Tempe police speak at a press conference to address the accident where a self-driving Uber killed a pedestrian. In this file photo taken in 2016, pilot models of the Uber self-driving car are displayed at the Uber Advanced Technologies Center in Pittsburgh, Pennsylvania. PHOENIX -- The operator behind the wheel of a self-driving Uber vehicle that hit and killed a 49-year-old woman Sunday night had served almost four years in an Arizona prison in the early 2000s on an attempted armed robbery conviction. A representative for Uber declined to comment on the conviction and the company's hiring policies, citing an active investigation. Elaine Herzberg was walking a bike across a street outside a crosswalk in Tempe, Ariz., at about 10 p.m. when she was hit, police said.
You are free to share this article under the Attribution 4.0 International license. A new method that uses deep learning to analyze vast amounts of personal health record data could identify early signs of heart failure, researchers say. A paper, which appears in the Journal of the American Medical Informatics Association (JAMIA), describes how the method addresses temporality in the data--something previously ignored by conventional machine learning models in health care applications. The research uses a deep learning model to allow earlier detection of the incidents and stages that often lead to heart failure within 6-18 months. To achieve this, researchers use a recurrent neural network (RNN) to model temporal relations among events in electronic health records.
Netradyne's advanced artificial intelligence and machine learning technologies are creating deep driver, vehicle and environmental analysis; yielding unique opportunities to create efficiencies in risk identification, data trend analysis and timely payment of insurance claims. Their robust commercial fleet platform currently captures and analyzes several million driving miles each month and Willis' expertise will allow the company to turn this insightful data into comprehensive offerings insurance companies can use to underwrite and analyze professional drivers across the country. In parallel, Willis will support customers who are actively reducing fleet risk by managing relationships with insurance carriers to provide evidence of improvement. He will also foster relationships with captive program managers to help drive fleet sales growth. "Netradyne is experiencing explosive growth, with new avenues and uses for our technology approach coming to life every day.
BOULDER, Colo.--(BUSINESS WIRE)--Artificial intelligence (AI) has worked its way into a variety of industries, from the obvious (autonomous vehicles) to the hidden (anti-money laundering due diligence). But according to a new report from Tractica, while organizations are clearly recognizing the value associated with incorporating AI into their business processes, they are also encountering a number of challenges with integrating this new intelligence into operational processes. Taking AI beyond the proof-of-concept phase to the enterprise scale will require a significant level of professional services to support large implementations, with key service categories including application integration, support and maintenance, training, customization, and installation. Tractica forecasts that the worldwide market for AI services will grow from $10.1 billion in 2017 to $188.3 billion by 2025. The market intelligence firm anticipates that the industry sectors using the highest levels of professional services to support AI deployments will include business services, consumer, healthcare, advertising, and automotive.
The UK's artificial intelligence sector is growing faster than rivals in America, Canada and Australia – putting Britain on course to be the global leader in AI technology. That's the finding of new data released by the world's largest job site, Indeed. AI technologies require highly skilled workers who can develop and maintain complex systems and applications. In the last three years, demand from UK employers for these types of workers has almost tripled, according to Indeed's data. Roles typically include data scientists and machine learning engineers, specialists who play a crucial part in teaching machines how to use and interpret data.
When Trevor McFedries set out last year to raise money for Brud, his robotics and artificial intelligence start-up, he found himself in many meetings with "a ton of white guy" venture capitalists. So Mr. McFedries, who is black, and his co-founder, Sara DeCou, a Latino woman, added a condition for investors: The pair would accept money only from venture firms that had a woman or a person of color in a position to write them a check. "It was counterintuitive for us to raise money from a bunch of white guys who want to extract all the value from the world," said Mr. McFedries, who eventually collected several million dollars from firms that met the condition. "We're interested in reshaping the way that tech looks." Mr. McFedries is one of more than 400 tech entrepreneurs and chief executives who have now banded together, in a loose coalition known as Founders for Change, to pressure the venture capital industry to diversify its ranks.
NARRATOR: The future unfolds before our eyes, but is it always beyond our grasp? What was once the province of the gods has now come more clearly into view, through mathematics and data. Out of some early observations about gambling, arose tools that guide our scientific understanding of the world and more, through the power of prediction. BOATSWAIN'S MATE 1 LUKE SCHAFFER (United States Coast Guard): Keep a good look out. NARRATOR: …every day mathematics and data combine to help us envision what might be. LIBERTY VITTERT (University of Glasgow): It's the best crystal ball that humankind can have. NARRATOR: Take a trip on the wings of probability, into the future. MONA CHALABI (The Guardian, United States Edition): We are thinking about luck or misfortune, but they just, basically, are a question of math, right? The Orange County Fair, held in Southern California: in theory, these crowds hold a predictive power that can have startling accuracy, but it doesn't belong to any individual, only the group. And even then, it has to be viewed through the lens of mathematics. The theory is known as the "wisdom of crowds," a phenomenon first documented about a hundred years ago. Statistician Talithia Williams is here to see if the theory checks out and to spend some time with the Fair's most beloved animal, Patches, a 14-year-old ox. TALITHIA WILLIAMS (Harvey Mudd College): It was a fair, kind of like this one, where, in 1906, Sir Francis Galton came across a contest where you had to guess the weight of an ox, like Patches, you see here behind me. NARRATOR: After the ox weight-guessing contest was over, Galton took all the entries home and analyzed them statistically. To his surprise, while none of the individual guesses were correct, the average of all the guesses was off by less than one percent. But is it still true? TALITHIA WILLIAMS: So, here's how I think we can test that today. What if we ask a random sample of people, here at the fair, if they can guess how many jellybeans they think are in the jar, and then we take those numbers and average them and see if that's actually close to the true number of jellybeans?
CARMEL, Ind., March 19, 2018 (GLOBE NEWSWIRE) -- TradeRev, a digital platform that facilitates live, dealer-to-dealer vehicle auctions, announced they will unveil H, the company's newest suite of artificial intelligence capabilities at next week's National Auto Dealers Association (NADA) Show 2018 in Las Vegas. TradeRev is a business unit of global remarketing and technology solutions provider KAR Auction Services, Inc. (NYSE:KAR). H leverages data and technology from across the KAR platform and uses TradeRev's machine learning and proprietary algorithms to deliver clear, easy, actionable intelligence to dealers. At NADA, TradeRev will demo H's AI-driven automated condition report visualization tool and several recently released data and predictive analytics capabilities.
People can read your emotions even if your facial movements don't give them away, a new report has found. Researchers constructed computer algorithms, based on the new findings, that can recognize human emotions by analyzing facial color patterns. New research suggests that humans can read other humans' moods based on facial colors alone - but that AI can do this more accurately than people can. Cognitive scientist and Ohio State professor Aleix Martinez explained how the report informs our understanding of the connection between our feelings and our anatomy. Professor Martinez said: 'We identified patterns of facial coloring that are unique to every emotion we studied.
Uber has parked its autonomous cars in North America after a crash early this morning in Tempe, Arizona that killed a female pedestrian. Tempe Police said in a statement provided by Uber that the car was operating in autonomous mode with a human driver when the accident occurred. Elaine Herzberg was taken to the hospital from the site of the accident, where she died from her injuries. She was 49 years old. According to the police's statement, she was walking outside of the crosswalk at the time of the crash.