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Papers that changed AI, with a plain-language explainer for each.
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- 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.
- 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.