Yann LeCun argues deep learning is essential for autonomous driving. He traces how learning replaced hand-engineered systems in OCR, speech, and vision, and contrasts today's heavily mapped, sensor-rich approaches like Waymo with a future built on self-supervised learning and reinforcement learning.
Foreign. Elon Musk is confident that large scale data and deep learning can solve the autonomous driving problem. What are your thoughts on the limits possibilities of deep learning in this space? It's obviously part of the solution. I mean, I don't think we'll ever have a self driving system, or at least not in the foreseeable future, that does not use deep learning. Maybe put it this way. So in the history of engineering, particularly sort of AI like systems, there's generally a first phase where everything is built by hand, then there is a second phase and that was the case for autonomous driving 20, 30 years ago. There's a phase where there's a little bit of learning is used, but there's a lot of engineering that's involved in kind of taking care of corner cases and putting limits, et cetera, because the learning system is not perfect. And then as technology progresses, we end up relying more and more on learning. That's the history of character recognition, it's the history of speech recognition now, computer vision, natural language processing. And I think the same is going to happen with autonomous driving that currently the methods that are closest to providing some level of autonomy, some decent level of autonomy, where you don't expect a driver to kind of do anything, is where you constrain the world. So you only run within 100 square kilometers or square miles in Phoenix, where the weather is nice and the roads are wide, which is what Waymo is doing. You completely over engineer the car with tons of lidars and sophisticated sensors that are too expensive for consumer cars, but they're fine. If you just run a fleet and you engineer the hell out of everything else, you map the entire world so you have complete 3D model of everything. So the only thing that the perception system has to take care of is moving objects and construction and sort of things that weren't in your map. And you can engineer a good slam system and all that stuff, right? So that's kind of the current approach that's closest to some level of autonomy. But I think eventually the long term solution is going to rely more and more on, on learning and possibly using a combination of self supervised learning and model based reinforcement or something like that.
