YouSaid · the spoken record

Sergey Levine

lines on the record: 110
first: 2025-09-12
most recent: 2025-09-12
sittings or episodes: 1
sources: podcast

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“Well, what education gives you is flexibility. So it's less about the particular facts you know as it is about your ability to acquire skills, acquire understanding. So it has to be good education.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Automation showing up in all sorts of places, probably not the places we expect first. So I think that the constants here that I think are really important is education is really, really valuable Like education is the best buffer somebody has against the negative effects of change. So if there is like one single lever that we can pull collectively as a society, it's more education because that's true.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I think at some level, that's a very reasonable way to look at things. But I think that if there's one thing that I've learned about technology, it's that Rarely evolves quite the way that people expect, and sometimes the journey is just as important as the destination. So I think it's actually very difficult to plan ahead for an end state. But I think directionally what you said makes a lot of sense. And I do think that it's very important for us collectively to think about how to structure the world around us in a way that is amenable to greater and greater automation across all sectors. But I think we should really think about the journey just as much as the destination, because things evolve in all sorts of unpredictable ways. And we'll find”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“like AI. And we Are tempted to lose track of other things, including things you've said, like, hey, there's a hardware component, there's an infrastructure component with compute and things like that. So I think that in general it's good to have a more holistic view of these things, and I wish we had more holistic conversations about that sometimes.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, yeah, and this is why I said before that I think something really important to get right here is a balanced robotics ecosystem. I think AI is tremendously exciting, but I think we should also recognize that getting AI right is not the only thing that we need to do. And we need to think about how to balance our priorities, our investment, the kind of things that we spend our time on. Just as an example, at physical intelligence, we do take hardware very seriously, actually. We build a lot of our own things, and we want to have a hardware roadmap alongside our AI roadmap. But I think that that's just us. I think that for the United States, for arguably for human civilization as a whole, I think we need to think about these problems very holistically. And I think it is easy to get distracted sometimes when there's a lot of excitement, a lot of progress in one area.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“It seems like it is an easier problem to address than, for example, the problem of digital devices, where work goes into creating computers, phones, et cetera, but the computers and phones don't themselves help with the work.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, again, for the specifics of how we make that happen, I think that's a very long conversation that I'm probably not the most qualified to speak to, but I think that in terms of the ingredients, the ingredient here that I think is important is that Robots help with physical things, physical work. And if producing robots is itself physical work, then getting really good at robotics should help with that. It's a little circular, of course, and as with all circular things,”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“And then from there, we have to solve for all the details that will help us get there. And that's not easy. I think there's a lot of complicated decisions that need to be made in terms of private industry, in terms of investment, in terms of the political dimension. But I'm very optimistic about it because it's like it seems to me the light at the end of the tunnel is kind of in the right direction.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Long term vision and the right kind of balance of investment. But what makes me really optimistic about this is that final state, that if I think we can all agree that in the United States we would like to have the kind of society where people are highly productive, where we have highly educated people doing high value work. And because that end state seems to me very compatible with automation, with robotics, there's a lot of, at some level, there should be a lot of incentive to get to that state.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Now, that's kind of like a final state, like a desirable final state. Now, there's a lot of complexity in how you get to that state, how you make that an appealing journey to society, how you navigate the geopolitical dimension of that. Like all of that stuff is actually pretty complicated, and it requires making a number of really good decisions, like good decisions about investing in a balanced robotics ecosystem, supporting Both software innovation and hardware innovation. I don't think any of those are insurmountable problems. It just requires A degree of”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. So this is a very complex question. I'll start with the broader themes and then try to drill a little bit into the details. One broader theme here is that if you want to have an economy where You get ahead by having a highly educated workforce, by having people that have high productivity, meaning that for each person's hour of work, lots of stuff gets done. Automation is really, really good because automation is what multiplies the amount of productivity that each person has. Again, same as LM coding tools. LM coding tools amplify the productivity of a software engineer. Robots will amplify the productivity of basically everybody that is doing work.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“But I think it's something that will get more clarity on as things progress, because as we basically, the AI systems of today are not pushing the hardware to the limit. So as the AI systems get better and better, the harder will get pushed to the limit, and then we'll hopefully have a much better answer to your question.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a tough question to answer, mainly because things are changing so fast. I think that to me, the things that I spend a significant amount of time thinking about on the hardware side is really more like reliability and cost. It's not that I'm that worried about cost. It's just that cost translates to number of robots, which translates to amount of data. And being an ML person, I really like having lots of data. So I really like having robots that are low cost because then I can have more of them and therefore more data. And reliability is important more or less for the same reason.