. Do you think it's possible for a human being to fall
in love with an AI system, AI assistant, as you mentioned, to have taken this big leap
from, uh, what's the weather to, you know, having a deep connection? Yeah. I think, uh, as people,
that's what we love to do. And, uh, I was at a, uh, a showing of HER where we had a panel discussion
and, and somebody asked me, uh, what other movie do you think HER is similar to? And my answer
was, uh, Life of Brian, which, which is not a science fiction movie. Uh, but both movies are
about wanting to believe in something that's not necessarily real. Yeah. By the way, for people
that don't know, it's Monty Python. Yeah. Yeah. That's brilliantly put. Right. So, so, I mean,
I think that's just the way we are. We, we want to trust, we want to believe, we want to fall in love
and, uh, it doesn't necessarily take that much, right? So, uh, you know, my kids, uh, fell in love
with their teddy bear and the teddy bear was not very interactive, right? So that's all us pushing
our feelings onto our devices and our things. And I think that that's what we like to do. So we'll
continue to do that. So yeah, as human beings, we long for that connection and just AI has to, uh,
do a little bit of work to, uh, to catch us in the other end. Yeah. And certainly, you know,
if you can get to a dog level, a lot of people have invested a lot of love in their pets and
their pets. Some, some people, as I've been told in working with autonomous vehicles have invested
a lot of love into their inanimate cars. Yeah. So it really doesn't take much. Yeah. So what is a
good test to linger on a topic that, uh, maybe silly or a little bit philosophical? What is a
good test of intelligence in your view? Is natural conversation like in the touring test, a good,
a good test, put another way, what would impress you if you saw a computer do it these days?
Yeah. I mean, I get impressed all the time. Right. So, uh, you know, go playing, uh, Starcraft
playing, uh, those are all pretty cool. You know, and I think, uh, sure, conversation is important.
I think, uh, you know, we sometimes have these tests where it's easy to fool the system where you
can have a chat bot that can have a conversation, but you never, uh, it never gets into a situation
where it has to be deep enough that, uh, it really reveals itself as, as being intelligent or not.
I think, uh, you know, Turing suggested that. Uh, but I think if he were alive, he'd say,
you know, I didn't really mean that seriously. Yeah. And I think, uh, you know, this is just
my opinion, but, but I think Turing's point was not that, uh, this test of conversation is a good
test. I think his point was having a test is the right thing. So rather than having the
philosopher say, oh no, AI is impossible, you should say, well, we'll just have a test. And
then the result of that will, will tell us the answer and doesn't necessarily have to be a
conversation test. That's right. And coming up with you about a test as the technology evolves
is probably the right way. Do you worry as a lot of the general public does about, not a lot, but
some vocal part of the general public about the existential threat of artificial intelligence.
So looking farther into the future, as you said, most of us are not able to predict much.
So when shrouded in such mystery, there's a concern of, well, you think it started thinking
about worst case. Is that something that occupies your mind space much?
So I certainly think about, uh, threats. I think about, uh, dangers. Uh, and I think, uh,
any new technology, uh, has positives and negatives. And if it's a powerful technology,
it can be used for bad as well as for good. So I'm certainly not worried about, uh, the robot
apocalypse, uh, and the Terminator type scenarios. I am worried about change in employment and, uh,
are we going to be able to react fast enough to deal with that? I think we're, you know,
we're already seeing it today where a lot of people are, are disgruntled about, uh,
the way income inequality is, is working. And, uh, and automation could help accelerate those kinds
of, uh, problems. I see powerful technologies can always be used as weapons, uh, whether they're
robots or drones or whatever. Uh, some of that, uh, we're seeing dude AI, a lot of it, uh, you
don't need AI. Uh, and I don't know what's a, what's a worst threat if it's a autonomous drone or, uh,
it's, uh, CRISPR technology becoming available or we have lots of, uh, threats to face and
some of them involve AI and some of them don't. So the threats that technology presents, are you
for the most part optimistic about technology also alleviating those threats or creating new
opportunities or protecting us from the more detrimental effects of these things?
Yeah, I don't know. It, it, again, it's hard to predict the future and, uh, as a society so far,
we've survived, uh, nuclear and other things. Of course, uh, only societies that have survived
are having this conversation. So, uh, uh, maybe that's, uh, survivorship bias there.
Yeah. What problem stands out to you as exciting, challenging, impactful to work on in the near
future for yourself, for the community and broadly? So, uh, you know, we talked about these, uh,
assistance and conversation. I think that's a great area. I think, uh, combining, uh,
common sense reasoning, uh, with, uh, the power of data is a great area.
Yeah. In which application? In, in conversational
issues, just broadly? Just in general, yeah. As a programmer, I'm interested in, uh,
programming tools, both in terms of, uh, you know, the current systems we have today with,
with TensorFlow and so on. Can we make them much easier to use for broader, uh, class of people?
And also, can we apply, uh, machine learning to, uh, the more traditional type of programming?
Right. So, you know, when you go to Google and you, uh, type in a query and you spell something
wrong, it says, did you mean, and the reason we're able to do that is cause lots of other people
made a similar error and then they corrected it. Uh, we should be able to go into our code
bases and our bugfix spaces. And, uh, when I type a line of code, it should be able to say,
did you mean such and such? If you type this today, you're probably going to fit,
type in this bugfix, uh, tomorrow.
Yeah. That's a really exciting application of, uh, almost, uh, an assistant for the coding
programming experience at every level. So I think I could safely speak for the entire AI community.
First of all, for thanking you for the amazing work you've done, uh, certainly for the amazing
work you've done with, uh, AI, a modern approach book. I think we're all looking forward very much
for the fourth edition and then the fifth edition and so on. So, uh, Peter, thank you so much for
talking today. Yeah, thank you. Pleasure.