Also, there’s a deep relationship between DL and symbolic reasoning:
The tensor networks in DL end up looking really similar to tensor representations of the diagrams equivalent to a type theory — down to convolutions being a way to “type” data in an input.
We’re just now exploring that, but this may be another case of “algebra-geometry equivalence” with DL giving us a differential/geometric interpretation and symbolic reasoning giving us an algebraic interpretation.
The tensor networks in DL end up looking really similar to tensor representations of the diagrams equivalent to a type theory — down to convolutions being a way to “type” data in an input.
We’re just now exploring that, but this may be another case of “algebra-geometry equivalence” with DL giving us a differential/geometric interpretation and symbolic reasoning giving us an algebraic interpretation.