It was an insight problem that supposedly led Archimedes to run naked through the streets of Syracuse when he solved it. In insight problems, the solver is given none of these. In path problems, the solver is given a representation, which includes a starting state, a goal state, and a set of tools or operators that can be applied to move through the representation. Instead, insight problems are characterized by a kind of restructuring of the solver’s approach to the problem. Insight problems generally cannot be solved by a step-by-step procedure, like an algorithm, or if they can, the process is extremely tedious. One group of these are so-called insight problems. This article is adapted from Herbert Roitblat’s book “ Algorithms Are Not Enough: Creating General Artificial Intelligence.” There are other problems that are much more typical of human intelligence and deserve a closer look. Focusing on these well-structured problems may be like looking for your lost keys where the light is brightest. But we humans are creative, irrational, and inconsistent. That is, problems that have a clear goal and a set number of possible solutions. The emphasis of intelligence testing and computational approaches to intelligence has been on well-structured and formal problems. They may change the kind of jobs that people do, but they will not spell the end of human existence.
Let’s get this out of the way: Improvements in machine intelligence will not lead to runaway machine-led revolutions. We’ve now succeeded in creating machines that can solve specific fairly narrow problems - “smart” machines that can diagnose disease, drive cars, understand speech, and beat us at chess - but general intelligence remains elusive.
At least since the 1950s, the idea that it would be possible to soon create a machine that was capable of matching the full scope and level of achievement of human intelligence has been greeted with equal amounts of hype and hysteria.
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