Safe For Humans AI
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Safe For Humans AI

A "humans-first" approach to designing and building AI systems.

About the Book

We take a dive into both general architectures for safe, human-first AI as well as concrete examples of building applications with LLMs with both prompt engineering and fine-tuning. While I favor using self-hosted LLMs for the technical book examples, most can also be used with OpenAI and Hugging Face APIs.

About the Author

Mark Watson
Mark Watson

Mark Watson is a consultant specializing in deep learning, machine learning, knowledge graphs, and general artificial intelligence software development. He uses Common Lisp, Clojure, Python, Java, Haskell, and Ruby for development.

He is the author of 20+ published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme. He has 55 US Patents.

Mark's consulting customer list includes: Google, Capital One, Olive AI, CompassLabs, Disney,,, and Webmind Corporation.

All of my Leanpub books are available to read for free on my web site:

Table of Contents

    • Preface
      • Running Software Examples
      • Technology Stack for Development of the Book Examples
      • Author’s Use of Large Language Models for Writing and Coding
      • The “Big Project Example” in this Book
      • About the Author
      • Book Cover
      • Acknowledgements
    • Part I - Risks Using AI
    • Road Map for Our Journey to Safe AI
      • Your Requirements for Privacy and AI Safety vs. Making Maximum Business Use of LLMs
      • A Prompt Template For Evaluating Safety vs. Opportunity Tradeoffs
    • Risks
      • Don’t Throw the Baby Out With the Bath Water
      • Government Control of AI and International Norms
      • Key Aspects of AI Safety
      • Leaking Customer Data
      • Inaccurate Results from LLMs
      • Legal Exposure to the Use of Possibly Private Data Used to Train LLMs
      • Protecting Your Business Processes from Competition
    • AI Weapons
      • Attempts to Mitigate Risks of AI Weapons
      • What About the United Nations?
    • Generative AI
      • Midjourney
      • Microsoft and GitHub Copilot
      • ChatGPT and Microsoft Bing with ChatGPT
    • Governance
    • Transparency in a World Where Corporations and Governments Do Not Want Transparency
      • Building Transparency Into AI Systems
    • Part II - Using AI Safely: a Technical Approach
    • Portability Library Supporting OpenAI, Hugging Face, and Self Hosted Models
      • Examples Using LlmLib
    • Using Local Large Language Models
      • Available Public Large Language Models
      • StabilityAI’s StableLM Using lil-parrot Library
      • Google’s Flan-T5-XXL
      • Running FastChat with Vicuna LLMs
      • Building a Chat Application Using Text From My Books and Prompt Engineering
    • Fine-Tuning LLMs Using Your Data
      • Fine-Tuning vs. Prompt Engineering
      • The Process of Fine-Tuning
      • Building a Chat Application Using Text From My Books and Fine-Tuning
    • Part III - What Does the Future Hold?
    • AI-Safety Respecting Architectures for Use in Education
    • AI-Safety Respecting Architectures for Use in Small and Medium Size Businesses
    • Trying to Predict the Future
      • Advances in LLMs
      • Small Devices and Edge Computing
      • Personalized AIs
      • New Kinds of Digital Devices
    • A Thought Experiment on Building a Safe AI in Alignment With Positive Human Values
      • A Design Based on Prompt Engineering
      • Safe AI Thought Experiment Implementation
    • Good Luck
    • Appendix A - Using Google Colab
    • Appendix B - Using GPU Servers on Lambda Labs

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