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 a Master's 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 for your bachelor's in Stanford in 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 submajor 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 class, 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. And 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, they're mathematical and philosophical
because we don't know so much of how difficult it is, how difficult is the 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. So 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, which, so you mentioned
Flakey or Shaky Flakey, and what was the robot that first captured your imagination of what's
possible? Right. Well, so the first robot I worked with was Flakey. 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, and really, everybody should
read the Shaky Tech Report because it has so many good ideas in it. I mean, they invented A Star Search
and symbolic planning and learning macro operators. They had low-level kind of configuration
space planning for the robot. They had vision. They had all this, the basic ideas of a ton of
things. Shaky was a mobile robot, but it could push objects, and so it would move things around.
With which actuator? With itself, with its base. So it could, and 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. I mean, it really had the basics of so many of the things
that we think about now. How did it represent the space around it? So 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 flaky come in? Yeah, okay. So 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 kind of there or involved
anymore. So we were kind of starting from scratch. And my advisor was Stan Rosenstein. He ended up
being my thesis advisor. And he was motivated by this idea of situated computation or situated
automata. And 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 in its head to prove
theorem. So that was kind of the distinction. And so the idea was to kind of use those principles to
make your 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 a hindrance? Oh, no. I mean, 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. Yeah. Or 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's
oscillates, right? So things become fashionable, and then they go out, and then something else
becomes cool, and then it goes out and so on. And I think there's some interesting sociological
process that actually drives a lot of what's going on. Early days was kind of cybernetics and
control, right? 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,
right? So, oh, 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, right? So it's not like we have better ways of doing the problem of the
expert systems people were 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, we worked 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, 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
to some 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 kinds 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, not even just encoding, but getting it out of you. 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 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. You cannot reason at
completely fine grain about everything in your life. 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... 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 254.
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
235 this afternoon anyway. Right. When you're trying to get a PhD.
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.
My biggest point about all of this is that the dogma is not the thing. 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. Well, 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 245 p.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 will 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 bomb MDPs, 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 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 could 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 think 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 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 special 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 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. So 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 computeable
in some reasonable time. When you think about optimality, you know, the community broadly
is shifted on that, I think, a little bit in how much they value the idea of optimality,
of chasing an optimal solution. How is the use 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,
right? 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, 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 that notion
is still somewhere deeply compelling to you. The notion that you can say you can drop
a 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, uh, 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 a while, people built 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, that sounds so 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 our 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
and believe space. Also, I mean, 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? There's this idea of hierarchical planning we've mentioned. So 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 you 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 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 then 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. 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 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. 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. You would never plan to fly somewhere if you 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
pre-image backchaining. This idea of starting at the goal and just forming these big clouds
of states, it's almost like saying to the airport, 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 states base 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
mere humans. Speaking of which, when you maybe, now we 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
go 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 mean,
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.
So it's a bunch of pieces together designed to solve a bunch of specific problems. One specific...
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-based 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 too 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. And so 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... And there could be the more human sides of falling in love,
having a relationship. That might be another style of... I have no idea.
How to model that, yeah. Let's first solve the algebra and the object manipulation.
Yeah. So what do you think is harder? Perception or planning?
Perception. That's why I'm not planning. That's why.
So what do you think is so hard about perception by understanding the world around you?
Well, I mean, I think that the question
is representational. Hugely, the question is representational. 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
believe in modularity... Okay, 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 starting 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 afield from perception, but 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
a 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
problem. You're teaching a course on embodied 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? Like, 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 with 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 kind of 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, I mean, 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, what's a good test of intelligence? Do you like the touring test and
natural language and the robotic space? Is there something where you would sit back and think,
oh, that's, 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 touring test
challenges, where everybody kind of gets together and tries to build a better robot because they
want to compete with 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 what, I mean, 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 I'm, 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,
the mix is 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, they,
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 Cunadaris, 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 were 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.
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 the 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 JMR 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,
you know, 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 at 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 that's probably going to be less than how many machine learning papers are
in a year now. I'm worried, I'm like an old person, so of course I'm going to say,
rah, rah, rah, things are moving too fast, I'm a stick in the mud. 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. It's exciting and there's value
in that and you get patted on the head for it and so on. 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 theses for
years. You picked a hard problem and then you worked and chewed on it and did stuff and wasted
time for a long time. Roughly when it was done, you would write papers. 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, what are your hopes and fears about
the future of AI and continuing on this theme? AI has gone through a few winters, ups and downs.
Do you see another winter of AI coming? 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. It's like climbing
some kind of landscape with a noisy optimizer. It's clear that the deep learning stuff has
made deep and important improvements. The high watermark is now higher. There's no question,
but of course, I think people are overselling and eventually investors and other people look
around and say, well, you're not quite delivering on this grand claim and that wild hypothesis,
instead of probably it's going to crash something out and then it's okay. I can't imagine that
there's some awesome monotonic improvement from here to human-level AI.
I have to ask this question. I probably anticipate the answers, but do you have a
worry short-term or long-term about the existential threats of AI and maybe short-term less existential
but more robots taking away jobs? 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. They were interesting, smart, well-educated guys who didn't know too
much about AI or machine learning. The first question they asked me was, has your robot ever
done something you didn't expect? I burst out laughing because anybody who's ever done something
other robot knows that they don't do much. What I realized was that their model of how we program
a robot was completely wrong. Their model of how we program a robot was like Lego Mindstorms,
like, oh, go forward a meter, turn left, take a picture, do this, do that. 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 operate
at least one or maybe many levels of abstraction about that. 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. 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 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, 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. Sometimes you get the optimal solution and you realize, man, that objective
was wrong. So pragmatically, in the shortish term, it seems to me 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 where she 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,
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 said 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 a, that's a beautiful note, actually. And Leslie, thank you so much for talking today.
Sure. It's been fun.