The "Large Language Models Agent's Handbook" serves as a comprehensive guide for utilizing large language models (LLMs) effectively. These models, such as GPT-3, have revolutionized natural language processing and are invaluable tools in various fields, including research, business, and creative endeavors. The handbook begins by elucidating the fundamental principles underlying LLMs, explaining their architecture, training process, and capabilities. It delves into the importance of data quality, model fine-tuning, and ethical considerations in deploying LLMs responsibly. Understanding the applications of LLMs is crucial, and the handbook provides detailed insights into their diverse uses. From generating text and code to aiding in decision-making processes, LLMs can augment human capabilities across industries. Case studies showcase real-world examples, illustrating how LLMs have been leveraged for tasks such as content creation, customer service automation, and scientific research. Ethical guidelines are paramount when employing LLMs, and the handbook emphasizes the ethical implications of LLM usage. Issues such as bias, misinformation, and privacy concerns are addressed, alongside strategies for mitigating these risks. Responsible AI practices, including transparency, fairness, and accountability, are advocated throughout. Practical considerations for working with LLMs are explored in detail, covering topics such as model selection, data preprocessing, and performance evaluation. Tips for optimizing model performance and troubleshooting common challenges are provided, empowering users to navigate the complexities of LLM implementation effectively. As LLMs continue to evolve, staying updated with the latest advancements and best practices is essential. The handbook offers resources for ongoing learning, including research papers, online communities, and development tools. Additionally, it encourages collaboration and knowledge sharing among LLM practitioners to foster innovation and collective growth. In conclusion, the "Large Language Models Agent's Handbook" equips readers with the knowledge and tools needed to harness the full potential of LLMs responsibly and effectively. By embracing ethical principles, staying informed about emerging trends, and leveraging practical strategies, agents can leverage LLMs to tackle complex challenges and drive meaningful progress in their respective domains

The Handbook on Socially Interactive Agents provides a comprehensive overview of the research fields of Embodied Conversational Agents;Intelligent Virtual Agents;and Social Robotics. Socially Interactive Agents (SIAs);whether virtually or physically embodied;are autonomous agents that are able to perceive an environment including people or other agents;reason;decide how to interact;and express attitudes such as emotions;engagement;or empathy. They are capable of interacting with people and one another in a socially intelligent manner using multimodal communicative behaviors;with the goal to support humans in various domains. Written by international experts in their respective fields;the book summarizes research in the many important research communities pertinent for SIAs;while discussing current challenges and future directions. The handbook provides easy access to modeling and studying SIAs for researchers and students;and aims at further bridging the gap between the research communities involved. In two volumes;the book clearly structures the vast body of research. The first volume starts by introducing what is involved in SIAs research;in particular research methodologies and ethical implications of developing SIAs. It further examines research on appearance and behavior;focusing on multimodality. Finally;social cognition for SIAs is investigated using different theoretical models and phenomena such as theory of mind or pro-sociality. The second volume starts with perspectives on interaction;examined from different angles such as interaction in social space;group interaction;or long-term interaction. It also includes an extensive overview summarizing research and systems of human–agent platforms and of some of the major application areas of SIAs such as education;aging support;autism;and games.

Proceedings of the 15th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Nice, France, 24-27 July 2024.

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Unleash the power of collaboration with AgentScope, a comprehensive platform designed to streamline the development of multi-agent Large Language Model (LLM) applications. This in-depth guide equips you with everything you need to know to leverage AgentScope's functionalities and build intelligent, scalable AI systems. Embrace the Future of AI: Multi-Agent Collaboration Made Easy AgentScope empowers you to construct a team of specialized LLMs, each with its own strengths and expertise. Imagine a system where one agent analyzes customer reviews for sentiment, another identifies key themes, and a third generates a comprehensive report – all working together seamlessly. This is the power of multi-agent LLMs, and AgentScope simplifies the process of bringing it to life. Dive Deep into AgentScope: From Agent Definition to Orchestrated Workflows This comprehensive guide takes you on a journey through the functionalities of AgentScope. Learn how to define and configure your agents, specifying their roles, LLM models, and communication protocols. Explore how to orchestrate tasks, ensuring a smooth workflow where subtasks are completed in the correct order and dependencies are managed effectively. Conquer Challenges: Error Handling, Security, and Explainability The guide doesn't shy away from the real-world considerations of multi-agent systems. Address potential errors and exceptions with AgentScope's robust error handling mechanisms. Safeguard your LLM application with built-in security features like authentication and data encryption. Foster trust and transparency by incorporating Explainable AI (XAI) techniques to understand the decision-making processes within your multi-agent system. Scale to New Heights: Optimizing Performance for Large Tasks As your LLM application tackles more complex tasks and works with ever-growing datasets, AgentScope provides the tools you need to maintain optimal performance. Discover strategies for resource allocation, communication optimization, and utilizing scalable LLM architectures. Employ monitoring and analytics to identify bottlenecks and ensure your multi-agent system continues to function efficiently. A Glimpse into the Future: Pioneering Applications with AgentScope Look ahead and explore the exciting potential of multi-agent LLM systems. Imagine AI-powered scientific discovery, personalized education, intelligent content creation, and advanced conversational AI for businesses – these are just a few possibilities on the horizon. AgentScope equips you to be a part of this revolution, empowering you to build groundbreaking applications that leverage the power of collaborative intelligence. Start Building Today: Unleash the Potential of Multi-Agent LLMs with AgentScope This guide provides a roadmap for your journey into the world of multi-agent LLM development with AgentScope. With its user-friendly interface, comprehensive documentation, and expansive capabilities, AgentScope makes complex AI development accessible. So, what are you waiting for? Start building the future of AI today!

"This book provide a comprehensive view of current developments in agent organizations as a paradigm for both the modeling of human organizations, and for designing effective artificial organizations"--Provided by publisher.

A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning--the deep, context-sensitive meaning that a person derives from spoken or written language.