The following is a conversation with Peter Abiel.
He's a professor at UC Berkeley and the director of the Berkeley Robotics Learning Lab.
He's one of the top researchers in the world working on how to make robots understand and
interact with the world around them, especially using imitation and deeper enforcement learning.
This conversation is part of the MIT course on artificial general intelligence
and the artificial intelligence podcast. If you enjoy it, please subscribe on YouTube,
iTunes, or your podcast provider of choice, or simply connect with me on Twitter at Lex
Freedman, spelled F R I D. And now here's my conversation with Peter Abiel.
You've mentioned that if there was one person you could meet, it would be Roger Federer. So let
me ask, when do you think we will have a robot that fully autonomously can beat Roger Federer
at tennis? Roger Federer level player at tennis? Well, first, if you can make it happen for me
to meet Roger, let me know. In terms of getting a robot to beat him at tennis, it's kind of an
interesting question because for a lot of the challenges we think about in AI, the software
is really the missing piece. But for something like this, the hardware is nowhere near either. To
really have a robot that can physically run around, the Boston Dynamics robots are starting to get
there, but still not really human level ability to run around and then swing a racket.
So you think that's a hardware problem? I don't think it's a hardware problem only. I think it's
a hardware and a software problem. I think it's both. And I think they'll have independent progress.
So I'd say the hardware maybe in 10, 15 years. On clay, not grass. I mean, grass is probably hard.
With the sliding? Yeah. Well, clay, I'm not sure what's harder, grass or clay. The clay involves
sliding, which might be harder to master actually. Yeah. But you're not limited to bipedal. I mean,
I'm sure there's no... Well, if we can build a machine, it's a whole different question, of
course. If you can say, okay, this robot can be on wheels, it can move around on wheels and
can be designed differently, then I think that can be done sooner probably than a full humanoid
type of setup. What do you think of swing a racket? So you've worked at basic manipulation.
How hard do you think is the task of swinging a racket with a be able to hit a nice backhand
or a forehand? Let's say we just set up stationery, a nice robot arm, let's say. You know,
a standard industrial arm, and it can watch the ball come and then swing the racket.
It's a good question. I'm not sure it would be super hard to do. I mean, I'm sure it would require
a lot... If we do it with reinforcement learning, it would require a lot of trial and error. It's
not going to swing it right the first time around, but yeah, I don't see why I couldn't
swing it the right way. I think it's learnable. I think if you set up a ball machine, let's say
on one side and then a robot with a tennis racket on the other side, I think it's learnable
and maybe a little bit of pre training and simulation. Yeah, I think that's feasible.
I think the swinging the racket is feasible. It'd be very interesting to see how much precision it
can get. I mean, that's where... I mean, some of the human players can hit it on the lines,
which is very high precision. With spin. The spin is an interesting whether RL can learn to
put a spin on the ball. Well, you got me interested. Maybe someday we'll set this up.
Your answer is basically, okay, for this problem, it sounds fascinating, but for the general problem
of a tennis player, we might be a little bit farther away. What's the most impressive thing
you've seen a robot do in the physical world? So physically, for me, it's
the Boston Dynamics videos always just ring home and just super impressed. Recently, the robot
running up the stairs during the parkour type thing. I mean, yes, we don't know what's underneath.
They don't really write a lot of detail, but even if it's hard coded underneath,
which it might or might not be just the physical abilities of doing that parkour,
that's a very impressive robot right there. So have you met Spotmini or any of those robots in
person? I met Spotmini last year in April at the Mars event that Jeff Bezos organizes. They
brought it out there and it was nicely falling around Jeff. When Jeff left the room, they had it
following him along, which is pretty impressive. So I think there's some confidence to know that
there's no learning going on in those robots. The psychology of it, so while knowing that,
while knowing there's not, if there's any learning going on, it's very limited,
I met Spotmini earlier this year and knowing everything that's going on,
having one on one interaction, so I get to spend some time alone.
