#81 – Anca Dragan: Human-Robot Interaction and Reward Engineering

Anca Dragan is a professor at Berkeley, working on human-robot interaction — algorithms that look beyond the robot’s function in isolation, and generate robot behavior that accounts for interaction and coordination with human beings. Support this podcast by supporting the sponsors and using the special code: – Download Cash App on the App Store https://apps.apple.com/us/app/cash-app/id711923939 or Google Play https://play.google.com/store/apps/details?id=com.squareup.cash&hl=en_US & use code “LexPodcast” EPISODE LINKS: Anca’s Twitter: https://twitter.com/ancadianadragan Anca’s Website: https://people.eecs.berkeley.edu/~anca/ This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584, follow on Spotify https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL, or support it on Patreon https://www.patreon.com/lexfridman. Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. OUTLINE: 00:00 – Introduction 02:26 – Interest in robotics 05:32 – Computer science 07:32 – Favorite robot 13:25 – How difficult is human-robot interaction? 32:01 – HRI application domains 34:24 – Optimizing the beliefs of humans 45:59 – Difficulty of driving when humans are involved 1:05:02 – Semi-autonomous driving 1:10:39 – How do we specify good rewards? 1:17:30 – Leaked information from human behavior 1:21:59 – Three laws of robotics 1:26:31 – Book recommendation 1:29:02 – If a doctor gave you 5 years to live… 1:32:48 – Small act of kindness 1:34:31 – Meaning of life

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Anca Jergan, a professor at Berkeley working on human robot interaction, algorithms that look beyond the robot's function in isolation and generate robot behavior that accounts for interaction and coordination with human beings. She also consults at Waymo, the autonomous vehicle company. But in this conversation, she is 100% wearing her Berkeley hat. She is one of the most brilliant and fun roboticists in the world to talk with. I had a tough and crazy day leading up to this conversation, so I was a bit tired, even more so than usual.

• 0:41Lex Fridman?

  But almost immediately as she walked in, her energy, passion, and excitement for human robot interaction was contagious. So I had a lot of fun and really enjoyed this conversation. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, review it with five stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter at LexFriedman, spelled F R I D M A N. As usual, I'll do one or two minutes of ads now and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience.

• 1:20Lex Fridman?

  This show is presented by Cash App, the number one finance app in the App Store. When you get it, use code LEXPODCAST. Cash App lets you send money to friends, buy Bitcoin, and invest in the stock market with as little as $1. Since Cash App does fractional share trading, let me mention that the order execution algorithm that works behind the scenes to create the abstraction of fractional orders is an algorithmic marvel. So big props to the Cash App engineers for solving a hard problem that in the end provides an easy interface that takes a step up to the next layer of abstraction over the stock market, making trading more accessible for new investors and diversification much easier.

• 2:05Lex Fridman?

  So again, if you get Cash App from the App Store or Google Play and use the code LexPodcast, you get $10 and Cash App will also donate $10 to FIRST, an organization that is helping to advance robotics and STEM education for young people around the world. And now here's my conversation with Anca Dragan.

• 2:44Lex Fridman?

  When did you first fall in love with robotics?

• 2:47Speaker 01

  I think it was a very gradual process and it was somewhat accidental, actually, because I first started getting into programming when I was a kid and then into math and then into, I decided computer science was the thing I was going to do. And then in college, I got into AI and then I applied to the Robotics Institute at Carnegie Mellon. And I was coming from this little school in Germany that nobody had heard of, but I had spent an exchange semester at Carnegie Mellon. So I had letters from Carnegie Mellon. So that was the only, MIT said no, Berkeley said no, Stanford said no. That was the only place I got into. So I went there to the Robotics Institute and I thought that robotics

• 3:32Speaker 01

  is a really cool way to actually apply the stuff that I knew and love, like optimization. So that's how I got into robotics. I have a better story how I got into cars, which is I, you know, I used to do mostly manipulation in my PhD, but now I do kind of a bit of everything application wise, including cars. And I got into cars because I was here in Berkeley while I was a PhD student still for RSS 2014. Peter Beal organized it.

• 4:05Speaker 01

  And he arranged for, it was Google at the time, to give us rides in self-driving cars. And I was in a robot and it was just making decision after decision, the right call. And it was so amazing. So it was a whole different experience, right? Just, I mean, manipulation is so hard. You can't do anything. And there it was.

• 4:25Lex Fridman?

  Was it the most magical robot you've ever met? So like, for me too, meeting Google's self-driving car for the first time was like a transformative moment. Like, I had two moments like that. That and Spot Mini. I don't know if you met Spot Mini for Boston Dynamics. I felt like, I felt like I fell in love or something like it. Cause I thought, I know how a Spot Mini works, right? It's just, I mean, there's nothing truly special. It's, it's great engineering work. But the anthropomorphism that went on into my brain, that it came to life, like it had a little arm and it like, and it looked at me. He, she looked at me. You know, I don't know. There's a magical connection there.

• 5:06Lex Fridman?

  And it made me realize, wow, robots can be so much more than things that manipulate objects. They can be things that have a human connection. Do you have, was the self-driving car the moment? Like, was there a robot that truly sort of inspired you?

• 5:21Speaker 01

  That was, I remember that experience very viscerally, riding in that car and being just wowed. I, I had the, they gave us a sticker that said I rode in a self-driving car. And it had this cute little firefly on it. And, or logo or something.

• 5:38Lex Fridman?

  Oh, that was like the smaller one, like the firefly.

• 5:40Speaker 01

  Yeah, the really cute one. Yeah. And, and I put it on my laptop and I had that for years until I finally changed my laptop out and, you know.

• 5:50Lex Fridman?

  What about if we walk back, you mentioned optimization. Like what beautiful ideas inspired you in math, computer science, early on? Like, why get into this field? Seems like a cold and boring field of math. Like, what was exciting to you about it?

• 6:06Speaker 01

  The thing is, I liked math from very early on, from.

• 6:11Speaker 01

  Fifth grade is when I got into the Math Olympiad and all of that.

• 6:14Lex Fridman?

  Oh, you competed too.

• 6:15Speaker 01

  Yeah, Romania is like our national sport, you've got to understand. So I got into that fairly early and it was a little maybe too just theory with no kind of, I didn't kind of had a, didn't really have a goal. And other than understanding, which was cool, I always liked learning and understanding, but there was no, okay, what am I applying this understanding to? And so I think that's how I got into, more heavily into computer science because it was, it was kind of math meets something you can do tangibly in the world.

• 6:48Lex Fridman?

  Do you remember like the first program you've written?

• 6:51Speaker 01

  Okay, the first program I've written was, I kind of do, it was in Cube Basic in fourth grade.

• 6:59Lex Fridman?

  Wow.

• 7:00Speaker 01

  And it was drawing like a circle.

• 7:03Lex Fridman?

  Graphics.

• 7:04Speaker 01

  Yeah, that was, I don't know how to do that anymore. But in fourth grade, that's like the first thing that they taught me. I was like, you could take a special, I wouldn't say it was an extracurricular, it's in a sense an extracurricular, so you could sign up for, you know, dance or music or programming. And I did the programming thing and my mom was like, what, why?

• 7:25Lex Fridman?

  Did you compete in program? Like, uh, these days Romania probably, that's like a big thing, there's a programming competition. Was that, did that touch you at all?

• 7:34Speaker 01

  I did a little bit of the Computer Science Olympian, but not, not as seriously as I did the Math Olympian. So it was programming, yeah, it's basically here's a hard math problem, solve it with a computer is kind of the deal.

• 7:46Lex Fridman?

  Yeah, it's more like algorithm.

• 7:47Speaker 01

  Exactly, it's always algorithmic.

• 7:49Lex Fridman?

  So, again, you kind of mentioned the Google self-driving car, but outside of that, oh, what's, like who or what is your favorite robot, real or fictional, that like captivated your imagination throughout? I mean, I guess you kind of alluded to the Google self-driving, the Firefly was a magical moment, but is there something else?

• 8:12Speaker 01

  It was the Firefly there, it was, I think that was the Lexus, by the way. This was back, back then. But yeah, so good question. I, my, okay, my favorite fictional robot is Wally.

• 8:25Speaker 01

  And I love how amazingly expressive it is. Some persons think a little bit about expressive motion, kinds of things you were saying with, you can do this and it's a head and it's the manipulator and what does it all mean? I like to think about that stuff. I love Pixar. I love animation.

• 8:43Lex Fridman?

  And Wally has two big eyes, I think, or no?

• 8:45Speaker 01

  Yeah, it has these, um, these cameras and they move. So yeah, that's it's, you know, it goes woo and then it's super cute. It's, yeah, it's, you know, the way it moves is just so expressive. The timing of that motion, what it's doing with its arms and what it's doing with these lenses is amazing. And so, um, I've, I've really liked that from the start. And then on top of that, sometimes I share this, it's a personal story I share with people or when I teach about AI or whatnot. My husband proposed to me by building a Wally. And he actuated it, so it's seven degrees of freedom, including the lens thing.

• 9:30Speaker 01

  And it kind of came in and it had the, he made it have like a, you know, the belly box opening thing. So it just did that. And then it spewed out this, uh, box made out of Legos that opened slowly and then bam.

• 9:46Lex Fridman?

  No.

• 9:47Speaker 01

  Yeah. Yeah. It was, uh, it was quite, quite, it set a bar.

• 9:51Lex Fridman?

  That could be like the, the most impressive thing I've ever heard. Okay.

• 9:56Speaker 01

  That was a special connection to Wally. Long story short, I like Wally because I like animation and I like robots and I like, uh, you know, the fact that this was, we still have this robot to this day.

• 10:06Lex Fridman?

  What, uh, how hard is that problem? Do you think of the expressivity of robots? Like the, with the Boston Dynamics, I've never talked to those folks about this particular element. I've, I've talked to them a lot, but it seems to be like almost an accidental side effect for them. That they weren't, I don't know if they're flaking it, they weren't trying to. Okay. They do say that the, the gripper on the, it was not intended to be a face.

• 10:35Lex Fridman?

  I don't know if that's an honest statement, but I think they're legitimately.

• 10:38Speaker 01

  It probably is.

• 10:39Lex Fridman?

