Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.
Andy Dufresne, the wrongly convicted character in The Shawshank Redemption, provocatively asks the prison guard early in the film: "Do you trust your wife?" It's a dead serious question regarding avoiding taxes on a recent financial windfall that had come the guard's way, and leads to events that eventually win freedom for Andy. And it's also a dead serious question being asked today with respect to AI. At this point we all recognize that successful deployment of AI is going to come down to something much more fundamental than the technical aspects of algorithms, neural networks and machine learning. It's going to come down to trust. Do we trust the black box calculations of AI? Do we trust it to drive our cars, diagnose our illnesses, and manage our finances?
A USC-led study of violent protest has found that moral rhetoric on Twitter may signal whether a protest will turn violent. The researchers also found that people are more likely to endorse violence when they moralize the issue that they are protesting--and when they believe that others in their social network moralize that issue, too. "Extreme movements can emerge through social networks," said the study's corresponding author, Morteza Dehghani, a researcher at the Brain and Creativity Institute at USC. "We have seen several examples in recent years, such as the protests in Baltimore and Charlottesville, where people's perceptions are influenced by the activity in their social networks. People identify others who share their beliefs and interpret this as consensus. In these studies, we show that this can have potentially dangerous consequences."
Many companies, especially retailers, are interested in machine learning, particular for its ability detect fraud. However, many of them are misunderstanding some of the fundamentals of the technology. Tuesday morning at MRC Dublin, an expert panel tried to clear up some of the confusion. Panteha Pedram, director of risk for Ingenico ePayments, advised attendees to know what they want from machine learning. "There's no recipe for everyone to follow," she said.
The arrests spurred a splash of publicity from state media, who are crowning Mr. Cheung--one of the Hong Kong megastars known as the "Four Heavenly Kings"--with a new title: "The Nemesis of Fugitives." China's police departments have been openly touting their use of technology to nab lawbreakers--a campaign that rights activists say is aimed at winning public support for growing state surveillance. Concert organizers in China have also increasingly deployed facial-recognition systems to curb scalping by verifying the identities of ticket-holders. Surveillance companies and local security agencies have experimented with deploying the technology at events around the country in recent years. The tests date back to 2015, when one company, Shenzhen-based Firs Technology Co. Ltd. said its facial-recognition system helped police identify drug-users, fugitives and ex-convicts at a jewelry exhibition in the city of Chenzhou, in central China's Hunan province.
Just over two years ago, Microsoft released a chatbot on Twitter named Tay. Created to mimic the speech and spelling of a 19-year-old American girl, the program was designed to interact with other Twitter users and get smarter as it discovered more about the world through their posts--a process called machine learning. Rather than becoming an after-school chum for bored teens, though, Tay was soon tweeting everything from "I'm smoking kush in front of the police" to "I fucking hate feminists and they should all die and burn in hell." She was shut down 16 hours after her launch. Tay's rants--which featured racist slurs and Holocaust denials--tapped into people's biggest anxieties about the future of artificial intelligence (AI).
An image from the product page of Amazon's Rekognition service, which provides image and video facial and item recognition and analysis. SAN FRANCISCO – Two years ago, Amazon built a facial and image recognition product that allows customers to cheaply and quickly search a database of images and look for matches. One of the groups it targeted as potential users of this service was law enforcement. At least two signed on: the Washington County Sheriff's Office outside of Portland, Ore., and the Orlando Police Department in Florida. Now the ACLU and civil rights groups are demanding that Amazon stop selling the software tool, called Rekognition, to police and other government entities because they fear it could be used to unfairly target protesters, immigrants and any person just going about their daily business.
Tech companies are trying to sell police real-time facial recognition systems, which can track and identify people as they walk down the street. As NPR reported two weeks ago, American police have generally held off, but there's new evidence that one police department -- Orlando, Fla. -- has decided to try it out. What's more, Orlando ordered its facial recognition system from Amazon. This information was uncovered by the ACLU, which noticed that law enforcement customers were mentioned in the marketing of Amazon's "Rekognition" service. Until now, American police have used facial recognition primarily to compare still photos from crime scenes with mug shots.