And there's immediately a deep connection on the psychological level,
even though you know the fundamentals, how it works, there's something magical.
So do you think about the psychology of interacting with robots in the physical world,
even you just showed me the PR2, the robot, and there was a little bit something like a face,
had a little bit something like a face, there's something that immediately draws you to it.
Do you think about that aspect of the robotics problem?
Well, it's very hard with Brett here. We'll give him a name, Berkeley Robot,
for the elimination of tedious tasks. It's very hard to not think of the robot as a person,
and it seems like everybody calls him a he for whatever reason, but that also makes it more
a person than if it was a it. And it seems pretty natural to think of it that way.
This past weekend really struck me, I've seen Pepper many times on videos,
but then I was at an event organized by, this was by Fidelity, and they had scripted Pepper to help
moderate some sessions, and they had scripted Pepper to have the personality of a child a
little bit. And it was very hard to not think of it as its own person in some sense, because it
was just kind of jumping, it would just jump into conversation making it very interactive.
Moderate would be saying Pepper would just jump in, hold on, how about me,
how about me, can I participate in this doing it, just like, okay, this is like like a person,
and that was 100% scripted. And even then it was hard not to have that sense of somehow
there is something there. So as we have robots interact in this physical world, is that a signal
that could be used in reinforcement learning? You've worked a little bit in this direction,
but do you think that that psychology can be somehow pulled in? Yes, that's a question I would
say a lot, a lot of people ask. And I think part of why they ask it is they're thinking about
how unique are we really still as people, like after they see some results, they see
a computer play go to say a computer do this that they're like, okay, but can it really have
emotion? Can it really interact with us in that way? And then once you're around robots,
you already start feeling it. And I think that kind of maybe methodologically, the way that I
think of it is, if you run something like reinforcement learnings about optimizing some
objective, and there's no reason that the objective couldn't be tied into how much
does a person like interacting with this system? And why could not the reinforcement learning system
optimize for the robot being fun to be around? And why wouldn't it then naturally become more
more interactive and more and more maybe like a person or like a pet? I don't know what it would
exactly be, but more and more have those features and acquire them automatically. As long as you
can formalize an objective of what it means to like something, how you exhibit what's the ground
truth? How do you get the reward from human? Because you have to somehow collect that information
from human. But you're saying if you can formulate as an objective, it can be learned.
There's no reason it couldn't emerge through learning. And maybe one way to formulate as an
objective, you wouldn't have to necessarily score it explicitly. So standard rewards are
numbers. And numbers are hard to come by. This is a 1.5 or 1.7 on some scale. It's very hard to do
for a person. But much easier is for a person to say, okay, what you did the last five minutes
was much nicer than we did the previous five minutes. And that now gives a comparison. And in fact,
there have been some results on that. For example, Paul Cristiano and collaborators at OpenEye had
the hopper, Mojoka hopper, one legged robot, the backflip, backflips purely from feedback. I like
this better than that. That's kind of equally good. And after a bunch of interactions, it figured
out what it was the person was asking for, namely a backflip. And so I think the same thing.
It wasn't trying to do a backflip. It was just getting a score from the comparison score from
the person based on person having a mind in their own mind. I wanted to do a backflip. But
the robot didn't know what it was supposed to be doing. It just knew that sometimes the person
said, this is better, this is worse. And then the robot figured out what the person was actually
after was a backflip. And I imagine the same would be true for things like more interactive
robots that the robot would figure out over time. Oh, this kind of thing apparently is appreciated
more than this other kind of thing. So when I first picked up Sutton's Richard Sutton's
reinforcement learning book, before sort of this deep learning, before the reemergence
of neural networks as a powerful mechanism for machine learning, IRL seemed to me like magic.
