Automation is now carriers' top priority. Automation is necessary to operate 5G and needed to fully monetize new services enabled by NFV and SDN – required to scale for IoT and more. The market makers at the start of the Zero Touch journey and the future of Network & Service Management presented in Madrid at the second Zero Touch Automation Congress. Fresh approaches such as Artificial Intelligence and Machine Learning are going to revolutionize telecoms networking operations and the strategic shift is already underway, in this transformation of Network Operations, NFV Orchestration & Services Management to an automated future.

While consumers across ASEAN are excited about 5G's promise of faster home broadband speeds and mobile internet connections, it is the technology's low network latency and power consumption that offers a variety of opportunities for businesses. Globe Telecom in the Philippines rolled out Southeast Asia's first 5G broadband service in select areas of the country in June, partnering with telecommunications giant Huawei to give the world's largest community of social media users a home broadband service that is now the fastest in ASEAN. About 20 times faster than 4G, everything from instant high-definition movie streaming to cloud gaming will be less than seconds away once 5G is commercially available across the rest of the region in the next few years. Businesses, meanwhile, can unlock increased value from Internet of Things (IoT) applications, artificial intelligence (AI), advanced robotics and a multitude of other uses. A recent report from consultancy firm A.T. Kearney predicted that this increased value will be worth around US$147 billion for ASEAN businesses by 2025 – with US$81 billion of that additional revenue gained through the growing use of AI in industries such as trade, transport and financial services.

Every good love story has a moment in which the precious ingénue, blind to the complexities of the world, misinterprets the lover's move. Romeo, believing Juliet to be dead, poisons himself. The folly of love is not so much about what we do when we are flooded with feelings, but what can happen when we have incomplete data. This is perhaps why a crop of new apps have arrived, harnessing the powers of artificial intelligence, to offer relationship advice. One of them, Mei, is billed as a "relationship assistant."

In the ever onward march of modern technology, we humans have sometimes felt uncomfortable about the increasing automation of telecommunications – from machines calling us with recorded messages to switchboard operators replaced by numbered menus to choose from. However, we seem to be turning a corner, with many people now happy to communicate with devices such as chatbots. For example, a report from media data specialists Comscore which looked at the future of voice technology in the US, found that half of smartphone users engage with voice technology and messaging apps on their device, and one in three do so on a daily basis. According to Call Centre Helper investment in artificial intelligence (AI) technologies will increase by more than 300 per cent over the next year, and eight out of ten businesses have already implemented AI as a customer service solution, or are planning to do so by 2020. A blog by NewGenApps has looked at the top 11 ways that companies are now using AI in their business processes, and they say that "What started as a rule-based automation is now capable of mimicking human interaction. It is not just the human-like capabilities that make artificial intelligence unique. An advanced AI algorithm offers far better speed and reliability at a much lower cost … compared to its human counterparts."

Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 548 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity via meta-learning to construct a series of "stacked" models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are also superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvement of these results.

Verizon is working with Mcity at the University of Michigan to advance transportation safety and shape the future of autonomous vehicles and smart cities using 5G. The Verizon 5G Ultra Wideband network is now live at the Mcity Test Facility where we are testing various 5G solutions designed to boost pedestrian safety and avoid car accidents. This includes installing 5G-connected cameras at every intersection inside the Mcity test track to help identify traffic and pedestrian patterns to prevent collisions. While connected cars have sensors that can "talk" to each other to help avoid accidents, cameras connecting to traffic light signals can help protect people walking or biking. "We've installed signal controllers at the intersections within Mcity that provide signal phase and timing data to the 5G network," said Eric Raamot, chief technology officer at Econolite.

CHIBA – At this year's Tokyo Game Show, the big draw was next-generation 5G networking -- setting pulses racing with the prospect of a radically more immersive gaming experience. Offering data transmission speeds around 100 times faster than 4G, 5G is expected to enable more seamless imagery -- particularly lower latency, more vivid images -- and sharper motion. Industry experts say it will dramatically improve the quality of augmented and virtual reality games. "It was very smooth, responsive and consistent," said Omar Alshiji, a 23-year-old game designer from Bahrain, after trying out the fighting game Tekken at the NTT Docomo Inc. booth. The major mobile carrier installed 5G base stations at its booth this year, making the high-speed network available at the show. The four-day industry event, held in the city of Chiba, ended Sunday.

We've seen how technological advancement has displaced jobs, especially so in the manufacturing and telecommunications industry. Now, the development of Artificial Intelligence (AI) is causing people to worry about being replaced by robots. Even Jack Ma himself acknowledged that AI is a threat to job security. "AI and robots are going to kill a lot of jobs, because in the future, these will be done by machines." How then, can we remain one step ahead?

CHIBA – Next-generation 5G networking was the big draw at Tokyo Game Show 2019, setting pulses racing with the prospect of a radically more immersive gaming experience. Offering data transmission speeds around 100 times faster than 4G, 5G is expected to enable more seamless imagery with lower latency, more vivid images and sharper motion. Industry experts say it will dramatically improve the quality of augmented and virtual reality games. "It was very smooth, responsive and consistent," said Omar Alshiji, a 23-year-old game designer from Bahrain, after trying out the fighting game "Tekken" at the NTT Docomo Inc. booth at the four-day game show in Chiba. The major mobile carrier installed 5G base stations at its booth this year, making the high-speed network available at the show. "My country does not have 5G, only 4G so I wanted to try it.

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. M. Chen is with the Chinese University of Hong Kong, Shenzhen, 518172, China, and also with the Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA, Email: mingzhec@princeton.edu. Z. Y ang is with the Centre for Telecommunications Research, Department of Informatics, King's College London, WC2B 4BG, UK, Email: yang.zhaohui@kcl.ac.uk. W . Saad is with the Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, V A, 24060, USA, Email: walids@vt.edu. C. Yin is with the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, 100876, China, Emails: ccyin@ieee.org. Poor is with the Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA, Email: poor@princeton.edu. S. Cui is with the Shenzhen Research Institute of Big Data and School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen, 518172, China, Email: robert.cui@gmail.com This work was supported in part by the U.S. National Science Foundation under Grants CNS-1836802 and CCF-0939370. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function.