We study hypergraph clustering under the weighted $d$-uniform hypergraph stochastic block model ($d$-WHSBM), where each edge consisting of $d$ nodes has higher expected weight if $d$ nodes are from the same community compared to edges consisting of nodes from different communities. We propose a new hypergraph clustering algorithm, which is a convex relaxation of truncated maximum likelihood estimator (CRTMLE), that can handle the relatively sparse, high-dimensional regime of the $d$-WHSBM with community sizes of different orders. We provide performance guarantees of this algorithm under a unified framework for different parameter regimes, and show that it achieves the order-wise optimal or the best existing results for approximately balanced community sizes. We also demonstrate the first recovery guarantees for the setting with growing number of communities of unbalanced sizes.

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.

Ethics is an ancient matter for human kind, from the origin of civilizations ethics have been related with the most relevant human concerns and determined human behavior. Ethics was initially related to religion, politics and philosophy to then be fragmented into specific disciplines and communities of practice. The undergoing digital revolution enabled by Artificial Intelligence and Big Data are bringing ethical wicked problems in the social application of these technologies. However, a broader perspective is also necessary. We now face global challenges that affect groups and individuals, specially those that are most vulnerable, but cannot reduced only to individual-oriented solutions. Thus, ethics has to consider the several scales in which the current complex society is organized and the interconnections between different systems. Ethics should also give a response to the systemic changes in individual to collective behavior produced by external factors and threats. Furthermore, Artificial Intelligence and digital technologies are global and make humans more connected and smart but also more homogeneous, predictable and ultimately controllable. Ethics must take a stand to preserve and keep promoting individuals rights and uniqueness and cultural heterogeneity. The digital revolution has been so far an industry-driven movement, so it is necessary to establish mechanisms to ensure that the society becomes conscious about its own future. Finally, Artificial Intelligence has advanced through the ambition to humanize matter, so we should expect ethics to give a response to the future status of machines and their interactions with humans.

In areas such as fraud detection, risk management, credit rating and wealth advisory, AI is already augmenting or even replacing human decision makers. In fact, not deploying AI capabilities in these fields can be considered disastrous. Withthe ever-increasing amounts of data that needs to be processed, AI systems are a must-have to improve accuracy. As technological capabilities continue to improve, the amount of available data grows, and competitive pressures mount, the use of AI in finance will be pervasive. However, as with any new technology the adoption of AI brings its very own set of challenges.

Nowadays we are hearing a lot of buzz around artificial intelligence or AI and robotics. We have even felt their presence in our surroundings in one or the other way. The technology sounds new but its seeds have been long sown. Do you know that AI found its way into film-business around 100 years ago? Yes, the 1927 release Metropolis depicts AI that takes the form of a humanoid robot with an intent on taking over the titular mega-city by inciting chaos.

Chris Bain is a professor of Digital Health in the Faculty of IT at Monash University, Melbourne, Australia. Abraham Oshni Alvandi is a research assistant in Digital Health in the Faculty of IT at Monash University, Melbourne, Australia.

Japan's National Institute of Informatics (NII) launched its inaugural NII Shonan Meeting in February 2011. It was the first international conference of informatics in Asia, following in the style of the Dagstuhl seminars in Germany, designed to bring together the world's leading researchers and engineers to discuss open problems and challenges. More than 140 meetings have been held since then, and the number of participants totaled approximately 3,500 by November 2019. NII supports all the administrative arrangements for organizers and covers approximately half the fee for every academic participant (including room, board, and meeting fees). Sometimes, we also have summer/winter schools.

India is turning to WhatsApp, the most popular app in the country, to create awareness about the coronavirus pandemic. Narendra Modi, India's Prime Minister, said on Saturday that citizens in the country can text a WhatsApp bot -- called MyGov Corona Helpdesk -- to get instant authoritative answers to their coronavirus queries such as the symptoms of the viral disease and how they could seek help. Here is an effort by WhatsApp and @mygovindia to ensure you receive accurate and verified information on Coronavirus. Please click on this link https://t.co/REabfIp5QT An individual is required to text 919013151515 (or click on this shortcut link) to connect to the bot.

"High demand and tight labor markets have made candidates with AI skills highly competitive, but hiring techniques and strategies have not kept up," said Peter Krensky, research director at Gartner. "In the recent Gartner AI and Machine Learning Development Strategies Study, respondents ranked "skills of staff" as the No. 1 challenge or barrier to the adoption of AI and machine learning (ML)." Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. These business units are using AI talent for customer churn modeling, customer profitability analysis, customer segmentation, cross-sell and upsell recommendations, demand planning, and risk management. A significant portion of AI use cases are reported from asset-centric industries supporting projects such as predictive maintenance, workflow and production optimization, quality control and supply chain optimization.

In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.