JOHANNESBURG, South Africa – 24 June 2020 – Now is the age of home entertainment and high-quality audio provides the finishing touch to the best of these experiences. The concept of the modern'home' goes way beyond being merely a residential space; it has become a place for relaxation, for recreation and for quality time with others. At the recentre of this change is the near-three-dimensional content experiences granted by today's ultra-large, ultra-high-definition, ultra-fine pixel TVs. With its 2020 QLED 8K TVs and AI sound technologies, Samsung has raised the bar for TV audio experiences. Object Tracking Sound uses AI-based software to match the movement of audio with movement on-screen; Active Voice Amplifier (AVA) tracks the user's audio environment; and Q-Symphony creates a more realistic, three-dimensional sound.

For a few seconds, it seems real. Then, on the horizon, the landscape gives way to rugged coastline, and, as the plane flies closer, we glimpse the rippling waves glinting in the evening sun. In real life, I have not seen the ocean for five months and, although I'm just sitting in my kitchen watching a virtual presentation of a video game, I feel a surge of emotion. When the latest instalment in Microsoft's decades-old Flight Simulator series was first shown at the E3 video game event last year, it drew gasps from the audience. Using two petabytes of geographic data culled from Bing Maps, together with cutting-edge, machine learning algorithms running on the company's Azure cloud computing network, the game presents a near-photorealistic depiction of the entire planet.

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

Text simplification is concerned with reducing the language complexity and improving the readability of professional content so that the text is accessible to readers at different ages and educational levels. As a promising practice to improve the fairness and transparency of text information systems, the notion of text simplification has been mixed in existing literature, ranging all the way through assessing the complexity of single words to automatically generating simplified documents. We show that the general problem of text simplification can be formally decomposed into a compact pipeline of tasks to ensure the transparency and explanability of the process. In this paper, we present a systematic analysis of the first two steps in this pipeline: 1) predicting the complexity of a given piece of text, and 2) identifying complex components from the text considered to be complex. We show that these two tasks can be solved separately, using either lexical approaches or the state-of-the-art deep learning methods, or they can be solved jointly through an end-to-end, explainable machine learning predictor. We propose formal evaluation metrics for both tasks, through which we are able to compare the performance of the candidate approaches using multiple datasets from a diversity of domains.

Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in next-generation wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

In recent years, multi-dimensional online decision making has been playing a crucial role in many practical applications such as online recommendation and digital marketing. To solve it, we introduce stochastic low-rank tensor bandits, a class of bandits whose mean rewards can be represented as a low-rank tensor. We propose two learning algorithms, tensor epoch-greedy and tensor elimination, and develop finite-time regret bounds for them. We observe that tensor elimination has an optimal dependency on the time horizon, while tensor epoch-greedy has a sharper dependency on tensor dimensions. Numerical experiments further back up these theoretical findings and show that our algorithms outperform various state-of-the-art approaches that ignore the tensor low-rank structure.

We consider structural impulse response analysis for sparse high-dimensional vector autoregressive (VAR) systems. Since standard procedures like the delta-method do not lead to valid inference in the high-dimensional set-up, we propose an alternative approach. First, we directly construct a de-sparsified version of the regularized estimators of the moving average parameters that are associated with the VAR process. Second, the obtained estimators are combined with a de-sparsified estimator of the contemporaneous impact matrix in order to estimate the structural impulse response coefficients of interest. We show that the resulting estimator of the impulse response coefficients has a Gaussian limiting distribution. Valid inference is then implemented using an appropriate bootstrap approach. Our inference procedure is illustrated by means of simulations and real data applications.

Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1-30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continental US, with rainfall quantized at 3 levels: light or no rain; moderate; and heavy rain. Three regions were selected, corresponding to three squares from a $5\times5$ grid covering the map area. Rainfall predictions up to 30 days ahead for these three regions were based on a support vector machine (SVM) applied to consecutive sequences of prior daily rainfall map images. The results show that predictions for corner squares in the grid were less accurate than predictions obtained by a simple untrained classifier. However, SVM predictions for a central region outperformed the other two regions, as well as the untrained classifier. We conclude that there is some evidence that SVMs applied to large-scale precipitation maps can under some conditions give useful information for predicting regional rainfall, but care must be taken to avoid pitfall

Distinguishing between individual animals is important for long-term monitoring of populations and protecting species from pressures such as climate change. However, it is also one of the most expensive, troublesome, and time-consuming aspects of animal behaviour research. While some creatures such as leopards have unique markings which allow humans to recognise individuals by eye, most species require additional visual identifiers such as coloured bands to be distinguished. Attaching bands to birds' legs can be stressful and disruptive to the animals, limiting the scope of research. Seeking an alternative method for distinguishing between individual birds, researchers from institutes in France, Germany, Portugal, and South Africa developed the first AI bird identification tool of its kind.

If you need a reason to feel good about the direction technology is going, look up Dell Technologies CTO John Roese on Twitter. The handle he composed back in 2006 is @theICToptimist. ICT stands for information and communication. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. "The reason for that acronym was because I firmly believed that the future was not about information technology and communication technology independently," says Roese, president and chief technology officer of products and operations at Dell Technologies. "It was about them coming together." Close to two decades later, it's hard not to call him right. Organizations are looking to the massive amounts of data they're collecting and generating to become fully digital, they're using the cloud to process and store all that data, and they're turning to new wireless technologies like 5G to power data-hungry applications such as artificial intelligence (AI) and machine learning. In this episode of Business Lab, Roese walks through this confluence of technologies and its future outcomes. For example, autonomous vehicles are developing fast, but fully driverless cars aren't plying are streets yet. And they won't until they tap into a "collaborative compute model"--smart devices that plug into a combination of cloud and edge-computing infrastructure to provide "effectively infinite compute." "One of the biggest problems isn't making the device smart; it's making the device smart and efficient in a scalable system," Roese says. So big things are ahead, but technology today is making huge strides, Roese says. He talks about machine intelligence, which taps AI and machine learning to mimic human intelligence and tackle complex problems, such as speeding up supply chains, or in health care, more accurately detecting tumors or types of cancer.