Discover the Papers That Shaped
Artificial Intelligence
A reference guide to important AI research papers. Explore the studies that introduced fundamental ideas and methods still used in artificial intelligence today.
Key Milestones in AI Research
A look at the most influential research papers that powered AI’s rise.
A Logical Calculus of the Ideas Immanent in Nervous Activity - McCulloch & Pitts
The first mathematical model of neuron‑like networks and showed how logical operations could be done by simple nerve cells — Starting point for neural networks.
Cybernetics: Or Control and Communication in the Animal and the Machine – Norbert Wiener
Introduced cybernetics, applied feedback and information theory to both machines and living beings, influencing later AI and control‑system research.
Computing Machinery and Intelligence – Alan Turing
Proposed the imitation game (Turing Test) and asked how to tell if a machine can think, which set the tone for AI research.
Dartmouth Summer Research Project on AI proposal – McCarthy et al.
This proposal said machines could simulate any aspect of intelligence and called for a summer study, kick‑starting AI as a field.
The Perceptron – Frank Rosenblatt
Introduced the perceptron learning rule and showed that simple neural nets could learn to classify patterns.
Machine Perception of Three‑Dimensional Solids – Lawrence G. Roberts
Proposed the first hidden‑surface removal algorithm, laying the base for computer vision and graphics.
Learning Representations by Back‑Propagating Errors – Rumelhart et al.
Introduced back‑propagation, allowing multi‑layer neural nets to learn and enabling modern deep learning.
Learning to Predict by the Methods of Temporal Differences – Sutton
Presented the TD learning method which became a core idea in reinforcement learning.
Probabilistic Reasoning in Intelligent Systems – Judea Pearl
Introduced Bayesian networks and algorithms that made reasoning under uncertainty practical.
Long Short‑Term Memory – Hochreiter & Schmidhuber
Proposed LSTM cells to overcome vanishing gradients, enabling sequence models for speech and language.
ImageNet Classification with Deep CNNs – Krizhevsky et al.
Showed that deep CNNs trained on GPUs vastly improve image recognition, sparking the deep‑learning boom.
Generative Adversarial Nets – Goodfellow et al.
Introduced GANs where a generator and discriminator compete to produce realistic samples, giving rise to powerful generative models.
Mastering the Game of Go with Deep Neural Networks and Tree Search – Silver et al.
Showed how deep networks plus tree search beat human Go champions, proving deep RL’s power.
Attention Is All You Need – Vaswani et al.
The paper introduced the transformer architecture based solely on attention, which became the foundation of modern NLP and vision models.
BERT: Pre‑training of Deep Bidirectional Transformers – Devlin et al.
Introduced masked‑language pre‑training on transformers, which quickly became a standard technique in NLP.
Recent Research Papers
Recent impactful researches in AI !
Classic Research Papers
Papers that shaped artificial intelligence field !