The following is a conversation with Sergei Levine, a professor at Berkeley and a world

class researcher in deep learning, reinforcement learning, robotics, and computer vision,

including the development of algorithms for end to end training of neural network policies

that combine perception and control, scalable algorithms for inverse reinforcement learning,

and, in general, deep RL algorithms.

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What's the difference between a state of the art human, such as you and I, well,

I don't know if we qualify as state of the art humans, but a state of the art human and a state

of the art robot? That's a very interesting question. Robot capability is, it's kind of a,

I think it's a very tricky thing to understand because there are some things that are difficult

that we wouldn't think are difficult and some things that are easy that we wouldn't think are easy.

And there's also a really big gap between capabilities of robots in terms of

hardware and their physical capability and capabilities of robots in terms of what they

can do autonomously. There is a little video that I think robotics researchers really like

to show, especially robotics learning researchers like myself from 2004 from Stanford,

which demonstrates a prototype robot called the PR1. And the PR1 was a robot that was designed as a

home assistance robot. And there's this beautiful video showing the PR1 tidying up a living room,

putting away toys, and at the end, bringing a beer to the person sitting on the couch,

which looks really amazing. And then the punchline is that this robot is entirely controlled by a

person. So you can, in some ways, the gap between a state of the art human and a state of the art

robot, if the robot has a human brain, is actually not that large. Now, obviously,

like human bodies are sophisticated and very robust and resilient in many ways. But on the whole,

if we're willing to like spend a bit of money and do a bit of engineering,

we can kind of close the hardware gap almost. But the intelligence gap, that one is very wide.

And when you say hardware, you're referring to the physical sort of the actuators, the actual

body of the robot as opposed to the hardware on which the cognition, the nervous, the hardware of

the nervous system. Yes, exactly. I'm referring to the body rather than the mind.

So that means that the kind of the work is cut out for us. While we can still make the

body better, we kind of know that the big bottleneck right now is really the mind.

And how big is that gap? How big is the difference in your sense of ability to learn,

ability to reason, ability to perceive the world between humans and our best robots?

The gap is very large, and the gap becomes larger the more unexpected events can happen

in the world. So essentially, the spectrum along which you can measure the size of that gap is

the spectrum of how open the world is. If you control everything in the world very tightly,

if you put the robot in like a factory and you tell it where everything is and you rigidly

program its motion, then it can do things, one might even say, in a superhuman way,

it can move faster, it's stronger, it can lift up a car and things like that. But as soon as

anything starts to vary in the environment, now it'll trip up. And if many, many things vary,

like they would like in your kitchen, for example, then things are pretty much like wide open.

Now, again, we're going to stick a bit on the philosophical questions, but

how much on the human side of the cognitive abilities in your sense is nature versus nurture?

So how much of it is a product of evolution and how much of it is something we'll learn from

sort of scratch from the day we're born? I'm going to read into your question as asking about

the implications of this for AI, because I'm not a biologist, I can't really like speak

authoritatively. So until we learn it, if it's all about learning, then there's more hope for AI.

Yeah. So the way that I look at this is that,

you know, well, first, of course, biology is very messy. And it's, if you ask the question,

how does a person do something, or how does a person's mind do something,

you can come up with a bunch of hypotheses, and oftentimes you can find support for many

different often conflicting hypotheses. One way that we can approach the question of

what the implications of this for AI are, is we can think about what's sufficient.

So, you know, maybe a person is, from birth, very, very good at some things like, for example,

recognizing faces. There's a very strong evolutionary pressure to do that. If you can

recognize your mother's face, then you're more likely to survive, and therefore people are good

at this. But we can also ask like, what's the minimum sufficient thing? And one of the ways

that we can study the minimal sufficient thing is we could, for example, see what people do in

unusual situations. If you present them with things that evolution couldn't have prepared them for,

you know, our daily lives actually do this to us all the time. We didn't evolve to deal with,

you know, automobiles and space flight and whatever. So, there are all these situations

that we can find ourselves in. And we do very well there. Like, I can give you a joystick

to control a robotic arm, which you've never used before. And you might be pretty bad for the

first couple of seconds. But if I tell you, like, your life depends on using this robotic arm to,

like, open this door, you'll probably manage it. Even though you've never seen this device before,

you've never used the joystick to control us, and you'll kind of muddle through it. And that's

not your evolved natural ability. That's your flexibility, your adaptability. And that's exactly

where our current robotic systems really kind of fall flat.

But I wonder how much general, almost what we think of as common sense,

pre trained models underneath all of that. So that ability to adapt to a joystick

requires you to have a kind of, you know, I'm human. So it's hard for me to introspect all

the knowledge I have about the world. But it seems like there might be an iceberg underneath

of the amount of knowledge we actually bring to the table. That's kind of the open question.

I think there's absolutely an iceberg of knowledge that we bring to the table. But

I think it's very likely that iceberg of knowledge is actually built up over our

lifetimes. Because we have, you know, we have a lot of prior experience to draw on. And it kind

of makes sense that the right way for us to, you know, to optimize our efficiency, our evolutionary

fitness, and so on, is to utilize all that experience to build up the best iceberg we can

get. And that's actually one, you know, while that sounds an awful lot like what machine

learning actually does, I think that for modern machine learning, it's actually one of the

things that for modern machine learning, it's actually a really big challenge to take this

unstructured mass of experience and distill out something that looks like a common sense

understanding of the world. And perhaps part of that is it's not because something about

machine learning itself is broken or hard, but because we've been a little too rigid

in subscribing to a very supervised, very rigid notion of learning, you know, kind of the input

output Xs go to Ys sort of model. And maybe what we really need to do is to view the world more

as like a massive experience that is not necessarily providing any rigid supervision,

but sort of providing many, many instances of things that could be. And then you take that

and you distill it into some sort of common sense understanding.

I see. Well, you're painting an optimistic, beautiful picture, especially from the robotics

perspective, because that means we just need to invest and build better learning algorithms,

figure out how we can get access to more and more data for those learning algorithms to extract

signal from and then accumulate that iceberg of knowledge. It's a beautiful picture. It's a

hopeful one. I think it's potentially a little bit more than just that. And this is where we

perhaps reach the limits of our current understanding. But one thing that I think that

the research community hasn't really resolved in a satisfactory way is how much it matters

where that experience comes from. Like, you know, do you just like download everything on the

internet and cram it into essentially the 21st century analog of the giant language model and

then see what happens? Or does it actually matter whether your machine physically experiences the

world or in the sense that it actually attempts things, observes the outcome of its actions and

kind of augments its experience that way? That it chooses which parts of the world it

gets to interact with and observe and learn from. Right. It may be that the world is so complex

that simply obtaining a large mass of sort of IID samples of the world is a very difficult way

to go. But if you are actually interacting with the world and essentially performing this sort of

hard negative mining by attempting what you think might work, observing the sometimes happy and

sometimes sad outcomes of that, and augmenting your understanding using that experience and

you're just doing this continually for many years, maybe that sort of data in some sense

is actually much more favorable to obtaining a common sense understanding. One reason we might

think that this is true is that what we associate with common sense or lack of common sense

is often characterized by the ability to reason about kind of counterfactual questions. Like,

if I were to... Here, this bottle of water is sitting on the table, everything is fine,

if I were to knock it over, which I'm not going to do, but if I were to do that, what would happen?

And I know that nothing good would happen from that, but if I have a bad understanding of the

world, I might think that that's a good way for me to gain more utility. If I actually go about

daily life doing the things that my current understanding of the world suggests will give

me high utility, in some ways, I'll get exactly the right supervision to tell me not to do those

bad things and to keep doing the good things. So, there's a spectrum between IID, random walk

through the space of data, and what we humans do. I don't even know if we do it optimal, but

there might be beyond. So, this open question that you raised, where do you think systems,

intelligent systems that would be able to deal with this world fall? Can we do pretty well

by reading all of Wikipedia, randomly sampling it, like language models do, or do we have to be

exceptionally selective and intelligent about which aspects of the world we try?

So, I think this is first an open scientific problem, and I don't have a clear answer,

but I can speculate a little bit. And what I would speculate is that you don't need to be

super, super careful. I think it's less about being careful to avoid the useless stuff,

and more about making sure that you hit on the really important stuff. So, perhaps it's okay

if you spend part of your day just guided by your curiosity, visiting interesting regions of

your state space, but it's important for you to, every once in a while, make sure that you really

try out the solutions that your current model of the world suggests might be effective,

and observe whether those solutions are working as you expect or not. And perhaps some of that

is really essential to have a perpetual improvement loop. This perpetual improvement

loop is really the key that's going to potentially distinguish the best current methods from the

best methods of tomorrow, in a sense. How important do you think is exploration or total out of the

box thinking exploration in this space to jump to totally different domains? So, you mentioned

there's an optimization problem, you explore the specifics of a particular strategy, whatever the

thing you're trying to solve. How important is it to explore totally outside of the strategies

that have been working for you so far? What's your intuition there?

