Middle east is one of the top tech destinations where artificial intelligence is playing a significant role. The region is known for its oil wells, which are major contributors to the region's economy. Slowly the economy is shifting its base from petrochemicals to technology. The region is slowly shifting its economic dependence on oil wells. According to an IDC report, spending on AI in the region is expected to grow at an annual growth of 19%.

We show that the Riemannian Gaussian distributions on symmetric spaces, introduced in recent years, are of standard random matrix type. We exploit this to compute analytically marginals of the probability density functions. This can be done fully, using Stieltjes-Wigert orthogonal polynomials, for the case of the space of Hermitian matrices, where the distributions have already appeared in the physics literature. For the case when the symmetric space is the space of $m \times m$ symmetric positive definite matrices, we show how to efficiently compute by evaluating Pfaffians at specific values of $m$. Equivalently, we can obtain the same result by constructing specific skew orthogonal polynomials with regards to the log-normal weight function (skew Stieltjes-Wigert polynomials). Other symmetric spaces are studied and the same type of result is obtained for the quaternionic case. Moreover, we show how the probability density functions are a particular case of diffusion reproducing kernels of the Karlin-McGregor type, describing non-intersecting Brownian motions, which are also diffusion processes in the Weyl chamber of Lie groups.

Most energy companies today struggle with the complex technological and economic challenges involved in detecting, monitoring and preventing cyberattacks on critical infrastructure. The operational technologies (OT) and information technologies (IT) responsible for running energy systems today were never engineered to be secured in a digital environment; doing so poses a technical challenge tough to solve and difficult for small and mid-sized operators to afford. Yet in today's digital energy ecosystem, the failure of weak links can take down critical infrastructure for all participants. Protecting the entire system requires all industrial operators – both large and small – to detect and defend against cyberattacks. New developments in artificial intelligence (AI) based solutions can help all energy companies put defenders ahead of attackers, while adapting to the changing energy landscape.

The world of technology moves fast, as Lux Research's annual list of the top technologies to watch over the next decades proves. After a tumultuous 2020, 10 of last year's 20 technologies don't appear on this year's list, showing how dynamic changes in the innovation landscape have been over the past year. Notably, 5G networks, the top-ranked technology in last year's report, are absent from this year's list because, as the 5G rollout begins, they are now firmly established on everyone's radar. The new report, Foresight 2021: Top Emerging Technologies to Watch, identifies and ranks 12 key technologies that will reshape the world. Autonomous vehicles: All levels of vehicle automation are seeing improvements in safety and efficiency, benefiting both consumers and commercial operations.

Though there are plenty of opportunities and evidences that AI adoption facilitate companies to improve its environmental sustainability, it is important for companies to be aware that AI has also the potential to produce significant carbon emissions, and also has the potential to offset or reduce those carbon emissions. Companies need to understand how this can affect the operations and financial performance. As companies are pressed to be more transparent in managing its sustainability issues, companies would also need to monitor and disclose on its strategy and performance related to AI and its impacts, thus questions on its effectiveness to mitigate carbon emissions as well as threats imposed on the environment, would also likely to be asked by the stakeholders.

Designing missiles' autopilot controllers has been a complex task, given the extensive flight envelope and the nonlinear flight dynamics. A solution that can excel both in nominal performance and in robustness to uncertainties is still to be found. While Control Theory often debouches into parameters' scheduling procedures, Reinforcement Learning has presented interesting results in ever more complex tasks, going from videogames to robotic tasks with continuous action domains. However, it still lacks clearer insights on how to find adequate reward functions and exploration strategies. To the best of our knowledge, this work is pioneer in proposing Reinforcement Learning as a framework for flight control. In fact, it aims at training a model-free agent that can control the longitudinal flight of a missile, achieving optimal performance and robustness to uncertainties. To that end, under TRPO's methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Not only does this work enhance the concept of prioritized experience replay into BPER, but it also reformulates HER, activating them both only when the training progress converges to suboptimal policies, in what is proposed as the SER methodology. Besides, the Reward Engineering process is carefully detailed. The results show that it is possible both to achieve the optimal performance and to improve the agent's robustness to uncertainties (with low damage on nominal performance) by further training it in non-nominal environments, therefore validating the proposed approach and encouraging future research in this field.

We present a reinforcement learning-based solution to autonomously race on a miniature race car platform. We show that a policy that is trained purely in simulation using a relatively simple vehicle model, including model randomization, can be successfully transferred to the real robotic setup. We achieve this by using novel policy output regularization approach and a lifted action space which enables smooth actions but still aggressive race car driving. We show that this regularized policy does outperform the Soft Actor Critic (SAC) baseline method, both in simulation and on the real car, but it is still outperformed by a Model Predictive Controller (MPC) state of the art method. The refinement of the policy with three hours of real-world interaction data allows the reinforcement learning policy to achieve lap times similar to the MPC controller while reducing track constraint violations by 50%.

Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting Internet of Everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service. As a rapid evolving paradigm, the AI-empowered mobile communications demand large amounts of data acquired from real network environment for systematic test and verification. Hence, we build the world's first true-data testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. In the TTIN, true network data acquisition, storage, standardization, and analysis are available, which enable system-level online verification of B5G/6G-orientated key technologies and support data-driven network optimization through the closed-loop control mechanism. This paper elaborates on the system architecture and module design of TTIN. Detailed technical specifications and some of the established use cases are also showcased.

Machine learning is a powerful computing capability for predicting or forecasting things that conventional algorithms find challenging. The machine learning journey begins with collecting and preparing data--a lot of it--then it builds mathematical models based on that data. While multiple tools can be used for these tasks, I like to use the shell. A shell is an interface for performing operations using a defined language. This language can be invoked interactively or scripted.

Quantum computing--considered to be the next generation of high-performance computing--is a rapidly-changing field that receives equal parts attention in academia and in enterprise research labs. Honeywell, IBM, and Intel are independently developing their own implementations of quantum systems, as are startups such as D-Wave Systems. In late 2018, President Donald Trump signed the National Quantum Initiative Act that provides $1.2 billion for quantum research and development. TechRepublic's cheat sheet for quantum computing is positioned both as an easily digestible introduction to a new paradigm of computing, as well as a living guide that will be updated periodically to keep IT leaders informed on advances in the science and commercialization of quantum computing. SEE: The CIO's guide to quantum computing (ZDNet/TechRepublic special feature) Download the free PDF version (TechRepublic) SEE: All of TechRepublic's cheat sheets and smart person's guides Quantum computing is an emerging technology that attempts to overcome limitations inherent to traditional, transistor-based computers. Transistor-based computers rely on the encoding of data in binary bits--either 0 or 1. Quantum computers utilize qubits, which have different operational properties.