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. With the sliding.
I'm not sure what's harder, grass or clay. The clay involves sliding, which might be harder to
master actually. But you're not limited to bipedal. I'm sure there's no... 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, swing a racket? So you've worked at basic manipulation.
How hard do you think is the task of swinging a racket? Would it 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 because, 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 spot right there. 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 follow 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
interactions, 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 Brad 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, like very interactive. Moderate would be saying,
Pepper would just jump in, hold on, 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 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 and 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, what how you exhibit with the ground
truth, how do you how do you get the reward from human? Because you have to somehow collect that
information from your 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, Mojoko 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. Oh, 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
as 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 RL works? Why do you think you can learn
anything under such sparse rewards, whether it's regular reinforcement learning or deep
reinforcement learning? What's your intuition? The kind of part of that is why is RL,
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 I 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 and either one. And so that's why
you need so many experiences. But once you have enough experiences, effectively RL is teasing
that apart. It's trying to say, Okay, when 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
and sometimes the policy grand 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 RL is very inefficient. But it seems to be way more efficient than one would imagine
on paper, that the 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? 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 weird 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 it 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'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 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 take a
high level action. 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 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 you 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 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. I mean, I would say when even with the initial kind of big breakthrough
in 2012 with AlexNet, the initial thing is okay, great. This does better on ImageNet,
hence image recognition. But then immediately thereafter, there was of course the notion that
wow, what was learned on ImageNet and you now want to solve a new task, you can fine-tune
AlexNet 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 that's a huge
application. That's probably the biggest success of transfer learning today, 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 looked at, for example, some of the OpenAI results on
language models, and some of 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 it 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 one of your interviews, maybe the one with
Yoshua Benjamin, not 100% sure. But I like the example. 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
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 experience.
It's not simple. But that's what physics researchers do, right? They say,
can I make this even simpler? How simple can I get this? What's the simplest equation I 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's a statistician,
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 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 in bodies, both groups,
right? The 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 that would be
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, kind of 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, 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? Immutation 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 in 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 into 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 I'll 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 another one of them. That's not identical to any one of them, but just another
one of them. Another sample from the distribution of simulators. Exactly. We do live in a simulation,
so this is just one other one. I'm not sure about that, but 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 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 in the physical world? How do you approach this
problem in an engineering kind of way, in a systematic way? 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. This could be completely unintentional. 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. I'm always
wondering, 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? Get a driver's license. What do they really do? I mean,
you fill out some tests, and then you drive 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 pull over again, and you're pretty much done. You're like, okay, if a self-driving car
did that, would you trust it that it can drive? 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. I think somehow for humans,
we've figured out representative tests of what it means if you can do this, what you can really do.
Of course, testing 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?
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 humans have accents every million or 10 million miles.
Something pretty phenomenal compared to that short test that is being done.
So let me ask, you've mentioned that Andrew Ang, by example, showed you the value of kindness.
Do you think the space of policies, good policies for humans and for AI,
is populated by policies that with kindness are 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 converge
down 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 Stephen 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. I hope so. So 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 a point on, 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? But I'll come back to it. So another question I 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
him 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
that humans feel among each other or with friendly animals and so forth? It's a 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.
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.