Diffractive deep neural network is an optical machine learning framework that blends deep learning with optical diffraction and light-matter interaction to engineer diffractive surfaces that collectively perform optical computation at the speed of light. A diffractive neural network is first designed in a computer using deep learning techniques, followed by the physical fabrication of the designed layers of the neural network using e.g., 3-D printing or lithography. Since the connection between the input and output planes of a diffractive neural network is established via diffraction of light through passive layers, the inference process and the associated optical computation does not consume any power except the light used to illuminate the object of interest. Developed by researchers at UCLA, diffractive optical networks provide a low power, low latency and highly-scalable machine learning platform that can find numerous applications in robotics, autonomous vehicles, defense industry, among many others. In addition to providing statistical inference and generalization to classes of data, diffractive neural networks have also been used to design deterministic optical systems such as a thin imaging system.
On Thursday, Uber filed a patent for Artificial Intelligence system to identify the drunken riders. Now, while booking the cab, the patent application will monitor the behaviour of the user on some factors like how many typos user make, typing & walking speed, how precisely the users click on link and buttons, and how long its request for the ride. It becomes easier for ride-sharing giant to tailor its ride options for users. As a result, patent notifies the Uber to change the riding option for the drunken user because Uber can't bar their service for them. In fact, the drivers warn with the rider state and have the authority to neglect the request.
In the race to develop fully autonomous vehicles, Israeli start-Up Lirhot Systems says they "see" the road ahead and assess potential hazards. While most leading industry actors have relied on and heavily invested in laser-based LiDAR (light detection and ranging) three-dimensional sensors for self-driving navigation, Tesla CEO Elon Musk has been the primary – and vocal – proponent of navigation based on using inexpensive cameras and radar. While developers continue to argue among themselves regarding the pros and cons of the two systems, Rehovot-based robotic vision start-up Lirhot Systems says it has developed a third method of navigation: a camera-like sensor inspired by insect navigation. "In nature, you have bugs and insects that navigate in a specific way, and we're copying that to enable autonomous vehicles to see," Lirhot CEO Shlomi Voro, an applied physicist with dozens of patents in the field of quantum physics, told The Jerusalem Post. "We were inspired by the heads of bees, their artificial intelligence-like neural network, size, accuracy of navigation, and how they see the world through their five eyes – two for vision and three for navigation."
California's passage of their "GDPR-lite" caught people off guard. We think this is part of a trend we've studied for a long time. Much of the current analysis misses key points, so it seems worth explaining. About two years ago, we asked several thought leaders in the U.S. about the odds we'd see legislation like the E.U. GDPR provides clear rights to E.U citizens, controlling data captured on-line.
Volkswagen Group and Qatar have agreed to develop a public transit system of autonomous shuttles and buses by 2022 for the capital city of Doha. The agreement signed Saturday by VW Group and the Qatar Investment Authority is an expansive project that will involve four brands under VW Group, including Volkswagen Commercial Vehicles, Scania, its shared ride service MOIA and Audi subsidiary Autonomous Intelligent Driving, or AID. The aim is to develop the entire transport system, including the electric autonomous shuttles and buses, legal framework, city infrastructure and ride-hailing software required to deploy a commercial service there. The autonomous vehicles will be integrated into existing public transit. "For our cities to progress we need a new wave of innovation," QIA CEO Mansoor Al Mahmoud said in a statement.
Cars are still all over the place. And we spend way too much time in traffic jams. For years we have been hearing that self-driving cars and buses are coming. But before that happens, those futuristic vehicles need to be tested in every possible way. In many cases this is done at Transpolis, Europe's largest testing ground for future mobility solutions.
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Whatever the future role of computers in society, Jeff Dean will have a powerful hand in the outcome. As the leader of Google's sprawling artificial intelligence research group, he steers work that contributes to everything from self-driving cars to domestic robots to Google's juggernaut online ad business. WIRED talked with Dean in Vancouver at the world's leading AI conference, NeurIPS, about his team's latest explorations--and how Google is trying to put ethical limits on them. WIRED: You gave a research talk about building new kinds of computers to power machine learning. What new ideas is Google testing?
One of the things that really annoys AI researchers is how supposedly "intelligent" machines are judged by much higher standards than are humans. Take self-driving cars, they say. So far they've driven millions of miles with very few accidents, a tiny number of them fatal. Yet whenever an autonomous vehicle kills someone there's a huge hoo-ha, while every year in the US nearly 40,000 people die in crashes involving conventional vehicles. Likewise, the AI evangelists complain, everybody and his dog (this columnist included) is up in arms about algorithmic bias: the way in which automated decision-making systems embody the racial, gender and other prejudices implicit in the data sets on which they were trained.