Big data is a big venture of most social media businesses and Pinterest is no exception. The underlying algorithms that make Pinterest successful and fun the ones that suggest new pins you might like based on things you’ve liked before.
And now, Pinterest is taking big data to another level. The social network has just announced a new visual search feature: a search tool that will allow users to select just a portion of an image, and then look for other similar images within the site. In collaboration with members of the Berkeley Vision and Learning Center, Pinterest uses deep machine learning to learn image features based on their richly annotated dataset of billions of Pins. Those features are then used to create a similarity score between any two images.
The result is that if you see a lamp you love in a pin of a living room, you can select the lamp, and search for other similar lamps as well as where to buy them.
Facebook announced that it has added a new feature to its Messenger app that will look at your phone’s camera roll for any photos you may have snapped of your Facebook friends, and then prompt you to share them with those friends. Facebook says it is solving a problem of the digital age: that you may have dozens of photos of friends on your phone that you never get around to sharing.
Facebook’s powerful facial recognition algorithm hopes to make that a problem of the past by recognizing your friends and prompting you to share the photos.
Users can opt out of facial recognition, and users must opt in to the new Photo Magic feature to get notifications about images they may want to share. But Facebook isn’t the only one putting the new algorithms to work. A recent update to the Photos app included with the Mac OS offers a smart album called “selfies” that you guessed it picks out all the photos it believes you’ve taken of yourself. In both cases, the technology represents yet another step forward in treating photos as quantifiable data.
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