The following is a conversation with Leslie Kailbling. She is a roboticist and professor at
MIT. She is recognized for her work in reinforcement learning, planning, robot navigation, and several
other topics in AI. She won the Ijkai Computers and Thought Award and was the editor in chief
of the prestigious journal machine learning research. This conversation is part of the
artificial intelligence podcast at MIT and beyond. If you enjoy it, subscribe on YouTube,
iTunes, or simply connect with me on Twitter at Lex Friedman, spelled F R I D. And now,
here's my conversation with Leslie Kailbling. What made me get excited about AI, I can say
that, is I read Girdle Escherbach when I was in high school. That was pretty formative for me
because it exposed the interestingness of primitives and combination and how you can
make complex things out of simple parts and ideas of AI and what kinds of programs might
generate intelligent behavior. So you first fell in love with AI reasoning logic versus robots?
Yeah, the robots came because my first job, so I finished an undergraduate degree in philosophy
at Stanford and was about to finish masters in computer science and I got hired at SRI
in their AI lab and they were building a robot. It was a kind of a follow on to shaky,
but all the shaky people were not there anymore. And so my job was to try to get this robot to
do stuff and that's really kind of what got me interested in robots. So maybe taking a small
step back to your bachelor's in Stanford philosophy, did master's in PhD in computer science,
but the bachelor's in philosophy. So what was that journey like? What elements of philosophy
do you think you bring to your work in computer science?
So it's surprisingly relevant. So part of the reason that I didn't do a computer science
undergraduate degree was that there wasn't one at Stanford at the time, but that there's part of
philosophy and in fact Stanford has a special sub major in something called now Symbolic Systems,
which is logic, model, theory, formal semantics of natural language. And so that's actually
a perfect preparation for work in AI and computer science.
That's kind of interesting. So if you were interested in artificial intelligence,
what kind of majors were people even thinking about taking? What is in your science? So besides
philosophies, what were you supposed to do if you were fascinated by the idea of creating
intelligence? There weren't enough people who did that for that even to be a conversation.
I mean, I think probably philosophy. I mean, it's interesting in my graduating class of
undergraduate philosophers, probably maybe slightly less than half went on in computer
science, slightly less than half went on in law, and like one or two went on in philosophy.
So it was a common kind of connection. Do you think AI researchers have a role,
be part time philosophers, or should they stick to the solid science and engineering
without sort of taking the philosophizing tangents? I mean, you work with robots,
you think about what it takes to create intelligent beings. Aren't you the perfect person to think
about the big picture philosophy at all? The parts of philosophy that are closest to AI,
I think, or at least the closest to AI that I think about are stuff like
belief and knowledge and denotation and that kind of stuff. It's quite formal, and it's
like just one step away from the kinds of computer science work that we do kind of routinely.
I think that there are important questions still about what you can do with a machine and what
you can't and so on. Although at least my personal view is that I'm completely a materialist,
and I don't think that there's any reason why we can't make a robot be
behaviorally indistinguishable from a human. And the question of whether it's
distinguishable internally, whether it's a zombie or not in philosophy terms, I actually don't,
I don't know, and I don't know if I care too much about that.
Right, but there is a philosophical notions there, mathematical and philosophical,
because we don't know so much of how difficult that is, how difficult is a perception problem.
How difficult is the planning problem? How difficult is it to operate in this world successfully?
Because our robots are not currently as successful as human beings in many tasks.
The question about the gap between current robots and human beings borders a little bit
on philosophy. The expanse of knowledge that's required to operate in this world and the ability
to form common sense knowledge, the ability to reason about uncertainty, much of the work
you've been doing, there's open questions there that, I don't know, require to activate a certain
big picture view. To me, that doesn't seem like a philosophical gap at all.
To me, there is a big technical gap. There's a huge technical gap,
but I don't see any reason why it's more than a technical gap.
Perfect. When you mentioned AI, you mentioned SRI, and maybe can you describe to me when you
first fell in love with robotics, with robots, or inspired, so you mentioned flaky or shaky flaky,
and what was the robot that first captured your imagination of what's possible?
Right. The first robot I worked with was flaky. Shaky was a robot that the SRI people had built,
but by the time, I think when I arrived, it was sitting in a corner of somebody's office
dripping hydraulic fluid into a pan, but it's iconic. Really, everybody should read the Shaky
Tech Report because it has so many good ideas in it. They invented ASTAR Search and symbolic
planning and learning macro operators. They had low level kind of configuration space planning for
the robot. They had vision. That's the basic ideas of a ton of things.
Can you take a step by it? Shaky was a mobile robot, but it could push objects,
and so it would move things around. With which actuator?
With its self, with its base. They had painted the baseboards black,
so it used vision to localize itself in a map. It detected objects. It could detect objects that
were surprising to it. It would plan and replan based on what it saw. It reasoned about whether
to look and take pictures. It really had the basics of so many of the things that we think about now.
How did it represent the space around it?
It had representations at a bunch of different levels of abstraction,
so it had, I think, a kind of an occupancy grid of some sort at the lowest level.
At the high level, it was abstract, symbolic kind of rooms and connectivity.
So where does Flakey come in?
Yeah, okay. I showed up at SRI and we were building a brand new robot. As I said, none of the people
from the previous project were there or involved anymore, so we were starting from scratch.
