Machine Learning Algorithms Help Predict Traffic Headaches

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Urban traffic roughly follows a periodic pattern associated with the typical "9 to 5" work schedule. However, when an accident happens, traffic patterns are disrupted. Designing accurate traffic flow models, for use during accidents, is a major challenge for traffic engineers, who must adapt to unforeseen traffic scenarios in real time. A team of Lawrence Berkeley National Lab computer scientists are working with the California Department of Transportation (Caltrans) to use high performance computing (HPC) and machine learning to help improve Caltrans' real-time decision making when incidents occur. The research was done in conjunction with California Partners for Advanced Transportation Technology (PATH), part of UC Berkeley's Institute for Transportation Studies (ITS), and Connected Corridors, a collaborative program to research, develop, and test an Integrated Corridor Management approach to managing transportation corridors in California.


Huawei and UC Berkeley Announce Strategic Partnership into Basic AI Research - huawei press center

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Huawei will provide a US 1 million fund to UC Berkeley for research into many subjects of interest in AI, including deep learning, reinforcement learning, machine learning, natural language processing and computer vision. Through close cooperation, the Research and Development (R&D) teams of Huawei and the Berkeley Artificial Intelligence Research (BAIR) Lab will strive to make significant breakthroughs in AI theories and key technologies. The two parties believe that this strategic partnership will fuel the advancement of AI technology and create completely new experiences for people, thus contributing greatly to society at large. As one of the world's leading higher education institutes, UC Berkeley has profound expertise in machine learning and other AI domains. Its newly founded BAIR Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, robotics, and research planning.


Advancement of AI Opens Health Data Privacy to Attack

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Advances in artificial intelligence have created new threats to the privacy of health data, according to a new study by University of California, Berkeley researchers. University of California, Berkeley (UC Berkeley) researchers have found that artificial intelligence (AI) innovations have created new threats to health data privacy against which current laws and regulations cannot adequately safeguard. The researchers demonstrated that AI can be used to identify individuals by learning daily patterns in step data--like that collected by activity trackers, smartwatches, and smartphones--and correlating it to demographic data. Said UC Berkeley's Anil Aswani, "In principle, you could imagine Facebook gathering step data from the app on your smartphone, then buying healthcare data from another company and matching the two. Now they would have healthcare data that's matched to names, and they could either start selling advertising based on that or they could sell the data to others."


Applying Machine Learning to the Universe's Mysteries

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Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory and international collaborators have demonstrated computers' readiness to solve the universe's mysteries. The team used thousands of images from simulated high-energy particle collisions to train neural networks to identify important features. They found the networks were up to 95-percent successful in recognizing important features in a sampling of about 18,000 images. The researchers say machine-learning algorithms enable the networks to improve their analysis as they process more images, with the underlying technology employed in facial recognition and other types of image-based object recognition applications. "With this type of machine learning, we are trying to identify a certain pattern or correlation of patterns that is a unique signature of the equation of state," says Long-Gang Pan of the University of California, Berkeley.


FTC Announces Agenda For March 9 FinTech Forum On Artificial Intelligence And Blockchain Technology

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The Federal Trade Commission today announced the agenda for its March 9, 2017, FinTech Forum focusing on the consumer implications of two rapidly developing technologies: artificial intelligence and blockchain. The forum, which is the third in an ongoing event series, will take place from 9:00 a.m. to approximately 12:30 p.m. Pacific Time in Berkeley, California. The event will bring together industry representatives, consumer advocates, government officials, and others with expertise regarding these technologies. The half-day forum will feature two panel discussions. The first panel will focus on the potential benefits and risks for consumers with the use of artificial intelligence, which involves the capability of machines to mimic human thinking or actions such as learning and problem solving, in consumer products or services in fields including personalized financial services.