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by simonw
832 days ago
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I love this essay so much. I read it 22 years ago and it's been stuck in my mind ever since: it taught me that any time you take on a new abstraction that you don't understand, you're effectively taking on mental debt that is likely to come due at some point in the future. This has made me quite a bit more cautious about the abstractions I take on: I don't have to understand them fully when I start using them, but I do need to feel moderately confident that I could understand them in depth if I needed to. And now I'm working with LLMs, the most opaque abstraction of them all! |
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You put a black box around it to fit it into the world of abstractions that traditional programs live in.
But I'd say the most interesting thing about neural networks is that they do not have any abstractions within them. They're programs, but programs created by an optimization algorithm just turning knobs to minimize the loss.
This creates very different kinds of programs - large, data-driven programs that can integrate huge amounts of information into their construction. It's a whole new domain with very different properties than traditional software built out of stacked abstractions.