My advisor was Stan Rosenstein. He ended up being my thesis advisor. He was motivated by this idea
of situated computation or situated automata. The idea was that the tools of logical reasoning were
important, but possibly only for the engineers or designers to use in the analysis of a system,
but not necessarily to be manipulated in the head of the system itself.
So I might use logic to prove a theorem about the behavior of my robot,
even if the robot's not using logic, and it's had to prove theorems. So that was kind of the
distinction. And so the idea was to kind of use those principles to make a robot do stuff.
But a lot of the basic things we had to kind of learn for ourselves, because I had zero
background in robotics. I didn't know anything about control. I didn't know anything about
sensors. So we reinvented a lot of wheels on the way to getting that robot to do stuff.
Do you think that was an advantage or hindrance?
Oh, no. I'm big in favor of wheel reinvention, actually. I mean, I think you learned a lot
by doing it. It's important though to eventually have the pointers so that you can see what's
really going on. But I think you can appreciate much better the good solutions once you've
messed around a little bit on your own and found a bad one.
Yeah, I think you mentioned reinventing reinforcement learning and referring to
rewards as pleasures, a pleasure, I think, which I think is a nice name for it.
It's more fun, almost. Do you think you could tell the history of AI, machine learning,
reinforcement learning, how you think about it from the 50s to now?
One thing is that it oscillates. So things become fashionable and then they go out and
then something else becomes cool and then it goes out and so on. So there's some interesting
sociological process that actually drives a lot of what's going on. Early days was cybernetics and
control and the idea that of homeostasis, people who made these robots that could,
I don't know, try to plug into the wall when they needed power and then come loose and roll
around and do stuff. And then I think over time, they thought, well, that was inspiring, but people
said, no, no, no, we want to get maybe closer to what feels like real intelligence or human
intelligence. And then maybe the expert systems people tried to do that, but maybe a little
too superficially. So we get this surface understanding of what intelligence is like,
because I understand how a steel mill works and I can try to explain it to you and you can write
it down in logic and then we can make a computer infer that. And then that didn't work out.
But what's interesting, I think, is when a thing starts to not be working very well,
it's not only do we change methods, we change problems. So it's not like we have better ways
of doing the problem of the expert systems people are trying to do. We have no ways of
trying to do that problem. Oh, yeah, no, I think maybe a few. But we kind of give up on that problem
and we switch to a different problem. And we work that for a while and we make progress.
As a broad community. As a community. And there's a lot of people who would argue,
you don't give up on the problem. It's just the decrease in the number of people working on it.
You almost kind of like put it on the shelf. So we'll come back to this 20 years later.
Yeah, I think that's right. Or you might decide that it's malformed. Like you might say,
it's wrong to just try to make something that does superficial symbolic reasoning behave like a
doctor. You can't do that until you've had the sensory motor experience of being a doctor or
something. So there's arguments that say that that problem was not well formed. Or it could be
that it is well formed, but we just weren't approaching it well. So you mentioned that your
favorite part of logic and symbolic systems is that they give short names for large sets.
So there is some use to this. They use symbolic reasoning. So looking at expert systems
and symbolic computing, what do you think are the roadblocks that were hit in the 80s and 90s?
Okay, so right. So the fact that I'm not a fan of expert systems doesn't mean that I'm not a fan
of some kind of symbolic reasoning. So let's see roadblocks. Well, the main roadblock, I think,
was that the idea that humans could articulate their knowledge effectively into some kind of
logical statements. So it's not just the cost, the effort, but really just the capability of
doing it. Right. Because we're all experts in vision, but totally don't have introspective access
into how we do that. Right. And it's true that, I mean, I think the idea was, well, of course,
even people then would know, of course, I wouldn't ask you to please write down the rules that you
use for recognizing a water bottle. That's crazy. And everyone understood that. But we might ask
you to please write down the rules you use for deciding, I don't know what tie to put on or
or how to set up a microphone or something like that. But even those things, I think people maybe,
I think what they found, I'm not sure about this, but I think what they found was that the
so called experts could give explanations that sort of post hoc explanations for how and why
they did things, but they weren't necessarily very good. And then they depended on maybe some
kinds of perceptual things, which again, they couldn't really define very well. So I think,
I think fundamentally, I think that the underlying problem with that was the assumption that people
could articulate how and why they make their decisions. Right. So it's almost encoding the
knowledge from converting from expert to something that a machine can understand and reason with.
No, no, no, not even just encoding, but getting it out of you. Not not not writing it. I mean,
yes, hard also to write it down for the computer. But I don't think that people can
produce it. You can tell me a story about why you do stuff. But I'm not so sure that's the why.
Great. So there are still on the hierarchical planning side,
places where symbolic reasoning is very useful. So as you've talked about, so
where so don't where's the gap? Yeah, okay, good. So saying that humans can't provide a
description of their reasoning processes. That's okay, fine. But that doesn't mean that it's not
good to do reasoning of various styles inside a computer. Those are just two orthogonal points.
So then the question is, what kind of reasoning should you do inside a computer?
