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Great discussion and summary of LLM generative AI.
I tend to agree that it's often "interpolation" how to string words or pixels together into something resembling reality.
For me, it's a great tool for drafting letters and reports. It's a shortcut for research on common reasonably common topics. But as the data gets thin, the interpolations get more extreme and it will start to show a lack of "thought".
But damn if I'm not impressed with what Gemini can do to help teach (or check) my kids' Algebra 2 homework. I will often just take a picture of the question and ask for an explanation, and then subsequently ask it to solve the problem showing work. It's stunningly good, concise, and logical.
It also checks homework and has noted things like "Your son did all the steps correctly, but transcribed the final answer wrong when he wrote it in the answer box" or, "it appears he was on the correct track, but erased those steps in the left hand margin". Gemini's ability to make these levels of insight seemingly goes beyond the correlation/interpolation explanation of LLM AI. I suspect the latest models are layering in more, at least for Gemini and math.
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