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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
by nervechannel 16y ago
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.
- _delirium 16y agoI 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.
- another 16y ago... 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.