Right. And the answer is, I think you need to do all different kinds of reasoning inside
a computer, depending on what kinds of problems you face. I guess the question is, what kind of
things can you encode symbolically so you can reason about? I think the idea about and and
even symbolic, I don't even like that terminology because I don't know what it means technically
and formally. I do believe in abstractions. So abstractions are critical, right? You cannot
reason at completely fine grain about everything in your life, right? You can't make a plan at the
level of images and torques for getting a PhD. So you have to reduce the size of the state space
and you have to reduce the horizon if you're going to reason about getting a PhD or even buying
the ingredients to make dinner. And so how can you reduce the spaces and the horizon of the
reasoning you have to do? And the answer is abstraction, spatial abstraction, temporal
abstraction. I think abstraction along the lines of goals is also interesting, like you might
or well, abstraction and decomposition. Goals is maybe more of a decomposition thing.
So I think that's where these kinds of, if you want to call it symbolic or discrete
models come in. You talk about a room of your house instead of your pose. You talk about
doing something during the afternoon instead of at 2.54. And you do that because it makes
your reasoning problem easier and also because you don't have enough information
to reason in high fidelity about your pose of your elbow at 2.35 this afternoon anyway.
Right. When you're trying to get a PhD.
Right. Or when you're doing anything really.
Yeah, okay. Except for at that moment. At that moment,
you do have to reason about the pose of your elbow, maybe. But then maybe you do that in some
continuous joint space kind of model. And so again, my biggest point about all of this is that
there should be, the dogma is not the thing, right? It shouldn't be that I am in favor
against symbolic reasoning and you're in favor against neural networks. It should be that just
computer science tells us what the right answer to all these questions is if we were smart enough
to figure it out. Yeah. When you try to actually solve the problem with computers, the right answer
comes out. You mentioned abstractions. I mean, neural networks form abstractions or rather,
there's automated ways to form abstractions and there's expert driven ways to form abstractions
and expert human driven ways. And humans just seems to be way better at forming abstractions
currently and certain problems. So when you're referring to 2.45 a.m. versus afternoon,
how do we construct that taxonomy? Is there any room for automated construction of such
abstractions? Oh, I think eventually, yeah. I mean, I think when we get to be better
and machine learning engineers, we'll build algorithms that build awesome abstractions.
That are useful in this kind of way that you're describing. Yeah. So let's then step from
the abstraction discussion and let's talk about BOMMDP's
Partially Observable Markov Decision Processes. So uncertainty. So first, what are Markov Decision
Processes? What are Markov Decision Processes? Maybe how much of our world can be models and
MDPs? How much when you wake up in the morning and you're making breakfast, how do you think
of yourself as an MDP? So how do you think about MDPs and how they relate to our world?
Well, so there's a stance question, right? So a stance is a position that I take with
respect to a problem. So I as a researcher or a person who designed systems can decide to make
a model of the world around me in some terms. So I take this messy world and I say, I'm going to
treat it as if it were a problem of this formal kind, and then I can apply solution concepts
or algorithms or whatever to solve that formal thing, right? So of course, the world is not
anything. It's not an MDP or a POMDP. I don't know what it is, but I can model aspects of it
in some way or some other way. And when I model some aspect of it in a certain way, that gives me
some set of algorithms I can use. You can model the world in all kinds of ways. Some have some
are more accepting of uncertainty, more easily modeling uncertainty of the world. Some really
force the world to be deterministic. And so certainly MDPs model the uncertainty of the world.
Yes. Model some uncertainty. They model not present state uncertainty, but they model uncertainty
in the way the future will unfold. Right. So what are Markov decision processes?
So Markov decision process is a model. It's a kind of a model that you can make that says,
I know completely the current state of my system. And what it means to be a state is that I have
all the information right now that will let me make predictions about the future as well as I
can. So that remembering anything about my history wouldn't make my predictions any better.
But then it also says that then I can take some actions that might change the state of the world
and that I don't have a deterministic model of those changes. I have a probabilistic model
of how the world might change. It's a useful model for some kinds of systems. I mean, it's
certainly not a good model for most problems. I think because for most problems, you don't
actually know the state. For most problems, it's partially observed. So that's now a different
problem class. So okay, that's where the problem depies, the partially observed Markov decision
process step in. So how do they address the fact that you can't observe most the incomplete
information about most of the world around you? Right. So now the idea is we still kind of postulate
that there exists a state. We think that there is some information about the world out there
such that if we knew that we could make good predictions, but we don't know the state.
And so then we have to think about how, but we do get observations. Maybe I get images or I hear
things or I feel things and those might be local or noisy. And so therefore they don't tell me
everything about what's going on. And then I have to reason about given the history of actions
I've taken and observations I've gotten, what do I think is going on in the world? And then
given my own kind of uncertainty about what's going on in the world, I can decide what actions to
take. And so difficult is this problem of planning under uncertainty in your view and your
long experience of modeling the world, trying to deal with this uncertainty in
especially in real world systems. Optimal planning for even discrete POMDPs can be
undecidable depending on how you set it up. And so lots of people say I don't use POMDPs
because they are intractable. And I think that that's a kind of a very funny thing to say because
the problem you have to solve is the problem you have to solve. So if the problem you have to
solve is intractable, that's what makes us AI people, right? So we solve, we understand that
the problem we're solving is wildly intractable that we will never be able to solve it optimally,
at least I don't. Yeah, right. So later we can come back to an idea about bounded optimality
and something. But anyway, we can't come up with optimal solutions to these problems.
