Design, develop, and deploy human-like AI solutions that chat with your customers, solve their problems, and streamline your support services. In Conversational AI, you will learn how to: Pick the right AI assistant type and channel for your needs Write dialog with intentional tone and specificity Train your AI’s classifier from the ground up Create question-and-direct-response AI assistants Design and optimize a process flow for web and voice Test your assistant’s accuracy and plan out improvements Conversational AI: Chatbots that work teaches you to create the kind of AI-enabled assistants that are revolutionizing the customer service industry. You’ll learn to build effective conversational AI that can automate common inquiries and easily address your customers' most common problems. This engaging and entertaining book delivers the essential technical and creative skills for designing successful AI solutions, from coding process flows and training machine learning, to improving your written dialog. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Create AI-driven chatbots and other intelligent agents that humans actually enjoy talking to! Adding intelligence to automated response systems saves time and money for you and your customers. Conversational AI systems excel at routine tasks such as answering common questions, classifying issues, and routing customers to the appropriate human staff. This book will show you how to build effective, production-ready AI assistants. About the book Conversational AI is a guide to creating AI-driven voice and text agents for customer support and other conversational tasks. This practical and entertaining book combines design theory with techniques for building and training AI systems. In it, you’ll learn how to find training data, assess performance, and write dialog that sounds human. You’ll go from building simple chatbots to designing the voice assistant for a complete call center. What's inside Pick the right AI for your needs Train your AI classifier Create question-and-direct-response assistants Design and optimize a process flow About the reader For software developers. Examples use Watson Assistant and Python. About the author Andrew R. Freed is a Master Inventor and Senior Technical Staff Member at IBM. He has worked in AI solutions since 2012. Table of Contents PART 1 FOUNDATIONS 1 Introduction to conversational AI 2 Building your first conversational AI PART 2 DESIGNING FOR SUCCESS 3 Designing effective processes 4 Designing effective dialogue 5 Building a successful AI assistant PART 3 TRAINING AND TESTING 6 Training your assistant 7 How accurate is your assistant? 8 Testing your dialogue flows PART 4 MAINTENANCE 9 Deployment and management 10 Improving your assistant PART 5 ADVANCED/OPTIONAL TOPICS 11 Building your own classifier 12 Additional training for voice assistants

This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.

Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important

Build enterprise chatbots for web, social media, voice assistants, IoT, and telephony contact centers with Google's Dialogflow conversational AI technology. This book will explain how to get started with conversational AI using Google and how enterprise users can use Dialogflow as part of Google Cloud. It will cover the core concepts such as Dialogflow essentials, deploying chatbots on web and social media channels, and building voice agents including advanced tips and tricks such as intents, entities, and working with context. The Definitive Guide to Conversational AI with Dialogflow and Google Cloud also explains how to build multilingual chatbots, orchestrate sub chatbots into a bigger conversational platform, use virtual agent analytics with popular tools, such as BigQuery or Chatbase, and build voice bots. It concludes with coverage of more advanced use cases, such as building fulfillment functionality, building your own integrations, securing your chatbots, and building your own voice platform with the Dialogflow SDK and other Google Cloud machine learning APIs. After reading this book, you will understand how to build cross-channel enterprise bots with popular Google tools such as Dialogflow, Google Cloud AI, Cloud Run, Cloud Functions, and Chatbase. ​​What You Will Learn Discover Dialogflow, Dialogflow Essentials, Dialogflow CX, and how machine learning is used Create Dialogflow projects for individuals and enterprise usage Work with Dialogflow essential concepts such as intents, entities, custom entities, system entities, composites, and how to track context Build bots quickly using prebuilt agents, small talk modules, and FAQ knowledge bases Use Dialogflow for an out-of-the-box agent review Deploy text conversational UIs for web and social media channels Build voice agents for voice assistants, phone gateways, and contact centers Create multilingual chatbots Orchestrate many sub-chatbots to build a bigger conversational platform Use chatbot analytics and test the quality of your Dialogflow agent See the new Dialogflow CX concepts, how Dialogflow CX fits in, and what’s different in Dialogflow CX Who This Book Is For Everyone interested in building chatbots for web, social media, voice assistants, or contact centers using Google’s conversational AI/cloud technology.

