As any hipster will tell you, that can change in an instant.Throughout 2016, a shift in tone and approach to presenting and discussing artists who exist outside of the traditional or mainstream (that has been crystallizing over the past few years) has continued in force. While it may be useful to identify a user’s tribe to understand them better, how that information is used depends on certain assumptions about what that tribe likes. Often is seems that Facebook thinks that just because a user is a woman, she will automatically be interested in news about celebrity diets. Ads and search results that have been tailored according to our gender can already be irritating. We have to ask ourselves if we want our internet experience to be tailored in this way. Just because a goth likes to dress in black doesn’t necessarily mean their taste in hobbies isn’t more closely aligned to those of a surfer. While 48% is better than chance, the researchers want their algorithm to perform as well as a human would and plan to keep working to improve its accuracy.īut then comes the deeper question of whether you can really make assumptions about what a person is interested in based on how they look. For a start, a 48% accuracy means that a Facebooking goth would be fairly likely to get ads for fixed-wheel bike repairs cropping up in their feed by mistake were the technology be deployed in its current state. There are some problems to this approach though. The idea is that if an algorithm can identify the kind of person you are from how you look, sites can offer you a more personally tailored experience. This type of learning problem would be perfectly suited to the machinery of Google, in that it might be possible to find the features indicative of particular social groups without needing to manually state the types of features such as face, head, top of the head (where a hat would be), neck, torso and arms.
The algorithm then uses the labelled pictures to learn a classifier.
Which is your tribe? Jacobs School of Engineering UC San Diego In this case, features such as the head, neck, torso and arms of each subject were scanned for attributes such as tattoos, colours, haircuts and jewellery. The algorithm uses a “parts and attributes” approach, by breaking down each picture into a set of feature values.
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This machine can do better than that but not as well as a human using the full power of their street savvy. If you were to guess the content of a picture (without seeing it), then you will guess the correct answer once every 11 times on average. The researchers say the algorithm is 48% accurate on average, while chance would get answers right only 9% of the time. So if it looks at a picture, sees a pair of horn-rimmed glasses, a waxed moustache and a lumberjack shirt, and is told that it is looking at a hipster, it can move on to a new photo and identify a quinoa lover just from their look. The algorithm leverages the assumption that pictures with a similar set of feature values are likely to have similar labels. It takes a set of photos, each with their own label, such as “cat”, “car”, “emo”, and then finds the features in the photos that best predict the label of a new photo. These are widely used in vision analysis to draw conclusions from clues that are found in images. The researchers are using what is known as a multi-label classification algorithm.
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