So we have to make approximations. Approximations in modeling approximations in solution algorithms
and so on. And so I don't have a problem with saying, yeah, my problem actually it is POMDP in
continuous space with continuous observations. And it's so computationally complex. I can't
even think about it's, you know, big O whatever. But that doesn't prevent me from it helps me
gives me some clarity to think about it that way. And to then take steps to make approximation
after approximation to get down to something that's like computable in some reasonable time.
When you think about optimality, you know, the community broadly has shifted on that, I think,
a little bit in how much they value the idea of optimality of chasing an optimal solution.
How is your views of chasing an optimal solution changed over the years when you work with robots?
That's interesting. I think we have a little bit of a methodological crisis, actually,
from the theoretical side. I mean, I do think that theory is important and that right now we're not
doing much of it. So there's lots of empirical hacking around and training this and doing that
and reporting numbers. But is it good? Is it bad? We don't know. It's very hard to say things.
And if you look at like computer science theory, so people talked for a while,
everyone was about solving problems optimally or completely. And then there were interesting
relaxations. So people look at, oh, can I, are there regret bounds? Or can I do some kind of,
you know, approximation? Can I prove something that I can approximately solve this problem or
that I get closer to the solution as I spend more time and so on? What's interesting, I think,
is that we don't have good approximate solution concepts for very difficult problems. Right?
I like to, you know, I like to say that I'm interested in doing a very bad job of very big
problems. Right. So very bad job, very big problems. I like to do that. But I wish I could say
something. I wish I had a, I don't know, some kind of a formal solution concept
that I could use to say, oh, this algorithm actually, it gives me something. Like, I know
what I'm going to get. I can do something other than just run it and get out. So that notion
is still somewhere deeply compelling to you. The notion that you can say, you can drop
thing on the table says this, you can expect this, this algorithm will give me some good results.
I hope there's, I hope science will, I mean, there's engineering and there's science,
I think that they're not exactly the same. And I think right now we're making huge engineering
like leaps and bounds. So the engineering is running away ahead of the science, which is cool.
And often how it goes, right? So we're making things and nobody knows how and why they work,
roughly. But we need to turn that into science. There's some form. It's, yeah,
there's some room for formalizing. We need to know what the principles are. Why does this work?
Why does that not work? I mean, for while people build bridges by trying, but now we can often
predict whether it's going to work or not without building it. Can we do that for learning systems
or for robots? See, your hope is from a materialistic perspective that intelligence,
artificial intelligence systems, robots are kind of just fancier bridges.
Belief space. What's the difference between belief space and state space? So we mentioned
MDPs, FOMDPs, you reasoning about, you sense the world, there's a state. What's this belief
space idea? Yeah. Okay, that sounds good. It sounds good. So belief space, that is, instead of
thinking about what's the state of the world and trying to control that as a robot, I think about
what is the space of beliefs that I could have about the world? What's, if I think of a belief
as a probability distribution of the ways the world could be, a belief state is a distribution,
and then my control problem, if I'm reasoning about how to move through a world I'm uncertain about,
my control problem is actually the problem of controlling my beliefs. So I think about taking
actions, not just what effect they'll have on the world outside, but what effect they'll have on my
own understanding of the world outside. And so that might compel me to ask a question or look
somewhere to gather information, which may not really change the world state, but it changes
my own belief about the world. That's a powerful way to empower the agent to reason about the
world, to explore the world. What kind of problems does it allow you to solve to
consider belief space versus just state space? Well, any problem that requires deliberate
information gathering. So if in some problems, like chess, there's no uncertainty, or maybe
there's uncertainty about the opponent. There's no uncertainty about the state.
And some problems, there's uncertainty, but you gather information as you go. You might say,
oh, I'm driving my autonomous car down the road, and it doesn't know perfectly where it is, but
the LiDARs are all going all the time. So I don't have to think about whether to gather information.
But if you're a human driving down the road, you sometimes look over your shoulder to see what's
going on behind you in the lane. And you have to decide whether you should do that now. And you
have to trade off the fact that you're not seeing in front of you, and you're looking behind you,
and how valuable is that information, and so on. And so to make choices about information
gathering, you have to reason in belief space. Also to just take into account your own uncertainty
before trying to do things. So you might say, if I understand where I'm standing relative to the
door jam, pretty accurately, then it's okay for me to go through the door. But if I'm really not
sure where the door is, then it might be better to not do that right now. The degree of your
uncertainty about the world is actually part of the thing you're trying to optimize in forming the
plan, right? So this idea of a long horizon of planning for a PhD or just even how to get out
of the house or how to make breakfast, you show this presentation of the WTF, where's the fork
of a robot looking to sink. And can you describe how we plan in this world is this idea of hierarchical
planning we've mentioned? Yeah, how can a robot hope to plan about something with such a long
horizon where the goal is quite far away? People since probably reasoning began have thought about
hierarchical reasoning, the temporal hierarchy in particular. Well, there's spatial hierarchy,
but let's talk about temporal hierarchy. So you might say, oh, I have this long
execution I have to do, but I can divide it into some segments abstractly, right? So maybe
have to get out of the house, I have to get in the car, I have to drive, and so on. And so
you can plan if you can build abstractions. So this we started out by talking about abstractions,
and we're back to that now. If you can build abstractions in your state space,
and abstractions, sort of temporal abstractions, then you can make plans at a high level. And you
can say, I'm going to go to town, and then I'll have to get gas, and I can go here, and I can do
this other thing. And you can reason about the dependencies and constraints among these actions,
again, without thinking about the complete details. What we do in our hierarchical planning work is
then say, all right, I make a plan at a high level of abstraction. I have to have some
reason to think that it's feasible without working it out in complete detail. And that's
actually the interesting step. I always like to talk about walking through an airport, like
you can plan to go to New York and arrive at the airport, and then find yourself in an office
building later. You can't even tell me in advance what your plan is for walking through the airport,
partly because you're too lazy to think about it maybe, but partly also because you just don't
have the information. You don't know what gate you're landing in or what people are going to be
in front of you or anything. So there's no point in planning in detail. But you have to have,
you have to make a leap of faith that you can figure it out once you get there. And it's really
interesting to me how you arrive at that. How do you, so you have learned over your lifetime to be
able to make some kinds of predictions about how hard it is to achieve some kinds of sub goals.
