Analytics has been changing the bottom line for businesses for quite some time. Now that more companies are mastering their use of analytics, they are delving deeper into their data to increase efficiency, gain a greater competitive advantage, and boost their bottom lines even more. That's why companies are looking to implement machine learning (ML) and artificial intelligence (AI); they want a more comprehensive analytics strategy to achieve these business goals. Learning how to incorporate modern machine learning techniques into their data infrastructure is the first step. For this many are looking to companies that already have begun the implementation process successfully. For call centers, using ML and AI means having conversation analytics software in place – in fact, decades ago call centers began using primitive forms of artificial intelligence.

This article presents an in-depth review of the topic of path following for autonomous robotic vehicles, with a specific focus on vehicle motion in two dimensional space (2D). From a control system standpoint, path following can be formulated as the problem of stabilizing a path following error system that describes the dynamics of position and possibly orientation errors of a vehicle with respect to a path, with the errors defined in an appropriate reference frame. In spite of the large variety of path following methods described in the literature we show that, in principle, most of them can be categorized in two groups: stabilization of the path following error system expressed either in the vehicle's body frame or in a frame attached to a "reference point" moving along the path, such as a Frenet-Serret (F-S) frame or a Parallel Transport (P-T) frame. With this observation, we provide a unified formulation that is simple but general enough to cover many methods available in the literature. We then discuss the advantages and disadvantages of each method, comparing them from the design and implementation standpoint. We further show experimental results of the path following methods obtained from field trials testing with under-actuated and fully-actuated autonomous marine vehicles. In addition, we introduce open-source Matlab and Gazebo/ROS simulation toolboxes that are helpful in testing path following methods prior to their integration in the combined guidance, navigation, and control systems of autonomous vehicles.

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.

What is the difference with autoencoders? What is the fundamental training algorithm of a GAN model? How can we make it learn meaningful representations? In what computer vision application can it be useful? How can one design one for her/his problem? We will address all these questions and much much! In this review article series, we will focus on a plethora of GANs for computer vision applications. Specifically, we will slowly build upon the ideas and the principles that led to the evolution of generative adversarial networks (GAN). We will encounter different tasks such as conditional image generation, 3D object generation, video synthesis. Let's start with reviewing our contents of the first part!

Artificial intelligence is probably the greatest transformative technology of our generation. Experts predict that the value of the AI market will reach over $266 billion by 2027, representing an 880% increase compared to 2019. As exciting as AI innovation might be from a practical viewpoint, there are also some issues to consider when it comes to ethics in AI. AI is a technology that aims to enhance and unlock human potential. It is here to augment or replicate problem-solving and decision-making capabilities that require a certain level of "human intelligence".

According to a survey of UK leaders conducted by bluQube, the adoption of digital transformation technologies is accelerating. "So-called'future' technologies, such as robotics or the Internet of Things, have now firmly entered the mainstream for businesses looking to grow and stand out from the chasing pack," commented Simon Kearsley, CEO of bluQube. Almost three-quarters (72%) of business leaders report their organisations have adopted mobile technology. An equal percentage say they're using the cloud for their operations. As established technologies, it's not particularly surprising.

Our ocean remains the least observed part of our planet. Ocean States often have exclusive economic zones that are significantly larger than their land mass, making management all the more challenging. Information sharing can present opportunities for innovative approaches to the way oceans are monitored, vessel activity is tracked and movements are cross referenced. An interactive discussion will showcase the use of transparent data and innovative technologies to effectively manage critical ocean areas. ATLAN Space, Global Fishing Watch and Skylight will highlight opportunities where AI can have real impact and showcase innovations which can revolutionize the way our ocean is governed.

Recently, stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review introduces the following sections, the biological background of spiking neurons and the theoretical basis, different neuronal models, the connectivity of neural circuits, the mainstream neural network learning mechanisms and network architectures, etc. This review hopes to attract different researchers and advance the development of brain-inspired intelligence and artificial intelligence.

Artificial Intelligence (AI) is reforming economies all across the world by proffering novel products and services which creates an avenue for the generation of greater productivity gains, improved efficiency and lower costs. This is a radical change from the usual practice and such that has the tendency to permeate every aspect of the economy of any given nation. Studies accentuate that Artificial Intelligence has a vital economic impact on developing economies in the world. Recent research conducted on 12 developed economies in the world, all of which together generate more than 0.5 % of the world's economic output, projected that by the year 2035, AI could double the annual global economic growth rates.1 This is because Artificial Intelligence has a massive impact on healthcare, communication, financial, legal and commercial services to mention but a few.

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