This is not a new sorting algorithm. It's much more interesting than that. AlphaDev appears to have produced a new technique for superoptimisation.
You can think of the superoptimisation problem as trying to take a code fragment, and finding the optimal sequence of instructions that does the same thing. The idea goes back at least to the 1970s; tools like BONSAI would start with a description of machine code expressed as some kind of formal logic, and then use heuristic search to find optimal code sequences.
The term "superoptimisation" was introduced by Henry Massalin in 1987. His was the first system to use exhaustive search, but it was made practical by using a limited subset of machine instructions. For many years, GCC was tuned using superoptimisations discovered by GNU superopt.
Superoptimisation is useful for discovering instruction selection templates and peephole optimisations, but the huge search space makes full optimisation prohibitive. Modern algorithms tend to use stochastic search and goal-directed search to allow for more instructions and longer instruction sequences.
What the AlphaDev appears to have achieved is the start of a new way to get even longer instruction sequences: use deep learning techniques to propose sequences, which would then presumably be automatically verified using a theorem prover like Z3.
This is a very interesting approach. Forget sort algorithms, it could revolutionise the way we construct compiler back-ends. It won't be feasible to run an ANN plus theorem prover on every compilation any time soon, but compiler developers could take larger patterns found in real code and generate special-purpose optimisations for them.