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LessWrong Curated Podcast

Jul 13, 2022

Over the last few years, deep-learning-based AI has progressed extremely rapidly in fields like natural language processing and image generation. However, self-driving cars seem stuck in perpetual beta mode, and aggressive predictions there have repeatedly been disappointing. Google's self-driving project started four years before AlexNet kicked off the deep learning revolution, and it still isn't deployed at large scale, thirteen years later. Why are these fields getting such different results?

Right now, I think the biggest answer is that ML benchmarks judge models by average-case performance, while self-driving cars (and many other applications) require matching human worst-case performance. For MNIST, an easy handwriting recognition task, performance tops out at around 99.9% even for top models; it's not very practical to design for or measure higher reliability than that, because the test set is just 10,000 images and a handful are ambiguous. Redwood Research, which is exploring worst-case performance in the context of AI alignment, got reliability rates around 99.997% for their text generation models.

By comparison, human drivers are ridiculously reliable. The US has around one traffic fatality per 100 million miles driven; if a human driver makes 100 decisions per mile, that gets you a worst-case reliability of ~1:10,000,000,000 or ~99.999999999%. That's around five orders of magnitude better than a very good deep learning model, and you get that even in an open environment, where data isn't pre-filtered and there are sometimes random mechanical failures. Matching that bar is hard! I'm sure future AI will get there, but each additional "nine" of reliability is typically another unit of engineering effort. (Note that current self-driving systems use a mix of different models embedded in a larger framework, not one model trained end-to-end like GPT-3.)