Machine learning has been a constant on tech trend lists for years. This year, it’s time to embrace what humans can learn by interacting with machine learning.
As Google’s head of Machine Intelligence, Blaise Aguera y Arcas, noted in a recent Medium article: “Machine intelligence will expand our understanding of both external reality and our perceptual and cognitive processes.”
In the spring of 2016, Google’s AlphaGo software, fueled by machine learning, beat the world’s greatest human Go player, Lee Sedol. The victory was a major milestone for a specific type of AI, called deep neural networks, more closely modeled on the way humans think.
The AlphaGo team refined the machine’s Go-playing prowess by training on 30 million moves from prior games, and also by pitting AlphaGo against human experts. While machine learning was the clear protagonist of the story, a funny thing happened during these human-machine face offs. In training and playing against AlphaGo, the human Go players also became better players. While the use of neural networks in AlphaGo was proof of how human thinking influences machine learning, AlphaGo’s interaction with human players also suggested a future in which machine learning could influence human thinking. We are already indirectly learning from machine learning in other ways, whether by refining our music tastes while helping Spotify refine its algorithm or by learning about the brain by observing neural networks learn.
What happens when we approach machine learning not as a replacement for human expertise, but as a partner in a collaborative relationship where humans and machines learn from each other? Could observing a computer make new connections between words make us more creative writers? Could we teach someone a new language and refine a computer’s translation abilities at the same time? Everyone is talking about machine learning. Let’s talk about human-machine learning as well.
Learning from machine learning could have an immediate impact on a number of industries. Below are five predictions for how human-machine learning could impact our lives in the coming years.
1. Education
Education is one of the areas with the clearest opportunity for embracing human-machine learning. For example, adaptive learning draws on machine learning to help tailor and evolve educational experiences based on a student’s learning style. Companies from education stalwart Pearson to start-up DuoLingo are embracing it, and the software — at least in the case of DuoLingo — also refines its translations over time as it draws on human input. As online and…