Leading Finnish telecommunications company Nokia has announced that it has bolstered its Customer Experience Index (CEI) with the unveiling of its latest Cognitive Analytics for Customer Insight software. Using machine learning and intelligent automation, the software will be able to provide transformational real time and personalized consumer experiences to business, IT and engineering companies. "Nokia CEI now taps advanced machine learning and deep learning algorithms co-developed with Nokia Bell Labs to provide new levels of prediction and automation capabilities to improve the subscriber experience," Nokia explains. "The algorithms optimize themselves over time, decreasing the time required for the initial tuning of the index from months to days, and delivering a far more accurate view of subscriber satisfaction." This will allow service providers to quickly identify issues, up to six times faster, and in turn prioritize improvements based on their unique circumstances, better delivering revenue-generating services.

Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-off between data exploration and exploitation plays a predominant role. This trade-off is of particular importance to several branches and applications of signal processing, such as data mining, statistical inference, multimedia indexing and wireless communications (to name but a few). With this in mind, the aim of this tutorial paper is to provide a gentle introduction to online optimization and learning algorithms that are asymptotically optimal in hindsight - i.e., they approach the performance of a virtual algorithm with unlimited computational power and full knowledge of the future, a property known as no-regret. Particular attention is devoted to identifying the algorithms' theoretical performance guarantees and to establish links with classic optimization paradigms (both static and stochastic). To allow a better understanding of this toolbox, we provide several examples throughout the tutorial ranging from metric learning to wireless resource allocation problems.

Instead of continuing to repurpose existing processors for smart home devices, Qualcomm has unveiled two new chipsets built specifically for IoT gadgets. The QCS605 and QCS603 are 10nm system-on-chips (SoCs) that will power things like 360-degree cameras, robot vacuums and smart displays from companies like Ricoh Theta and Kedacom. Qualcomm also launched a Vision Intelligence Platform today that provides frameworks like the company's AI Engine, and the Snapdragon Neural Processing Engine (NPE) to enhance on-device machine learning. It'll also offer SDKs for camera processing and computer vision so that companies who buy these processors can more easily create applications for their products. Some of the uses that the Vision Intelligence Platform will support include object detection, tracking, obstacle avoidance and facial recognition.

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.