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.

Huawei has stated that a dispute with T-Mobile was settled in 2017 after a report said US authorities had opened a criminal investigation into the Chinese telecommunications company. The US justice department (DoJ) was, according to the Wall Street Journal which cited anonymous sources, in the "advanced" stages of a criminal inquiry that could result in an indictment of Huawei. The newspaper said the DoJ was looking into allegations of theft of trade secrets from Huawei's US business partners, including a T-Mobile robotic device used to test smartphones. Huawei and the DoJ declined to comment directly on the report. Huawei said: "Huawei and T-Mobile settled their disputes in 2017 following a US jury verdict finding neither damage, unjust enrichment nor wilful and malicious conduct by Huawei in T-Mobile's trade secret claim."

WASHINGTON - U.S. authorities are in the "advanced" stages of a criminal probe that could result in an indictment of Chinese technology giant Huawei, a report said Wednesday. The Wall Street Journal, citing anonymous sources, said the Justice Department is looking into allegations of theft involving trade secrets from Huawei's U.S. business partners, including a T-Mobile robotic device used to test smartphones. The Justice Department declined to comment on the report and Huawei did not respond to a request for comment. The move would further escalate tensions between the U.S. and China after the arrest last year in Canada of Huawei's chief financial officer Meng Wanzhou, who is the daughter of the company's founder and remains under house arrest, awaiting proceedings. The Meng case has inflamed U.S.-China and Canada-China relations.

Artificial intelligence (AI) is in vogue. It's almost impossible to read an article in any media outlet that doesn't mention AI and the possibility it will reshape the world in which we live. In fact, according to research conducted by AT&T, AI has the potential to double GDP growth across geographies by 2035. Consumers are already interacting with a variety of low-level AI assistants, such as Siri, Cortana, and Alexa. With respect to the telecom sector, AI – supported by machine learning (ML) – is fundamental to controlling and operating communications networks of the future.