Look, of course statistics isn't reasoning because you can't build a proof with statistics (or probabilities). But, we have to wonder: when people say "reasoni

Look, of course statistics isn't reasoning because you can't build a proof with statistics (or probabilities). But, we have to wonder: when people say "reasoning", do they really, really mean, drawing inferences from axioms and theorems using some set of inference rules? Or do they just mean that they can ask a question and get an answer that makes sense, back? I certainly think it's the latter. People are imprecise when they speak about reasoning, just as they are imprecise when reasoning. Most people who are going to be using LLMs will not be people looking for precise, correct answers derived by sound inference procedures. Those who need precision will seek it elsewhere, where precision can be obtained. The rest will be happy with "reasoning", quote-unquote. Basically the use case for LLMs reminds me of a couple pieces of work that were published in the past, where people used neural nets to approximate the results of a precise calculation. One team trained a neural net to predict the chaotic motion of three astronomical bodies ("the three body problem"). Another trained a neural net to approximate an automated planner programmed to drive a drone around in a thicket of trees without crashing. In both cases the trained model could obviously only return approximately correct results, and it could only approximate results that had already been calculated classically, but, at least in the second case, if I remember correctly, there was a significant speedup of the operation of the drone, compared with the automated planner- very reasonably so, since the model approximating the planner didn't have to do all the hard work of actually, you know, calculating a plan. My bottom line is that no matter what you (or I, or the article's author, or anyone else) can say or show about the true capabilities of LLMs to reason, people are totally going to use them _as if_ they could reason, and the job of proving that they can't is going to get all that much harder for that experience, misguided as it may be. Ultimately the question of whether LLMs can reason is going to be, outside specialist circles, as relevant as "Does Netflix distribute art?". Sure, it doesn't. But people watch it anyway. Most people seemingly don't need art as much, and they don't need reasoning as much, either. Which is a tragic conclusion, of course. At least some of us are still working on AI whose purpose is to make humans better at thinking, not that takes away their motivation to think.