NAAI Annual AI Reading List 2026

Essential Books & Papers for the Age of Artificial Intelligence

Official Release Statement

On the occasion of World Book Day,
the National Academy of Artificial Intelligence is pleased to officially release:

NAAI Annual AI Reading List 2026

This initiative aims to provide a structured, systematic, and authoritative knowledge framework for the global artificial intelligence community, including researchers, engineers, and interdisciplinary scholars.

In an era where knowledge is increasingly generated, processed, and mediated by artificial intelligence systems, reading remains not only relevant, but fundamental. It is through reading that deep understanding is cultivated, intellectual boundaries are expanded, and the foundations of responsible AI are established.

This list is not merely a recommendation—it is a systematic articulation of knowledge in the age of AI.

I. Top 10 Foundational AI Books

1. Artificial Intelligence: A Modern Approach
   — Stuart Russell · Peter Norvig

2. Deep Learning
   — Ian Goodfellow · Yoshua Bengio · Aaron Courville

3. Pattern Recognition and Machine Learning
   — Christopher M. Bishop

4. The Elements of Statistical Learning
   — Trevor Hastie · Robert Tibshirani · Jerome Friedman

5. Reinforcement Learning: An Introduction
   — Richard S. Sutton · Andrew G. Barto

6. Life 3.0
   — Max Tegmark

7. Superintelligence
   — Nick Bostrom

8. Human Compatible
   — Stuart Russell

9. Prediction Machines
   — Ajay Agrawal · Joshua Gans · Avi Goldfarb

10. Atlas of AI
    — Kate Crawford

 II. Top 10 Landmark AI Papers

1. Attention Is All You Need
   — Ashish Vaswani et al., 2017

2. ImageNet Classification with Deep Convolutional Neural Networks
   — Alex Krizhevsky et al., 2012

3. Deep Residual Learning for Image Recognition
   — Kaiming He et al., 2015

4. Generative Adversarial Nets
   — Ian Goodfellow et al., 2014

5. Playing Atari with Deep Reinforcement Learning
   — Volodymyr Mnih et al., 2013

6. BERT: Pre-training of Deep Bidirectional Transformers
   — Jacob Devlin et al., 2018

7. Scaling Laws for Neural Language Models
   — Jared Kaplan et al., 2020

8. Neural Radiance Fields
   — Ben Mildenhall et al., 2020

9. Segment Anything
   — Alexander Kirillov et al., 2023

10. GPT-4 Technical Report
    — OpenAI, 2023

 III. Interdisciplinary AI Readings (Top 5)

1. The Age of Surveillance Capitalism
   — Shoshana Zuboff

2. The Alignment Problem
   — Brian Christian

3. AI Ethics
   — Mark Coeckelbergh

4. The Master Algorithm
   — Pedro Domingos

5. Machines of Loving Grace
   — John Markoff

 Academic Note

This list is curated based on the following principles:

Foundational contributions to the development of AI

Long-term impact on both theory and practice

Interdisciplinary relevance and intellectual depth

Sustained academic and societal significance

 Closing Statement

In the age of artificial intelligence,
reading is no longer merely about acquiring information—
it is a deliberate act of shaping understanding and defining intellectual boundaries.

National Academy of Artificial Intelligence (NAAI)
Official Release · 2026