AI Library
The essential reading list for academics working with AI. We've curated the books that shaped the field and the papers that define its frontier.
8 books · 6 articles
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Books
Foundational texts, practical guides, and critical perspectives on artificial intelligence — curated for researchers, students, and educators.
Foundational AI
Life 3.0: Being Human in the Age of Artificial Intelligence
Essential ReadMax Tegmark · 2017
A sweeping exploration of how AI will transform every aspect of society — from jobs and law to war and consciousness. Essential reading for anyone entering the AI field.
Artificial Intelligence: A Modern Approach
Textbook StandardStuart Russell & Peter Norvig · 2020
The definitive AI textbook used in over 1,500 universities. Covers search, knowledge, planning, learning, perception, and robotics with rigorous depth.
AI Ethics
Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence
Kate Crawford · 2021
A revelatory investigation into the material and political costs of AI — from lithium mines to data labourers. Critical for understanding AI's societal impact.
Human Compatible: Artificial Intelligence and the Problem of Control
Stuart Russell · 2019
A leading AI researcher makes the case that we need to fundamentally rethink how we build AI systems to ensure they remain beneficial to humanity.
Practical Guides
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Best for BeginnersAurelien Geron · 2022
The most practical ML book available. Takes you from zero to building production-ready models with Python, with clear code examples throughout.
The Prompt Engineering Guide
Free ResourceDAIR.AI · 2024
A comprehensive, regularly updated guide to prompt engineering techniques for LLMs. Covers zero-shot, few-shot, chain-of-thought, and advanced prompting strategies.
Ace the Data Science Interview
Nick Singh & Kevin Huo · 2021
201 real interview questions from top tech companies. Covers statistics, ML, SQL, product sense, and case studies — ideal for academics transitioning to industry.
Articles & Papers
Landmark research papers and critical analyses that every AI researcher should know — from the Transformer paper to AI safety and ethics.
Landmark Papers
Attention Is All You Need
LandmarkVaswani et al. · NeurIPS 2017 · 2017
The paper that introduced the Transformer architecture — the foundation of GPT, BERT, and virtually every modern language model.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin et al. · NAACL 2019 · 2018
Introduced bidirectional pre-training for language models, revolutionising NLP benchmarks and spawning an entire family of models.
Industry Research
GPT-4 Technical Report
OpenAI · arXiv · 2023
OpenAI's technical report on GPT-4 — a large multimodal model that exhibits human-level performance on various professional and academic benchmarks.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck et al. (Microsoft Research) · arXiv · 2023
Microsoft Research's extensive evaluation of GPT-4's capabilities across mathematics, coding, vision, and reasoning — arguing it shows 'sparks' of AGI.