  And but so do we automatically just anthropomorphize any something we can see about a robot? So like, the the question is, how hard is it to create a Wally-type robot that connects so deeply with us humans? What do you think?

• 10:54Speaker 01

  It's really hard, right? So it depends on what setting. So if you wanna do it in this very particular, narrow setting where it does only one thing, and it's expressive, then you can get an animator, you know, can have Pixar on call, come in, design some trajectories. There was Anki had a robot called Cosmo, where they put in some of these animations. That part is is easy, right? The the hard part is doing it not via these kind of handcrafted behaviors, but doing it generally autonomously. Like, I want robots. I don't work on, just to clarify, I don't, I used to work a lot on this. I don't work on that quite as much these days. But but have the notion of having robots that, you know,

• 11:38Speaker 01

  when they pick something up and put it in a place, they can do that with various forms of of style. Or you can say, well, this robot is, you know, succeeding at this task and is confident versus it's hesitant versus, you know, maybe it's happy or it's, you know, disappointed about something, some failure that it had. Or I think that when robots move,

• 12:00Speaker 01

  they can communicate so much about internal states or perceived internal states that they have. And I think that's really useful and an element that we'll want in the future. Because I was reading this article about how kids are

• 12:21Speaker 01

  kids are being rude to Alexa because they can be rude to it and it doesn't really get angry, right? It doesn't reply in any way, it just says the same thing. So I think there's, at least for that, for the for the correct development of children. It's important that these things, you kind of react differently. I also think, you know, you walk in your home and you have a personal robot. And if you're really pissed, presumably, robots should kind of behave slightly differently than when you're super happy and excited. But it's really hard because it's, I don't know, the, you know, the way I would think about it and the way I thought about it when it came to in expressing goals or intent, sorry, intentions for robots, it's

• 13:02Speaker 01

  Well, what's really happening is that instead of doing robotics where you have your state and you have your action space and you have your space, the reward function to try and optimize, now you kind of have to expand the notion of state to include this human internal state. What is the person actually perceiving? What do they think about the robots, something rather? And then you have to optimize in that system. And so that means you have to understand how your motion, your actions end up sort of influencing the observer's kind of perception of you. And it's very, it's very hard to write math about that.

• 13:41Lex Fridman?

  Right. So when you start to think about incorporating the human into the state model, I apologize for the philosophical question, but how complicated are human beings, do you think? Like, can they be reduced to a, to a kind of, almost like an object that moves and maybe has some basic intents? Or is there something, do we have to model things like mood and general aggressiveness and I mean, all these kinds of human qualities or like game theoretic qualities? Like, what's your sense?

• 14:15Speaker 01

  How complicated is?

• 14:17Lex Fridman?

  How hard is the problem of human robot interaction?

• 14:20Speaker 01

  Yeah. Should we talk about what the problem of human robot interaction is?

• 14:24Lex Fridman?

  Yeah, this is what is?

• 14:26Speaker 01

  And then?

• 14:27Lex Fridman?

  Human robot interaction?

• 14:27Speaker 01

  And then talk about how that, yeah. So, and by the way, I'm going to talk about this very particular view of human robot interaction, right? Which is not so much on the social side or on the side of how do you have a good conversation with the robot? What should the robot's appearance be? It turns out that if you make robots taller versus shorter, this has an effect on how people act with them. So I'm not, I'm not talking about that. But I'm talking about this very kind of narrow thing, which is you take, if you want to take a task that a robot can do in isolation, in a lab, out there in the world, but in isolation. And now you're asking, what does it mean for the robot to be able to do this task for presumably what its actually end goal is,

• 15:11Speaker 01

  which is to help some person? That ends up

• 15:17Speaker 01

  Changing the problem in two ways. The first way changes the problem is that the robot is no longer the single agent acting. You have humans who also take actions in that same space. You know, cars navigating around people, robots around an office, navigating around the people in that office. If I send a robot to over there in the cafeteria to get me a coffee, then there's probably other people reaching for stuff in the same space. And so now you have your robot and you're in charge of the actions that the robot is taking. Then you have these people who are also making decisions and taking actions in that same space. And even if, you know, the robot knows what it's, what it should do and all of that, just coexisting with these people, right?

• 15:59Speaker 01

  Kind of getting the actions to gel well, to mesh well together. That's sort of the kind of problem number one. And then there's problem number two, which is, goes back to this notion of I, if I'm a programmer, I can specify some objective for the robot to go off and optimize and specify the task. But if I put the robot in your home, presumably you might have your own opinions about, well, okay, I want my house clean, but how do I want it cleaned and how should robot move how close to me it should come and all of that. And so I think those are the two differences that you have. You're acting around people and you, what you should be optimizing for

• 16:43Speaker 01

  should satisfy the preferences of that end user, not of your programmer who programmed you.

• 16:47Lex Fridman?

  Yeah, and the preferences thing is tricky. So figuring out those preferences, be able to interactively adjust. So understand what the human is doing. So it really boils down to be, understand the humans in order to interact with them and in order to please them.

• 17:02Speaker 01

  Right.

• 17:04Lex Fridman?

  So why is this hard?

• 17:05Speaker 01

  Why, yeah, why is understanding humans hard? So I think.

• 17:12Speaker 01

  There's two tasks about understanding humans that in my mind are very, very similar, but not everyone agrees. So there is the task of being able to just anticipate what people will do. We all know that cars need to do this, right? We all know that, well, if I navigate around some people, the robot has to get some notion of, okay, where, where is this person gonna be? So that's kind of the prediction side. And then there's what you were saying, satisfying the preferences, right? So adapting to the person's preferences, knowing what to optimize for, which is more this inference side, this what is, what does this person want? What is their intent? What are their preferences? And to me, those kind of go together because I think that in, if you, at the very least,

• 17:56Speaker 01

  if you can understand, if you can look at human behavior and understand what it is that they want, then that's sort of the key enabler to being able to anticipate what they'll do in the future. Because I think that, you know, we're not arbitrary. We make these decisions that we make, we act in the way we do because we're trying to achieve certain things. And so I think that's the relationship between them. Now, how complicated do these models need to be in order to be able to understand what people want? So we've gotten a long way in robotics with something called inverse reinforcement learning, which is the notion of someone asks, demonstrates what, how they want the thing done.

• 18:39Lex Fridman?

  What is inverse reinforcement learning? You briefly said it.

• 18:42Speaker 01

  Yeah, right. So it's, it's the problem of take human behavior and infer reward function from this. Figure out what it is that that behavior is optimal with respect to. And it's a great way to think about learning human preferences in the sense of, you know, you have a car and the person can drive it and then you can say, well, okay, I can actually learn what the person is optimizing for. I can learn their driving style. Or you can, you can have people demonstrate how they want the house clean. And then you can say, okay, this is, this is, I'm getting the trade offs that they're, that they're making, I'm getting the preferences that they want out of this. And so we've been successful in robotics somewhat with this.

• 19:27Speaker 01

  And it's based on a very simple model of human behavior. It was remarkably simple, which is that human behavior is optimal with respect to whatever it is that people want, right? So you make that assumption and now you can kind of inverse through. That's why it's called inverse, well, really optimal control, but also inverse reinforcement learning. So this is based on utility maximization in economics. Back in the 40s by Neumann-Morgenstein, we're like, okay, people are making choices by maximizing utility. Go. And then in the late 50s, we had Lou Stent Shepherd come in and say, people are a little

• 20:12Speaker 01

  bit noisy and approximate in that process. So they might choose something kind of stochastically with probability proportional to how much utility something has. There's a bit of noise in there. This has translated into robotics and something that we call Boltzmann rationality. So it's a kind of an evolution of inverse reinforcement learning that accounts for human noise. And we've had some success with that, too, for these tasks where it turns out people act noisily enough that you can't just do vanilla, the vanilla version. Ah, you can account for noise and still infer what they seem to want based on this. Then now we're hitting tasks where that's not enough.

• 20:57Lex Fridman?

  What are examples?

• 21:00Speaker 01

  What are examples? So imagine you're trying to control some robot that's fairly complicated. You're trying to control a robot arm because maybe you're a patient with a motor impairment and you have this wheelchair-mounted arm and you're trying to control it around. Or one task that we've looked at with Sergey is, and our students did, is a lunar lander. So I don't know if you know this Atari game. It's called Lunar Lander. It's really hard. People really suck at landing the same. Mostly they just crash it left and right. Okay, so this is the kind of task where imagine you're trying to provide some assistance to a person operating such a robot where you want the autonomy to kick in, figure out what it is that you're trying to do and help you do it.

• 21:43Speaker 01

  It's really hard to do that for, say, Lunar Lander, because people are all over the place. And so they seem much more noisy than really irrational. That's an example of a task where these models are kind of failing us. And it's not surprising because, so we, you know, we talked about the 40s, utility, late 50s, sort of, uh, noisy. Then the 70s came and behavioral economics started being a thing, where people are like, no, no, no, no, no. People are not rational. People are messy and emotional and irrational and have all sorts of heuristics that might be domain-specific and they're just, they're just a mess.

• 22:26Lex Fridman?

  A mess.

• 22:27Speaker 01

  So, so what do you, so what does my robot do to understand what you want? And it's a very, it's very, that's why it's complicated. It's, you know, for the most part, we get away with pretty simple models until we don't. And then the question is, what do you do then? And I've, I had days when I wanted to, you know, pack my bags and go home and switch jobs because it's just, it feels really daunting to make sense of human behavior enough that you can reliably understand what people want, especially as, you know, robot capabilities will continue to get developed. You'll get these systems that are more and more capable of all sorts of things. And then you really want to make sure that you're telling them the right thing to do. What is that thing?

• 23:09Speaker 01

  Well, read it in human behavior.

• 23:13Lex Fridman?

  So if I just sat here quietly and tried to understand something about you by listening to you talk, it would be harder than if I got to say something and ask you and interact and control. Can you, can the robot help its understanding of the human by influencing the behavior by actually acting?

