As an area of research, AI has been around since it was first identified and given its name during a conference at Dartmouth University in 1956. It hit a peak of excitement and media attention in the mid-1980s, when AI was overhyped as a technology that was about to change the world. One fervent branch at the time was expert systems—building a computer and software that could recreate the knowledge of an expert. A brewing company, for instance, could capture a master brewer in software, possibly making human master brewers less necessary.
The exuberance was hindered by a couple of snags that led to disenchantment with AI. For one, computers of the time weren't powerful enough to even come close to mimicking a human's processing power. Two, AI was trying to do too much. Creating a complete intelligence was too hard—and still is.
Knowing One Thing Well
These days, that's less of a barrier. Computers have gotten exponentially more powerful every year. Now, a PC is capable of running some serious AI software. And AI researchers have learned to aim at pieces of human capacity, building software that knows it can't know everything but can know one thing really well. That's how IBM's Deep Blue beat champion Gary Kasparov in chess. Together, the developments have "led to a blossoming of real-world applications," Horvitz says.
Those applications are taking on all forms.
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