[parpfish]

I'm reminded of how our understanding of human object recognition was affected by computer vision research.

For decades we knew that there were neurons with simple receptive fields in V1/V2 that extracted low-level visual features, and that those neurons passed information along the ventral visual stream, and by the end of that processing stream we had neurons in IT that represented different objects.

However, we couldn't really comprehend what sort of algorithm/process was capable of this seemingly magical inference. Coming up with an object representation that was invariant to out of plane rotation was seen as impossibly complex.

But then computer vision came along and showed us that with a relatively simple neuralnet and enough training data... it just kind of works.

Same thing is happening with LLMs right now -- a seemingly impossible, mysterious human capability (e.g., "understanding") isn't as complex as we think. Throw enough data into a network that does pattern matching/autocomplete and human-like intelligence pops out.