Yeah, I think it's a very problem dependent kind of question. And I think that that's actually,

you know, in some ways, that question gets at one of the big differences between

sort of the classic formulation of a reinforcement learning problem and some of the sort of more

open ended reformulations of that problem that had been explored in recent years. So,

classically, reinforcement learning is framed as a problem of maximizing utility, like any kind of

rational AI agent, and then anything you do is in service to maximizing that utility.

But a very interesting kind of way to look at, I'm not necessarily saying this is the best way to

look at it, but an interesting alternative way to look at these problems is as something where

you first get to explore the world however you please, and then afterwards you will be tasked

with doing something. And that might suggest a somewhat different solution. So, if you don't

know what you're going to be tasked with doing, and you just want to prepare yourself optimally

for whatever you're on certain future holds, maybe then you will choose to attain some sort of

coverage, build up sort of an arsenal of cognitive tools, if you will, such that later on when someone

tells you, now your job is to fetch the coffee for me, you will be well prepared to undertake that

task. And that you see that as the modern formulation of the reinforcement learning

problem as a kind of the more multitask, the general intelligence kind of formulation.

I think that's one possible vision of where things might be headed. I don't think that's by any means

the mainstream or standard way of doing things, and it's not like if I had to...

But I like it. It's a beautiful vision. So, maybe actually take a step back. What is the goal of

robotics? What's the general problem of robotics we're trying to solve? You actually kind of painted

two pictures here, one of sort of the narrow, one of the general. What in your view is the big

problem of robotics? Again, ridiculously philosophical question.

I think that maybe there are two ways I can answer this question. One is there's a very

pragmatic problem, which is what would make robots... What would sort of maximize the

usefulness of robots? And there the answer might be something like a system where a system that

a system that can perform whatever task a human user sets for it, within the physical constraints,

of course. If you teleport to another planet, it probably can't do that. But if you ask it to do

something that's within its physical capability, then potentially with a little bit of additional

training or a little bit of additional trial and error, it ought to be able to figure it out

in much the same way as like a human teleoperator ought to figure out how to drive the robot to

do that. That's kind of the very pragmatic view of what it would take to kind of solve the robotics

problem, if we will. But I think that there is a second answer, and that answer is a lot closer

to why I want to work on robotics, which is that I think it's less about what it would take to do a

really good job in the world of robotics, but more the other way around of what robotics

can bring to the table to help us understand artificial intelligence.

So your dream, fundamentally, is to understand intelligence?

Yes. I think that's the dream for many people who actually work in this space. I think that

there's something very pragmatic and very useful about studying robotics. But I do think that a

lot of people that go into this field, actually, the things that they draw inspiration from are the

potential for robots to help us learn about intelligence and about ourselves.

So that's fascinating that robotics is basically the space by which you can get closer to

understanding the fundamentals of artificial intelligence. So what is it about robotics

that's different from some of the other approaches? So if we look at some of the early breakthroughs

in deep learning or in the computer vision space and the natural language processing,

there's really nice, clean benchmarks that a lot of people competed on, and thereby came

up with a lot of brilliant ideas. What's the fundamental difference between computer vision

purely defined and ImageNet and kind of the bigger robotics problem?

So there are a couple of things. One is that with robotics, you kind of have to take away

many of the crutches. So you have to deal with both the particular problems of perception,

control, and so on. But you also have to deal with the integration of those things.

And, you know, classically, we've always thought of the integration as kind of a separate problem.

So a classic kind of modular engineering approach is that we solve the individual

sub problems, then wire them together, and then the whole thing works. And one of the

things that we've been seeing over the last couple of decades is that we'll maybe

studying the thing as a whole might lead to just like very different solutions than if we were

to study the parts and wire them together. So the integrative nature of robotics research

helps us see, you know, the different perspectives on the problem. Another part of the answer is that

with robotics, it casts a certain paradox into very clever relief. So this is sometimes referred

to as Morvix paradox, the idea that in artificial intelligence, things that are very hard for people

can be very easy for machines. And vice versa, things that are very easy for people can be

very hard for machines. So, you know, integral and differential calculus is pretty difficult to

learn for people. But if you program a computer, do it, it can derive derivatives and integrals

for you all day long without any trouble. Whereas some things like, you know, drinking from a cup

of water, very easy for a person to do very hard for a robot to deal with. And sometimes when we

see such blatant discrepancies, that gives us a really strong hint that we're missing something

important. So if we really try to zero in on those discrepancies, we might find that little bit

that we're missing. And it's not that we need to make machines better or worse at math and better

at drinking water, but just that by studying those discrepancies, we might find some new insight.

So that could be, that could be in any space, doesn't have to be robotics. But you're saying,

I mean, I get, it's kind of interesting that robotics seems to have a lot of those discrepancies.

So the, the, the Hans Maravak paradox is probably referring to the space of the physical

interaction, like you said, object manipulation, walking, all the kind of stuff we do in the physical

world. How do you make sense if you were to try to disentangle the Maravaks paradox? Like, why is

there such a gap in our intuition about it? Why do you think manipulating objects is so

hard from everything you've learned from applying reinforcement learning in this space?

Yeah, I think that one reason is maybe that for many of the, for many of the other problems that

we've studied in AI and computer science and so on, the notion of input, output and supervision

is much, much cleaner. So computer vision, for example, deals with very complex inputs,

but it's comparatively a bit easier, at least up to some level of abstraction, to cast it as a very

tightly supervised problem. It's comparatively much, much harder to cast robotic manipulation as a

very tightly supervised problem. You can do it. It just doesn't seem to work all that well. So you

could say that, well, maybe we get a labeled data set where we know exactly which motor commands

to send and then we train on that. But for various reasons, that's not actually like such a great

solution. And it also doesn't seem to be even remotely similar to how people and animals learn

to do things because we're not told by like our parents, here's how you fire your muscles in order

to walk. We do get some guidance, but the really low level detailed stuff we figure out mostly on

our own. And that's what you mean by tightly coupled that every single little subaction gets a

supervised signal of whether it's a good one or not. Right. So while in computer vision, you

could sort of imagine up to a level of abstraction that maybe somebody told you this is a car and

this is a cat and this is a dog. In motor control, it's very clear that that was not the case.

If we look at sort of the subspaces of robotics that, again, as you said, robotics integrates

all of them together and we get to see how this beautiful mess interplays. But so there's nevertheless

still perception. So it's the computer vision problem, broadly speaking, understanding the

environment. Then there's also, maybe you can correct me on this kind of categorization of the

space, then there's prediction in trying to anticipate what things are going to do into

the future in order for you to be able to act in that world. And then there's also this game

theoretic aspect of how your actions will change the behavior of others. In this kind of space,

and this is bigger than reinforcement learning, this is just broadly looking at the problem

of robotics. What's the hardest problem here? Or is there, or is what you said

true that when you start to look at all of them together, that's a whole

another thing. You can't even say which one individually is harder because all of them

together, you should only be looking at them all together. I think when you look at them all together,

some things actually become easier. And I think that's actually pretty important.

Back in 2014, we had some work, basically our first work on end to end reinforcement learning

for robotic manipulation skills from vision, which at the time was something that seemed

a little inflammatory and controversial in the robotics world. But other than the inflammatory

and controversial part of it, the point that we were actually trying to make in that work is that

for the particular case of combining perception and control, you could actually do better if you

treat them together than if you try to separate them. And the way that we tried to demonstrate

this is we picked a fairly simple motor control task where a robot had to insert a little red

trapezoid into a trapezoidal hole. And we had our separated solution, which involved first

detecting the hole using a pose detector, and then actuating the arm to put it in,

and then our intent solution, which just mapped pixels to the torques. And one of the things we

observed is that if you use the intent solution, essentially the pressure on the perception part

of the model is actually lower. It doesn't have to figure out exactly where the thing is in 3D

space. It just needs to figure out where it is distributing the errors in such a way that

the horizontal difference matters more than the vertical difference, because vertically it just

pushes it down all the way until it can't go any further. And their perceptual errors are a lot

less harmful, whereas perpendicular to the direction of motion, perceptual errors are much

more harmful. So the point is that if you combine these two things, you can trade off errors between

the components optimally to best accomplish the task. And the components can actually be weaker

while still leading to better overall performance.

