Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Generative AI Foundations in Python

You're reading from   Generative AI Foundations in Python Discover key techniques and navigate modern challenges in LLMs

Arrow left icon
Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835460825
Length 190 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Carlos Rodriguez Carlos Rodriguez
Author Profile Icon Carlos Rodriguez
Carlos Rodriguez
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Foundations of Generative AI and the Evolution of Large Language Models FREE CHAPTER
2. Chapter 1: Understanding Generative AI: An Introduction 3. Chapter 2: Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers 4. Chapter 3: Tracing the Foundations of Natural Language Processing and the Impact of the Transformer 5. Chapter 4: Applying Pretrained Generative Models: From Prototype to Production 6. Part 2: Practical Applications of Generative AI
7. Chapter 5: Fine-Tuning Generative Models for Specific Tasks 8. Chapter 6: Understanding Domain Adaptation for Large Language Models 9. Chapter 7: Mastering the Fundamentals of Prompt Engineering 10. Chapter 8: Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI 11. Index 12. Other Books You May Enjoy

Ethical norms and values in the context of generative AI

The ethical norms and values guiding the development and deployment of generative AI are rooted in transparency, equity, accountability, privacy, consent, security, and inclusivity. These principles can serve as a foundation for developing and adopting systems aligned with societal values and supporting the greater good. Let’s explore these in detail:

  • Transparency involves clearly explaining the methodologies, data sources, and processes behind large language model (LLM) construction. This practice builds trust by enabling stakeholders to understand the technology’s reliability and limits. For example, a company could publish a detailed report on the types of data trained on their LLM and the steps taken to ensure data privacy and bias mitigation.
  • Equity in the context of LLMs ensures fair treatment and outcomes for all users by actively preventing biases in models. This requires thorough analysis and...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image