Imagine you were traveling on business and you just arrived into town and had an emergency. Your luggage was sent to the wrong city or you were late for a critical meeting and you needed directions to the convention center. The check-in counter has a dozen guests waiting and the concierge is busy. Well, you may be in luck, because a four-foot robot with an open tablet computer and no waiting line is standing in the corner, and just may be able to come to your assistance. Pepper is an intelligent assistant that costs a bit more than a smart speaker, and can engage you in ways that go beyond ordering pizza or playing your favorite Top 40 tunes on the radio.

Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big data analytics and machine learning, this work contributes in three ways. First, we utilize the call detail records (CDR) data to detect anomalies in the network. For authentication and verification of anomalies, we use k-means clustering, an unsupervised machine learning algorithm. Through effective detection of anomalies, we can proceed to suitable design for resource distribution as well as fault detection and avoidance. Second, we prepare anomaly-free data by removing anomalous activities and train a neural network model. By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data. Lastly, we use an autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Through simple visualization, we show that anomaly free data better generalizes the learning models and performs better on prediction task.

A group of computer vision researchers from ETH Zurich want to do their bit to enhance AI development on smartphones. To wit: They've created a benchmark system for assessing the performance of several major neural network architectures used for common AI tasks. They're hoping it will be useful to other AI researchers but also to chipmakers (by helping them get competitive insights); Android developers (to see how fast their AI models will run on different devices); and, well, to phone nerds -- such as by showing whether or not a particular device contains the necessary drivers for AI accelerators. The app, called AI Benchmark, is available for download on Google Play and can run on any device with Android 4.1 or higher -- generating a score the researchers describe as a "final verdict" of the device's AI performance. AI tasks being assessed by their benchmark system include image classification, face recognition, image deblurring, image super-resolution, photo enhancement or segmentation.

A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. PCNet takes the channel coefficients as input and outputs the transmit power of all users. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. Observing that a single PCNet does not globally outperform the existing solutions, we further propose ePCNet, a network ensemble with multiple PCNets trained independently. Simulation results show that for the standard symmetric multi-user Gaussian interference channel, ePCNet can outperform all state-of-the-art power control methods by 1.2%-4.6% under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.

Every decade or so, a new generation of telecom network technology comes along that promises more speed, more capacity, better quality and new uses for customers. With each generation, network operators invest capital to upgrade their infrastructure, with the firm belief that doing so will lead to happier customers and reinvigorated revenues and profits. This formulation has been true ever since the early days of cell phone service in the 1980s; it has held up through 2G in the 1990s, 3G in the 2000s and 4G in the 2010s. But this time around, something has changed. When it comes to the next generation, 5G, some telecom executives seem to have lost their faith in the power of technology.

Rose is a'yuppie' with an'unorthodox family'. While carrying out research for this piece at a startup workspace in London, I procrastinated by asking Rose to tell me the meaning of life. 'There is no meaning, life is just to be,' she responds. Nothing strange there, that would probably have been my response to such an exhausted question. But, there is a difference between Rose and I.

The internet of things has evolved from just connecting and transferring data between devices like sensors, cameras, and thermostats to making these devices smarter with decision-making capabilities. Thanks to machine learning (ML) and artificial intelligence (AI) technologies, that help these connected edge devices perform faster, in smarter ways. Artificial intelligence plays a significant role in helping users analyze myriad of data generated by sensors and act upon them in a manner that is beneficial to users in different ways, such as environmental monitoring, weather analysis, predicting equipment failure in industries, disease prediction, etc. Machine learning and neural networks as parts of the AI technology help detect anomalies and patterns of data generated by sensors and devices, which help in extracting better insights for intelligent decision-making. AI-enabled IoT edge devices also help companies to increase operational efficiency and reduce downtimes, giving a competitive edge to business performance. Let us understand how AI empowers smart and powerful devices on the network edge.

CSG, a business support solutions (BSS) provider said that telecom carriers are increasingly leveraging the cloud to bring down the recurring operational costs, and with India's top service provider Bharti Airtel as one of the telcos to deploy revenue management platform, the US-headquartered company feels that the IoT and AI would fundamentally the change the way we live, work and play. How have you been supporting businesses to digitally transform? Almost every industry is faced with digital disruption and the need to transform to survive and thrive. Among our primary client base of communications service providers, digital transformation encompasses every aspect of their business, from rolling out new 5G networks to launching new services designed to attract consumers on-the-go. CSG supports the digital transformation of companies in ways such as investments in our solution portfolio that enable our customers to meet these increased demands, and through the deep expertise of our people across digital strategy, processes, and technology domains.

After having tested the waters with artificial intelligence in its top-end devices, Huawei will now open up its platform for developers to exploit it to the fullest. "Developers need to understand how AI works so they can build those capabilities into their apps. For us, the biggest challenge is natural interaction, and direct service access, when it comes to AI," Huawei's director of AI James Lu told indianexpress.com The telecom giant and smartphone manufacturer plans to hold a global developer conference on the lines of Apple's WWDC soon and hopes to rope in more developers, who will use the company's Application Programming Interface (APIs) in their apps. However, this seems to the natural progression of what has been a clear focus on AI for Huawei.

Huawei Consumer Business Group (CBG), the global smartphone giant, has announced the launch of its new HUAWEI nova 3 and HUAWEI nova 3i in the UAE. Both smartphones are powered by Artificial Intelligence (AI) in its overall functioning and camera features. With dual front camera (24MP 2MP), users can expect the best-in-technology'selfie' that a smartphone can offer. HUAWEI nova 3 series was initially designed with the younger and trendier consumers in mind, for whom a smartphone is not just part of their lifestyle but also represents their personal style. The series is designed to offer an AI-enhanced lifestyle, which includes a huge focus on an outstanding selfie experience with its AI beautification features and front camera that allows one to capture AI selfies.