• 23:35Speaker 01

  Yeah, absolutely. So, one of the things that's been exciting to me lately is this notion that when you try to, that, that, that, when you try to think of the robotics problem as, okay, I have a robot and it needs to optimize for whatever it is that a person wants it to optimize, as opposed to maybe what a programmer said. That problem. We think of as a human-robot collaboration problem in which both agents get to act, in which the robot knows less than the human because the human actually has access to, you know, at least implicitly to what it is that they want. They can't write it down, but they can, they can talk about it. They can give all sorts of signals. They can demonstrate.

• 24:20Speaker 01

  And, and, but the robot doesn't need to sit there and passively observe human behavior and try to make sense of it. The robot can act too. And so there's these information gathering actions that the robot can take to sort of solicit responses that are actually informative. So for instance, this is not for the purpose of assisting people, but with kind of back to coordinating with people in cars and all of that. One thing that, um, uh, Dorsa did was, so we were looking at cars being able to navigate around people. And, uh, you might not know exactly the driving style of a particular individual that's next to you, but you want to change lanes in front of them.

• 25:02Lex Fridman?

  Uh, navigating a, a, around other humans inside cars?

• 25:05Speaker 01

  Yeah, good, good clarification question. So, um, you, you have an autonomous car and it's trying to navigate the road around human-driven vehicles. Similar things, ideas apply to pedestrians as well, but let's just take human-driven vehicles. So now you're trying to change a lane. Well, you could be trying to infer the, the driving style of this person next to you. You'd like to know if they're in particular, if they're sort of aggressive or defensive, if they're gonna let you kind of go in or if they're gonna not.

• 25:36Lex Fridman?

  Right.

• 25:37Speaker 01

  And, and it's very difficult to just, you know, when, if you think that, if you want to hedge your bets and say, ah, maybe they're actually pretty aggressive. I shouldn't try this. You kind of end up driving next to them and driving next to them, right? And then you, you don't know because you're not actually getting the observations that you get. The way someone drives when they're next to you, um, and they just need to go straight. It's kind of the same regardless of they're aggressive or defensive. And so you need to enable the robot. The reason about how it might actually be able to gather information by changing the actions that it's taking. And then the robot comes up with these cool things where it kind of nudges towards you and then sees if you're gonna slow down or not.

• 26:22Speaker 01

  And if you slow down, it sort of updates its model of you and says, oh, okay, you're more on the defensive side. So now I can actually.

• 26:29Lex Fridman?

  That's a fascinating dance. That's so, that's so cool that you could sort of use your own actions to gather information. That's a, that feels like a totally open, exciting new world of robotics. I mean, how many people are even thinking about that kind of thing?

• 26:44Speaker 01

  A handful of us, I'd say.

• 26:47Lex Fridman?

  It's rare because it's, it's actually leveraging human. I mean, most roboticists, I've talked to a lot of, you know, colleagues and so on, are kind of, being honest, kind of afraid of humans.

• 26:59Speaker 01

  Because they're messy and complicated, right? I understand. Going back to what we're talking about earlier, right? Now we're kind of in this dilemma of, okay, there are tasks that we can just assume people are approximately rational for, and we can figure out what they want. We can figure out their goals. We can figure out their driving styles, whatever. Cool. There are these tasks that we can't. So what do we do, right? Do we pack our bags and go home? And this one, this, this, I've had a little bit of hope recently. And I'm kind of doubting myself because what do I know that, you know, 50 years of behavioral economics hasn't figured out? But maybe it's not really in contradiction with what, with, with the way that field is headed.

• 27:41Speaker 01

  But basically, one thing that we've been thinking about is, instead of kind of giving up and saying people are too crazy and irrational for us to make sense of them, maybe we can give them a bit the benefit of the doubt. And maybe we can think of them as actually being relatively rational, but just under different assumptions about the world, about how the world works, about, you know, they don't have, we, when we think about rationality, implicit assumption is, oh, they're rational under all the same assumptions and constraints as the robot, right? What, if this is the state of the world, that's what they know. This is the transition function, that's what they know. This is the horizon, that's what they know. But.

• 28:25Speaker 01

  Maybe maybe the kind of this difference, the way, the reason they can seem a little messy and hectic, especially to robots, is that perhaps they just make different assumptions or have different beliefs.

• 28:38Lex Fridman?

  Yeah.

• 28:39Speaker 01

  So.

• 28:39Lex Fridman?

  I mean, that's that's another fascinating idea, that this our kind of anecdotal desire to say that humans are irrational, perhaps grounded in behavioral economics, is is that we just don't understand the constraints and the rewards under which they operate. And so our goal shouldn't be to throw our hands up and say they're irrational, is to say, let's try to understand what are the constraints.

• 29:03Speaker 01

  What it is that they must be assuming that makes this behavior make sense. A good life lesson, right? Good life lesson.

• 29:10Lex Fridman?

  That's true. It's just outside of robotics. That's just good to, that's communicating with humans, that's just a good assume that you just don't sort of empathy, right? It's a.

• 29:21Speaker 01

  This maybe there's something you're missing and you and it's, you know, it especially happens to robots because they're kind of dumb and they don't know things. And oftentimes, people are sort of super irrational in that they actually know a lot of things that robots don't. Sometimes, like with the lunar lander, the robot, you know, knows much more. So it turns out that if you try to say, look, maybe people are operating this thing, but assuming a much more simplified physics model, because they don't get the complexity of this kind of craft, or the robot arm with seven degrees of freedom with these inertia and whatever. So so maybe they have this intuitive physics model, which is not, you know, this this notion of intuitive physics is something that you studied actually in cognitive science,

• 30:03Speaker 01

  was like Josh Dennebaum, Tom Griffith's work on this stuff. And and what we found is that you can actually try to figure out what what physics model kind of best explains human actions. And then you can use that to sort of correct what it is that they're commanding the craft to do. So they might, you know, be sending the craft somewhere, but instead of executing that action, you can sort of take a step back and say, according to their intuitive, if the world worked according to their intuitive physics model, where do they think that the craft is going? Where are they trying to send it to? And then you can use the real physics, right? The inverse of that to actually figure out what you should

• 30:47Speaker 01

  do so that you do that instead of where they were actually sending you in the real world. And I kid you not, at work, people land the damn thing and, you know, in between the two flags and all that. So it's not conclusive in any way, but I'd say it's evidence that, yeah, maybe we're kind of underestimating humans in some ways when we're giving up and saying, yeah, they're just crazy noisy.

• 31:10Lex Fridman?

  So then you try to explicitly try to model the kind of worldview that they have.

• 31:15Speaker 01

  That they have. That's right. That's right. And it's not too, I mean, there's things in behavioral economics too, that, for instance, have touched upon the planning horizon. So there's this idea that there's bounded rationality essentially, and the idea that, well, maybe we work under computational constraints. And I think kind of our view recently has been, take the Bellman update in AI and just break it in all sorts of ways by saying, state? No, no, no. The person doesn't get to see the real state. Maybe they're estimating somehow. Transition function? No, no, no, no, no. Even the actual reward evaluation? Maybe they're still learning about what it is that they want. Like, you know, when you watch Netflix and, you know, you have all the things and then you have to pick

• 31:57Speaker 01

  something. Imagine that, you know, the AI system interpreted that choice as this is the thing you prefer to see. How are you gonna know? You're still trying to figure out what you like, what you don't like, et cetera. So I think it's important to also account for that. So it's not irrationality, they're doing the right thing under the things that they know.

• 32:16Lex Fridman?

  Yeah, that's brilliant. You mentioned recommender systems. What kind of? And when we're talking about human robot interaction, what kind of problem spaces are you thinking about? So is it robots, like wheeled robots with autonomous vehicles? Is it object manipulation? Like, when you think about human robot interaction in your mind, and maybe, I'm sure you can speak for the entire community of human robot interaction. But like, what are the problems of interest here? Is, and does it, you know, I kind of think of

• 32:54Lex Fridman?

  open domain dialogue as human robot interaction. And that happens not in the physical space, but it could just happen in the virtual space. So where's the boundaries of this field for you? When you're thinking about the things we've been talking about?

• 33:09Speaker 01

  Yeah, so I, I try to find kind of underlying, I don't know what to even call them. I try to work on, you know, I might call what I do, the kind of working on the foundations of algorithmic human robot interaction and trying to make contributions there. And it's important to me that whatever we do is actually somewhat domain agnostic when it comes to, is it about, you know, autonomous cars? Or is it about quad rotors? Or is it about, is it sort of, the same underlying principles apply? Of course, when you're trying to get a particular domain to work, you usually have to do some extra work to adapt that to that particular domain.

• 33:53Speaker 01

  But these things that we were talking about around, well, you know, how do you model humans? It turns out that a lot of systems need to core benefit from a better understanding of how human behavior relates to what people want and need to predict human behavior, physical robots of all sorts and beyond that. And so I used to do manipulation. I used to be, you know, picking up stuff. And then I was picking up stuff with people around. And now it's Sort of very broad when it comes to the application level, but in a sense, very focused on, okay, how does the problem need to change? How do the algorithms need to change when we're not doing a robot by itself?

• 34:37Speaker 01

  You know, emptying the dishwasher, but we're stepping outside of that.

• 34:40Lex Fridman?

  I thought that popped into my head just now. On the game theoretic side, I think you said this really interesting idea of using actions to gain more information. But if we think of sort of game theory,

• 34:57Lex Fridman?

  the humans that are interacting with you, with you, the robot, I'm taking the identity of the robot.

• 35:03Speaker 01

  Yeah, I did that all the time.

• 35:04Lex Fridman?

  Yeah. Is, they also have a world model of you. And you can manipulate that. I mean, if we look at autonomous vehicles, people have a certain viewpoint. You said with the kids, people see Alexa as a, in a certain way. Is there some value in trying to also optimize how people see you as a robot? Is that, or is that a little too far in, away from the specifics of what we can solve right now?

