Consumer AI in 2016

In October of 1994, Netscape released Netscape Navigator 1.0, the first commercial web browser. Over the next decade, the web went mainstream as it became increasingly usable.

In October of 2011, Apple announced that Siri — the mobile personal assistant it acquired in 2010 — would ship on the iPhone 4S. Siri has continued to improve, as have Google Now, Amazon Echo, and a host of other solutions. 2016 will be the year that consumer AI goes mainstream.

The Future Arrives

For twenty years voice recognition had been “the future.” While Watson and Wolfram Alpha captured the attention of the press, they both had negligible impact on consumers. While the world obsessed over screen size and Angry Birds, Siri kept getting better.

With WatchOS2 and an iPhone 6, Siri finally feels usable. I find myself tapping the phone less, and talking to my wrist more. How did this happen, and what can it tell us about the near future?

• Experience effects. Like most machine intelligence, the more heavily Siri is used, the better it gets. Apple’s acquisition of VocalIQ, the world’s first self-learning dialogue system, should further increase the rate of improvement.
• Moore’s law. AI problems are often bound by processing speed. CPUs double in performance every two years, creating a “natural” rate of improvement for AI systems. So, for the same CapEx, Apple now has four times as much processing power to throw at the problem. AI performance is further improved by algorithmic enhancements, like migrating processing work away from general-purpose CPUs and onto GPUs (graphical processing units), which are faster but more difficult to work with.
• Apple built a search engine. Apple built a search engine to make it easy and worthwhile for app developers to extend Siri — without even knowing it. The Search API lets developers to describe their app to Apple (and Siri) with simple markup. Search is Apple’s way to evolve Siri into a tightly controlled platform.

Siri is Now A Toy that Works

Siri is now the main way I handle use cases where I can articulate a clear question (“what is the weather tomorrow?”) or command (“remind me to buy milk tonight”). While I emphasize ‘clear question’, the future will likely behold Siri’s ability to handle increasingly complex natural language questions that deliver an optimal solution to a problem. For example, “Given the tastes of my dinner guests, what meals should I prepare?”

It Keeps Getting Better

The ecosystem around Apple’s AI implementation is strengthening every day. Developers are exposing more and more functionality to Siri through the Search API. There are, however, bottlenecks imposed by Apple’s privacy policies that prevent it from having access to the rich user-generated datasets it helps create.

The Kids Think It’s Normal

And by “kids” I don’t mean “millennials,” I mean the toddlers running about my house. The older one now orders Siri around. She expects to be able to talk to a computer. Remember when kindergarteners suddenly expected all screens to be touch sensitive? Generational shifts like these are great leading indicators of what’s next.

Conclusion: Next Year…

By the end of next year, consumer AI will be everywhere. Operating systems will expose key features of installed applications or replace them altogether.

Facebook M, Operator, WhatsApp, WeChat, Slack, Kik, and every service with a natural language interface is, at its heart, an AI platform. Algorithms can either establish a direct rapport with users or monitor what is being said in a privacy-sensitive way, collecting intelligence and offering assistance.

For example, Apple will integrate Siri within Messages and Mail, Emu-style.

…and Beyond

Consumer AI will continue to improve by a factor of two every two years. This sustained, exponential improvement will bring startling results. Incremental projects will overtake attempts at big-bang disruption. Think less “Google Self-Driving Car” and more “Tesla Autopilot.”

Incumbent players will accelerate the acquisition of consumer AI applications to bolster their teams, and to defend their positions. Nobody wants to have done to them what Google did to Yahoo.

Updated on December 16, 2016 based on feedback from Nathan Benaich and Ahmad Nassri, as well as what I learned at the Machine Learning and the Market for Intelligence hosted by the @creativedlab and the University of Toronto. Inspired in part by Shivon Zilis’s excellent work in the space.