It was beautiful. So that seemed like what intelligence is, RRL reinforcement learning. So how
do you think we can possibly learn anything about the world when the reward for the actions is delayed
is so sparse? Like where is, why do you think RRL works? Why do you think you can learn anything
under such sparse rewards, whether it's regular reinforcement learning or deeper reinforcement
learning? What's your intuition? The kind of part of that is, why is RRL, why does it need
so many samples, so many experiences to learn from? Because really what's happening is when you
have a sparse reward, you do something maybe for like, I don't know, you take 100 actions and then
you get a reward, or maybe you get like a score of three. And I'm like, okay, three. Not sure what
that means. You go again and now you get two. And now you know that that sequence of 100 actions
that you did the second time around somehow was worse than the sequence of 100 actions you did
the first time around. But that's tough to now know which one of those were better or worse.
Some might have been good and bad in either one. And so that's why you need so many experiences.
But once you have enough experiences, effectively RRL is teasing that apart. It's starting to say,
okay, what is consistently there when you get a higher reward and what's consistently there when
you get a lower reward? And then kind of the magic of sometimes the policy grant update is to say,
now let's update the neural network to make the actions that were kind of present when things are
good, more likely, and make the actions that are present when things are not as good, less likely.
So that's that is the counterpoint. But it seems like you would need to run it a lot more than
you do. Even though right now, people could say that RRL is very inefficient. But it seems to be
way more efficient than one would imagine on paper, that the simple updates to the policy,
the policy gradient that somehow you can learn is exactly as I said, what are the common actions
that seem to produce some good results, that that somehow can learn anything.
It seems counterintuitive, at least. Is there some intuition behind it?
Yeah, so I think there's a few ways to think about this. The way I tend to think about it
mostly originally. And so when we started working on deep reinforcement learning here at Berkeley,
which was maybe 2011, 12, 13, around that time, John Shulman was a PhD student initially kind of
driving it forward here. And kind of the way we thought about it at the time was if you think
about rectified linear units or kind of rectifier type neural networks, what do you get? You get
something that's piecewise linear feedback control. And if you look at the literature,
linear feedback control is extremely successful, can solve many, many problems surprisingly well.
I remember, for example, when we did helicopter flight, if you're in a stationary flight regime,
not a non stationary, but a stationary flight regime like hover, you can use linear feedback
control to stabilize the helicopter, a very complex dynamical system. But the controller
is relatively simple. And so I think that's a big part of is that if you do feedback control,
even though the system you control can be very, very complex, often,
relatively simple control architectures can already do a lot. But then also just linear
is not good enough. And so one way you can think of these neural networks is that in some of the
tile the space, which people were already trying to do more by hand or with finite state machines,
say this linear controller here, this linear controller here, neural network,
learns to tell the spin say linear controller here, another linear controller here,
but it's more subtle than that. And so it's benefiting from this linear control aspect is
benefiting from the tiling, but it's somehow tiling it one dimension at a time. Because if
let's say you have a two layer network, even that hidden layer, you make a transition from active
to inactive or the other way around, that is essentially one axis, but not axis aligned, but
one direction that you change. And so you have this kind of very gradual tiling of the space,
we have a lot of sharing between the linear controllers that tile the space. And that was
always my intuition as to why to expect that this might work pretty well. It's essentially
leveraging the fact that linear feedback control is so good. But of course, not enough. And this
is a gradual tiling of the space with linear feedback controls that share a lot of expertise
across them. So that that's, that's really nice intuition. But do you think that scales to the
more and more general problems of when you start going up the number of control dimensions,
when you start going down in terms of how often you get a clean reward signal,
does that intuition carry forward to those crazy or weirder worlds that we think of as the real
world? So I think where things get really tricky in the real world compared to the things we've
looked at so far with great success and reinforcement learning is
the time scales, which takes us to an extreme. So when you think about the real world, I mean,
I don't know, maybe some student decided to do a PhD here, right? Okay, that's that's a decision,
that's a very high level decision. But if you think about their lives, I mean, any person's life,
it's a sequence of muscle fiber contractions and relaxations. And that's how you interact with
the world. And that's a very high frequency control thing. But it's ultimately what you do
and how you affect the world. Until I guess we have brain readings, you can maybe do it slightly
differently. But typically, that's how you affect the world. And the decision of doing a PhD is
like so abstract relative to what you're actually doing in the world. And I think that's where
credit assignment becomes just completely beyond what any current RL algorithm can do. And we need
hierarchical reasoning at a level that is just not available at all yet. Where do you think we can
pick up hierarchical reasoning by which mechanisms? Yeah, so maybe let me highlight what I think the
limitations are of what already was done 20, 30 years ago. In fact, you'll find reasoning systems
that reason over relatively long horizons. But the problem is that they were not grounded in the real
world. So people would have to hand design some kind of logical, dynamical descriptions of the
world. And that didn't tie into perception. And so that didn't tie into real objects and so forth.
