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This matches my experience. Today Codex generated a PR for us that fixed a bug, but it missed three more instances of the same bug, broke some functionality with the fix, and didn't have a hint of the work we'd need to do to reenable that functionality for existing users while migrating them to a newer implementation with wider compatibility.
BUT: all that extra work won't take nearly as long as it would have taken the human Codex user to find the initial bug. Still a big win.
At this rate, though, how long will it be until we don't need the human user? Centaur chess lasted maybe a decade, being generous, but at this point last year AI had only basic "much better search engine" utility for me, and at this point two years ago it was downright counterproductive to try to sort out real answers from hallucinations. Where will we be in another five years?
No, but somehow these days they're tuning their final models to get to "I don't know" anyways. Maybe they're not just glorified autocomplete? 10 months ago was the first time I got an LLM to admit it couldn't answer a question of mine (although it did still make helpful suggestions); not only did the other models back then give me wrong answers, IIRC one of them went on to gaslight me about it rather than admit the mistake. (two years ago this gaslighting would have been the rule rather than the exception) IMHO that "I don't know" was the exact point at which AI started to have positive utility for me. Sometimes an AI still isn't helpful, but it's at least often worth throwing a problem at now, not a waste of time.
That's good to hear. I'm not saying that the hallucination problem can't be mitigated, I'm just saying that it's a struggle and it's likely to continue to be a struggle, even if LLMs continue to get smarter for a long time. The way I think of it - which is definitely an oversimplification but possibly a useful one - is that next-token prediction really isn't the kind of intelligence that we wanted to develop, but it's what we discovered first. So in some ways - keeping models focused on tasks, preventing malicious usage, learning in real time, avoiding hallucinations - we're paying the cost of trying to pound that square peg into a round hole. With enough effort, paying enough training/inference costs, we often can do it. But perhaps at some point we'll discover a different framework for AI that better matches our own sapience at lower cost.
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