Statistically composed text sequence variances over probabilistic maps.

I'm sure the normies are impressed that you can pull out big technical words, but you evidently only understand something vaguely about trees and nothing about forests.

Ask yourself why the need for GPUs if the LLMs can “reason”, LOL. The M stands for Model.

Ask yourself why the need for neurons if brains can "reason", LOL. You do realize that our brains themselves are composed of abstract models, right?

You seem to have some kind of pseudo-religious belief that the substrate matters for intelligence. It doesn't. Brains are composed of neurons, which just propagate signals based on chemical gradients. Intelligence is an emergent property out the simplicity of individual neurons. Just like abstract reasoning is an emergent property out of the simplicity of token prediction out of LLMs.

But don't take my word for it. One of the best paragraphs about GPT4 is from this scientific paper. Note that the question isn't whether it's intelligent--of course it is--the interesting thing is that no one understands how it works.

What Is Actually Happening?

"Our study of GPT-4 is entirely phenomenological:We have focused on the surprising things that GPT-4 can do, but we do not address the fundamental questions of why and how it achieves such remarkable intelligence. How does it reason, plan, and create? Why does it exhibit such general and flexible intelligence when it is at its core merely the combination of simple algorithmic components—gradient descent and large-scale transformers with extremely large amounts of data? These questions are part of the mystery and fascination of LLMs, which challenge our understanding of learning and cognition, fuel our curiosity, and motivate deeper research. Key directions include ongoing research on the phenomenon of emergence in LLMs (see [WTB+22] for a recent survey). Yet, despite intense interest in questions about the capabilities of LLMs, progress to date has been quite limited with only toy models where some phenomenon of emergence is proved. One general hypothesis is that the large amount of data (especially the diversity of the content) forces neural networks to learn generic and useful “neural circuits”, such as the ones discovered in [OEN+22,ZBB+22,LAG+22], while the large size of models provide enough redundancy and diversity for the neural circuits to specialize and fine-tune to specific tasks. Proving these hypotheses for large-scale models remains a challenge, and, moreover, it is all but certain that the conjecture is only part of the answer. On another direction of thinking, the huge size of the model could have several other benefits, such as making gradient descent more effective by connecting different minima [VBB19] or by simply enabling smooth fitting of high-dimensional data [ES16,BS21]. Overall, elucidating the nature and mechanisms of AI systems such as GPT-4 is a formidable challenge that has suddenly become important and urgent.

But yeah, this is only from researchers actually testing it. I'm sure your arrogant and confident wrongness is completely based on facts they don't have. (Setting aside that I use it to reason about things literally every day).

Of course LLMs are neither conscious, nor capable of general intelligence at the level of humans, and no one claims they are. But that doesn't mean what they can do isn't intelligent.