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.

The quantification of positively buoyant marine plastic debris is critical to understanding how concentrations of trash from across the world's ocean and identifying high concentration garbage hotspots in dire need of trash removal. Currently, the most common monitoring method to quantify floating plastic requires the use of a manta trawl. Techniques requiring manta trawls (or similar surface collection devices) utilize physical removal of marine plastic debris as the first step and then analyze collected samples as a second step. The need for physical removal before analysis incurs high costs and requires intensive labor preventing scalable deployment of a real-time marine plastic monitoring service across the entirety of Earth's ocean bodies. Without better monitoring and sampling methods, the total impact of plastic pollution on the environment as a whole, and details of impact within specific oceanic regions, will remain unknown. This study presents a highly scalable workflow that utilizes images captured within the epipelagic layer of the ocean as an input. It produces real-time quantification of marine plastic debris for accurate quantification and physical removal. The workflow includes creating and preprocessing a domain-specific dataset, building an object detection model utilizing a deep neural network, and evaluating the model's performance. YOLOv5-S was the best performing model, which operates at a Mean Average Precision (mAP) of 0.851 and an F1-Score of 0.89 while maintaining near-real-time speed.

Post-deployment monitoring of the performance of ML systems is critical for ensuring reliability, especially as new user inputs can differ from the training distribution. Here we propose a novel approach, MLDemon, for ML DEployment MONitoring. MLDemon integrates both unlabeled features and a small amount of on-demand labeled examples over time to produce a real-time estimate of the ML model's current performance on a given data stream. Subject to budget constraints, MLDemon decides when to acquire additional, potentially costly, supervised labels to verify the model. On temporal datasets with diverse distribution drifts and models, MLDemon substantially outperforms existing monitoring approaches. Moreover, we provide theoretical analysis to show that MLDemon is minimax rate optimal up to logarithmic factors and is provably robust against broad distribution drifts whereas prior approaches are not.