{"product_id":"introduction-to-large-language-models-for-business-leaders-responsible-ai-strategy-beyond-fear-and-hype-9780648635956","title":"Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype","description":"\u003cp\u003eNow in hardback color edition! \u003cstrong\u003eResponsible AI Strategy Beyond Fear and Hype - 2024 Edition\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003eFinalist for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction.\u003c\/em\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eIn this comprehensive guide, business leaders will gain a nuanced understanding of large language models (LLMs) and generative AI. The book covers the rapid progress of LLMs, explains technical concepts in non-technical terms, provides business use cases, offers implementation strategies, explores impacts on the workforce, and discusses ethical considerations. Key topics include: \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eThe Evolution of LLMs: \u003c\/strong\u003e From early statistical models to transformer architectures and foundation models.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHow LLMS Understand Language: \u003c\/strong\u003eDemystifying key components like self-attention, embeddings, and deep linguistic modeling.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eThe Art of Inference: \u003c\/strong\u003e Exploring inference parameters for controlling and optimizing LLM outputs.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAppropriate Use Cases: \u003c\/strong\u003e A nuanced look at LLM strengths and limitations across applications like creative writing, conversational agents, search, and coding assistance.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eProductivity Gains: \u003c\/strong\u003e Synthesizing the latest research on generative AI's impact on worker efficiency and satisfaction.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eThe Perils of Automation: \u003c\/strong\u003eExamining risks like automation blindness, deskilling, disrupted teamwork and more if LLMs are deployed without deliberate precautions.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eThe LLM Value Chain: \u003c\/strong\u003eAnalyzing key components, players, trends and strategic considerations.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e Computational Power: \u003c\/strong\u003e A deep dive into the staggering compute requirements behind state-of-the-art generative AI.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOpen Source vs Big Tech: \u003c\/strong\u003e Exploring the high-stakes battle between open and proprietary approaches to AI development.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eThe Generative AI Project Lifecycle: \u003c\/strong\u003eA blueprint spanning use case definition, model selection, adaptation, integration and deployment.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEthical Data Sourcing: \u003c\/strong\u003eWhy the training data supply chain proves as crucial as model architecture for responsible development.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEvaluating LLMs: \u003c\/strong\u003eSurveying common benchmarks, their limitations, and holistic alternatives.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEfficient Fine-Tuning: \u003c\/strong\u003eExamining techniques like LoRA and PEFT that adapt LLMs for applications with minimal compute.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHuman Feedback: \u003c\/strong\u003eHow reinforcement learning incorporating human ratings and demonstrations steers models towards helpfulness.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEnsemble Models and Mixture-of-Experts: \u003c\/strong\u003eParallels between collaborative intelligence in human teams and AI systems.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAreas of Research and Innovation: \u003c\/strong\u003eRetrieval augmentation, program-aided language models, action-based reasoning and more.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eEthical Deployment: \u003c\/strong\u003ePragmatic steps for testing, monitoring, seeking feedback, auditing incentives and mitigating risks responsibly.\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe book offers an impartial narrative aimed at informing readers for thoughtful adoption, maximizing real-world benefits while proactively addressing risks. With this guide, leaders gain integrated perspectives essential to setting sound strategies amidst generative AI's rapid evolution.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eMore Than a Book\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eBy purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. No credit card required.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e I. Almeida\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Now Next Later AI\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 12\/22\/2023\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 236\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.93lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 8.50h x 5.50w x 0.56d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9780648635956\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003ci\u003eAlmeida, I.:\u003c\/i\u003e\u003c\/b\u003e - I. Almeida is the Chief Transformation Officer at Now Next Later AI, an AI advisory, training, and publishing business supporting organizations with their AI strategy, transformation, and governance. With a wealth of experience spanning over 26 years, I. Almeida held senior positions at companies such as Thoughtworks, Salesforce, and Publicis Sapient, where she advised hundreds of executive customers on digital- and technology-enabled Business Strategy and Transformation. She is the author of several books, including three AI guides with a clear aim to provide an independent, balanced and responsible perspective on Generative AI business adoption. I. Almeida serves as an AI advisory member in the Adelaide Institute of Higher Education Course Advisory Committee.She is a regular speaker at industry events such as Gartner Symposium, SXSW, and ADAPT. Her latest books show her extensive knowledge and insights, displaying her unique perspective and invaluable contributions to the field.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eThis title is not returnable\u003c\/i\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Now Next Later AI","offers":[{"title":"Hardcover","offer_id":43646231216243,"sku":"9.78065E+12","price":37.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/files\/img_9d714d40-9e36-43c6-a3a9-434f5454f395.jpg?v=1755947974","url":"https:\/\/bookstorenmore.com\/products\/introduction-to-large-language-models-for-business-leaders-responsible-ai-strategy-beyond-fear-and-hype-9780648635956","provider":"Bookstore N More","version":"1.0","type":"link"}