WEBVTT 00:00.000 --> 00:05.440 The following is a conversation with Leslie Kailbling. She is a roboticist and professor at 00:05.440 --> 00:12.080 MIT. She is recognized for her work in reinforcement learning, planning, robot navigation, and several 00:12.080 --> 00:18.560 other topics in AI. She won the Ijkai Computers and Thought Award and was the editor in chief 00:18.560 --> 00:24.320 of the prestigious journal machine learning research. This conversation is part of the 00:24.320 --> 00:30.400 artificial intelligence podcast at MIT and beyond. If you enjoy it, subscribe on YouTube, 00:30.400 --> 00:37.760 iTunes, or simply connect with me on Twitter at Lex Friedman, spelled F R I D. And now, 00:37.760 --> 00:45.360 here's my conversation with Leslie Kailbling. What made me get excited about AI, I can say 00:45.360 --> 00:49.920 that, is I read Girdle Escherbach when I was in high school. That was pretty formative for me 00:49.920 --> 00:59.280 because it exposed the interestingness of primitives and combination and how you can 00:59.280 --> 01:06.000 make complex things out of simple parts and ideas of AI and what kinds of programs might 01:06.000 --> 01:12.800 generate intelligent behavior. So you first fell in love with AI reasoning logic versus robots? 01:12.800 --> 01:18.240 Yeah, the robots came because my first job, so I finished an undergraduate degree in philosophy 01:18.240 --> 01:24.160 at Stanford and was about to finish masters in computer science and I got hired at SRI 01:25.440 --> 01:30.960 in their AI lab and they were building a robot. It was a kind of a follow on to shaky, 01:30.960 --> 01:35.840 but all the shaky people were not there anymore. And so my job was to try to get this robot to 01:35.840 --> 01:41.200 do stuff and that's really kind of what got me interested in robots. So maybe taking a small 01:41.200 --> 01:46.160 step back to your bachelor's in Stanford philosophy, did master's in PhD in computer science, 01:46.160 --> 01:51.440 but the bachelor's in philosophy. So what was that journey like? What elements of philosophy 01:52.320 --> 01:55.200 do you think you bring to your work in computer science? 01:55.200 --> 02:00.080 So it's surprisingly relevant. So part of the reason that I didn't do a computer science 02:00.080 --> 02:04.560 undergraduate degree was that there wasn't one at Stanford at the time, but that there's part of 02:04.560 --> 02:09.200 philosophy and in fact Stanford has a special sub major in something called now Symbolic Systems, 02:09.200 --> 02:15.520 which is logic, model, theory, formal semantics of natural language. And so that's actually 02:15.520 --> 02:19.680 a perfect preparation for work in AI and computer science. 02:19.680 --> 02:23.760 That's kind of interesting. So if you were interested in artificial intelligence, 02:26.000 --> 02:32.560 what kind of majors were people even thinking about taking? What is in your science? So besides 02:32.560 --> 02:37.120 philosophies, what were you supposed to do if you were fascinated by the idea of creating 02:37.120 --> 02:41.440 intelligence? There weren't enough people who did that for that even to be a conversation. 02:41.440 --> 02:49.920 I mean, I think probably philosophy. I mean, it's interesting in my graduating class of 02:49.920 --> 02:57.120 undergraduate philosophers, probably maybe slightly less than half went on in computer 02:57.120 --> 03:02.240 science, slightly less than half went on in law, and like one or two went on in philosophy. 03:03.360 --> 03:07.920 So it was a common kind of connection. Do you think AI researchers have a role, 03:07.920 --> 03:12.480 be part time philosophers, or should they stick to the solid science and engineering 03:12.480 --> 03:17.200 without sort of taking the philosophizing tangents? I mean, you work with robots, 03:17.200 --> 03:22.960 you think about what it takes to create intelligent beings. Aren't you the perfect person to think 03:22.960 --> 03:27.440 about the big picture philosophy at all? The parts of philosophy that are closest to AI, 03:27.440 --> 03:30.400 I think, or at least the closest to AI that I think about are stuff like 03:30.400 --> 03:38.400 belief and knowledge and denotation and that kind of stuff. It's quite formal, and it's 03:38.400 --> 03:44.000 like just one step away from the kinds of computer science work that we do kind of routinely. 03:45.680 --> 03:53.040 I think that there are important questions still about what you can do with a machine and what 03:53.040 --> 03:57.680 you can't and so on. Although at least my personal view is that I'm completely a materialist, 03:57.680 --> 04:01.920 and I don't think that there's any reason why we can't make a robot be 04:02.800 --> 04:06.800 behaviorally indistinguishable from a human. And the question of whether it's 04:08.480 --> 04:13.600 distinguishable internally, whether it's a zombie or not in philosophy terms, I actually don't, 04:14.720 --> 04:16.960 I don't know, and I don't know if I care too much about that. 04:16.960 --> 04:22.080 Right, but there is a philosophical notions there, mathematical and philosophical, 04:22.080 --> 04:27.520 because we don't know so much of how difficult that is, how difficult is a perception problem. 04:27.520 --> 04:32.640 How difficult is the planning problem? How difficult is it to operate in this world successfully? 04:32.640 --> 04:37.920 Because our robots are not currently as successful as human beings in many tasks. 04:37.920 --> 04:44.320 The question about the gap between current robots and human beings borders a little bit 04:44.320 --> 04:52.400 on philosophy. The expanse of knowledge that's required to operate in this world and the ability 04:52.400 --> 04:57.280 to form common sense knowledge, the ability to reason about uncertainty, much of the work 04:57.280 --> 05:05.040 you've been doing, there's open questions there that, I don't know, require to activate a certain 05:06.320 --> 05:09.840 big picture view. To me, that doesn't seem like a philosophical gap at all. 05:10.640 --> 05:14.240 To me, there is a big technical gap. There's a huge technical gap, 05:15.040 --> 05:19.360 but I don't see any reason why it's more than a technical gap. 05:19.360 --> 05:28.400 Perfect. When you mentioned AI, you mentioned SRI, and maybe can you describe to me when you 05:28.400 --> 05:37.680 first fell in love with robotics, with robots, or inspired, so you mentioned flaky or shaky flaky, 05:38.400 --> 05:42.720 and what was the robot that first captured your imagination of what's possible? 05:42.720 --> 05:47.920 Right. The first robot I worked with was flaky. Shaky was a robot that the SRI people had built, 05:47.920 --> 05:53.360 but by the time, I think when I arrived, it was sitting in a corner of somebody's office 05:53.360 --> 06:00.640 dripping hydraulic fluid into a pan, but it's iconic. Really, everybody should read the Shaky 06:00.640 --> 06:07.840 Tech Report because it has so many good ideas in it. They invented ASTAR Search and symbolic 06:07.840 --> 06:15.520 planning and learning macro operators. They had low level kind of configuration space planning for 06:15.520 --> 06:20.160 the robot. They had vision. That's the basic ideas of a ton of things. 06:20.160 --> 06:27.920 Can you take a step by it? Shaky was a mobile robot, but it could push objects, 06:27.920 --> 06:31.680 and so it would move things around. With which actuator? 06:31.680 --> 06:40.080 With its self, with its base. They had painted the baseboards black, 06:40.080 --> 06:48.320 so it used vision to localize itself in a map. It detected objects. It could detect objects that 06:48.320 --> 06:54.800 were surprising to it. It would plan and replan based on what it saw. It reasoned about whether 06:54.800 --> 07:02.240 to look and take pictures. It really had the basics of so many of the things that we think about now. 07:03.280 --> 07:05.360 How did it represent the space around it? 07:05.360 --> 07:09.680 It had representations at a bunch of different levels of abstraction, 07:09.680 --> 07:13.920 so it had, I think, a kind of an occupancy grid of some sort at the lowest level. 07:14.880 --> 07:20.000 At the high level, it was abstract, symbolic kind of rooms and connectivity. 07:20.000 --> 07:22.160 So where does Flakey come in? 07:22.160 --> 07:29.600 Yeah, okay. I showed up at SRI and we were building a brand new robot. As I said, none of the people 07:29.600 --> 07:34.240 from the previous project were there or involved anymore, so we were starting from scratch. 