Comment by mjburgess

It's not a matter of making points, it's at least a semester's worth of courses on causal analysis, animal intelligence, the scientific method, explanation.

Causality isnt ordering. Take two contrary causal mechanisms (eg., filling a bathtube with a hose, and emptying it with a bucket). The level of the bath is arbitrarily orderable with respect to either of these mechanisms.

cf. https://en.wikipedia.org/wiki/Collider_(statistics)

Go on youtube and find people growing a nervous system in a lab, and you'll notice its an extremely plastic, constantly physically adapting, and so on system. You'll note the very biochemcial "signalling" you're talking about itself is involved in the change to the physical structure of the system.

This physical structure does not encode all prior activations of the system, nor even a compression of them.

To see this consider Plato's cave. Outside the cave passes by a variety of objects which cast a shadow on the wall. The objects themselves are not compressions of these shadows. Inside the cave, you can make one of these yourself: take clay from the floor and fashion a pot. This pot, like the one outside, are not compressions of their shadows.

All statistical algorithms which average over historical cases are compressions of shadows, and replay these shadows on command, ie., they learn the distribution of shadows and sample from this distribution demand.

Animals, and indeed all science, is not concerned with shadows. We don't model patterns in the night sky -- this is astrology -- we model gravity: we build pots.

The physical structure of our bodies encodes their physical structure and that of reality itself. They do so by sensor-motor modulation of organic processes of physical adaption. If you like: our bodies are like clay and this is fashioned by reality into the right structure.

In any case, we haven't the time or space to convince you of this formally. Suffice it to say that it is a very widespread consensus that modelling conditional probabilities with generative models fails to model causality. You can read Judea Pearl on this if you want to understand more.

Perhaps more simply: a video game model of a pot can generate an infinite number of shadows in an infinite number of conditions. And no statistical algorithm with finite space and finite time requirements will ever model this video game. The video game model does not store a compression of past frames -- since it has a real physical model, it can create new frames from this model.