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John Hopfield

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For his father, the Polish-American spectroscopist, see John J. Hopfield (spectroscopist).

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. He is known for the development of the Hopfield network. Previous to its invention, research in artificial intelligence (AI) was in a decay period or AI winter, Hopfield work revitalized large scale interest in this field.[2][3]

John Hopfield
Hopfield in 2016
Born
John Joseph Hopfield

(1933-07-15) July 15, 1933 (age 91)
Chicago, Illinois, U.S.
EducationSwarthmore College (AB)
Cornell University (PhD)
Known forHopfield network
Modern Hopfield network
Hopfield dielectric
Polariton
Kinetic proofreading
Awards
Scientific career
FieldsPhysics
Molecular biology
Complex systems
Neuroscience
InstitutionsBell Labs
Princeton University
University of California, Berkeley
California Institute of Technology
ThesisA quantum-mechanical theory of the contribution of excitons to the complex dielectric constant of crystals (1958)
Doctoral advisorAlbert Overhauser
Doctoral studentsSteven Girvin
Gerald Mahan
Bertrand Halperin
David J. C. MacKay
José Onuchic
Terry Sejnowski
Erik Winfree
Li Zhaoping

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.[4][2] He has been awarded various major physics awards for his work in multidisciplinary fields including condensed matter physics, statistical physics and biophysics.

Biography

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Early life and education

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John Joseph Hopfield was born in 1933 in Chicago[1] to physicists John Joseph Hopfield (born in Poland as Jan Józef Chmielewski) and Helen Hopfield (née Staff).[5][6]

Hopfield received a Bachelor of Arts with a major in physics from Swarthmore College in Pennsylvania in 1954 and a Doctor of Philosophy in physics from Cornell University in 1958.[1] His doctoral dissertation was titled "A quantum-mechanical theory of the contribution of excitons to the complex dielectric constant of crystals".[7] His doctoral advisor was Albert Overhauser.[1]

Career

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He spent two years in the theory group at Bell Laboratories working on optical properties of semiconductors working with David Gilbert Thomas[8] and later on a quantitative model to describe the cooperative behavior of hemoglobin in collaboration with Robert G. Shulman.[1][5][9] Subsequently he became a faculty member at University of California, Berkeley (physics, 1961–1964),[2] Princeton University (physics, 1964–1980),[2] California Institute of Technology (Caltech, chemistry and biology, 1980–1997)[2] and again at Princeton (1997–),[2][1] where he is the Howard A. Prior Professor of Molecular Biology, emeritus.[10]

In 1976, he participated in a science short film on the structure of the hemoglobin, featuring Linus Pauling.[11]

From 1981 to 1983 Richard Feynman, Carver Mead and Hopfield gave a one-year course at Caltech called "The Physics of Computation".[12] Hopfield was invited by Feynman to teach on associative neural networks.[12][13] This collaboration inspired the Computation and Neural Systems PhD program at Caltech in 1986, co-founded by Hopfield.[14][12]

His former PhD students include Gerald Mahan (PhD in 1964),[15] Bertrand Halperin (1965),[16] Steven Girvin (1977),[16] Terry Sejnowski (1978),[16] Erik Winfree (1998),[16] José Onuchic (1987),[16] Li Zhaoping (1990)[17] and David J. C. MacKay (1992).[16]

Work

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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.[18][19] He wrote: "The polarization field 'particles' analogous to photons will be called 'polaritons'."[19] His polariton model is sometimes known as the Hopfield dielectric.[20]

From 1959 to 1963, Hopfield and David G. Thomas investigated the exciton structure of cadmium sulfide from its reflection spectra. Their experiments and theoretical models allowed to understand the optical spectroscopy of II-VI semiconductor compounds.[21]

Condensed matter physicist Philip W. Anderson reported that John Hopfield was his "hidden collaborator" for his 1961–1970 works on the Anderson impurity model which explained the Kondo effect. Hopfield was not included as a co-author in the papers but Anderson admitted the importance of Hopfield's contribution in various of his writings.[22]

William C. Topp and Hopfield introduced the concept of norm-conserving pseudopotentials in 1973.[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.[26][27]

Hopfield published his first paper in neuroscience in 1982, titled "Neural networks and physical systems with emergent collective computational abilities" where he introduced what is now known as Hopfield network, a type of artificial network that can serve as a content-addressable memory, made of binary neurons that can be 'on' or 'off'.[28][5] He extended his formalism to continuous activation functions in 1984.[29] The 1982 and 1984 papers represent his two most cited works.[10] Hopfield has said that the inspiration came from his knowledge of spin glasses from his collaborations with P. W. Anderson.[30]

Together with David W. Tank, Hopfield developed a method in 1985–1986[31][32] for solving discrete optimization problems based on the continuous-time dynamics using a Hopfield network with continuous activation function. The optimization problem was encoded in the interaction parameters (weights) of the network. The effective temperature of the analog system was gradually decreased, as in global optimization with simulated annealing.[33]

Hopfield is one of the pioneers of the critical brain hypothesis, he was the first to link neural networks with self-organized criticality in reference to the Olami–Feder–Christensen model for earthquakes in 1994.[34][35] In 1995, Hopfield and Andreas V. Herz showed that avalanches in neural activity follow power law distribution associated to earthquakes.[36][37]

