| Symbolic AI needs computing power to counteract combinatorial explosion. https://en.wikipedia.org/wiki/Combinatorial_explosion The vulnerability of early logic-based AI to combinatorial explosion was the main argument against funding AI research put forward in the Lighthill Report, the document that shut down AI research in the UK in the 1970s and contributed to the AI winter on the other side of the Atlantic, also. Obviously, today we have more powerful computers so combinatorial explosion is less of an issue, or anyway it's possible to go a bit further and do a bit more than it was in the '70s. One area of symbolic AI that actually does benefit from parallel architectures (though not GPUs) is logic programming with Prolog. Prolog's execution model is basically a depth-first search, which lends itself naturally to parallelisation (one branch per search). Even more so given that data in Prolog is immutable (no mutable state, no concurrency headaches). But, in general, anything people did 20 or 30 years ago with comptuers can be done better today. Not just symbolic AI or neural networks. I mean, even office work like printing a document is faster today and that doesn't even depend on GPUs and parallel processors. |
Oops, sorry. Meant "one branch per processor".