The State of AI: Fad or Future? 224

Like nearly every technology trend that came before it, it's worth asking whether artificial intelligence is just a fad (PalmPilots, anyone?) or whether it's here to stay. Before you shrug it off, know that AI is nothing new. In fact, the field of artificial intelligence has been steadily growing in the public spotlight for more than 60 years.

The term "artificial intelligence" was coined in 1956, when Dartmouth College professor John McCarthy led a small team in a summer project to see if machines could learn like young children, developing formal reasoning through trial and error. According to the original project proposal, they sought to make machines "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

Since then, the technology has mostly stayed in the academic realm, going through what some have called "AI winters." That is, until now.

In essence, three main factors are driving AI into the mainstream spotlight today:

There is more data than ever before. So much so, in fact, that 90% of the world's data was created in the last two years. Keep in mind that machine learning draws on its ability to recognize patterns and form algorithms from large data sets.

Algorithms are becoming increasingly sophisticated. Both computers and computer scientists are getting smarter. By building machines with neural networks, machines are able to replicate the way the human brain works and form complex associations.

Computing power is unlike anything it was half a century ago. According to Moore's Law (although some would reasonably argue it's now outdated), computing power has doubled every two years since McCarthy first began exploring AI.

Put all of this together, and you have the potential for a flood of tech experts, CEOs, and venture capitalists diving into a new era of AI. Ari Sheinkin, VP of marketing analytics at IBM, acknowledges that applications of AI have just intersected with ordinary people's lives in unprecedented ways.

"Being able to have access to [massive amounts of] data, and having the actual ability to understand it, process it, and tools—10 years ago, [it] would have been a research project," Sheinkin said.