And that's critical. Like you would never plan to fly somewhere if you couldn't,
didn't have a model of how hard it was to do some of the intermediate steps.
So one of the things we're thinking about now is how do you do this kind of very aggressive
generalization to situations that you haven't been in and so on to predict how long will it
take to walk through the Kuala Lumpur airport? Like you could give me an estimate and it wouldn't
be crazy. And you have to have an estimate of that in order to make plans that involve
walking through the Kuala Lumpur airport, even if you don't need to know it in detail.
So I'm really interested in these kinds of abstract models and how do we acquire them.
But once we have them, we can use them to do hierarchical reasoning, which I think is very
important. Yeah, there's this notion of goal regression and preimage backchaining.
This idea of starting at the goal and just forming these big clouds of states. I mean,
it's almost like saying to the airport, you know, you know, once you show up to the airport,
you're like a few steps away from the goal. So thinking of it this way is kind of interesting.
I don't know if you have further comments on that of starting at the goal. Yeah, I mean,
it's interesting that Herb Simon back in the early days of AI talked a lot about
means ends reasoning and reasoning back from the goal. There's a kind of an intuition that people
have that the number of the state space is big, the number of actions you could take is really big.
So if you say, here I sit and I want to search forward from where I am, what are all the things
I could do? That's just overwhelming. If you say, if you can reason at this other level and say,
here's what I'm hoping to achieve, what can I do to make that true that somehow the
branching is smaller? Now, what's interesting is that like in the AI planning community,
that hasn't worked out in the class of problems that they look at and the methods that they tend
to use, it hasn't turned out that it's better to go backward. It's still kind of my intuition
that it is, but I can't prove that to you right now. Right. I share your intuition, at least for us
mirror humans. Speaking of which, when you maybe now we take it and take a little step into that
philosophy circle, how hard would it, when you think about human life, you give those examples
often, how hard do you think it is to formulate human life as a planning problem or aspects of
human life? So when you look at robots, you're often trying to think about object manipulation,
tasks about moving a thing. When you take a slight step outside the room, let the robot
leave and he'll get lunch or maybe try to pursue more fuzzy goals. How hard do you think is that
problem? If you were to try to maybe put another way, try to formulate human life as a planning
problem. Well, that would be a mistake. I mean, it's not all a planning problem, right? I think
it's really, really important that we understand that you have to put together pieces and parts
that have different styles of reasoning and representation and learning. I think it seems
probably clear to anybody that it can't all be this or all be that. Brains aren't all like this
or all like that, right? They have different pieces and parts and substructure and so on.
So I don't think that there's any good reason to think that there's going to be like one true
algorithmic thing that's going to do the whole job. Just a bunch of pieces together,
design to solve a bunch of specific problems. Or maybe styles of problems. I mean,
there's probably some reasoning that needs to go on in image space. I think, again,
there's this model base versus model free idea, right? So in reinforcement learning,
people talk about, oh, should I learn? I could learn a policy just straight up a way of behaving.
I could learn it's popular in a value function. That's some kind of weird intermediate ground.
Or I could learn a transition model, which tells me something about the dynamics of the world.
If I take a, imagine that I learn a transition model and I couple it with a planner and I
draw a box around that, I have a policy again. It's just stored a different way, right?
But it's just as much of a policy as the other policy. It's just I've made, I think,
the way I see it is it's a time space trade off in computation, right? A more overt policy
representation. Maybe it takes more space, but maybe I can compute quickly what action I should
take. On the other hand, maybe a very compact model of the world dynamics plus a planner
lets me compute what action to take to just more slowly. There's no, I mean, I don't think,
there's no argument to be had. It's just like a question of what form of computation is best
for us. For the various sub problems. Right. So, and so like learning to do algebra manipulations
for some reason is, I mean, that's probably going to want naturally a sort of a different
representation than riding a unicycle. At the time constraints on the unicycle are serious.
The space is maybe smaller. I don't know. But so I could be the more human size of
falling in love, having a relationship that might be another another style of no idea how to model
that. Yeah, that's, that's first solve the algebra and the object manipulation. What do you think
is harder perception or planning perception? That's why I'm understanding that's why.
So what do you think is so hard about perception by understanding the world around you?
