Comment by andy99
10 hours 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
> 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.
“AI is whatever hasn't been done yet” — Larry Tesler
> 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.