And so that was a big gap. Now with deep learning, we start having the ability to really see with
sensors process that and understand what's in the world. And so it's a good time to try to
bring these things together. I see a few ways of getting there. One way to get there would be to say
deep learning can get bolted on somehow to some of these more traditional approaches.
Now bolted on would probably mean you need to do some kind of end to end training,
where you say, my deep learning processing somehow leads to a representation that in term
uses some kind of traditional underlying dynamical systems that can be used for planning.
And that's, for example, the direction of Eve Tamar and Thanard Kuritach here have been pushing
with causal info again. And of course, other people too, that that's that's one way. Can we
somehow force it into the form factor that is amenable to reasoning?
Another direction we've been thinking about for a long time and didn't make any progress on
was more information theoretic approaches. So the idea there was that what it means to take
high level action is to take and choose a latent variable now that tells you a lot about what's
going to be the case in the future, because that's what it means to to take a high level action.
I say, okay, what I decide I'm going to navigate to the gas station because I need to get
gas from my car. Well, that'll now take five minutes to get there. But the fact that I get
there, I could already tell that from the high level action I took much earlier.
That we had a very hard time getting success with, not saying it's a dead end,
necessarily, but we had a lot of trouble getting that to work. And then we started revisiting
the notion of what are we really trying to achieve? What we're trying to achieve is
not necessarily a hierarchy per se, but you could think about what does hierarchy give us?
What we hope it would give us is better credit assignment. What is better credit assignment
is giving us, it gives us faster learning. And so faster learning is ultimately maybe
what we're after. And so that's where we ended up with the RL squared paper on learning to
reinforcement learn, which at a time Rocky Dwan led. And that's exactly the meta learning
approach where we say, okay, we don't know how to design hierarchy. We know what we want to get
from it. Let's just enter and optimize for what we want to get from it and see if it might emerge.
And we saw things emerge. The maze navigation had consistent motion down hallways,
which is what you want. A hierarchical control should say, I want to go down this hallway.
And then when there is an option to take a turn, I can decide whether to take a turn or not and
repeat, even had the notion of, where have you been before or not to not revisit places you've
been before? It still didn't scale yet to the real world kind of scenarios I think you had in mind,
but it was some sign of life that maybe you can meta learn these hierarchical concepts.
I mean, it seems like through these meta learning concepts, we get at the, what I think is one of
the hardest and most important problems of AI, which is transfer learning. So it's generalization.
How far along this journey towards building general systems are we being able to do transfer
learning? Well, so there's some signs that you can generalize a little bit. But do you think
we're on the right path or totally different breakthroughs are needed to be able to transfer
knowledge between different learned models? Yeah, I'm pretty torn on this in that I think
there are some very impressive results already, right? I mean, I would say when even with the
initial kind of big breakthrough in 2012 with Alex net, right, the initial, the initial thing is,
okay, great. This does better on image net hands image recognition. But then immediately thereafter,
there was of course the notion that wow, what was learned on image net, and you now want to solve
a new task, you can fine tune Alex net for new tasks. And that was often found to be the even
bigger deal that you learn something that was reusable, which was not often the case before
usually machine learning, you learn something for one scenario. And that was it. And that's
really exciting. I mean, that's just a huge application. That's probably the biggest
success of transfer learning today, if in terms of scope and impact. That was a huge breakthrough.
