I don’t think the bottleneck is iterative testing more importantly than in language/image models, all of them need iterative testing to gather the massive amounts of data required to cover all the edge cases you could care about. Your second statement about robustness seems correct to me, I don’t think that self-driving is especially harder or easier than language modelling in some absolute way, it’s just that the bar to deployment is much higher for cars because mistakes cost a lot more. If you wanted language models to be 99.999% robust to weird bugs, that would also be ridiculously hard. But if you want to hear the opinion of people right next to the problem, here’s Andrej Karpathy, the (former) director of self-driving at Tesla, explaining why self-driving is hard.
I don’t think the bottleneck is iterative testing more importantly than in language/image models, all of them need iterative testing to gather the massive amounts of data required to cover all the edge cases you could care about. Your second statement about robustness seems correct to me, I don’t think that self-driving is especially harder or easier than language modelling in some absolute way, it’s just that the bar to deployment is much higher for cars because mistakes cost a lot more. If you wanted language models to be 99.999% robust to weird bugs, that would also be ridiculously hard. But if you want to hear the opinion of people right next to the problem, here’s Andrej Karpathy, the (former) director of self-driving at Tesla, explaining why self-driving is hard.