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Foundational
Papers

in computer science & artificial intelligence

10 frames · 1936 → 2020 · read 

  1. 01

    On Computable Numbers, with an Application to the Entscheidungsproblem

    Alan Turing · 1936 · Proceedings of the London Mathematical Society

    Defines the abstract machine — tape, states, a head that reads and writes — that every computer is still an instance of.

  2. 02

    A Mathematical Theory of Communication

    Claude Shannon · 1948 · Bell System Technical Journal

    Turns information into a measurable quantity and names its unit: the bit.

  3. 03

    The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain

    Frank Rosenblatt · 1958 · Psychological Review

    The first trainable artificial neuron — a machine that learns its weights from examples.

  4. 04

    Perceptrons

    Marvin Minsky & Seymour Papert · 1969 · MIT Press

    Proves what a single-layer perceptron cannot learn — XOR among them — cooling the field for a decade. A book; no official free PDF.

  5. 05

    Time, Clocks, and the Ordering of Events in a Distributed System

    Leslie Lamport · 1978 · Communications of the ACM

    Shows that “before” in a distributed system is about causal order, not wall-clock time.

  6. 06

    Learning Representations by Back-Propagating Errors

    Rumelhart, Hinton & Williams · 1986 · Nature

    Backpropagation makes multi-layer networks trainable — the answer to the 1969 critique.

  7. 07

    The Anatomy of a Large-Scale Hypertextual Web Search Engine

    Sergey Brin & Lawrence Page · 1998 · Computer Networks

    PageRank ranks a page by who links to it — the paper that became Google.

  8. 08

    ImageNet Classification with Deep Convolutional Neural Networks

    Krizhevsky, Sutskever & Hinton · 2012 · NeurIPS

    A deep CNN trained on GPUs crushes ImageNet, igniting the modern deep-learning era.

  9. 09

    Attention Is All You Need

    Vaswani et al. · 2017 · Google Brain / NeurIPS

    Replaces recurrence with attention — the Transformer behind today’s language models.

  10. 10

    Language Models are Few-Shot Learners

    Brown et al. · 2020 · OpenAI / NeurIPS

    Scaling a Transformer to 175 billion parameters lets it learn new tasks from a few examples in the prompt.

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