>In any case, it doesn't matter if one instance of the class of human minds hasn't invented anything, in the same way that it doesn't matter if one car can't do 80mph.
It does matter, depending on what claim you're making. We've not reached the upper bound of transformer ability. Until we clearly do, then it very much does matter.
>I'm with LeCun and Bengio. There's a fair amount of confusion about what a "model" is in that sense: a theory of the world. There's no reason why LLMs should have that.
See this is my problem with Lecun's arguments. He usually starts with the premise that it's not possible and works his way from there. If you disagree with the premise then there's very little left. "Well it shouldn't be possible" is not a convincing argument, especially when we really have very little clue on the nature of intelligence.
>Sutskever's bet is that a model can be learned from text generated by entities that already have a world model, i.e. us, but LeCun is right in pointing out that a lot of what we know about the world is never transmitted by text or language.
A lot of the world is transmitted by things humans don't have access to. Wouldn't birds that can naturally sense electromagnetic waves to intuit direction say humans have no model of the world ? Would they be right ? Nobody is trained on the world. Everything that exists is trained on small slices of it. A lot of the world is transmitted by text and language. And if push comes to shove then text and language is not the only thing you can train a transformer on.
>Sutskever again seems to think that, that kind of information, can somehow be guessed from the text, but that seems like a very tall order,
I don't think this is as tall an order as you believe
>and Transformers don't look like the right architecture. You need something that can learn hidden (latent) variables. Transformers can't do that.
But they do this all the time.
Transformer trained on only protein sequences learns biological structure and function - https://www.pnas.org/doi/full/10.1073/pnas.2016239118
Toy example on binary addition (transformer trained on inputs and outputs of addition sequences) learn an algorithm for it - https://www.alignmentforum.org/posts/N6WM6hs7RQMKDhYjB/a-mec...
Unless i'm misunderstanding what you mean by hidden variables, it's very clear a transformer is regularly learning not just the sequences themselves but what might produce them.