Nature-Inspired Local Propagation
Alessandro Betti Marco Gori
The spectacular results achieved in machine learning, including the recent
advances in generative AI, rely on large data collections. On the opposite,
intelligent processes in nature arises without the need for such collections,
but simply by online processing of the environmental information. In
particular, natural learning processes rely on mechanisms where data
representation and learning are intertwined in such a way to respect
spatiotemporal locality. This paper shows that such a feature arises from a
pre-algorithmic view of learning that is inspired by related studies in
Theoretical Physics. We show that the algorithmic interpretation of the derived
"laws of learning", which takes the structure of Hamiltonian equations, reduces
to Backpropagation when the speed of propagation goes to infinity. This opens
the doors to machine learning studies based on full on-line information
processing that are based the replacement of Backpropagation with the proposed
spatiotemporal local algorithm.