The original Hopfield networks had a limited memory, this problem was addressed by Hopfield and Dimitry Krotov in 2016.[33][38] Large memory storage Hopfield networks are now known as modern Hopfield networks.[39]

Views on artificial intelligence

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In March 2023, Hopfield signed an open letter titled "Pause Giant AI Experiments", calling for a pause on the training of artificial intelligence (AI) systems more powerful than GPT-4. 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.[40][41]

Upon being jointly awarded the 2024 Nobel Prize in Physics, Hopfield revealed he was very unnerved by recent advances in AI capabilities, and said "as a physicist, I'm very unnerved by something which has no control".[42] In a followup press conference in Princeton University, Hopfield compared AI with discovery of nuclear fission, which led to nuclear weapons and nuclear power.[2]

Awards and honors

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The 1969 ceremony of the Oliver E. Buckley Prize of condensed matter physics. Luis Walter Alvarez (left) congratulates David Gilbert Thomas (middle) and John Hopfield (right).

Hopfield received a Sloan Research Fellowship[43] in 1962 and as his father, he received a Guggenheim Fellowship (1968).[44] Hopfield was elected as a member of the American Physical Society (APS) in 1969,[45][46] a member of the National Academy of Sciences in 1973, a member of the American Academy of Arts and Sciences in 1975, and a member of the American Philosophical Society in 1988.[47][48][49] He was the President of the APS in 2006.[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".[51]

In 1983 he was awarded the MacArthur Foundational Prize by the MacArthur Fellows Program.[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.[9] In 1988, he received the Michelson–Morley Award by Case Western Reserve University.[54] Hopfield received the Neural Networks Pioneer Award in 1997 by the Institute of Electrical and Electronics Engineers (IEEE).[55]

He was awarded the Dirac Medal of the International Centre for Theoretical Physics in 2001 "for important contributions in an impressively broad spectrum of scientific subjects"[56][57] including "an entirely different [collective] organizing principle in olfaction" and "a new principle in which neural function can take advantage of the temporal structure of the 'spiking' interneural communication".[57]

Hopfield received the Harold Pender Award in 2002 for his accomplishments in computational neuroscience and neural engineering from the Moore School of Electrical Engineering, University of Pennsylvania.[58] He received the Albert Einstein World Award of Science in 2005 in the field of life sciences.[59] In 2007, he gave the Fritz London Memorial Lecture at Duke University, titled "How Do We Think So Fast? From Neurons to Brain Computation".[60] Hopfield received the IEEE Frank Rosenblatt Award in 2009 for his contributions in understanding information processing in biological systems.[61] In 2012 he was awarded the Swartz Prize by the Society for Neuroscience.[62] In 2019 he was awarded the Benjamin Franklin Medal in Physics by the Franklin Institute,[63] and in 2022 he shared the Boltzmann Medal award in statistical physics with Deepak Dhar.[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]

References

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  1. ^ a b c d e f "Hopfield, John J." Physics History Network American Institute of Physics. Retrieved October 8, 2024.
  2. ^ a b c d e f g Taylor, D.B.; et al. (October 8, 2024), "Nobel Physics Prize Awarded for Pioneering A.I. Research by 2 Scientists", The New York Times, archived from the original on October 8, 2024, retrieved October 8, 2024
  3. ^ Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3.
  4. ^ "Press release: The Nobel Prize in Physics 2024". NobelPrize.org. Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  5. ^ a b c Lindsay, Grace (March 4, 2021). Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain. Bloomsbury Publishing. ISBN 978-1-4729-6645-2. Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  6. ^ "American Men of Science: A Biographical Directory". Science Press. 1966.
  7. ^ John Hopfield (1958). A Quantum-Mechanical Theory of the Contribution of Excitons to the Complex Dielectric Constant of Crystals. ISBN 979-8-6578-5817-4. OCLC 63226906. Wikidata Q130468423.
  8. ^ Orton, John W. (December 11, 2008). The Story of Semiconductors. OUP Oxford. ISBN 978-0-19-156544-1.
  9. ^ a b "American Physical Society Meets in Baltimore". Physics Today. 38 (3): 87–93. March 1, 1985. Bibcode:1985PhT....38c..87.. doi:10.1063/1.2814495. ISSN 0031-9228.
  10. ^ a b Office of Communications (October 8, 2024). "Princeton's John Hopfield receives Nobel Prize in physics". Princeton University. Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  11. ^ "The Life and the Structure of Hemoglobin, American Institute of Physics". Orego State Documentary History of Linus Pauling. 1976. Retrieved October 9, 2024.
  12. ^ a b c Hey, Anthony (March 8, 2018). Feynman And Computation. CRC Press. ISBN 978-0-429-96900-3.
  13. ^ Hillis, W. Daniel (February 1, 1989). "Richard Feynman and the Connection Machine". Physics Today. 42 (2): 78–83. Bibcode:1989PhT....42b..78H. doi:10.1063/1.881196. ISSN 0031-9228.
  14. ^ "Caltech Celebrates 30 Years of its Computation and Neural Systems Option | Caltech Alumni". