Scientific research is generating far more data than the average researcher can get through. Meanwhile, modern computing has yet to catch up with the superior discernment of the human eye. The solution? Enlist the help of citizen scientists. British astronomer and web developer Robert Simpson is part of the online platform Zooniverse, which lets more than one million volunteers from around the world lend a hand to a variety of projects — everything from mapping the Milky Way to hunting for exoplanets to counting elephants to identifying cancer cells — accelerating important research and making their own incredible discoveries along the way.
At TED2014, Simpson took us through a few of Zooniverse’s 20-plus projects (with more on the way), some of which have led to startling discoveries — including a planet with four suns. Below, an edited transcript of our conversation.
Are you a scientist?
Well, I’m a distracted astronomer. Yes, I’m an astronomer at University of Oxford. But I’m there to create crowdsourcing projects where we put data — usually images, but sometimes videos or sound — online, and ask the public to do research tasks that we used to ask postgrads to do. This helps us go through lots and lots and lots of data very quickly — which means scientists are free to concentrate on the hard, analytical parts of the problem.
So you’re giving volunteers the grunt work, basically?
Yeah, but what’s weird is that people love it. And not only do they enjoy it, and engage with each other online, they make discoveries, too. That’s what’s so special about it. We don’t just get the scientists’ science done. We open up the possibility for everyone to start participating in creating their own science projects using data.
We have really sophisticated computers. What can the human eye detect that a machine can’t?
A lot. I mean, a lot. With Zooniverse’s original project, Galaxy Zoo – which asked volunteers to discern between spiral galaxies versus elliptical galaxies — that was something that computers really couldn’t do at the time. Actually, they still really can’t do it unless we use the human data that we’ve gathered to train them. The computer can get it right maybe 85 percent of the time. But the 15 percent where it fails are the most interesting objects. So the reason it fails is they’re weird, funny shapes or funny colors. There’s something about them that’s slightly abnormal. These are the objects people can identify that the computer can’t — and those are precisely the ones that are scientifically interesting. So by definition, the computer isn’t doing the bit we want it to do.
Having said that, we’ve been able to train the computer to do a much better job based on the human answer, which is great news. But still, we want to ask for more — we want to cover weird, harder galaxies. So that project will just keep going, because people will always be looking at the harder set.
In another example, our project Planet Hunters has people looking through light curve data from stars, gathered using Kepler. So we stare at 150,000 stars, and watch the light from them. The whole point of doing this is to occasionally catch a planet passing in front of the star, and see a dip in light as it goes past. That dip can tell you how big the planet is, how often the planet’s going around the star, all sorts of stuff. You’ve just got to stare for long enough, and you’ve got to do it with a really, really, really good instrument.
Now, NASA and the Kepler team have used computer algorithms to look through this data for years, and they find lots of planets. But based on our experience with galaxies, we thought there must be stuff in this data that people will see that the computer can’t, because a computer is trained to look for certain things. It’s programmed by a person. Sure enough, we found planets that they didn’t find. And we found ones that are in weird, amazing configurations — some of which don’t make any physical sense — but they exist. For example, we found a planet in a seven-planet system around a sun-like star. That was an amazing discovery, because the more planets you have, the more crazy and chaotic all these dips get as they go back and forth.
To read the full interview, visit the TED Blog >>>