Your understanding that problem solving has to do with "steps" is the correct one in my opinion. Also, with data driven approaches, the model isn't necessarily

Your understanding that problem solving has to do with "steps" is the correct one in my opinion. Also, with data driven approaches, the model isn't necessarily learned in any meaningful way. If you train with certain inputs to get certain realistic looking outputs, you can build sophisticated parrots or chameleons. That's what GPT and stable diffusion are: compressed knowledge bases to get an output without a knowledge model. (No, language models are not knowledge models). Thinking, rational or otherwise, requires causal steps. Since none of these data driven approaches have even fuzzy causal models, they require memorizing infinite universes to see if search can find a particular universe that's seen this before. That's why they're not intelligent and never will be. An intelligent animal only needs one universe and limited experiences to solve a problem because it knows the latent structure of the problem and can generate approaches. Intelligent entities, unlike these autistic Rain Man automatons, do not need to memorize every book in the library to multiply two large numbers together.