Progress in computer animation has gained such a speed that, before long, computer-generated human faces and figures on screen will be indistinguishable from those of real humans. The potential both for scripted films and real-time interaction with users is enormous. However, in order to cope with this potential, these faces and figures must be guided by autonomous personality agents. This carefully arranged volume presents the state of the art in research and development in making synthetic actors more autonomous. The papers describe the different approaches and solutions developed by computer animation specialists, computer scientists, experts in AI, psychologists and philosophers, from leading laboratories world-wide. Finally, a bibliography comprising more than 200 entries enable further study.

This practical, yet comprehensive, text offers an up-to-date look at one of the most rapidly expanding media industries - corporate and organizational video. The book features preproduction, production, post production, and management insights from over 20 writers, most of whom are practitioners in the field.

In 1995 Chinese animated filmmaking ceased to be a state-run enterprise and was plunged into the free market. Using key animated films as his case studies, Shaopeng Chen examines new generation Chinese animation in its aesthetic and industrial contexts. He argues that, unlike its predecessors, this new generation does not have a distinctive national identity, but represents an important stage of diversity and exploration in the history of Chinese animation. Chen identifies distinct characteristics of new generation filmmaking, including an orientation towards young audiences and the recurring figure of the immortal monkey-like Sun Wukong. He explores how films such as Lotus Lantern/Baolian Deng (1999) responded to competition from American imports such as The Lion King (1994), retaining Chinese iconography while at the same time adopting Hollywood aesthetics and techniques. Addressing the series Boonie Bears/Xiong Chumo (2014-5), Chen focuses on the films' adaptation from the original TV series, and how the films were promoted across generations and by means of both online and offline channels. Discussing the series Kuiba/Kui Ba (2011, 2013, 2014), Chen examines Vasoon Animation Studio's ambitious attempt to create the first Chinese-style high fantasy fictional universe, and considers why the first film was a critical success but a failure at the box-office. He also explores the relationship between Japanese anime and new generation Chinese animation. Finally, Chen considers how word-of-mouth social media engagement lay behind the success of Monkey King: Hero is Back (2015).

Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI in Action is the comprehensive and concrete guide to generative AI you’ve been searching for. It introduces both AI’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: • A practical overview of of generative AI applications • Architectural patterns, integration guidance, and best practices for generative AI • The latest techniques like RAG, prompt engineering, and multi-modality • The challenges and risks of generative AI like hallucinations and jailbreaks • How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You’ll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside • Best practices for deploying Generative AI apps • Production-quality RAG • Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1 1 Introduction to generative AI 2 Introduction to large language models 3 Working through an API: Generating text 4 From pixels to pictures: Generating images 5 What else can AI generate? Part 2 6 Guide to prompt engineering 7 Retrieval-augmented generation: The secret weapon 8 Chatting with your data 9 Tailoring models with model adaptation and fine-tuning Part 3 10 Application architecture for generative AI apps 11 Scaling up: Best practices for production deployment 12 Evaluations and benchmarks 13 Guide to ethical GenAI: Principles, practices, and pitfalls A The book’s GitHub repository B Responsible AI tools