alt Hacker News> When the data fed into an AI model is based on real data you can get predictability. When the AI inputs start coming from the AI outputs the wobble is introduced which results inevitably into delirium.
This is asserted without evidence. There is absolutely no proof that AI requires humans in the loop to function or improve itself.
Compare it with Deepmind's go-playing program, AlphaGo, which mainly involved training a neural network on a large database of high-level human games. It defeated one of the top-ranked players in the world, Lee Sedol, but arguably it was drawing from human experience just like you described.
But it didn't stop there. After that, Deepmind developed AlphaGo Zero, a version that was trained exclusively through self-play, with no human feedback in the loop. That's what you would call "AI inputs coming from AI outputs" but it didn't have the result of “resulting in delirium”: instead, it became orders of magnitudes stronger than the original version (which it defeated in a 100 to 0 competition after 3 days of training).
This shows that AI can improve itself without having access to any human knowledge, and indeed transcend human performance by orders of magnitudes. There is absolutely nothing to suggest that general AI cannot improve itself the same way.
People who claim otherwise are engaging in wishful thinking; they just assert their conclusion, but have no rational arguments to back it up.
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Both programs had programmed in to their routines the rules of go (and for Alpha Zero the rules of chess and shogi). The programs then followed an optimization program. Absent the extremely specific "rules of this game" set by a human none of the programs work.
Amusingly, all three games are in the game theory sense trivial because they provably have a correct solution (probably a draw for chess), but the search space is so large that it is unlikely we will ever collectively invest the resources to discover the answer.
You have no evidence that self improvement can work at generalized tasks. Go is a simple game with a clear win condition, but deep strategy and near infinite permutations of how a game plays out. Winning a game of Go is a task well suited to machine learning.
One might say you are engaging in wishful thinking by believing it’ll just continue to work across all domains. The world is much bigger than a Go board.
You are referring to gans which have complete understanding of the rules and an api interface to leverage interacting with the world.
While I think there could be an argument made here you are mixing apples and rocks if you believe ChatGPT is using gans.
I hate to be that person but you are showing that you are unable to differentiate between linear regressions, clustering, gradient descent, and generative adversarial networks. You are over your ski-tips and don't see itc
>>When the AI inputs start coming from the AI outputs the wobble is introduced which results inevitably into delirium.
>This is asserted without evidence. There is absolutely no proof that AI requires humans in the loop to function or improve itself.
Nope, it's actually fairly well established.
AlphaZero works in simple and fully observable environments like board games.
RLVR for LLMs works for coding and math domains which can be verified by code.
AlphaZero, RLVR and other RL methods don't work for learning dextrous manipulation for robotics, for example. Learning in robotics depends on human data.