According to Gartner, 37% of enterprise companies have currently embraced AI. This shows that the business world is taking a keen interest in how AI adoption can deliver a return on investment, as the number of organizations implementing such technologies has increased by 270% in the last four years. Recently, Gartner revealed that artificial intelligence (AI) adoption has tripled in the past year alone, with approximately 37% of companies currently implementing artificial intelligence in some form. According to Gartner's 2019 CIO survey, artificial intelligence (AI) is now being used in multiple applications. In this particular context, artificial intelligence is not related to the creation of'true,' self-conscious AI.

The number of organisations using AI has rocketed 270 per cent since 2015, a survey out today reveals. Firms across all industries are increasingly using artificial intelligence as they seek to gain an edge over competitors, research firm Gartner's 2019 CIO Survey found. The huge increase means the percentage of businesses incorporating AI has grown from 25 per cent last year to 37 per cent now, the poll of 3,000 chief information officers across 89 countries showed, and up from 10 per cent since 2015. Gartner vice president Chris Howard said the tech's nascent maturity level means most of those firms are now able to use it in a role designed to assist human workers, rather than do human tasks itself. "We still remain far from general AI that can wholly take over complex tasks, but we have now entered the realm of AI-augmented work and decision science -- what we call'augmented intelligence'," he said. "If you are a CIO and your organisation doesn't use AI, chances are high that your competitors do and this should be a concern."

ARTIFICIAL intelligence (AI) is a technology that can easily provide companies with a strong competitive edge. It's a unique weapon in the arsenal of the biggest and brightest companies, who are using it to disrupt the industry and defeat their competitors. As a result, even those without the most sophisticated technology infrastructure or talent pool, are looking at implementing AI solutions. The 2019 Gartner CIO Survey revealed that the deployment of AI has tripled in the past year -- rising from 25 percent in 2018 to 37 percent today. The growth is attributed to the fact that AI capabilities have matured significantly in the recent past, as a result, enterprises are more willing to give the technology a shot.

It seems the enterprise is taking a serious interest in how the adoption of artificial intelligence (AI) can provide a return on investment (ROI), as the number of companies implementing these technologies has grown by 270 percent in the past four years. On Monday, Gartner said that AI adoption has tripled in the last year alone, with an estimated 37 percent of firms now implementing AI in some form. According to the research agency's 2019 CIO Survey, AI is being used in a variety of applications. See also: GE is piloting'humble AI' to introduce business risk to algorithms AI in this context does not relate to the development of'true,' self-aware artificial intelligence. Rather, it can be considered an umbrella term for a range of applications including image recognition, natural language processing, cognitive computing, automatic Big Data analysis, and machine learning (ML), among other technologies.

It's a bright September morning in San Carlos, California, and Masayoshi Son, chairman of SoftBank, is throwing me off schedule. I'd come, as he had, to meet with the people he's tapped to run the Vision Fund, his $100 billion bet on the future of, well, everything. After almost four decades of building SoftBank into a telecom conglomerate, Son, an inveterate dealmaker, launched this unprecedented venture two years ago to back startups that he believes are driving a new wave of digital upheaval. He has staked everything on its success–his company, his reputation, his fortune. We'd both arrived with the same basic question: Where is this massive vehicle heading? But because I wasn't the one footing the 12-figure allowance, I understood that I'd be the one to wait. When I finally arrive at the Vision Fund's offices, just off California's Highway 101, I'm struck by how mundane they are. Son is known for big, showy statements. He reportedly paid $117 million for a home in Woodside in 2013, the highest price ever in the U.S. This glass and concrete building, on the other hand, could be found in any part of suburban America. The room where I wait is spartan.

Abstract--As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual banditframework arises as a natural candidate for such a task, its extension to a parallel setting is not straightforward: one needs to carefully adapt existing methods to fully leverage the multi-agent structure of this problem. We propose two approaches: one derived from a deterministic UCB-like method and the other relying on Thompson sampling. Thanks to its bayesian nature, the latter is intuited to better preserve the exploration-exploitation balance in the bandit batch. This is verified on toy experiments, where Thompson sampling shows robustness to the variability of the contexts. Finally, we apply both methods on a real base station network dataset and evidence that Thompson sampling outperforms both manual tuning and contextual UCB. I. INTRODUCTION The land area covered by a cellular wireless network, such as a mobile phone network, is divided into small areas called cells, each cell being covered by the antenna of a fixed base station (see Figure 1).

Chinese telecom giant Huawei introduced a new data center switch powered by an artificial intelligence (AI) chip designed to improve performance and reduce latency to near zero. The new switch follows the announcement of a 64-core ARM server processor. The CloudEngine 16800 series of data center switches use AI to improve network operations and also provide an underlying network foundation for companies to build new apps that utilize AI for network performance. Huawei claims the CloudEngine 16800 is the first data center switch use an embedded AI chip, using the iLossless algorithm to implement auto-sensing and auto-optimization of the traffic model, thereby lowering latency and providing higher throughput based on zero packet loss. The CloudEngine 16800 has an internal analyzer called FabricInsight, which identifies faults in seconds and automatically locates the faults in minutes, helping to drive an autonomous network.

How might your everyday working life change if you have artificial intelligence and machine learning? Consider an employee who normally fills out his weekly time card on Thursday afternoon, because he doesn't work most Fridays. Machine learning that's built into a payroll application could help the app learn the individual working habits of each employee. Having learned this specific pattern, the app could ask him if he meant to fill out the time card when he goes to log out of the system Thursday. There's no policy there: It's a behavior pattern that machine learning can pick up on.

A churn prediction system guides telecom service providers to reduce revenue loss. Development of a churn prediction system for a telecom industry is a challenging task, mainly due to size of the data, high dimensional features, and imbalanced distribution of the data. In this paper, we focus on a novel solution to the inherent problems of churn prediction, using the concept of Transfer Learning (TL) and Ensemble-based Meta-Classification. The proposed method TL-DeepE is applied in two stages. The first stage employs TL by fine tuning multiple pre-trained Deep Convolution Neural Networks (CNNs). Telecom datasets are in vector form, which is converted into 2D images because Deep CNNs have high learning capacity on images. In the second stage, predictions from these Deep CNNs are appended to the original feature vector and thus are used to build a final feature vector for the high-level Genetic Programming and AdaBoost based ensemble classifier. Thus, the experiments are conducted using various CNNs as base classifiers with the contribution of high-level GP-AdaBoost ensemble classifier, and the results achieved are as an average of the outcomes. By using 10-fold cross-validation, the performance of the proposed TL-DeepE system is compared with existing techniques, for two standard telecommunication datasets; Orange and Cell2cell. In experimental result, the prediction accuracy for Orange and Cell2cell datasets were as 75.4% and 68.2% and a score of the area under the curve as 0.83 and 0.74, respectively.

British telecommunications provider BT tapped SevOne's Data Platform for performance management. The provider will leverage SevOne to provide monitoring and reporting capabilities to its service teams to resolve issues on customer networks. The SevOne Data Platform is a cloud-based platform that collects heterogenous raw data and turns it into insights. It provides a collection and analysis of infrastructure and network metrics by integrating this analysis with flow, log, and user experience data on the platform. The platform is meant to help enterprises maintain visibility over technologies like NFV, SDN, and SD-WAN.