The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do

Treating intelligence as problem-solving thus gives us narrow applications.

Mythology about AI is bad, then, because it covers up a scientific mystery in endless talk of ongoing progress.

Gödel proved that there must exist some statements in any formal (mathematical or computational) system that are True, with capital-T standing, yet not provable in the system itself using any of its rules. The True statement can be recognized by a human mind, but is (provably) not provable by the system it’s formulated in.

we humans can see that the Gödel statement is in fact true, but because of Gödel’s result, we also know that the system’s rules can’t prove it—the system is in effect blind to something not covered by its rules.4 Truth and provability pull apart. Perhaps mind and machine do, as well. The purely formal system has limits, at any rate. It cannot prove

This book explains two important aspects of the AI myth, one scientific and one cultural. The scientific part of the myth assumes that we need only keep “chipping away” at the challenge of general intelligence by making progress on narrow feats of intelligence, like playing games or recognizing images. This is a profound mistake: success on narrow

As we successfully apply simpler, narrow versions of intelligence that benefit from faster computers and lots of data, we are not making incremental progress, but rather picking low-hanging fruit. The jump to general “common sense” is completely different, and there’s no known path from the one to the other.