As Silicon Valley investors and tech giants continue to pour cash into burgeoning artificial intelligence technologies such as machine learning and chatbots, the relatively nascent A.I. industry is emerging as the latest mega-hot new ticket in town—the heir to online delivery apps, anything-hailing services, and virtual reality start-ups. But much like another buzz-worthy predecessor, Big Data, many A.I. cheerleaders and investment check signatories probably don’t quite understand it. But in Silicon Valley, when has that ever stopped anyone?
The obsession in the Valley with artificial intelligence is almost palpable. Just last week, Twitter announced its acquisition of London-based machine learning start-up Magic Pony Technology. Then on Wednesday of this week, Pinterest announced that it was rolling out new features that will use machine-learning algorithms and A.I. to help users identify and purchase items on the social-media platform. The same day, Microsoft C.E.O. Satya Nadella authored what can only be described as a love letter to A.I. for Slate, in which he touted the potential of the emerging technology: “ . . . imagine what’s possible when human and machine work together to solve society’s greatest challenges like beating disease, ignorance and poverty.”
Among the powerful in tech, Nadella is certainly not alone in his sentiments. Mark Zuckerberg kicked off the new year with a Facebook post stating that his personal challenge in 2016 is, “to build a simple A.I. to run my home and help me with my work. You can think of it kind of like Jarvis in Iron Man.” Despite his fears of a calamitous, Skynet-style robot takeover, Elon Musk also dreams of building an A.I. assistant. Earlier this month, the Tesla C.E.O. announced that he is specifically interested in building a robotic servant that will do household chores (to begin with, anyway). In a May interview with Forbes, Google C.E.O. Sundar Pichai announced his bold plans to make A.I. a cornerstone of the tech behemoth’s business with the launch of Google assistant, Google Home and Allo, a smart-messaging app. “Imagine all the kinds of problems that many many teams around the world are working on being able to tap and apply machine learning and A.I. to their problems,” Pichai said.
Based on findings from CB Insights, A.I. is trending at a rate among investors that puts it on track to soon eclipse Big Data. As of February this year, A.I. start-ups have raised a net total of $967 million in funding since 2010. But the question remains: do people really understand what they are investing in? “It is clear that 9 of 10 investors have very little idea what A.I. is so if you’re a founder raising money, you should sprinkle some A.I. into your pitch deck. Use of ‘artificial intelligence,’ ‘A.I.,’ ‘chatbot,’ or ‘bot’ are winners now and might get you a little valuation bump or get the process to move quicker,” writes Anand Sanwal, C.E.O. and co-founder of CB Insights.
This isn’t to say that A.I. is brand new. Every time you talk to Siri, or Facebook gives you suggestions as to who to tag in a photo, machine learning is at play. But now these techniques are becoming increasingly advanced and more functional, and few truly understand exactly how your phone is suggesting that perfect restaurant. Benedict Evans, an employee at venture-capital firm Andreessen Horowitz, sums it up well. In a blog post he writes, “there are two things that make it hard to talk about the A.I. explosion. The first is that ‘A.I.’ is an impossibly broad term that implies we have a new magic hammer that turns every problem into a nail. We don’t—we have a bunch of new tools that solve some, but not all, problems, and the promise of extracting new insight from all sorts of data pools will not always be met. It might be the wrong data, or the wrong insight. The second is that this field is growing and changing very fast, and things that weren’t working now are, and new things are being discussed all the time.”
In March, Google’s DeepMind A.I. program AlphaGo made headlines when it beat the top Go player in the world at the complex board game. At the time, however, you would have been hard-pressed to find anyone able to explain to you what deep neural networks are, or the importance of the fact that the program didn’t use brute force to beat its human opponent, but a technique called “Monte-Carlo tree search.” While the Amazon Echo is certainly handy and many of us would be lost without auto-correction on our smartphones, there seems to be a gap in the understanding of A.I. amid the growing hype.
