Introduction to Large Language Models for Business Leaders

Responsible AI Strategy Beyond Fear and Hype - 2025 Edition. Finalist for the 2023 HARVEY CHUTE Book Awards recognizing emerging talent and outstanding works in the genre of Business and Enterprise Non-Fiction.

In 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:

1. The Evolution of LLMs: From early statistical models to transformer architectures and foundation models.
2. How LLMs Understand Language: Demystifying key components like self-attention, embeddings, and deep linguistic modeling.
3. The Art of Inference: Exploring inference parameters for controlling and optimizing LLM outputs.
4. Appropriate Use Cases: A nuanced look at LLM strengths and limitations across applications like creative writing, conversational agents, search, and coding assistance.
5. Productivity Gains: Synthesizing the latest research on generative AI's impact on worker efficiency and satisfaction.
6. The Perils of Automation: Examining risks like automation blindness, deskilling, disrupted teamwork and more if LLMs are deployed without deliberate precautions.
7. The LLM Value Chain: Analyzing key components, players, trends and strategic considerations.
8. Computational Power: A deep dive into the staggering compute requirements behind state-of-the-art generative AI.
9. Open Source vs Big Tech: Exploring the high-stakes battle between open and proprietary approaches to AI development.
10. The Generative AI Project Lifecycle: A blueprint spanning use case definition, model selection, adaptation, integration and deployment.
11. Ethical Data Sourcing: Why the training data supply chain proves as crucial as model architecture for responsible development.
12. Evaluating LLMs: Surveying common benchmarks, their limitations, and holistic alternatives.
13. Efficient Fine-Tuning: Examining techniques like LoRA and PEFT that adapt LLMs for applications with minimal compute.
14. Human Feedback: How reinforcement learning incorporating human ratings and demonstrations steers models towards helpfulness.
15. Ensemble Models and Mixture-of-Experts: Parallels between collaborative intelligence in human teams and AI systems.
16. Areas of Research and Innovation: Retrieval augmentation, program-aided language models, action-based reasoning and more.
17. Ethical Deployment: Pragmatic steps for testing, monitoring, seeking feedback, auditing incentives and mitigating risks responsibly.

The 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.

The perfect Generative AI book for beginners.

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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.