I think both brains and ML in practice "solve equations". The main thrust of machine learning is using linear algebra as a universal function fitter. Sure, th

I think both brains and ML in practice "solve equations". The main thrust of machine learning is using linear algebra as a universal function fitter. Sure, the state space may be transformed in interesting ways and the overall process may be relatively opaque, but you could easily end up with the quadratic formula in there. The equations may be very strange from the point of view of the underlying physics/kinematics problem. There's a lot of representations of the problem that fit well and may map better to what our perceptions see and the level of precision that our muscles need. And it's likely very messy--- we don't have some clean, nested set of controllers here, but strange ways that many kinds of feedback are combined.