The resulting tool is easier and more accurate than keeping a food diary and cheaper than consulting a nutritionist.
"Just taking pictures won't make you healthier," warns Gajos. "You have to actually reflect on this information. You have to be motivated to change. But if you have this motivation, then PlateMate will make it easier for you to follow through."
In the future, he suggests, some of the contextual problems could be avoided by pairing the photos with location data.
Intended primarily as a foray into the capabilities of human-computer systems, PlateMate may not solve, once and for all, the challenge of eating well. It is, however, one of the first attempts to use multiple human-computational approaches to solve a very complex, real-world problem.
"A lot of prior crowdsourcing research has been about making crowds do things that we wish computers could do, like shorten an 800-word essay to 500 words and have it still make sense," explains Noronha. "That's something computers can almost do, but it's just beyond their reach."
"What makes the nutrition application so interesting as a problem in crowdsourcing is that computers are so very far away from doing it on their own -- because food is such a human thing."
Computations and algorithms cannot yet evaluate a meal, but it turns out that they can build an effective workforce. The PlateMate project proves that a well-managed crowd can play the role of an expert, and that opens the door to a wealth of new opportunities.
"Any problem that can be broken down into logical steps is a great candidate for crowdsourcing," says Haoqi Zhang '07, a doctoral candidate at SEAS who brought crowdsourcing expertise to the PlateMate project. "The only question is, what would you like the crowd to do for you?"
"Take, for example, comparing travel packages or making slides
|Contact: Caroline Perry|