It has a profound idea. I mean, in the space of pegs and things like that, it's quite simple.

It almost is tempting to overlook. But that seems to be at least intuitively an idea that should

generalize the basically all aspects of perception and control.

Of course.

That one strengthens the other.

Yeah. And people who have studied perceptual heuristics in humans and animals find things

like that all the time. So one very well known example is something called the gaze heuristic,

which is a little trick that you can use to intercept a flying object. So if you want to

catch a ball, for instance, you could try to localize it in 3D space, estimate its velocity,

estimate the effect of wind resistance, solve a complex system of differential equations in your

head. Or you can maintain a running speed so that the object stays in the same position

as in your field of view. So if it dips a little bit, you speed up. If it rises a little bit,

you slow down. And if you follow the simple rule, you'll actually arrive at exactly the

place where the object lands and you'll catch it. And humans use it when they play baseball.

Human pilots use it when they fly airplanes to figure out if they're about to collide with

somebody. Frogs use us to catch insects and so on and so on. So this is something that

actually happens in nature. And I'm sure this is just one instance of it that we were able to

identify just because all that scientists were able to identify because it's so prevalent,

but there are probably many others.

Do you have a, just so we can zoom in as we talk about robotics, do you have a canonical problem,

sort of a simple, clean, beautiful representative problem in robotics that you think about when

you're thinking about some of these problems? We talked about robotic manipulation. To me,

that seems intuitively, at least the robotics community has conversed towards that as a space

that's the canonical problem. If you agree, then maybe you do zoom in in some particular

aspect of that problem that you just like. Like if we solve that problem perfectly,

it'll unlock a major step towards human level intelligence.

I don't think I have like a really great answer to that. And I think partly the reason I don't

have a great answer kind of has to do with the, it has to do with the fact that the difficulty

is really in the flexibility and adaptability rather than in doing a particular thing really,

really well. So it's hard to just say like, oh, if you can shuffle a deck of cards as fast as like

a Vegas casino dealer, then you'll be very proficient. It's really the ability to quickly

figure out how to do some arbitrary new thing well enough to move on to the next arbitrary thing.

But the source of newness and uncertainty, have you found problems in which it's easy to

generate new newnessnessness is new types of newness?

Yeah. So a few years ago, if you'd asked me this question around like 2016, maybe,

I would have probably said that robotic grasping is a really great example of that because

it's a task with great real world utility. Like you will get a lot of money if you can do it well.

What is robotic grasping?

Picking up any object.

With a robotic hand.

Exactly. So you will get a lot of money if you do it well because lots of people want to run

warehouses with robots. And it's highly non trivial because very different objects will

require very different grasping strategies. But actually, since then, people have gotten

really good at building systems to solve this problem to the point where I'm not actually

sure how much more progress we can make with that as like the main guiding thing.

But it's kind of interesting to see the kind of methods that have actually worked well in that

space because robotic grasping classically used to be regarded very much as kind of almost like

a geometry problem. So people who have studied the history of computer vision will find this

very familiar that kind of in the same way that in the early days of computer vision,

people thought of it very much as like an inverse graphics thing. In robotic grasping,

people thought of it as an inverse physics problem. Essentially, you look at what's in front of you,

figure out the shapes, then use your best estimate of the laws of physics to figure out

where to put your fingers on, you pick up the thing. And it turns out that what works really

well for robotic grasping instantiated in many different recent works, including our own, but

also ones from many other labs is to use learning methods with some combination of either exhaustive

simulation or like actual real world trial and error. And it turns out that those things actually

work really well. And then you don't have to worry about solving geometry problems or physics

problems. So just by the way in the grasping, what are the difficulties that have been worked on?

So one is like the materials of things, maybe occlusions on the perception side. Why is it

such a difficult? Why is picking stuff up such a difficult problem? Yeah, it's a difficult problem

because the number of things that you might have to deal with or the variety of things that you

have to deal with is extremely large. And oftentimes, things that work for one class of

objects won't work for other class of objects. So if you get really good at picking up boxes,

and now you have to pick up plastic bags, you just need to employ a very different strategy.

And there are many properties of objects that are more than just their geometry,

it has to do with the bits that are easier to pick up, the bits that are hard to pick up,

the bits that are more flexible, the bits that will cause the thing to pivot and bend and drop

out of your hand versus the bits that result in a nice secure grasp, things that are flexible,

things that if you pick them up the wrong way, they'll fall upside down and the contents will

spill out. So there's all these little details that come up. But the task is still kind of can

be characterized as one task, like there's a very clear notion of you did it or you didn't do it.

So in terms of spilling things, there creeps in this notion that starts to sound and feel like

common sense reasoning. Do you think solving the general problem of robotics requires

common sense reasoning, requires general intelligence, this kind of human level capability of,

you know, like you said, be robust and deal with uncertainty, but also be able to sort of reason

and assimilate different pieces of knowledge that you have. Yeah. What are your thoughts on

the needs of common sense reasoning in the space of the general robotics problem?

So I'm going to slightly dodge that question and say that I think maybe actually,

it's the other way around is that studying robotics can help us understand how to put

common sense into RAI systems. One way to think about common sense is that, and why our current

systems might lack common sense, is that common sense is an emergent property of

actually having to interact with a particular world, a particular universe, and get things done

in that universe. So you might think that, for instance, like an image captioning system,

maybe it looks at pictures of the world and it types out English sentences. So it kind of

deals with our world. And then you can easily construct situations where image captioning

systems do things that defy common sense, like give it a picture of a person wearing a fur coat,

and we'll say it's a teddy bear. But what I think what's really happening in those settings

is that the system doesn't actually live in our world, it lives in its own world that consists

of pixels and English sentences, and doesn't actually consist of having to put on a fur coat

in the winter so you don't get cold. So perhaps the reason for the disconnect is that the

systems that we have now simply inhabit a different universe. And if we build AI systems

that are forced to deal with all of the messiness and complexity of our universe, maybe they will

have to acquire common sense to essentially maximize their utility. Whereas the systems

we're building now don't have to do that, they can take some shortcut.

That's fascinating. You've a couple of times already sort of reframed the role of robotics

in this whole thing. And for some reason, I don't know if my way of thinking is common,

but I thought like, we need to understand and solve intelligence in order to solve robotics.

And you're kind of framing it as, no, robotics is one of the best ways to just study

artificial intelligence and build sort of like robotics is like the right space in which

you get to explore some of the fundamental learning mechanisms, fundamental sort of

multimodal, multitask aggregation of knowledge mechanisms that are required for general

intelligence. That's a really interesting way to think about it. But let me ask about learning.

Can the general sort of robotics, the epitome of the robotics problem be solved purely

through learning, perhaps end to end learning, sort of learning from scratch,

as opposed to injecting human expertise and rules and heuristics and so on?

I think that in terms of the spirit of the question, I would say yes. I mean, I think that

though in some ways it may be like an overly sharp dichotomy, like, you know, I think that

in some ways when we build algorithms, at some point, a person does something.

A person turned on the computer, a person implemented TensorFlow.

But yeah, I think that in terms of the point that you're getting at, I do think the answer

is yes. I think that we can solve many problems that have previously required meticulous manual

engineering through automated optimization techniques. And actually, one thing I will say

on this topic is I don't think this is actually a very radical or very new idea. I think people

have been thinking about automated optimization techniques as a way to do control for a very,

very long time. And in some ways, what's changed is really more the name. So today we would say that,

oh, my robot does machine learning, it does reinforcement learning, maybe in the 1960s,

you'd say, oh, my robot is doing optimal control. And maybe the difference between typing out a

system of differential equations and doing feedback linearization versus training in neural net,

maybe it's not such a large difference. It's just pushing the optimization deeper and deeper into

the thing. Well, it is interesting, you think that way, but especially with deep learning,

that the accumulation of experiences in data form to form deep representations starts to feel

like knowledge as opposed to optimal control. So this feels like there's an accumulation of

knowledge through the learning process. Yes. Yeah. So I think that is a good point that

one big difference between learning based systems and classic optimal control systems is that

learning based systems in principle should get better and better the more they do something.

And I do think that that's actually a very, very powerful difference.

So look back at the world of expert systems, the symbolic AI and so on,

of using logic to accumulate expertise, human expertise, human encoded expertise.

