No matter how graciously good customer service is rendered, online reviews are more likely to cite "bad customer service," than the opposite. Problematic service will dominate reviews. People are more likely to angrily pen a disgruntled review than they are a positive one, and the latter acknowledgement can boost a business' morale and bring more people to their business. The coronavirus pandemic caused stress and anxiety among those who were overwhelmed as they faced unprecedented challenges, and sought help in navigating the technology necessitated by social distancing and isolation. Yet, there's a bright light at the end of the tunnel: Issues have been resolved by the judicious use of artificial intelligence (AI)-enabled chatbots and virtual agents, according to a new IBM report, "The value of virtual technology."

Arm DevSummit was one of several virtual tech conferences and launch events taking place over the past month week and it included everything from a fireside chat with the CEOs of Arm and Nvidia about the proposed acquisition to new technology announcements and the deep dive training tracks one would expect. One of the most interesting announcements was around a new platform of cores for SoCs specifically targeting autonomous machines. While there is a growing focus on autonomous vehicles, few companies look at the broader opportunity for autonomous control across a wide variety of platforms that include autonomous systems for materials handling and production for everything from consumer products to agriculture. Arm announced a complete platform that can span a wide range of applications that meet the safety critical requirements of autonomous systems. As Arm expanded its technology offerings in mobile, it began releasing a complete platform of compute cores and related technologies together as a single platform.

Various countries across the Middle East have placed an emphasis on AI. Clear examples of that have been nation-wide strategies around AI and part of wider government digital transformations. In the Gulf Cooperation Council (GCC) region (Saudi Arabia, Qatar, Oman, Bahrain, Kuwait and the United Arab Emirates (UAE), economic development diversifications such as Saudi Vision 2030 has prioritised wider future and innovative economies. Sectors such as fintech and AI-both fintech and non-fintech related- play a strong role in that. It is not just nation-wide economic development diversification strategies such as Vision 2030 but also, complimenting and in parallel, strategies purely around AI.

Machine intelligence is the last invention that humanity will ever need to make.

Machine learning model and Neural Networks helps in extracting archaic information about human civilization. Archaeology is the gateway to our past. It describes events which shaped the world how it is today and the transition that led humans from animal-hunter to a knowledgeable-mosaic. In archaeology, Stone Age holds the key relevance. It establishes the patterns of human behavior and helps in identifying the transitions that hurled humans to the path of development.

Hospitals are likely to increasingly rely on Internet of Things systems as the coronavirus pandemic ... [ ] persists or worsens. The European Commission has launched an €8 million project that aims to use the Internet of Things (IoT) to increase and enhance the remote care provided by hospitals. At a time when the coronavirus pandemic is stretching health systems to their limits, the project is one of several actions the EC is funding with the aim of developing "Next-Generation Internet of Things" tech that could help hospitals and other organisations operate more efficiently. Dubbed IntellIoT, the project is a consortium of 13 participating companies and institutions, including Siemens, Philips, EURECOM, Aalborg University, University of Oulu, Philips, Sphynx Analytics, and the University of St. Gallen. Over the next three years, the 13 partners will trial a range of initiatives and tools intended to autonomously conduct health monitoring and interventions, while also analysing large quantities of medical data.

Agriculture worldwide is a US $5 trillion industry. And artificial intelligence (AI) is revolutionizing this industry every step of the way -- from preparing soils and sowing seeds to getting products to the kitchen table. AI-powered technologies are increasing productivity and reducing costs significantly throughout the production and supply chain. The market value of global AI in the agricultural sector is currently estimated at $852.2 million. In the next decade alone this value is expected to grow more than 10 times, exceeding $8 billion annually.

When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's prediction on abnormal inputs. However, there is a scarcity of literature analyzing this problem on medical data, especially on mixed-type tabular data such as Electronic Health Records. We close this gap by presenting a series of tests including a large variety of contemporary uncertainty estimation techniques, in order to determine whether they are able to identify out-of-distribution (OOD) patients. In contrast to previous work, we design tests on realistic and clinically relevant OOD groups, and run experiments on real-world medical data. We find that almost all techniques fail to achieve convincing results, partly disagreeing with earlier findings.

Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making. Improvement of the forecasting accuracy, even by a small percentage, may have a substantial impact on companies' production and financial planning, marketing strategies, inventory controls, supply chain management, and eventually stock prices. Specifically, our research goal is to forecast the sales of each product in each store in the near future. Motivated by tensor factorization methodologies for personalized context-aware recommender systems, we propose a novel approach called the Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS), which achieves accurate and individualized prediction for sales by building a single tensor-factorization model across multiple stores and products. Our contribution is a combination of: tensor framework (to leverage information across stores and products), a new regularization function (to incorporate demand dynamics), and extrapolation of tensor into future time periods using state-of-the-art statistical (seasonal auto-regressive integrated moving-average models) and machine-learning (recurrent neural networks) models. The advantages of ATLAS are demonstrated on eight product category datasets collected by the Information Resource, Inc., where a total of 165 million weekly sales transactions from more than 1,500 grocery stores over 15,560 products are analyzed.

We study the effect of mini-batching on the loss landscape of deep neural networks using spiked, field-dependent random matrix theory. We demonstrate that the magnitude of the extremal values of the batch Hessian are larger than those of the empirical Hessian. We also derive similar results for the Generalised Gauss-Newton matrix approximation of the Hessian. As a consequence of our theorems we derive an analytical expressions for the maximal learning rates as a function of batch size, informing practical optimisation schemes for both stochastic gradient descent (linear scaling) and adaptive algorithms such as Adam (square root scaling). Whilst the linear scaling for stochastic gradient descent has been derived under more restrictive conditions, which we generalise, the square root scaling rule for adaptive optimisers is, to our knowledge, completely novel. For stochastic Second-order methods and adaptive methods, we derive that the minimal damping coefficient is proportional to the ratio of the learning rate to batch size.