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How Computational Complexity Will Revolutionize Philosophy

Technology Review (08/10/11)

Massachusetts Institute of Technology computer scientist Scott Aaronson argues that computational complexity theory will have a transformative effect on philosophical thinking about a broad spectrum of topics such as the challenge of artificial intelligence (AI). The theory focuses on how the resources required to solve a problem scale with some measure of the problem size, and how problems typically scale either reasonably slowly or unreasonably rapidly. Aaronson raises the issue of AI and whether computers can ever become capable of human-like thinking. He contends that computability theory cannot provide a fundamental impediment to computers passing the Turing test. A more productive strategy is to consider the problem’s computational complexity, Aaronson says. He cites the possibility of a computer that records all the human-to-human conversations it hears, accruing a database over time with which it can make conversation by looking up human answers to questions it is presented with. Aaronson says that although this strategy works, it demands computational resources that expand exponentially with the length of the conversation. This, in turn, leads to a new way of thinking about the AI problem, and by this reasoning, the difference between humans and machines is basically one of computational complexity.



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