Tokyo and Washington are exploring the possibility of deploying U.S. military drones to a Maritime Self-Defense Force base in Kyushu – the first time American drones would be sent to an SDF base. Defense Minister Nobuo Kishi said Friday that the government was considering the temporary deployment of U.S. Air Force MQ-9 unmanned surveillance aircraft to the MSDF's Kanoya Air Base in Kagoshima Prefecture. Around seven MQ-9 drones would be deployed to the base, with about 100 U.S. personnel expected to operate and maintain the aircraft, according to media reports. The move to deploy the drones would be "part of efforts to improve the alliance's surveillance capabilities," Kishi told a news conference. Prime Minister Fumio Kishida pledged to bolster Japan's alliance with the U.S. during a virtual summit earlier this month with U.S. President Joe Biden, and the deployment of U.S. drones to an SDF base could be part of that.

This story is limited to Techwire Insider members. This story is limited to Techwire Insider members. Login below to read this story or learn about membership. An independent oversight agency charged with probing and making recommendations on government operations and policy has issued several recommendations on artificial intelligence. In a recent "Lessons from Research" post on "How California Can Better Harness the Power of Artificial Intelligence," the Little Hoover Commission (LHC) offers a bit of a follow-up to its 2018 report, Artificial Intelligence: A Roadmap for California.

Coastal communities around the world are especially vulnerable to flooding, storms, hurricanes and heavy rainfall. Now, scientists are studying whether artificial intelligence can better predict the impact of the storms. More information would help areas like New Orleans, Louisiana, which is forced to fix and rebuild after severe flooding. Clint Dawson, a professor at the University of Texas Austin, is part of a team of investigators working on a project funded by the Department of Energy's Office of Advanced Scientific Computing Research. "The only reason that place still exists is because there is fairly extensive levy system that protects it," Dawson said.

Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large "fair ranking" research literature has been developed around making these systems fair to the individuals, providers, or content that are being ranked. Most of this literature defines fairness for a single instance of retrieval, or as a simple additive notion for multiple instances of retrievals over time. This work provides a critical overview of this literature, detailing the often context-specific concerns that such an approach misses: the gap between high ranking placements and true provider utility, spillovers and compounding effects over time, induced strategic incentives, and the effect of statistical uncertainty. We then provide a path forward for a more holistic and impact-oriented fair ranking research agenda, including methodological lessons from other fields and the role of the broader stakeholder community in overcoming data bottlenecks and designing effective regulatory environments.

Although machine learning (ML) has been successful in automating various software engineering needs, software testing still remains a highly challenging topic. In this paper, we aim to improve the generative testing of software by directly augmenting the random number generator (RNG) with a deep reinforcement learning (RL) agent using an efficient, automatically extractable state representation of the software under test. Using the Cosmos SDK as the testbed, we show that the proposed DeepRNG framework provides a statistically significant improvement to the testing of the highly complex software library with over 350,000 lines of code. The source code of the DeepRNG framework is publicly available online.

In recent years there has been a growing interest in the estimation of causal effects using machine learning algorithms, particularly in the field of economics (Athey, 2018). The newly emerging synthesis of machine learning methods with causal inference has a large potential for a more comprehensive estimation of causal effects (Lechner, 2018). On the one hand, it enables a more flexible estimation of average effects which are of main interest in microeconometrics (Imbens & Wooldridge, 2009). On the other hand, it advances the estimation beyond the average effects and allows for a systematic analysis of effect heterogeneity (Athey & Imbens, 2017). Both of these aspects contribute to a better description of the causal mechanisms and thus to a possibly more efficient treatment allocation (Zhao, Zeng, Rush, & Kosorok, 2012; Kitagawa & Tetenov, 2018; Athey & Wager, 2021; Nie, Brunskill, & Wager, 2021). Hence, applied empirical researchers can greatly benefit from the usage of machine learning methods ranging from evaluation of public policies and business decisions to designing personalized interventions (Andini, Ciani, de Blasio, D'Ignazio, & Salvestrini, 2018; Bansak et al., 2018). Machine learning estimators as such are, however, primarily designed for prediction problems and thus cannot be used directly for causal inference. Therefore, new approaches for the estimation of causal parameters using machine learning emerged (see Athey & Imbens, 2019, for an overview). In particular, the development of the so-called meta-learners have received considerable attention (see e.g.

Research into the therapeutic potential of psychedelic drugs was pioneered by psychiatrists way back in the 1950s, but the emergence of advanced technologies in pharma appears to have breathed new life into the field. As interest in the psychedelics market gains stream, a number of drug companies are now employing artificial intelligence (AI) methods in their search for new psychedelic compounds to treat a range of mental and physical conditions. One in four people in the UK will experience some kind of mental health problem every year, and figures are almost identical in the US. Despite this, treatments for psychological conditions are relatively limited – and for many patients, the drugs that are available come with side effects that negatively impact their quality of life. Psychedelics are hallucinogenic drugs that alter a person's perception and mood and affect their thought processes.

It is my view that deficiencies in the current classification system undermine our national security, as well as critical democratic objectives, by impeding our ability to share information in a timely manner

Waymo has sued the California Department of Motor Vehicles. In a case first reported by The Los Angeles Times, the Alphabet subsidiary filed a complaint with the Sacramento County Superior Court on January 21st to prevent the agency from disclosing what it believes to be trade secrets. At the center of the lawsuit is a public records request an unidentified party made to obtain Waymo's driverless deployment application. Before sharing the requested documents, the DMV allowed the company to redact any sections it believed would reveal its trade secrets, including questions that were asked by the agency. When the DMV eventually forwarded the package to the requester, that individual or group challenged the redactions.

China has confirmed it's joining forces with Russia to build a research station on the moon by 2035, which will rival NASA's Lunar Gateway. Confirmation of plans to build the International Lunar Research Station (ILRS) came on Friday from officials at China National Space Administration (CNSA), the country's national space agency. Russia and China aim to complete basic infrastructure construction for ILRS by 2035, Wu Yanhua, CNSA deputy director, told a briefing in Beijing. ILRS will rival NASA's Lunar Gateway, which is set to play a'vital' role in the US space agency's upcoming Artemis program. However, NASA's Lunar Gateway will only orbit the moon, while ILRS will have both an orbiter and a base on the lunar surface, as well as multiple exploration rovers.