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AI has scaled well according to convenient measures. It (neural networks) have the property that whatever you define, they can rapidly be trained master it. We’
by andy99 5mo ago
AI has scaled well according to convenient measures. It (neural networks) have the property that whatever you define, they can rapidly be trained master it. We’re able to show that various tasks of increasing complication do not require intelligence and can be framed as autoregressive RL problems. I personally don’t think AI is any closer to sentient intelligence than LeNet; it’s almost trivially clear, we know how it works. So we’re measuring something orthogonal, basically how well a universal function approximator can fit to a function we define, given arbitrary computing power, and calling that progress. What will be really interesting is if we’re able to find a way to properly measure what they can’t do and what’s different about real intelligence.
Edit: in particular I don’t agree with
But if someone claims that the trend toward increasing AI capabilities will never reach some particular scary level...
One has to agree that the benchmark results are getting “scarier”, which is not automatically implied by finding more goals to optimize for
- ordu 5mo ago> We’re able to show that various tasks of increasing complication do not require intelligence and can be framed as autoregressive RL problems. The important thing we can show it in hindsight only. We don't know which other tasks we are currently mistaken about requiring intelligence. Maybe none of them are? We don't know. We don't know what intelligence is. If we look at decades and even centuries of attempts to define intelligence, it is all looks like a goalposts moving. When a definition of intelligence starts to include people or things we don't like to think as of intelligent ones, we change the definition.
- ryeights 5mo ago“AI is whatever hasn't been done yet” — Larry Tesler
- winwang 5mo agoYep. No one bats an eye at eyewitnesses "hallucinating" details, or that I'd rather have Opus as a coworker vs a random middle schooler (err, labor laws notwithstanding). I think perhaps too much of the dialogue around intelligence has to do with the word (and its connotations) itself. The poster you replied to even used the word "sentient", which is quite interesting (warning: opinionated tangent ahead). Merriam-Webster defines it as "capable of sensing or feeling: conscious of or responsive to the sensations of seeing, hearing, feeling, tasting, or smelling". Feels like qualia. Or if we don't want to go the qualia route... Of course, we wouldn't call Helen Keller non-sentient, so presumably we "really" mean "can it sense or feel" -- well, sense is just "act/feel according to the environment", which you could argue in the case of an LLM would be their context... so we should "really" remove "sense" from the definition, probably. So "do LLMs feel" is probably closer to what "sentient" is being used for here. Since we don't have the obvious symmetry of "you are like me and I feel (therefore you probably feel)", it's way better/easier/feel-good-ier to prefer "LLMs don't feel" rather than "oh shit, it feels and model training is actually just torturing it into the right shape". LLMs as fundamentally non-intelligent also avoids the problems of "what does that say about people" or "we may have made 'AGI' and it wasn't what we thought it would be" or "we're not ready to talk about this yet".
- red75prime 5mo ago> basically how well a universal function approximator can fit to a function we define That's what you've got wrong. We don't define functions that an LLM approximates. Autoregressive pretraining approximates an unknown function that produces text (that is what the brain does). RL doesn't approximate functions, it optimizes objective by finding an unknown function that performs better.
- imtringued 5mo agoWhat an interesting perspective! There is just a small problem. How does the human brain work in the phase between being born and learning written language? Seems like a significant bootstrapping problem for your theory.
- red75prime 5mo agoLLMs capture a snapshot of the "collective brain" functionality as represented by literate people. I don't speculate how our brains get to this point.
- jbjbjbjb 5mo agoIs it not the case that a lot of the recent gains are just the coding harness that directs the LLM? That coding harness isn’t all that intelligent, simple pattern matching that maps to well defined tasks a programmer might do.
- naasking 5mo agoI wouldn't say "just", but harnesses are a big deal responsible for a lot of improvements, yes. Models are also fine-tuned on these, like MetaClaw.