Another area where we see the application of ConvNets is in the prevention of fraud, which is a big concern for telecom companies. In a bid to develop algorithms that detect early potential frauds and/or prevent them, deep learning techniques, especially ConvNets are being used to detect fraudsters in mobile communications. In a research paper, published in Science Direct, fraud datasets culled from customer details records (CDR) are used and learning features are extracted and classified to fraudulent and non-fraudulent events activity. The paper revealed how deep convolution neural networks surpassed other traditional machine learning algorithms such as random forest, support vector machines and gradient boosting classifier, especially in terms of accuracy.

Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of datacenter networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned and aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table, and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of reinforcement learning (RL) algorithms-the first of which is traditional reinforcement learning algorithm based while the other is deep reinforcement learning based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead, and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method given a fixed size flow table of 4KB.

5th generation networks are envisioned to provide seamless and ubiquitous connection to 1000-fold more devices and is believed to provide ultra-low latency and higher data rates up to tens of Gbps. Different technologies enabling these requirements are being developed including mmWave communications, Massive MIMO and beamforming, Device to Device (D2D) communications and Heterogeneous Networks. D2D communication is a promising technology to enable applications requiring high bandwidth such as online streaming and online gaming etc. It can also provide ultra- low latencies required for applications like vehicle to vehicle communication for autonomous driving. D2D communication can provide higher data rates with high energy efficiency and spectral efficiency compared to conventional communication. The performance benefits of D2D communication can be best achieved when D2D users reuses the spectrum being utilized by the conventional cellular users. This spectrum sharing in a multi-tier heterogeneous network will introduce complex interference among D2D users and cellular users which needs to be resolved. Motivated by limited number of surveys for interference mitigation and resource allocation in D2D enabled heterogeneous networks, we have surveyed different conventional and artificial intelligence based interference mitigation and resource allocation schemes developed in recent years. Our contribution lies in the analysis of conventional interference mitigation techniques and their shortcomings. Finally, the strengths of AI based techniques are determined and open research challenges deduced from the recent research are presented.

A.I. offers a precise navigational tool, he says, to find the right nuggets of data as more and more information pours into companies, a trend that's expected to accelerate with the widespread adoption of blur-fast 5G networks later this year. Handling data properly is critical because misusing it, or allowing it to be compromised, can badly damage a brand, he added. He spoke with Barron's Wednesday before delivering a speech on "Democratization of A.I." in San Francisco. Stine, who has been with AT&T nearly 40 years, won the newly created title of chief data officer a year ago. He's part of a wave of CDOs at Fortune 500 companies, where the position didn't exist a few years ago.

Successfully deploying conversational artificial intelligence (AI) is like no other digital business-process upgrade. In fact, it's not an IT upgrade in the conventional sense; conversational AI does nothing less than usher sophisticated robotics into the front office. The surest route to project failure would be taking this fact for granted. Where these cross-channel AI systems--designed to interact naturally and fluidly with internal users and/or customers in text or verbal conversation--are most like traditional business systems is in how short-sighted decisions can doom development and hobble future productivity. What should you keep in mind when deploying conversational AI?

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.

You might feel better if you confide your worries to someone--or something. So tell your woes to the Worry Capsule Tree, the brainchild of Xiran Yang & Hau Yuan, students of the Interactive Telecommunications Program at New York University's Tisch School of the Arts. This interactive sculpture will glow in response to your voice, and it can also save your concerns as text. After a set period has elapsed, you'll receive an email reminding you of your past woes--and ideally, this time capsule will allow you to revisit your past self.

Among the critical elements required to support expanding Internet of Things deployments are wider wireless pipes that consume less power. That's the promise of IoT networks supported by emerging fifth-generation, or 5G, wireless network rollouts that go well beyond mobile phones to support networks of connected devices. As wireless carriers jockey for position--including a proposed merger between T-Mobile and Sprint--emerging IoT applications such as low-power, wide area connections are forecast to grow at a more than 200-percent annual rate through 2021. Industry tracker IHS Markit predicts in an updated forecast released this week that emerging 5G-based connections will help boost machine-to-machine connections as well new IoT platforms and services. "In order to scale up IoT revenues, operators will need to pursue a broader IoT strategy that goes beyond connectivity into IoT platforms, vertical offerings and ecosystem orchestration," notes Julian Watson, senior IoT analyst at IHS. "By pre-integrating partner platforms, hardware and sensors into their IoT offerings, operators can make it easier for enterprises to plan and budget for IoT projects."

Earth right now is in the beginning stages of a radical shift, where us unreliable, distracted money loving human drivers are going to be replaced by artificial intelligence algorithms, that never need to rest and will work for free. This global transition to Driverless Vehicles is going to advance much faster than most people realize, spurred on by tech and auto companies ferociously competing against each other, and also given a huge helping hand by the rapidly approaching fifth generation of cellular mobile communications, also known as 5G. The dramatic increase in the speed of data will also pave the way for a whole new generation of features and methods for businesses to make money. These will include targeted in-car ads based on information relayed back to tech and automakers from the onboard sensors and vehicle analytics, greatly improved 3d Mapping capabilities, as well as crowd-sourced reporting on the road conditions and better real-time analysis on the vehicles performance. Right now there is massive infrastructure development underway by leading Telecom players such as Verizon, T-Mobile and AT&T, who are racing to implement the new generation of 5g services in places like Dallas, Houston, L.A and New York, which will begin initially with broadband capability by the end of this year, followed by the mobile version in 2019.

It should have been a great week for Huawei. Following the news that it had overtaken Apple as the No. 2 phone maker in the world, the company set the stage for the next generation of must-have phones with the unveiling of its Kirin 980 processor and the launch date for the highly anticipated Mate 20 Pro. But instead of a series of positive headlines about what's to come, Huawei's old phones were in the news for all the wrong reasons. It started with an AnandTech report that uncovered some major inconsistencies with benchmark results. Inside the latest version of software on the P20, P20 Pro, and Honor Play, Huawei was discovered gaming its scores by optimizing the system for certain benchmarking apps, most notably the popular 3DMark and GFXBench suites.