Peter Norvig: Artificial Intelligence: A Modern Approach
嘉宾：Peter Norvig
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§01Transcript · 00:00–09:25
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, A 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
@LexFriedman, 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, A Modern Approach."
It's a field-defining textbook, as many people are 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 first edition,
we said, "Here's predicate logic,"
but that only goes so far 'cause pretty soon
you have millions of short little predicate expressions
and they couldn't possibly fit in memory.
So we're gonna 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?
- Yeah. - So resource constraints
were loosened significantly for the second?
- Yeah, yeah.
- And that was 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 you'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 gonna find AI
"as maximizing expected utility.
"And you tell me your utility function."
And now we've got 27 chapters worth of 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 touched that topic in this edition.
You get a little bit more into utility.
- Yeah, yeah. - That's really interesting.
On a technical level,
or 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 certain ways.
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 like 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 a six out of 10,
then I want that to mean the same,
whether no matter what race I'm on, right?
So I want to have a 60% chance of reoccurring regardless.
And the makers of the,
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 trade off point.
- But nevertheless, then we can,
as humans, deliberate where that trade off 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,
where there's some fuzzier things,
like 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.
- I think that's a really important issue too,
certainly 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 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.
And we built 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 we 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
that seemed to be on the easier part of that,
which is feeding the dopamine, the rush.
- Right.
- But, so maybe take a quick step back
at the beginning of the "Artificial Intelligence,
A 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,
§02Transcript · 09:27–19:30
which is one of the only books
that has successfully done that for AI.
And actually in a lot of other computer science fields,
it's a huge undertaking.
So it must've 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.
"It 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 Lab.
So I thought, well, that's the end
of my possible academic publishing career.
But I met Stuart again at a conference 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?"
(laughing)
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 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,
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 were moving more towards probability
rather than Boolean logic,
and we were moving more towards machine learning
rather than knowledge engineering.
And the other books hadn't caught that wave.
They were still more in the old school.
So 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.
You telnet it into one shell and you type cat file name
and you hoped it was captured 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 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 gonna first develop
different types of representations.
And we're gonna 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, you know, there's some sense
in which the deep learning craze
is extremely successful for a particular set of problems.
And, you know, 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 gonna return to AI,
a modern approach type breadth in edition 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 gonna 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, 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 gonna play out.
- So do you think looking at the some success,
but certainly eventual demise,
a 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 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 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?
'Cause they have necessary and sufficient conditions.
But things in the real world don't.
The real world 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 started getting these paradoxes like,
well if I have a mountain and I remove one grain of sand,
then it's still a mountain.
But if I do that repeatedly at some point it's not.
And with logic, 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,
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 integers, 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 in 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 wanna use these systems unless we trust them,
§03Transcript · 19:33–28:18
and we wanna understand where they work
and where they don't work.
And an explanation can be part of that, right?
So I apply for a 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 we are 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
"'cause 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 wanna have the explanations,
and I wanna 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 wanna have that kind of capability.
You wanna 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."
- And 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?
So-- - In a different way perhaps.
- Right, 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.
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.
- You know, you look-- - I like that phrase.
- You look in a textbook and you say,
"Okay, now we've mapped out the space."
And you know, a 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 gonna 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 a 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 gonna 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 it back to 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,
there's a dance,
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 gonna go crazy wrong.
And yet to AI, when we look at AI systems,
we seem to approach through 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 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."
(laughing)
- Yeah.
- He says, chimpanzees cannot do that.
- Yeah, right.
(laughing)
If a chimpanzee's 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.
- Yeah, yeah.
But we humans have figured that out, right?
- 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 gonna 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 wanna 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,
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 example
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 gotta 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,
'cause 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 gonna happen.
And then in doing the course, I learned,
well, yeah, you gotta 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,
'cause 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
§04Transcript · 28:21–37:37
that are ready to do that.