Do you think that will have a role at some points? The deep learning, machine learning,

reinforcement learning has shown incredible results and breakthroughs and just inspired

thousands, maybe millions of researchers. But there's this less popular now,

but it used to be popular idea of symbolic AI. Do you think that will have a role?

I think in some ways, the kind of the descendants of symbolic AI actually already have a role. So

this is the highly biased history from my perspective. You say that, well, initially we

thought that rational decision making involves logical manipulation. So you have some model of

the world expressed in terms of logic. You have some query like, what action do I take in order to

for X to be true? And then you manipulate your logical symbolic representation to get an answer.

What that turned into somewhere in the 1990s is, well, instead of building kind of predicates

and statements that have true or false values, we'll build probabilistic systems where things

have probabilities associated and probabilities of being true and false. And that turned into

Bayes Nets. And that provided sort of a boost to what were really still essentially logical

inference systems, just probabilistic logical inference systems. And then people said, well,

let's actually learn the individual probabilities inside these models. And then people said, well,

let's not even specify the nodes in the models, let's just put a big neural net in there. But in

many ways, I see these as actually kind of descendants from the same idea. It's essentially

instantiating rational decision making by means of some inference process, and learning by means

of an optimization process. So in a sense, I would say yes, that it has a place. And in many

ways, that place is over, you know, it already holds that place. It's already in there. Yeah,

it's just by different, it looks slightly different than it was before. But in some,

there are some things that we can think about that make this a little bit more obvious. Like,

if I train a big neural net model to predict what will happen in response to my robot's actions,

and then I run probabilistic inference, meaning I invert that model to figure out the actions that

lead to some plausible outcome. Like, to me, that seems like a kind of logic. You have a model of

the world, it just happens to be expressed by a neural net. And you are doing some inference

procedure, some sort of manipulation on that model to figure out, you know, the answer to a

query that you have. It's the interpretability, it's the explainability, though, that seems to

be lacking more so because the nice thing about sort of expert systems is you can follow the

reasoning of the system that to us, mere humans is somehow compelling. It would, it's just,

I don't know what to make of this fact that there's a human desire for intelligent systems to be able

to convey in a poetic way to us why it made the decisions it did. Like, tell a convincing story.

And perhaps that's like a silly human thing. Like, we shouldn't expect that of intelligent

systems. Like, we should be super happy that there is intelligent systems out there. But

if I were to sort of psychoanalyze the researchers at the time, I would say expert systems connected

to that part, that desire for AI researchers for systems to be explainable. I mean, maybe on that

topic, do you have a hope that sort of inference systems of learning based systems will be as

explainable as the dream was with expert systems, for example? I think it's a very complicated

question because I think that in some ways, the question of explainability is kind of very closely

tied to the question of performance. Like, why do you want your system to explain itself? Well,

it's so that when it screws up, you can kind of figure out why it did it. But in some ways,

that's a much bigger problem, actually. Like, your system might screw up and then it might

screw up in how it explains itself, or you might have some bug somewhere so that it's not actually

doing what it was supposed to do. So, maybe a good way to view that problem is really as a

problem, as a bigger problem of verification and validation of which explainability is sort of

one component. I see. I just see it differently. I see explainability. You put it beautifully. I

think you actually summarized the field of explainability. But to me, there's another

aspect of explainability, which is like storytelling that has nothing to do with errors or with

like the sort of it doesn't it uses errors as as elements of its story, as opposed to a fundamental

need to be explainable when errors occur. It's just that for other intelligence systems to be in

our world, we seem to want to tell each other stories. And that that's true in the political

world. That's true in the academic world. And that I, you know, neural networks are less capable

of doing that, or perhaps they're equally capable of storytelling, storytelling, maybe it doesn't

matter what the fundamentals of the system are, you just need to be a good storyteller.

Maybe one specific story I can tell you about in that space is actually about some work that was

done by about my former collaborator, who's now a professor at MIT named Jacob Andreas. Jacob actually

works in natural language processing, but he had this idea to do a little bit of work in reinforcement

learning, and how on how natural language can basically structure the internals of policies

trained with RL. And one of the things he did is he set up a model that attempts to perform some

tasks that's defined by a reward function. But the model reads in a natural language instruction.

So this is a pretty common thing to do an instruction following. So you tell it like,

you know, go to the red house, and then it's supposed to go to the red house. But then one

of the things that Jacob did is he treated that sentence not as a command from a person,

but as a representation of the internal kind of state of the mind of this policy, essentially,

so that when it was faced with a new task, what it would do is it would basically try to think of

possible language descriptions, attempt to do them and see if they led to the right outcome.

So would it kind of think out loud, like, you know, I'm faced with this new task, what am I

going to do? Let me go to the red house. Oh, that didn't work. Let me go to the blue room or something,

let me go to the green plant. And once it got some reward, it would say, oh, go to the green

plant, that's what's working, I'm going to go to the green plant. And then you could look at the

string that it came up with, and that was a description of how it thought it should solve

the problem. So you could do, you could basically incorporate language as internal state, and you

can start getting some handle on these kinds of things. And then what I was kind of trying to

get to is that also if you add to the reward function, the convincingness of that story.

So I have another reward signal of like, people who review that story, how much they like it.

So that, you know, initially that could be a hyper parameter sort of hard coded heuristic

type of thing, but it's an interesting notion of the convincingness of the story becoming part

of the reward function, the objective function of the explainability. It's in the world of sort of

Twitter and fake news that might be a scary notion that the nature of truth may not be as

important as the convincingness of the how convincing you are in telling the story around

the facts. Well, let me ask the basic question. You're one of the world class researchers in

reinforcement learning, deeper enforcement learning, certainly in the robotics space.

What is reinforcement learning? I think that what reinforcement learning

refers to today is really just the kind of the modern incarnation of learning based control.

So classically, reinforcement learning has a much more narrow definition, which is that

it's, you know, literally learning from reinforcement, like the thing does something

and then it gets a reward or punishment. But really, I think the way the term is used today is

it's used for more broadly to learning based control. So some kind of system that's supposed

to be controlling something and it uses data to get better. And what does control mean? So this

action is the fundamental element there. It means making rational decisions.

And rational decisions are decisions that maximize a measure of utility.

And sequentially, so you made decisions time and time and time again. Now, like,

it's easier to see that kind of idea in the space of maybe games and the space of robotics.

Do you see it bigger than that? Is it applicable? Like, where are the limits of the applicability

of reinforcement learning? Yeah, so rational decision making is essentially the

encapsulation of the AI problem viewed through a particular lens. So any problem that we would

want a machine to do, an intelligent machine can likely be represented as a decision making problem.

Classifying images is a decision making problem, although not a sequential one typically.

You know, controlling a chemical plant as a decision making problem,

deciding what videos to recommend on YouTube is a decision making problem.

And one of the really appealing things about reinforcement learning is, if it does encapsulate

the range of all these decision making problems, perhaps working on reinforcement learning is,

you know, one of the ways to reach a very broad swath of AI problems.

But what do you use the fundamental difference between reinforcement learning and maybe supervised

machine learning? So reinforcement learning can be viewed as a generalization of supervised

machine learning. You can certainly cast supervised learning as a reinforcement learning problem.

You can just say your loss function is the negative of your reward. But you have stronger

assumptions. You have the assumption that someone actually told you what the correct answer was,

that your data was IID and so on. So you could view reinforcement learning essentially relaxing

some of those assumptions. Now, that's not always a very productive way to look at it,

because if you actually have a supervised learning problem, you'll probably solve it

much more effectively by using supervised learning methods, because it's easier. But

you can view reinforcement learning as a generalization of that.

No, for sure. But they're fundamentally different. That's a mathematical statement.

That's absolutely correct. But it seems that reinforcement learning, the kind of tools

we're bringing to the table today, after today, so maybe down the line, everything will be a

reinforcement learning problem. Just like you said, image classification should be mapped to a

reinforcement learning problem. But today, the tools and ideas, the way we think about them are

different. Sort of supervised learning has been used very effectively to solve basic,

narrow AI problems. Reinforcement learning kind of represents the dream of AI. It's very much so

in the research space now, in sort of captivating the imagination of people of what we can do with

intelligent systems. But it hasn't yet had as wide of an impact as the supervised learning

approaches. So my question comes in a more practical sense. What do you see as the

gap between the more general reinforcement learning and the very specific, yes,

it's a question of decision making with one step in the sequence of the supervised learning?