Build a chatbot using the Microsoft Conversational AI platform. This book will teach you, step by step, how to save time and money by including chatbots in your enterprise's strategy. You will learn how to be proficient at every phase of development, from collaboration on a chatbot in an end-to-end scenario, to the first mock-up phase, and on through to the deployment and evaluation phases. Microsoft built a cloud service ecosystem for running artificial intelligence workloads in public cloud scenarios and a robust AI platform that offers a broad range of services targeting conversational artificial intelligence solutions such as chatbots. Building a chatbot requires not just developer coding skills but special considerations, including input from business stakeholders such as domain matter experts and power users. You will learn by example how to use a great set of tools and services to bridge the gap between business and engineering. You will learn how to successfully morph business requirements into actionable IT and engineering requirements. You will learn about Bot Framework Composer, which allows power users to initiate the building of a chatbot that can then be handed over to the development team to add capabilities through code. Coverage is given to the process of sharing implementation tasks and workloads between power users, who are using a low-code or no-code approach, and developers, who are building out the enhanced features for the chatbot. What You Will Learn Understand Microsoft’s comprehensive AI ecosystem and its services and solutions Recognize which solutions and services should be applied in each business scenario Discover no-code/low-code approaches for building chatbots Develop chatbots using the conversational AI stack Align business and development for improved chatbot outcomes and reduced time-to-market Who This Book Is For Developers and power users who want to build chatbots. An understanding of the core principles of writing code (.NET or JavaScript) for modern web applications is expected.

Create next-level AI assistants and transform how customers communicate with businesses with the power of natural language understanding and dialogue management using Rasa Key FeaturesUnderstand the architecture and put the underlying principles of the Rasa framework to practiceLearn how to quickly build different types of chatbots such as task-oriented, FAQ-like, and knowledge graph-based chatbotsExplore best practices for working with Rasa and its debugging and optimizing aspectsBook Description The Rasa framework enables developers to create industrial-strength chatbots using state-of-the-art natural language processing (NLP) and machine learning technologies quickly, all in open source. Conversational AI with Rasa starts by showing you how the two main components at the heart of Rasa work – Rasa NLU (natural language understanding) and Rasa Core. You'll then learn how to build, configure, train, and serve different types of chatbots from scratch by using the Rasa ecosystem. As you advance, you'll use form-based dialogue management, work with the response selector for chitchat and FAQ-like dialogs, make use of knowledge base actions to answer questions for dynamic queries, and much more. Furthermore, you'll understand how to customize the Rasa framework, use conversation-driven development patterns and tools to develop chatbots, explore what your bot can do, and easily fix any mistakes it makes by using interactive learning. Finally, you'll get to grips with deploying the Rasa system to a production environment with high performance and high scalability and cover best practices for building an efficient and robust chat system. By the end of this book, you'll be able to build and deploy your own chatbots using Rasa, addressing the common pain points encountered in the chatbot life cycle. What you will learnUse the response selector to handle chitchat and FAQsCreate custom actions using the Rasa SDKTrain Rasa to handle complex named entity recognitionBecome skilled at building custom components in the Rasa frameworkValidate and test dialogs end to end in RasaDevelop and refine a chatbot system by using conversation-driven deployment processingUse TensorBoard for tuning to find the best configuration optionsDebug and optimize dialogue systems based on RasaWho this book is for This book is for NLP professionals as well as machine learning and deep learning practitioners who have knowledge of natural language processing and want to build chatbots with Rasa. Anyone with beginner-level knowledge of NLP and deep learning will be able to get the most out of the book.

A vision of the future of education in which the classroom experience is distributed across space and time without compromising learning. What if there were a model for learning in which the classroom experience was distributed across space and time--and students could still have the benefits of the traditional classroom, even if they can't be present physically or learn synchronously? In this book, two experts in online learning envision a future in which education from kindergarten through graduate school need not be tethered to a single physical classroom. The distributed classroom would neither sacrifice students' social learning experience nor require massive development resources. It goes beyond hybrid learning, so ubiquitous during the COVID-19 pandemic, and MOOCs, so trendy a few years ago, to reimagine the classroom itself. David Joyner and Charles Isbell, both of Georgia Tech, explain how recent developments, including distance learning and learning management systems, have paved the way for the distributed classroom. They propose that we dispense with the dichotomy between online and traditional education, and the assumption that online learning is necessarily inferior. They describe the distributed classroom's various delivery modes for in-person students, remote synchronous students, and remote asynchronous students; the goal would be a symmetry of experiences, with both students and teachers able to move from one mode to another. With The Distributed Classroom, Joyner and Isbell offer an optimistic, learner-centric view of the future of education, in which every person on earth is turned into a potential learner as barriers of cost, geography, and synchronicity disappear.