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.

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security. In this work, we present a novel method for detecting fraud transactions by leveraging patterns from both users' static profiles and users' dynamic behaviors in a unified framework. To address and explore the information of users' behaviors in continuous time spaces, we propose to use \emph{time attention based recurrent layers} to embed the detailed information of the time interval, such as the durations of specific actions, time differences between different actions and sequential behavior patterns,etc., in the same latent space. We further combine the learned embeddings and users' static profiles altogether in a unified framework. Extensive experiments validate the effectiveness of our proposed methods over state-of-the-art methods on various evaluation metrics, especially on \emph{recall at top percent} which is an important metric for measuring the balance between service experiences and risk of potential losses.

Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behaviour prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their superior performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behaviour prediction in this paper. We firstly give an overview of the generic problem of vehicle behaviour prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The paper also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions.

Sudheesh Nair, CEO of ThoughtSpot earnestly campaigns for artificial intelligence as a panacea for restoring our humanity - by making us able to do more work. Whether AI is helping a commuter navigate through a city or supporting a doctor's medical diagnosis, it relieves humans from mind-numbing, repetitive and error-prone tasks. This scares some business leaders, who worry AI could make people lazy, feckless and over-dependent. The more utopian minded - me included - see AI improving society and business while individuals get to enjoy happier, more fulfilling lives. Fortunately, this need not launch yet another polarised debate.

British Airways is set to trial artificial intelligence powered robots at Heathrow Terminal 5. In an announcement Thursday, the airline said the autonomous robots had been programmed to "interact with passengers" in multiple languages and would be able to answer "thousands" of questions, providing passengers with services such as real-time flight information. The robots are being provided by a technology company called BotsAndUs and the trial will start in 2020. British Airways added that the robots would also have the capacity to escort passengers to locations such as special assistance zones. "These smart robots are the latest innovation allowing us to free up our people to deal with immediate issues and offer that one-on-one service we know our customers appreciate," Ricardo Vidal, who is head of innovation at British Airways, said in a statement.

Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

On 7 November 2019, the Australian Federal Government's Minister for Industry, Innovation, and Science, Karen Andrews, announced the Australian …