Traffic accidents are a major unsolved problem worldwide. Yearly, it causes around 1.35 million deaths and 10 million people sustain nonfatal injuries9 in addition to having substantial negative economic and social effects. With approximately 90% of accidents being due to human errors, autonomous driving (AD) will play a vital role in saving human lives and substantial property damage. Moreover, it promises far greater mobility, energy saving, and less air pollution. Despite the recent advances to achieve such promising vision, enabling autonomous vehicles in complex environments is still decades away.6
When a secretive start-up scraped the internet to build a facial-recognition tool, it tested a legal and ethical limit -- and blew the future of privacy in America wide open. In May 2019, an agent at the Department of Homeland Security received a trove of unsettling images. Found by Yahoo in a Syrian user's account, the photos seemed to document the sexual abuse of a young girl. One showed a man with his head reclined on a pillow, gazing directly at the camera. The man appeared to be white, with brown hair and a goatee, but it was hard to really make him out; the photo was grainy, the angle a bit oblique. The agent sent the man's face to child-crime investigators around the country in the hope that someone might recognize him. When an investigator in New York saw the request, she ran the face through an unusual new facial-recognition app she had just started using, called Clearview AI. The team behind it had scraped the public web -- social media, employment sites, YouTube, Venmo -- to create a database with three billion images of people, along with links to the webpages from which the photos had come. This dwarfed the databases of other such products for law enforcement, which drew only on official photography like mug shots, driver's licenses and passport pictures; with Clearview, it was effortless to go from a face to a Facebook account. The app turned up an odd hit: an Instagram photo of a heavily muscled Asian man and a female fitness model, posing on a red carpet at a bodybuilding expo in Las Vegas. The suspect was neither Asian nor a woman. But upon closer inspection, you could see a white man in the background, at the edge of the photo's frame, standing behind the counter of a booth for a workout-supplements company. On Instagram, his face would appear about half as big as your fingernail. The federal agent was astounded. The agent contacted the supplements company and obtained the booth worker's name: Andres Rafael Viola, who turned out to be an Argentine citizen living in Las Vegas.
Florida State University researchers report they have found a way to automatically infer parameters used in an important quantum Boltzmann machine algorithm for machine learning applications. The work could help build artificial neural networks used for training computers to solve complicated, interconnected problems, such as image recognition, drug discovery and the creation of new materials. The findings of Professor William Oates, the Cummins Inc. Professor in Mechanical Engineering and chair of the Department of Mechanical Engineering at the FAMU-FSU College of Engineering, and postdoctoral researcher Guanglei Xu were published in Scientific Reports. "There's a belief that quantum computing, as it comes online and grows in computational power, can provide you with some new tools, but figuring out how to program it and how to apply it in certain applications is a big question," said Oates. Quantum bits, unlike binary bits in a standard computer, can exist in more than one state at a time, a concept known as superposition.
Lawrence Livermore National Laboratory (LLNL) computer scientists have developed a new framework and an accompanying visualization tool that leverages deep reinforcement learning for symbolic regression problems, outperforming baseline methods on benchmark problems. The paper was recently accepted as an oral presentation at the International Conference on Learning Representations (ICLR 2021), one of the top machine learning conferences in the world. The conference takes place virtually May 3-7. In the paper, the LLNL team describes applying deep reinforcement learning to discrete optimization--problems that deal with discrete "building blocks" that must be combined in a particular order or configuration to optimize a desired property. The team focused on a type of discrete optimization called symbolic regression--finding short mathematical expressions that fit data gathered from an experiment.
The coronavirus pandemic has been tough on the global call-center industry, and nowhere more than in the Philippines, the world leader in the field. Hundreds of thousands of employees in the former U.S. colony field queries from the other side of the planet, and for the past year many of them have had to work alone from home through the night, grappling with frequent electricity outages, isolation from friends, and the snores of parents, partners, siblings, or children crammed into tight quarters. What comes after Covid-19 is likely to be even worse. The lockdowns of the past year have accelerated the shift to greater automation in responding to inquiries to lenders, insurers, and telecom operators. And when they do connect with a human, it's more frequently in a chat window with someone who's engaged in multiple conversations at once.
The U.S. government's highway safety agency is sending a team to Detroit to investigate a crash involving a Tesla that drove beneath a semitrailer. The National Highway Traffic Safety Administration says Monday night that a special crash investigation team will go to the city to investigate the "violent crash." Two people were critically injured in the crash that happened last Thursday on the city's southwest side. The crash circumstances are similar to two others in Florida in which Teslas drove beneath tractor-trailers, causing two deaths. In both crashes, in 2016 and 2019, the cars were being driven while using Tesla's Autopilot partially automated driving software.