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Half of your examples are, in the grand scheme of things, trivial applications of decades-old techniques. Recommendation, for example, is based on techniques wh
by cdavid 15y ago
Half of your examples are, in the grand scheme of things, trivial applications of decades-old techniques. Recommendation, for example, is based on techniques which are almost 100 years old (SVD). Winning chess has been rendered possible through increased machine power, the algorithms necessary for it are hardly ground breaking. If this is AI, then many things are AI: almost any quantitative usage of statistics is AI, for example.
There is no agreement on this, but some leading AI researchers consider that most AI problems should be solvable with decades old computers (i.e. we need some completely new paradigms that nobody has yet thought about). See Mc Carthy for example: http://www-formal.stanford.edu/jmc/whatisai/node1.html http://www-formal.stanford.edu/jmc/whatisai/node1.html
- nhaehnle 15y agoThat was exactly his point, wasn't it? His examples used to be considered problems in AI. Now that the problems are understood, they are simply considered to be algorithmic problems. Lots of output of the MIT AI labs in the early days is now simply taught in algorithms courses, yet back then, those researchers considered themselves to be doing AI research. That is the moving of the goalposts.
- ilamont 15y agoAs I recall, Kurzweil referred to such applications as "narrow A.I." Human-level AI capable of passing the Turing Test is called "strong A.I." in his lexicon.