AI Books and Papers

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

A

  • AI 2041: Ten Visions for Our Future Amazon
    Type: Book
    Author: Kai-Fu Lee (with Chen Qiufan)
    Year: 2021
    Notes: Combines imaginative storytelling with technical insights to explore how AI will transform our world by 2041.

  • Artificial Intelligence: A Modern Approach Amazon
    Type: Book
    Authors: Stuart Russell & Peter Norvig
    Notes: Often considered the definitive textbook for introductory AI.

  • Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence Amazon
    Type: Book
    Author: Kate Crawford
    Year: 2021
    Notes: A critical exploration of the environmental, political, and social implications of AI.

  • Artificial Intelligence for Business: A Roadmap for Getting Started with AI Amazon
    Type: Book
    Author: Jason L. Anderson, Jeffrey L. Coveyduc Year: 2020
    Notes: Provides guidance for business leaders on integrating AI technologies into enterprise strategies.

  • Artificial Intelligence in Finance: A Python-Based Guide Amazon
    Type: Book
    Author: Yves Hilpisch
    Year: 2020
    Notes: Explores how AI is transforming the financial industry through theory and hands‑on practice.

  • Attention Is All You Need
    Type: Research Paper
    Authors: Vaswani et al.
    Year: 2017 last revised 2023 Notes: Introduced the Transformer architecture that revolutionized natural language processing.

B

  • Bayesian Reasoning and Machine Learning Amazon
    Type: Book
    Author: David Barber
    Notes: Provides an in-depth look at Bayesian methods in AI and machine learning.

  • Beyond Intelligence: The Future of AI and Humanity Amazon
    Type: Book
    Author: Abhishek thakur
    Year: 2025
    Notes: Examines how AI is reshaping human capabilities and the nature of life itself.

C

  • Computing Machinery and Intelligence
    Type: Academic Paper
    Author: Alan Turing
    Year: 1950
    Notes: The seminal work that posed the question “Can machines think?” and introduced the Turing Test.

D

  • Deep Learning Amazon
    Type: Book
    Authors: Ian Goodfellow, Yoshua Bengio & Aaron Courville
    Year: 2016
    Notes: A comprehensive resource on deep learning theory and practice.

  • Deep Residual Learning for Image Recognition
    Type: Research Paper
    Authors: He et al.
    Year: 2015
    Notes: Introduced ResNet architectures that have become a cornerstone in computer vision.

E

F

G

  • Generative Adversarial Networks
    Type: Research Paper
    Authors: Goodfellow et al.
    Year: 2014
    Notes: Introduced GANs, a major breakthrough for generative modeling in AI.

  • Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World Amazon
    Type: Book
    Author: Cade Metz
    Year: 2021
    Notes: Chronicles the people and breakthroughs that have driven the AI revolution.

H

  • Human Compatible: Artificial Intelligence and the Problem of Control Amazon
    Type: Book
    Author: Stuart Russell
    Year: 2019
    Notes: Explores the challenges in designing AI systems that are aligned with human values.

I

  • Intelligence Unbound: The Future of Uploaded and Machine Minds Amazon
    Type: Book
    Editors: Russell Blackford & Damien Broderick
    Year: 2014
    Notes: Explores the future implications of AI and human enhancement.

  • ImageNet Classification with Deep Convolutional Neural Networks
    Type: Research Paper
    Authors: Krizhevsky, Sutskever & Hinton
    Year: 2012
    Notes: A landmark paper that sparked widespread adoption of deep convolutional networks.

J

K

  • Knowledge Representation and Reasoning Amazon
    Type: Book
    Authors: Ronald Brachman & Hector Levesque
    Year: 2004
    Notes: A foundational text on methods for representing and processing knowledge in AI systems.

L

  • Life 3.0: Being Human in the Age of Artificial Intelligence Amazon
    Type: Book
    Author: Max Tegmark
    Year: 2017
    Notes: Explores the future of AI and its impact on society.

M

  • Machine Learning: A Probabilistic Perspective Amazon
    Type: Book
    Author: Kevin P. Murphy
    Year: 2012
    Notes: A comprehensive guide to probabilistic approaches in machine learning.

  • Mastering the Game of Go with Deep Neural Networks and Tree Search
    Type: Research Paper
    Authors: Silver et al.
    Year: 2016
    Notes: Describes the breakthrough AlphaGo system that combined deep learning and tree search.

N

  • Neural Networks and Deep Learning
    Type: Book (Online Resource)
    Author: Michael Nielsen
    Year: 2015
    Notes: An accessible introduction to the concepts behind neural networks.

O

P

  • Probabilistic Graphical Models: Principles and Techniques Amazon
    Type: Book
    Authors: Daphne Koller & Nir Friedman
    Year: 2009
    Notes: A comprehensive resource on modeling complex distributions with graphs.

  • Playing Atari with Deep Reinforcement Learning
    Type: Research Paper
    Authors: Mnih et al.
    Year: 2013
    Notes: Pioneering work that demonstrated deep reinforcement learning on video games.

Q

R

  • Real World AI: A Practical Guide for Responsible Machine Learning Amazon
    Type: Book
    Authors: Alyssa Simpson Rochwerger & Wilson Pang
    Year: 2021
    Notes: Explores methods for building AI systems that are both effective and ethically sound.

  • Reinforcement Learning: An Introduction Amazon
    Type: Book
    Authors: Richard S. Sutton & Andrew G. Barto
    Year: 2018 (Second Edition)
    Notes: The go-to textbook for understanding reinforcement learning fundamentals.

  • Rethinking Generalization in Deep Learning
    Type: Research Paper
    Authors: Zhang et al.
    Year: 2021
    Notes: Investigates how deep networks generalize beyond training data.

S

  • Speech and Language Processing Amazon
    Type: Book
    Authors: Daniel Jurafsky & James H. Martin
    Notes: A comprehensive resource on natural language processing and speech recognition.

  • Superintelligence: Paths, Dangers, Strategies Amazon
    Type: Book
    Author: Nick Bostrom
    Year: 2014
    Notes: An influential work on the future risks and strategic challenges of advanced AI.

T

  • The Age of AI: And Our Human Future Amazon
    Type: Book
    Authors: Henry Kissinger, Eric Schmidt & Daniel Huttenlocher
    Year: 2021
    Notes: Examines the profound impact of AI on global affairs, society, and human identity.

  • The Book of Why: The New Science of Cause and Effect Amazon
    Type: Book
    Author: Judea Pearl
    Year: 2018
    Notes: Explores causal reasoning and its importance in the development of AI.

U

V

W

  • Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Amazon
    Type: Book
    Author: Cathy O’Neil
    Year: 2016
    Notes: Explores the societal impacts and ethical concerns of algorithmic decision-making.

  • Weakly Supervised Learning
    Type: Research Paper
    Author: Zhou
    Year: 2018
    Notes: Reviews techniques for learning with limited or imprecise labeled data.

X

Y

Z

This list does not claim to be complete. The field of AI is changing extremely quickly and is subject to almost daily changes. And we are sure that we missed here a lot of books and papers. So feel free to send us more input for our lists.