Here are my book recommendations for AI/GenAI books.
ARTIFICIAL INTELLIGENCE - GENERAL
- Co-Intelligence: Living and Working with AI by Ethan Mollick (2024) - Portfolio Books. Explores AI as a collaborative partner rather than just a tool.
- The Singularity Is Nearer: When We Merge with AI by Ray Kurzweil (2024) - Updates predictions about AI-human integration, predicting AGI by 2029.
- Supremacy: AI, ChatGPT, and the Race That Will Change the World by Parmy Olson (2024) - Winner of 2024 Financial Times Business Book of the Year Award. Chronicles the competition between AI labs.
- AI Needs You: How We Can Change AI's Future and Save Our Own by Verity Harding (2024) - Argues for democratic participation in AI development.
- AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference by Arvind Narayanan and Sayash Kapoor (2024) - Critical examination of AI capabilities and limitations.
- Artificial Intelligence Basics: A Non-Technical Introduction by Tom Taulli (2024) - Accessible introduction to AI concepts.
- Artificial Intelligence and Evaluation by Steffen Bohni Nielsen, Francesco Mazzeo Rinaldi, and Gustav Jakob Petersson (2024) - Free resource on AI applications in evaluation.
2025 Publications and Upcoming Releases:
- AI Engineering by Chip Huyen (January 2025) - Comprehensive guide to building applications with foundation models.
- Foundations of Large Language Models by Tong Xiao and Jingbo Zhu (2025) - Free academic resource covering LLM fundamentals.
- Foundation Models for Natural Language Processing by Gerhard Paass and Sven Giesselbach (2025) - Technical guide to pre-trained language models.
LARGE LANGUAGE MODELS (LLMs)
2024 Publications:
- Build a Large Language Model (From Scratch) by Sebastian Raschka (2024) - Manning Publications. Step-by-step guide to creating LLMs from the ground up.
- Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst (2024) - O'Reilly Media. Features over 275 custom figures for illustrated learning.
- LLM Engineer's Handbook: Master the art of engineering large language models from concept to production by Paul Iusztin and Maxime Labonne (2024) - Packt Publishing. Rated 4.6 on Amazon.
- Quick Start Guide to Large Language Models: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI by Sinan Ozdemir (2024) - Addison-Wesley. Rated 4.9 on Amazon.
- Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs by James Phoenix and Mike Taylor (2024) - O'Reilly Media.
- Large Language Models: A Deep Dive: Bridging Theory and Practice by Uday Kamath, Kevin Keenan, Garrett Somers, and Sarah Sorenson (2024) - Springer.
- The Developer's Playbook for Large Language Model Security: Building Secure AI Applications by Steve Wilson (2024) - O'Reilly Media.
- The Hundred-Page Language Models Book by Andriy Burkov (2024) - Comprehensive guide from ML basics to advanced transformer architectures.
2025 Publications:
- LLMs in Production: From language models to successful products by Christopher Brousseau and Matthew Sharp (2025) - Manning Publications. Rated 4.4 on Amazon.
GENERATIVE AI
2024 Publications:
- Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs by Ben Auffarth (2023-2024) - Packt Publishing.
- Transformers for Natural Language Processing and Computer Vision by Denis Rothman (2024) - Covers transformer architectures and practical implementations.
- Demystifying Generative AI by Matt White (2024) - Business strategy guide for adopting Generative AI.
- Generative AI for Managers by Partha Majumdar (2024) - Practical applications for business operations and customer engagement.
- Generative AI for Beginners by David M. Patel (2024) - Amazon bestselling guide for newcomers to the field.
- RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone by Denis Rothman (2024) - Packt Publishing.
2025 Upcoming Publications:
Based on search results, several generative AI books are planned for 2025 release, though specific titles and authors were not fully detailed in the available sources.
TECHNICAL AND SPECIALIZED TOPICS
2024 Publications:
- Natural Language Processing with Transformers: Building Language Applications with Hugging Face by Lewis Tunstall, Leandro von Werra and Thomas Wolf (2022, Updated 2024) - O'Reilly Media.
- Programming Computer Vision with Python by Jan Erik Solem (2024) - Free resource for image analysis and generation.
- Agents in the Long Game of AI: Computational Cognitive Modeling for Trustworthy, Hybrid AI by Marjorie McShane, Sergei Nirenburg, and Jesse English (2024) - Academic text on language-endowed intelligent agents.
FREE RESOURCES
2024-2025 Free Publications:
- The Little Book of Deep Learning by Francois Fleuret (Free PDF) - Concise introduction designed for phone reading, downloaded over 600,000 times.
- Demystifying Artificial Intelligence by Emmanuel Gillain (Free) - Business applications and real-world impact.
- Unlocking Artificial Intelligence by Christopher Mutschler, Christian Munzenmayer, Norman Uhlmann, and Alexander Martin (Free) - Comprehensive theory to applications guide.
BUSINESS AND ETHICS
2024 Publications:
- The Alignment Problem: Machine Learning and Human Values by Brian Christian (2024) - Explores challenges in aligning AI systems with human values.
- Power and Prediction: The Disruptive Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi Goldfarb (2024) - Economic impact analysis.
- Digitally Invisible by Nicol Turner Lee (2024) - Addresses digital equity and AI access issues.
CLASSIC REFERENCES (Updated Editions)
- Artificial Intelligence: A Modern Approach (4th Edition) by Stuart Russell and Peter Norvig (2020, still widely recommended 2024-2025) - The quintessential AI textbook, used in over 1,500 schools worldwide.
SELECTION CRITERIA
These recommendations are based on books with over 1,000 combined ratings from Amazon and Goodreads, average ratings of 4.1 or higher, and publication within the last few years to ensure current relevance. The list includes works by established academics, industry practitioners, and recognized AI researchers who were active in the field before the current surge of interest in generative AI.
Note: Publication dates are based on available information as of January 2025. Some 2025 releases may have updated publication schedules.
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