07:34.240 --> 07:43.920 My advisor was Stan Rosenstein. He ended up being my thesis advisor. He was motivated by this idea 07:43.920 --> 07:52.400 of situated computation or situated automata. The idea was that the tools of logical reasoning were 07:52.400 --> 08:01.200 important, but possibly only for the engineers or designers to use in the analysis of a system, 08:01.200 --> 08:05.600 but not necessarily to be manipulated in the head of the system itself. 08:06.400 --> 08:09.920 So I might use logic to prove a theorem about the behavior of my robot, 08:10.480 --> 08:14.400 even if the robot's not using logic, and it's had to prove theorems. So that was kind of the 08:14.400 --> 08:22.160 distinction. And so the idea was to kind of use those principles to make a robot do stuff. 08:22.800 --> 08:28.960 But a lot of the basic things we had to kind of learn for ourselves, because I had zero 08:28.960 --> 08:32.160 background in robotics. I didn't know anything about control. I didn't know anything about 08:32.160 --> 08:36.640 sensors. So we reinvented a lot of wheels on the way to getting that robot to do stuff. 08:36.640 --> 08:39.120 Do you think that was an advantage or hindrance? 08:39.120 --> 08:45.600 Oh, no. I'm big in favor of wheel reinvention, actually. I mean, I think you learned a lot 08:45.600 --> 08:51.920 by doing it. It's important though to eventually have the pointers so that you can see what's 08:51.920 --> 08:58.080 really going on. But I think you can appreciate much better the good solutions once you've 08:58.080 --> 09:00.400 messed around a little bit on your own and found a bad one. 09:00.400 --> 09:04.880 Yeah, I think you mentioned reinventing reinforcement learning and referring to 09:04.880 --> 09:10.960 rewards as pleasures, a pleasure, I think, which I think is a nice name for it. 09:12.800 --> 09:18.960 It's more fun, almost. Do you think you could tell the history of AI, machine learning, 09:18.960 --> 09:23.520 reinforcement learning, how you think about it from the 50s to now? 09:23.520 --> 09:29.440 One thing is that it oscillates. So things become fashionable and then they go out and 09:29.440 --> 09:34.480 then something else becomes cool and then it goes out and so on. So there's some interesting 09:34.480 --> 09:41.600 sociological process that actually drives a lot of what's going on. Early days was cybernetics and 09:41.600 --> 09:48.320 control and the idea that of homeostasis, people who made these robots that could, 09:48.320 --> 09:54.400 I don't know, try to plug into the wall when they needed power and then come loose and roll 09:54.400 --> 10:00.960 around and do stuff. And then I think over time, they thought, well, that was inspiring, but people 10:00.960 --> 10:04.880 said, no, no, no, we want to get maybe closer to what feels like real intelligence or human 10:04.880 --> 10:15.040 intelligence. And then maybe the expert systems people tried to do that, but maybe a little 10:15.040 --> 10:21.760 too superficially. So we get this surface understanding of what intelligence is like, 10:21.760 --> 10:25.840 because I understand how a steel mill works and I can try to explain it to you and you can write 10:25.840 --> 10:31.520 it down in logic and then we can make a computer infer that. And then that didn't work out. 10:32.400 --> 10:37.520 But what's interesting, I think, is when a thing starts to not be working very well, 10:38.720 --> 10:44.480 it's not only do we change methods, we change problems. So it's not like we have better ways 10:44.480 --> 10:48.160 of doing the problem of the expert systems people are trying to do. We have no ways of 10:48.160 --> 10:56.800 trying to do that problem. Oh, yeah, no, I think maybe a few. But we kind of give up on that problem 10:56.800 --> 11:01.520 and we switch to a different problem. And we work that for a while and we make progress. 11:01.520 --> 11:04.960 As a broad community. As a community. And there's a lot of people who would argue, 11:04.960 --> 11:09.760 you don't give up on the problem. It's just the decrease in the number of people working on it. 11:09.760 --> 11:13.920 You almost kind of like put it on the shelf. So we'll come back to this 20 years later. 11:13.920 --> 11:19.360 Yeah, I think that's right. Or you might decide that it's malformed. Like you might say, 11:21.600 --> 11:26.800 it's wrong to just try to make something that does superficial symbolic reasoning behave like a 11:26.800 --> 11:34.000 doctor. You can't do that until you've had the sensory motor experience of being a doctor or 11:34.000 --> 11:38.560 something. So there's arguments that say that that problem was not well formed. Or it could be 11:38.560 --> 11:44.160 that it is well formed, but we just weren't approaching it well. So you mentioned that your 11:44.160 --> 11:49.120 favorite part of logic and symbolic systems is that they give short names for large sets. 11:49.840 --> 11:56.320 So there is some use to this. They use symbolic reasoning. So looking at expert systems 11:56.960 --> 12:01.760 and symbolic computing, what do you think are the roadblocks that were hit in the 80s and 90s? 12:02.640 --> 12:08.320 Okay, so right. So the fact that I'm not a fan of expert systems doesn't mean that I'm not a fan 12:08.320 --> 12:16.640 of some kind of symbolic reasoning. So let's see roadblocks. Well, the main roadblock, I think, 12:16.640 --> 12:25.040 was that the idea that humans could articulate their knowledge effectively into some kind of 12:25.040 --> 12:30.560 logical statements. So it's not just the cost, the effort, but really just the capability of 12:30.560 --> 12:37.120 doing it. Right. Because we're all experts in vision, but totally don't have introspective access 12:37.120 --> 12:45.440 into how we do that. Right. And it's true that, I mean, I think the idea was, well, of course, 12:45.440 --> 12:49.040 even people then would know, of course, I wouldn't ask you to please write down the rules that you 12:49.040 --> 12:54.000 use for recognizing a water bottle. That's crazy. And everyone understood that. But we might ask 12:54.000 --> 13:00.800 you to please write down the rules you use for deciding, I don't know what tie to put on or 13:00.800 --> 13:08.240 or how to set up a microphone or something like that. But even those things, I think people maybe, 13:08.880 --> 13:12.720 I think what they found, I'm not sure about this, but I think what they found was that the 13:12.720 --> 13:19.120 so called experts could give explanations that sort of post hoc explanations for how and why 13:19.120 --> 13:27.680 they did things, but they weren't necessarily very good. And then they depended on maybe some 13:27.680 --> 13:33.280 kinds of perceptual things, which again, they couldn't really define very well. So I think, 13:33.280 --> 13:38.800 I think fundamentally, I think that the underlying problem with that was the assumption that people 13:38.800 --> 13:45.280 could articulate how and why they make their decisions. Right. So it's almost encoding the 13:45.280 --> 13:51.440 knowledge from converting from expert to something that a machine can understand and reason with. 13:51.440 --> 13:58.880 No, no, no, not even just encoding, but getting it out of you. Not not not writing it. I mean, 13:58.880 --> 14:03.680 yes, hard also to write it down for the computer. But I don't think that people can 14:04.240 --> 14:10.080 produce it. You can tell me a story about why you do stuff. But I'm not so sure that's the why. 14:11.440 --> 14:16.960 Great. So there are still on the hierarchical planning side, 14:16.960 --> 14:25.120 places where symbolic reasoning is very useful. So as you've talked about, so 14:27.840 --> 14:34.400 where so don't where's the gap? Yeah, okay, good. So saying that humans can't provide a 14:34.400 --> 14:40.560 description of their reasoning processes. That's okay, fine. But that doesn't mean that it's not 14:40.560 --> 14:44.880 good to do reasoning of various styles inside a computer. Those are just two orthogonal points. 14:44.880 --> 14:50.560 So then the question is, what kind of reasoning should you do inside a computer? 