> There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don’t fully understand why current LLMs are bad at these tasks.

I thought we did know for things like playing Wordle, that its because they deal with words as sequence of tokens that correspond to whole words not sequences of letters, so a game that involves dealing with sequences of letters constrained to those that are valid words doesn’t match the way they process information?

> Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the “reasoning steps” and handing them to the LLM, instead of the LLM figuring them out by itself. We have “reasoning machines” that are intelligent but seem to be hitting fundamental limits we don’t understand.

But providing examples with different, contextually-appropriate sets of reasoning steps results can enable the model to choose its own, more-or-less appropriate, set of reasoning steps for particular questions not matching the examples.

> It’s unclear if better prompting and bigger models using existing attention mechanisms can achieve AGI.

Since there is no objective definition of AGI or test for it, there’s no basis for any meaningful speculation on what can or cannot achieve it; discussions about it are quasi-religious, not scientific.