When 450,000 People Can’t be Wrong – Forbes.com on TopCoder
Originally posted on forbes.com February 19, 2013 by Andy Boynton.
A couple of years ago, I dropped in on Jack Hughes of the software development firm TopCoder, while researching a book about ideas. It was one of the most intellectually energizing visits I’ve ever made to a corporate front office. Based in Glastonbury, Connecticut, TopCoder has pioneered the model of crowdsourcing in that industry, spawning a global community of 450,000 freelance software developers from 200 countries.
I was deeply impressed by the innovation: These independent developers do all the software coding for TopCoder, which has provided solutions to companies such as Best Buy and GEICO. But I was even more struck by the way Hughes and his team members go looking for business ideas. They do so practically everywhere except within their own field of software development. “I almost never look to the existing discipline for new ideas. Most of the time, I actively avoid it,” Hughes told me. I’ll get back in a minute to how the business was built with that attitude.
Last week I paid another visit to TopCoder amid a groundswell of interest in the company and its approach to crowdsourcing, or what Hughes often labels as “open innovation.” He spoke about how this model has allowed the company to leverage global expertise to address a “broad variety of problems and difficulties.” He wasn’t kidding.
Among other projects, TopCoder is now taking on complex biological problems, together with researchers from Harvard Medical School, HarvardBusiness School, and London Business School. These partners are using the company’s innovation platform—in other words, that far-flung network of freelancers—to surmount the challenge of analyzing mind-boggling amounts of genetic data. Their recent study was detailed in an item published in the February 7 edition of the journal Nature Biotechnology.
Reporter Carolyn Y. Johnson of the Boston Globe found a perfect prototype for this story:
Faced with a tough data analysis challenge as he struggled to answer questions about how the immune system works, Dr. Ramy Arnaout of Beth Israel Deaconess Medical Center took an unusual step. He went beyond his circle of Harvard colleagues and beyond the expertise of fellow biologists; he turned to software programmers scattered around the world who had little expertise in the life sciences.
The result: A deeply biological problem — analyzing the makeup of genes that produce proteins involved in the immune system’s ability to identify microbes — could be rapidly and efficiently answered by a community of more than 400,000 computer programmers who try to solve competitive coding challenges posted on TopCoder, a platform used by big companies such as Google, Intel, and Facebook.
At its news blog, Nature described the task even more colorfully: “Mercenary computer coders are helping scientists cope with the deluge of data pouring out of research labs.”
Prizes of up to $500 each were offered to coders who came up with credible solutions. This isn’t a gimmick or a one-off strategy. It’s the modus operandi of TopCoder. Members of this coding community take part in contests to come up with creative designs and solutions.
During a two-week contest, 122 coders from 69 countries came up with software code. Among their solutions, 16 were deemed better—faster and more accurate—than the standard algorithm used by the National Institutes ofHealth, according to a Feb. 7 announcement by Harvard Medical School. The entire competition cost $6,000.
In a traditional research setting, “a life scientist who needs large volumes of data analyzed will hire a postdoc to create a solution, and it could take well over a year,” explained Karim Lakhani, associate professor in the Technology and Operations Management Unit at Harvard Business School. “We’re showing that in certain instances, existing platforms and communities might solve these problems better, cheaper and faster.”
More to the point I’m stressing: the study illustrates what can happen when disparate worlds of knowledge are pulled together to come up with answers to difficult problems. Consider the kinds of connections underway here—between an algorithm specialist in (for all I know) Algeria, or a software developer in Slovenia, and a radiation oncologist at Dana-Farber Cancer Institute. Not the most likely collaborators.