Except that these aren't thoughts. These techniques are improvements to how the model breaks down input data, and how it evaluates its responses to arrive at a

Except that these aren't thoughts. These techniques are improvements to how the model breaks down input data, and how it evaluates its responses to arrive at a result that most closely approximates patterns it was previously rewarded for. Calling this "thinking" is anthropomorphizing what's really happening. "AI" companies love to throw these phrases around, since it obviously creates hype and pumps up their valuation. Human thinking is much more nuanced than this mechanical process. We rely on actually understanding the meaning of what the text represents. We use deduction, intuition and reasoning that involves semantic relationships between ideas. Our understanding of the world doesn't require "reinforcement learning" and being trained on all the text that's ever been written. Of course, this isn't to say that machine learning methods can't be useful, or that we can't keep improving them to yield better results. But these are still methods that mimic human intelligence, and I think it's disingenuous to label them as such.