KT Corporation and Portugal-based WeDo Technologies have signed a Cooperation Agreement for AI-FMS (Artificial Intelligence based Fraud Management System) development and sales. KT's Deep Learning-based Artificial Intelligence (AI) module has been implemented and tested on WeDo's RAID FMS system. This AI module, trained with KT Big Data, has showed strong results for fraud detection and prevention, and has reportedly proved to be effective for a number of fraud use cases, with a high degree of accuracy. KT and WeDo plan to supply the AI-based International Revenue Share Fraud (AI-IRSF) module with the RAID platform to communication service providers (CSPs) by the end of 2019. KT's DL (Deep Learning) based AI module has been implemented and tested on WeDo's RAID FMS system.

According to an analysis released by OpenAI, the demand for computing power has increased by more than 300,000 times in the six years after 2012. It grows by about factor of 10 each year, far exceeding the pace set by Moore's Law. As a latecomer to artificial intelligence (AI), Huawei boldly proposed to provide the industry with computing power that is accessible, affordable, and easy to use, to meet the exponentially increasing demand for AI computing. Now, one year after the AI strategy was proposed, has Huawei found a way to address the computing power challenges? In the late 17th century, the British mining industry, particularly the coal mine, was developed to a considerable scale.

Metis Data Science Bootcamp has been rigorous, and this is my third project. The goal is to predict customer churn in a Telecommunication company. Customer attrition, customer turnover, or customer defection -- they all refer to the loss of clients or customers, ie, churn. This can be due to voluntary reasons (by choice) or involuntary reasons (for example relocation). In this article, we will explore 8 predictive analytic models to assess customers' propensity or risk to churn.

Editor's Note: Andrea Frittoli and Kyra Wulffert are presenting their talk"Machine Learning for Continuous Integration" at ODSC 2019 Europe. As more applications move to a DevOps model with CI/CD pipelines, the testing required for this development model to work inevitably generates lots of data. This is also true for large open-source projects, that may see millions of tests executed on a daily basis. The data produced by such CI systems contains information about several aspects of the continuous testing system; engineers with specific domain experience usually parse such data on a daily basis in an effort to maintain the system running smoothly. After years of experience in the field, we wanted to investigate if machine learning could help us extract valuable insights from CI data with minimal human intervention.

We are at the threshold of a fully connected, intelligent world, where innovation happens in the blink of an eye, and the impact on every person, home, and organization is nothing short of profound. An intelligent world is right around the corner, with wide application of AI and 5G across all industries. More and more companies have come to realize the value of AI and 5G, and with eagerness to leverage them, are now focusing on how technologies can accelerate their migration to the cloud.

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case $R^{2}$ = 0.878). Also, we present several use-cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.

Spiro Technologies has added capabilities to its CRM platform to help companies extract notes and capture action items from calls, emails, or text messages with sales contacts. Based on advanced natural language processing (NLP), Spiro uses this data to automatically create reminders for salespeople. For example, if a prospect indicates during a phone call, email, or text that he wants to be contacted at the beginning of the new year, the Spiro platform will automatically generate a reminder about the communication as well as what it judges to be the preferred communication method for the first business day the following January. According to the company, the new capability enables Spiro to make more precise judgments on an organization's pipeline, giving sales leaders a more accurate forecast. For example, during a call if a salesperson says: "I'll make a note that you need more information on the implementation process," or suggests a date or a time for a follow-up, as mentioned earlier, the platform will include the pertinent details in follow-up communications to the salesperson.

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose a band switching approach based on machine learning that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g. 3.5 GHz) band and a millimeter wave band (e.g. 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5%.

Verizon's acquisition will include Jaunt's volumetric capture and machine learning technology. It's been a rocky couple of months for Jaunt XR. Late last year the software development startup conducted a series of layoffs to its staff as part of the companies transition from VR content to the development of AR technology with a focus on volumetric video capture. In November, the company began seeking buyers for its VR business and this past December company co-founder Arthur van Hoff announced his departure from the organization. Today, Jaunt announced the sale of all company assets to Verizon Communications Inc. as part of a new acquisition by the multimedia corporation.

The Square Kilometre Array (SKA) has a celestial ambition. The project aims to discover the origins of the universe through the lens of the world's largest radio telescope. The telescope will search outer space for signals travelling through the cosmos and could reveal how the first stars and galaxies were formed after the big bang and the nature of the mysterious force of dark energy. It may even answer the question that humans have pondered since the dawn of civilisation: are we alone in the universe? It will also provide the scientific foundations for more practical applications.