I enjoy the planetary motion analogy immensely. However, a curious complexity is evident in biological systems - it's physics imbued with 'meaning'. A cell that

I enjoy the planetary motion analogy immensely. However, a curious complexity is evident in biological systems - it's physics imbued with 'meaning'. A cell that behaves one way instead of another will die. This is true from e. coli up to skin cells. I suspect this adds a lot of complexity to the ultimate 'algorithm' of intelligence. In fact, it's probably prudent to stipulate whether the goal is to specify the developmental algorithm that gives rise to intelligence, or intelligence itself. The generating process might be a lot more simple than the finished product, in the same way that the output of a pseudorandom number generator is very complex in terms of entropy (but not kolmogorov complexity, I guess), but the generating algorithm is very simple. Or the Rado graph, which is sorta maximally complex in the sense that it contains all finite and countably infinite graphs as induced subgraphs, yet it has a simple generating scheme. As a last example, consider No Man's Sky - relatively simple algorithm compared to the astonishing complexity of the worlds. I do believe there is a relatively simple high-level description of neural development. But it's curious how it's relatively robust to genetic manipulations. I remember early in graduate school listening to a lecture about a particular mouse model for autism, caused by just one gene. The lecturer excitedly told us that it had a very high rate of behavioral manipulation - 75% or so. Not coming from a mouse-model background I was astonished that it was 'only' 75%. What happens in the other 25% - the gene doesn't just magically reappear, there are other compensatory mechanisms. What drives those? I suspect that's the more abstract level of description that a simple algorithm might describe. https://en.wikipedia.org/wiki/Rado_graph https://en.wikipedia.org/wiki/Rado_graph