AI megathread

I think there’s some confusion here. A DSL is based on a grammar that’s used to transform DSL statements into a syntax tree. It’s then the developer’s task to implement deterministic rules in code for each node in such a syntax tree. AI, however, works fundamentally differently. The logic doesn’t reside in the code lines but emerges from patterns in data. This is why AI is such a breakthrough—it challenges the idea that intelligence is something transcendent or mysterious. Instead, it shows that what we think of as intelligence might simply be the ability to recognize patterns, make predictions, and adapt based on data.

Much like how babies learn about the world through experience—observing patterns, experimenting, and refining their understanding—AI models learn from enormous datasets to generalize and solve problems. Whether it’s natural language, image recognition, or protein folding, AI demonstrates that complex problem-solving can arise from computational processes rather than any mystical property. This suggests that intelligence, far from being an ineffable quality, could simply be the result of sufficiently rich patterns and processing. In this context, I think @Laird makes a fair point.

I think you’re missing my point. It’s not that there are ASTs  being used, it’s that a lot of domain knowledge can be captured programmatically by using the right structures.

Why I said combine that knowledge with the fact the AI companies have humans-in-the-loop working as wage-slaves around the world to aid AIs with their seeming semantic understanding.

Also you’re ignoring the important point @Valmar and I keep raising which is that AI looks impressive but one has to actually look under the hood - such as the way “temperature” is randomized - to see how the magic trick is actually done.

My contention is that the “black box” aspect of machine “learning” is all that separates current LLMs from the old claims about GPS.