Fake video and audio streams that appear to be real can ruin the reputations of your executives and your company. They can cost you money. They can even cost you your job. Fortunately, there are steps you can take that can help. If you're at all familiar with the term "deep fake" you probably think about it in terms of fake videos about celebrities or politicians where they're already being used as parts of disinformation campaigns.

When Carol Clark was diagnosed with sepsis, a potentially deadly condition caused by her body's response to a foot infection, her future was touch and go. "I was a very, very sick lady," she said. "Thankfully, they didn't want me up there yet." As soon as the ambulance collected Ms Clark from her Guildford home and took her to Westmead Hospital, a computer system began collecting her data. Every blood-pressure and heart-rate check was meticulously recorded into the hospital's electronic medical database.

The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves linking together off-the-shelf binary classifiers in a chain structure, such that class label predictions become features for other classifiers. Such methods have proved flexible and effective and have obtained state-of-the-art empirical performance across many datasets and multi-label evaluation metrics. This performance led to further studies of how exactly it works, and how it could be improved, and in the recent decade numerous studies have explored classifier chains mechanisms on a theoretical level, and many improvements have been made to the training and inference procedures, such that this method remains among the state-of-the-art options for multi-label learning. Given this past and ongoing interest, which covers a broad range of applications and research themes, the goal of this work is to provide a review of classifier chains, a survey of the techniques and extensions provided in the literature, as well as perspectives for this approach in the domain of multi-label classification in the future. We conclude positively, with a number of recommendations for researchers and practitioners, as well as outlining a number of areas for future research.

In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) model is presented. The setting of J-RFDL aims at improving the data representations by enhancing the robustness to outliers and noise in data, encoding the reconstruction error more accurately and obtaining hybrid salient coefficients with accurate reconstruction ability. Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient. To make the encoding process robust to noise in data, J-RFDL clearly uses sparse L2, 1-norm that can potentially minimize the factorization and reconstruction errors jointly by forcing rows of the reconstruction errors to be zeros. To deliver salient coefficients with good structures to reconstruct given data well, J-RFDL imposes the joint low-rank and sparse constraints on the embedded coefficients with a synthesis dictionary. Based on the hybrid salient coefficients, we also extend J-RFDL for the joint classification and propose a discriminative J-RFDL model, which can improve the discriminating abilities of learnt coeffi-cients by minimizing the classification error jointly. Extensive experiments on public datasets demonstrate that our formulations can deliver superior performance over other state-of-the-art methods.

We use synthetic data and a reinforcement learning algorithm to train a system controlling a full-size real-world vehicle in a number of restricted driving scenarios. The driving policy uses RGB images as input. We analyze how design decisions about perception, control and training impact the real-world performance.

Microsoft is devising a new roadmap for Cortana by integrating it with various applications. Earlier in 2019, Satya Nadela, CEO of Microsoft, said that the firm no longer sees Cortana as a competitor to other prominent virtual assistants Alexa and Google Now. The company is rather working towards revamping the way Cortana is being leveraged across the world. In an announcement on Friday, Microsoft notified users of several countries that they are pulling out Cortana support from Android and iOS platforms. Come 31 January 2020, Cortana will disappear from India, UK, Australia, Mexico, Spain, Canada, Germany, and China.

Wagering giant Tabcorp is preparing to install artificial intelligence-powered video surveillance in its 400 TAB agencies in what it says is a world-first bid to prevent underage gambling. The $9.5 billion group recently completed an eight-week trial in three Melbourne TAB agencies to test software that identifies when someone potentially under the age of 18 enters a betting shop. The AI software alerts staff if a potential underage punter has entered the venue. Tabcorp's executive general manager of wagering Andy Wright said the company was satisfied with the trial and would start rolling out the new technology in agencies across Australia from the middle of 2020. "In retail, you have the anonymity of cash and there's a heightened level of risk around that," Mr Wright said.

The main objective of any supply chain remains to be the management of inventory, from procurement to supply the right product at the right time in the right place. And, for traditional supply chain companies, it has always been a challenge to achieve it as they focus majorly on optimizing a particular segment of the supply chain, rather than optimizing the entire value chain. This limits their operational efficiency to meet the need for granularity in customers' unique expectations. Artificial Intelligence (AI) can help supply chain companies in breaking the silos to reinvent their operational models. AI in the supply chain helps companies in procuring and processing large datasets and provides better visibility within the supply chain.

Ola, the popular ride-hailing company this week announced the roll-out of its artificial intelligence-enabled safety feature, Guardian in 17 markets across India and Australia. After running a successful pilot across multiple cities in India and international markets, the Guardian feature is going live in 16 Indian cities as well as Perth in Australia. Ola aims to take Guardian to more cities in the coming quarter. The Guardian feature, developed by Ola as a world-first, uses real-time data from rides to automatically detect irregular trip activity, including prolonged stops and unexpected route deviations. These alerts are flagged off in real-time to Ola's dedicated 24 7 Safety Response Team, who immediately reach out to customers and drivers to confirm if they're safe and offer on-the-call assistance until ride completion.

Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain.