In the 2010s, automation got better, cheaper, and way less avoidable. It’s still mysterious, but no longer foreign; the most Extremely Online among us interact with dozens of AIs throughout the day. That means driving directions are more reliable, instant translations are almost good enough, and everyone gets to be an adequate portrait photographer, all powered by artificial intelligence. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. And by now, everyone from fruit pickers to hedge fund managers has suffered through headlines about being replaced.
Humans and tech have always coexisted and coevolved, but this decade brought us closer together—and closer to the future—than ever. These days, you don’t have to be an engineer to participate in AI projects; in fact, you have no choice but to help, as you’re constantly offering your digital behavior to train AIs.
So here’s how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.
We Made Them Smarter
All those little operational tweaks in our day come courtesy of a specific scientific approach to AI called machine learning, one of the most popular techniques for AI projects this decade. That’s when AI is tasked not only with finding the answers to questions about data sets but with finding the questions themselves; successful deep learning applications require vast amounts of data and the time and computational power to self-test over and over again.
Deep learning, a subset of machine learning, uses neural networks to extract its own rules and adjust them until it can return the right results; other machine learning techniques might use Bayesian networks, vector maps, or evolutionary algorithms to achieve the same goal.
Technology Review’s Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. “Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video,” Hao wrote. Domestic spying is now a lucrative application for AI technologies, thanks to this powerful new development.
Hao’s report suggests that the age of deep learning is finally drawing to a close, but the next big thing may have already arrived. Reinforcement learning, like generative adversarial networks (GANs), pits neural nets against one another by having one evaluate the work of the other and distribute rewards and punishments accordingly—not unlike the way dogs and babies learn about the world.
The future of AI could be in structured learning. Just as young humans are thought to learn their first languages by processing data input from fluent caretakers with their internal language grammar, computers can also be taught how to teach themselves a task—especially if the task is to imitate a human in some capacity.
We Invited Them In
This decade, artificial intelligence went from being employed chiefly as an academic subject or science fiction trope to an unobtrusive (though occasionally malicious) everyday companion. AIs have been around in some form since the 1500s or the 1980s, depending on your definition. The first search indexing algorithm was AltaVista in 1995, but it wasn’t until 2010 that Google quietly introduced personalized search results for all customers and all searches. What was once background chatter from eager engineers has now become an inescapable part of daily life.