And then recently, I feel like similar kind of by scaling things up, it seems like this has been
expanded upon like people training even bigger networks, they might transfer even better. If
you look that, for example, some of the opening results on language models. And so in the recent
Google results on language models, they are learned for just prediction. And then they get
reused for other tasks. And so I think there is something there where somehow if you train a
big enough model on enough things, it seems to transfer some deep mind results that I thought
were very impressive, the unreal results, where it was learning to navigate mazes in ways where
it wasn't just doing reinforcement learning, but it had other objectives was optimizing for. So I
think there's a lot of interesting results already. I think maybe where it's hard to wrap my head
around this, to which extent or when do we call something generalization, right? Or the levels
of generalization involved in these different tasks, right? So you draw this, by the way, just
to frame things. I've heard you say somewhere, it's the difference in learning to master versus
learning to generalize. That it's a nice line to think about. And I guess you're saying it's a gray
area of what learning to master and learning to generalize where one starts.
I think I might have heard this. I might have heard it somewhere else. And I think it might have
been one of your interviews, maybe the one with Yoshua Benjamin, 900% sure. But I like the example
and I'm going to not sure who it was, but the example was essentially if you use current deep
learning techniques, what we're doing to predict, let's say the relative motion of our planets,
it would do pretty well. But then now if a massive new mass enters our solar system,
it would probably not predict what will happen, right? And that's a different kind of
generalization. That's a generalization that relies on the ultimate simplest explanation
that we have available today to explain the motion of planets, whereas just pattern recognition
could predict our current solar system motion pretty well. No problem. And so I think that's
an example of a kind of generalization that is a little different from what we've achieved so far.
And it's not clear if just, you know, regularizing more and forcing it to come up with a simpler,
simpler, simpler explanation. Look, this is not simple, but that's what physics researchers do,
right, to say, can I make this even simpler? How simple can I get this? What's the simplest
equation that can explain everything, right? The master equation for the entire dynamics of the
universe. We haven't really pushed that direction as hard in deep learning, I would say. Not sure
if it should be pushed, but it seems a kind of generalization you get from that that you don't
get in our current methods so far. So I just talked to Vladimir Vapnik, for example, who was
a statistician in statistical learning, and he kind of dreams of creating the E equals Mc
squared for learning, right, the general theory of learning. Do you think that's a fruitless pursuit
in the near term, within the next several decades?
I think that's a really interesting pursuit. And in the following sense, in that there is a
lot of evidence that the brain is pretty modular. And so I wouldn't maybe think of it as the theory,
maybe, the underlying theory, but more kind of the principle where there have been findings where
people who are blind will use the part of the brain usually used for vision for other functions.
And even after some kind of, if people get rewired in some way, they might be able to reuse parts of
their brain for other functions. And so what that suggests is some kind of modularity. And I think
it is a pretty natural thing to strive for to see, can we find that modularity? Can we find this
thing? Of course, it's not every part of the brain is not exactly the same. Not everything can be
rewired arbitrarily. But if you think of things like the neocortex, which is a pretty big part of
the brain, that seems fairly modular from what the findings so far. Can you design something
equally modular? And if you can just grow it, it becomes more capable, probably. I think that would
be the kind of interesting underlying principle to shoot for that is not unrealistic.
Do you think you prefer math or empirical trial and error for the discovery of the essence of what
it means to do something intelligent? So reinforcement learning embodies both groups, right?
