When people say "If it was reasoning, then it would be able to know, How many Rs does the word Strawberry have in it?", but that's not quite right, but I would

When people say "If it was reasoning, then it would be able to know, How many Rs does the word Strawberry have in it?", but that's not quite right, but I would say this instead "If it was reasoning THE SAME WAY HUMANS reason....then it would be able to...". Humans do reasoning a certain way. LLMs do reasoning a different way. But both are doing it. But since it's not reasoning the way people do (but very differently), yes it can make mistakes that look silly to us, but still be higher IQ than any human. Intelligence is a spectrum and has different "types". You can fail at one thing but be highly intelligent at something else. Think of Savantism. Savants are definitely "reasoning" but many of savants are essentially mentally disabled by many standards of measurement, up to and including not being able to count letters in words. So saying you don't think LLMs can reason, and giving examples fails as evidence of that, is just a kind of category error, to put it politely. The fact that LLMs can fix bugs in pretty much any code base shows it's definitely not doing just simple "word completion" (despite that way of training), but is indeed doing some kind of reasoning FAR FAR beyond what humans can yet understand. I have a feeling only coders truly understand the power of LLMs reasoning because the kind of prompts we do absolutely require extremely advanced reasoning and are definitley NOT answerable because some example somewhere already had my exact scenario (or even a remotely similar one) that the model weights essentially had just 'compressed'. Sure there is a compression aspect to what LLMs do, but that's totally orthogonal to the reasoning aspect.