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methods even if better ones are available. Davis subsequently earned another cash award in a challenge that sought help with a hair removal product; a memory of rolling chewing gum on his leg as a kid led him to a solution.
When I asked Davis if he was prone to framing problems with distant analogies from random experiences outside his field, he had to ponder it for a moment. Does he do that in his daily chemistry problems, I asked? “You know, I don’t, not really,” he said. “It’s these other puzzles or problems where you have to think outside the box.”
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InnoCentive works in part because, as specialists become more narrowly focused, “the box” is more like Russian nesting dolls. Specialists divide into subspecialties, which soon divide into sub-subspecialties. Even if they get outside the small doll, they may get stuck inside the next, slightly larger one. Cragin and Davis were outside the box to begin with, and saw straightforward solutions that eluded insiders with seemingly every training and resource advantage. Solvers themselves were often bewildered when they overcame a challenge that stumped entire companies or industries.
“It took me three evenings to write it up,” an outside solver told the journal Science after he answered Johnson & Johnson’s request for help with a production problem in the manufacture of tuberculosis medication. “I think it’s strange that a major pharma company cannot solve this kind of problem.” Karim Lakhani, codirector of the Laboratory for Innovation Science at Harvard, had InnoCentive solvers rate problems on how relevant they were to their own field of specialization, and found that “the further the problem was from the solver’s expertise, the more likely they were to solve it.”
As organizational boxes get smaller and smaller, and as outsiders are more easily engaged online, “exploration [of new solutions] now increasingly resides outside the boundaries of the traditional firm,” Lakhani and colleagues wrote. Our intuition might be that only hyperspecialized experts can drive modern innovation, but increasing specialization actually creates new opportunities for outsiders.
As Alph Bingham noticed, for difficult challenges organizations tend toward local search. They rely on specialists in a single knowledge domain, and methods that have worked before. (Think about the lab with only E. coli specialists from chapter 5.) If those fail, they’re stuck. For the most intractable problems, “our research shows that a domain-based solution is often inferior,” according to Lakhani. “Big innovation most often happens when an outsider who may be far away from the surface of the problem reframes the problem in a way that unlocks the solution.”
Since InnoCentive demonstrated the concept, other organizations have arisen to capitalize on outside-in solvers in normally highly specialized fields. Kaggle is like InnoCentive but specifically for posting challenges in the area of machine learning—artificial intelligence designed to teach itself without human intervention.
Shubin Dai, who lives in Changsha, China, was the top-ranked Kaggle solver in the world as of this writing, out of more than forty thousand contributors. His day job is leading a team that processes data for banks, but Kaggle competitions gave him an opportunity to dabble in machine learning. His favorite problems involve human health or nature conservation, like a competition in which he won $30,000 by wielding satellite imagery to distinguish human-caused from natural forest loss in the Amazon. Dai was asked, for a Kaggle blog post, how important domain expertise is for winning competitions. “To be frank, I don’t think we can benefit from domain expertise too much. . . . It’s very hard to win a competition just by using [well-known] methods,” he replied. “We need more creative solutions.”
“The people who win a Kaggle health competition have no medical training, no biology training, and they’re also often not real machine learning experts,” Pedro Domingos, a computer science professor and machine learning researcher, told me. “Knowledge is a double-edged sword. It allows you to do some things, but it also makes you blind to other things that you could do.”
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Don Swanson saw it coming—the opportunities for people like Bruce Cragin and John Davis, outsiders who merge strands of disparate knowledge. Swanson earned a physics PhD in 1952, and then worked as an industry computer systems analyst, where he became fascinated with organizing information. In 1963, the University of Chicago took a chance on him as dean of the Graduate Library School. As a thirty-eight-year-old from private industry, he was an oddball. The hiring announcement declared, “Swanson is the first physical scientist to head a