Field
Computation, information, algorithms, and the machine-learning systems driving the modern artificial-intelligence era.
AlphaFold solved a 50-year grand challenge in biology: predicting a protein's three-dimensional shape from its amino-acid sequence. Its accuracy approached that of experimental methods, transforming structural biology.
The paper that introduced the Transformer, an architecture built entirely on attention mechanisms with no recurrence or convolution. It became the foundation of modern large language models such as GPT and BERT.
AlphaGo became the first program to beat a professional human at the ancient board game Go, long considered a grand challenge for AI because of its astronomical complexity. It combined deep neural networks with Monte Carlo tree search.
A landmark review by three pioneers of the field, synthesising how deep neural networks learn layered representations of data. It explains why depth works and surveys the advances that made deep learning dominant across vision, speech, and language.
Goodfellow and colleagues introduced generative adversarial networks (GANs), a way to train generative models by pitting two networks against each other — one creating fake data, the other trying to detect it — until the fakes are indistinguishable from real data.
'AlexNet' showed that deep convolutional neural networks, trained on GPUs, could crush the competition at large-scale image recognition. Its dramatic win on the ImageNet benchmark ignited the deep-learning revolution. (Linked DOI is the journal version of record; the original appeared at NeurIPS 2012.)
The paper describing the prototype of Google. Brin and Page presented a web search engine that ranks pages using the link structure of the web (PageRank), producing far more relevant results than keyword matching alone.
Dijkstra's three-page note introduced two classic graph algorithms, including the shortest-path algorithm that now bears his name. It finds the minimum-cost route from a source to all other nodes in a weighted graph.
Shannon founded information theory. He defined information quantitatively in terms of entropy, introduced the 'bit', and proved fundamental limits on how much data a noisy channel can carry — and that error-free communication is possible up to that limit.
Turing introduced an abstract machine — now called the Turing machine — to make 'computation' precise. He used it to prove that some problems are undecidable, settling Hilbert's Entscheidungsproblem in the negative and founding the theory of computation. (Read in 1936; published in the 1937 volume.)