So from a practical standpoint, I think that one thing that is potentially a little tough now,

and this is, I think, something that we'll see, this is a gap that we might see closing over

the next couple of years, is the ability of reinforcement learning algorithms to effectively

utilize large amounts of prior data. So one of the reasons why it's a bit difficult today

to use reinforcement learning for all the things that we might want to use it for,

is that in most of the settings where we want to do rational decision making,

it's a little bit tough to just deploy some policy that does crazy stuff and learns purely

through trial and error. It's much easier to collect a lot of data, a lot of logs of some

other policy that you've got, and then maybe you, you know, if you can get a good policy out of that,

then you deploy it and let it kind of fine tune a little bit. But algorithmically,

it's quite difficult to do that. So I think that once we figure out how to get reinforcement learning

to bootstrap effectively from large datasets, then we'll see very, very rapid growth in

applications of these technologies. So this is what's referred to as off policy reinforcement

learning or offline RL or batch RL. And I think we're seeing a lot of research right now that

does bring us closer and closer to that. Can you maybe paint the picture of the different methods,

as you said, off policy, what's value based, reinforcement learning, what's policy based,

what's model based, what's off policy on policy, what are the different categories of reinforcement

learning? Yeah. So one way we can think about reinforcement learning is that it's in some

very fundamental way. It's about learning models that can answer kind of what if questions. So

what would happen if I take this action that I hadn't taken before? And you do that, of course,

from experience, from data. And oftentimes you do it in a loop. So you build a model that answers

these what if questions, use it to figure out the best action you can take, and then go and try

taking that and see if the outcome agrees with what you predicted. So the different kinds of

techniques basically refer to different ways of doing it. So model based methods answer a question

of what state you would get, basically, what would happen to the world if you were to take a

certain action value based methods, they answer the question of what value you would get, meaning

what utility you would get. But in a sense, they're not really all that different, because

they're both really just answering these what if questions. Now, unfortunately, for us, with

current machine learning methods, answering what if questions can be really hard, because

they are really questions about things that didn't happen. If you wanted to answer what if questions

about things that did happen, you wouldn't need to learn model, you would just like repeat the

thing that worked before. And that's really a big part of why RL is a little bit tough. So if you

have a purely on policy kind of online process, then you ask these what if questions, you make

some mistakes, then you go and try doing those mistaken things. And then you observe kind of

the counter examples that will teach you not to do those things again. If you have a bunch of

off policy data, and you just want to synthesize the best policy you can out of that data, then

you really have to deal with the challenges of making these counterfactual.

First of all, what's a policy?

A policy is a model or some kind of function that maps from observations of the world to actions.

So in reinforcement learning, we often refer to the current configuration of the world as the

state. So we say the state kind of encompasses everything you need to fully define where the

world is at at the moment. And depending on how we formulate the problem, we might say you either

get to see the state or you get to see an observation, which is some snapshots and piece of the state.

So policy is just includes everything in it in order to be able to act in this world.

Yes.

And so what does off policy mean?

Yeah, so the terms on policy and off policy refer to how you get your data.

So if you get your data from somebody else who was doing some other stuff, maybe you get your data

from some manually programmed system that was just running in the world before,

that's referred to as off policy data. But if you got the data by actually acting in the world based

on what your current policy thinks is good, we call that on policy data. And obviously,

on policy data is more useful to you because if your current policy makes some bad decisions,

you will actually see that those decisions are bad. Off policy data, however, might be much easier

to obtain because maybe that's all the log data that you have from before.

So we talked about offline, talked about autonomous vehicles, so you can envision

off policy kind of approaches in robotic spaces where there's already a ton of robots out there,

but they don't get the luxury of being able to explore based on a reinforced learning framework.

So how do we make, again, open question, but how do we make off policy methods work?

Yeah, so this is something that has been kind of a big open problem for a while. And in the last

few years, people have made a little bit of progress on that. I can tell you about, and it's

not by any means solved yet, but I can tell you some of the things that, for example, we've done to

try to address some of the challenges. It turns out that one really big challenge with off policy

reinforcement learning is that you can't really trust your models to give accurate predictions

for any possible action. So if I've never tried to, if in my data set I never saw somebody steering

the car off the road onto the sidewalk, my value function or my model is probably not going to

predict the right thing if I ask what would happen if I were to steer the car off the road onto the

sidewalk. So one of the important things you have to do to get off policy RL to work is you have

to be able to figure out whether a given action will result in a trustworthy prediction or not.

And you can use kind of distribution estimation methods, kind of density estimation methods

to try to figure that out. So you could figure out that, well, this action, my model is telling me

that it's great, but it looks totally different from any action I've taken before. So my model is

probably not correct. And you can incorporate regularization terms into your learning objective

that will essentially tell you not to ask those questions that your model is unable to answer.

What would lead to breakthroughs in this space, do you think? Like what's needed? Is this a data set

question? Do we need to collect big benchmark data sets that allows to explore the space?

Is it a new kinds of methodologies? Like what's your sense? Or maybe coming together in a space

of robotics and defining the right problem to be working on? I think for off policy reinforcement

learning in particular, it's very much an algorithms question right now. And this is something that

I think is great because an algorithms question is that that just takes some very smart people to

get together and think about it really hard. Whereas if it was like a data problem or hardware

problem, that would take some serious engineering. So that's why I'm pretty excited about that

problem because I think that we're in a position where we can make some real progress on it just

by coming up with the right algorithms in terms of which algorithms they could be. The problems at

their core are very related to problems in things like causal inference because what you're really

dealing with is situations where you have a model, a statistical model that's trying to make predictions

about things that it hadn't seen before. And if it's a model that's generalizing properly,

that'll make good predictions. If it's a model that picks up on various correlations that will

not generalize properly, and then you have an arsenal of tools you can use, you could for example

figure out what are the regions where it's trustworthy, or on the other hand, you could try

to make it generalize better somehow or some combination of the two. Is there a room for

mixing where most of it, like 90, 95% is off policy, you already have the data set,

and then you get to send the robot out to do a little exploration? What's that role of

mixing them together? Yeah, absolutely. I think that this is something that you actually

described very well at the beginning of our discussion when you talked about the iceberg.

This is the iceberg, that the 99% of your prior experience, that's your iceberg. You'd use that

for off policy reinforcement learning. And then of course, if you've never opened that particular

kind of door with that particular lock before, then you have to go out and fiddle with it a little

bit. And that's that additional 1% to help you figure out a new task. And I think that's actually

like a pretty good recipe going forward. Is this to you the most exciting space of reinforcement

learning now? Or is there, what's, maybe taking a step back, not just now, but what's to use the

most beautiful idea? Apologize for the romanticized question, but the beautiful idea or concept in

reinforcement learning? In general, I actually think that one of the things that is a very beautiful

idea in reinforcement learning is just the idea that you can obtain a near optimal control or

near optimal policy without actually having a complete model of the world. It's something that

feels perhaps kind of obvious if you just hear the term reinforcement learning or you think about

trial and error learning. But from a control's perspective, it's a very weird thing because

classically, we think about engineered systems and controlling engineered systems as the problem

of writing down some equations and then figuring out, given these equations, basically like solve

for x, figure out the thing that maximizes its performance. And the theory of reinforcement

learning actually gives us a mathematically principled framework to think, to reason about

optimizing some quantity when you don't actually know the equations that govern that system.

And to me, that actually seems kind of very elegant, not something that

becomes immediately obvious, at least in the mathematical sense.

Does it make sense to you that it works at all?

Well, I think it makes sense when you take some time to think about it, but it is a little

surprising. Well, then taking a step into the more deeper representations, which is also very

surprising of sort of the richness of the state space, the space of environments that

this kind of approach can operate in. Can you maybe say what is deep reinforcement learning?

Well, deep reinforcement learning simply refers to taking reinforcement learning algorithms and

combining them with high capacity neural net representations, which might at first seem like

a pretty arbitrary thing, just take these two components and stick them together. But the

reason that it's something that has become so important in recent years is that reinforcement

learning, it kind of faces an exacerbated version of a problem that has faced many other machine

learning techniques. So if we go back to the early 2000s or the late 90s, we'll see a lot

of research on machine learning methods that have some very appealing mathematical properties,

like they reduce the convex optimization problems, for instance. But they require very

special inputs. They require a representation of the input that is clean in some way, like for

example, clean in the sense that the classes in your multi class classification problems

separate linearly. So they have some kind of good representation, and we call this a feature

representation. And for a long time, people were very worried about features in the world of supervised

learning, because somebody had to actually build those features, so you couldn't just take an image

and plug it into your logistic regression or your SVM or something, someone had to take that image

and process it using some handwritten code. And then neural nets came along and they could

actually learn the features. And suddenly, we could apply learning directly to the raw inputs,

which was great for images, but it was even more great for all the other fields where people hadn't

come up with good features yet. And one of those fields actually reinforcement learning,

because in reinforcement learning, the notion of features, if you don't use neural nets and you

have to design your own features, is very opaque. It's very hard to imagine, let's say I'm playing

chess or Go. What is a feature with which I can represent the value function for Go or even the

optimal policy for Go linearly? I don't even know how to start thinking about it. And people

tried all sorts of things that would write down an expert chess player looks for whether the

knight is in the middle of the board or not. So that's a feature is knight in middle of board.

