The Complete, if Flawed, Guide to Generative AI
3 stars
David Clinton’s The Complete Obsolete Guide to Generative AI is a must-read for computer science students looking to break into the exciting field of AI. This book offers a comprehensive overview of generative AI, covering everything from the basics of how AI models work to the practical applications of AI in various industries.
What makes this book especially valuable for students is its focus on practical skills and real-world applications. You’ll learn how to use generative AI to:
●Create text, code, images, videos, and audio.
● Speed up your work with tools like boilerplate code generation and specialized document creation.
●Analyze data and generate insights.
Clinton provides hands-on exercises and examples using simple Python code to help you build your AI skills23. He also emphasizes the importance of prompt engineering, teaching you how to write effective prompts to get the most out of generative AI models5
However, while “The Complete …
David Clinton’s The Complete Obsolete Guide to Generative AI is a must-read for computer science students looking to break into the exciting field of AI. This book offers a comprehensive overview of generative AI, covering everything from the basics of how AI models work to the practical applications of AI in various industries.
What makes this book especially valuable for students is its focus on practical skills and real-world applications. You’ll learn how to use generative AI to:
●Create text, code, images, videos, and audio.
● Speed up your work with tools like boilerplate code generation and specialized document creation.
●Analyze data and generate insights.
Clinton provides hands-on exercises and examples using simple Python code to help you build your AI skills23. He also emphasizes the importance of prompt engineering, teaching you how to write effective prompts to get the most out of generative AI models5
However, while “The Complete Obsolete Guide to Generative AI” excels in its technical explanations and practical guidance, it falls short in one critical area: it completely ignores the issue of bias in AI.
This omission is a serious flaw. As computer scientists, it’s crucial to understand that AI models are trained on data, and that data can reflect and amplify existing societal biases. If left unaddressed, these biases can lead to unfair and discriminatory outcomes. From my blog: hackscience.education/2024/12/01/the-complete-if-flawed-guide-to-generative-ai/
Therefore, while I highly recommend this book for its comprehensive introduction to generative AI, I urge you to supplement your reading with materials that specifically address the ethical implications of AI, including bias. Understanding these issues is not just an ethical imperative; it’s essential for building responsible and trustworthy AI systems.