To prove that something converges, prove the bounds. And then at the same time, a lot of those
successes are, well, let's try this and see if it works. So which do you gravitate towards? How do
you think of those two parts of your brain? So maybe I would prefer we could make the progress
with mathematics. And the reason maybe I would prefer that is because often if you have something you
can mathematically formalize, you can leapfrog a lot of experimentation. And experimentation takes
a long time to get through. And a lot of trial and error, reinforcement learning, your research
process. But you need to do a lot of trial and error before you get to a success. So if you can
leapfrog that, to my mind, that's what the math is about. And hopefully once you do a bunch of
experiments, you start seeing a pattern, you can do some derivations that leapfrog some experiments.
But I agree with you. I mean, in practice, a lot of the progress has been such that we have not
been able to find the math that allows it to leapfrog ahead. And we are kind of making gradual
progress one step at a time. A new experiment here, a new experiment there that gives us new
insights and gradually building up, but not getting to something yet where we're just, okay,
here's an equation that now explains how, you know, that would be have been two years of
experimentation to get there. But this tells us what the results going to be. Unfortunately,
unfortunately, not so much yet. Not so much yet. But your hope is there. In trying to teach robots
or systems to do everyday tasks, or even in simulation, what do you think you're more excited
about? imitation learning or self play. So letting robots learn from humans, or letting robots plan
their own, try to figure out in their own way, and eventually play, eventually interact with humans,
or solve whatever problem is. What's the more exciting to you? What's more promising you think
is a research direction? So when we look at self play, what's so beautiful about it is,
goes back to kind of the challenges in reinforcement learning. So the challenge
of reinforcement learning is getting signal. And if you don't never succeed, you don't get any signal.
In self play, you're on both sides. So one of you succeeds. And the beauty is also one of you
fails. And so you see the contrast, you see the one version of me that did better than the other
version. And so every time you play yourself, you get signal. And so whenever you can turn
something into self play, you're in a beautiful situation where you can naturally learn much
more quickly than in most other reinforcement learning environments. So I think, I think if
somehow we can turn more reinforcement learning problems into self play formulations, that would
go really, really far. So far, self play has been largely around games where there is natural
opponents. But if we could do self play for other things, and let's say, I don't know,
a robot learns to build a house, I mean, that's a pretty advanced thing to try to do for a robot,
but maybe it tries to build a hut or something. If that can be done through self play, it would
learn a lot more quickly if somebody can figure it out. And I think that would be something where
it goes closer to kind of the mathematical leapfrogging where somebody figures out a
formalism to say, okay, any RL problem by playing this and this idea, you can turn it
into a self play problem where you get signal a lot more easily.
Reality is many problems, we don't know how to turn to self play. And so either we need to provide
detailed reward. That doesn't just reward for achieving a goal, but rewards for making progress,
and that becomes time consuming. And once you're starting to do that, let's say you want a robot
to do something, you need to give all this detailed reward. Well, why not just give a
demonstration? Because why not just show the robot. And now the question is, how do you show
the robot? One way to show is to tally operator robot and then robot really experiences things.
And that's nice, because that's really high signal to noise ratio data. And we've done a lot
of that. And you teach your robot skills. In just 10 minutes, you can teach your robot a new basic
skill, like, okay, pick up the bottle, place it somewhere else. That's a skill, no matter where
the bottle starts, maybe it always goes on to a target or something. That's fairly easy to teach
your robot with teleop. Now, what's even more interesting, if you can now teach your robot
through third person learning, where the robot watches you do something, and doesn't experience
it, but just watches it and says, okay, well, if you're showing me that, that means I should
be doing this. And I'm not going to be using your hand, because I don't get to control your hand,
but I'm going to use my hand, I do that mapping. And so that's where I think one of the big breakthroughs
has happened this year. This was led by Chelsea Finn here. It's almost like learning a machine
translation for demonstrations where you have a human demonstration and the robot learns to
translate it into what it means for the robot to do it. And that was a meta learning formulation,
learn from one to get the other. And that I think opens up a lot of opportunities to learn a lot
more quickly. So my focus is on autonomous vehicles. Do you think this approach of third
person watching is the autonomous driving is amenable to this kind of approach?
