If facial recognition is good enough for Taylor Swift, is it good enough for you?

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In this Oct. 31, 2018, file photo, a man, who declined to be identified, has his face painted to represent efforts to defeat facial recognition during a protest at Amazon headquarters over the company's facial recognition system, "Rekognition," in Seattle. San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies. These days, with facial recognition technology, you've got a face that can launch a thousand applications, so to speak. Sure, you may love the ease of opening your phone just by facing it instead of tapping in a code. But how do you feel about having your mug scanned, identifying you as you drive across a bridge, when you board an airplane or to confirm you're not a stalker on your way into a Taylor Swift concert?

'Godfathers of AI' Receive Turing Award, the Nobel Prize of Computing - AI Trends


The 2018 Turing Award, known as the "Nobel Prize of computing," has been given to a trio of researchers who laid the foundations for the current boom in artificial intelligence. Yoshua Bengio, Geoffrey Hinton, and Yann LeCun -- sometimes called the'godfathers of AI' -- have been recognized with the $1 million annual prize for their work developing the AI subfield of deep learning. The techniques the trio developed in the 1990s and 2000s enabled huge breakthroughs in tasks like computer vision and speech recognition. Their work underpins the current proliferation of AI technologies, from self-driving cars to automated medical diagnoses. In fact, you probably interacted with the descendants of Bengio, Hinton, and LeCun's algorithms today -- whether that was the facial recognition system that unlocked your phone, or the AI language model that suggested what to write in your last email.

AI Weekly: How to regulate facial recognition to preserve freedom


Today Microsoft president Brad Smith called for federal regulation of facial recognition software. "In a democratic republic, there is no substitute for decision making by our elected representatives regarding the issues that require the balancing of public safety with the essence of our democratic freedoms. Facial recognition will require the public and private sectors alike to step up -- and to act," Smith wrote in a blog post. Recent events explain why Smith is speaking out now. Last month, while the majority of U.S. citizens was outraged about the idea of separating families who unlawfully entered the United States, Microsoft was criticized by the public and hundreds of its own employees for its contract with Immigration and Customs Enforcement (ICE).

'Deep learning' quest drives autonomous startup


Imagine a driverless vehicle capable of using a variety of emojis, honks and signs to communicate its intentions to nearby drivers and pedestrians. Drive.ai, a new entrant in the autonomous vehicle race, has begun testing a fleet of such vehicles near its home base in Mountain View, Calif. The company, staffed by researchers from Stanford University's artificial intelligence laboratory, is getting an assist from Steve Girsky, a former General Motors executive who has been named to the Drive.ai Girsky stepped down from GM's board in June after holding several posts at the automaker, including vice chairman of GM and chairman of its Adam Opel subsidiary. "We all know that the automotive industry is in the midst of a foundational shift," Girsky said in a statement.

Machine learning in the wild


The following interview is one of many included in the report. Benjamin Recht is an associate professor in the electrical engineering and computer sciences department as well as the statistics department at the University of California at Berkeley. His research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning -- exploring the intersections of convex optimization, mathematical statistics, and randomized algorithms. David Beyer: You're known for thinking about computational issues in machine learning, but you've recently begun to relate it to control theory. Can you talk about some of that work?