The first image of China's rouge Long March 5B rocket in orbit has been released by astronomers. The Italy-based Virtual Telescope Project captured the craft, which appears like a glowing light, as it passed above the group's'Elena' robotic telescope. The Chinese rocket made headlines this week when new surfaced the massive 21-ton vehicle would make an uncontrolled reentry weekend, with the possibility of landing in inhabited areas. The rocket was moving'extremely fast' when it soared 435 miles above the Virtual Telescopes Project's telescope Wednesday evening, researchers said. Gianluca Masi, an astronomer with the Virtual Telescope Project who snapped the image, stated that'while the Sun was just a few degrees below the horizon, so the sky was incredibly bright: these conditions made the imaging quite extreme, but our robotic telescope succeeded in capturing this huge debris.' 'This is another bright success, showing the amazing capabilities of our robotic facility in tracking these objects.'

While healthcare has lagged behind other industries in the deployment of artificial intelligence (AI) and many other advanced technologies, the COVID-19 pandemic is proving to be the mother of invention when it comes to technological innovation. Machine learning -- a key part of AI where computer algorithms automatically improve through experience -- has been called upon to leverage healthcare data to help deal with many of the challenges COVID-19 has presented. Public health systems have turned to machine learning to complement their contact tracing and other efforts to control the disease and track outbreaks. Private healthcare operators have embraced machine learning to remain competitive when faced with a drop in demand for elective surgery or, in many countries, a reluctance or inability to visit hospitals or clinics. The pace of AI and machine learning adoption is also accelerating in hospitals.

HomePod owners have had the ability to use third-party music services for several months now, but thus far only Pandora directly works with Apple's smart speakers. However, that's changing today with the introduction of Deezer integration for the original (and since discontinued) HomePod and its replacement, the cheaper HomePod Mini. By launching and connecting the music streaming app with an Apple speaker, paying subscribers can tell Siri to play specific tracks, artists, albums, favorites or playlists. To keep things succinct, you can set Deezer as your default music service in iOS. That way you don't have to ask Siri to play a song or artist "on Deezer" at the end of every command.

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

In this independent report fAshIon after fashion, we examine the development of fAshIon (artificial intelligence (AI) in fashion) and explore its potentiality to become a major disruptor of the fashion industry in the near future. To do this, we investigate AI technologies used in the fashion industry through several lenses. We summarise fAshIon studies conducted over the past decade and categorise them into seven groups: Overview, Evaluation, Basic Tech, Selling, Styling, Design, and Buying. The datasets mentioned in fAshIon research have been consolidated on one GitHub page for ease of use. We analyse the authors' backgrounds and the geographic regions treated in these studies to determine the landscape of fAshIon research. The results of our analysis are presented with an aim to provide researchers with a holistic view of research in fAshIon. As part of our primary research, we also review a wide range of cases of applied fAshIon in the fashion industry and analyse their impact on the industry, markets and individuals. We also identify the challenges presented by fAshIon and suggest that these may form the basis for future research. We finally exhibit that many potential opportunities exist for the use of AI in fashion which can transform the fashion industry embedded with AI technologies and boost profits.

Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully utilize the historical records, since most of the documents are not written in a modern language and part of the contents are damaged over time. As a result, restoring the damaged or unrecognizable parts as well as translating the records into modern languages are crucial tasks. In response, we present a multi-task learning approach to restore and translate historical documents based on a self-attention mechanism, specifically utilizing two Korean historical records, ones of the most voluminous historical records in the world. Experimental results show that our approach significantly improves the accuracy of the translation task than baselines without multi-task learning. In addition, we present an in-depth exploratory analysis on our translated results via topic modeling, uncovering several significant historical events.

We develop a formal framework for automatic reasoning about the obligations of autonomous cyber-physical systems, including their social and ethical obligations. Obligations, permissions and prohibitions are distinct from a system's mission, and are a necessary part of specifying advanced, adaptive AI-equipped systems. They need a dedicated deontic logic of obligations to formalize them. Most existing deontic logics lack corresponding algorithms and system models that permit automatic verification. We demonstrate how a particular deontic logic, Dominance Act Utilitarianism (DAU), is a suitable starting point for formalizing the obligations of autonomous systems like self-driving cars. We demonstrate its usefulness by formalizing a subset of Responsibility-Sensitive Safety (RSS) in DAU; RSS is an industrial proposal for how self-driving cars should and should not behave in traffic. We show that certain logical consequences of RSS are undesirable, indicating a need to further refine the proposal. We also demonstrate how obligations can change over time, which is necessary for long-term autonomy. We then demonstrate a model-checking algorithm for DAU formulas on weighted transition systems, and illustrate it by model-checking obligations of a self-driving car controller from the literature.

Google has a new way to preserve endangered languages: give cultures the AI tools they need to protect the languages themselves. The company has launched Woolaroo, an open source photo translation web app (also available through Google Arts & Culture for Android and iOS) that uses machine learning and image recognition to help preserve languages on the brink. As a user, you just have to point your phone's camera at an object to have the AI recognize and describe it in a given language, complete with pronunciation. Woolaroo's real power comes from its open nature, however. Communities can use the platform to expand vocabularies on their own terms.

A number of AI researchers are pushing back and developing ways to make sure AIs can't learn from personal data. Two of the latest are being presented this week at ICLR, a leading AI conference. "I don't like people taking things from me that they're not supposed to have," says Emily Wenger at the University of Chicago, who developed one of the first tools to do this, called Fawkes, with her colleagues last summer: "I guess a lot of us had a similar idea at the same time." Actions like deleting data that companies have on you, or deliberating polluting data sets with fake examples, can make it harder for companies to train accurate machine-learning models. But these efforts typically require collective action, with hundreds or thousands of people participating, to make an impact.

Phillip Crawley, the Englishman who runs Canadian national news title the Globe and Mail, is a newsroom executive of the old school. He has tales of working alongside Rupert Murdoch, sparring with Conrad Black, and editing the South China Morning Post during the 1989 Tiananmen Square protests. But Crawley, who also edited the Newcastle Journal between 1979 and 1987, has some strikingly new ideas about the future of journalism. "If you'd asked me ten years ago, I'd have said that newsrooms would be largely resistant to being told what to do by the machine," he tells Press Gazette in a phone interview. The'machine' he refers to is Sophi, an artificial intelligence (AI) programme developed by the G&M to drive up digital subscriptions.