If you’re thinking of a human who’s been implanted with sort of computer chip, that’s not the kind of cyborg citizen scientist that Kevin Schawinski who developed the Galaxy Zoo citizen science project is writing about in his March 17, 2016 essay for The Conversation. Schawinski introduces the concept of citizen science and his premise,
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.
He also explains why people power (until recently) has been superior to algorithms,
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.
But the people power advantage has changed somewhat with deep brains (deep neural networks), which can learn and develop intuition the way humans do. One of these deep neural networks has made recent news,
Recently, the team behind Google’s DeepMind has thrown down the gauntlet to the world’s best Go players, claiming that their deep mind can beat them. Go has remained an intractable challenge to computers, with good human players still routinely beating the most powerful computers – until now. Just this March AlphaGo, Google’s Go-playing deep mind, beat Go champion Lee Sedol 4-1.
Schawinski goes on to make his case for this new generation of machine intelligence,
We’re now entering an era in which machines are starting to become competitive with humans in terms of analyzing images, a task previously reserved for human citizen scientists clicking away at galaxies, climate records or snapshots from the Serengeti. This landscape is completely different from when I was a graduate student just a decade ago – then, the machines just weren’t quite up to scratch in many cases. Now they’re starting to outperform people in more and more tasks.
He then makes his case for citizen science cyborgs while explaining what he means by that,
But the machines still need help – our help! One of the biggest problems for deep neural nets is that they require large training sets, examples of data (say, images of galaxies) which have already been carefully and accurately classified. This is one way in which the citizen scientists will be able to contribute: train the machines by providing high-quality training sets so the machines can then go off and deal with the rest of the data.
There’s another way citizen scientists will be able to pitch in: by helping us identify the weird things out there we don’t know about yet, the proverbial Rumsfeldian [Donald Rumsfeld, a former US Secretary of Defense under both the Gerald Ford and George H. Bush administrations] “unknown unknowns.” Machines can struggle with noticing unusual or unexpected things, whereas humans excel at it.
So envision a future where a smart system for analyzing large data sets diverts some small percentage of the data to human citizen scientists to help train the machines. The machines then go through the data, occasionally spinning off some more objects to the humans to improve machine performance as time goes on. If the machines then encounter something odd or unexpected, they pass it on to the citizen scientists for evaluation.
Thus, humans and machines will form a true collaboration: citizen science cyborgs.
H/t March 17, 2016 phys.org news item.
I recommend reading Schwawinski’s article, which features an embedded video, in its entirety should you have the time.