As of a few months ago they still have trouble, with low thinking, at the "should I drive to a car wash that is 100 m away" kind of question.

nothing indicated otherwise at the time. IMO he just underestimated RL-scaling. chinese models improved a lot too, they are not parrots anymore, there's some real intelligence, at 27B params.

consider me optimist now, but just few months ago, even frontier models were dumb, doing stupid mistakes all the time, all of them were so dumb I'd never expect anything to change in just few months.

I thought it was more because of fundamental limitations in the architecture. As in, no matter the training data, it could not be consistently and generally represented

Actually, I think my fundamental challenge with AI is that it has no common sense. The way it builds things, writes, and operates is out of touch with reality.

Incidents like hugging face are partly rooted in the lack of common sense. It still functions like a supercharged toddler.

I'd love to overcome this because it'd mean I spend less time guiding the the LLM to produce usable outputs.

Last week I asked a frontier model draw me a backplane PCB and it placed daughterboard slots side by side in a chain.

No?

This is always the issues in the discussions.

There’s the outcomes camp (objectivists?), which points at the things LLMs can do.

Then there’s the process methods camp, which talks about what is actually going on.

If you only care about the outcome, then the process does t matter.

If you are talking about what is happening, what the underlying mechanics and science of it is, then the process matters.

These models aren’t thinking. They simulate cognition well enough to do useful work in several fields and domains.

Both are true.

> A statement that we can hopefully all agree has been thoroughly refuted.

Uh, no? So much of what we learn and take for granted as common sense is not learned via language, and not even expressible in it.