Well, I mean, I think the big question is representational. A hugely the question is
representation. So perception has made great strides lately, right? And we can classify images and we
can play certain kinds of games and predict how to steer the car and all this sort of stuff.
I don't think we have a very good idea of what perception should deliver, right? So if you
if you believe in modularity, okay, there's there's a very strong view which says
we shouldn't build in any modularity, we should make a giant gigantic neural network,
train it end to end to do the thing. And that's the best way forward.
And it's hard to argue with that except on a sample complexity basis, right? So you might say,
oh, well, if I want to do end to end reinforcement learning on this giant giant neural network,
it's going to take a lot of data and a lot of like broken robots and stuff. So
then the only answer is to say, okay, we have to build something in build in some structure
or some bias, we know from theory of machine learning, the only way to cut down the sample
complexity is to kind of cut down somehow cut down the hypothesis space, you can do that by
building in bias. There's all kinds of reason to think that nature built bias into humans.
Convolution is a bias, right? It's a very strong bias and it's a very critical bias.
So my view is that we should look for more things that are like convolution, but that address other
aspects of reasoning, right? So convolution helps us a lot with a certain kind of spatial
reasoning that's quite close to the imaging. I think there's other ideas like that,
maybe some amount of forward search, maybe some notions of abstraction, maybe the notion that
objects exist, actually, I think that's pretty important. And a lot of people won't give you
that to start with, right? So almost like a convolution in the
in the object semantic object space or some kind of some kind of ideas in there. That's right.
And people are like the graph, graph convolutions are an idea that are related to
relational representations. And so I think there are, so you, I've come far field from perception,
but I think, I think the thing that's going to make perception that kind of the next step is
actually understanding better what it should produce, right? So what are we going to do with
the output of it, right? It's fine when what we're going to do with the output is steer,
it's less clear when we're just trying to make one integrated intelligent agent,
what should the output of perception be? We have no idea. And how should that hook up to the other
stuff? We don't know. So I think the pressing question is, what kinds of structure can we
build in that are like the moral equivalent of convolution that will make a really awesome
superstructure that then learning can kind of progress on efficiently?
I agree. Very compelling description of actually where we stand with the perception from
you're teaching a course on embodying intelligence. What do you think it takes to
build a robot with human level intelligence? I don't know if we knew we would do it.
If you were to, I mean, okay, so do you think a robot needs to have a self awareness,
consciousness, fear of mortality? Or is it, is it simpler than that? Or is consciousness a simple
thing? Do you think about these notions? I don't think much about consciousness. Even most philosophers
who care about it will give you that you could have robots that are zombies, right, that behave
like humans but are not conscious. And I, at this moment, would be happy enough for that. So I'm not
really worried one way or the other. So the technical side, you're not thinking of the use of self
awareness? Well, but I, okay, but then what does self awareness mean? I mean, that you need to have
some part of the system that can observe other parts of the system and tell whether they're
working well or not. That seems critical. So does that count as, I mean, does that count as
self awareness or not? Well, it depends on whether you think that there's somebody at home who can
articulate whether they're self aware. But clearly, if I have like, you know, some piece of code
that's counting how many times this procedure gets executed, that's a kind of self awareness,
right? So there's a big spectrum. It's clear you have to have some of it.
Right. You know, we're quite far away on many dimensions, but is the direction of research
that's most compelling to you for, you know, trying to achieve human level intelligence
in our robots? Well, to me, I guess the thing that seems most compelling to me at the moment is this
question of what to build in and what to learn. I think we're, we don't, we're missing a bunch of
ideas. And, and we, you know, people, you know, don't you dare ask me how many years it's going
to be until that happens, because I won't even participate in the conversation. Because I think
we're missing ideas and I don't know how long it's going to take to find them. So I won't ask you
how many years, but maybe I'll ask you what it, when you will be sufficiently impressed that we've
achieved it. So what's a good test of intelligence? Do you like the Turing test and natural language
in the robotic space? Is there something where you would sit back and think, oh, that's pretty
impressive as a test, as a benchmark. Do you think about these kinds of problems?
No, I resist. I mean, I think all the time that we spend arguing about those kinds of things could
be better spent just making their robots work better. So you don't value competition. So I mean,
there's a nature of benchmark, benchmarks and data sets, or Turing test challenges, where
everybody kind of gets together and tries to build a better robot because they want to outcompete
each other, like the DARPA challenge with the autonomous vehicles. Do you see the value of that?
Or can get in the way? I think you can get in the way. I mean, some people, many people find it
motivating. And so that's good. I find it anti motivating personally. But I think you get an
interesting cycle where for a contest, a bunch of smart people get super motivated and they hack
their brains out. And much of what gets done is just hacks, but sometimes really cool ideas emerge.
And then that gives us something to chew on after that. So it's not a thing for me, but I don't
I don't regret that other people do it. Yeah, it's like you said, with everything else that
makes us good. So jumping topics a little bit, you started the journal machine learning research
and served as its editor in chief. How did the publication come about?
