NASA is set to launch its'CAPSTONE' spacecraft mission later this month to orbit around the moon, in preparation for a new lunar space station. The craft, which is around the size of a microwave oven and weighs just 55 pounds, will blast off from the Māhia Peninsula, New Zealand some time between June 13-22. It will test the stability of a halo-shaped orbit around the moon before this is used by Lunar Gateway, NASA's planned lunar outpost. Lunar Gateway will one day serve as a'staging area' for landing humans on the moon and potentially as a jumping point for missions to Mars. Pictured is an artist's impression of CAPSTONE in orbit around the moon with the Earth in the background.

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.

BambooHR, the industry's leading cloud-hosted software provider dedicated to powering the strategic evolution of human resources, announced it has added Ease to its Marketplace. Ease, an online benefits enrollment solution built for insurance brokers and used by employers, and BambooHR will now offer joint customers an integration that will eliminate duplicative data entries between HR and benefits systems, reducing the risk of errors, and saving joint customers time. The BambooHR Marketplace includes 119 applications that integrate with BambooHR and gives partners the opportunity to leverage the BambooHR open API to build these connectors and integrations. They can also promote and showcase their solutions to over 27,000 BambooHR customers, from hiring and onboarding, people and culture, compensation, benefits and more, all designed to give joint customers the ability to streamline their HR experience across the board. Selects Riskified to Help Deliver a More Secure and Frictionless Shopping Experience for Customers in Australia and New Zealand "Ease provides a simple way to set up and manage benefits, giving employees access to key details related to their benefits and employment in one place," said Shama Madhvani, director of business development and strategic partnerships at BambooHR.

Prime Video is playing host to a vast range of sci-fi stories, from the absolute classics to the hidden gems more people need to watch. One of those is Coherence, widely regarded as the best hidden sci-fi gem out there. How cool and intriguing is that title? Beyond the Infinite Two Minutes is a low-budget time travel comedy from Japanese filmmaker Junta Yamaguchi. The wild premise: A café worker discovers his TV can show him what happens 2 minutes into the future.

Monitoring whales in remote areas is important for their conservation; however, using traditional survey platforms (boat and plane) in such regions is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote locations, is gaining interest and momentum. However, the development of this emerging technology relies on accurate automated systems to detect whales, which are currently lacking. Such detection systems require access to an open source library containing examples of whales annotated in satellite images to train and test automatic detection systems. Here we present a dataset of 633 annotated whale objects, created by surveying 6,300 km2 of satellite imagery captured by various very high-resolution satellites (i.e. WorldView-3, WorldView-2, GeoEye-1 and Quickbird-2) in various regions across the globe (e.g. Argentina, New Zealand, South Africa, United States, Mexico). The dataset covers four different species: southern right whale (Eubalaena glacialis), humpback whale (Megaptera novaeangliae), fin whale (Balaenoptera physalus), and grey whale (Eschrichtius robustus).

I am lucky to have a large collection of historical family photos. Scanned by my relatives in Finland, I have photos dating back a century showing my great-grandparents with my grandparents as children. Identifying who's who in these photos is a fascinating trip back in time. I've been exploring using People tags in Mylio Photos to catalog and organize my collection. The People tags in Mylio Photos uses AI to match faces. The more people you tag, the more accurate it gets.

High-precision vehicle positioning is key to the implementation of modern driving systems in urban environments. Global Navigation Satellite System (GNSS) carrier phase measurements can provide millimeter- to centimeter-level positioning, provided that the integer ambiguities are correctly resolved. Abundant code measurements are often used to facilitate integer ambiguity resolution (IAR), however, they suffer from signal blockage and multipath in urban canyons. In this contribution, a lidar-aided instantaneous ambiguity resolution method is proposed. Lidar measurements, in the form of 3D keypoints, are generated by a learning-based point cloud registration method using a pre-built HD map and integrated with GNSS observations in a mixed measurement model to produce precise float solutions, which in turn increase the ambiguity success rate. Closed-form expressions of the ambiguity variance matrix and the associated Ambiguity Dilution of Precision (ADOP) are developed to provide a priori evaluation of such lidar-aided ambiguity resolution performance. Both analytical and experimental results show that the proposed method enables successful instantaneous IAR with limited GNSS satellites and frequencies, leading to centimeter-level vehicle positioning.

Digital transformation has become a business imperative in the wake of the pandemic. Companies that have not modernized their processes and integrated cloud and analytics capabilities are severely disadvantaged. Genpact's latest study, "Data-Driven Business Transformation," reveals that only 8% of enterprises have fully modernized. This digital divide will result in those companies who can move quickly and take advantage of new opportunities, leaving behind those who are not. In addition to Genpact, Peak's latest report, "State of AI 2022," revealed that 55% of enterprises will be AI-First by 2025.

Eyal Ben-Ari just took his first shot on a goal of bringing professional-class analytics to amateur soccer players. The CEO of startup Track160, in Tel Aviv, has seen his company's AI-powered sports analytics software tested and used in the big leagues. Now he's turning his attention to underserved amateurs in the clubs and community teams he says make up "the bigger opportunity" among the world's 250 million soccer players. "Almost everyone in professional sports uses data analytics today. Now we're trying to enable any team at any level to capture their own data and analytics, and the only way to do it is leveraging AI," he said.

The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games, such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey, we seek to unify the field of AutoRL, provide a common taxonomy, discuss each area in detail and pose open problems of interest to researchers going forward.