Archives April 2018

There have always been outsiders who criticized the tech industry — even if their concerns have been drowned out by the oohs and aahs of consumers, investors, and journalists. But today, the most dire warnings are coming from the heart of Silicon Valley itself. The man who oversaw the creation of the original iPhone believes the device he helped build is too addictive. The inventor of the World Wide Web fears his creation is being “weaponized.” Even Sean Parker, Facebook’s first president, has blasted social media as a dangerous form of psychological manipulation. “God only knows what it’s doing to our children’s brains,” he lamented recently.

If the tech industry likes to assume the trappings of a religion, complete with a quasi-messianic story of progress, the Church of Tech is now giving rise to a new sect of apostates, feverishly confessing their own sins. And the internet’s original sin, as these programmers and investors and CEOs make clear, was its business model.

The advertising model of the internet was different from anything that came before. Whatever you might say about broadcast advertising, it drew you into a kind of community, even if it was a community of consumers. The culture of the social-media era, by contrast, doesn’t draw you anywhere. It meets you exactly where you are, with your preferences and prejudices — at least as best as an algorithm can intuit them. “Microtargeting” is nothing more than a fancy term for social atomization — a business logic that promises community while promoting its opposite.

Artificial intelligence has been used to create 3D models of people’s bodies for virtual reality avatars, surveillance, visualizing fashion, or movies. But it typically requires special camera equipment to detect depth or to view someone from multiple angles. A new algorithm creates 3D models using standard video footage from one angle.

The system has three stages. First, it analyzes a video a few seconds long of someone moving—preferably turning 360° to show all sides—and for each frame creates a silhouette separating the person from the background. Based on machine learning techniques—in which computers learn a task from many examples—it roughly estimates the 3D body shape and location of joints. In the second stage, it “unposes” the virtual human created from each frame, making them all stand with arms out in a T shape, and combines information about the T-posed people into one, more accurate model. Finally, in the third stage, it applies color and texture to the model based on recorded hair, clothing, and skin.

The researchers tested the method with a variety of body shapes, clothing, and backgrounds and found that it had an average accuracy within 5 millimeters, they will report in June at the Computer Vision and Pattern Recognition conference in Salt Lake City. The system can also reproduce the folding and wrinkles of fabric, but it struggles with skirts and long hair. With a model of you, the researchers can change your weight, clothing, and pose—and even make you perform a perfect pirouette. No practice necessary.