Re: definitions, problem spaces, methods

Hi Mike,

GPT3’s weaknesses in respect to coherence, etc. is unsurprising and should encourage research on moving on from text and image prediction to work on reasoning and continuous learning. This in turn motivates work on imprecise and imperfect knowledge, where I expect neural networks will shine. That includes the role of causal explanations as a basis for learning, and the ability to reason about past, present and imagined situations, including the beliefs of others. I very much believe that hand authoring of knowledge representations will give way to machine generated KR. I hope you can respect my view that KR is a subset of AI, as in the Wikipedia definition.  The media hype around AI is frustrating in that attention grabbing headlines detract from a clear exposition of the underlying concepts.

p.s. you may be interested in the talk I’ve prepared for next week’s NSF/EU workshop on research priorities.

 https://www.w3.org/2022/11/Raggett-AI-Priorities.pdf

Best regards,

Dave Raggett <dsr@w3.org>