Much as a character in the 1960s movie The Graduate said "the future is plastics," government today has identified digital technology as key to its ability to operate both now and in the future. Government's growing focus on technologies like artificial intelligence, machine learning and automated processes is well-documented. While many of these goals are aspirational, some advances already demonstrate the way forward for agency operations. Take, for example, robotic process automation. Using RPA to automate routine, repetitive and often tedious tasks is increasingly common, and NASA and the General Services Administration are two front-runners in this RPA adoption.

Japanese and U.S. foreign and defense chiefs on Friday shared their concerns about China's attempts to "undermine the rule-based order" and challenges they pose to the region and world, vowing to cooperate in deterring and responding to "destabilizing activities." In a joint statement issued after their virtual "two-plus-two" talks, the ministers highlighted the "importance of peace and stability in the Taiwan Strait," while opposing any unilateral actions threatening Japan's administration of the Senkaku Islands in the East China Sea, controlled by Japan but claimed by China. Foreign Minister Yoshimasa Hayashi, Defense Minister Nobuo Kishi, and their U.S. counterparts Secretary of State Antony Blinken and Defense Secretary Lloyd Austin, also aired "serious and ongoing concerns" about human rights issues in China's Xinjiang autonomous region and Hong Kong. Hayashi said at the outset of the talks that Japan is "fully committed" to constantly enhancing the alliance toward realizing "a free and open Indo-Pacific," and noted that "it is more important than ever that Japan and the United States are united and exhibit leadership" in the face of a range of challenges. Blinken reaffirmed the alliance as a cornerstone of peace and security in the region, and said the two countries must not only strengthen the tools they have, but also develop "new ones" to address the evolving threats posed by countries seeking to undermine the international rules-based order, including China and North Korea.

IN RECENT YEARS, underground chemists have increasingly made small chemical tweaks on known recreational drugs to skirt laws, creating novel designer versions. Instead of cannabis, for instance, these chemists could offer up XLR-11, or instead of PCP they might have 3-MeO-PCE. Novel designer drugs, also called research chemicals or legal highs, still produce physiological and psychological effects, though experts say that they can come with a slew of risks. Synthetic opioids such as fentanyl, for instance, are increasingly cited among the number of opioid-related deaths in the United States, which reached more than 75,000 this year. According to the Centers for Disease Control and Prevention, synthetic cannabinoids can cause heart attacks, kidney failure, and, in some cases, death.

Stephen Ritter is Chief Technology Officer at Mitek, a global leader in mobile deposit and digital identity verification solutions. When the director of the White House Office of Science and Technology Policy proposes an artificial intelligence (AI) bill of rights, you know the conversation is leaping from technologists' private discussions to mainstream thought. But, make no mistake, even though Facebook says it's backing away from the use of facial recognition, there's no turning back. In the coming years, AI-based systems are going to change everything about how we live and work. And, currently, there are no rules of the road.

Understanding capabilities and limitations of different network architectures is of fundamental importance to machine learning. Bayesian inference on Gaussian processes has proven to be a viable approach for studying recurrent and deep networks in the limit of infinite layer width, $n\to\infty$. Here we present a unified and systematic derivation of the mean-field theory for both architectures that starts from first principles by employing established methods from statistical physics of disordered systems. The theory elucidates that while the mean-field equations are different with regard to their temporal structure, they yet yield identical Gaussian kernels when readouts are taken at a single time point or layer, respectively. Bayesian inference applied to classification then predicts identical performance and capabilities for the two architectures. Numerically, we find that convergence towards the mean-field theory is typically slower for recurrent networks than for deep networks and the convergence speed depends non-trivially on the parameters of the weight prior as well as the depth or number of time steps, respectively. Our method exposes that Gaussian processes are but the lowest order of a systematic expansion in $1/n$. The formalism thus paves the way to investigate the fundamental differences between recurrent and deep architectures at finite widths $n$.

Simulators are an essential tool for behavioural and interaction research on driving, due to the safety, cost, and experimental control issues of on-road driving experiments. The most advanced simulators use expensive 360 degree projections systems to ensure visual fidelity, full field of view, and immersion. However, similar visual fidelity can be achieved affordably using a virtual reality (VR) based visual interface. We present DReyeVR, an open-source VR based driving simulator platform designed with behavioural and interaction research priorities in mind. DReyeVR (read "driver") is based on Unreal Engine and the CARLA autonomous vehicle simulator and has features such as eye tracking, a functional driving heads-up display (HUD) and vehicle audio, custom definable routes and traffic scenarios, experimental logging, replay capabilities, and compatibility with ROS. We describe the hardware required to deploy this simulator for under $5000$ USD, much cheaper than commercially available simulators. Finally, we describe how DReyeVR may be leveraged to answer an interaction research question in an example scenario.

The cybersecurity market already top $100 billion per year and will reach $300 billion by 2024. With that kind of money at stake, this market is one of the hottest areas for IT innovation.

BREXIT Britain will launch its plan to become a global superpower in Artificial Intelligence (AI) technology as it breaks free from EU red tape. Boris Johnson's Government will release its 10-year plan to put Britain at the forefront of AI technology early this year as it grapples back its freedoms post-Brexit.

Amazon have teamed up with Cisco and Lockheed Martin to develop an artificial intelligence system -- dubbed'Callisto' -- for NASA's Artemis I lunar mission. Callisto will combine into one interface the voice control technology of Amazon's Alexa virtual assistant with Cisco's Webex video conferencing system. Lockheed Martin engineers, meanwhile, will be leading the development of the custom-built system and its integration into their Orion space capsule design. Unlike Alexa, Callisto will contain technology and software that allows it to respond to commands in-flight without reliance on an internet connection. The video conferencing system, in contrast, will require a communications system to function -- and will operate via NASA's Deep Space Network.

When we stride down the aisles at our local grocer, shelves are full of products vying for our attention. To make their way into our shopping carts, some tout their superior performance on their packaging, and some even try to back their claims up with some magical ingredient. Yet when the rubber meets the road, few of us expect a laundry detergent empowered by such a magical compound to truly get rid of all traces of stains from holiday cooking. While the stakes may be high if our favorite pair of trousers is involved, they are surely higher when picking a security solution. In cybersecurity, most offerings tout some level of AI.