At MIT Technology Review, Tom Simonite writes about Facebook’s efforts to make its automated assistant M answer pretty much any request that comes its way, not matter how obscure. And for a very small group of beta testers, the bot actually works, delivering results so good you’d swear you were talking to a human being. Because you are.
M is so smart because it cheats. It works like Siri in that when you tap out a message to M, algorithms try to figure out what you want. When they can’t, though, M doesn’t fall back on searching the Web or saying “I’m sorry, I don’t understand the question.” Instead, a human being invisibly takes over, responding to your request as if the algorithms were still at the helm. (Facebook declined to say how many of those workers it has, or to make M available to try.)
That design is too expensive to scale to the 1.2 billion people who use Facebook Messenger, so Facebook offered M to a few thousand users in 2015 as a kind of semi-public R&D project. Entwining human workers and algorithms was intended to reveal how people would react to an omniscient virtual assistant, and to provide data that would let the algorithms learn to take over the work of their human “trainers.”
This is the way I’ve been prototyping chatbots, too: start with simple human-to-human interactions.
I’m a big fan of this kind of prototype that put people where the pipes will eventually go. In a way, Uber is a similar prototype for self-driving cars: until the robots get the go-ahead to drive on their own, we’ll put a human in the driver’s seat and automate the rest of the experience (calling a car, giving directions, paying the tab).
When you’re trying out new interactions for untested or emerging technologies, the best MVP is often no tech at all. Powering a bot with people instead of artificial intelligence gets you early info about what people want, how they respond, and the kind of language to use. It proves out the demand of the service, hints at the shape it should take, and offers training data to give to the bots down the road.
Eventually the AI steps in. At Facebook, they’re still trying to use all that data to train the bots well enough so that they can take over. Simonite shares some of the techniques the M team is using, with mixed results. Even though machine-learning breakthroughs are coming fast and furious, the holy grail of broad and instant natural-language understanding is still tantalizingly out of reach. “Sometimes we say this is three years, or five years,“ M’s leader Laurent Landowski told Simonite. ”But maybe it’s 10 years or more.”