And they would write these like long lists of kind of arbitrary made up stuff. And that was

really kind of getting us nowhere. And that's a little chess is a little more accessible than

the robotics problem. Absolutely. Right. That's there's at least experts in the in the different

features for chess. But still like the neural network there, to me, that's, I mean, you put it

eloquently and almost made it seem like a natural step to add neural networks. But the fact that

neural networks are able to discover features in the control problem. It's very interesting. It's

hopeful. I'm not sure what to think about it, but it feels hopeful that the control problem has

features to be learned. Like, I guess my question is, is it surprising to you how far the deep side

of deeper enforcement learning was able to like what the space of promise has been able to tackle

from especially in games with the Alpha star and and Alpha zero and just the representation power

there and in the robotics space. And what is your sense of the limits of this representation power

and the control context? I think that in regard to the limits that here, I think that one thing

that makes it a little hard to fully answer this question is because in settings where we would

like to push push these things to the limit, we encounter other bottlenecks. So like the reason

that I can't get my robot to learn how to like, I don't know, do the dishes in the kitchen. It's

not because it's neural net is not big enough. It's because when you try to actually do trial

and error learning, reinforcement learning directly in the real world, where you have the

potential to gather these large, very, you know, highly varied and complex data sets,

you start running into other problems, like one problem you run into very quickly. It's it'll

first sound like a very pragmatic problem, but it actually turns out to be a pretty deep scientific

problem. Take the robot put in your kitchen, have it try to learn to do the dishes with trial and

error, it'll break all your dishes, and then we'll have no more dishes to clean. Now you might think

this is a very practical issue, but there's something to this, which is that if you have a

person trying to do this, you know, a person will have some degree of common sense, they'll

break one dish, they'll be a little more careful with the next one. And if they break all of them,

they're going to go and get more or something like that. So there's all sorts of scaffolding

that that comes very naturally to us for our learning process. Like, you know, if I have to

learn something through trial and error, I have a common sense to know that I have to, you know,

try multiple times. If I screw something up, I ask for help or I reset things or something like that.

And all of that is kind of outside of the classic reinforcement learning problem formulation.

There are other things that are that can also be categorized as kind of scaffolding,

but are very important, like for example, where you do get your reward function. If I want to

learn how to pour a cup of water, well, how do I know if I've done it correctly? Now that probably

requires an entire computer vision system to be built just to determine that. And that seems a

little bit inelegant. So there are all sorts of things like this that start to come up when we

think through what we really need to get reinforcement learning to happen at scale in the real world.

And many of these things actually suggest a little bit of a shortcoming in the problem

formulation and a few deeper questions that we have to resolve. That's really interesting. I

talked to like David Silver, about AlphaZero. And it seems like there's no, again, that we

haven't hit the limit at all in the context when there's no broken dishes. So in the case of Go,

you can, it's really about just scaling compute. So again, like the bottleneck is the amount of

money you're willing to invest in compute, and then maybe the different, the scaffolding around

how difficult it is to scale compute, maybe. But there there's no limit. And it's interesting.

Now we move to the real world, and there's the broken dishes, there's all the, and the reward

function like you mentioned, that's really nice. So what, how do we push forward there? Do you think

there's, there's this kind of sample efficiency question that people bring up, you know, not

having to break 100,000 dishes? Is this an algorithm question? Is this a data selection

like question? What do you think? How do we, how do we not break too many dishes?

Yeah. Well, one way we can think about that is that maybe we need to be better at

at reusing our data, building that, that iceberg. So perhaps, perhaps it's too much to hope that

you can have a machine that in isolation in the vacuum without anything else can just master

complex tasks in like, in minutes, the way that people do. But perhaps it also doesn't have to,

perhaps what it really needs to do is have an existence, a lifetime where it does many things

and the previous things that it has done, prepare it to do new things more efficiently.

And, you know, the study of these kinds of questions typically falls under categories

like multitask learning or meta learning. But they all fundamentally deal with the same

general theme, which is use experience for doing other things to learn to do new things

efficiently and quickly. So what do you think about if you just look at the one particular

case study of Tesla autopilot that has quickly approaching towards a million vehicles on the

road, where some percentage of the time 30, 40% of the time is driving using the computer vision,

multitask, hydranet, right? And then the other percent, that's what they call it hydranet.

The other percent is human controlled. From the human side, how can we use that data? What's

your sense? So like, what's the signal? Do you have ideas in this autonomous vehicle space

when people can lose their lives? You know, it's a safety critical environment. So how do we use

that data? So I think that actually the kind of problems that come up when we want systems that

are reliable and that can kind of understand the limits of their capabilities, they're actually

very similar to the kind of problems that come up when we're doing off policy reinforcement

learning. So as I mentioned before, in off policy reinforcement learning, the big problem is you

need to know when you can trust the predictions of your model, because if you're trying to evaluate

some pattern of behavior for which your model doesn't give you an accurate prediction, then you

shouldn't use that to modify your policy. It's actually very similar to the problem that we're

faced when we actually then deploy that thing. And we want to decide whether we trust it in the

moment or not. So perhaps we just need to do a better job of figuring out that part. And that's

a very deep research question, of course. But it's also a question that a lot of people are

working on. So I'm pretty optimistic that we can make some progress on that over the next few years.

What's the role of simulation in reinforcement learning, deeper reinforcement learning,

reinforcement learning? Like how essential is it? It's been essential for the breakthroughs so far,

for some interesting breakthroughs. Do you think it's a crutch that we rely on? I mean,

again, it's the connection to our off policy discussion. But do you think we can ever get rid

of simulation? Or do you think simulation will actually take over? Will create more and more

realistic simulations that will allow us to solve actual real world problems, like transfer the models

we'll learn in simulation to real world problems? I think that simulation is a very pragmatic tool

that we can use to get a lot of useful stuff to work right now. But I think that in the long run,

we will need to build machines that can learn from real data, because that's the only way that we'll

get them to improve perpetually. Because if we can't have our machines learn from real data,

if they have to rely on simulated data, eventually the simulator becomes the bottleneck.

In fact, this is a general thing. If your machine has any bottleneck that is built by humans,

and that doesn't improve from data, it will eventually be the thing that holds it back.

And if you're entirely reliant on your simulator, that'll be the bottleneck. If you're entirely

reliant on a manually designed controller, that's going to be the bottleneck. So simulation is very

useful. It's very pragmatic. But it's not a substitute for being able to utilize real experience.

And this is, by the way, this is something that I think is quite relevant now, especially in the

context of some of the things we've discussed, because some of these kind of scaffolding issues

that I mentioned, things like the broken dishes and the unknown reward functions, like these are

not problems that you would ever stumble on when working in a purely simulated kind of environment.

But they become very apparent when we try to actually run these things in the real world.

Do you throw a brief wrench into our discussion? Let me ask, do you think we're living in a simulation?

Oh, I have no idea.

Do you think that's a useful thing to even think about the fundamental physics nature of reality?

Or another perspective? The reason I think the simulation hypothesis is interesting is

to think about how difficult is it to create sort of a virtual reality game type situation

that will be sufficiently convincing to us humans or sufficiently enjoyable that we wouldn't want

to leave. That's actually a practical engineering challenge. And I personally really enjoy virtual

reality, but it's quite far away. But I kind of think about, what would it take for me to want

to spend more time in virtual reality versus the real world? And that's a, that's a sort of a nice

clean question. Because at that point, we've reached, if I want to live in a virtual reality,

that means we're just a few years away, we're a majority, the population lives in a virtual

reality. And that's how we create the simulation, right? You don't need to actually simulate the

the quantum gravity and just every aspect of the of the universe. And that's a really,

that's an interesting question for reinforcement learning too, is if we want to make sufficiently

realistic simulations that may, it blend the difference between sort of the real world and

the simulation, thereby, just some of the things we've been talking about, kind of the problems

go away, if we can create actually interesting, rich simulations. It's an interesting question.