• 35:38Speaker 01

  So, well, we can, both, right? So it's really interesting. And we've seen a little bit of progress on this problem, on pieces of this problem. So you can, again, it kind of comes down to how complicated the human model need to be. But in one piece of work that we were looking at, we just said, okay, there's these, there's these parameters that are internal to the robot. And they're what the, they're what the robot is about to do, or maybe what objective, what driving style the robot has, or something like that. And what we're going to do is we're going to set up a system where part of the state is the person's belief over those parameters. And now, when the robot acts,

• 36:22Speaker 01

  that the person gets new evidence about this robot internal state. And so they're updating their mental model of the robot, right? So if they see a card that sort of cuts someone off, they're like, oh, that's an aggressive card. They know more, right? If they see sort of a robot head towards a particular door, they're like, oh, yeah, the robot's trying to get to that door. So this thing that we have to do with humans to try and understand their goals and intentions, humans are inevitably gonna do that to your robots. And then that raises this interesting question that you asked, which is, can we do something about that? This is gonna happen inevitably, but we can sort of be more confusing or less confusing to people. And it turns out you can optimize for being more informative and less confusing

• 37:06Speaker 01

  if you if you have an understanding of how your actions are being interpreted by the human, how they're using these actions to update their belief. And honestly, all we did is just Bayes rule. Basically, okay, the person has a belief. They see an action, they make some assumptions about how the robot generates its actions, presumably as being rational, because robots are rational. It's reasonable to assume that about them. And then they incorporate that that new piece of evidence, the Bayesian sense in their belief, and they obtain a posterior. And now the robot is trying to figure out what actions to take such that it steers the person's belief to put as much probability mass as possible on the correct on the correct parameters.

• 37:48Lex Fridman?

  So that's kind of a mathematical formalization of that. But my worry, and I don't know if you want to go there with me, but I I talk about this quite a bit. The the kids talking to Alexa disrespectfully worries me. I worry in general about human nature. I guess I grew up in Soviet Union, World War II. I'm a Jew too, so with the Holocaust and everything. I just worry about how we humans sometimes treat the other, the group that we call the other, whatever it is. Through human history, the group that's the other has been changed faces. But it seems like the robot will be the other, the other, the next the other.

• 38:32Lex Fridman?

  And one thing is, it feels to me that robots don't get no respect.

• 38:39Speaker 01

  They get shoved around.

• 38:40Lex Fridman?

  Shoved around. And is there, one, at the shallow level, for better experience, it seems that robots need to talk back a little bit. Like, my intuition says, I mean, most companies from Roomba, autonomous vehicle companies, might not be so happy with the idea that a robot has a little bit of an attitude. But I feel, it feels to me that that's necessary to create a compelling experience. Like, we humans don't seem to respect anything that doesn't give us some attitude. That, or like, a mix of mystery and attitude and anger and that threatens us subtly, maybe passive aggressively. I don't, it seems like we humans, yeah, need that.

• 39:25Lex Fridman?

  Do, what are your, is there something, you have thoughts on this?

• 39:29Speaker 01

  I'll give you two thoughts on this.

• 39:30Lex Fridman?

  Okay, sure.

• 39:31Speaker 01

  One is, one is, it's, we respond to, you know, someone being assertive, but we also respond to someone being vulnerable. So I think robots, my first thought is that robots get shoved around and bullied a lot because they're sort of, you know, tempting and they're sort of showing off, or they appear to be showing off. And so I think, going back to these things we were talking about in the beginning of making robots a little more, a little more expressive, a little bit more like, eh, that wasn't cool to do, and now I'm bummed. Right? I think that that can actually help because people can't help but anthropomorphize and respond to that. Even that, though, the emotion being communicated is not in any way a real thing.

• 40:16Speaker 01

  And people know that it's not a real thing because they know it's just a machine. We're still inter, you know, we were, we watch, there's this famous psychology experiment with little triangles and kind of dots on a screen, and a triangle is chasing the square, and you get really angry at the darn triangle because why is it not leaving the square alone? So that's, yeah, we can't help. So that was the first thought.

• 40:38Lex Fridman?

  The vulnerability, that's really interesting. That I, I, I think of like being pushing back, being assertive as the only mechanism of getting, of forming a connection, of getting respect, but perhaps vulnerability, perhaps there's other mechanisms that are less threatening.

• 40:57Speaker 01

  Yeah.

• 40:58Lex Fridman?

  Is there?

• 40:59Speaker 01

  Well, I think, well, a little bit, yes, but then this, this other thing that we can think about is, it goes back to what you were saying, that interaction is really game theoretic. Right, so the moment you're taking actions in a space, the humans are taking actions in that same space, but you have your own objective, which is, you know, you're a car, you need to get your passenger to the destination, and then the human nearby has their own objective, which somewhat overlaps with you, but not entirely. You're not interested in getting into an accident with each other, but you have different destinations and you want to get home faster and they want to get home faster. And that's a general sum game at that point. And so that's, I think that's what, treating it as such is kind of a way we can step outside

• 41:42Speaker 01

  of this kind of mode that where you try to anticipate what people do and you don't realize you have any influence over it, while still protecting yourself because you're understanding that people also understand that they can influence you. And it's just kind of back and forth, this, this negotiation, which is really, really talking about different equilibrium of a game. The very basic way to solve coordination is to just make predictions about what people will do and then stay out of their way. And that's hard for the reasons we talked about, which is I have to understand people's intentions implicitly, explicitly, who knows, but somehow you have to get enough of an understanding of that, be able to anticipate what happens next.

• 42:26Speaker 01

  And so that's challenging, but then it's further challenged by the fact that people change what they're do based on what you do, because they don't, they don't plan in isolation either, right? So when you see cars trying to merge on a highway. And not succeeding, one of the reasons this can be is because you you they they look at traffic that keeps coming, they predict what these people are planning on doing, which is to just keep going, and then they stay out of the way because there's not there's no feasible plan, right? Any plan would actually intersect with one of these other people. So that's bad. So you get stuck there. So now kind of if if you start thinking about

• 43:10Speaker 01

  it as no, no, no, actually, these people change what they do depending on what the car does. If the car actually tries to kind of inch itself forward, they might actually slow down and let the car in. And now taking advantage of that, well, that you know, that's kind of the next level. We call this like this underactuated system idea where it's kind of underactuation robotics, but it's kind of it's you don't you're influenced these other degrees of freedom, but you don't get to decide what they do.

• 43:42Lex Fridman?

  I've somewhere seen you mention it, the the human element in this picture as underactuated. So you know, you understand underactuated robotics is, you know, that you can't fully control the system.

• 43:57Speaker 01

  You can't go in arbitrary directions in the configuration space.

• 44:01Lex Fridman?

  Under your control.

• 44:03Speaker 01

  Yeah, it's a very simple way of underactuation where basically there's literally these degrees of freedom that you can control and these degrees of freedom that you can't, but you influence. And I think that's the important part is that they don't do whatever regardless of what you do, that what you do influences what they end up doing.

• 44:19Lex Fridman?

  I just also like the the poetry of calling human robot interaction and underactuated robotics problem. And you also mentioned sort of nudging. It seems that there, I don't know, I think about this a lot in the case of pedestrians have collected hundreds of hours of videos. I like to just watch pedestrians and it seems that.

• 44:39Speaker 01

  It's a funny hobby.

• 44:40Lex Fridman?

  Yeah, it's weird because I learn a lot. I learn a lot about myself, about our human behavior from watching pedestrians, watching people in their environment. Basically, crossing the street is like you're putting your life on the line. You know, I don't know, tens of millions of time in America every day is people are just like playing this weird game of chicken when they cross the street, especially when there's some ambiguity about the right of way. That has to do either with the rules of the road or with the general personality of the intersection based on the time of day and so on. And this nudging idea, I don't, you know, it seems that people don't even nudge, they

• 45:24Lex Fridman?

  just aggressively take, make a decision. Somebody, there's a runner that gave me this advice. I sometimes run in the, in the street and, you know, not in the street, on the sidewalk. And he said that if you don't make eye contact with people when you're running, they will all move out of your way.

• 45:42Speaker 01

  It's called civil inattention.

• 45:44Lex Fridman?

  Civil inattention, that's a thing. Oh, wow, I need to look this up, but it works. What is that? My sense was if you communicate like confidence in your actions, that you're unlikely to deviate from the action that you're following, that's a really powerful signal to others that they need to plan around your actions as opposed to nudging where you're sort of hesitantly, the hesitation might communicate that you're now, you're still in the dance and the game that they can influence with their own actions. I've recently had a conversation with Jim Keller, who's a sort of this legendary chip architect, but he also led the autopilot team for a while.

• 46:29Lex Fridman?

  And his intuition, that driving is fundamentally still like a ballistics problem, like you can ignore the human element, that it's just not hitting things and you can kind of learn the right dynamics required to do the merger and all those kinds of things. And then my sense is, and I don't know if I can provide sort of definitive proof of this, but my sense is like an order of magnitude or more, more difficult when humans are involved. Like it's not simply a object, a collision avoidance problem. What's, where does your intuition, of course, nobody knows the right answer here, but where does your intuition fall on the difficulty, fundamental difficulty of the driving problem

• 47:14Lex Fridman?

  when humans are involved?

• 47:15Speaker 01

  Yeah. Good question. I have many opinions on this. Imagine.

• 47:22Speaker 01

  Downtown San Francisco?

• 47:24Lex Fridman?

  Yeah.

• 47:24Speaker 01

  Yeah? It's crazy, busy, everything. Okay, now take all the humans out. No pedestrians, no human-driven vehicles, no cyclists, no people on little electric scooters zipping around, nothing. I think we're done. I think driving at that point is done. We're done. There's nothing really that's still needs to be solved about that.

• 47:46Lex Fridman?

  Well, let's pause there. I, I, I think, I, I agree with you, like, and I think a lot of people that will hear will agree with that. But we need to sort of internalize that idea. So what's the problem there? Because we might not quite yet be done with that. Because a lot of people kind of focus on the perception problem. A lot of people kind of map autonomous driving into how close are we to solving being able to detect all the, you know, the, the drivable area, the objects in the scene. Do you see that as a, how hard is that problem? So your intuition there behind your statement was we might have not solved it yet, but we're close to solving basically the perception problem.

• 48:31Speaker 01

  I think the perception problem, I mean, and by the way, a bunch of years ago, this would not have been true. And a lot of issues in the space came, were coming from the fact that, oh, we don't really, you know, we don't know what's, what's where. But I think it's fairly safe to say that at this point, although you could always improve on things and all of that, you can drive through downtown San Francisco if there are no people around. There's no really perception issue standing in your way there. I think perception is hard, but yeah, it's, we've made a lot of progress on the perception side. To undermine the difficulty of the problem, I think everything about robotics is really difficult, of course. I think that, you know, the, the, the planning problem, the control problem, all very difficult.

