John Joseph Hopfield (born July 15, 1933)[1] is an American physicist and emeritus professor of Princeton University, most widely known for his study of associative neural networks in 1982.

[2][3] In 2024 Hopfield, along with Geoffrey Hinton, was awarded the Nobel Prize in Physics for their foundational contributions to machine learning, particularly through their work on artificial neural networks.

[16] In his doctoral work of 1958, he wrote on the interaction of excitons in crystals, coining the term polariton for a quasiparticle that appears in solid-state physics.

[23][24][25] In 1974 he introduced a mechanism for error correction in biochemical reactions known as kinetic proofreading to explain the accuracy of DNA replication.

[34][35] In 1995, Hopfield and Andreas V. Herz showed that avalanches in neural activity follow power law distribution associated to earthquakes.

The letter, signed by over 30,000 individuals including AI researchers Yoshua Bengio and Stuart Russell, cited risks such as human obsolescence and society-wide loss of control.

[50] In 1969 Hopfield and David Gilbert Thomas were awarded the Oliver E. Buckley Prize of condensed matter physics by the APS "for their joint work combining theory and experiment which has advanced the understanding of the interaction of light with solids".

[52] In 1985, Hopfield received the Golden Plate Award of the American Academy of Achievement[53] and the Max Delbruck Prize in Biophysics by the APS.

[54] Hopfield received the Neural Networks Pioneer Award in 1997 by the Institute of Electrical and Electronics Engineers (IEEE).

[60] Hopfield received the IEEE Frank Rosenblatt Award in 2009 for his contributions in understanding information processing in biological systems.

[64] He was jointly awarded the 2024 Nobel Prize in Physics with Geoffrey E. Hinton for "foundational discoveries and inventions that enable machine learning with artificial neural networks".

[65][66] In 2025 he was awarded the Queen Elizabeth Prize for Engineering jointly with Yoshua Bengio, Bill Dally, Geoffrey E. Hinton, Yann LeCun, Jen-Hsun Huang and Fei-Fei Li for the development of modern machine learning.