Introduction to Large Language Models for Business Leaders

Responsible AI Strategy Beyond Fear and Hype - 2024 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:

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

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