14:50.560 --> 14:55.680 Right. And the answer is, I think you need to do all different kinds of reasoning inside 14:55.680 --> 15:01.680 a computer, depending on what kinds of problems you face. I guess the question is, what kind of 15:01.680 --> 15:12.880 things can you encode symbolically so you can reason about? I think the idea about and and 15:12.880 --> 15:18.080 even symbolic, I don't even like that terminology because I don't know what it means technically 15:18.080 --> 15:24.240 and formally. I do believe in abstractions. So abstractions are critical, right? You cannot 15:24.240 --> 15:30.240 reason at completely fine grain about everything in your life, right? You can't make a plan at the 15:30.240 --> 15:37.680 level of images and torques for getting a PhD. So you have to reduce the size of the state space 15:37.680 --> 15:43.040 and you have to reduce the horizon if you're going to reason about getting a PhD or even buying 15:43.040 --> 15:50.080 the ingredients to make dinner. And so how can you reduce the spaces and the horizon of the 15:50.080 --> 15:53.200 reasoning you have to do? And the answer is abstraction, spatial abstraction, temporal 15:53.200 --> 15:58.080 abstraction. I think abstraction along the lines of goals is also interesting, like you might 15:58.800 --> 16:03.840 or well, abstraction and decomposition. Goals is maybe more of a decomposition thing. 16:03.840 --> 16:08.880 So I think that's where these kinds of, if you want to call it symbolic or discrete 16:08.880 --> 16:15.440 models come in. You talk about a room of your house instead of your pose. You talk about 16:16.800 --> 16:22.560 doing something during the afternoon instead of at 2.54. And you do that because it makes 16:22.560 --> 16:30.000 your reasoning problem easier and also because you don't have enough information 16:30.000 --> 16:37.120 to reason in high fidelity about your pose of your elbow at 2.35 this afternoon anyway. 16:37.120 --> 16:39.440 Right. When you're trying to get a PhD. 16:39.440 --> 16:41.600 Right. Or when you're doing anything really. 16:41.600 --> 16:44.400 Yeah, okay. Except for at that moment. At that moment, 16:44.400 --> 16:48.160 you do have to reason about the pose of your elbow, maybe. But then maybe you do that in some 16:48.160 --> 16:55.680 continuous joint space kind of model. And so again, my biggest point about all of this is that 16:55.680 --> 17:01.440 there should be, the dogma is not the thing, right? It shouldn't be that I am in favor 17:01.440 --> 17:06.320 against symbolic reasoning and you're in favor against neural networks. It should be that just 17:07.600 --> 17:12.240 computer science tells us what the right answer to all these questions is if we were smart enough 17:12.240 --> 17:16.960 to figure it out. Yeah. When you try to actually solve the problem with computers, the right answer 17:16.960 --> 17:22.880 comes out. You mentioned abstractions. I mean, neural networks form abstractions or rather, 17:22.880 --> 17:30.320 there's automated ways to form abstractions and there's expert driven ways to form abstractions 17:30.320 --> 17:35.920 and expert human driven ways. And humans just seems to be way better at forming abstractions 17:35.920 --> 17:44.080 currently and certain problems. So when you're referring to 2.45 a.m. versus afternoon, 17:44.960 --> 17:49.920 how do we construct that taxonomy? Is there any room for automated construction of such 17:49.920 --> 17:55.200 abstractions? Oh, I think eventually, yeah. I mean, I think when we get to be better 17:56.160 --> 18:02.240 and machine learning engineers, we'll build algorithms that build awesome abstractions. 18:02.240 --> 18:06.720 That are useful in this kind of way that you're describing. Yeah. So let's then step from 18:07.840 --> 18:14.400 the abstraction discussion and let's talk about BOMMDP's 18:14.400 --> 18:21.440 Partially Observable Markov Decision Processes. So uncertainty. So first, what are Markov Decision 18:21.440 --> 18:27.520 Processes? What are Markov Decision Processes? Maybe how much of our world can be models and 18:27.520 --> 18:32.080 MDPs? How much when you wake up in the morning and you're making breakfast, how do you think 18:32.080 --> 18:38.080 of yourself as an MDP? So how do you think about MDPs and how they relate to our world? 18:38.080 --> 18:43.040 Well, so there's a stance question, right? So a stance is a position that I take with 18:43.040 --> 18:52.160 respect to a problem. So I as a researcher or a person who designed systems can decide to make 18:52.160 --> 18:58.960 a model of the world around me in some terms. So I take this messy world and I say, I'm going to 18:58.960 --> 19:04.640 treat it as if it were a problem of this formal kind, and then I can apply solution concepts 19:04.640 --> 19:09.120 or algorithms or whatever to solve that formal thing, right? So of course, the world is not 19:09.120 --> 19:14.080 anything. It's not an MDP or a POMDP. I don't know what it is, but I can model aspects of it 19:14.080 --> 19:19.280 in some way or some other way. And when I model some aspect of it in a certain way, that gives me 19:19.280 --> 19:25.600 some set of algorithms I can use. You can model the world in all kinds of ways. Some have some 19:26.400 --> 19:32.880 are more accepting of uncertainty, more easily modeling uncertainty of the world. Some really 19:32.880 --> 19:40.720 force the world to be deterministic. And so certainly MDPs model the uncertainty of the world. 19:40.720 --> 19:47.200 Yes. Model some uncertainty. They model not present state uncertainty, but they model uncertainty 19:47.200 --> 19:53.840 in the way the future will unfold. Right. So what are Markov decision processes? 19:53.840 --> 19:57.680 So Markov decision process is a model. It's a kind of a model that you can make that says, 19:57.680 --> 20:05.600 I know completely the current state of my system. And what it means to be a state is that I have 20:05.600 --> 20:10.720 all the information right now that will let me make predictions about the future as well as I 20:10.720 --> 20:14.640 can. So that remembering anything about my history wouldn't make my predictions any better. 20:18.720 --> 20:23.680 But then it also says that then I can take some actions that might change the state of the world 20:23.680 --> 20:28.800 and that I don't have a deterministic model of those changes. I have a probabilistic model 20:28.800 --> 20:35.600 of how the world might change. It's a useful model for some kinds of systems. I mean, it's 20:35.600 --> 20:43.280 certainly not a good model for most problems. I think because for most problems, you don't 20:43.280 --> 20:49.680 actually know the state. For most problems, it's partially observed. So that's now a different 20:49.680 --> 20:56.480 problem class. So okay, that's where the problem depies, the partially observed Markov decision 20:56.480 --> 21:03.600 process step in. So how do they address the fact that you can't observe most the incomplete 21:03.600 --> 21:09.360 information about most of the world around you? Right. So now the idea is we still kind of postulate 21:09.360 --> 21:14.080 that there exists a state. We think that there is some information about the world out there 21:14.640 --> 21:18.800 such that if we knew that we could make good predictions, but we don't know the state. 21:18.800 --> 21:23.840 And so then we have to think about how, but we do get observations. Maybe I get images or I hear 21:23.840 --> 21:29.520 things or I feel things and those might be local or noisy. And so therefore they don't tell me 21:29.520 --> 21:35.440 everything about what's going on. And then I have to reason about given the history of actions 21:35.440 --> 21:40.000 I've taken and observations I've gotten, what do I think is going on in the world? And then 21:40.000 --> 21:43.920 given my own kind of uncertainty about what's going on in the world, I can decide what actions to 21:43.920 --> 21:51.120 take. And so difficult is this problem of planning under uncertainty in your view and your 21:51.120 --> 21:57.840 long experience of modeling the world, trying to deal with this uncertainty in 21:57.840 --> 22:04.240 especially in real world systems. Optimal planning for even discrete POMDPs can be 22:04.240 --> 22:12.000 undecidable depending on how you set it up. And so lots of people say I don't use POMDPs 22:12.000 --> 22:17.600 because they are intractable. And I think that that's a kind of a very funny thing to say because 22:18.880 --> 22:23.120 the problem you have to solve is the problem you have to solve. So if the problem you have to 22:23.120 --> 22:28.160 solve is intractable, that's what makes us AI people, right? So we solve, we understand that 22:28.160 --> 22:34.320 the problem we're solving is wildly intractable that we will never be able to solve it optimally, 22:34.320 --> 22:41.360 at least I don't. Yeah, right. So later we can come back to an idea about bounded optimality 22:41.360 --> 22:44.960 and something. But anyway, we can't come up with optimal solutions to these problems. 