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Not right now. Maybe there will be someday. Really like maybe I'm being idealistic, but I would really like to see a world where there's a lot of heterogeneity”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I think my job as I see it right now is to figure out what sort of the minimal package we can get away with. And I really like to think about robots in terms of minimal package because I don't think that we will have the one ultimate robot, like sort of the mechanical person, basically. I think what we will have is a bunch of things that good effective robots need to satisfy, just like good smartphones need to have a touchscreen. Like that's something that we all kind of agreed on. And then a bunch of other stuff that's kind of optional depending on the need, depending on the cost point, et cetera. And I think there will be a lot of innovation where once we have very capable AI systems that can be plugged into any robot to endow it with some basic level of intelligence, then lots of different people can innovate on how to get the robot hardware to be optimal for each niche it needs to be.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, maybe you still want more than that, but still finding the bare minimum that still lets you have good functionality, that's important. That's in the question mark box. And there's some things that I think we probably don't need. We probably don't need the robot to be super duper precise because we know that feedback can compensate for that.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think it's something that is worth thinking about, and a particularly important question for researchers like myself is how can AI affect how we think about hardware. Because there are some things that I think are going to be really, really important. Like you probably want your thing to not break all the time. There are some things that are firmly in that category of question marks, like how many fingers do we need? You said yourself before that you were kind of surprised that a robot with two fingers can do a lot.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, economies are very good at filling demand when there's a lot of demand, right? How many iPhones were in the world in 2001?”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I don't know the answer to that question, but it's also a tricky question to answer because not all arms are made equal. Like, arguably the kind of robots that are assembling cars in a factory are just not the right kind to think about.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“That is a great question for my co founder, Anan Ismail, who is probably like the best person, arguably in the world, to ask that question of.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, there are a few things. So one, of course, has to do with economies of scale, so custom built, high-end research hardware, of course, is going to be much more expensive than... Kind of more productionized hardware. But the other, and then, of course, there's a technological element that as we get better at building actuated machines, they become cheaper. But there's also a software element, which is the smarter your AI system gets, the less you need the hardware to satisfy certain requirements. So traditional robots in factories, they need to make motions that are highly repeatable, and therefore it requires a degree of precision and robustness that you don't need if you can use cheap visual feedback. So, AI also makes robots more affordable and lowers the requirements on the hardware.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“We need it for that. These are very tough questions. And also Economies of scale and robotics so far have not functioned the same way that they probably would in the long term. Just to give you an example, when I started working in robotics in 2014, I used a very nice research robot called a PR2 that cost $400,000 to purchase. When I started my research lab at UC Berkeley, I bought robot arms that were $30,000, the kind of robots that we are using now at physical intelligence. Each arm costs about $3,000, and we think they can be made for a small fraction of that So these things”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“You can, you know, for example, I'm not an expert on data centers by any means, but you could build your data centers in a very remote location because the robots don't have to worry about whether there's like a shopping center nearby.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“It possible. I mean, in principle, quite a lot, right? I think that we have a tendency sometimes to think about robots as like mechanical people. But that's not the case, right? Like, people are people, and robots are robots. The better analogy for the robot, it's like your car or a bulldozer. It has much lower maintenance requirements. You can put them into all sorts of weird places, and they don't have to look like people at all. You can make a robot that's 100 feet tall. You can make a robot that's tiny. If you have the intelligence to power very heterogeneous robotic systems, you can probably actually do a lot better than just having mechanical people in effect. And it can be a big productivity boost for the real people, and it can allow you to solve problems that are very difficult to solve now.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“That's cool. So you're basically saying, how much concrete should I buy now to build the data center so that by 2030 I can power all the robots? Yeah, yeah. That is a more ambitious way of thinking about it than that has occurred to me. But it's a cool question. I mean, the good thing, of course, is that the robots can help you build that stuff. Right.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Optimal decision making at its core, regardless of how you do it, requires considering counterfactuals. You basically have to ask yourself, if I did this instead of that, would it be better? And you have to answer that question somehow. And whether you answer that question by using a learned simulator or whether you answer that question by using a value function or something like that, by using a reward model, in the end, it's kind of all the same. As long as you have some mechanism for considering counterfactuals and figuring out which counterfactual is better, you've got it. I like thinking about it this way because it kind of simplifies things. It tells us that the key is not necessarily to do really good simulation. The key is to figure out how to answer counterfactuals.