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Papers that changed AI, with a plain-language explainer for each.
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- 1998
The Anatomy of a Large-Scale Hypertextual Web Search Engine
A 1998 conference paper by Sergey Brin and Lawrence Page describing PageRank, an algorithm that ranks web pages by the number and quality of links pointing to them, and the search engine built on that algorithm.
- 1998
MNIST database of handwritten digits
Proceedings of the IEEE
MNIST is a database of 60,000 training and 10,000 test images of handwritten digits, normalized to 28×28 pixels and described in the 1998 paper by LeCun and colleagues as a shared benchmark for document recognition.
- 1997
Long Short-Term Memory
Neural Computation
Published in 1997 by Sepp Hochreiter and Jürgen Schmidhuber, the LSTM paper introduced a memory cell with gates and a "constant error carousel" that lets recurrent networks learn across long time gaps. It shaped the recurrent models that came before Transformers.
- 1995
Support-vector networks
Machine Learning
Corinna Cortes and Vladimir Vapnik's 1995 paper presents a learning machine for two-group classification that maps inputs nonlinearly into a high-dimensional feature space, constructs a linear decision surface, and extends earlier work from separable to non-separable data.
- 1993
Building a Large Annotated Corpus of English: The Penn Treebank
Computational Linguistics
A 1993 journal article by Mitchell P. Marcus, Beatrice Santorini and Mary Ann Marcinkiewicz describing the Penn Treebank: over one million words of Wall Street Journal text annotated with syntactic bracketing, built as a resource for work on English sentence structure.
- 1992
A Training Algorithm for Optimal Margin Classifiers
Published in 1992, this paper by Boser, Guyon and Vapnik presents a training algorithm that maximizes the margin between training patterns and the decision boundary, with experiments on optical character recognition.[^1]
- 1992
Q-learning
Machine Learning
A 1992 technical note by Watkins and Dayan that set out Q-learning, an incremental and computationally light way for an agent to learn optimal action values, and proved it converges to the optimum with probability 1.
- 1989
Backpropagation Applied to Handwritten Zip Code Recognition
Neural Computation
In 1989 LeCun and colleagues at AT&T Bell Laboratories trained a convolutional network with backpropagation to read handwritten zip-code digits from U.S. mail, reaching 5.0% test error. The work showed that the convolutional design could be learned end to end on a real task.
- 1986
Learning representations by back-propagating errors
Nature
In a 1986 Nature paper, Rumelhart, Hinton and Williams showed that backpropagation lets hidden units in a multilayer network learn useful internal features of a task. The method is standard in most neural networks today.
- 1985
A Learning Algorithm for Boltzmann Machines
Cognitive Science 9
A 1985 Cognitive Science paper by Ackley, Hinton and Sejnowski that presents a general learning rule for connectionist networks and reports simple examples of efficient internal representations.
- 1982
Neural networks and physical systems with emergent collective computational abilities
PNAS
A 1982 paper by John J. Hopfield showing that large networks of simple neuron-like units can collectively store and recall memories. It became known as the Hopfield network, and the work was recognised by the 2024 Nobel Prize in Physics.
- 1969
Perceptrons: An Introduction to Computational Geometry
A 1969 book by Marvin Minsky and Seymour Papert that proved mathematical limits on what single-layer perceptrons can compute — notably that they cannot compute parity or connectedness — and has long been credited with steering research away from neural networks.
- 1958
The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
Psychological Review
In 1958 Frank Rosenblatt described the perceptron, a model whose connection strengths change with training so that it learns to classify stimuli by trial and error. It became the starting point for trainable neural networks.
- 1948
A Mathematical Theory of Communication
Bell System Technical Journal 27
C. E. Shannon's 1948 paper set out a mathematical framework for communication, defining information in bits, channel capacity, and entropy, and treating messages statistically while setting aside semantic meaning.
- 1943
A Logical Calculus of the Ideas Immanent in Nervous Activity
Bull. Math. Biophys.
In 1943 Warren McCulloch and Walter Pitts described a simplified model of a neuron and showed that networks of such units can carry out logical reasoning, an early link between brain models and computation.