22:45.520 --> 22:51.200 So we have to make approximations. Approximations in modeling approximations in solution algorithms 22:51.200 --> 22:58.160 and so on. And so I don't have a problem with saying, yeah, my problem actually it is POMDP in 22:58.160 --> 23:02.880 continuous space with continuous observations. And it's so computationally complex. I can't 23:02.880 --> 23:10.320 even think about it's, you know, big O whatever. But that doesn't prevent me from it helps me 23:10.320 --> 23:17.360 gives me some clarity to think about it that way. And to then take steps to make approximation 23:17.360 --> 23:22.080 after approximation to get down to something that's like computable in some reasonable time. 23:22.080 --> 23:27.920 When you think about optimality, you know, the community broadly has shifted on that, I think, 23:27.920 --> 23:35.600 a little bit in how much they value the idea of optimality of chasing an optimal solution. 23:35.600 --> 23:42.240 How is your views of chasing an optimal solution changed over the years when you work with robots? 23:42.240 --> 23:49.920 That's interesting. I think we have a little bit of a methodological crisis, actually, 23:49.920 --> 23:54.000 from the theoretical side. I mean, I do think that theory is important and that right now we're not 23:54.000 --> 24:00.640 doing much of it. So there's lots of empirical hacking around and training this and doing that 24:00.640 --> 24:05.440 and reporting numbers. But is it good? Is it bad? We don't know. It's very hard to say things. 24:08.240 --> 24:15.920 And if you look at like computer science theory, so people talked for a while, 24:15.920 --> 24:21.280 everyone was about solving problems optimally or completely. And then there were interesting 24:21.280 --> 24:27.520 relaxations. So people look at, oh, can I, are there regret bounds? Or can I do some kind of, 24:27.520 --> 24:33.280 you know, approximation? Can I prove something that I can approximately solve this problem or 24:33.280 --> 24:38.160 that I get closer to the solution as I spend more time and so on? What's interesting, I think, 24:38.160 --> 24:47.680 is that we don't have good approximate solution concepts for very difficult problems. Right? 24:47.680 --> 24:52.640 I like to, you know, I like to say that I'm interested in doing a very bad job of very big 24:52.640 --> 25:02.960 problems. Right. So very bad job, very big problems. I like to do that. But I wish I could say 25:02.960 --> 25:09.600 something. I wish I had a, I don't know, some kind of a formal solution concept 25:10.320 --> 25:16.640 that I could use to say, oh, this algorithm actually, it gives me something. Like, I know 25:16.640 --> 25:21.760 what I'm going to get. I can do something other than just run it and get out. So that notion 25:21.760 --> 25:28.640 is still somewhere deeply compelling to you. The notion that you can say, you can drop 25:28.640 --> 25:33.440 thing on the table says this, you can expect this, this algorithm will give me some good results. 25:33.440 --> 25:38.960 I hope there's, I hope science will, I mean, there's engineering and there's science, 25:38.960 --> 25:44.720 I think that they're not exactly the same. And I think right now we're making huge engineering 25:45.600 --> 25:49.840 like leaps and bounds. So the engineering is running away ahead of the science, which is cool. 25:49.840 --> 25:54.800 And often how it goes, right? So we're making things and nobody knows how and why they work, 25:54.800 --> 26:03.200 roughly. But we need to turn that into science. There's some form. It's, yeah, 26:03.200 --> 26:07.200 there's some room for formalizing. We need to know what the principles are. Why does this work? 26:07.200 --> 26:12.480 Why does that not work? I mean, for while people build bridges by trying, but now we can often 26:12.480 --> 26:17.520 predict whether it's going to work or not without building it. Can we do that for learning systems 26:17.520 --> 26:23.600 or for robots? See, your hope is from a materialistic perspective that intelligence, 26:23.600 --> 26:28.000 artificial intelligence systems, robots are kind of just fancier bridges. 26:29.200 --> 26:33.600 Belief space. What's the difference between belief space and state space? So we mentioned 26:33.600 --> 26:42.000 MDPs, FOMDPs, you reasoning about, you sense the world, there's a state. What's this belief 26:42.000 --> 26:49.040 space idea? Yeah. Okay, that sounds good. It sounds good. So belief space, that is, instead of 26:49.040 --> 26:54.880 thinking about what's the state of the world and trying to control that as a robot, I think about 26:55.760 --> 27:01.120 what is the space of beliefs that I could have about the world? What's, if I think of a belief 27:01.120 --> 27:06.640 as a probability distribution of the ways the world could be, a belief state is a distribution, 27:06.640 --> 27:13.040 and then my control problem, if I'm reasoning about how to move through a world I'm uncertain about, 27:14.160 --> 27:18.880 my control problem is actually the problem of controlling my beliefs. So I think about taking 27:18.880 --> 27:23.120 actions, not just what effect they'll have on the world outside, but what effect they'll have on my 27:23.120 --> 27:29.920 own understanding of the world outside. And so that might compel me to ask a question or look 27:29.920 --> 27:35.280 somewhere to gather information, which may not really change the world state, but it changes 27:35.280 --> 27:43.440 my own belief about the world. That's a powerful way to empower the agent to reason about the 27:43.440 --> 27:47.840 world, to explore the world. What kind of problems does it allow you to solve to 27:49.040 --> 27:54.560 consider belief space versus just state space? Well, any problem that requires deliberate 27:54.560 --> 28:02.800 information gathering. So if in some problems, like chess, there's no uncertainty, or maybe 28:02.800 --> 28:06.320 there's uncertainty about the opponent. There's no uncertainty about the state. 28:08.400 --> 28:14.000 And some problems, there's uncertainty, but you gather information as you go. You might say, 28:14.000 --> 28:18.240 oh, I'm driving my autonomous car down the road, and it doesn't know perfectly where it is, but 28:18.240 --> 28:23.280 the LiDARs are all going all the time. So I don't have to think about whether to gather information. 28:24.160 --> 28:28.800 But if you're a human driving down the road, you sometimes look over your shoulder to see what's 28:28.800 --> 28:36.320 going on behind you in the lane. And you have to decide whether you should do that now. And you 28:36.320 --> 28:40.400 have to trade off the fact that you're not seeing in front of you, and you're looking behind you, 28:40.400 --> 28:45.440 and how valuable is that information, and so on. And so to make choices about information 28:45.440 --> 28:56.080 gathering, you have to reason in belief space. Also to just take into account your own uncertainty 28:56.080 --> 29:03.280 before trying to do things. So you might say, if I understand where I'm standing relative to the 29:03.280 --> 29:08.880 door jam, pretty accurately, then it's okay for me to go through the door. But if I'm really not 29:08.880 --> 29:14.240 sure where the door is, then it might be better to not do that right now. The degree of your 29:14.240 --> 29:18.800 uncertainty about the world is actually part of the thing you're trying to optimize in forming the 29:18.800 --> 29:26.560 plan, right? So this idea of a long horizon of planning for a PhD or just even how to get out 29:26.560 --> 29:32.720 of the house or how to make breakfast, you show this presentation of the WTF, where's the fork 29:33.360 --> 29:42.000 of a robot looking to sink. And can you describe how we plan in this world is this idea of hierarchical 29:42.000 --> 29:52.000 planning we've mentioned? Yeah, how can a robot hope to plan about something with such a long 29:52.000 --> 29:58.400 horizon where the goal is quite far away? People since probably reasoning began have thought about 29:58.400 --> 30:02.560 hierarchical reasoning, the temporal hierarchy in particular. Well, there's spatial hierarchy, 30:02.560 --> 30:06.240 but let's talk about temporal hierarchy. So you might say, oh, I have this long 30:06.240 --> 30:13.680 execution I have to do, but I can divide it into some segments abstractly, right? So maybe 30:14.400 --> 30:19.360 have to get out of the house, I have to get in the car, I have to drive, and so on. And so 30:20.800 --> 30:25.920 you can plan if you can build abstractions. So this we started out by talking about abstractions, 30:25.920 --> 30:30.080 and we're back to that now. If you can build abstractions in your state space, 30:30.080 --> 30:37.760 and abstractions, sort of temporal abstractions, then you can make plans at a high level. And you 30:37.760 --> 30:42.320 can say, I'm going to go to town, and then I'll have to get gas, and I can go here, and I can do 30:42.320 --> 30:47.360 this other thing. And you can reason about the dependencies and constraints among these actions, 30:47.920 --> 30:55.600 again, without thinking about the complete details. What we do in our hierarchical planning work is 30:55.600 --> 31:00.960 then say, all right, I make a plan at a high level of abstraction. I have to have some 31:00.960 --> 31:06.640 reason to think that it's feasible without working it out in complete detail. And that's 31:06.640 --> 31:10.800 actually the interesting step. I always like to talk about walking through an airport, like 31:12.160 --> 31:16.720 you can plan to go to New York and arrive at the airport, and then find yourself in an office 31:16.720 --> 31:21.520 building later. You can't even tell me in advance what your plan is for walking through the airport, 31:21.520 --> 31:26.320 partly because you're too lazy to think about it maybe, but partly also because you just don't 31:26.320 --> 31:30.960 have the information. You don't know what gate you're landing in or what people are going to be 31:30.960 --> 31:37.040 in front of you or anything. So there's no point in planning in detail. But you have to have, 31:38.000 --> 31:43.760 you have to make a leap of faith that you can figure it out once you get there. And it's really 31:43.760 --> 31:52.000 interesting to me how you arrive at that. How do you, so you have learned over your lifetime to be 31:52.000 --> 31:56.800 able to make some kinds of predictions about how hard it is to achieve some kinds of sub goals. 31:57.440 --> 32:01.440 And that's critical. Like you would never plan to fly somewhere if you couldn't, 32:02.000 --> 32:05.200 didn't have a model of how hard it was to do some of the intermediate steps. 32:05.200 --> 32:09.440 So one of the things we're thinking about now is how do you do this kind of very aggressive 32:09.440 --> 32:16.400 generalization to situations that you haven't been in and so on to predict how long will it 32:16.400 --> 32:20.400 take to walk through the Kuala Lumpur airport? Like you could give me an estimate and it wouldn't 32:20.400 --> 32:26.800 be crazy. And you have to have an estimate of that in order to make plans that involve 32:26.800 --> 32:30.080 walking through the Kuala Lumpur airport, even if you don't need to know it in detail. 32:31.040 --> 32:35.520 So I'm really interested in these kinds of abstract models and how do we acquire them. 32:35.520 --> 32:39.760 But once we have them, we can use them to do hierarchical reasoning, which I think is very 32:39.760 --> 32:46.400 important. Yeah, there's this notion of goal regression and preimage backchaining. 32:46.400 --> 32:53.760 This idea of starting at the goal and just forming these big clouds of states. I mean, 32:54.560 --> 33:01.840 it's almost like saying to the airport, you know, you know, once you show up to the airport, 33:01.840 --> 33:08.560 you're like a few steps away from the goal. So thinking of it this way is kind of interesting. 33:08.560 --> 33:15.600 I don't know if you have further comments on that of starting at the goal. Yeah, I mean, 33:15.600 --> 33:22.400 it's interesting that Herb Simon back in the early days of AI talked a lot about 33:22.400 --> 33:26.960 means ends reasoning and reasoning back from the goal. There's a kind of an intuition that people 33:26.960 --> 33:34.960 have that the number of the state space is big, the number of actions you could take is really big. 33:35.760 --> 33:39.440 So if you say, here I sit and I want to search forward from where I am, what are all the things 33:39.440 --> 33:45.040 I could do? That's just overwhelming. If you say, if you can reason at this other level and say, 33:45.040 --> 33:49.520 here's what I'm hoping to achieve, what can I do to make that true that somehow the 33:49.520 --> 33:54.000 branching is smaller? Now, what's interesting is that like in the AI planning community, 33:54.000 --> 33:59.120 that hasn't worked out in the class of problems that they look at and the methods that they tend 33:59.120 --> 34:04.400 to use, it hasn't turned out that it's better to go backward. It's still kind of my intuition 34:04.400 --> 34:10.000 that it is, but I can't prove that to you right now. Right. I share your intuition, at least for us 34:10.720 --> 34:19.920 mirror humans. Speaking of which, when you maybe now we take it and take a little step into that 34:19.920 --> 34:27.280 philosophy circle, how hard would it, when you think about human life, you give those examples 34:27.280 --> 34:32.400 often, how hard do you think it is to formulate human life as a planning problem or aspects of 34:32.400 --> 34:37.600 human life? So when you look at robots, you're often trying to think about object manipulation, 34:38.640 --> 34:46.240 tasks about moving a thing. When you take a slight step outside the room, let the robot 34:46.240 --> 34:54.480 leave and he'll get lunch or maybe try to pursue more fuzzy goals. How hard do you think is that 34:54.480 --> 35:00.720 problem? If you were to try to maybe put another way, try to formulate human life as a planning 35:00.720 --> 35:05.680 problem. Well, that would be a mistake. I mean, it's not all a planning problem, right? I think 35:05.680 --> 35:11.920 it's really, really important that we understand that you have to put together pieces and parts 35:11.920 --> 35:18.640 that have different styles of reasoning and representation and learning. I think it seems 35:18.640 --> 35:25.680 probably clear to anybody that it can't all be this or all be that. Brains aren't all like this 35:25.680 --> 35:30.160 or all like that, right? They have different pieces and parts and substructure and so on. 35:30.160 --> 35:34.400 So I don't think that there's any good reason to think that there's going to be like one true 35:34.400 --> 35:39.600 algorithmic thing that's going to do the whole job. Just a bunch of pieces together, 35:39.600 --> 35:48.160 design to solve a bunch of specific problems. Or maybe styles of problems. I mean, 35:48.160 --> 35:52.880 there's probably some reasoning that needs to go on in image space. I think, again, 35:55.840 --> 35:59.440 there's this model base versus model free idea, right? So in reinforcement learning, 35:59.440 --> 36:06.000 people talk about, oh, should I learn? I could learn a policy just straight up a way of behaving. 36:06.000 --> 36:11.360 I could learn it's popular in a value function. That's some kind of weird intermediate ground. 36:13.360 --> 36:17.440 Or I could learn a transition model, which tells me something about the dynamics of the world. 36:18.320 --> 36:22.560 If I take a, imagine that I learn a transition model and I couple it with a planner and I 36:22.560 --> 36:29.520 draw a box around that, I have a policy again. It's just stored a different way, right? 36:30.800 --> 36:34.560 But it's just as much of a policy as the other policy. It's just I've made, I think, 36:34.560 --> 36:41.920 the way I see it is it's a time space trade off in computation, right? A more overt policy 36:41.920 --> 36:47.680 representation. Maybe it takes more space, but maybe I can compute quickly what action I should 36:47.680 --> 36:52.880 take. On the other hand, maybe a very compact model of the world dynamics plus a planner 36:53.680 --> 36:58.240 lets me compute what action to take to just more slowly. There's no, I mean, I don't think, 36:58.240 --> 37:04.240 there's no argument to be had. It's just like a question of what form of computation is best 37:04.240 --> 37:12.720 for us. For the various sub problems. Right. So, and so like learning to do algebra manipulations 37:12.720 --> 37:17.280 for some reason is, I mean, that's probably going to want naturally a sort of a different 37:17.280 --> 37:22.640 representation than riding a unicycle. At the time constraints on the unicycle are serious. 37:22.640 --> 37:28.640 The space is maybe smaller. I don't know. But so I could be the more human size of 37:28.640 --> 37:36.240 falling in love, having a relationship that might be another another style of no idea how to model 37:36.240 --> 37:43.280 that. Yeah, that's, that's first solve the algebra and the object manipulation. What do you think 37:44.160 --> 37:50.480 is harder perception or planning perception? That's why I'm understanding that's why. 37:51.920 --> 37:55.440 So what do you think is so hard about perception by understanding the world around you? 37:55.440 --> 38:03.520 Well, I mean, I think the big question is representational. A hugely the question is 38:03.520 --> 38:12.560 representation. So perception has made great strides lately, right? And we can classify images and we 38:12.560 --> 38:17.760 can play certain kinds of games and predict how to steer the car and all this sort of stuff. 