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Comment by _delirium | Hacker News Reader

I don't think it's true that it's no longer used; I certainly still consider my primary research field to be "AI", and the main journals and conferences I publish in have "AI" in the title. I do agree that some subfields have split off more, especially machine-learning, robotics, and vision, but there's plenty of stuff that still goes on under the rubric of AI. Even the more split-off fields are still very present in big-tent AI conferences and journals, especially when presenting work on integrated systems.

Despite being mostly known as an ML researcher, Leslie Pack Kaelbling's AAAI-10 keynote slides make something of an argument for why AI is important as well: http://people.csail.mit.edu/lpk/AAAI10LPK.pdf

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The skills and techniques required are very very different between the fields though; my PhD was in natural language processing but I don't have the first clue about machine vision, for example.

Hell, I was working on parsing biomedical text -- meaning I don't really have much of a clue about speech processing, machine translation, sentiment analysis, question answering or most of the other subfields of linguistic computing.

Of course there's plenty of room for cross-fertilization, but that's true across comp sci as a whole. Thinking of all of these things as being part of some coherent topic called AI is more of a historical legacy than a useful category.

I guess taking a mostly integrated-systems, applications-focused view, I see AI as a useful organizing concept for what to build, what the issues are in building it, etc. For example, I'm not sure what field Alan Newell's "knowledge level" talk would go in if not into AI.

I do agree there are lots of areas of research that are maybe more "algorithms" than "AI" (e.g. improving SAT-solving), but I disagree that that sort of specialization is the only way to research. From my perspective, those areas of algorithms provide the raw-material research that can be used to build AI systems. Even building AI systems is often specialized to some extent as well, but I think it's useful to have a semi-coherent body of knowledge and shared community around "AI" when doing so, rather than just the domain-specific algorithms and approaches.

... and as someone whose research involves both SAT and statistical learning, I'd agree: putting a variety of specialties under the AI umbrella can make it easier to connect different areas in useful ways.

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