The following is a conversation with Leslie Kaelbling. 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 IJCAI Computers and Thought Award and was the editor in chief

of the prestigious Journal of 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 Kaelbling.

What made me get excited about AI, I can say that, is I read Gödel Escher Bach 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...

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 to your bachelor's in Stanford in philosophy,

did master's and 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 the 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 a 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 it 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, 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

to 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 that's, you know,

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. You know, the expanse of knowledge that's required to operate in a human world,

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, required 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, so you mentioned Flaky or Shaky Flaky,

and what was the robot that first captured your imagination, what's possible?

Right. Well, so 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 and really everybody should read the

Shaky Tech Report because it has so many good ideas in it. I mean, they invented ASTAR search

and symbolic planning and learning macro operators. They had low level kind of

configuration space planning for their robot. They had vision. That's the basic ideas of

a ton of things. Can you take a step back? Shaky have arms. What was the job? 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. Okay, great. 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 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 a hindrance? Oh no, I mean, I'm big in favor of wheel reinvention actually. I mean,

I think you learn a lot by doing it. It's important though to eventually have the pointers

to, 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, pleasure. Yeah. Or I think, which I think is a nice name for it.

Yeah. It's more fun almost. Do you think you could tell the history of AI machine learning

reinforcement learning and how you think about it from the fifties 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 that goes out and so on. And I think there's, so 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,

right? People have 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, the

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 the 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 and for 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 switched to a different problem and we worked that for a while

and we make progress. As a broad community. As a community, yeah. And there's a lot of people who

would argue, you don't give up on the problem, it's just you decrease the number of people working

on it. You almost kind of like put it on the shelf, say, 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?

Ah, 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, right? So let's see, roadblocks. Well, the main road

block, 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, right? 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 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 fundamentally, I think 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 could understand and reason with.

No, no, no, no, not even just encoding, but getting it out of you.

Right. 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'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, 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, 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

and you do that because it makes your reasoning problem easier. And also because

you have, 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.

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 you, maybe you do that in some continuous joint space kind of model.

And so again, I, my biggest point about all of this is that there should be the dogma is not

the thing, right? We shouldn't, it shouldn't be that I'm in favor against symbolic reasoning

and you're in favor against neural networks. It should be that just, 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. But you mentioned abstractions. I mean, neural networks form abstractions or rather

there's, there's automated ways to form abstractions and there's expert driven ways to

form abstractions and expert human driven ways. And humans just seem to be way better at forming

abstractions currently and certain problems. So when you're referring to 2.45 PM 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, the abstraction discussion and let's talk about POMM MDPs. Partially observable

Markov decision processes. So uncertainty. So first, what are Markov decision processes?

What are Markov decision processes? And 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 designs 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

POMM DP. 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, some are, 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, that all the, 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.

And, 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, it's a useful model for some kinds of systems.

I think it's a, 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 you, it's partially observed. So

that's now a different problem class. So, okay. That's where the POMDPs, the part that we observe

with the Markov decision processes 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 how 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 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 can't, 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 the 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, 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 has 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, are there regret bounds or can I do some kind of 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. 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, 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 a, 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? So your hope is from a materialistic

perspective that intelligence, artificial intelligence systems, robots are just fancier

bridges. Belief space. What's the difference between belief space and state space? So you

mentioned MDPs, FOMDPs, reasoning about, you sense the world, there's a state.

Uh, what, what's this belief space idea? 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, to empower the agent, to reason about the world, to explore the world. So what kind of

problems does it allow you to solve to, to consider belief space versus just state space?

Well, any problem that requires deliberate information gathering, right? 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, right? You might say, Oh, I'm driving my autonomous car down

the road and it doesn't know perfectly where it is, but the light hours 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 reasonably

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 robot looking at a sink. And can you describe how we plan in this world

of 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 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 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, like you can plan to go to New York and arrive at the airport, and then

find yourself 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 is, 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, once you show up to the airport

that you're like a few steps away from the goal. So like thinking of it this way, it's kind of

interesting. I don't know if you have sort of further comments on that of starting at the goal.