• 49:16Speaker 01

  But I think what's, what makes it really, yeah. It might be, I mean, you know, and I picked downtown San Francisco, I, it's.

• 49:26Speaker 01

  Adapting to, well, now it's snowing. Now it's no longer snowing. Now it's slippery in this way. Now the dynamics part could, I could imagine being,

• 49:37Speaker 01

  being still somewhat challenging.

• 49:40Lex Fridman?

  But no, the thing that I think worries us and our intuition is not good there is the perceptual problem at the edge cases. Sort of downtown San Francisco, the nice thing. It's not actually, it may not be a good example because.

• 49:56Speaker 01

  Because you know what you're getting for. Well, there's like crazy construction zones and all of that.

• 50:00Lex Fridman?

  Yeah, but the thing is you're traveling at slow speeds. So like, it doesn't feel dangerous. To me, what feels dangerous is highway speeds. When everything is, to us humans, super clear.

• 50:11Speaker 01

  Yeah, I'm assuming LiDAR here, by the way. I think it's kind of irresponsible to not use LiDAR. That's just my personal opinion.

• 50:19Lex Fridman?

  That's.

• 50:20Speaker 01

  I mean, depending on your use case, but I think like, you know, if you, if you have the opportunity to use LiDAR, in a lot, in a lot of cases, you might not.

• 50:29Lex Fridman?

  Good, your intuition makes more sense now. So you don't think vision.

• 50:32Speaker 01

  I really just don't know enough to say, well, vision alone. What, you know, what's like, there's a lot of, how many cameras do you have? Is it how are you using them? I don't know. There's all, there's a sorts of details. I imagine there's stuff that's really hard to actually see. I don't know, how do you deal with, with glare? Exactly what you were saying, stuff that people would see that, that, that you don't. I think I have more, my intuition comes from systems that can actually use LiDAR as well.

• 51:01Lex Fridman?

  Yeah, and until we know for sure, it's, makes sense to be using LiDAR. That's kind of the safety focus. But then the sort of the, I also sympathize with the Elon Musk statement of LiDAR is a crutch. It's, it's, it's a, it's a fun notion to think that the things that work today is a crutch for the invention of the things that will work tomorrow. Right?

• 51:29Lex Fridman?

  It's kind of true in the sense that if, you know, we want to stick to the conference. You see this in academic and research settings all the time. The things that work force you to not explore outside, think outside the box. I mean, that happens all the time. The problem is in the safety critical systems. You kind of want to stick with the things that work. So it's, it's, it's an interesting and difficult trade off in the, in the, in the case of real world sort of safety critical robotic systems. But so your intuition is, just to clarify.

• 52:03Speaker 01

  Yes.

• 52:04Lex Fridman?

  How, I mean, how hard is this human element for, like, how hard is driving when this human element is involved? Are we years, decades away from solving it? But perhaps actually the year isn't the, the thing I'm asking. It doesn't matter what the timeline is, but do you think we're, how many breakthroughs are we away from in, in solving the human robot interaction problem to get this, to get this right?

• 52:32Speaker 01

  I think it, in a sense, it really depends. I think that, you know, we, we were talking about how, well, look, it's really hard because anticipation of what people do is hard. And on top of that, playing the game is hard. But I think we sort of have the fundamental, some of the fundamental understanding for that. And then you already see that these systems are being deployed in the real world, you know, even, even driverless, because I think now a few companies that don't have a driver in the car in some small areas.

• 53:13Lex Fridman?

  I got a chance to, I went to Phoenix and I, I shot a video with Waymo. I need to get that video out. People have been giving me slack, but there's incredible engineering work being done there. And it's one of those other seminal moments for me in my life to be able to, it sounds silly, but to be able to drive without a, with a, uh, ride, sorry, without a driver in the seat. I mean, that was an incredible robotics. I was driven by a robot. And without being able to take over, without being able to take the steering wheel, that's a magical, that's a magical moment. So in that regard, in those domains, at least for like Waymo, they're, they're, they're solving that human, there's, I mean, they

• 53:58Lex Fridman?

  were, they're going, I mean, it felt fast because you're like freaking out at first. That was, this is my first experience, but it's going like the speed limit, right? Thirty, forty, whatever it is. And there's humans and it deals with them quite well. It detects them and negotiates the intersections, left turns and all that. So at least in those domains, it's solving them. The open question for me is like, how quickly can we expand? You know, that's the, you know, outside of the weather conditions, all those kinds of things, how quickly can we expand to like cities like San Francisco?

• 54:31Speaker 01

  Yeah. And I wouldn't say that it's just, you know, now it's just pure engineering and it's probably the, I mean, and by the way, I'm speaking kind of very generally here as hypothesizing, but I, I think that, that there are successes and yet no one is everywhere out there. So that seems to suggest that things can be expanded and can be scaled and we know how to do a lot of things, but there's still probably, you know, new algorithms or modified algorithms that, that you still need to put in there as you, as you learn more and more about new challenges that get, you get faced with.

• 55:12Lex Fridman?

  How much of this problem do you think can be learned through end to end? Is the success of machine learning and reinforcement learning, how much of it can be learned from sort of data from scratch? And how much, which most of the success of autonomous vehicle systems have a lot of heuristics and rule-based stuff on top, like human expertise injected, forced into the system to make it work? What's your, what's your sense? How much, what's, what will be the role of learning in the near term?

• 55:44Speaker 01

  I think, I, I think.

• 55:48Speaker 01

  On the one hand, that learning is inevitable here, right? I think on the other hand, that when people characterize the problem as it's a bunch of rules that some people wrote down versus it's an end-to-end RL system or imitation learning, then maybe there's kind of something missing from maybe that's that's more. So for instance, I think a very, very useful tool in this sort of problem, both in how to generate the car's behavior and robots in general, and how to model human beings, is actually planning, search optimization, right?

• 56:32Speaker 01

  So robotics is a sequential decision-making problem. And when when a robot can figure out on its own how to achieve its goal without hitting stuff and all that stuff, right? All the good stuff for motion planning 101. I think of that as very much AI, not this is some rule or something. There's nothing rule-based around that, right? It's just you're you're searching through a space and figuring out, or you're optimizing through a space and figure out what seems to be the right thing to do. And I think it's hard to just do that because you need to learn models of the world. And I think it's hard to just do the learning part where you don't, you know, you don't bother with any of that because then you're saying,

• 57:17Speaker 01

  well, I could do imitation, but then when I go off distribution, I'm really screwed. Or you can say, I can do reinforcement learning, which adds a lot of robustness. But then you have to do either reinforcement learning in the real world, which sounds a little challenging, or that trial and error, you know? Or you have to do reinforcement learning in simulation. And then that means, well, guess what? You need to model things, at least to to model people, model the world enough that you, you know, whatever policy you get of that is like actually fine to roll out in the world and do some additional learning there. So.

• 57:54Lex Fridman?

  Do you think simulation, by the way, just a quick tangent, has a role in the human robot interaction space? Like, is it useful? It seems like humans, everything we've been talking about, are difficult to model and simulate. Do you think simulation has a role in this space?

• 58:10Speaker 01

  I do. I think so because you can take models and train with them ahead of time, for instance. You can.

• 58:23Lex Fridman?

  But the models, sorry to interrupt, the models are sort of human constructed or learned?

• 58:27Speaker 01

  I think they have to be a combination. Because if you get some human data and then you say, this is how, this is going to be my model of the person, whether for simulation and training or for just deployment time, and that's what I'm planning with as my model of how people work. Regardless, if you take some data and you don't assume anything else, and you just say, okay, this is some data that I've collected. Let me fit a policy to how people work based on that. What does to happen is you collected some data and some distribution, and then now your robot sort of computes a best response to that, right?

• 59:10Speaker 01

  It's sort of like, what should I do if this is how people work? And easily goes off of distribution, where that model that you've built of the human completely sucks. Because out of distribution, you have no idea, right? There's, if you think of all the possible policies, and then you take only the ones that are consistent with the human data that you've observed, that still leads a lot of, a lot of things could happen outside of that distribution where you're confident and you know what's going on.

• 59:37Lex Fridman?

  By the way, that's, I mean, I've gotten used to this terminology of out of distribution. But it's such a machine learning terminology, because it kind of assumes, so distribution is referring to the data that you've seen.

• 59:53Speaker 01

  The set of states that you encountered.

• 59:55Lex Fridman?

  That you've encountered so far at training time.

• 59:57Speaker 01

  Yeah.

• 59:57Lex Fridman?

  But it kind of also implies that there's a nice, like, statistical model that represents that data. So out of distribution feels like, I don't know. It raises to me philosophical questions of how we humans reason out of distribution, reason about things that are completely, we haven't seen before.

• 1:00:20Speaker 01

  And so, and what we're talking about here is how do we reason about what other people do in, you know, situations where we haven't seen them. And somehow, we just magically navigate that. I, you know, I can anticipate what will happen in situations that are even novel in many ways. And I have a pretty good intuition for, I don't always get it right, but you know, and I might be a little uncertain and so on. I think it's, it's this, that if you just rely on data, you know, you, there's, there's just too many possibilities, there's too many policies out there that fit the data. And by the way, it's not just state, it's really kind of history of state, because to really be able to anticipate what the person will do, it kind of depends on what they've been doing so far, because that's

• 1:01:03Speaker 01

  the information you need to kind of, at least implicitly, sort of say, oh, this is the kind of person that this is, this is probably what they're trying to do. So anyways, like you're trying to map history states to actions, there's many mappings.

• 1:01:13Lex Fridman?

  And history meaning like the last few seconds, or the last few minutes, or the last few months?

• 1:01:19Speaker 01

  Who knows? Who knows how much you need, right? In terms of if your state is really like the positions of everything or whatnot, and velocities. Who knows how much you need? And then, and then there's this, there's so many mappings. And so now you're talking about, how do you regularize that space? What priors do you impose? Or what's the inductive bias? So, you know, there's all very related things to think about it. Basically, what are assumptions that we should be making such that these models actually generalize outside of the data that we've seen? And now you're talking about, well, I don't know, what can you assume? Maybe you can assume that people like actually have intentions, and that's what drives their actions. Maybe that's, you know, the right thing to do when

• 1:02:04Speaker 01

  you haven't seen data very nearby that tells you otherwise. I don't know, it's, it's a very open question.

