Mist's Bob Friday: An AI-driven network maximises the user experience through better performance and reliability while lowering IT costs through better efficiencies. Artificial intelligence (AI) – it's a nebulous term that means many things to different people. What is true is that one day in the near future, machines will be likely to possess'human-level' intelligence, providing organisations with efficiencies that they have never seen before. But what role is AI playing inside organisations today, particularly when it comes to providing a good experience for internal users and external customers across their wide area networks? Tech execs gathered in Sydney in September to discuss the benefits of using artificial intelligence technologies inside their wired and wireless networks.

Even governments are vying for 5G supremacy.

Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work we examine the ML-based Quality-of-Transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. We examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their accuracy and training time. We show that the distributed QoT models outperform the centralized QoT model, especially as the number of diverse QoT requirements increases.

In this paper, we consider data consisting of multiple networks, each comprised of a different edge set on a common set of nodes. Many models have been proposed for such multi-view data, assuming that the data views are closely related. In this paper, we provide tools for evaluating the assumption that there is a relationship between the different views. In particular, we ask: is there an association between the latent community memberships of the nodes within each data view? To answer this question, we extend the stochastic block model for a single network view to two network views, and develop a new hypothesis test for the null hypothesis that the latent community structure within each data view is independent. We apply our test to protein-protein interaction data sets from the HINT database (Das & Yu 2012). We find evidence of a weak association between the latent community structure of proteins defined with respect to binary interaction data and with respect to co-complex association data. We also extend this proposal to the setting of a network with node covariates.

Artificial intelligence technology is proliferating fast across the world and is being deployed in applications from speech recognition to deepfake videos and monitoring traffic congestion. It's also increasingly being used to monitor and track citizens, according to a new report. At least 75 out of 176 nations surveyed globally are actively using AI technologies for surveillance purposes, according to the Carnegie Endowment for International Peace. These include facial recognition systems, smart policing tools, and the establishment of safe city platforms. The leading vendors of these systems globally are Chinese firms, led by Huawei, which has supplied these technologies to at least 50 states worldwide.

AI-IRSF is an AI system that prevents a fraud that involves hacking of IP-PBX (IP telephony exchange) to generate illegal calls to international numbers. With this Cooperation Agreement, KT will develop and supply more AI based FMS modules to integrate with WeDo's Fraud and Risk Management system. Additional AI based modules will also run on the WeDo's system, and the modular capability of RAID will allow CSPs to choose from different fraud detection models for their market similar to how one chooses applications from a smartphone app store. The open architecture of RAID will also allow other CSPs to develop their own models as well. WeDo Technologies is part of the Mobileum group, a leading enterprise software and analytics company in roaming, security, fraud and risk management serving more than 700 telecommunication providers in more than 180 countries.

In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantum-clustering framework based on the quantum deep SVM formulation, deep convolutional neural networks, and quantum K-Means clustering. We have investigated the run time computational complexity of the proposed quantum deep clustering framework and compared with the possible classical implementation. Our investigation shows that the proposed quantum version of deep clustering formulation demonstrates a significant performance gain (exponential speed up gains in many sections) against the possible classical implementation. The proposed theoretical quantum deep clustering framework is also interesting & novel research towards the quantum-classical machine learning formulation to articulate the maximum performance.

The world needs more computing power and Huawei wants to build the architectures needed to answer the call, spanning processors, networks, devices, and cloud services. It also believes artificial intelligence (AI) will fuel much of this need and is gearing up a "full stack" portfolio to tap the growing enterprise demand. Statistical computing, which is needed in dealing with undefinable tasks such as voice and image recognition, will soon become mainstream and Huawei believes AI computing, five years from now, will account for more than 80% of computing power used worldwide. At the Chinese tech vendor's annual Connect conference Wednesday, deputy chairman Ken Hu noted that training AI algorithms requires a metric ton of computing power, while other more complex applications such as autonomous driving and weather forecasting requires even more compute power. Touting its low latency and high speeds, Ericsson says 5G can introduce a multitude of new applications for businesses and give telcos the cost efficiencies they seek, but the persistent controversy over cybersecurity--specifically involving Huawei--is leading to uncertainty and a general slowdown in the market.

What is the smart factory? In simple terms, it is the aspirational concept of a digitally transformed factory or warehouse. It is a cyber-physical ecosystem of virtualized and connected assets, people, and processes that constitute the manufacturing platform of the 4th industrial revolution that is Industry 4.0. One of the great expectations of 5G is that it will bring a new level of flexibility to the shop floor of a Smart Factory, allowing a growing number and range of mobile autonomous robots and vehicles to safely operate and coexist with humans in a manufacturing environment. The net expected outcome is the transition of today's static production models toward highly dynamic and software-defined production models.

You know a technology has reached a tipping point when your kids ask about it. This happened recently when my eighth grade daughter asked, "What is Machine Learning and why is it so important?". Answering her question, I explained how Machine Learning is part of AI, where we teach machines to reason and learn like human beings. I used the example of fraud detection. In many ways catching fraud is like finding needles in a haystack – you must sort and make sense of massive amounts of data in order to find your "needles" or in this case, your fraudsters.