We are interested in the problem of efficiently determining the data complexity of answering queries mediated by non-Horn description logic ontologies and constructing their optimal rewritings to standard database queries. In general, this problem is known to be extremely complex. In this article, we strip it to the bare bones and focus on conjunctive queries mediated by a simple covering axiom stating that one class is covered by the union of two other classes. We develop a novel technique to prove that, quite surprisingly, deciding first-order rewritability of even such simple ontology-mediated queries is PSpace-hard. The main result of this article is a complete and transparent syntactic AC0/NL/P/coNP tetrachotomy of path queries under the assumption that the covering classes are disjoint. We also obtain a number of syntactic and semantic sufficient conditions (without the path query assumption) for membership in AC0, L, NL, and P.

In this chapter we review the main literature related to the recent advancement of deep neural-kernel architecture, an approach that seek the synergy between two powerful class of models, i.e. kernel-based models and artificial neural networks. The introduced deep neural-kernel framework is composed of a hybridization of the neural networks architecture and a kernel machine. More precisely, for the kernel counterpart the model is based on Least Squares Support Vector Machines with explicit feature mapping. Here we discuss the use of one form of an explicit feature map obtained by random Fourier features. Thanks to this explicit feature map, in one hand bridging the two architectures has become more straightforward and on the other hand one can find the solution of the associated optimization problem in the primal, therefore making the model scalable to large scale datasets. We begin by introducing a neural-kernel architecture that serves as the core module for deeper models equipped with different pooling layers. In particular, we review three neural-kernel machines with average, maxout and convolutional pooling layers. In average pooling layer the outputs of the previous representation layers are averaged. The maxout layer triggers competition among different input representations and allows the formation of multiple sub-networks within the same model. The convolutional pooling layer reduces the dimensionality of the multi-scale output representations. Comparison with neural-kernel model, kernel based models and the classical neural networks architecture have been made and the numerical experiments illustrate the effectiveness of the introduced models on several benchmark datasets.

When the coronavirus outbreak first hit the Plaza Minorista market, Edison Palacio knew that it would take more than disinfectant and face masks to contain it. So he decided to use artificial intelligence. Mr Palacio is the director of the densely packed market which sits in the heart of the Colombian city of Medellín. Every day, up to 15,000 people flood into the giant building where more than 3,300 vendors sell fruits, vegetables, meats, eggs, spices, grains and clothes. They are a crucial link bringing food grown on farms to a metropolitan area of nearly four million people.

Artificial Intelligence in Education Market Report aims to provide an overview of the industry through detailed market segmentation. The report offers thorough information about the overview and scope of the market along with its drivers, restraints and trends. This report is designed to include both qualitative and quantitative aspects of the industry in each region and country participating in the study. Key players in global Artificial Intelligence in Education market include: Blackboard,Fujitsu,Cisco Systems,Pearson,Samsung,Instructure,Discovery Communications,Dell,Echo360,Adobe systems,SAP,Microsoft,Jenzabar,Ellucian,Promethean World,Oracle,IBM and more. This study specially analyses the impact of Covid-19 outbreak on the Artificial Intelligence in Education, covering the supply chain analysis, impact assessment to the Artificial Intelligence in Education market size growth rate in several scenarios, and the measures to be undertaken by Artificial Intelligence in Education companies in response to the COVID-19 epidemic.

This year, the world changed in the span of a few months, in unprecedented ways that surprised and overwhelmed every country on this planet. The greatest global crisis since World War II and the largest global pandemic since the 1918–19 Spanish Flu fell upon us. Everybody spent a better part of their day looking at the daily rise of the death toll and the rapid, exponential spread of this novel strain of the COVID-19 virus. Millions of people lost their jobs, unemployment rose through the roof, global travel and hospitality industries were all but decimated, international relationships were frayed, healthcare systems were stressed to the limits. Of course, the fight was not one-sided.

InForGrowth Market Research offers a most recent distributed report on Global Big Data & Machine Learning in Telecom industry examination and figure 2019-2026 conveying key bits of knowledge and giving an upper hand to customers through a point by point report. The Global pandemic of COVID19 calls for redefining of business strategies. Worldwide Big Data & Machine Learning in Telecom Market inspect reports consolidate market designs nuances, genuine scene, feature assessment, cost structure, capability, bargains, net advantage, and movement and measuring of business. Major Key players covered in this report:– Allot, Argyle data, Ericsson, Guavus, HUAWEI, Intel, NOKIA, Openwave mobility, Procera networks, Qualcomm, ZTE, Google, AT&T, Apple, Amazon, Microsoft. The overall market is set up for energetic advancement with progressively moving of various gathering methodology to more affordable objectives in rising economies.

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Artificial Intelligence (AI) has gone mainstream when it comes to customer interactions, according to a new report from the Capgemini Research Institute. More than half of customers (54 percent) have daily AI-enabled interactions with organizations – a significant increase from the 21 percent reported in Capgemini's 2018 research on the subject. The report, 'The Art of Customer-Centric Artificial Intelligence: How organizations can unleash the full potential of AI in the customer experience', reveals the factors that have significantly contributed to AI adoption among customers, including increasing customer trust in AI; an increase in human-like AI interactions; increasing customer concerns arising from COVID-19; and organizations stepping up their AI deployments. COVID-19 has accelerated customer adoption of non-touch AI-based systems, such as voice assistants and facial recognition – a shot in the arm for AI adoption. Over three-quarters of customers (77 percent) expect to increase the use of touchless interfaces to avoid direct interactions with humans or touchscreens during COVID-19, and 62 percent will continue to do so post-COVID.

Guavus, a pioneer in Artificial Intelligence-based analytics for communications service providers (CSPs), today announced the launch of Guavus-IQ -- a comprehensive product portfolio that provides a unique multi-perspective analytics experience for CSPs.

Facial recognition software has been the subject of debate for a long time now. Despite the controversy, law enforcement has been using such AI-powered software to catch criminals all around the world, and most particularly in large nations with less strict privacy laws. The use is prevalent despite the fact that the software may not work accurately when used on ethnic communities, youngsters and even women. One company which is leading the news headlines these days is Clearview AI founded by an Australian entrepreneur Hoan Ton-That. Although Clearview AI hasn't devised a groundbreaking facial recognition app, what it sells can be deemed as useful to law enforcement agencies.