The Smiling Chatbot

Significant advances in the field of artificial intelligence have given a strong tailwind to the spread of technologies based on it. AI technologies are increasingly influencing private contexts but also the service sector where more and more service encounters are handled by chatbots. In part, however, the spread of chatbots and the associated focus on their functional advantages seem to contradict extant research findings that service interactions are also driven by emotional components. A central role is played here by so-called emotional contagion (i.e., the unconscious transfer of emotions from employees to customers). Against the background that this contagion with positive emotions can lead to a better evaluation of the service transaction in the further course, it is the goal of this book to investigate the effects of positive displayed emotions of a chatbot. For this purpose, six consecutive studies were conducted. The results show that the expression of positive emotions by a chatbot enriches the service interactions by transmitting positive emotions that, in the further course, do also lead to a better evaluation of the service experienced. Moreover, it is shown that these emotional reactions are dependent on the customer’s personality and the chatbot’s appearance, expressed through an avatar.

This book constitutes the proceedings of the 4th International Workshop on Chatbot Research and Design, CONVERSATIONS 2020, which was held during November 23-24, 2020, hosted by the University of Amsterdam. The conference was planned to take place in Amsterdam, The Netherlands, but changed to an online format due to the COVID-19 pandemic. The 14 papers included in this volume were carefully reviewed and selected from a total of 36 submissions. The papers in the proceedings are structured in four topical groups: Chatbot UX and user perceptions, social and relational chatbots, chatbot applications, and chatbots for customer service. The papers provide new knowledge through empirical, theoretical, or design contributions.

A chatbot is expected to be capable of supporting a cohesive and coherent conversation and be knowledgeable, which makes it one of the most complex intelligent systems being designed nowadays. Designers have to learn to combine intuitive, explainable language understanding and reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot construction: 1. Build a bot platform with universal NLP and ML capabilities so that a bot developer for a particular enterprise, not being an expert, can populate it with training data; 2. Accumulate a huge set of training dialogue data, feed it to a deep learning network and expect the trained chatbot to automatically learn “how to chat”. Although these two approaches are reported to imitate some intelligent dialogues, both of them are unsuitable for enterprise chatbots, being unreliable and too brittle. The latter approach is based on a belief that some learning miracle will happen and a chatbot will start functioning without a thorough feature and domain engineering by an expert and interpretable dialogue management algorithms. Enterprise high-performance chatbots with extensive domain knowledge require a mix of statistical, inductive, deep machine learning and learning from the web, syntactic, semantic and discourse NLP, ontology-based reasoning and a state machine to control a dialogue. This book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning. The foci of this book are applications of discourse analysis in text relevant assessment, dialogue management and content generation, which help to overcome the limitations of platform-based and data driven-based approaches. Supplementary material and code is available at https://github.com/bgalitsky/relevance-based-on-parse-trees

Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications

The second volume of this research monograph describes a number of applications of Artificial Intelligence in the field of Customer Relationship Management with the focus of solving customer problems. We design a system that tries to understand the customer complaint, his mood, and what can be done to resolve an issue with the product or service. To solve a customer problem efficiently, we maintain a dialogue with the customer so that the problem can be clarified and multiple ways to fix it can be sought. We introduce dialogue management based on discourse analysis: a systematic linguistic way to handle the thought process of the author of the content to be delivered. We analyze user sentiments and personal traits to tailor dialogue management to individual customers. We also design a number of dialogue scenarios for CRM with replies following certain patterns and propose virtual and social dialogues for various modalities of communication with a customer. After we learn to detect fake content, deception and hypocrisy, we examine the domain of customer complaints. We simulate mental states, attitudes and emotions of a complainant and try to predict his behavior. Having suggested graph-based formal representations of complaint scenarios, we machine-learn them to identify the best action the customer support organization can chose to retain the complainant as a customer.

A must-read for every dentist who wants more patients! Are you frustrated by stagnant growth or uncertain about the future of your dental practice? If so, then this book is for you. Dental marketing expert Dan Delmain shares insights from his years of experience in the industry, outlining proven systems for booking more of the right patients through digital marketing and a modern website. From Search to Smile takes you on a journey through the eyes of your perfect patient, providing a unique perspective on how to tailor your marketing strategy to meet their needs. This easy-to-read dental marketing book is packed with actionable advice that will help your practice stop just surviving and start thriving, including: -How to identify your perfect patient -Setting your dental marketing budget -Organic marketing vs paid advertising -Dental marketing techniques that actually work -How to modernize your branding and website design -Tips for retaining patients and minimizing no-shows and cancellations -Measuring the success of your dental marketing efforts -The worst (but most common) marketing mistakes dentists make -> PLUS: Hear stories from real dentists that show exactly what's possible Dentists using these strategies have seen incredible success, including: -Increased year-over-year collections -Converting more website visitors to patients -Lower cost-per-lead for Google Ads -Growth in SEO keyword rankings -Improved social media marketing ROI From Search to Smile presents the topic of dental marketing in a concise, simple-to-digest format. You'll finish this book knowing how to create a proven marketing plan that drives a steady flow of high-quality new patients. Learn, apply the techniques in this book, and grow your practice with confidence.

Chatbots offer exceptional services to end-users due to various factors, including the ability to respond to customer requests quickly according to their convenience. Given the magnitude of research and interest in chatbots, further study on several vital and evolving concerns including human-bot interaction, chatbot adoption, chatbot architecture, design considerations, and chatbot applications in various domains including education and customer support is necessary. Trends, Applications, and Challenges of Chatbot Technology provides novel research content and reviews of current chatbot technology and sheds light on challenges and open questions as well as possible research directions. Covering key topics such as human-computer interaction, education, customer support, and algorithms, this reference work is ideal for computer scientists, industry professionals, policymakers, researchers, academicians, practitioners, scholars, instructors, and students.

This book is a collection of papers that were presented at the 6th International Conference on Big Data Cloud and Internet of Things, BDIoT 2022. The conference took place on October 25-27, 2022, Tangier, Morocco. The book consisted of 49 chapters, which correspond to the four major areas that are covered during the conference, namely Big Data & Cloud Computing, Cybersecurity, Machine Learning, Deep Learning, E-Learning, Internet of Things, Information System and Natural Language Processing. Every year BDIoT attracted researchers from all over the world, and this year was not an exception – the authors received 98 submissions from 7 countries. More importantly, there were participants from many countries, which indicates that the conference is truly gaining more and more international recognition as it brought together a vast number of specialists who represented the aforementioned fields and share information about their newest projects. Since the authors strived to make the conference presentations and proceedings of the highest quality possible, the authors only accepted papers that presented the results of various investigations directed to the discovery of new scientific knowledge in the area of Big Data, IoT and their applications. All the papers were reviewed and selected by the Program Committee, which comprised 96 reviewers from over 58 academic institutions. As usual, each submission was reviewed following a double process by at least two reviewers. When necessary, some of the papers were reviewed by three or four reviewers. Authors’ deepest thanks and appreciation go to all the reviewers for devoting their precious time to produce truly through reviews and feedback to the authors.