38:17.760 --> 38:28.160 I don't think we have a very good idea of what perception should deliver, right? So if you 38:28.160 --> 38:32.000 if you believe in modularity, okay, there's there's a very strong view which says 38:34.640 --> 38:40.400 we shouldn't build in any modularity, we should make a giant gigantic neural network, 38:40.400 --> 38:44.000 train it end to end to do the thing. And that's the best way forward. 38:44.000 --> 38:51.280 And it's hard to argue with that except on a sample complexity basis, right? So you might say, 38:51.280 --> 38:55.120 oh, well, if I want to do end to end reinforcement learning on this giant giant neural network, 38:55.120 --> 39:00.800 it's going to take a lot of data and a lot of like broken robots and stuff. So 39:02.640 --> 39:10.000 then the only answer is to say, okay, we have to build something in build in some structure 39:10.000 --> 39:14.080 or some bias, we know from theory of machine learning, the only way to cut down the sample 39:14.080 --> 39:20.320 complexity is to kind of cut down somehow cut down the hypothesis space, you can do that by 39:20.320 --> 39:24.880 building in bias. There's all kinds of reason to think that nature built bias into humans. 39:27.520 --> 39:32.800 Convolution is a bias, right? It's a very strong bias and it's a very critical bias. 39:32.800 --> 39:39.520 So my view is that we should look for more things that are like convolution, but that address other 39:39.520 --> 39:43.440 aspects of reasoning, right? So convolution helps us a lot with a certain kind of spatial 39:43.440 --> 39:51.520 reasoning that's quite close to the imaging. I think there's other ideas like that, 39:52.400 --> 39:58.240 maybe some amount of forward search, maybe some notions of abstraction, maybe the notion that 39:58.240 --> 40:02.560 objects exist, actually, I think that's pretty important. And a lot of people won't give you 40:02.560 --> 40:06.720 that to start with, right? So almost like a convolution in the 40:08.640 --> 40:13.600 in the object semantic object space or some kind of some kind of ideas in there. That's right. 40:13.600 --> 40:17.680 And people are like the graph, graph convolutions are an idea that are related to 40:17.680 --> 40:26.240 relational representations. And so I think there are, so you, I've come far field from perception, 40:26.240 --> 40:33.200 but I think, I think the thing that's going to make perception that kind of the next step is 40:33.200 --> 40:38.000 actually understanding better what it should produce, right? So what are we going to do with 40:38.000 --> 40:41.920 the output of it, right? It's fine when what we're going to do with the output is steer, 40:41.920 --> 40:48.880 it's less clear when we're just trying to make one integrated intelligent agent, 40:48.880 --> 40:53.520 what should the output of perception be? We have no idea. And how should that hook up to the other 40:53.520 --> 41:00.240 stuff? We don't know. So I think the pressing question is, what kinds of structure can we 41:00.240 --> 41:05.520 build in that are like the moral equivalent of convolution that will make a really awesome 41:05.520 --> 41:10.240 superstructure that then learning can kind of progress on efficiently? 41:10.240 --> 41:14.080 I agree. Very compelling description of actually where we stand with the perception from 41:15.280 --> 41:19.120 you're teaching a course on embodying intelligence. What do you think it takes to 41:19.120 --> 41:24.800 build a robot with human level intelligence? I don't know if we knew we would do it. 41:27.680 --> 41:34.240 If you were to, I mean, okay, so do you think a robot needs to have a self awareness, 41:36.000 --> 41:44.160 consciousness, fear of mortality? Or is it, is it simpler than that? Or is consciousness a simple 41:44.160 --> 41:51.680 thing? Do you think about these notions? I don't think much about consciousness. Even most philosophers 41:51.680 --> 41:56.560 who care about it will give you that you could have robots that are zombies, right, that behave 41:56.560 --> 42:01.360 like humans but are not conscious. And I, at this moment, would be happy enough for that. So I'm not 42:01.360 --> 42:06.240 really worried one way or the other. So the technical side, you're not thinking of the use of self 42:06.240 --> 42:13.760 awareness? Well, but I, okay, but then what does self awareness mean? I mean, that you need to have 42:13.760 --> 42:18.800 some part of the system that can observe other parts of the system and tell whether they're 42:18.800 --> 42:24.560 working well or not. That seems critical. So does that count as, I mean, does that count as 42:24.560 --> 42:30.560 self awareness or not? Well, it depends on whether you think that there's somebody at home who can 42:30.560 --> 42:35.600 articulate whether they're self aware. But clearly, if I have like, you know, some piece of code 42:35.600 --> 42:41.120 that's counting how many times this procedure gets executed, that's a kind of self awareness, 42:41.120 --> 42:44.560 right? So there's a big spectrum. It's clear you have to have some of it. 42:44.560 --> 42:48.800 Right. You know, we're quite far away on many dimensions, but is the direction of research 42:49.600 --> 42:54.720 that's most compelling to you for, you know, trying to achieve human level intelligence 42:54.720 --> 43:00.880 in our robots? Well, to me, I guess the thing that seems most compelling to me at the moment is this 43:00.880 --> 43:10.320 question of what to build in and what to learn. I think we're, we don't, we're missing a bunch of 43:10.320 --> 43:17.120 ideas. And, and we, you know, people, you know, don't you dare ask me how many years it's going 43:17.120 --> 43:22.320 to be until that happens, because I won't even participate in the conversation. Because I think 43:22.320 --> 43:26.240 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 43:26.240 --> 43:34.160 how many years, but maybe I'll ask you what it, when you will be sufficiently impressed that we've 43:34.160 --> 43:41.280 achieved it. So what's a good test of intelligence? Do you like the Turing test and natural language 43:41.280 --> 43:47.520 in the robotic space? Is there something where you would sit back and think, oh, that's pretty 43:47.520 --> 43:52.800 impressive as a test, as a benchmark. Do you think about these kinds of problems? 43:52.800 --> 43:58.480 No, I resist. I mean, I think all the time that we spend arguing about those kinds of things could 43:58.480 --> 44:04.800 be better spent just making their robots work better. So you don't value competition. So I mean, 44:04.800 --> 44:11.280 there's a nature of benchmark, benchmarks and data sets, or Turing test challenges, where 44:11.280 --> 44:15.440 everybody kind of gets together and tries to build a better robot because they want to outcompete 44:15.440 --> 44:21.360 each other, like the DARPA challenge with the autonomous vehicles. Do you see the value of that? 44:23.600 --> 44:27.440 Or can get in the way? I think you can get in the way. I mean, some people, many people find it 44:27.440 --> 44:34.880 motivating. And so that's good. I find it anti motivating personally. But I think you get an 44:34.880 --> 44:41.200 interesting cycle where for a contest, a bunch of smart people get super motivated and they hack 44:41.200 --> 44:47.120 their brains out. And much of what gets done is just hacks, but sometimes really cool ideas emerge. 44:47.120 --> 44:53.360 And then that gives us something to chew on after that. So it's not a thing for me, but I don't 44:53.360 --> 44:58.480 I don't regret that other people do it. Yeah, it's like you said, with everything else that 44:58.480 --> 45:03.840 makes us good. So jumping topics a little bit, you started the journal machine learning research 45:04.560 --> 45:09.920 and served as its editor in chief. How did the publication come about? 