• 1:02:10Lex Fridman?

  Do you think, sort of, that one of the dreams of artificial intelligence was to solve common sense reasoning, whatever the heck that means? Do you think something like common sense reasoning has to be solved in part to be able to solve this dance of human robot interaction, the driving space? Or human robot interaction in general? Do you have to be able to reason about these kind of common sense concepts? Of physics, of, you know, all the things we've been talking about humans. I don't even know how to express them with words. But the basics of human behavior, a fear of death.

• 1:02:51Lex Fridman?

  So like, to me, it's really important to encode in some kind of sense. Maybe not, maybe it's implicit, but it feels it's important to explicitly encode the fear of death, that people don't want to die. Because it seems silly, but like that, the game of chicken that involves with the pedestrian crossing the street is playing with the idea of mortality. Like, we really don't want to die. It's not just like a negative reward. I don't know. It just feels like all these human concepts have to be encoded. Do you share that sense? Or is this a lot simpler than I'm making out to be?

• 1:03:33Speaker 01

  I think it might be simpler. And I'm the person who likes to complicate things. I think it might be simpler than that. Because it turns out, for instance, if you say model people in the very, I'll call it traditional way, I don't know if it's fair to look at it as a traditional way, but, you know, calling people as, okay, they're rational somehow, the utilitarian perspective. Well, in that, once you say that, you automatically capture that they have an incentive to keep on being. You know, Stuart likes to say, you can't fetch the coffee if you're dead.

• 1:04:12Lex Fridman?

  Stuart Russell, by the way. That's a good line.

• 1:04:17Speaker 01

  So when you're sort of treating agents as having these objectives, these incentives, humans or artificial, you're kind of implicitly modeling that they'd like to stick around so that they can accomplish those goals. So I think, I think in a sense, maybe that's what draws me so much to the rationality framework, even though it's so broken, we've been able to. It's been such a useful perspective. And like we were talking about earlier, what's the alternative? I give up and go home or, you know, I just use complete black boxes. But then I don't know what to assume out of distribution that come back to this.

• 1:04:57Speaker 01

  It's just, it's been a very fruitful way to think about the problem in a very more positive way. Right? It's just people aren't just crazy. Maybe they make more sense than we think. But, um, but I think we also have to somehow be ready for it to be, to be wrong, be able to detect when these assumptions aren't holding, be all of that stuff.

• 1:05:20Lex Fridman?

  Let me ask sort of another small side of this that we've been talking about, the pure autonomous driving problem. But there's also relatively successful systems already deployed out there in what you may call like level two autonomy or semi autonomous vehicles, whether that's Tesla Autopilot, I've worked quite a bit with Cadillac Super Group System, which has a driver facing camera that detects your state. There's a bunch of basically lane centering systems. What's your sense about this kind of way of dealing with the human robot interaction problem by having a really dumb robot?

• 1:06:03Lex Fridman?

  And, and relying on the human to help the robot out to keep them both alive? Is that, uh, is that from the research perspective? How difficult is that problem? And from a practical deployment perspective, is that a fruitful way to approach this human robot interaction problem?

• 1:06:25Speaker 01

  I think what we have to be careful about there is to not make, it seems like some of these systems, not all, are making this underlying assumption that.

• 1:06:38Speaker 01

  If, so I'm a driver and I'm now really not driving but supervising and my job is to intervene, right? And so we have to be careful with this assumption that when I'm, if I'm supervising, I will be just as safe as when I'm driving. Like that I will, you know, if I, if I wouldn't get into some kind of accident if I'm driving, I will be able to avoid that accident when I'm supervising too. And I think I'm concerned about this assumption from a few perspectives. So from a technical perspective, it's that when you let something kind of take control and do its thing, and it depends on what that thing is, obviously, and how much it's

• 1:07:22Speaker 01

  taking control and how, what things are you trusting it to do. But if you let it do its thing and take control, it will go to what we might call off policy from the person's perspective state. So states that the person wouldn't actually find themselves in if they were the ones driving. And the assumption that the person functions just as well there as they function in the states that they would normally encounter is a little questionable. Now, another part is the, kind of the human factor side of this, which is that, I don't know about you, but I think I definitely feel like I'm experiencing things very differently when I'm actively engaged in the task versus when I'm a passive observer.

• 1:08:04Speaker 01

  Even if I try to stay engaged, right, it's very different than when I'm actually actively making decisions. And you see this in, in life in general, like you see students who are actively trying to come up with the answer, learn the thing better than when they're passively told the answer. I think that's somewhat related. And I think people have studied this in human factors for airplanes. And I think it's actually fairly established that these two are not the same.

• 1:08:28Lex Fridman?

  Yeah, I, on, on that point, because I've gotten a huge amount of heat on this and I stand by it.

• 1:08:34Speaker 01

  Okay.

• 1:08:35Lex Fridman?

  Um, because I know the human factors community well, and the work here is really strong, and there's many decades of work showing exactly what you're saying. Nevertheless, I've been continuously surprised that much of the predictions of that work has been wrong in what I've seen.

• 1:08:51Speaker 01

  Hmm.

• 1:08:52Lex Fridman?

  So what we have to do, I still agree with everything you said, but we have to be a little bit more.

• 1:09:01Lex Fridman?

  Open minded. So the, the, I'll tell you, there's a few surprising things that supervi- like, everything you said to the word is actually exactly correct. But it doesn't say, what you didn't say is that these systems are, you said you can't assume a bunch of things, but we don't know if these systems are fundamentally unsafe. That's still unknown.

• 1:09:24Speaker 01

  Mm hmm.

• 1:09:25Lex Fridman?

  There's, there's a lot of interesting things, like, I'm surprised by the fact, not, not the fact, that what seems to be anecdotally from, well, from large data collection that we've done, but also from just talking to a lot of people, when in the supervisory role of semi-autonomous systems that are sufficiently dumb, at least, which is the, that might be the key element, is the systems have to be dumb. The people are actually more energized as observers. So they're actually better, they're, they're better at observing the situation. So there might be cases in systems, if you get the interaction right, where you, as a supervisor, will do a better job with the system together.

• 1:10:10Speaker 01

  I agree. I think that is actually really possible. I guess mainly I'm pointing out that if you do it naively, you're implicitly assuming something, that assumption might actually really be wrong. But I do think that if you explicitly think about what the agent should do such that the person still stays engaged, what the, so that you essentially empower the person to more than they could, that's really the goal, right? Is you still have a driver, so you want to empower them to be so much better than they would be by themselves. And that's different, it's a very different mindset than, I want them to basically not drive, right?

• 1:10:51Speaker 01

  And, but be ready to, to sort of take over.

• 1:10:57Lex Fridman?

  So one of the interesting things we've been talking about is the rewards, that they seem to be fundamental to the, the way robots behave. So broadly speaking, we've been talking about utility functions, so, but could you comment on how do we approach the design of reward functions? Like, how do we come up with good reward functions?

• 1:11:18Speaker 01

  Mm hmm. Wow. Really good question, because the answer is, we don't.

• 1:11:27Speaker 01

  This was, you know, I used to think, I used to think about how, well, it's actually really hard to specify rewards for interaction because it's really supposed to be what the people want. And then you really, you know, we talked about how you have to customize what you want to do to the end user. But I kind of realized that even if you take the interactive component away, it's still really hard to design reward functions. So what do I mean by that? I mean, if we assume this sort of AI paradigm in which there's an agent and his

• 1:12:06Speaker 01

  job is to optimize some objectives, some reward, utility, loss, whatever, cost. If you write it out, maybe it's a set depending on situation or whatever it is. If you write it out and then you deploy the agent, you'd want to make sure that whatever you specify incentivizes the behavior you want from the agent in any situation that the agent will be faced with, right? So I do motion planning on my robot arm. I specify some cost function like, you know, this is how far away you should try to stay so much it matters to stay away from people. And this is how much it matters to be able to be efficient and blah, blah,

• 1:12:49Speaker 01

  blah, right? I need to make sure that whatever I specify those constraints or trade offs or whatever they are, that when the robot goes and solves that problem in every new situation, that behavior is the behavior that I want to see. And what I've been finding is that we have no idea how to do that. Basically, what I can do is I can sample, I can think of some situations that I think are representative of what the robot will face. And I can tune and add and tune some reward function until the optimal behavior is what I want on those situations, which first of all is super frustrating because,

• 1:13:34Speaker 01

  you know, through the miracle of AI, we've take, we don't have to specify rules for behavior anymore, right? The, we were saying before, the robot comes up with the right thing to do, you plug in the situation, it optimizes, bring that situation, it optimizes. But you have to spend still a lot of time on actually defining what it is that that criteria should be, making sure you didn't forget about 50 bazillion things that are important and how they all should be combining together to tell the robot what's good and what's bad and how good and how bad. And so I think this is, this is a lesson that, I don't know, kind of, I guess I closed my eyes to it for a while because I've been,

• 1:14:17Speaker 01

  you know, tuning cost functions for 10 years now. But it's, it's really strikes me that, yeah, we've moved the tuning and the like designing of features or whatever from the behavior side into the reward side. And yes, I agree that there's way less of it, but it still seems really hard to anticipate any possible situation and make sure you specify a reward function that when optimized will work well in every possible situation.

• 1:14:52Lex Fridman?

  So you're kind of referring to unintended consequences or just in general, any kind of suboptimal behavior that emerges outside of the things you said, out of distribution?

• 1:15:02Speaker 01

  Suboptimal behavior that is, you know, actually optimal. I mean, this, I guess the idea of unintended consequences, you know, it's optimal in respect to what you specified, but it's not what you want. And there's a difference between those.

• 1:15:14Lex Fridman?

  But that's not fundamentally a robotics problem, right? That's a human problem.

• 1:15:18Speaker 01

  So like That's the thing.

• 1:15:19Lex Fridman?

  Yeah.

