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>> For those of us used to the continual disappointment that pre-LLM AI was, the current crop of LLM's are amazing, mind blowing things.

Aren't you overgeneralising a bit? Not even I would say that CNNs for image classification, or Deep-RL for board game-playing are a "continual disappointment" and they certainly predate LLMs. Are you talking about NLP? Even Neural Turing Machines were quite capable in language pairs with large parallel corpora (and similar linguistic structure).

Basically, what do you mean by "continual disappointment"? What I'm aware of is an incessant hype crescendo that crashing over everything like a relentless wave.



Pre-2022 most AI was disappointing not because it was unimpressive but because it near exclusively took the form of blog posts or university papers that announced something interesting but unusable. Either because it was merely an academic curiosity (AlphaGo) or because the tech wasn't being released for public usage at all, and wasn't really easy to replicate externally either, or because there was no obvious way to apply it to normal problems. So we all got used to this constant year-in-year-out stream of "amazing" "breakthroughs" that ended up being a thats-cool followed by a shrug.

RLHF trained GPT3 and then DALLE/Midjourney/Stable Diffusion changed all that. Suddenly AI not only got good, but the field broke loose of the inane and insane obsession with pseudo-safety that had been holding it back. Now the rest of us can use it without dropping $5M on a GPU cluster and hiring a dozen researchers first. AI is no longer a disappointment.




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