45:11.680 --> 45:17.040 And what do you think about the current publishing model space in machine learning 45:17.040 --> 45:23.040 artificial intelligence? Okay, good. So it came about because there was a journal called machine 45:23.040 --> 45:29.840 learning, which still exists, which was owned by Clure. And there was I was on the editorial 45:29.840 --> 45:33.520 board and we used to have these meetings annually where we would complain to Clure that 45:33.520 --> 45:37.280 it was too expensive for the libraries and that people couldn't publish. And we would really 45:37.280 --> 45:41.920 like to have some kind of relief on those fronts. And they would always sympathize, 45:41.920 --> 45:49.120 but not do anything. So we just decided to make a new journal. And there was the Journal of AI 45:49.120 --> 45:54.880 Research, which has was on the same model, which had been in existence for maybe five years or so, 45:54.880 --> 46:01.920 and it was going on pretty well. So we just made a new journal. It wasn't I mean, 46:03.600 --> 46:07.600 I don't know, I guess it was work, but it wasn't that hard. So basically the editorial board, 46:07.600 --> 46:17.440 probably 75% of the editorial board of machine learning resigned. And we founded the new journal. 46:17.440 --> 46:25.280 But it was sort of it was more open. Yeah, right. So it's completely open. It's open access. 46:25.280 --> 46:31.600 Actually, I had a postdoc, George Conrad Harris, who wanted to call these journals free for all. 46:33.520 --> 46:37.600 Because there were I mean, it both has no page charges and has no 46:40.080 --> 46:45.520 access restrictions. And the reason and so lots of people, I mean, for there were, 46:45.520 --> 46:50.240 there were people who are mad about the existence of this journal who thought it was a fraud or 46:50.240 --> 46:55.200 something, it would be impossible, they said, to run a journal like this with basically, 46:55.200 --> 46:59.200 I mean, for a long time, I didn't even have a bank account. I paid for the 46:59.840 --> 47:06.640 lawyer to incorporate and the IP address. And it just didn't cost a couple hundred dollars a year 47:06.640 --> 47:12.880 to run. It's a little bit more now, but not that much more. But that's because I think computer 47:12.880 --> 47:19.920 scientists are competent and autonomous in a way that many scientists in other fields aren't. 47:19.920 --> 47:23.920 I mean, at doing these kinds of things, we already type set around papers, 47:23.920 --> 47:28.000 we all have students and people who can hack a website together in the afternoon. 47:28.000 --> 47:32.960 So the infrastructure for us was like, not a problem, but for other people in other fields, 47:32.960 --> 47:38.960 it's a harder thing to do. Yeah. And this kind of open access journal is nevertheless, 47:38.960 --> 47:45.840 one of the most prestigious journals. So it's not like a prestige and it can be achieved 47:45.840 --> 47:49.920 without any of the papers. Paper is not required for prestige, turns out. Yeah. 47:50.640 --> 47:56.960 So on the review process side of actually a long time ago, I don't remember when I reviewed a paper 47:56.960 --> 48:01.360 where you were also a reviewer and I remember reading your review and being influenced by it. 48:01.360 --> 48:06.480 It was really well written. It influenced how I write feature reviews. You disagreed with me, 48:06.480 --> 48:15.280 actually. And you made it my review, but much better. But nevertheless, the review process 48:16.880 --> 48:23.600 has its flaws. And what do you think works well? How can it be improved? 48:23.600 --> 48:27.600 So actually, when I started JMLR, I wanted to do something completely different. 48:28.720 --> 48:34.800 And I didn't because it felt like we needed a traditional journal of record and so we just 48:34.800 --> 48:40.800 made JMLR be almost like a normal journal, except for the open access parts of it, basically. 48:43.200 --> 48:47.600 Increasingly, of course, publication is not even a sensible word. You can publish something by 48:47.600 --> 48:54.400 putting it in an archive so I can publish everything tomorrow. So making stuff public is 48:54.400 --> 49:04.800 there's no barrier. We still need curation and evaluation. I don't have time to read all of 49:04.800 --> 49:20.480 archive. And you could argue that kind of social thumbs uping of articles suffices, right? You 49:20.480 --> 49:25.440 might say, oh, heck with this, we don't need journals at all. We'll put everything on archive 49:25.440 --> 49:30.400 and people will upvote and downvote the articles and then your CV will say, oh, man, he got a lot 49:30.400 --> 49:44.000 of upvotes. So that's good. But I think there's still value in careful reading and commentary of 49:44.000 --> 49:48.480 things. And it's hard to tell when people are upvoting and downvoting or arguing about your 49:48.480 --> 49:55.440 paper on Twitter and Reddit, whether they know what they're talking about. So then I have the 49:55.440 --> 50:01.360 second order problem of trying to decide whose opinions I should value and such. So I don't 50:01.360 --> 50:06.240 know. If I had infinite time, which I don't, and I'm not going to do this because I really want to 50:06.240 --> 50:11.920 make robots work, but if I felt inclined to do something more in a publication direction, 50:12.880 --> 50:16.160 I would do this other thing, which I thought about doing the first time, which is to get 50:16.160 --> 50:22.480 together some set of people whose opinions I value and who are pretty articulate. And I guess we 50:22.480 --> 50:27.520 would be public, although we could be private, I'm not sure. And we would review papers. We wouldn't 50:27.520 --> 50:31.600 publish them and you wouldn't submit them. We would just find papers and we would write reviews 50:32.720 --> 50:39.120 and we would make those reviews public. And maybe if you, you know, so we're Leslie's friends who 50:39.120 --> 50:45.200 review papers and maybe eventually if we, our opinion was sufficiently valued, like the opinion 50:45.200 --> 50:50.800 of JMLR is valued, then you'd say on your CV that Leslie's friends gave my paper a five star reading 50:50.800 --> 50:58.800 and that would be just as good as saying I got it accepted into this journal. So I think we 50:58.800 --> 51:04.800 should have good public commentary and organize it in some way, but I don't really know how to 51:04.800 --> 51:09.120 do it. It's interesting times. The way you describe it actually is really interesting. I mean, 51:09.120 --> 51:15.040 we do it for movies, IMDB.com. There's experts, critics come in, they write reviews, but there's 51:15.040 --> 51:20.960 also regular non critics humans write reviews and they're separated. I like open review. 51:22.240 --> 51:31.600 The eye clear process, I think is interesting. It's a step in the right direction, but it's still 51:31.600 --> 51:39.840 not as compelling as reviewing movies or video games. I mean, it sometimes almost, it might be 51:39.840 --> 51:44.400 silly, at least from my perspective to say, but it boils down to the user interface, how fun and 51:44.400 --> 51:50.400 easy it is to actually perform the reviews, how efficient, how much you as a reviewer get 51:51.200 --> 51:56.560 street cred for being a good reviewer. Those human elements come into play. 51:57.200 --> 52:03.600 No, it's a big investment to do a good review of a paper and the flood of papers is out of control. 52:05.280 --> 52:08.960 There aren't 3,000 new, I don't know how many new movies are there in a year, I don't know, 52:08.960 --> 52:15.440 but there's probably going to be less than how many machine learning papers there are in a year now. 52:19.840 --> 52:22.320 Right, so I'm like an old person, so of course I'm going to say, 52:23.520 --> 52:30.240 things are moving too fast, I'm a stick in the mud. So I can say that, but my particular flavor 52:30.240 --> 52:38.240 of that is, I think the horizon for researchers has gotten very short, that students want to 52:38.240 --> 52:46.000 publish a lot of papers and it's exciting and there's value in that and you get padded on the 52:46.000 --> 52:58.320 head for it and so on. And some of that is fine, but I'm worried that we're driving out people who 52:58.320 --> 53:05.280 would spend two years thinking about something. Back in my day, when we worked on our theses, 53:05.280 --> 53:10.560 we did not publish papers, you did your thesis for years, you picked a hard problem and then you 53:10.560 --> 53:16.320 worked and chewed on it and did stuff and wasted time and for a long time. And when it was roughly, 53:16.320 --> 53:22.800 when it was done, you would write papers. And so I don't know how to, and I don't think that 53:22.800 --> 53:26.720 everybody has to work in that mode, but I think there's some problems that are hard enough 53:27.680 --> 53:31.680 that it's important to have a longer research horizon and I'm worried that 53:31.680 --> 53:39.600 we don't incentivize that at all at this point. In this current structure. So what do you see 53:41.440 --> 53:47.280 what are your hopes and fears about the future of AI and continuing on this theme? So AI has 53:47.280 --> 53:53.440 gone through a few winters, ups and downs. Do you see another winter of AI coming? 