Built CS231n.

Transcript context

…You’re imagining in the short term that instead of a tutor being able to probe your understanding, if you have enough self-awareness to be able to probe yourself, you’re never going to be stuck. You can find the right answer between talking to the TA or talking to an LLM and looking at the reference implementation. It sounds like automation or AI is not a significant part. So far, the big alpha here is your ability to explain AI codified in the source material of the class. That’s fundamentally what the course is. You always have to be calibrated to what capability exists in the industry. A lot of people are going to pursue just asking ChatGPT, etc. But I think right now, for example, if you go to ChatGPT and you say, teach me AI, there’s no way. It’s going to give you some slop. AI is never going to write nanochat right now. But nanochat is a really useful intermediate point. I’m collaborating with AI to create all this material, so AI is still fundamentally very helpful. Earlier on, I built CS231n at Stanford, which I think was the first deep learning class at Stanford, which became very popular. The difference in building out 231n then and LLM101N now is quite stark. I feel really empowered by the LLMs as they exist right now, but I’m very much in the loop. They’re helping me build the materials, I go much faster. They’re doing a lot of the boring stuff, etc. I feel like I’m developing the course much faster, and it’s LLM-infused, but it’s not yet at a place where it can creatively create the content. I’m still there to do that. The trickiness is always calibrating yourself to what exists. When you imagine what is available through Eureka in a couple of years, it seems like the big bottleneck is going to be finding Karpathys in field after field who can convert their understanding into these ramps.…

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