Huawei today launched the world's first AI-Native database GaussDB, and the high-performance distributed storage FusionStorage 8.0. Its aim is to activate intelligence in data to help enterprises embrace intelligence. Huawei is also working with its customers and partners to innovate and build a data industry ecosystem that features openness, collaboration, and shared success. The ultimate goal is to make intelligent industries a reality. Humanity will enter the intelligent world in two to three decades.

This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops. We present experimental measurement results for three AI-aaS applications deployed on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders; 2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway segment classification in smart transportation.

We propose networkmetrics, a new data-driven approach for monitoring, troubleshooting and understanding communication networks using multivariate analysis. Networkmetric models are powerful machine-learning tools to interpret and interact with data collected from a network. In this paper, we illustrate the application of Multivariate Big Data Analysis (MBDA), a recently proposed networkmetric method with application to Big Data sets. We use MBDA for the detection and troubleshooting of network problems in a campus-wide Wi-Fi network. Data includes a seven-year trace (from 2012 to 2018) of the network's most recent activity, with approximately 3,000 distinct access points, 40,000 authenticated users, and 600,000 distinct Wi-Fi stations. This is the longest and largest Wi-Fi trace known to date. To analyze this data, we propose learning and visualization procedures that extend MBDA. These procedures result in a methodology that allows network analysts to identify problems and diagnose and troubleshoot them, optimizing the network performance. In the paper, we go through the entire workflow of the approach, illustrating its application in detail and discussing processing times for parallel hardware.

Products/Services Visa agreed to acquire the token and electronic ticketing business of Rambus for $75 million in cash. The business involved is part of the Smart Card Software subsidiary of Rambus. It includes the former Bell ID mobile-payment businesses and the Ecebs smart-ticketing systems for transit providers. Meanwhile, Rambus expanded its CryptoManager Root of Trust product line. "Security is a mission-critical imperative for SoC designs serving virtually every application space," Neeraj Paliwal, vice president of products, cryptography at Rambus, said in a statement.

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

In this paper, the trajectory optimization problem for a multi-aerial base station (ABS) communication network is investigated. The objective is to find the trajectory of the ABSs so that the sum-rate of the users served by each ABS is maximized. To reach this goal, along with the optimal trajectory design, optimal power and sub-channel allocation is also of great importance to support the users with the highest possible data rates. To solve this complicated problem, we divide it into two sub-problems: ABS trajectory optimization sub-problem, and joint power and sub-channel assignment sub-problem. Then, based on the Q-learning method, we develop a distributed algorithm which solves these sub-problems efficiently, and does not need significant amount of information exchange between the ABSs and the core network. Simulation results show that although Q-learning is a model-free reinforcement learning technique, it has a remarkable capability to train the ABSs to optimize their trajectories based on the received reward signals, which carry decent information from the topology of the network.

Telecom companies are currently scrambling to implement fifth-generation cellular network technology. But the world of 5G is a world where all objects are wired and constantly communicating data to one another. The dark truth is that the development of 5G networks and the various networked products that they will give rise to in the global smart city infrastructure, represent the greatest threat to freedom in the history of humanity. STEVE MOLLONKOPF: 5G will upgrade the human experience at home and across industries as we connect virtually everything. By 2020, analysts estimate that there will be more than 20 billion installed IoT devices around the world, generating massive amounts of data. With access to this kind of information, industries of all kinds will be able to reach new levels of efficiency as they add products, services, and capabilities. As you may have heard by now, telecom companies are currently scrambling to implement fifth-generation cellular network technology.

Try the Loan Repayment Prediction machine learning demo to see it in action. Speak to a deep learning model to see if it understands you. Try out an easy-to-understand classification algorithm. See how a deep learning model can identify a thousand different objects in an image. Handwrite a number and let the ML model guess what it is.

There's simply no Plan B for submarine cables, which are the size of a common garden hose and are situated in the abysses of oceans the world over. This means that as an industry, we must continually innovate, adopt, and adapt to ensure these submerged engineering marvels continually evolve to meet the ever-changing demands from end-users, both humans and machines. Spatial Division Multiplexing (SDM) submarine cables, Open Cables, Shannon's Limit, and the increasing adoption of Artificial Intelligence and Machine Learning are hot topics across the submarine network industry. Increasingly, technologies borne in data centers and terrestrial networks are finding their way into submarine networks, and that's a good thing. The network can and should be viewed from end-to-end along the entire service path, overland and undersea, and increasingly, right into data centers.

Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in RoFSO systems. The proposed problem is a weighted total capacity maximization problem with two constraints of total power limitation and eye safety concern. The model-based Stochastic Dual Gradient algorithm is presented first, which solves the problem exactly by exploiting the null duality gap. The model-free Primal-Dual Deep Learning algorithm is then developed to learn and optimize the power allocation policy with Deep Neural Network (DNN) parametrization, which can be utilized without any knowledge of system models. Numerical simulations are performed to exhibit significant performance of our algorithms compared to the average equal power allocation.