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Whatever I called it, it was going to be big. It was the pitch for my new startup, a company that promised to deliver one of the world's most popular resources in the most high-tech way imaginable: an on-demand drone delivery service for bottled water. In my mind I was already picking out my Gulfstream private jet, bumping fists with Apple's Tim Cook, and staging hostile takeovers of Twitter. I just needed to convince a panel of venture capitalists that I (and they) were onto a good thing. There were three VCs in total.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. At some point, every one of us has had the feeling that online applications like YouTube and Amazon and Spotify seem to know us better than ourselves, recommending content that we like even before we say it. At the heart of these platforms' success are artificial intelligence algorithms--or more precisely, machine learning models--that can find intricate patterns in huge sets of data. Corporations in different sectors leverage the power of machine learning along with the availability of big data and compute resources to bring remarkable enhancement to all sorts of operations, including content recommendation, inventory management, sales forecasting, and fraud detection. Yet, despite their seemingly magical behavior, current AI algorithms are very efficient statistical engines that can predict outcomes as long as they don't deviate too much from the norm.