Beyond today’s crowdsourced science to tomorrow’s citizen science cyborgs
Source: Kevin Schawinski
Millions of citizen scientists have been flocking to projects that pool their time and brainpower to tackle big scientific problems, from astronomy to zoology. Projects such as those hosted by the Zooniverse get people across the globe to donate some part of their cognitive surplus, pool it with others' and apply it to scientific research.
But the way in which citizen scientists contribute to the scientific enterprise may be about to change radically: rather than trawling through mountains of data by themselves, they will teach computers how to analyze data. They will teach these intelligent machines how to act like a crowd of human beings.
We’re on the verge of a huge change – not just in how we do citizen science, but how we do science itself.
The awesome human brain
The human mind is pretty amazing. A young child can tell one human face from another without any trouble, yet it took computer scientists and engineers over a decade to build software that could do the same. And that’s not human beings' only advantage: we are far more flexible than computers. Give a person some example images of galaxies instead of human faces, and she’ll soon outperform any computer running a neural net in classifying galaxies.
I hit on that reality when I was trying to classify about 50,000 galaxy images for my Ph.D. research in 2007. I took a brief overview of what computers could do and decided that none of the state-of-the-art solutions available was really good enough for what I wanted. So I went ahead and sorted nearly 50,000 galaxies “by eye.” This endeavor led to the Galaxy Zoo citizen science project, in which we invited the public to help astronomers classify a million galaxies by shape and discover the “weird things” out there that nobody knew are out there, such as Hanny’s Voorwerp, the giant glowing cloud of gas next to a massive galaxy.
Enter the deep minds
Computer scientists have made significant steps forward in machine learning over the last few years, and some of their inventions have started to hit the public consciousness.
“Deep neural networks” (or deep minds) learn in a way that is closer to how our brains learn. They try to model data – say, photos of people – by turning them into high-level abstractions using multiple layers where each different layer may focus on different tasks (hence the “deep” in deep neural nets).
These machines are learning in a way that is more akin to what humans do: they start to develop their own intuition. Games are where computer scientists put their machine lab rats to the test. With their learned artificial intelligence, machines are starting to intuit which moves in a game are better than others. This is fundamentally different from previous approaches where the computers would try to “brute force” the game by calculating as many moves as possible and use smart statistics to figure out the best move that way.
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