China is imposing curfews and regulations on video game playing minors. China is imposing curfews and regulations on video game playing minors. Chinese officials are cracking down on youth online gaming, which they say negatively affects the health and learning of minors. Official guidelines released Tuesday outline a new curfew and time restrictions for gamers under 18. Six measures were outlined in the guidelines, aimed at preventing minors "from indulging in online games."

In this Q&A on Explainable AI, Andrea Brennen speaks with In-Q-Tel's Peter Bronez about descriptive vs. prescriptive models, "white box" vs. "black box" explanation techniques, and why some models are easier to explain than others. Peter also discusses the reproducibility crisis in Psychology and why good experiment design is so important. Peter is a VP on the technical staff at IQT. Could you tell me about your experience with machine learning and AI? PETER: As an undergraduate, I studied econometrics and operations research, so my exposure to machine learning was in the context of designing models of the world that you could test mathematically -- basically, doing hypothesis testing using statistics. Afterwards, I worked at the Department of Defense and used a lot of the same techniques. From there, I went to the private sector and [worked on] social media and data mining in marketing applications, trying to create mathematical models to categorize people, activities, and messages in order to understand them better.

The key question on the mind of policymakers now is whether Artificial Intelligence would be able to deliver on its promises instead of entering another season of scepticism and stagnation. The quest for Artificial Intelligence (AI) has travelled through multiple "seasons of hope and despair" since the 1950s. The introduction of neural networks and deep learning in late 1990s has generated a new wave of interest in AI and growing optimism in the possibility of applying it to a wide range of activities, including diplomacy. The key question on the mind of policymakers now is whether AI would be able to deliver on its promises instead of entering another season of scepticism and stagnation. This paper evaluates the potential of IA to provide reliable assistance in areas of diplomatic interest such as in consular services, crisis management, public diplomacy and international negotiations, as well as the ratio between costs and contributions of AI applications to diplomatic work.

We examine the impacts of different proposed AI regulations on managers' intentions to adopt AI technologies and on their AI-related business strategies. We conduct a randomized online survey experiment on more than a thousand managers in the U.S. We randomly present managers with different proposed AI regulations, and ask them to make decisions about AI adoption, budget allocation, hiring, and other issues. We have four main findings: (1) information about AI regulation generally reduces the rate of adoption of AI technologies. Nonetheless, industry- and agency-specific AI regulation has a smaller impact than general AI regulation. That is, firms spend more on developing AI strategy and hire more managers.

Forward-Looking Safe Harbor Statement This press release contains certain forward-looking statements, as that term is defined in the Private Securities Litigation Reform Act of 1995, including those relating to the products and services described herein and to fiscal 2020 business performance and beyond and the Company's plans for growth and improvement in profitability and cash flow. You can identify these statements by the use of the words "may," "will," "could," "should," "would," "plans," "expects," "anticipates," "continue," "estimate," "project," "intend," "likely," "forecast," "probable," "potential," and similar expressions. These forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those projected or anticipated. Such risks and uncertainties include, but are not limited to, continued funding of defense programs, the timing and amounts of such funding, general economic and business conditions, including unforeseen weakness in the Company's markets, effects of any U.S. Federal government shutdown or extended continuing resolution, effects of continued geopolitical unrest and regional conflicts, competition, changes in technology and methods of marketing, delays in completing engineering and manufacturing programs, changes in customer order patterns, changes in product mix, continued success in technological advances and delivering technological innovations, changes in, or in the U.S. Government's interpretation of, federal export control or procurement rules and regulations, market acceptance of the Company's products, shortages in components, production delays or unanticipated expenses due to performance quality issues with outsourced components, inability to fully realize the expected benefits from acquisitions and restructurings, or delays in realizing such benefits, challenges in integrating acquired businesses and achieving anticipated synergies, increases in interest rates, changes to cyber-security regulations and requirements, changes in tax rates or tax regulations, changes to interest rate swaps or other cash flow hedging arrangements, changes to generally accepted accounting principles, difficulties in retaining key employees and customers, unanticipated costs under fixed-price service and system integration engagements, and various other factors beyond our control. These risks and uncertainties also include such additional risk factors as are discussed in the Company's filings with the U.S. Securities and Exchange Commission, including its Annual Report on Form 10-K for the fiscal year ended June 30, 2019.

Artificial intelligence has already started to shape our lives in ubiquitous and occasionally invisible ways. In its new documentary, In The Age of AI, FRONTLINE examines the promise and peril this technology. AI systems are being deployed by hiring managers, courts, law enforcement, and hospitals -- sometimes without the knowledge of the people being screened. And while these systems were initially lauded for being more objective than humans, it's fast becoming clear that the algorithms harbor bias, too. It's an issue Joy Buolamwini, a graduate researcher at the Massachusetts Institute of Technology, knows about firsthand. She founded the Algorithmic Justice League to draw attention to the issue, and earlier this year she testified at a congressional hearing on the impact of facial recognition technology on civil rights. "One of the major issues with algorithmic bias is you may not know it's happening," Buolamwini told FRONTLINE.

NIOSH has started an open competition for artificial intelligence programmers as part of a search for ways to automate data processing in occupational safety and health surveillance systems. In an Oct. 24 press release, the agency describes injury recording as "a person writing a narrative about the incident," adding that someone else then reads these narratives and assigns codes to classify the injuries, "which has resulted in time, cost and the risk of human error influencing occupational safety and health data." NIOSH, in conjunction with the NASA Tournament Lab and a crowdsourcing vendor, is asking programmers to develop an algorithm that uses AI to read the injury reports and code them according to the Occupational Injury and Illness Classification System. "We're thrilled to be hosting this competition along with our partners," Carlos Siordia, lead project officer at NIOSH, said in the release. "Not only do these partnerships help support our extramural crowdsourcing AI competition, but they can also support others at [the Centers for Disease Control and Prevention] who want to crowdsource software programming to come up with the most innovative and efficient solutions to improve public health."

Raw video: Cameras mounted inside the car catches the fatal moment. Authorites are investigating the cause of the crash. The National Transportation Safety Board (NTSB) says that an Uber self-driving car that struck and killed a pedestrian in Arizona in 2018 was unable to detect jaywalkers. Elaine Herzberg died in March 2018 when an Uber vehicle struck her as she walked across a darkened street in Tempe. The board said the Uber autonomous driving system spotted Herzberg before hitting her but a system used to automatically apply brakes in potentially dangerous situations had been automatically disabled.

This amendment delays the due date for the request for information to this program element in order to provide the community with more time to respond. The due date has been extended to November 14, 2019. FDL is an applied artificial intelligence (AI) research accelerator leveraging the newest developments in AI and Machine Learning (ML) technologies from academia and the private sector and applying them to challenges relevant to NASA's goals in space and earth sciences. Teams of computer scientists and space and earth scientists work to solve problems important to NASA and humanity's future within a given time frame. Each team is made up of four participants (two computer scientists and two domain scientists).

Over the last 15 years, the United States military has developed a new addition to its arsenal. The weapon is deployed around the world, largely invisible, and grows more powerful by the day. That weapon is a vast database, packed with millions of images of faces, irises, fingerprints, and DNA data -- a biometric dragnet of anyone who has come in contact with the U.S. military abroad. The 7.4 million identities in the database range from suspected terrorists in active military zones to allied soldiers training with U.S. forces. "Denying our adversaries anonymity allows us to focus our lethality. It's like ripping the camouflage netting off the enemy ammunition dump," wrote Glenn Krizay, director of the Defense Forensics and Biometrics Agency, in notes obtained by OneZero.