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, yeah, I mean, certainly when you sleep, your brain does stuff that looks an awful lot, like what it does when it's awake, that looks an awful lot like playing back experience or perhaps generating new statistically similar experience. And so I think it's very reasonable to guess that perhaps simulation through a learned model is part of how your brain figures out counterfactuals basically. But something that's kind of even more fundamental than that is that”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So, in a sense, what you said, I think, is actually quite right in that a very powerful AI system can simulate a lot of stuff. But also at that point, it kind of almost doesn't matter because viewed as a black box, what's going on with that system is that information comes in and capability comes out. And whether the way processed that information is by imagining some stuff and simulating or by some model free method is kind of irrelevant in understanding its capabilities.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“In robotics, classically, people have often thought about simulation as a way to inject human knowledge, because a person knows how to write down the differential equations, they can code it up, and that gives the robot more knowledge than had before. But I think that increasingly what we're learning from experiences in other fields, from how the video generation stuff goes, from synthetic data for LLMs, is that actually probably the most powerful way to create synthetic experience is from a really good model because the model probably knows more than a person does about those fine-grained details. But then, of course, where does that model get the knowledge from experiencing the world?”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So here's what I would say that Deep down at a very fundamental level, the synthetic experience that you create yourself doesn't allow you to learn more about the world. It allows you to rehearse things. It allows you to consider counterfactuals, but somehow Information about the world needs to get injected into this system. And I think the way you pose this question actually elucidates this very nicely.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“But the reason they're able to leverage that synthetic data effectively is because they have this starting point that has train on lots of real data that kind of gets it. And once it gets it, then it's more able to leverage all this other stuff. So, I think perhaps ironically, the key to leveraging other data sources, including simulation, is to get really good at using real data, understand what's up with the world, and then now you can fruitfully use all the stimulus.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think that, again, we can look to the examples of what happened in other fields. Like these days, if someone trains an LLM for solving complex problems, they're using lots of synthetic data.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think that's actually the key. And coming back to your airplane pilot, the airplane pilot is trained on a real world objective. Their objective is to be a good airplane pilot, to be successful, to have a good career, and all of that kind of propagates back into the actions they take in leveraging all these other data sources. So what I think is actually the key here to leveraging auxiliary data sources, including simulation, is to build the right foundation model that is really good Has those immersion abilities. And to your point, to get really good like that, it has to have the right objective. Now, we know how to get the right objective out of real-world data. Maybe we can get out of other things, but that's harder right now.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“That's right. But here's the thing with this. There's a set of these ideas that are all going to be like something like train to make it better on the real thing by leveraging something else. And the key lynchpint for all of that is the ability to train it to be better on the real thing. The thing is, I actually suspect in reality we might not even do something quite so explicit because Middle learning is a merchant, as you pointed out before, right? Like LLMs essentially do a kind of metal learning via in context learning. I mean, we can debate as to how much that's learning or not, but the point is that large, powerful models trained on the right objective on real data get much better at leveraging all the other stuff.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“What I was trying to say. So I think what you're trying to say is basically that, well, maybe if we have a really smart model that's doing metal learning, perhaps it can figure out that its performance on a downstream problem, a real world problem, is increased by doing something in a simulator.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“And when we train models on data from multiple different domains, the models don't know that they're supposed to solve a particular task. They just see like, hey, here's one thing I need to master, here's another thing I need to master. So, maybe, like a better analogy there is if you're playing a video game where you can fly an airplane and then eventually someone puts you in the cockpit of a real one. It's not that the video game is useless, but it's not the same thing. And if you're trying to play that video game and your goal is to really master the video game, you're not going to go about it in quite the same way.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“This is a very subtle question. Your example with the airplane pilot using simulation is really interesting, but something to remember is that When a pilot is using a simulator to learn to fly an airplane, they're extremely goal directed. So their goal in life is not to learn to use a simulator. Their goal in life is to learn to fly the airplane. They know there will be a test afterwards, and they know that eventually they'll be in charge of like a few hundred passengers, and they really need to not crash that thing.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, and I should say that the coding is probably like the pinnacle of abstract knowledge work in the sense that just by the mathematical nature of computer programming, it's an extremely abstract activity, which is why people struggle with it so much.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“The second one is that understanding the physical world at a very deep fundamental level, at a level that goes beyond just what we can articulate with language, can actually help you solve other problems. And we experience this all the time. Like when we talk about abstract concepts, we say like, This company has a lot of momentum. We'll use social metaphors to describe inanimate objects. My computer hates me. Experience the world in a particular way and are subjective experience shapes how we think about in very profound ways, and then we use that as a hammer to basically hit all sorts of other nails that are far too abstract to handle any other way.