01:04:54 · Karol Hausman
[01:04:54] S0: I think in general, don't pay enough attention to this problem of liability. And that that's maybe a big difference between something like a language model or a chatbot and a robot. When you talk to a chatbot and it makes a small mistake or it just like puts not quite the right word, it's totally fine because you're right there and you can absorb all of this. You can correct it. You can ask it again, or you can you can interpret it the right way. You're very forgiving. And the physical world is is the opposite of that. That's not very forgiving. If you make any small mistake, if you know, if you grab the porta filter just like a little bit off or you don't insert it just quite right, then the whole task is going to fail in a catastrophic way. So I think the the bar on performance is just much much higher because there isn't someone to help you, someone to catch you. And I think this is a very big problem. Now the the other thing you point out is more about the model being self aware of where it's where it's not going well. And the power of reinforcement learning is that it very often needs to have an additional function that tries to predict how close you are to success. We call this a value function. And that value function should have an understanding of how well you're doing so that then you can improve upon it. And what we found so far is that you can train these value functions to be pretty good. You can train them the extent where we don't fully know that the robot is about to fail yet, that everything is looking okay, but the value function already knows. And they start seeing that, like, the the the the expectation of of of success is starting to go down. This is actually something that we've also seen with with works like AlphaGo, where we had a computer program learn how to play Go. Where if you if you watch some of the games that this AI system had against the the the champion at at Go, What was quite intriguing is that in many of these games, you have world's experts at Go talking about and commentating on the game. They're all thinking that the game is basically head to head that's like, you know, it's very unclear who is winning, whether it's AI or Lee Sedol. Then But you look at the value function predictions, and it's basically over. Like, the the AI system knows 100% that the game is over.
01:07:10 · Karol Hausman
It's already won by AI, and Lee Sedol has no chance. But the entire world, you know, all the experts, including probably Lee Sedol himself think that that this is very, very close game. So I think we will probably get to to similar models in in robots where they will be able to predict how well they're doing or how likely the success is much better than we will. One of
01:07:31 · Mario Gabriele
[01:07:31] S1: my favorite activities to do with my my son is he loves to look at my bookcase and just pull out whatever covers he finds interesting. He's, you know, 15 old, so he's just doing it based on, you know, what interesting faces on the front or whatever. And he's gotten really interested in Oliver Sacks' biography called On the Move. Because, I don't know, it's a picture of Oliver Sacks. It's, you know, engaging. And I was just paging through it with him, like, last week, and I was already starting to think quite a lot about our conversation. And it lands on this page where Oliver Sacks talks about being obsessed with proprioception, and how this is, you know, the sixth sense, and it's actually the most important sense because, you know, you can strip humans of of all the other five, and they can more or less get a... Get along. But if you, you know, get rid of this sense of where your body is, life becomes extremely hard. And he talks about working with these patients who, you know, maybe have a virus and they lose this this sense of of their own their own body. And he has this one case, which made me think a lot about what you're doing and and maybe what the, final state of it might look like, which is this guy gets a virus, and he loses the sense of proprioception, but he compensates with visuals where, you know, if the lights go off in a room, he can no longer move. But as long as he can see where he is, he can sort of walk, but not walk and talk, etcetera, etcetera. And so it's it's basically the way that the the brain compensates for the loss of this sense. Is that sort of the stage we're at at the moment with physical intelligence where we don't have true proprioception, and so we're sort of compensating with these other senses. And is there a point in the future, do you think, where however a computer is able to emulate that, we have something that really feels equivalent to it. Yeah.
01:09:31 · Karol Hausman
[01:09:31] S0: There are many neuroscientific experiments showing something like this, how you can you can disable one sensing modality and then compensate for compensate for it with another one. I think in general, it's quite unclear what the right sensing modalities should be. I think our intuitions are often wrong about this because also these machine learning algorithms can extract the right level of information from signals that to us seem like insufficient. Like, a common question I get is, you know, about touch sensing on our robots. And instead, we put wrist cameras, and it turns out that they can compensate for the lack of touch just fine. And they probably find some kind of regularities in the data where maybe you see the deformation on the gripper or something like this that indicates to you how much pressure you're applying, and that's enough for it to to fully compensate for the sense of touch. It might be that we'll find some tests where that's not the case, and you really need some other sensing modality to tell you the the the... To to to to give you that signal. But so far, I think it's been quite surprising to the entire field how far you can push it with with just simple cameras. I think in general, though, a lot about intelligence is about compensating. It's... And what I mean by this is kinda like predicting what's going to happen and then trying to figure out if if what actually happened... How how different it is to what you predicted. And it doesn't just apply to sensing modalities. I think it also applies to precision. So for instance, as a as a human, you're very imprecise when it comes to movements. Right? Like, if I ask you to put your finger on a specific point 100 times in a row and I measure this exactly, there will be quite a bit quite a bit of variance, especially compared to modern industrial robots that can go to sub millimeter accuracy and very good repeatability. But if you compare our dexterity to dexterity of those robots, you know, it's night and day. We're way more dexterous even though we're way less precise of a machine. And I think the reason for this is that we have physical intelligence that's gonna compensate for it. We have enough feedback that if we just look where where our fingers are or if we just feel it, we can compensate for it immediately.
01:11:44 · Karol Hausman, Mario Gabriele
And I think we'll see that being translated to robot design as well, where maybe the the robots of the future don't need to be as precise as we had thought or, you know, they they can they can accommodate for a lot of things like a little bit of backlash in the motors or just noise because with the right intelligence, you can you can compensate for it. And that's what we've seen here at at Physical Intelligence. We show some of the most impressive demos ever on robots. But these robots are really bad robots. If you compare them to to any state of the art industrial machine, they're really, really bad. They're very imprecise, not very reliable, a lot of backlash and other problems, but it doesn't really matter because the right intelligence can compensate for it. [01:12:28] S1: Well, you're... That was a very interesting and gracious way of answering a very baggy question from me. So I'm glad I asked it because, yeah, that was that was fascinating. As a final wrap up question, I always like to ask folks that if they had the chance to assign a book to everyone on Earth to read and know that they would understand it, what is a book you would you would love to give everyone? I reread recently a book that I'm
01:12:28 · Mario Gabriele, Karol Hausman, Mario Gabriele
[01:12:28] S1: Well, you're... That was a very interesting and gracious way of answering a very baggy question from me. So I'm glad I asked it because, yeah, that was that was fascinating. As a final wrap up question, I always like to ask folks that if they had the chance to assign a book to everyone on Earth to read and know that they would understand it, what is a book you would you would love to give everyone? I reread recently a book that I'm [01:12:56] S0: a big fan of. I'm not sure if this is the the book I would recommend to everyone, but this is the book I think it's... That is really, really good. It's Why Greatness Cannot Be Planned by by Ken Stanley. Yes. Yeah. I think it's just a wonderful counterintuitive book that shows multiple examples of how we arrive at something really spectacular without planning for it. I find it quite inspiring and quite motivational in a way that is maybe a little less straightforward than than than usual. So I think it's just like a very insightful book that would that that would help a lot of people to to show... Shed a new light on how to think about innovation and achieving something spectacular. [01:13:41] S1: Well, that's a perfect place to end. Thank you so much, Karol. [01:13:45] S0: Thank you. [01:13:46] S2: That's it. Thank you for