So for autonomous driving, I would say it's third person is slightly easier. And the reason I'm
going to say it's slightly easier to do with third person is because the car dynamics are very well
understood. So the easier than first person, you mean, or easier than. So I think the distinction
between third person and first person is not a very important distinction for autonomous driving.
They're very similar. Because the distinction is really about who turns the steering wheel.
And or maybe let me put it differently. How to get from a point where you are now to a point,
let's say a couple of meters in front of you. And that's a problem that's very well understood.
And that's the only distinction between third and first person there. Whereas with the robot
manipulation, interaction forces are very complex. And it's still a very different thing.
For autonomous driving, I think there's still the question imitation versus RL.
Well, so imitation gives you a lot more signal. I think where imitation is lacking and needs
some extra machinery is it doesn't in its normal format, doesn't think about goals or objectives.
And of course, there are versions of imitation learning, inverse reinforcement learning type
imitation, which also thinks about goals. I think then we're getting much closer. But I think it's
very hard to think of a fully reactive car generalizing well, if it really doesn't have a notion
of objectives to generalize well to the kind of general that you would want, you want more than
just that reactivity that you get from just behavioral cloning slash supervised learning.
So a lot of the work, whether it's self play or even imitation learning would benefit
significantly from simulation, from effective simulation, and you're doing a lot of stuff
in the physical world and in simulation, do you have hope for greater and greater
power of simulation loop being boundless, eventually, to where most of what we need
to operate in the physical world, what could be simulated to a degree that's directly
transferable to the physical world? Are we still very far away from that? So I think
we could even rephrase that question in some sense, please. And so the power of simulation,
as simulators get better and better, of course, becomes stronger, and we can learn more in
simulation. But there's also another version, which is where you say the simulator doesn't
even have to be that precise. As long as it's somewhat representative. And instead of trying
to get one simulator that is sufficiently precise to learn and transfer really well to the real
world, I'm going to build many simulators, ensemble of simulators, ensemble of simulators,
not any single one of them is sufficiently representative of the real world such that
it would work if you train in there. But if you train in all of them, then there is something
that's good in all of them. The real world will just be, you know, another one of them. That's,
you know, not identical to any one of them, but just another one of them.
Now, this sample from the distribution of simulators.
Exactly.
We do live in a simulation. So this is just one, one other one.
I'm not sure about that. But yeah, it's definitely a very advanced simulator if it is.
Yeah, it's a pretty good one. I've talked to Russell. It's something you think about a little bit
too. Of course, you're like really trying to build these systems. But do you think about the future
of AI? A lot of people have concern about safety. How do you think about AI safety as you build
robots that are operating the physical world? What is, yeah, how do you approach this problem
in an engineering kind of way in a systematic way?
So when a robot is doing things, you kind of have a few notions of safety to worry about. One is that
the robot is physically strong and of course could do a lot of damage. Same for cars, which we can
think of as robots do in some way. And this could be completely unintentional. So it could be not
the kind of long term AI safety concerns that, okay, AI is smarter than us. And now what do we do?
But it could be just very practical. Okay, this robot, if it makes a mistake,
what are the results going to be? Of course, simulation comes in a lot there to test in simulation.
It's a difficult question. And I'm always wondering, like I always wonder, let's say you look at,
let's go back to driving, because a lot of people know driving well, of course.
What do we do to test somebody for driving, right, to get a driver's license? What do they
really do? I mean, you fill out some tests, and then you drive and I mean, for a few minutes,
it's suburban California, that driving test is just you drive around the block, pull over, you
do a stop sign successfully, and then, you know, you pull over again, and you're pretty much done.
And you're like, okay, if a self driving car did that, would you trust it that it can drive?
And I'd be like, no, that's not enough for me to trust it. But somehow for humans,
we've figured out that somebody being able to do that is representative
of them being able to do a lot of other things. And so I think somehow for humans,
we figured out representative tests of what it means if you can do this, what you can really do.
