00:36:44 · Jim Fan
[00:36:44] S1: Well, I get asked this question a lot, and thank you so much, Molly, for bringing this up. So I think the community tends to, in general, overestimate the short term and underestimate the long term. I will say I don't know for sure exactly when the GPT-4 moment for robotics will happen, but I know for sure when it will certainly happen. So here... Here's kind of my my reasoning. Right? So this year is 2025, and AlexNet was introduced in 2012. Actually, AlexNet was, you know, held as this milestone, right, huge revolutionary moment. But does the audience know how crappy AlexNet was? It's a horrible model. What AlexNet did is classifying objects, right, like one out of a thousand categories, like cats versus dogs versus airplanes, and its accuracy was only 62.5%, meaning that it confuses cats and dogs and airplanes more than a third of the time. It's completely unusable. Right? Even though it was a revolution, from a methodology point of view, performance was super bad. And then fast forward to 2025, what do we have today? We have LMs that pass the Turing test. We got the latest coding models and the recent models winning gold medals at IMO, and then AlphaFold just won a Nobel Prize, and that's what's happening in 2025. It's crazy what happened in the last twelve years, and then it so happens that technology doesn't progress linearly. It's exponential. So if we add... If we take 2025 as the middle point and we add thirteen years from now, that will be 2038, right, where we're in the middle between AlexNet and 2038, and then PhDs procrastinate, so let's round up. Give them two more years. By 2040, I believe the number of robots, intelligent robots, in the world will be more than the number of iPhones, and I say that with more than 95% certainty that it's gonna happen.
00:38:56 · Molly Welch
[00:38:56] S0: It's a provocative framing, and I want to think shorter term for a moment. When you think about models today and the next two to three years, many folks are thinking about what the next chapter of their journey looks like, whether that's starting a company or joining. Models' capabilities today, which application areas do you think have the right combination of market readiness and technical feasibility to be really tractable in the next two to three years?
00:39:26 · Jim Fan
[00:39:26] S1: That is a that is a great question. And let me also zoom in a little bit on the on the shorter time horizon. And, again, this year is 2025. And if we look back to May 2020, that was when GPT-three was first introduced. And from GPT-three to winning a gold medal at IMO, I think that roughly takes three roughly three iterations. Right? So there is SFT, and then there is ROHF, right, reinforcement learning from human preference, as kind of two big breakthroughs, and then reinforcement learning, like the the OpenAI O one and O three style, like RO for reasoning. So roughly three major model capability breakthroughs carried us from 2020 to 2025. So if we add five more year... Five more years, that will be 2030. So I think in terms of methodology, right, like the the breakthrough in robot foundation model, probably three takes five years will will get us there. So now what do we do in the next five years horizon in terms of applications if we have this moment of GPT-three for robot foundation models? I think the following application areas I'm most excited about. The first is programmable factories. I think factories and industrial use cases are probably the first to be tackled because they are more regular and also more structured and less demanding on safety. You can... You don't need humans to be around robots inside a factory. You can have the factory basically wall off, and then the robots operate themselves, so it doesn't need very strict safety criterion, And also, the manufacturing process is quite well understood with very good specifications, so it doesn't need too much generalization. It does need some generalization, but not too much. And if we have a GPT-three or GPT-four level robot foundation model, then we can quickly reprogram a set of intelligent robots so they can manufacture a new product almost overnight, and you can basically show the robots, let's say, a few demonstrations, and then you can program them. You program by demonstration, by data, instead of programming by hand, and that's why robots are very difficult to to be reprogrammed these days because people do it by hand, and then big factories are really hard to repivot. You need months, if not years, of expertise to reprogram something. But once we have these foundation models, the factories will be a lot more programmable, so I think that's one of the first areas.
00:42:06 · Jim Fan
And the second thing... Second area I'm excited about, also really regular, very structured, and isn't that demanding on safety, is self driving wet labs. A lot of scientific experiments, like, you know, in chemistry or in drug discovery, are still carried out by humans. Even though there are special equipment to do chemical mixing, that people don't do it by hand anymore. But think about those biology experiments. Still, there are a lot of humans in the process using those equipments. We can completely replace that with an automated robot pipeline, and it's gonna look very similar to a programmable factory, but this time applied to scientific discoveries. So you can collect data and program this whole line by demonstration instead of by hard coding. And then there is the agentic fleet. So we discuss a lot about single robot, but what about kind of having this life mind of a whole fleet of robots all working together? Then we can run these agentic flows, right, these multi agent flows controlled by a central O1 brain to break down and distribute a task to a whole fleet of robot so they can better support these factory assembly lines and also multistage, right, like scientific experimentation. And then finally, home use. I I think home use is just a lot harder to be fully automated. It's way more unstructured. Right? Every house is messy in a different way, and then you you for sure have a lot of safety concerns. Right? There are kids, elderly people, and these robots, they weigh a lot, and it's not fun having any of those robots fall down and fall on a person. So I think home use, and then there's also privacy and all of those concerns. I would say home use is still a bit out there, but I really wish that we can see that in five years. If not five years, definitely by 2040.
00:44:06 · Molly Welch
[00:44:06] S0: Very inspiring examples and application areas. Thank you, Jim. You've been listening to a special episode of Radical Talks from the Radical AI Founders Masterclass series. A special thank you to NVIDIA's director of AI and distinguished scientist, Jim. If you like what you hear, please be sure to subscribe and leave us a comment. Thanks for listening.