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Published online by Cambridge University Press: 05 August 2013
In some of the just-preceding chapters, we have seen the important role of data and of machine learning techniques for distilling and using these data. If one single theme has lately begun to unite the several disparate approaches to AI, ranging from logical representations and reasoning in Cyc to decision and estimation by neural networks, it is their dependence on massive amounts of data. Moving beyond toy problems and simple puzzles into real-world problems requires real-world data. In this chapter, I'll explore how the latest systems for natural language processing and computer vision exploit data that are representative of the inputs they must deal with.
Natural Language Processing
The growing need for systems able to deal with written and spoken languages, together with new technical advances, large databases, and increased computational power, has led to improved systems for performing such tasks as summarizing pieces of text, answering queries, and translating languages. In this section, I'll describe some of the technical developments in NLP during the past two or three decades. Impressive as they are though, they have not yet allowed us to realize Terry Winograd's hope back in 1971 that “We will talk to [computer systems] just as we talk to a research assistant, librarian, or secretary, and they will carry out our commands and provide us with the information we ask for.“ Many people say that the problem of realizing such systems is “AI complete,“ in the sense that they must be generally as intelligent as humans, being able to reason and to solve problems as well as humans do those things. In any case, it is probable that such systems, when we finally do have them, will employ some or all of the technology I'll be describing here.
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