The rest of the people just don't see
or don't have the motivation
and don't see how if they push through
and were 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 some of it is based on technology,
but more of it's based on the idea of community,
that you gotta 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.
- There's an intentional mechanism
that we've developed a short attention span,
especially younger people,
because sort of shorter and shorter videos online.
The way the brain is developing now
with people that have grown up with the internet,
they have quite a short attention span.
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 falling 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.
- Right, 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 wanna 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,
you know, Stanford, MIT, Berkeley,
all these places go online only in the next few decades?
- Yeah, probably not, 'cause, you know,
they've got a big commitment to a physical campus.
- Sure, so there's a momentum
that's both financial and cultural.
- 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 were 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,
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 wanna solve
without achieving that professional-level status, right?
So I'm not gonna 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 wanna 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.
- You know, and I see myself being stuck sometimes
in kind of the old ways, right?
So, you know, I'll 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."
- Yep. - 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 100 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 wanna 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.
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.
It's sort of the spectrum between Donald and Don 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 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 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,
§05Transcript · 37:40–47:06
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?
'Cause it's still, it's a 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, 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 could solve problems of large scale data
and so on.
How do you build a team like that
through the interviewing process?
- Yeah, 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'd 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 experience,
as you've also mentioned,
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 the flexibility
of the design of, yes, you know, this API solves
this problem, but where is it gonna go in the future?
Who else is gonna wanna 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's 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 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 gonna 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 Lisp 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, 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 what really matters is the total time
it takes to get the project done.
And most of that's gonna 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 Lisp 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 a lot of people say, well, then that's too simple.
Here's all these things I wanna do.
And 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 gonna give you
eight or so syntax rules,
but we're gonna 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 of 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.
One of the jokes is the acronym for Lisp
is lots of irritating, silly parentheses.
(laughing)
My acronym was Lisp is syntactically pure,
saying all you need is parentheses and atoms.
But I remember, so we had the AI textbook
and because we did it in the 90s,
we had pseudocode in the book,
but then we said, well, we'll have Lisp online
'cause that's the language of AI at the time.
And I remember some of the students complaining
'cause 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 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
'cause I don't wanna, 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 gotta have another language.
Java was the most popular language at the time.
I started doing that and then I said,
§06Transcript · 47:09–56:15
it's really hard to have a one-to-one correspondence
between the pseudocode and the Java
'cause Java is so verbose.
So then I said, I'm gonna do a survey
and find the language that's most like my pseudocode.
And it turned out Python basically was my pseudocode.
Somehow I had channeled Guido
and 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
'cause it's been a good match.
- So what's the story in Python behind PyTudes,
your GitHub repository with puzzles and exercises
in Python is pretty fun.
- Yeah, it seems like fun.
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 the--
- 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 just, 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 were a part of that, right?
People discovering that information.
So what were some of the challenges
on a 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.
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 they get are ordered
by the desirability for them of those results.
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 the 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,
maybe here's a type of query that we're not doing well on
and we try to fix that.
Early on, we started to realize
that we were in an adversarial position.
So we started thinking,
well, we're kind of like the card catalog in the library.
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 a 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 gonna be?
Is that gonna 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 of new content.
- Yeah, so that's certainly true.
So we definitely changed the structure of the network.
So if you think back in the very early days,
Larry and Sergey had the PageRank 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 link to.
That doesn't happen anymore.
People don't bother to say my page of cool links,
'cause 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 and deployed.
- Yeah, it's always been surprising to me.
I guess I'm not very good at predicting the future.
- Okay.
- I remember being a graduate student in 1980 or so,
and we had the ARPANET,
and then there was this proposal to commercialize it,
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 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, in many ways, at its founding,
is different than 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 we shouldn't be so obsessed
about creating human-level intelligence.
We just create systems that are very useful for humans.
But what do you think it takes to 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 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
"'cause we already have humans can take on those tasks."
So as you say, I like to focus on what's a useful tool.
And in some cases, being at human level
is an important part of crossing that threshold
to make the tool useful.
