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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.

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Key Milestones in AI Research

A look at the most influential research papers that powered AI’s rise.

1943

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.

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1948

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.

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1950

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.

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1955

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.

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1958

The Perceptron – Frank Rosenblatt

Introduced the perceptron learning rule and showed that simple neural nets could learn to classify patterns.

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1963

Machine Perception of Three‑Dimensional Solids – Lawrence G. Roberts

Proposed the first hidden‑surface removal algorithm, laying the base for computer vision and graphics.

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1986

Learning Representations by Back‑Propagating Errors – Rumelhart et al.

Introduced back‑propagation, allowing multi‑layer neural nets to learn and enabling modern deep learning.

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1988

Learning to Predict by the Methods of Temporal Differences – Sutton

Presented the TD learning method which became a core idea in reinforcement learning.

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1988

Probabilistic Reasoning in Intelligent Systems – Judea Pearl

Introduced Bayesian networks and algorithms that made reasoning under uncertainty practical.

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1997​

Long Short‑Term Memory – Hochreiter & Schmidhuber

Proposed LSTM cells to overcome vanishing gradients, enabling sequence models for speech and language.

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2012

ImageNet Classification with Deep CNNs – Krizhevsky et al.

Showed that deep CNNs trained on GPUs vastly improve image recognition, sparking the deep‑learning boom.

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2014

Generative Adversarial Nets – Goodfellow et al.

Introduced GANs where a generator and discriminator compete to produce realistic samples, giving rise to powerful generative models.

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2016

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.

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2017

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.

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2018

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.

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Recent Research Papers

Recent impactful researches in AI !

 
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Classic Research Papers

Papers that shaped artificial intelligence field !

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