alt Hacker NewsBig difference between a perfect information, completely specified zero sum game and the real world.
As a simple analogy, read out the following sentence multiple times, stressing a different word each time.
"I never said she stole my money"
Note how the meaning changes and is often unique?
That is a lens I to the frame problem and it's inverse, the specification problem.
The above problem quickly becomes tower-complete, and recent studies suggest that RL is reinforcing or increasing the weight of existing patterns.
As the open domain frame problem and similar challenges are equivalent to HALT, finding new ways to extract useful information will be important for generalization IMHO.
Synthetic data is useful, but not a complete solution, especially for tower problems.
The one we use is "I always pay my taxes"
and as far as synthetic vs real data, there's a lot of gaps in LLM knowledge; and vision models suffer from "limited tags", which used to have workarounds with textual embeddings and the like, but those went by the wayside as LoRA, controlnet, etc. appeared.
There's people who are fairly well known that LLMs have no idea about. There's things in books i own that the AI confidently tells me are either wrong or don't exist.
That one page about compressing 1 gig wikipedia as small as possible implicitly and explicitly states that AI is "basically compression" - and if the data isn't there, it's not in the compressed set (weights) either.
And i'll reply to another comment here, about "24/7 rolling/ for looped" AI - i thought of doing this when i first found out about LLMs, but context windows are the enemy, here. I have a couple of ideas about how to have a continuous AI, but i don't have the capital to test it out.