161–170 of 274 posts
Bayesian models solve this problem but they occupy model capacity which practitioners have traditionally preferred to devote to improving point estimates.
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1-turn instruction following and multi-turn instruction following are not the same exact capability, and some AIs only "get good" at the former. 1-turn gets more training attention - because it's more noticeable, in casual use and benchmarks both, and also easier to train for. With weak multi-turn instruction following, context data will often dominate over user instructions. Resulting in very "loopy" AI - and more s…
This is a good point, and to drive this home to people, if you have a conversation of this pattern: User: Fix this problem ... Assistant: X User: No, don't do X Assistant: Y User: No, Y is wrong too. Assistant: X It is generally pointless to continue. You now have a context that is full of the assistant explaining to you and itself why X and Y are the right answers, and much less context of you explaining why it is w…
I think often it's not required to completely start over: just identify the part where it goes off the rails, and modify your prompt just before that point. But yeah, basically the same process.
It's literally just a statistical model that guesses what you want based on the prompt and a whole bunch of training data.
If we want a black box that's AGI/SGI, we need a completely new paradigm. Or we apply a bunch of old-school AI techniques (aka. expert systems) to augment LLMs and get something immediately useful, yet slightly limited.
RIght now LLMs do things and are somewhat useful. Short of some expectations, butter than others, but yeah, a statistical model was never going to be more than the sum of its training data.
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Yeah I think our jobs are safe. Why doesn’t anyone acknowledge loops like this? They happen all the time and I’m only using it once a week at the most
> Yeah I think our jobs are safe. I give myself 6-18 months before I think top-performing LLM's can do 80% of the day-to-day issues I'm assigned. > Why doesn’t anyone acknowledge loops like this? Thisis something you run into early-on using LLM's and learn to sidestep. This looping is a sort of "context-rot" -- the agent has the problem statement as part of it's input, and then a series of incorrect solutions. Now wh…
This is going to age like "full self driving cars in 5 years". Yeah it'll gain capabilities, maybe it does do 80% of the work, but it still can't really drive itself, so it ultimately won't replace you like people are predicting. The money train assures that AGI/FSD will always be 6-18 months away, despite no clear path to solving glaring, perennial problems like the article points out.
The angle that being to detect confidently wrong, which then helps kicks off new learning is interesting. Has anyone had any success with continuous learning type AI products? Seems like there’s a lot of hype around RL to specialise.
There's no known good recipe for continuous learning that's "worth it". No ready-made solution for everyone to copy. People are working on it, no doubt, but it's yet to get to the point of being readily applicable.
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No. The experts in the field are past this argument. People have moved on. It is clear to everyone who builds LLMs that the AI is intelligent. The algorithm was autocomplete, but we are finding as an autocomplete bot is basically autocompleting things with humanity changing intelligent content. Your opinion is a minority now and not shared by people on the forefront of building these things. Your holding onto the ini…
LLMS don't "hallucinate" they generate a stochastic sequence of plausible tokens that, in context when read by a human, are a false statement or nonsensical. They also dont have an internal world model. Well I don't think so, but the debate is far from settled. "Experts" like the cofounders of various AI companies (whose livelihood depends on selling these things) seem to believe that. Others do not. https://aiguide.…
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> Yeah I think our jobs are safe. I give myself 6-18 months before I think top-performing LLM's can do 80% of the day-to-day issues I'm assigned. > Why doesn’t anyone acknowledge loops like this? Thisis something you run into early-on using LLM's and learn to sidestep. This looping is a sort of "context-rot" -- the agent has the problem statement as part of it's input, and then a series of incorrect solutions. Now wh…
> This looping is a sort of "context-rot" -- the agent has the problem statement as part of it's input, and then a series of incorrect solutions. While I agree, and also use your work around, I think it stands to reason this shouldn't be a problem. The context had the original problem statement along with several examples of what not to do and yet it keeps repeating those very things instead of coming up with a diffe…
In theory you should be able to get a multiplicative effect on context window size by consolidating context into it's most distilled form.
30,000 tokens of wheel spinning to get the model back on track consolidated to 500 tokens of "We tried A, and it didn't work because XYZ, so avoid A" and kept in recent context
The key feature of formalization is the ability to create statements, and test statements for correctness. ie, we went from fuzzy feel-good thinking to precise thinking thanks to the formalization.
Furthermore, the ingenuity of humans is to create new worlds and formalize them, ie we have some resonance with the cosmos so to speak, and the only resonance that the LLMs have is with their training datasets.
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There are people convinced that if we throw a sufficient amount of training data and VC money at more hardware, we'll overcome the gap. Technically, I can't prove that they're wrong, novel solutions sometimes happen, and I guess the calculus is that it's likely enough to justify a trillion dollars down the hole.
There's a guy, Ken Stanley, who wrote the NEAT[0]/HyperNEAT[1] algorithms. His big idea is that evolution/advancements don't happen incrementally, but rather in unpredictable large leaps. He wrote a whole book about it that's pretty solid IMO: "Why Greatness Cannot Be Planned: The Myth of the Objective." [0] https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t... [1] https://en.wikipedia.org/wiki/HyperNEAT
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No. The experts in the field are past this argument. People have moved on. It is clear to everyone who builds LLMs that the AI is intelligent. The algorithm was autocomplete, but we are finding as an autocomplete bot is basically autocompleting things with humanity changing intelligent content. Your opinion is a minority now and not shared by people on the forefront of building these things. Your holding onto the ini…
> Your opinion is a minority now and not shared by people on the forefront of building these things. Minority != wrong, with many historic examples that imploded in spectacular fashion. People at the forefront of building these things aren't immune from grandiose beliefs, many of them are practically predisposed to them. They also have a vested interest in perpetuating the hype to secure their generational wealth.
The ai can easily answer correctly complex questions NOT in its data set. If it is generating answers to questions like these out of thin air which fits our colloquial definition of intelligence.