![]() ![]() ![]() For howhot.io we simplified the process and just learned the ‘objective’ criteria… which is very difficult, because it is really a subjective thing!” he adds.īlinq currently has some 200,000 monthly active users, with the biggest markets being Switzerland, Germany, Turkey, the U.K., the U.S. if you like men with a beard, after you have liked a couple of men with a beard the proposed system would recognize that and know that you prefer men with a beard (without ever telling the system what a beard is). “In that paper we tried to learn personalized preferences. Although that aspect of the research is not being fed into the Blinq implementation at this point - so the forthcoming photo-judging feature in the app will purely be a median measure of attractiveness. if you have beautiful eyes, a large nose, no hair, a sexy beard, etc… ”īut beauty’s in the eye of the beholder right? So how can an algorithm meaningfully assess hotness? Rothe says the team’s initial experiments actually involved trying to learn to be more subjective (he wrote another paper on this). “Visualizations showed that it tends to focus on parts of the face which are ‘non-standard’, i.e. “The neural network itself then learns what parts of the face to look at,” he says. On the hotness front, Rothe says the team created an attractiveness ranking for men and women from the data supplied by Blinq to enable the algorithm to learn which specific features contribute to an individual being ranked in the top 10 per cent (or 20 per cent, or 50 per cent) of their gender. But hey, humans sometimes still think I’m this old too… Which perhaps explains its far worse accuracy level in my case. Obviously the algorithm lacks any such context - so it’s effectively guessing ‘blind’, as it were. you know that person graduated college a year ago and thus must be 23+/-1 year, or is in the same friend group and thus must be of similar age),” he adds. “This is also due to that in many cases when you estimate the age of a person you have a lot of context (i.e. The problem is that people have high expectations at such a system so 3 years might seem a lot. “Humans can be up to 3.5 years across the full age range (usually you are better at guessing the age for people who have a similar age as you)… so it should be slightly better than human prediction. “The average error should be around 3 years,” says Rothe. In my case across more than a decade, despite the sample photos being taken but a few years apart… So, yeah, age is a hard problem. So the algorithm’s guesses can range pretty widely/wildly. And the visual expression of age can hardly be described as an exact science. Of course guessing age is a hard problem, even for humans. On the age front, Rothe says it was trained on images from IMDb and Wikipedia - along with “some other smaller datasets”. “We won the age estimation challenge at International Conference of Computer Vision 2015 in Chile ( the paper) against 130 other teams with this method,” he notes. ![]() “We used more than 100,000 images and more than 20 million ratings between users from our data base,” says Berchtold, explaining the role the app’s data played in the algorithm’s aesthetic training. The tech powering the algorithm was developed by third year PhD student Rasmus Rothe, of the Computer Vision Lab at ETH Zurich, including using image data and attractiveness ratings supplied by Bling - the latter gleaned from the binary ‘hi or bye’ choices Blinq users make as they swipe through potential matches. By doing so they can test which of them will probably perform better.” “The users will have the possibility to upload several images before they set up their account. “We are going to integrate the algorithm in the Blinq app,” co-founder Jan Berchtold tells TechCrunch. In the meanwhile, it’s launched the feature as a standalone website, called howhot.io, to test how much appetite there is for robotically judged hotness. (The website launched last week and, inevitably, after two days had racked up more than two million unique visits, so it’s not hard to see why they’re ploughing this click-festy furrow…) Swiss dating app startup Blinq is playing around with a little algorithmic hot or not catnip, with a plan to add a machine-learning powered attractiveness assessment feature to help its users pick the photos that show them at their best.
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