this post was submitted on 26 Jun 2023
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I guess the important thing to understand about spurious output (what gets called "hallucinations") is that it's neither a bug nor a feature, it's just the nature of the program. Deep learning language models are just probabilities of co-occurrence of words; there's no meaning in that. Deep learning can't be said to generate "true" or "false" information, or rather, it can't be meaningfully said to generate information at all.
So then people say that deep learning is helping out in this or that industry. I can tell you that it's pretty useless in my industry, though people are trying. Knowing a lot about the algorithms behind deep learning, and also knowing how fucking gullible people are, I assume that—if someone tells me deep learning has ended up being useful in some field, they're either buying the hype or witnessing an odd series of coincidences.
I think it can be useful. I have used it myself, even before chatgpt was there and it was just gpt 3. For example I take a picture, OCR it and then look for mistakes with gpt because it's better than a spell check. I've used it to write code in a language I wasn't familiar with and having seen the names of the commands needed I could fix it to do what I wanted. I've also used it for some inspiration, which I could also have done with an online search. The concept just blew up and people were overstating what it can do, but I think now a lot of people know the limitations.