[merizian]

The fallacy being made in this argument is that computers need to perform tasks the same way as humans to achieve equal or better performance on them. While having better "system 2" abilities may improve performance, it's plausible that scaled-up next-token prediction along with a bit of scaffolding and finetuning could match human performance on the same diversity of tasks while doing them a completely different way.

If I had to critique Hinton's claims, I would say his usage of the word "understand" can be vague and communicate assumptions because it's from an ontology used for reasoning about human reasoning, not this new alien form of reasoning which language models embody.

[edot]

I believe it was Feynman who said something to the effect of "airplanes do not fly like birds do, but they fly much faster and can carry much more". So yes, we do not need to exactly replicate how humans do things in order to do human-like things in a useful manner. Planes do not flap their wings, but the jet engine (which is completely unnatural) does a great job of making things fly when paired with fixed wings of a certain shape.

[mcmoor]

Tbf planes have access to much more energy than birds just like LLM does. Maybe that will be the next challenge.

[BurningFrog]

> The fallacy being made in this argument is that computers need to perform tasks the same way as humans to achieve equal or better performance

Especially since I don't think we know that much about how human intelligence actually works.

[metanonsense]

In addition to that, the "system 2" abilities might already be there with "epi" strategies like chain-of-thought prompting. Talking / writing to yourself might not be the most efficient way to think but at least I do it often enough when pondering a problem.