Does your mopping robot or your food processor have names? Do you ascribe a character to them? Unless you're a robot yourself, you probably do. The entire history of engineering has been about making the most human-like machines possible. We humans are hardwired to process social interactions, so we subconsciously expect machines to be at least somewhat human.

Huawei's latest phone is something rather special. The Mate 20 Pro packs more cutting-edge technology into it than any other, but unlike most bleeding-edge devices it makes for a great experience. It looks like an iPhone XS mated with a Samsung Galaxy Note 9, with a huge curved 6.39in OLED display and big notch in the top. The screen is great and hides a good pressure-sensitive in-display fingerprint scanner. The curved sides and narrow width makes the massive screen manageable.

Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and investigated separately. Moreover, the accuracy of the previously proposed methods still needs to be improved for heterogenous dynamic optical networks. In this paper, we present the application of machine learning (ML) in NLI modeling and monitoring. In particular, we first propose to use ML approaches to calibrate the errors of current fiber nonlinearity models. The Gaussian-noise (GN) model is used as an illustrative example, and significant improvement is demonstrated with the aid of an artificial neural network (ANN). Further, we propose to use ML to combine the modeling and monitoring schemes for a better estimation of NLI variance. Extensive simulations with 1603 links are conducted to evaluate and analyze the performance of various schemes, and the superior performance of the ML-aided combination of modeling and monitoring is demonstrated.

ABSTRACT Massive multiple-input multiple-output antenna systems, millimeter wave communications, and ultra-dense networks have been widely perceived as the three key enablers that facilitate the development and deployment of 5G systems. This article discusses the intelligent agent that combines sensing, learning, and optimizing to facilitate these enablers. We present a flexible, rapidly deployable, and cross-layer artificial intelligence (AI)-based framework to enable the imminent and future demands on 5G and beyond. We present example AIenabled 5G use cases that accommodate important 5G-specific capabilities and discuss the value of AI for enabling network evolution. I. Introduction Does 5G cellular communications technology in the age of intelligence really look like the Thomas W. Lawson Schooner (the last of the large cargo sailing ships) of modern times? However, concerns are raised whether this is a revolutionary leap from today's wireless communications or a simple piling upof less innovative wireless functionalities. The International TelecommunicationUnion (ITU) classifies 5G into three categories of usage scenarios: enhanced mobile broadband (eMBB),massive machine-type communication (mMTC), and ultra-reliable and low latency communication (URLLC) to account for more diverse services and resourcehungry applications.eMBB is a service category that addresses bandwidth-hungryapplications, such as massive video streaming and virtual/augmented reality (VR/AR). URLLC is a service category that supports latency sensitive services including autonomous driving,drones and the tactile Internet.

SoftBank Group Corp. is introducing a new robot that, unlike the talkative Pepper, skips the chit chat and just mops the floor. Whiz, an autonomous floor-cleaning machine for businesses, will go on sale in Japan in February, the company announced Monday. The 32-kg machine is powered by self-driving software and an array of sensors from Brain Corp., a San Diego-based startup that is part of SoftBank's $100 billion Vision Fund. It will be available for rent for ¥25,000 a month. Pepper, SoftBank's first foray into robotics, was marketed as a companion in the home and as a sales assistant on the shop floor.

When U.N. member states unanimously adopted the 2030 Agenda in 2015, the narrative around global development embraced a new paradigm of sustainability and inclusion--of planetary stewardship alongside economic progress, and inclusive distribution of income. This comprehensive agenda--merging social, economic and environmental dimensions of sustainability--is not supported by current modes of data collection and data analysis, so the report of the High-Level Panel on the post-2015 development agenda called for a "data revolution" to empower people through access to information.1 Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process. Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.

With the increasing number of base stations (BSs) and network densification in 5G, interference management using link scheduling and power control are vital for better utilization of radio resources. However, the complexity of solving link scheduling and the power control problem grows exponentially with the number of BS. Due to high computation time, previous methods are useful for research purposes but impractical for real time usage. In this paper we propose to use deep neural networks (DNNs) to approximate optimal link scheduling and power control for the case with multiple small cells. A deep Q-network (DQN) estimates a suitable schedule, then a DNN allocates power for the corresponding schedule. Simulation results show that the proposed method achieves over five orders of magnitude speed-up with less than nine percent performance loss, making real time usage practical.

Smartphone maker Huawei is planning on taking its popular voice assistant outside of China and competing with Amazon, Google and Apple internationally, according to a report from CNBC. The Chinese technology firm is apparently working on a version of its voice assistant Xiaoyi that will work outside of China, though it hasn't revealed what languages the AI will speak, nor when it will be available for other markets. Prior to developing Xiaoyi, Huawei was reliant on third-party voice assistants including Google Assistant and Amazon Alexa. The company's first smart speaker, the AI Cube, relied on Alexa. Huawei has been building on its own voice assistant in recent months. Its newest device, the AI Speaker, uses Xiaoyi, as does its latest line of smartphones.

Huawei has unveiled its new smart cities digital platform utilising artificial intelligence (AI) and Internet of Things (IoT) capabilities, which it said could be used across smart public safety, environmental protection, transportation, government, education, and agriculture. Huawei's AI Digital Platform connects what it calls the brain, or command centre; the central nervous system, or network; and the peripheral nervous system, made up of sensors across a city. "Just like an operating system, the platform is compatible with different city sensors, creates a city digital twin, and supports diverse city applications," Huawei Enterprise Business Group VP Ma Yue said. The smart cities digital platform combines AI, IoT, big data, a geographic information system, video, cloud, converged communications, and security. "Huawei has also developed a middleware platform to provide services to software application partners. This is designed to help application partners quickly develop upper-layer applications to accelerate transformation and innovation in city management, city services, and industry development," the Chinese networking giant added.

Space may be the final frontier, but it continues to pose myriad technical challenges as commercial and government-driven space investment continues. One of those challenges is developing more effective space-based communication systems for the increasing number of satellites and spacecrafts that need to interact with one another in the void. A team of researchers has developed an algorithm to enable cognitive radio functions on satellite communications systems to adapt themselves autonomously. Current space communication systems deploy radio-resource selection algorithms, but they are rudimentary and work with a pre-programmed look-up table. Furthermore, they have little flexibility regarding the various parameters for the performance goals the system needs to achieve.