So we see in things like these personal assistants now
that you get either on your phone
or on a speaker that sits on the table,
you wanna be able to have a conversation with those.
And I think as an industry,
we haven't quite figured out what the right model is
for what these things can do.
And we're aiming towards,
well, you just have a conversation with them
the way you can with a person.
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 five or six other things,
and then you run out of stuff that it can do.
- In terms of a 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.
So taking this big leap from what's the weather
to having a deep connection.
- Yeah, I think as people, that's what we love to do.
And I was at a showing of "Her"
where we had a panel discussion,
and somebody asked me,
"What other movie do you think 'Her' is similar to?"
And my answer was "Life of Brian,"
which is not a science fiction movie,
but both movies are about wanting to believe
in something that's not necessarily real.
- Yeah, by the way, for people who don't know,
it's Monty Python.
Yeah, that's been brilliantly put.
- Right, so I think that's just the way we are.
We want to trust, we want to believe,
we want to fall in love,
and it doesn't necessarily take that much, right?
So my kids fell in love with their teddy bear,
§07Transcript · 56:20–1:03:05
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 do a little bit of work
to catch us in the other end.
- Yeah, and certainly, if you can get to dog level,
a lot of people have invested a lot of love in their pets.
- In their pets.
Some people, as I've been told,
in working with autonomous vehicles
have invested a lot of love into their inanimate cars,
so it really doesn't take much.
So what is a good test to linger on a topic
that may be silly or a little bit philosophical?
What is a good test of intelligence in your view?
Is natural conversation like in the Turing test
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.
(laughing)
Right, so-- - But like really impressive.
- You know, Go playing, StarCraft playing,
those are all pretty cool.
You know, and I think, sure, conversation is important.
I think, you know, we sometimes have these tests
where it's easy to fool the system,
where you can have a chatbot that can have a conversation,
but it never gets into a situation
where it has to be deep enough
that it really reveals itself as being intelligent or not.
I think, you know, Turing suggested that,
but I think if he were alive, he'd say,
you know, I didn't really mean that seriously.
Right? - Yeah.
- And I think, you know, this is just my opinion,
but I think Turing's point was not that
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 tell us the answer.
And it doesn't necessarily have to be a conversation test.
- That's right, and coming up with a new, better 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 start thinking about worst case.
Is that something that occupies your mind space much?
- So I certainly think about threats.
I think about dangers, and I think any new technology
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 the robot apocalypse
and the Terminator-type scenarios.
I am worried about change in employment,
and are we gonna be able to react fast enough
to deal with that?
I think we're already seeing it today,
where a lot of people are disgruntled
about the way income inequality is working,
and automation could help accelerate
those kinds of problems.
I see powerful technologies can always be used as weapons,
whether they're robots or drones or whatever.
Some of that we're seeing due to AI.
A lot of it, you don't need AI.
And I don't know what's a worst threat,
if it's an autonomous drone,
or it's CRISPR technology becoming available,
or we have lots of 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 new technologies?
- No, again, it's hard to predict the future.
And as a society so far,
we've survived nuclear bombs and other things.
Of course, only societies that have survived
are having this conversation.
So maybe that's a survivorship bias there.
- 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 we talked about these assistance in conversation.
I think that's a great area.
I think combining common sense reasoning
with the power of data is a great area.
- In which application, in conversation,
or just broadly speaking?
- Just in general, yeah.
As a programmer, I'm interested in programming tools,
both in terms of the current systems we have today
with TensorFlow and so on.
Can we make them much easier to use
for a broader class of people?
And also, can we apply machine learning
to the more traditional type of programming?
So when you go to Google and you 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.
We should be able to go into our code bases
and our bug fix bases, and 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 gonna
type in this bug fix tomorrow.
- Yeah, that's a really exciting application
of almost 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,
certainly for the amazing work you've done
with 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 Peter, thank you so much for talking today.
- Yeah, thank you, pleasure.
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