Of course, testing humans, humans don't want to be tested at all times. Self driving cars or
robots could be tested more often probably, you can have replicas that get tested and are known
to be identical because they use the same neural net and so forth. But still, I feel like we don't
have this kind of unit tests or proper tests for robots. And I think there's something very
interesting to be thought about there, especially as you update things, your software improves,
you have a better self driving car suite, you update it. How do you know it's indeed more
capable on everything than what you had before that you didn't have any bad things creep into it?
So I think that's a very interesting direction of research that there is no real solution yet,
except that somehow for humans, we do because we say, okay, you have a driving test, you passed,
you can go on the road now and you must have accents every like a million or 10 million miles,
something pretty phenomenal compared to that short test that is being done.
So let me ask, you've mentioned, you've mentioned that Andrew Ang, by example,
showed you the value of kindness. And do you think the space of
of policies, good policies for humans and for AI is populated by policies that
with kindness or ones that are the opposite, exploitation, even evil. So if you just look
at the sea of policies we operate under as human beings, or if AI system had to operate in this
real world, do you think it's really easy to find policies that are full of kindness,
like we naturally fall into them? Or is it like a very hard optimization problem?
I mean, there is kind of two optimizations happening for humans, right? So for humans,
there's kind of the very long term optimization, which evolution has done for us. And we're kind of
predisposed to like certain things. And that's in some sense, what makes our learning easier,
because I mean, we know things like pain and hunger and thirst. And the fact that we know about those
is not something that we were taught. That's kind of innate. When we're hungry, we're unhappy.
When we're thirsty, we're unhappy. When we have pain, we're unhappy. And ultimately evolution
built that into us to think about those things. And so I think there is a notion that it seems
somehow humans evolved in general to prefer to get along in some ways. But at the same time,
also to be very territorial and kind of centric to their own tribe. It seems like that's the kind
of space we converged on to. I mean, I'm not an expert in anthropology, but it seems like we're
very kind of good within our own tribe, but need to be taught to be nice to other tribes.
Well, if you look at Steven Pinker, he highlights this pretty nicely in
Better Angels of Our Nature, where he talks about violence decreasing over time consistently.
So whatever tension, whatever teams we pick, it seems that the long arc of history goes
towards us getting along more and more. So do you think that
do you think it's possible to teach RRL based robots this kind of kindness, this kind of ability
to interact with humans, this kind of policy, even to let me ask, let me ask upon one, do you think
it's possible to teach RRL based robot to love a human being and to inspire that human to love
the robot back? So to like a RRL based algorithm that leads to a happy marriage? That's an interesting
question. Maybe I'll answer it with another question, right? Because I mean, but I'll come
back to it. So another question you can have is okay. I mean, how close does some people's
happiness get from interacting with just a really nice dog? Like, I mean, dogs, you come home,
that's what dogs do. They greet you. They're excited. It makes you happy when you come home
to your dog. You're just like, okay, this is exciting. They're always happy when I'm here.
I mean, if they don't greet you, because maybe whatever, your partner took them on a trip or
something, you might not be nearly as happy when you get home, right? And so the kind of,
it seems like the level of reasoning a dog has is pretty sophisticated, but then it's still not yet
at the level of human reasoning. And so it seems like we don't even need to achieve human level
reasoning to get like very strong affection with humans. And so my thinking is, why not, right?
Why couldn't, with an AI, couldn't we achieve the kind of level of affection that humans feel
among each other or with friendly animals and so forth? So question, is it a good thing for us
or not? That's another thing, right? Because I mean, but I don't see why not. Why not? Yeah.
So Elon Musk says love is the answer. Maybe he should say love is the objective function and
then RL is the answer, right? Well, maybe. Oh, Peter, thank you so much. I don't want to take
up more of your time. Thank you so much for talking today. Well, thanks for coming by.
Great to have you visit.