3,262 karma · joined December 17, 2016
Would you like to hear more examples of humans using these techniques?
(actually I am wrong. You would introduce a new proof, and then step the verifier on all ongoing proofs, so non-termination isn't a driving concern)
Or, maybe, he’s considered the arguments on the object level, an activity OP participates in to a depth not exceeding “Robots are pretty hard to make right now”
Instead, what is extremely likely is that you will pay more than the cost of tokens, and get back AI generation. You won't make this mistake more than a few times before you stop trying.
This leads to impoverishment once we get to a point where employing a human to do anything is hard- try to get your sink fixed, exercise your moral principles to pay extra for a human plumber ($100 bucks! The robot plumbing service only charges 99c!), human shows up with a robot and doomscrolls on your porch while the robot does the work. Times are tough and you don't have that much money to waste on bullshit like this. Next time you just hire the robot.
This leads to extinction once paying UBI to a human is hard because robots are much better at applying for UBI than humans.
These models will absolutely remember a brilliant insight that appeared a single time in the pretrain corpus, because if they couldn't-- they would get a slightly worse loss. I don't know what this wishy-washing "well maybe we trained on it but we didn't read it" is supposed to mean.
(Example of Fable knowing the content of a deeply unimportant LessWrong post I wrote: https://claude.ai/share/a907b46c-bf7b-4fca-9c71-8582cf8507cc a working Navier Stokes solution would be way more salient)
But yeah, small number of large dependencies is the way to go.