Rebooting AI: Adding Knowledge to Deep Learning

Gary Marcus argues for a shift in research priorities, towards four cognitive prerequisites for building robust artificial intelligence:

• Hybrid architectures that combine large-scale learning with the representational and computational powers of symbol-manipulation

• Large-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledge

• Reasoning mechanisms capable of leveraging those knowledge bases in tractable ways

• And rich cognitive models that work together with those mechanisms and knowledge bases.

Although there are real problems to be solved here, and a great deal of effort must go into constraining symbolic search well enough to work in real time for complex problems, Google Knowledge Graph seems to be at least a partial counterexample to this objection, as do large scale recent successes in software and hardware verification.

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