Loyalty defines the strength of your customer relationships. High-loyalty customers can have a higher average spend and can have more repeat purchases than low-loyalty customers. For example, research has shown loyalty program customers spend 6% more than non-loyalty customers. Not only that, but loyal customers are often your biggest fans; these are the customers that tag your brand on social media, use your hashtags, and tell their friends about your products. In some cases, a loyal customer can be more valuable than a new customer who makes a single purchase, which means the more loyal customers you have--and the more loyal they are--the better.

We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT). The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications. Benchmarking on a 3rd Generation Partnership Project (3GPP) urban microcell (UMi) channel model with random drops of users against a state-of-the-art baseline shows that the proposed method enables up to 8 dB gains in false negative rate (FNR) as well as significant gains in false positive rate (FPR) and ToA and CFO estimation accuracy. Moreover, our simulations indicate that the proposed algorithm enables gains over a wide range of channel conditions, CFOs, and transmission probabilities. The introduced synchronization method operates at the base station (BS) and, therefore, introduces no additional complexity on the user devices. It could lead to an extension of battery lifetime by reducing the preamble length or the transmit power. Our code is available at: https://github.com/NVlabs/nprach_synch/.

Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today's optical neural networks are mainly developed to perform optical inference after in silico training on digital simulators. However, various physical imperfections that cannot be accurately modeled may lead to the notorious "reality gap" between the digital simulator and the physical system. To address this challenge, we demonstrate hybrid training of optical neural networks where the weight matrix is trained with neuron activation functions computed optically via forward propagation through the network. We examine the efficacy of hybrid training with three different networks: an optical linear classifier, a hybrid opto-electronic network, and a complex-valued optical network. We perform a study comparative to in silico training, and our results show that hybrid training is robust against different kinds of static noise. Our platform-agnostic hybrid training scheme can be applied to a wide variety of optical neural networks, and this work paves the way towards advanced all-optical training in machine intelligence. Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Machine learning powered by artificial neural networks has reshaped the landscape in many different areas over the last decade.

The best 5G startups are making groundbreaking innovations in wireless technology and communications. This is attributable to the importance of 5G in the world today so much that IHS Markit projects that 5G would increase the global GDP by $13.2 trillion by 2035. The benefits of 5G range from a dramatically fast network to lower latency, resulting in innovative solutions such as better connectivity for individual users and businesses. Here is a closer look at the top 10 5G startup companies. Some of these technologies include artificial intelligence (AI), the Internet of Things (IoT), and augmented reality (AR).

Data scientists have had to put up with sluggish machine learning and challenges in providing truly predictive analytics. But with no other options, moving data from a database to the machine learning software and then back to the database has been the only option these data scientists have had until recently. In-database machine learning is where data analytics is headed and it's making a huge difference in our ability to provide truly predictive analytics and make data actionable at the time we receive it. Let's look at some ways that various industries are applying in-database machine learning and the impact it is having. In-database machine learning is ideal for a variety of industries and it's the future of data analytics.

Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, configurations, traffic flows). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real networks. As a result, such models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in its unprecedented generalization capabilities when applied to other networks and configurations unseen during training, which is a critical feature for achieving practical data-driven solutions for networking. This article comprises a brief tutorial on GNNs and their possible applications to communication networks. To showcase the potential of this technology, we present two use cases with state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, we delve into the key open challenges and opportunities yet to be explored in this novel research area.

In this position paper, we discuss the critical need for integrating zero trust (ZT) principles into next-generation communication networks (5G/6G). We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components. While network virtualization, software-defined networking (SDN), and service-based architectures (SBA) are key enablers of 5G networks, operating in an untrusted environment has also become a key feature of the networks. Further, seamless connectivity to a high volume of devices has broadened the attack surface on information infrastructure. Network assurance in a dynamic untrusted environment calls for revolutionary architectures beyond existing static security frameworks. To the best of our knowledge, this is the first position paper that presents the architectural concept design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks. We introduce key ZT principles as real-time Monitoring of the security state of network assets, Evaluating the risk of individual access requests, and Deciding on access authorization using a dynamic trust algorithm, called MED components. To ensure ease of integration, the envisioned architecture adopts an SBA-based design, similar to the 3GPP specification of 5G networks, by leveraging the open radio access network (O-RAN) architecture with appropriate real-time engines and network interfaces for collecting necessary machine learning data. Therefore, this work provides novel research directions to design machine learning based components that contribute towards i-ZTA for the future 5G/6G networks.

Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next. The infusion of artificial intelligence (AI) into everything is literally changing our lives on a day-to-day basis. People use speech interfaces, purchase items based on recommendations, and sign into phones with facial recognition routinely because it makes their lives easier. However, one of the few industries that has yet to jump on the AI bandwagon is the telecom sector. This is a massive opportunity because AI has the potential to transform telco operations and improve efficiency in areas such as call center automation and much more.

Service providers have to deal with increasing complexity in their networks and services, amid what have been highly manual and ponderous service assurance deployments that almost inevitably result in visibility gaps and too much data that needs highly skilled human interpretation to resolve issues. As Charles Thompson, VP of product management at Spirent Communications with a history of more than two decades in service assurance, put it, "Service assurance for operators has traditionally been very burdensome. It's been a very big deployment, [with] multi-week, multi-month services engagement to do, to get things up and running." There's typically a highly manual process of deploying software or hardware-based test agents: Figuring out where you want them, configuring them, pushing them out and often still being left with "massive visibility gaps" as new data centers and other new network elements come online or, in a software-defined networking world, as virtual machines spin up and down and routing changes in response Spirent Communications aims to simplify and automate with the launch if a new service assurance solution, Vantage. Spirent is "hyperfocused… on the mobile core" with the Vantage release, said Thompson, and invested in the solution with an eye toward how it expects networks to continue evolving over the next three to five years: A continuing push for 5G and the use of open, cloud-native and containerized infrastructure and multi-vendor environments, combined with massive data demand, and the need to assure new use cases, features and services that have not existed before, like Ultra-Reliable Low Latency Communication (URLLC).

Traditional approaches to classify the political bias of news articles have failed to generate accurate, generalizable results. Existing networks premised on CNNs and DNNs lack a model to identify and extrapolate subtle indicators of bias like word choice, context, and presentation. In this paper, we propose a network architecture that achieves human-level accuracy in assigning bias classifications to articles. The underlying model is based on a novel Mesh Neural Network (MNN),this structure enables feedback and feedforward synaptic connections between any two neurons in the mesh. The MNN ontains six network configurations that utilize Bernoulli based random sampling, pre-trained DNNs, and a network modelled after the C. Elegans nematode. The model is trained on over ten-thousand articles scraped from AllSides.com which are labelled to indicate political bias. The parameters of the network are then evolved using a genetic algorithm suited to the feedback neural structure. Finally, the best performing model is applied to five popular news sources in the United States over a fifty-day trial to quantify political biases in the articles they display. We hope our project can spur research into biological solutions for NLP tasks and provide accurate tools for citizens to understand subtle biases in the articles they consume.