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“I really hope that they will actually be the same. And obviously I'm extremely biased. I love robotics. I think it's very fundamental to AI. But I think that it's optimistically that it's actually the other way around, that the robotics... Element of the equation will make all the other stuff better. And there are two reasons for this that I could tell you about. One has to do with representations and focus. So what I said before, with video prediction models, if you just want to predict everything that happens, it's very hard to figure out what's relevant. If you have the focus that comes from the... Allows you to more fruitfully utilize the other signals. That could be extremely powerful”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think it makes total sense that we would expect basically any foundation model effort to follow the same trajectory where we first build out the foundation, essentially in a somewhat brute force way. And the stronger that foundation gets, the easier it is to then make it even better with much more accessible training”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“And again, this is not a new idea. This is exactly what we've seen with LLMs, right? LMs started off being trained purely with Next Token prediction, and that provided an excellent starting point, first for all sorts of synthetic data generation, and then for RL.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“So the key here is prior knowledge. So, in order to effectively learn from your own experience, it turns out that it's really, really important to already know something about what you're doing. Otherwise, it takes far too long. It's just like it takes a person when they're a child a very long time to learn very basic things, to learn to write for the first time, for example, once you already have some knowledge, then you can learn new things very quickly. The purpose of Training the models with supervised learning now is to build out that foundation that provides the prior knowledge so they can figure things out much more quickly later”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“A more basic level of abstraction. And again, this comes back to representations. Figure out which representations are sufficient for kind of planning and advancing and then unrolling, which representations require a tight feedback loop. And for that tight feedback loop, where are you doing feedback on? If I'm driving a vehicle, maybe I'm doing feedback on the position of a lane marker so that I stay straight. And then at a lower frequency, I sort of gauge where I am in traffic.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Plan out movements, there is definitely a real planning process that happens in the brain. If you record from a monkey brain, you will actually find neural correlates of planning. And there is something that happens in advance of a movement, and when that movement actually takes place, the shape of the movement correlates with what happened before the movement. Like that's planning, right? So that means that you put something in place and set the initial conditions of some kind of process and then unroll that process and that's the movement. And that means that during that movement, you're doing less processing and you kind of batch it up in advance. But you're not entirely in open loop. It's not like you're playing back a tape recorder. You are actually reacting as you go. You're just reacting at a different level of abstraction.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't know, but if I were to guess, I would guess that we'll actually see both, that we'll see low cost systems with offboard inference and more reliable systems, for example in settings where if you have an outdoor robot or something where you can't rely on connectivity that are costlier and have onboard inference. A few things I'll say. From a technical standpoint, that might contribute to understanding this. While a real time system obviously needs to be controlled in real time, often at high frequency, the amount of thinking you actually need to do for every time step might be surprisingly low. And again, we see this in humans and animals.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's also a really fascinating systems problem. To be truthful, I haven't gotten to the systems problem because you want to implement the system once you sort of know the shape of the machine learning. Solution, but I think there's a lot of cool stuff to do there.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Like figuring out the right representations, concisely representing both your past observations, but also changes in observation, right? Like, you know, your sensory stream is extremely temporally correlated, which means that the marginal information gained from each additional observation is not the same as the entirety of that observation because the image that I'm seeing now is very correlated to the image I saw before. So in principle, I want to represent it concisely. I can get away with a much more compressed representation than if I represent the images independently. So there's a lot that can be done on the algorithm side to get this right, and that's really interesting algorithms work.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“What's happened in the five years? I think there are a lot of things to this question. I think certainly there's a really fascinating systems problem. I'm by no means a systems expert, but I would imagine that the right architecture in practice, especially if you want an affordable, low-cost system, would be to externalize at least part of the thinking. You could imagine maybe in the future we'll have a robot that has your internet connection is not very good, the robot is in kind of like dumber reactive mode. But if you have a good internet connection, then it can be a little smarter. That's pretty cool. But I think there are also research and algorithms things that can help here.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source
“Mathematically, this highly parallel thing where you're doing perception and proprioception and planning all at the same time is actually necessarily needs to look that different from a transformer, although its practical implementation will be different. And you could imagine that the system will in parallel think about, okay, here's like my long-term memory, like here's what I've seen a decade ago, here's my short-term kind of spatial stuff, here's my semantic stuff, here's what I'm seeing now, here's what I'm planning. And all of that can be implemented in a way that there's some very familiar kind of attentional mechanism, but in practice all running in parallel may be at different rates, maybe with a more complex things, running slower, the faster reactive stuff running faster.”
2025-09-12 · Dwarkesh Podcast · Fully autonomous robots are much closer than you think – Sergey Levine · IDENTIFIED FROM THE TRANSCRIPT · source