A year ago I wrote that 2016 would be the year that consumer AI went mainstream. In one sense, I was wrong. The average consumer still doesn’t interact with an AI application on a typical day. Siri, Cortana, Google Home, and Allo are making inroads but still have small reach when compared to, say, Android or iOS as a whole.
Looking back, I realize that my angle of attack was wrong. “Consumer artificial intelligence” will not mostly be about putting AI in end-user devices. Siri, chatbots, and other natural language interfaces are a piece of the picture. However, the really interesting stuff is happening just below the surface.
Last week, The New York Times published a long, breezy piece about AI. It centered on the migration of Google Translate from “conventional software” to neural networks. The article is a wonderful ramble through the AI countryside and I highly recommend it.
What was done to Google Translate is a great illustration of where the action is with AI right now. Rather than transforming how we interact with technology, AI is, instead, first transforming what happens behind the scenes in applications like web search and machine translation. This is analogous to what happened with other, historic new technologies.
Horseless Carriages, then Cars
In the early steam age, manufacturing plants were still laid out in the same way that they were when they were powered by water wheels. When workplaces were electrified, machines were still placed as though they had to be connected to a steam engine and desks were left near large windows, and so on. It takes time to learn how to apply a new technology. But we are learning.
The Machine Intelligence Landscape
For the past three years, Shivon Zilis and the team at Bloomberg Beta have been mapping the Machine Intelligence Landscape. 2016 saw an explosion of startups applying artificial intelligence to specific business problems. This is because it is easy to estimate, capture, and charge for the value an AI solution creates for a business. It is also relatively easy to identify and process training data.
Because of this, AI is becoming ubiquitous in business software. As a result, two years from now you won’t talk about “AI SaaS companies,” or “AI technology companies selling to business” — you’ll just assume that every piece of software marketing to business incorporates AI appropriately, just as that software uses a relational database or runs on the internet.
We’ll have optimized the heck out of what’s inside the little boxes of a business (sales, marketing, etc.) and will move on to the interesting stuff — creating new things that couldn’t happen without AI, and that blow up the boxes altogether.
But Back to the Consumer
This is where things get interesting; what happens when these new companies — with their blown up boxes, and their AI powered businesses — interact with consumers, directly through their devices? We were given a glimpse of this with Amazon Go a few weeks ago — a grocery store that uses machine vision and AI, along with other technologies, to end the checkout line. The thin end of the wedge were the Google Now and iPhone prompts that tell you when you need to leave to get to your best appointment, or warn you about a transit delay.
A tough question: What do we even want?
If we take a step further back — what do we, as consumers, ultimately want AI to do? Clearly, automating routine work and improving our safety is important. But then what? The answer is definitely not “generate more notifications on my phone.” Or “give me another awful pseudo-human to talk to,” one that’s just as frustrating as the typical Comcast customer-service representative.
AI will break down barriers and connect people to what they need
So now we truly begin to apply AI to the long project of connecting humans with the things that they value. The things they need to complete their day, raise their families, and run their lives. We need to create new connections from human needs, across time and space, going deep into the organizations that can satisfy those needs. Re-think our businesses, re-think our lives, embrace what is possible, and blow up the little boxes. To create revolutionary change with accelerating, incremental change.