Edgewater Wireless aera access point products will offer A.I.R. as a standard feature to deliver the world's first intelligent, real-time device tracking within any network coverage zone. A.I.R. uses an in-real-time artificial intelligence engine to learn from and adapt to any network environment or coverage zone with no setup required by the network operator. A.I.R. relies on patent-pending advanced algorithms to detect, range and track any WiFi-enabled device without the expense of additional monitoring hardware and no additional demands on network throughput or performance. A.I.R.'s intelligence engine automatically learns and adapts to the physical location or floor plan of any WiFi environment – with or without line of sight, and is accurate to within 6ft. Combined with the advanced features of Edgewater Wireless multi-channel WiFi3 including real-time, integrated Spectral Surveillance Architecture, Edgewater Wireless aera access point products with A.I.R. will give network operators accurate locationing of devices and their users physically moving through the network.

The market for AI is so overhyped, virtually anybody looking for a fast buck can re-package an old abacus and sell it as a "machine learning" platform. Misinformed people buy the idea and the mounting frustration makes it extremely difficult for legitimate AI companies to get their message above the din. To successfully stand out in a crowded market, creative marketers have found it critical to create unconventional educational content and enlist the support of credible B2B influencers in their space. In the ever-shifting AI solutions ecosystem, even expert-led and well-thought out marketing campaigns can -- and often -- fail. To reduce costs and save time, marketers of AI products need to learn at the onset, or as early as possible, exactly which specific method doesn't deliver the results for their solutions.

Through 5G related research, the door to the so called millimeter wave (mmWave) frequencies reopened, unlocking a huge chunk of untapped bandwidth [1]. With mmWaves, the propagation changes dramatically: the resulting radiation has severe path loss properties and reflects on most visible obstacles [2]. To counteract the aforementioned characteristics, beamforming (BF) is usually employed in systems containing multiple-input and multiple-output (MIMO) antennas, enabling steerable and focused radiation patterns. With that recent focus on mmWaves, new positioning systems based on these frequencies were proposed [3]. The achievable accuracy in controlled conditions is remarkable, with sub-meter accuracy in indoor [4] and ultra-dense line-ofsight (LOS) outdoor scenarios [5]. Nevertheless, in order to be useful in outdoor scenarios, a mmWave positioning system must also be able to deal with devices in non-line-of-sight (NLOS) locations. The works developed in [6]-[9] attempt to address this concern, being capable of locating devices in both LOS and NLOS situations. The method in [6] applies compressed sensing on information gathered from static listeners, while in [7] multiple access points are used to create a location fingerprint database of received powers and angles-of-arrival (AoA). In [8], the authors use multiple BF transmissions and an iterative algorithm to estimate the position and orientation of the device.

"There is only one boss. And he can fire everybody in the company from the chairman on down, simply by spending his money somewhere else." -Sam Walton Companies typically spend most of their effort and resources on customer acquisition, even though the cost of retaining an existing customer is 5 times lower than acquiring a new one. Customer retention is a measure of how many of your customers continue to buy from you over time and are therefore loyal to your brand. Churn, sometimes known as customer attrition, is at the opposite end of the spectrum, i.e. how many customers stop buying from your company. Industries that use a subscription-based business model have traditionally focused more on churn than others. Banks, telecom companies, insurance firms, energy services companies, are among the many types of businesses that often use customer attrition analysis and customer churn rates as one of their key business metrics.

SK Telecom is considered one of most progressive operators when it comes to 5G development. Last year Juniper Research ranked the operator No. 1 on its list of "most promising" 5G operators for its extensive time in development; the breadth and value of the operators' 5G partnerships; and its progress in 5G network testing. So it is not particularly surprising that the South Korean operator is already working on ways to use artificial intelligence (AI) to make its 5G network more efficient. The company realizes without AI there will be a lot manual operations necessary to constantly change the network parameters and settings and to adjust capacity. "We want to talk about how to efficiently operate 5G infrastructure using AI," said Haesung Park, senior manager of the ICT R&D Center, network technology R&D center, access network lab at SK Telecom. Park will be speaking at the New Horizons Symposium in Austin, Texas, May 16-17.

Machine learning in telecom can help network operators improve services, increase profits, as well as reduce customer churn. As the number of smartphones users is increasing, the chances for the telecommunications industry to increase sales is always on the rise. As the market seems to move ahead every day, telecom providers look to improve services to ensure customer retention. Mapping key trends and focusing on how their strategies work are some of the challenges that a telecommunication provider currently faces. Apart from merely mapping a company's strategies and fixing towers, mapping competitor's strategies and social media help businesses to achieve a broader base to reach out to their customers.

The telecommunications services industry is one of the fastest growing industries in the world and is already using machine learning (ML), artificial intelligence (AI) and Internet of Things (IoT) to enhance their customer service. According to a study by Transparency Market Research (TMR), the global market for artificial intelligence is estimated to post an impressive 36.1% CAGR between 2016 and 2024, rising to a valuation of US$3,061.35 billion by the end of 2024 from US$126.14 billion in 2015. As the market is seeing a rapid growth in Europe, North America and Latin America, the telecom services spending in Asia-Pacific region was projected to grow by around 2.06% in 2016 compared to 2015. Technavio's market research analysts predict that the global telecom IoT market to grow steadily and post an impressive CAGR of more than 42% by 2020. Even as IoT is set to revolutionize industries in its own way, it is worth noting that IoT would produce terabytes, petabytes and exabytes of big data.

David Morton, director of networks and telecommunications for the University of Washington, manages a wireless network for three college campuses, three hospital sites and 30 additional clinics; several research locations; and up to 85,000 users connecting up to 200,000 devices. If anyone asked, he could pinpoint a single device and single user and know whether that person was standing inside a building or was walking outside on the way to class. What provides Morton such deep visibility are network analytics tools driven by machine learning and artificial intelligence principles. Users of the university's network expect it to always work, no matter how much usage grows, Morton said. Applying new analytics tools provides a much better look into network performance and helps the university keep up with demands.

The Office of the U.S. Trade Representative has issued a list targeting 1,300 Chinese products, including industrial robots and telecommunications equipment. But the proposed tariffs wouldn't take effect before a public comment period ends May 11.

Call scoring is a crucial part of a call center quality assurance. It enables organizations to fine-tune the workflow so that call center agents can do their job faster and more efficiently, and also avoid meaningless routine work. With call center productivity in mind, our R&D team has been working on automated call scoring for the last couple of months. They've come up with an algorithm that processes all incoming calls and divides them into suspicious and neutral. All calls that are defined as suspicious go directly to a quality assurance team.