Language versus knowledge in AI

Refining what AI needs to do with language

The view while running around the lake near Puos D’Alpago, about an hour north of Venice in Italy. Europe has some amazing researchers in AI and related fields, while also having some great natural environments to take in.

My recent trip to Europe and Canada to refine my theory of knowledge is drawing to a close. We have visited a number of experts at universities in Europe and Canada to refine the challenge of knowledge representation for generalization, common sense and lossless accuracy.

The key to science, in my view, is to understand what the experts have discovered while maintaining a critical view and refining my own model. In the case of knowledge representation, I can see how easily a human brain deals with the vast number of semantic (meaning) distinctions we make with language, while limiting our understanding to what makes sense.

This is the distinction in AI known as the frame problem (described in Wiki and Stanford’s Plato very well). Paraphrased: how experts only seem to consider things that are relevant to the problem at hand. It’s common sense to only consider relevant possibilities that we also need to use in human language by excluding possible sentence meanings that aren’t relevant to the current context.

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In my recorded talks on the trip, they can be seen in YouTube. The recordings aren’t studio quality, but the questions are excellent given the audience’s expertise on the topics. Each…