And it actually, I think your question casts your previous question in a very interesting light,

because in some ways, asking whether we can, well, the more practical version is like,

can we build simulators that are good enough to train essentially AI systems that will work

in the world? And it's kind of interesting to think about this, about what this implies. If true,

it kind of implies that it's easier to create the universe than it is to create a brain.

And that seems like put this way, it seems kind of weird.

The aspect of the simulation most interesting to me is the simulation of other humans.

That seems to be a complexity that makes the robotics problem harder. Now,

I don't know if every robotics person agrees with that notion. Just as a quick aside,

what are your thoughts about when the human enters the picture of the robotics problem? How

does that change the reinforcement learning problem, the learning problem in general?

Yeah, I think that's a kind of a complex question. And I guess my hope for a while had been that

if we build these robotic learning systems that are multitask, that utilize lots of prior data,

and that learn from their own experience, the bit where they have to interact with people

will be perhaps handled in much the same way as all the other bits. So if they have prior

experience of attracting with people and they can learn from their own experience of attracting

with people for this new task, maybe that'll be enough. Now, of course, if it's not enough,

there are many other things we can do. And there's quite a bit of research in that area.

But I think it's worth a shot to see whether the multi agent interaction, the ability to understand

that other beings in the world have their own goals and intentions and thoughts and so on,

whether that kind of understanding can emerge automatically from simply learning to do things

with and maximize utility. That information arises from the data. You've said something

about gravity, sort of that you don't need to explicitly inject anything into the system,

they can be learned from the data. And gravity is an example of something that could be learned

from data, sort of like the physics of the world. What are the limits of what we can learn from

data? So a very simple, clean way to ask that is, do you really think we can learn gravity

from just data, the idea, the laws of gravity? So something that I think is a common kind of

pitfall when thinking about prior knowledge and learning is to assume that just because we know

something, then that it's better to tell the machine about that rather than have it figured out

and so on. In many cases, things that are important, that affect many of the events

that the machine will experience are actually pretty easy to learn. If every time you drop

something, it falls down. Yeah, you might get the Newton's version, not Einstein's version,

but it'll be pretty good and it will probably be sufficient for you to act rationally in the world

because you see the phenomenon all the time. So things that are readily apparent from the data,

we might not need to specify those by hand. It might actually be easier to let the machine

figure them out. It just feels like that there might be a space of many local

minima in terms of theories of this world that we would discover and get stuck on.

Yeah, of course.

That Newtonian mechanics is not necessarily easy to come by.

Yeah, and well, in fact, in some fields of science, for example, human civilizations

that sell full of these local optimism. So for example, if you think about how people

try to figure out biology and medicine for the longest time, the kind of rules, the kind of

principles that serve us very well in our day to day lives actually serve us very poorly

in understanding medicine and biology. We had very superstitious and weird ideas about how

the body worked until the advent of the modern scientific method. So that does seem to be

a failing of this approach, but it's also a failing of human intelligence arguably.

Yeah, maybe a smaller side, but the idea of self play is fascinating in reinforcement learning,

sort of these competitive, creating a competitive context in which agents can play against each

other in sort of at the same skill level and thereby increasing each other skill level.

It seems to be this kind of self improving mechanism is exceptionally powerful in the

context where it could be applied. First of all, is that beautiful to you that this mechanism

work as well as it does and also can be generalized to other contexts like in the robotic space

or anything that's applicable to the real world?

I think that it's a very interesting idea, but I suspect that the bottleneck to actually

generalizing it to the robotic setting is actually going to be the same as

the bottleneck for everything else, that we need to be able to build machines that can get better

and better through natural interaction with the world. And once we can do that, then they can go

out and play with each other, they can play with people, they can play with the natural environment.

But before we get there, we've got all these other problems we have to get out of the way.

So there's no shortcut around that. You have to interact with the natural environment that...

Well, because in a self play setting, you still need a mediating mechanism. So the reason that

self play works for a board game is because the rules of that board game

mediate the interaction between the agents. So the kind of intelligent behavior that will

emerge depends very heavily on the nature of that mediating mechanism.

So on the side of reward functions, that's coming up with good reward function seems to

be the thing that we associate with general... like human beings seem to value the idea of

developing our own reward functions, of arriving at meaning and so on. And yet for reinforcement

learning, we often specify that's the given. What's your sense of how we develop good reward

functions? Yeah, I think that's a very complicated and very deep question. And you're completely

right that classically in reinforcement learning, this question has been treated as an on issue,

that you treat the reward as this external thing that comes from some other bit of your biology

and you don't worry about it. And I do think that that's actually a little bit of a mistake that

we should worry about it. And we can approach it in a few different ways. We can approach it,

for instance, by thinking of reward as a communication medium. We can say, well,

how does a person communicate to a robot what its objective is? You can approach it also as

sort of more of an intrinsic motivation medium. You could say, can we write down

kind of a general objective that leads to good capability? Like, for example, can you write

down some objectives such that even in the absence of any other task, if you maximize that objective,

you'll sort of learn useful things. This is something that has sometimes been called unsupervised

reinforcement learning, which I think is a really fascinating area of research, especially today.

We've done a bit of work on that recently. One of the things we've studied is whether

we can have some notion of unsupervised reinforcement learning by means of

information theoretic quantities, like, for instance, minimizing a Bayesian measure of surprise. This

is an idea that was pioneered actually in the computational neuroscience community by folks

like Carl Friston. And we've done some work recently that shows that you can actually learn

pretty interesting skills by essentially behaving in a way that allows you to make accurate predictions

about the world. It seems a little circular. Do the things that will lead to you getting the right

answer for prediction. But by doing this, you can sort of discover stable niches in the world.

You can discover that if you're playing Tetris, then correctly clearing the rows will let you

play Tetris for longer and keep the board nice and clean, which sort of satisfies some desire

for order in the world. And as a result, get some degree of leverage over your domain.

So we're exploring that pretty actively. Is there a role for a human notion of curiosity

in itself being the reward sort of discovering new things about the world?

So one of the things that I'm pretty interested in is actually whether

discovering new things can actually be an emergent property of some other objective

that quantifies capability. So new things for the sake of new things, maybe might not by itself

be the right answer, but perhaps we can figure out an objective for which discovering new things

is actually the natural consequence. That's something we're working on right now,

but I don't have a clear answer for you there yet. That's still a work in progress.

You mean just as a curious observation to see sort of creative patterns of curiosity

on the way to optimize for a particular task?

On the way to optimize for a particular measure of capability.

Is there ways to understand or anticipate unexpected, unintended consequences of

particular reward functions? Sort of anticipate the kind of strategies that might be developed

and try to avoid highly detrimental strategies?

Yeah. So classically, this is something that has been pretty hard in reinforcement learning

because it's difficult for a designer to have good intuition about what a learning algorithm

will come up with when they give it some objective. There are ways to mitigate that.

One way to mitigate it is to actually define an objective that says, don't do weird stuff.

You can actually quantify it and say just don't enter situations that have low probability

under the distribution of states you've seen before.

It turns out that that's actually one very good way to do off policy reinforcement learning,

actually. So we can do some things like that.

If we slowly venture in speaking about reward functions into greater and greater levels of

intelligence, there's, I mean, Stuart Russell thinks about this, the alignment of AI systems

with us humans. So how do we ensure that AGI systems align with us humans?

It's kind of a reward function question of specifying the behavior of AI systems

such that their success aligns with the broader intended success interests of human beings.

Do you have thoughts on this? Do you have concerns of where reinforcement learning fits into this?

Or are you really focused on the current moment of us being quite far away and trying to solve

the robotics problem? I don't have a great answer to this. And I do think that this is a problem

that's important to figure out. For my part, I'm actually a bit more concerned about the other

side of this equation that maybe rather than unintended consequences for objectives that

are specified too well, I'm actually more worried right now about unintended consequences for

objectives that are not optimized well enough, which might become a very pressing problem

when we, for instance, try to use these techniques for safety critical systems like

cars and aircraft and so on. I think at some point we'll face the issue of objectives being

optimized too well, but right now I think we're more likely to face the issue of them not being

optimized well enough. But you don't think unintended consequences can arise even when

you're far from optimality, sort of like on the path to it? Oh, no, I think unintended

consequences can absolutely arise. It's just I think right now the bottleneck for improving

reliability, safety and things like that is more with systems that need to work better,

that need to optimize their objective better. Do you have thoughts, concerns about existential

threats of human level intelligence? If we put on our hat of looking in 10, 20, 100, 500 years from

now, do you have concerns about existential threats of AI systems? I think there are absolutely

existential threats for AI systems just like there are for any powerful technology.