Yeah. I mean, it's interesting that Simon, 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 that 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 could 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 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. 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

on 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 it, imagine that I learned

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 don't, 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, and so like

learning to do algebra manipulations for some reason is, I mean, that's probably gonna want

naturally a sort of a different representation than writing 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 how to

model that. Yeah. Let's first solve the algebra and the object manipulation. What do you think

is harder perception or planning perception? That's why 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. 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. Um, 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 reasons to think that nature built bias into humans. Um, convolution is a bias, right? It's a

very strong bias and it's a very critical bias. So my own view is that we should look for more

things that are like convolution, but the 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, uh,

uh, 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 relation, relational representations. And so, so I think there are, so you, I've come

I've come far field from perception, but I think, um, 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 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 in many dimensions, but is there a 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. Um, 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, uh, maybe I'll ask you what it, when you'll be sufficiently impressed that we've

achieved it. So what's, what's a good test of intelligence? Do you like the Turing test, the

natural language in the robotic space? Is there something where you would sit back and think,

Oh, that's, that's pretty impressive. Uh, 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 the robots work better. Uh, so you don't value

competition. So, I mean, there's a nature of benchmark benchmarks and datasets or Turing

test challenges where everybody kind of gets together and tries to build a better robot

cause they want to out compete each other. Like the DARPA challenge with the autonomous vehicles.

Do you see the value of that or it can get in the way? I think it 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. Uh, 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 that it makes us good. So jumping

topics a little bit, you started the journal of machine learning research and served as its editor

in chief. Uh, 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

Cluer and there was, I was on the editorial board and we used to have these meetings annually where

we would complain to Cluer 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, uh, we just decided to make a new journal and, uh,

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, uh, we just made a new journal.

It wasn't, I mean, um, 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, uh, 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, uh, uh, I had a postdoc, George Conidaris who wanted to call

these journals free for all, uh, because there were, I mean, it both has no page charges and has

no, uh, uh, access restrictions. And the reason, and so lots of people, I mean, 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. Uh, I paid for the lawyer to incorporate and the

IP address and it just did cost a couple of 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 and other fields aren't. I mean, at doing these kinds

of things, we already types out our own papers. We all have students and people who can hack a

website together in an 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, uh,

prestige and it can be achieved without any of the... Paper is not required for prestige.

Yeah. It 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 being influenced by it and it was really well written. It influenced how I write

feature reviews. Uh, you disagreed with me actually. Uh, and you made it, uh, my review,

but much better. So, but nevertheless, the review process, you know, has its, uh, flaws.

And how do you think, 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. Um,

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 upping 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 upvotes. So, uh, that's good. Um, 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, right? So then I have the second order problem of trying to decide whose

opinions I should value and such. So I don't know what I w 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, 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 rating. And that would be just as good as saying, I got it,

so, you know, accepted into this journal. So I think, 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 iClear 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 elements, 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.

Right. So, you know, there aren't 3000 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. And I'm worried, you know, I, 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 there's a huge, there's value. It's exciting. And

there's value in that and you get patted on the head for it and so on. But, 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 thesis, 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 long

research horizon. And I'm worried that we don't incentivize that at all at this point.

In this current structure. Yeah. So what do you see as, 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? 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, so, you

know, it's like climbing some kind of landscape with a noisy optimizer. So it's clear that the,

you know, the deep learning stuff has made deep and important improvements. And so the high water

mark is now higher. There's no question. But of course, I think people are overselling and

eventually investors, I guess, and other people will look around and say, well, you're not quite

delivering on this grand claim and that wild hypothesis. It's probably, it's going to crash

some amount and then 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 or

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 on the robot right knows that they don't do it. And what I realized was that their

model of how we program a robot was completely wrong. Their model of how we can program a robot

was like Lego mind storms, 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 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, 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. Yes. 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 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 so we're

gonna you started at Stanford philosophy, that's where she could be. 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 the 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 it. And there's a bunch

of different things you could do at the extremes, right? One super extreme is, what's the most

effective strategy? 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 goo, 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, and again, this isn't a theological question or anything like that. It's just like,

what's the middle ground? 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? Is that, that's the emphasis?

Okay. 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 from... 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.