There's lots of room for cross-pollination between bio/life sciences and ML/AI. One key insight is the importance of what you pick as your primary representatio

There's lots of room for cross-pollination between bio/life sciences and ML/AI. One key insight is the importance of what you pick as your primary representation of data (is everything a number, a symbolic structure, a probability distribution, etc). I believe a lot of these bio-inspired approaches over-emphasize the embodied nature of intelligence and how much it needs to be situated in space and time, which downplays all the sub-problems that need to be solved in other "spaces" with less obvious "spatiotemporal" structure. I believe space and time are emergent, at least for the purposes of defining intelligence, and there are representations where both space and time arise as dimensions of their structure and evolution.