Facial recognition and machine learning programs have officially been democratized, and of course the internet is using the tech to make porn. As first reported by Motherboard, people are now creating AI-assisted face-swap porn, often featuring a celebrity's face mapped onto a porn star's body, like Gal Gadot's likeness in a clip where she's supposedly sleeping with her stepbrother. But while stopping these so-called deepfakes has challenged Reddit, Pornhub, and other communities, GIF-hosting company Gfycat thinks it's found a better answer.
Microsoft has added Windows 10 Pro support for palm-vein authentication, as part of its Windows Hello facial and fingerprint-recognition system.
The palm-vein authentication comes by way of a collaboration with Fujitsu, a Windows 10 enterprise hardware partner that is in the process of deploying its own palm-vein biometric technology to 80,000 employees in Japan.
Sixteen years ago, on a cold February day at Yale University, a poster caught Gil Kalai’s eye. It advertised a series of lectures by Michel Devoret, a well-known expert on experimental efforts in quantum computing. The talks promised to explore the question “Quantum Computer: Miracle or Mirage?” Kalai expected a vigorous discussion of the pros and cons of quantum computing. Instead, he recalled, “the skeptical direction was a little bit neglected.” He set out to explore that skeptical view himself.
In early 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who’d left IBM in 2005 to head Amazon’s recommendations team, had come to propose a sweeping new plan for incorporating the latest advances in artificial intelligence into his division.
In the early ’90s, Elizabeth Behrman, a physics professor at Wichita State University, began working to combine quantum physics with artificial intelligence — in particular, the then-maverick technology of neural networks. Most people thought she was mixing oil and water. “I had a heck of a time getting published,” she recalled. “The neural-network journals would say, ‘What is this quantum mechanics?’ and the physics journals would say, ‘What is this neural-network garbage?’”
I just picked up a Vivo phone, laid my thumb on its screen and voila: I unlocked it. That might not sound like much, but titans like Apple and Samsung have reportedly struggled to squeeze fingerprint sensors beneath their own displays. And yet here I am, with a prototype phone from a company most have never heard of, touching a finger to glass and watching a phone come to life.
As soon as the iPhone X became official, various reports said that Android device makers will be quick to steal one its signature features, the 3D facial recognition system that’s more sophisticated than what’s found on Android counterparts.
Since then, more reports have emerged detailing Apple’s various moves to secure 3D modules supply for future device supposed to support Face ID, including 2018 iPhone X successors and new iPads. Android device makers are also looking to make use of the same components, but they may take their time copying Face ID.
On November 3, 2007, six vehicles made history by successfully navigating a simulated urban environment—and complying with California traffic laws—without a driver behind the wheel. Five of the six were sporting a revolutionary new type of lidar sensor that had recently been introduced by an audio equipment maker called Velodyne.
A decade later, Velodyne's lidar continues to be a crucial technology for self-driving cars. Lidar costs are coming down but are still fairly expensive. Velodyne and a swarm of startups are trying to change that.
On November 7, Waymo announced it would begin regularly testing fully driverless cars—without a safety driver—on public roads. It was a momentous announcement. A technology that had seemed like science fiction a decade earlier became a reality. And the announcement was greeted with a yawn by much of the media and the public—if they noticed at all.
Kubernetes at its core is a container orchestration system. But simply running containers for their own sake has little purpose, as at the end of the day what really matters are applications.
Among the most interesting and often challenging types of application workloads are machine learning ones, which can often be difficult to deploy and operate. On Dec. 21, the Kubeflow project was officially announced by Google engineers as a new stack to easily deploy and run machine learning workloads.