Massively parallel symbolic computing

Abstract

Advances in hardware and computer architecture continue to change the economics of various AI (as well as all other) computing paradigms. The new generation of massively parallel machines extends the potential for applications at the high end of the computing spectrum, offering higher computing and I/O performance, much larger memories, and MIMD as well as SIMD capabilities. Computing costs for the same level of performance are substantially less, and will continue to drop steeply for the foreseeable future.

All this has clear consequences for AI: for example, larger knowledge bases can be stored; hand coding will continue to become less cost-effective relative to learning and simple-to-program brute-force methods as time goes on; and just about any parallel AI paradigm should be capable of executing efficiently.

A brief overview will be provided of recent successful Connection Machine projects: automatic keyword assignment for news articles using MBR nearest-neighbor methods (MBR = Memory-Based Reasoning); automatic classification of Census Bureau returns; protein structure prediction using MBR together with backpropagation nets, and statistics; work on “database mining”; and Karl Sims' generation of graphics using genetically-inspired operations on s-expressions.