[jgelsey]

Well, much of "AI" in its modern form is statistical analysis. SIMD machines are great for linear algebra (integrated vector/scalar machines like 70's CDC machines, Cray and Convex through the 90's, most Intel and AMD processors today through their SSE and AVX instruction set extensions). Any statistical calculations that use linear algebra can be accelerated with a SIMD architecture.

[eggy]

True, and as hardware keeps evolving towards array-based setups like GPUs/ASICS with FPGAs and other hybrids, languages like J/APL/K fit them like a glove.

Using qdb from Kx or Jd for J, both columnar databases with billion row operations on capable vector-processing hardware is the modern realization of this type of computing.

These languages are not currently too popular, but I think a cousin of theirs will come on the scene and really light things up. Some current attempts at newer GPGPU targeted languages have popped up these past few years [1, 2, 3, 4].

The difference between these array languages and something like NumPy and other libs to add array processing and better numerics, is that the columnar databases or stores, have the array language built-in; you can abstract out, but if you program with them, there are no onion layers between you and speed and processing.

[1] https://github.com/melsman/apltail/

[2] http://futhark-lang.org/index.html

[3] PDF - http://www.elsman.com/pdf/array14_final.pdf

[4] PDF - http://futhark-lang.org/publications/fhpc16.pdf