• 1:15:20Speaker 01

  Right? So there's this thing called good heart's law, which is you set a metric for an organization and the moment it becomes a target that people actually optimize for, it's no longer a good metric.

• 1:15:31Lex Fridman?

  What's it called?

• 1:15:32Speaker 01

  Good heart's law.

• 1:15:33Lex Fridman?

  Good heart's law. So the moment you specify a metric, it stops doing its job.

• 1:15:38Speaker 01

  Yeah, it stops doing its job. So there's, yeah, there's such a thing as over optimizing for things and, and, you know, failing to, to

• 1:15:48Speaker 01

  Think ahead of time of all the possible things that might be important. And so that's, so that's interesting because historically I work a lot on reward learning from the perspective of customizing to the end user, but it really seems like it's not just the interaction with the end user that's a problem of the human and the robot collaborating so that the robot can do what the human wants, right? This kind of back and forth, the robot probing, the person being informative, all of that stuff. Might be actually just as applicable to this kind of maybe new form of human robot interaction, which is the interaction between the robot and the expert programmer, roboticist

• 1:16:30Speaker 01

  designer in charge of actually specifying what the heck the robot should do, specifying the task for the robot.

• 1:16:37Lex Fridman?

  Fascinating, that's so cool, like collaborating on the reward.

• 1:16:40Speaker 01

  Right, collaborating on the reward design. And so what, what does it mean, right? What is it, when we think about the problem, not as someone specifies, all of your job is to optimize, and we start thinking about you're in this interaction and this collaboration. And the first thing that comes up is when the person specifies a reward, it's not, you know, gospel, it's not like the letter of the law, it's not the definition of the reward function you should be optimizing, because they're doing their best, but they're not some magic perfect oracle. And the sooner we start understanding that, I think the sooner we'll get to more robust robots that function better in different situations.

• 1:17:23Speaker 01

  And then, then you have kind of say, okay, well, it's, it's almost like robots are over learning, over putting too much weight on the reward specified by definition, and maybe leaving a lot of other information on the table, like what are other things we could do to actually communicate to the robot about what we want them to do besides attempting to specify a reward function?

• 1:17:46Lex Fridman?

  Yeah, you have this awesome, again, I love the poetry of leaked information. You mentioned humans leak information about what they want.

• 1:17:57Lex Fridman?

  You know, leak reward signal for the for the robot. So how do we detect these leaks?

• 1:18:04Speaker 01

  What is that? Yeah, what are these leaks?

• 1:18:07Lex Fridman?

  By the way, just, I don't know. Those were, just recently saw it, read it, I don't know where, from you. And that's gonna stick with me for a while, for some reason. Because it's not explicitly expressed. It kind of leaks indirectly from our behavior.

• 1:18:21Speaker 01

  From what we do. Yeah, absolutely. So I think maybe some surprising bits, right? So we were talking before about, I'm a robot arm. It needs to move around people, carry stuff, put stuff away, all of that. And now imagine that, you know, the robot has some initial objective that the programmer gave it so they can do all these things functionally. It's capable of doing that. And now I notice that it's doing something and maybe it's coming too close to me. Right? And maybe I'm the designer, maybe I'm the end user and this robot is now in my home. And I push it away.

• 1:19:05Speaker 01

  So I push it away because, you know, it's a reaction to what the robot is currently doing. And this is what we call physical human robot interaction. And now there's a lot of, there's a lot of interesting work on how the heck do you respond to physical human robot interaction? What should the robot do if such an event occurs? And there's sort of different schools of thought. It's, well, you know, you can sort of treat it the control theoretically and say, this is a disturbance that you must reject. You can sort of treat it more kind of heuristically and say, I'm gonna go into some like gravity compensation mode so that I'm easily maneuverable around. I'm gonna go in the direction that the person pushed me. And, and to us, part of realization has been that that is signal that communicates about the reward.

• 1:19:47Speaker 01

  Because if my robot was moving in an optimal way and I intervened, that means that I disagree with its notion of optimality. Right? Whatever it thinks is optimal is not actually optimal. And sort of optimization problems aside, that means that the cost function, the reward function is, is incorrect. At least it's not what I want it to be.

• 1:20:10Lex Fridman?

  How difficult is that signal to?

• 1:20:14Lex Fridman?

  To interpret and make actionable. So like, I, cause this connects to autonomous vehicle discussion, whether in the semi-autonomous vehicle or autonomous vehicle, when the safety driver disengages the car, like, but they could have disengaged it for a million reasons.

• 1:20:28Speaker 01

  Yeah, yeah, so that's true. Again, it comes back to, can you, can you structure a little bit your assumptions about how human behavior relates to what they want? And you, you know, you can't, one thing that we've done is literally just treated this external torque that they applied as, you know, when you take that and you add it with what the torque the robot was already applying, that overall action is probably relatively optimal in respect to whatever it is that the person wants. And then that gives you information about what it is that they want. So you can learn that people want you to stay further away from them. Now, you're right that there might be many things that explain just that one signal and that you might need much more data than that for, for, for the person to be able to

• 1:21:12Speaker 01

  shape your reward function over time. You can also do this info gathering stuff that we were talking about. Not that we've done that in that context, just to clarify, but it's definitely something we thought about where you can have the robot start acting in a way, like if there are a bunch of different explanations, right? It moves in a way where it sees if you corrected in some other way or not, and then kind of actually plans its motion so that it can disambiguate and collect information about what you want. Anyway, so that's one way, that's kind of sort of leaked information, maybe even more subtle leaked information is if I just press the e-stop, right? I just, I'm doing it out of panic because the robot is about to do something bad. There's again, information there, right?

• 1:21:55Speaker 01

  Okay, the robot should definitely stop, but it should also figure out that whatever it was about to do was not good. And in fact, it was so not good that stopping and remaining stopped for a while was better, a better trajectory for it than whatever it is that it was about to do. And that again is information about what are my preferences? What do I want?

• 1:22:14Lex Fridman?

  Speaking of e-stops, what are your expert opinions on the three laws of robotics from Isaac Asimov? That don't harm humans, obey orders, protect yourself. I mean, it's a, it's such a silly notion, but I speak to so many people these days, just regular folks, just, I don't know, my, my parents and so on about robotics. And they kind of operate in that space of.

• 1:22:40Lex Fridman?

  You know, imagining our future with robots and thinking, what are the ethical? How do we get that dance right? I know the three laws might be a silly notion, but do you? Do you think about like what universal reward functions there might be that we should enforce on the robots of the future? Or is that a little too far out? And it doesn't? Or is the mechanism that you just described? There shouldn't be three laws, it should be constantly adjusting kind of thing.

• 1:23:12Speaker 01

  I think it should constantly be adjusting. And I think that, you know, the issue with the laws is I don't even, you know, they're words and I have to write math and have to translate them into math. What does it mean to?

• 1:23:24Lex Fridman?

  What does harm mean? What is?

• 1:23:27Speaker 01

  Obey.

• 1:23:28Lex Fridman?

  It's not math.

• 1:23:29Speaker 01

  Obey what? Right? Because we just talked about how you try to say what you want, but you don't always get it right. And you want these machines to do what you want, not necessarily exactly what you're literally, so you want to, you don't want them to take you literally. You want to take what you say and interpret it in context. And that's what we do with the specified rewards. We don't take them literally anymore from the designer. We, not we as a community, we as, you know, some members of my group. We, and some of our collaborators, like Peter Beal and Stuart Russell. We sort of say, okay, the designer specified this thing, but I'm gonna interpret it not

• 1:24:12Speaker 01

  as this is the universal reward function that I shall always optimize always and forever, but as this is good evidence about what the person wants. And I should interpret that evidence in the context of these situations that it was specified for. Because ultimately, that's what the designer thought about. That's what they had in mind. And really, them specifying reward function that works for me in all these situations is really kind of telling me that whatever behavior that incentivizes must be good behavior in respect to. The thing that I should actually be optimizing for. And so now the robot kind of has uncertainty about what it is that it should be, what its reward function is. And then there's all these additional signals we've been finding

• 1:24:54Speaker 01

  that it can kind of continually learn from and adapt its understanding of what people want. Every time the person corrected, maybe they demonstrate, maybe they stop, hopefully not, right? One really, really crazy one is the environment itself. Like our world, you don't, it's not, you know, you observe our world and the state of it. And it's not that you're seeing behavior and you're saying, oh, people are making decisions that are rational, blah, blah, blah. It's, but, but, but our world is something that we've been acting with according to our preferences. So I have this example where like the robot walks into my home and my shoes are laid down on the floor kind of in a line, right?

• 1:25:38Speaker 01

  It took effort to do that. So even though the robot doesn't see me doing this, you know, actually aligning the shoes, it should still be able to figure out that I want the shoes aligned. Because there's no way for them to have magically instantiated themselves in that way. Someone must have actually

• 1:25:58Lex Fridman?

  Yes.

• 1:25:58Speaker 01

  Taken the time to do that. So it must be important.

• 1:26:01Lex Fridman?

  So the environment actually tells, the environment

• 1:26:04Speaker 01

  Leaks information.

• 1:26:05Lex Fridman?

  Leaks information. I mean, the environment is the way it is because humans somehow manipulated it. So you have to kind of reverse engineer the narrative that happened to create the environment as it is. And that leaks the

• 1:26:16Speaker 01

  Yeah, yeah.

• 1:26:16Lex Fridman?

  Preference information.

• 1:26:17Speaker 01

  Yeah. And you have to be careful.

• 1:26:19Lex Fridman?

  Yeah.

• 1:26:20Speaker 01

  Right? Because, because people don't have the bandwidth to do everything. So just because, you know, my house is messy, doesn't mean that I want it to be messy, right? But that just, but that, it's because I, you know, I didn't put the effort into that. I put the effort into something else. So the robot should figure out, well, that's something else was more important. But it doesn't mean that, you know, the house being messy is not. So it's a little subtle, but yeah, we really think of it, the state itself is, is kind of like a choice that people implicitly made about how they want their world.

• 1:26:49Lex Fridman?

