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. 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. Let's get this out of the way: Improvements in machine intelligence will not lead to runaway machine-led revolutions. They may change the kind of jobs that people do, but they will not spell the end of human existence. There will be no robo-apocalypse. The emphasis of intelligence testing and computational approaches to intelligence has been on well-structured and formal problems. That is, problems that have a clear goal and a set number of possible solutions. But we humans are creative, irrational, and inconsistent.

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. 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. Let's get this out of the way: Improvements in machine intelligence will not lead to runaway machine-led revolutions. They may change the kind of jobs that people do, but they will not spell the end of human existence. There will be no robo-apocalypse. The emphasis of intelligence testing and computational approaches to intelligence has been on well-structured and formal problems. That is, problems that have a clear goal and a set number of possible solutions. But we humans are creative, irrational, and inconsistent.

In this paper we discuss an elaboration account of insight that provides answers to two of the main questions regarding insight problem solving: why insight problems are so difficult for humans and why insight is so rapid in nature. We claim that the difficulty in insight problems is due to misguided heuristic search and that this difficulty is overcome using a reformulation mechanism. Furthermore, we claim that search is carried out quickly when the heuristics are good--explaining the rapid nature of insight. We clarify our account by providing examples and initial empirical results. In conclusion, we review related work and discuss possible future work.