Successfully deploying conversational artificial intelligence (AI) is like no other digital business-process upgrade. In fact, it's not an IT upgrade in the conventional sense; conversational AI does nothing less than usher sophisticated robotics into the front office. The surest route to project failure would be taking this fact for granted. Where these cross-channel AI systems--designed to interact naturally and fluidly with internal users and/or customers in text or verbal conversation--are most like traditional business systems is in how short-sighted decisions can doom development and hobble future productivity. What should you keep in mind when deploying conversational AI?

AI and automation are changing the business environment across industries, delivering new opportunities through intelligent, automated solutions. Some companies are ahead of the curve, while others are stagnating in adopting the technology. Operators and enterprises are aware of the benefits of AI and automation, but the questions that always remain are, "What does it bring to my business? How will it solve my problems?" Artificial intelligence (AI) is a constellation of technologies that describes the processes of intelligent automation, like machine learning, natural language processing (NLP), cognitive computing, and deep learning.

In this paper we use a time-evolving graph which consists of a sequence of graph snapshots over time to model many real-world networks. We study the path classification problem in a time-evolving graph, which has many applications in real-world scenarios, for example, predicting path failure in a telecommunication network and predicting path congestion in a traffic network in the near future. In order to capture the temporal dependency and graph structure dynamics, we design a novel deep neural network named Long Short-Term Memory R-GCN (LRGCN). LRGCN considers temporal dependency between time-adjacent graph snapshots as a special relation with memory, and uses relational GCN to jointly process both intra-time and inter-time relations. We also propose a new path representation method named self-attentive path embedding (SAPE), to embed paths of arbitrary length into fixed-length vectors. Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.

The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behavior-related data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss the findings and potential research directions.

Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing and cognitive computing. Industry 4.0 is commonly referred to as the fourth industrial revolution. Industry 4.0 fosters what has been called a "smart factory". Within modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions.

Digital technologies are changing the world as we know it, but a select few are responsible for the biggest transformations. Artificial intelligence and machine learning are at the forefront of that change, collectively driving both innovation and improvement. The MIT Sloan Management Review reveals that more than 72 percent of respondents believe AI will have a significant impact on the technology, media and telecommunications industries over the next five years. Almost 85 percent believe AI will allow their organizations to obtain or sustain a competitive advantage in the market. But that change, believe it or not, is happening right now. The technology is already being used to transform business operations and to improve age-old practices.

SoftBank Group saw its operating income increase by 81% over the year to March 31, 2019, driven by its SoftBank Vision and Delta Funds more than tripling their operating income. Here's how it's related to artificial intelligence, how it works and why it matters. Overall, the SoftBank conglomerate took in ¥9.6 trillion in sales, an increase of 5%, from which it made ¥2.35 trillion in earnings before interest and taxes, up 81%, and ¥1.4 trillion of net income. Broken down, both of its telcos in the form of Sprint and SoftBank Corporation contributed around ¥3.7 trillion in sales, with Yahoo Japan making ¥947 billion in sales, and others making up the remaining ¥1.2 trillion. For operating income, the SoftBank Vision Fund hit ¥1.26 trillion, SoftBank made ¥725 billion, Sprint contributed ¥280 billion, and Yahoo Japan made ¥135 billion.

A decentralized learning mechanism, Federated Learning (FL), has attracted much attention, which enables privacy-preserving training using the rich data and computational resources of mobile clients. However, data on mobile clients is typically not independent and identically distributed (IID) owing to diverse of mobile users' interest and usage, and FL on non-IID data could degrade the model performance. This work aims to extend FL to solve the performance degradation problem resulting from non-IID data of mobile clients. We assume that a limited number (e.g., less than 1%) of clients who allow their data to be uploaded to a server, and we propose a novel learning mechanism referred to as Hybrid-FL, where the server updates the model using data gathered from the clients and merge the model with models trained by clients. In Hybrid-FL, we design a heuristic algorithms that solves the data and client selection problem to construct "good" dataset on the server under bandwidth and time limitation. The algorithm increases the amount of data gathered from clients and makes the data approximately IID for improving model performance. Evaluations consisting of network simulations and machine learning (ML) experiments show that the proposed scheme achieves a significantly higher classification accuracy than previous schemes in the non-IID case.

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in 5G and beyond. Forecasting extreme demand spikes and troughs is essential to avoiding outages and improving energy efficiency. However, current forecasting methods predominantly focus on overall forecast performance and/or do not offer probabilistic uncertainty quantification. Here, we design a feature embedding (FE) kernel for a Gaussian Process (GP) model to forecast traffic demand. The FE kernel enables us to trade-off overall forecast accuracy against peak-trough accuracy. Using real 4G base station data, we compare its performance against both conventional GPs, ARIMA models, as well as demonstrate the uncertainty quantification output. The advantage over neural network (e.g. CNN, LSTM) models is that the probabilistic forecast uncertainty can directly feed into decision processes in self-organizing-network (SON) modules.

In various application areas, networked data is collected by measuring interactions involving some specific set of core nodes. This results in a network dataset containing the core nodes along with a potentially much larger set of fringe nodes that all have at least one interaction with a core node. In many settings, this type of data arises for structures that are richer than graphs, because they involve the interactions of larger sets; for example, the core nodes might be a set of individuals under surveillance, where we observe the attendees of meetings involving at least one of the core individuals. We model such scenarios using hypergraphs, and we study the problem of core recovery: if we observe the hypergraph but not the labels of core and fringe nodes, can we recover the "planted" set of core nodes in the hypergraph? We provide a theoretical framework for analyzing the recovery of such a set of core nodes and use our theory to develop a practical and scalable algorithm for core recovery. The crux of our analysis and algorithm is that the core nodes are a hitting set of the hypergraph, meaning that every hyperedge has at least one node in the set of core nodes. We demonstrate the efficacy of our algorithm on a number of real-world datasets, outperforming competitive baselines derived from network centrality and core-periphery measures.