And what do you think about the current publishing model space in machine learning
artificial intelligence? Okay, good. So it came about because there was a journal called machine
learning, which still exists, which was owned by Clure. And there was I was on the editorial
board and we used to have these meetings annually where we would complain to Clure that
it was too expensive for the libraries and that people couldn't publish. And we would really
like to have some kind of relief on those fronts. And they would always sympathize,
but not do anything. So we just decided to make a new journal. And there was the Journal of AI
Research, which has was on the same model, which had been in existence for maybe five years or so,
and it was going on pretty well. So we just made a new journal. It wasn't I mean,
I don't know, I guess it was work, but it wasn't that hard. So basically the editorial board,
probably 75% of the editorial board of machine learning resigned. And we founded the new journal.
But it was sort of it was more open. Yeah, right. So it's completely open. It's open access.
Actually, I had a postdoc, George Conrad Harris, who wanted to call these journals free for all.
Because there were I mean, it both has no page charges and has no
access restrictions. And the reason and so lots of people, I mean, for there were,
there were people who are mad about the existence of this journal who thought it was a fraud or
something, it would be impossible, they said, to run a journal like this with basically,
I mean, for a long time, I didn't even have a bank account. I paid for the
lawyer to incorporate and the IP address. And it just didn't cost a couple hundred dollars a year
to run. It's a little bit more now, but not that much more. But that's because I think computer
scientists are competent and autonomous in a way that many scientists in other fields aren't.
I mean, at doing these kinds of things, we already type set around papers,
we all have students and people who can hack a website together in the afternoon.
So the infrastructure for us was like, not a problem, but for other people in other fields,
it's a harder thing to do. Yeah. And this kind of open access journal is nevertheless,
one of the most prestigious journals. So it's not like a prestige and it can be achieved
without any of the papers. Paper is not required for prestige, turns out. Yeah.
So on the review process side of actually a long time ago, I don't remember when I reviewed a paper
where you were also a reviewer and I remember reading your review and being influenced by it.
It was really well written. It influenced how I write feature reviews. You disagreed with me,
actually. And you made it my review, but much better. But nevertheless, the review process
has its flaws. And what do you think works well? How can it be improved?
So actually, when I started JMLR, I wanted to do something completely different.
And I didn't because it felt like we needed a traditional journal of record and so we just
made JMLR be almost like a normal journal, except for the open access parts of it, basically.
Increasingly, of course, publication is not even a sensible word. You can publish something by
putting it in an archive so I can publish everything tomorrow. So making stuff public is
there's no barrier. We still need curation and evaluation. I don't have time to read all of
archive. And you could argue that kind of social thumbs uping of articles suffices, right? You
might say, oh, heck with this, we don't need journals at all. We'll put everything on archive
and people will upvote and downvote the articles and then your CV will say, oh, man, he got a lot
of upvotes. So that's good. But I think there's still value in careful reading and commentary of
things. And it's hard to tell when people are upvoting and downvoting or arguing about your
paper on Twitter and Reddit, whether they know what they're talking about. So then I have the
second order problem of trying to decide whose opinions I should value and such. So I don't
know. If I had infinite time, which I don't, and I'm not going to do this because I really want to
make robots work, but if I felt inclined to do something more in a publication direction,
I would do this other thing, which I thought about doing the first time, which is to get
together some set of people whose opinions I value and who are pretty articulate. And I guess we
would be public, although we could be private, I'm not sure. And we would review papers. We wouldn't
publish them and you wouldn't submit them. We would just find papers and we would write reviews
and we would make those reviews public. And maybe if you, you know, so we're Leslie's friends who
review papers and maybe eventually if we, our opinion was sufficiently valued, like the opinion
of JMLR is valued, then you'd say on your CV that Leslie's friends gave my paper a five star reading
and that would be just as good as saying I got it accepted into this journal. So I think we
should have good public commentary and organize it in some way, but I don't really know how to
do it. It's interesting times. The way you describe it actually is really interesting. I mean,
we do it for movies, IMDB.com. There's experts, critics come in, they write reviews, but there's
also regular non critics humans write reviews and they're separated. I like open review.
The eye clear process, I think is interesting. It's a step in the right direction, but it's still
not as compelling as reviewing movies or video games. I mean, it sometimes almost, it might be
silly, at least from my perspective to say, but it boils down to the user interface, how fun and
easy it is to actually perform the reviews, how efficient, how much you as a reviewer get
street cred for being a good reviewer. Those human elements come into play.
No, it's a big investment to do a good review of a paper and the flood of papers is out of control.
There aren't 3,000 new, I don't know how many new movies are there in a year, I don't know,
but there's probably going to be less than how many machine learning papers there are in a year now.
Right, so I'm like an old person, so of course I'm going to say,
things are moving too fast, I'm a stick in the mud. So I can say that, but my particular flavor
of that is, I think the horizon for researchers has gotten very short, that students want to
publish a lot of papers and it's exciting and there's value in that and you get padded on the
head for it and so on. And some of that is fine, but I'm worried that we're driving out people who
would spend two years thinking about something. Back in my day, when we worked on our theses,
we did not publish papers, you did your thesis for years, you picked a hard problem and then you
worked and chewed on it and did stuff and wasted time and for a long time. And when it was roughly,
when it was done, you would write papers. And so I don't know how to, and I don't think that
everybody has to work in that mode, but I think there's some problems that are hard enough
that it's important to have a longer research horizon and I'm worried that
we don't incentivize that at all at this point. In this current structure. So what do you see
what are your hopes and fears about the future of AI and continuing on this theme? So AI has
gone through a few winters, ups and downs. Do you see another winter of AI coming?