53:53.440 --> 54:02.480 Or are you more hopeful about making robots work, as you said? I think the cycles are inevitable, 54:03.040 --> 54:10.080 but I think each time we get higher, right? I mean, it's like climbing some kind of 54:10.080 --> 54:19.600 landscape with a noisy optimizer. So it's clear that the deep learning stuff has 54:19.600 --> 54:25.760 made deep and important improvements. And so the high watermark is now higher. There's no question. 54:25.760 --> 54:34.400 But of course, I think people are overselling and eventually investors, I guess, and other people 54:34.400 --> 54:40.640 look around and say, well, you're not quite delivering on this grand claim and that wild 54:40.640 --> 54:47.680 hypothesis. It's like probably it's going to crash something out and then it's okay. I mean, 54:47.680 --> 54:54.000 it's okay. I mean, but I don't I can't imagine that there's like some awesome monotonic improvement 54:54.000 --> 55:01.760 from here to human level AI. So in, you know, I have to ask this question, I probably anticipate 55:01.760 --> 55:09.120 answers, the answers. But do you have a worry short term, a long term about the existential 55:09.120 --> 55:18.880 threats of AI and maybe short term, less existential, but more robots taking away jobs? 55:20.480 --> 55:28.000 Well, actually, let me talk a little bit about utility. Actually, I had an interesting conversation 55:28.000 --> 55:32.480 with some military ethicists who wanted to talk to me about autonomous weapons. 55:32.480 --> 55:39.360 And they're, they were interesting, smart, well educated guys who didn't know too much about AI or 55:39.360 --> 55:43.600 machine learning. And the first question they asked me was, has your robot ever done something you 55:43.600 --> 55:49.120 didn't expect? And I like burst out laughing because anybody who's ever done something other robot 55:49.120 --> 55:54.720 right knows that they don't do much. And what I realized was that their model of how we program 55:54.720 --> 55:59.440 a robot was completely wrong. Their model of how we could put program robot was like, 55:59.440 --> 56:05.600 program robot was like, Lego Mindstorms, like, Oh, go forward a meter, turn left, take a picture, 56:05.600 --> 56:11.120 do this, do that. And so if you have that model of programming, then it's true, it's kind of weird 56:11.120 --> 56:16.240 that your robot would do something that you didn't anticipate. But the fact is, and actually, 56:16.240 --> 56:21.840 so now this is my new educational mission, if I have to talk to non experts, I try to teach them 56:22.720 --> 56:28.080 the idea that we don't operate, we operate at least one or maybe many levels of abstraction 56:28.080 --> 56:33.280 about that. And we say, Oh, here's a hypothesis class, maybe it's a space of plans, or maybe it's a 56:33.280 --> 56:38.400 space of classifiers, or whatever. But there's some set of answers and an objective function. And 56:38.400 --> 56:44.800 then we work on some optimization method that tries to optimize a solution in that class. 56:46.080 --> 56:50.560 And we don't know what solution is going to come out. Right. So I think it's important to 56:50.560 --> 56:55.520 communicate that. So I mean, of course, probably people who listen to this, they know that lesson. 56:55.520 --> 56:59.600 But I think it's really critical to communicate that lesson. And then lots of people are now 56:59.600 --> 57:05.600 talking about, you know, the value alignment problem. So you want to be sure, as robots or 57:06.480 --> 57:11.280 software systems get more competent, that their objectives are aligned with your objectives, 57:11.280 --> 57:17.680 or that our objectives are compatible in some way, or we have a good way of mediating when they have 57:17.680 --> 57:22.240 different objectives. And so I think it is important to start thinking in terms, like, 57:22.240 --> 57:28.480 you don't have to be freaked out by the robot apocalypse to accept that it's important to think 57:28.480 --> 57:33.760 about objective functions of value alignment. And that you have to really, everyone who's done 57:33.760 --> 57:38.160 optimization knows that you have to be careful what you wish for that, you know, sometimes you get 57:38.160 --> 57:45.280 the optimal solution. And you realize, man, that was that objective was wrong. So pragmatically, 57:45.280 --> 57:51.360 in the shortest term, it seems to me that that those are really interesting and critical questions. 57:51.360 --> 57:55.680 And the idea that we're going to go from being people who engineer algorithms to being people 57:55.680 --> 58:00.800 who engineer objective functions, I think that's, that's definitely going to happen. And that's 58:00.800 --> 58:03.360 going to change our thinking and methodology and stuff. 58:03.360 --> 58:07.520 We're going to, you started at Stanford philosophy, that's wish you could be science, 58:07.520 --> 58:13.840 and I will go back to philosophy maybe. Well, I mean, they're mixed together because, because, 58:13.840 --> 58:18.240 as we also know, as machine learning people, right? When you design, in fact, this is the 58:18.240 --> 58:23.360 lecture I gave in class today, when you design an objective function, you have to wear both hats. 58:23.360 --> 58:28.320 There's the hat that says, what do I want? And there's the hat that says, but I know what my 58:28.320 --> 58:34.240 optimizer can do to some degree. And I have to take that into account. So it's, it's always a 58:34.240 --> 58:40.480 trade off. And we have to kind of be mindful of that. The part about taking people's jobs, 58:40.480 --> 58:47.360 that I understand that that's important, I don't understand sociology or economics or people 58:47.360 --> 58:51.840 very well. So I don't know how to think about that. So that's, yeah, so there might be a 58:51.840 --> 58:56.640 sociological aspect there, the economic aspect that's very difficult to think about. Okay. 58:56.640 --> 59:00.000 I mean, I think other people should be thinking about it, but I'm just, that's not my strength. 59:00.000 --> 59:04.320 So what do you think is the most exciting area of research in the short term, 59:04.320 --> 59:08.560 for the community and for your, for yourself? Well, so, I mean, there's this story I've been 59:08.560 --> 59:16.480 telling about how to engineer intelligent robots. So that's what we want to do. We all kind of want 59:16.480 --> 59:20.960 to do, well, I mean, some set of us want to do this. And the question is, what's the most effective 59:20.960 --> 59:25.840 strategy? And we've tried, and there's a bunch of different things you could do at the extremes, 59:25.840 --> 59:32.000 right? One super extreme is we do introspection and we write a program. Okay, that has not worked 59:32.000 --> 59:37.360 out very well. Another extreme is we take a giant bunch of neural guru and we try and train it up to 59:37.360 --> 59:43.040 do something. I don't think that's going to work either. So the question is, what's the middle 59:43.040 --> 59:49.840 ground? And again, this isn't a theological question or anything like that. It's just, 59:49.840 --> 59:57.040 like, how do, just how do we, what's the best way to make this work out? And I think it's clear, 59:57.040 --> 1:00:01.840 it's a combination of learning, to me, it's clear, it's a combination of learning and not learning. 1:00:02.400 --> 1:00:05.920 And what should that combination be? And what's the stuff we build in? So to me, 1:00:05.920 --> 1:00:10.080 that's the most compelling question. And when you say engineer robots, you mean 1:00:10.080 --> 1:00:15.600 engineering systems that work in the real world. That's the emphasis. 1:00:17.600 --> 1:00:23.200 Last question, which robots or robot is your favorite from science fiction? 1:00:24.480 --> 1:00:32.960 So you can go with Star Wars or RTD2, or you can go with more modern, maybe Hal. 1:00:32.960 --> 1:00:37.040 No, sir, I don't think I have a favorite robot from science fiction. 1:00:37.040 --> 1:00:45.520 This is, this is back to, you like to make robots work in the real world here, not, not in. 1:00:45.520 --> 1:00:50.000 I mean, I love the process. And I care more about the process. 1:00:50.000 --> 1:00:51.040 The engineering process. 1:00:51.600 --> 1:00:55.760 Yeah. I mean, I do research because it's fun, not because I care about what we produce. 1:00:57.520 --> 1:01:01.920 Well, that's, that's a beautiful note, actually. And Leslie, thank you so much for talking today. 1:01:01.920 --> 1:01:07.920 Sure, it's been fun.