But I think that these kinds of problems can take many forms and some of those forms will

come down to people with nefarious intent. Some of them will come down to AI systems that have

some fatal flaws and some of them will of course come down to AI systems that are too capable in

some way. But among this set of potential concerns, I would actually be much more concerned about the

first two right now and principally the one with nefarious humans because just through all

of human history actually it's the nefarious humans that have been the problem not the nefarious

machines than I am about the others. And I think that right now the best that I can do to make

sure things go well is to build the best technology I can and also hopefully promote

responsible use of that technology. Do you think RL systems has something to teach us humans?

You said nefarious humans getting us in trouble. I mean machine learning systems have in some ways

have revealed to us the ethical flaws in our data in that same kind of way. Can reinforcement learning

teach us about ourselves? Has it taught something? What have you learned about yourself from trying

to build robots and reinforcement learning systems? I'm not sure what I've learned about myself but

maybe part of the answer to your question might become a little bit more apparent once we see

more widespread deployment of reinforcement learning for decision making support in domains

like healthcare, education, social media, etc. And I think we will see some interesting stuff

emerge there. We will see for instance what kind of behaviors these systems come up with

in situations where there is interaction with humans and where they have possibility of

influencing human behavior. I think we're not quite there yet but maybe in the next two years

we'll see some interesting stuff come out in that area. I hope outside the research space because

the exciting space where this could be observed is sort of large companies that deal with large

data and I hope there's some transparency. One of the things that's unclear when I look at social

networks and just online is why an algorithm did something or whether even an algorithm was involved

and that'd be interesting as a from a research perspective just to observe the results of algorithms

to open up that data or to at least be sufficiently transparent about the behavior of these AS systems

in the real world. What's your sense? I don't know if you looked at the blog post bitter lesson

by Rich Sutton where it looks at sort of the big lesson of research in AI and reinforcement learning

is that simple methods, general methods that leverage computation seem to work well. So basically

don't try to do any kind of fancy algorithms just wait for computation to get fast. Do you share

this kind of intuition? I think the high level idea makes a lot of sense. I'm not sure that my

takeaway would be that we don't need to work on algorithms. I think that my takeaway would be that

we should work on general algorithms and actually I think that this idea of needing to better automate

the acquisition of experience in the real world actually follows pretty naturally from Rich

Sutton's conclusion. So if the claim is that automated general methods plus data leads to good

results, then it makes sense that we should build general methods and we should build the kind of

methods that we can deploy and get them to go out there and collect their experience autonomously.

I think that one place where I think that the current state of things falls a little bit short

of that is actually that the going out there and collecting the data autonomously, which is easy to

do in a simulated board game but very hard to do in the real world. Yeah, it keeps coming back to

this one problem, right? So your mind is focused there now in this real world. It just seems scary

the step of collecting the data and it seems unclear to me how we can do it effectively.

Well, you know, 7 billion people in the world, each of them had to do that at some point in

their lives. And we should leverage that experience that they've all done. We should be able to try

to collect that kind of data. Okay, big questions. Maybe stepping back to your life, wood book or

books, technical or fiction or philosophical had a big impact on the way you saw the world,

and the way you thought about in the world, your life in general. And maybe what books,

if it's different, would you recommend people consider reading on their own intellectual

journey? It could be within reinforcement learning, but it could be very much bigger.

I don't know if this is like a scientifically, like, particularly meaningful answer, but

like, the honest answer is that I actually found a lot of the work by Isaac Hasimov to be very

inspiring when I was younger. I don't know if that has anything to do with AI necessarily.

You don't think it had a ripple effect in your life?

Maybe it did. But yeah, I think that a vision of a future where, well, first of all,

artificial, I might say artificial intelligence system, artificial robotic systems,

robotic systems have, you know, kind of a big place, a big role in society,

and where we try to imagine the sort of the limiting case of technological advancement

and how that might play out in our future history. But yeah, I think that that was

in some way influential. I don't really know how, but I would recommend it. I mean,

if nothing else, you'd be well entertained. When did you first, yourself, like fall in love with

idea of artificial intelligence get captivated by this field?

So my honest answer here is actually that I only really started to think about it as a,

that's something that I might want to do actually in graduate school pretty late.

And a big part of that was that until, you know, somewhere around 2009, 2010,

then just wasn't really high on my priority list because I didn't think that it was something

where we're going to see very substantial advances in my lifetime. And, you know, maybe

in terms of my career, the time when I really decided I wanted to work on this was when I

actually took a seminar course that was taught by Professor Andrew Ng. And, you know, at that

point, I of course had some, had like a decent understanding of the technical things involved.

But one of the things that really resonated with me was when he said in the opening lecture,

something the effect of like, well, he used to have graduate students come to him and talk about

how they want to work on AI and he would kind of chuckle and give them some math problem to deal

with. But now he's actually thinking that this is an area where we might see like substantial

advances in our lifetime. And that kind of got me thinking because, you know, in some abstract

sense, yeah, like you can kind of imagine that. But in a very real sense, when someone who had

been working on that kind of stuff their whole career suddenly says that, yeah, like that,

that had some effect on me. Yeah, this might be a special moment in the history of the field.

That this is where we might see some, some interesting breakthroughs. So in the space of

advice, somebody who's interested in getting started in machine learning or reinforcement

learning, what advice would you give to maybe an undergraduate student or maybe even younger,

how what are the first steps to take? And further on, what are the steps to take on that journey?

So something that I think is important to do is to not be afraid to spend time imagining

the kind of outcome that you might like to see. So one outcome might be a successful career,

a large paycheck or something, or state of the art results on some benchmark.

But hopefully that's not the thing that's like the main driving force for somebody.

But I think that if someone who's a student considering a career in AI, like, takes a

little while, sits down and thinks like, what do I really want to see? What do I want to see a machine

do? What do I want? What do I want to see a robot do? What do I want to do? And what do I want to

see a natural language system? Just like imagine, you know, imagine it almost like a commercial

for a future product or something or like like something that you'd like to see in the world,

and then actually sit down and think about the steps that are necessary to get there.

And hopefully that thing is not a better number on image net classification. It's like it's

probably like an actual thing that we can't do today that would be really awesome, whether it's

a robot butler or a, you know, a really awesome healthcare decision making support system,

whatever it is that you find inspiring. And I think that thinking about that and then

backtracking from there and imagining the steps needed to get there will actually

lead to much better research. It'll lead to rethinking the assumptions. It'll lead to

working on the bottlenecks that other people aren't working on.

And then naturally to turn to you, we've talked about reward functions, and you just give an

advice on looking forward to how you'd like to see what kind of change you would like to make

in the world. What do you think, ridiculous, big question? What do you think is the meaning

of life? What is the meaning of your life? What gives you fulfillment, purpose, happiness, and

meaning? That's a very big question. What's the reward function under which you're operating?

Yeah, I think one thing that does give, you know, if not meaning at least satisfaction is

some degree of confidence that I'm working on a problem that really matters. I feel like it's

less important to me to actually solve a problem, but it's quite nice to take things to spend my

time on that I believe really matter. And I try pretty hard to look for that.

I don't know if it's easy to answer this, but if you're successful, what does that look like?

What's the big dream? Of course, success is built on top of success and you keep going forever,

but what is the dream? Yeah, so one very concrete thing or maybe as concrete as it's going to get

here is to see machines that actually get better and better the longer they exist in the world.

And that kind of seems like on the surface, one might even think that that's something that we

have today, but I think we really don't. I think that there is an ending complexity in the universe

and to date, all of the machines that we've been able to build don't sort of improve up to the limit

of that complexity. They hit a wall somewhere. Maybe they hit a wall because they're in a simulator

that has that is only a very limited, very pale limitation of the real world or they hit a wall

because they rely on a labeled dataset, but they never hit the wall of like running out of stuff

to see. So, you know, I'd like to build a machine that can go as far as possible.

And that runs up against the ceiling of the complexity of the universe. Yes.

Well, I don't think there's a better way to end it, Sergei. Thank you so much. It's a huge honor.

I can't wait to see the amazing work that you have to publish and in education space in terms

of reinforcement learning. Thank you for inspiring the world. Thank you for the great research you

do. Thank you. Thanks for listening to this conversation with Sergei Levine and thank you

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is a bird without wings. Thank you for listening and hope to see you next time.