Or are you more hopeful about making robots work, as you said? I think the cycles are inevitable,
but I think each time we get higher, right? I mean, it's like climbing some kind of
landscape with a noisy optimizer. So it's clear that the deep learning stuff has
made deep and important improvements. And so the high watermark is now higher. There's no question.
But of course, I think people are overselling and eventually investors, I guess, and other people
look around and say, well, you're not quite delivering on this grand claim and that wild
hypothesis. It's like probably it's going to crash something out and then it's okay. I mean,
it's okay. I mean, but I don't I can't imagine that there's like some awesome monotonic improvement
from here to human level AI. So in, you know, I have to ask this question, I probably anticipate
answers, the answers. But do you have a worry short term, a long term about the existential
threats of AI and maybe short term, less existential, but more robots taking away jobs?
Well, actually, let me talk a little bit about utility. Actually, I had an interesting conversation
with some military ethicists who wanted to talk to me about autonomous weapons.
And they're, they were interesting, smart, well educated guys who didn't know too much about AI or
machine learning. And the first question they asked me was, has your robot ever done something you
didn't expect? And I like burst out laughing because anybody who's ever done something other robot
right knows that they don't do much. And what I realized was that their model of how we program
a robot was completely wrong. Their model of how we could put program robot was like,
program robot was like, Lego Mindstorms, like, Oh, go forward a meter, turn left, take a picture,
do this, do that. And so if you have that model of programming, then it's true, it's kind of weird
that your robot would do something that you didn't anticipate. But the fact is, and actually,
so now this is my new educational mission, if I have to talk to non experts, I try to teach them
the idea that we don't operate, we operate at least one or maybe many levels of abstraction
about that. And we say, Oh, here's a hypothesis class, maybe it's a space of plans, or maybe it's a
space of classifiers, or whatever. But there's some set of answers and an objective function. And
then we work on some optimization method that tries to optimize a solution in that class.
And we don't know what solution is going to come out. Right. So I think it's important to
communicate that. So I mean, of course, probably people who listen to this, they know that lesson.
But I think it's really critical to communicate that lesson. And then lots of people are now
talking about, you know, the value alignment problem. So you want to be sure, as robots or
software systems get more competent, that their objectives are aligned with your objectives,
or that our objectives are compatible in some way, or we have a good way of mediating when they have
different objectives. And so I think it is important to start thinking in terms, like,
you don't have to be freaked out by the robot apocalypse to accept that it's important to think
about objective functions of value alignment. And that you have to really, everyone who's done
optimization knows that you have to be careful what you wish for that, you know, sometimes you get
the optimal solution. And you realize, man, that was that objective was wrong. So pragmatically,
in the shortest term, it seems to me that that those are really interesting and critical questions.
And the idea that we're going to go from being people who engineer algorithms to being people
who engineer objective functions, I think that's, that's definitely going to happen. And that's
going to change our thinking and methodology and stuff.
We're going to, you started at Stanford philosophy, that's wish you could be science,
and I will go back to philosophy maybe. Well, I mean, they're mixed together because, because,
as we also know, as machine learning people, right? When you design, in fact, this is the
lecture I gave in class today, when you design an objective function, you have to wear both hats.
There's the hat that says, what do I want? And there's the hat that says, but I know what my
optimizer can do to some degree. And I have to take that into account. So it's, it's always a
trade off. And we have to kind of be mindful of that. The part about taking people's jobs,
that I understand that that's important, I don't understand sociology or economics or people
very well. So I don't know how to think about that. So that's, yeah, so there might be a
sociological aspect there, the economic aspect that's very difficult to think about. Okay.
I mean, I think other people should be thinking about it, but I'm just, that's not my strength.
So what do you think is the most exciting area of research in the short term,
for the community and for your, for yourself? Well, so, I mean, there's this story I've been
telling about how to engineer intelligent robots. So that's what we want to do. We all kind of want
to do, well, I mean, some set of us want to do this. And the question is, what's the most effective
strategy? And we've tried, and there's a bunch of different things you could do at the extremes,
right? One super extreme is we do introspection and we write a program. Okay, that has not worked
out very well. Another extreme is we take a giant bunch of neural guru and we try and train it up to
do something. I don't think that's going to work either. So the question is, what's the middle
ground? And again, this isn't a theological question or anything like that. It's just,
like, how do, just how do we, what's the best way to make this work out? And I think it's clear,
it's a combination of learning, to me, it's clear, it's a combination of learning and not learning.
And what should that combination be? And what's the stuff we build in? So to me,
that's the most compelling question. And when you say engineer robots, you mean
engineering systems that work in the real world. That's the emphasis.
Last question, which robots or robot is your favorite from science fiction?
So you can go with Star Wars or RTD2, or you can go with more modern, maybe Hal.
No, sir, I don't think I have a favorite robot from science fiction.
This is, this is back to, you like to make robots work in the real world here, not, not in.
I mean, I love the process. And I care more about the process.
The engineering process.
Yeah. I mean, I do research because it's fun, not because I care about what we produce.
Well, that's, that's a beautiful note, actually. And Leslie, thank you so much for talking today.
Sure, it's been fun.