An exercise in data collection it’s cool, but do we really need to democratize artistic talent with data?
Big data isn’t just about the size of the data set: The discovery of new data sources is also important. There’s web data, sensor data, location data and, now, there’s artistic data. No, not data about the properties of the world’s masterpieces, but data about the actual strokes we use while were drawing.
Two studies, both being presented at this week’s SIGGRAPH conference in Los Angeles, have demonstrated that it’s possible to learn a lot about how people draw if you just have the right data. In one case, it was a team from Carnegie Mellon University and Microsoft Research having subjects play a cross of Pictionary and Wheel of Fortune on their iPhones in order to generate data. That study is particularly interesting, if only because of how it took advantage of the iPhone’s ubiquity in order to crowdsource data generation and ended up with a data set that now contains more than 17,000 drawings.
Subjects played a game called DrawAFriend that had them trace images of celebrities or mutual friends with their fingers. Once they were done, the drawing was presented to the other player stroke by stroke, and he or she guessed the letters in the subject’s name based on their confidence in who they’re looking at. The fewer guesses it took someone to guess the subject, the better those strokes were scored for the purpose of comparing them.