  What book or books, technical or fiction or philosophical, had a, when you like, look back at your life, had a big impact, maybe it was a turning point, was inspiring in some way, maybe we're talking about some silly book that nobody in their right mind would want to read, or maybe it's a book that you would recommend to others to read, or maybe those could be two different recommendations that, of books that could be useful for people on their journey.

• 1:27:18Speaker 01

  When I was in, this is kind of a personal story, when I was in 12th grade, I got my hands on a PDF copy in Romania of Russell Norvig, AI Modern Approach. I didn't know anything about AI at that point. I was, you know, I had watched the movie The Matrix. That was my exposure. And, and so I started going through this thing, and you know, you were asking in the beginning, what are, what are, you know, it's math and it's algorithms, what's interesting. It was so captivating. This notion that you could just have a goal

• 1:27:59Speaker 01

  and figure out your way through a kind of a messy, complicated situation, sort of what sequence of decisions you should make to autonomously to achieve that goal. That was so cool. I'm, you know, I'm biased, but that's a cool book.

• 1:28:17Lex Fridman?

  Yeah, you can convert, you know, the goal, the goal of intel, the process of intelligence and mechanize it. I had the same experience. I was really interested in psychiatry and trying to understand human behavior. And then AI Modern Approach is like, wait, you can just reduce it all to-

• 1:28:33Speaker 01

  You can write math about human behavior, right?

• 1:28:36Lex Fridman?

  Yeah.

• 1:28:36Speaker 01

  Yeah. So that's, and I think that stuck with me because, you know, a lot of what I do, a lot of what we do in my lab is write math about human behavior, combine it with data and learning, put it all together, give it to robots to plan with and, you know, hope that instead of writing rules for the robots, writing heuristics, designing behavior, they can actually autonomously come up with the right thing to do around people. That's kind of our, you know, that's our signature move. We wrote some math and then instead of kind of handcrafting this and that and that, and the robot figuring stuff out and isn't that cool? And I think that is the same enthusiasm that I got from the robot figuring out how to reach that goal in that graph. Isn't that cool?

• 1:29:19Lex Fridman?

  So apologize for the romanticized questions, but and the silly ones. If a doctor gave you five years to live, sort of emphasizing the finiteness of our existence, what would you try to accomplish?

• 1:29:37Speaker 01

  It's like my biggest nightmare, by the way. I really like living. So I'm actually, I really don't like the idea

• 1:29:45Lex Fridman?

  Dying?

• 1:29:45Speaker 01

  of being told that I'm gonna die.

• 1:29:48Lex Fridman?

  Sorry to linger on that for a second. Do you, I mean, do you meditate or ponder on your mortality or our human? The fact that this thing ends, it seems to be a fundamental feature. Do you think of it as a feature or a bug too? Is it, you, you said you don't like the idea of dying, but if I were to give you a choice of living forever, like you're not allowed to die.

• 1:30:09Speaker 01

  Yeah, now I'll say that I want to live forever, but I watch this show, it's very still, it's called The Good Place. And they reflect a lot on this. And, you know, the, the moral of the story is that you have to make the afterlife be finite too, because otherwise people just kind of, it's like Wally. It's like, ah, whatever. Fly around. So, so I think the finiteness helps. But, um, but yeah, it's just, um, you know, I don't, I don't, I'm not a religious person. I don't think that there's something after. And so I think it just ends and you stop existing. And I really like existing. It's just, it's such a great privilege to exist. That, um, that, yeah, it's just, I think that's, that's the scary part.

• 1:30:52Lex Fridman?

  I still think that we, we like existing so much because it ends. And that's so sad. Like, it's so sad to me every time. Like, I find almost everything about this life beautiful. Like, the silliest, most mundane things are just beautiful. And I think I'm cognizant of the fact that I find it beautiful because it ends.

• 1:31:12Lex Fridman?

  Like it, and it's so, I don't know, I don't know how to feel about that. I also feel like there's a lesson in there for robotics and AI. That is not like, the finiteness of things seems to be a fundamental nature of human existence. I think some people sort of accuse me of just being Russian and melancholic and romantic or something, but that seems to be a fundamental nature of our existence that should be incorporated in our reward functions. But anyway, if you were, speaking of reward functions, if you only had five years, what would you try to accomplish?

• 1:31:55Speaker 01

  This is the thing, I, I'm thinking about this question and have a pretty joyous moment because I don't know that I would

• 1:32:04Lex Fridman?

  Change anything?

• 1:32:04Speaker 01

  Change much.

• 1:32:05Lex Fridman?

  Oh, that's a beautiful thing.

• 1:32:06Speaker 01

  I'm, I'm, you know, I'm trying to make some contributions to how we understand human AI interaction. I don't think I would change that. Maybe I'll take, you know, I'd take more trips to the Caribbean or something, but I try to some of that already from time to time. So, yeah, I mean, I try to do the things that bring me joy and thinking about these things bring me joy is the Mary Kondo thing, you know, don't do stuff that doesn't spark joy. For the most part, I do things that spark joy. Maybe I'll do like less service in the department or something. I'd be like, death card. I'm not dealing with admissions anymore.

• 1:32:48Speaker 01

  But, but no, I mean, I think I have amazing colleagues and amazing students and amazing family and friends and kind of spending time and some balance with all of them is what I do and that's what I'm doing already. So I don't know that I would really change anything.

• 1:33:05Lex Fridman?

  So on the spirit of positiveness, what small act of kindness, if one pops to mind, will you want to shown that you will never forget?

• 1:33:14Speaker 01

  Hmm. When I was in high school, my friends, my classmates, did some tutoring. We were gearing up for our baccalaureate exam. And they did some tutoring on, well, some on math, some on whatever. I was comfortable enough with with some of those subjects, but physics was something that I hadn't focused on in a while. And so they were all working with this one teacher. And I started working with that teacher. Her name is Nicole Buchanan. And she she was the one who kind of opened up this whole world for me.

• 1:33:56Speaker 01

  Because she sort of told me that I should take the SATs and apply to go to college abroad and, you know, do better on my English and all of that. And when it came to, well, financially, I couldn't. My parents couldn't really afford to do all these things. She started tutoring me on physics for free. And on top of that, sitting down with me to kind of train me for SATs and all that jazz that she had experience with.

• 1:34:27Lex Fridman?

  Wow. And obviously, that has taken you to be to here today, also one of the world experts in robotics. It's funny, those little.

• 1:34:38Speaker 01

  Yeah, people do these these small or large.

• 1:34:40Lex Fridman?

  For no reason, really.

• 1:34:41Speaker 01

  Acts of kindness.

• 1:34:42Lex Fridman?

  Just out of karma.

• 1:34:45Speaker 01

  Wanting to support someone, yeah.

• 1:34:47Lex Fridman?

  Yeah. So we talked a ton about reward functions. Let me talk about the the most ridiculous big question. What is the meaning of life? What's the reward function under which we humans operate? Like, what, maybe to your life, maybe broader to human life in general? What do you think?

• 1:35:09Lex Fridman?

  What gives life fulfillment, purpose, happiness, meaning?

• 1:35:14Speaker 01

  You can't even ask that question with a straight face. That's how ridiculous this is like.

• 1:35:18Lex Fridman?

  I can't, I can't.

• 1:35:19Speaker 01

  Okay, so.

• 1:35:22Speaker 01

  You know.

• 1:35:22Lex Fridman?

  You're gonna try to answer it anyway, aren't you?

• 1:35:26Speaker 01

  So I was in a planetarium once.

• 1:35:31Lex Fridman?

  Yes.

• 1:35:31Speaker 01

  And, you know, they show you the thing and then they zoom out and zoom out and this whole like you're a speck of dust kind of thing. I think I was conceptualizing that we're kind of, you know, what are humans? We're just on this little planet, whatever. We don't matter much in the grand scheme of things. And then my mind got really blown because they talked about this multiverse theory where they kind of zoomed out and were like, this is our universe. And then like, there's a bazillion other ones and it's just these pop in and out of existence. So like our whole thing that's that we can't even fathom how big it is was like a blimp that went in and out. And at that point, I was like, okay, like, I'm done. This is not, there is no meaning.

• 1:36:11Speaker 01

  And clearly what we should be doing is try to impact whatever local thing we can impact. Our communities leave a little bit behind there, our friends, our family, our local communities, and just try to be there for other humans. Because I just, everything beyond that seems ridiculous.

• 1:36:30Lex Fridman?

  I mean, are you like, how do you make sense of these multiverses? Like, are you inspired by the immensity of it? That do you, I mean, you, is there, um,

• 1:36:44Lex Fridman?

  like, is it amazing to you? Or is it almost paralyzing in the mystery of it?

• 1:36:51Speaker 01

  It's frustrating. I'm frustrated by my inability to comprehend.

• 1:36:59Speaker 01

  It just feels very frustrating. It's like, there's, there's some stuff that, you know, we should time, blah, blah, blah, that we should really be understanding. And I definitely don't understand it, but you know, the, the, the amazing physicists of the world have a much better understanding than me. But it's still in epsilon and the grand scheme of things. So it's very frustrating. It's just, it sort of feels like our brains don't have some fundamental capacity yet. Well, yet or ever, I don't know, but.

• 1:37:25Lex Fridman?

  Well, this, one of the dreams of artificial intelligence is to create systems that will aid, expand our cognitive capacity in order to understand the, build the, the theory of everything, what the physics, and understand what the heck these multiverses are.

• 1:37:42Lex Fridman?

  So I think there's no better way to end it than talking about the meaning of life and the fundamental nature of the universe and multiverses.

• 1:37:49Speaker 01

  And the multiverse.

• 1:37:50Lex Fridman?

  So Anka, it's a huge honor, one of my favorite conversations I've had. I really, really appreciate your time. Thank you for talking today.

• 1:37:59Speaker 01

  Thank you for coming. Come back again.

• 1:38:02Lex Fridman?

  Thanks for listening to this conversation with Anka Dugan. And thank you to our presenting sponsor, Cash App. Please consider supporting the podcast by downloading Cash App and using code LEXPODCAST. If you enjoy this podcast, subscribe on YouTube, review it with five stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter at @LexFriedman. And now, let me leave you with some words from Isaac Asimov. "Your assumptions are your windows in the world. Scrub them off every once in a while, or the light won't come in." Thank you for listening and hope to see you next time.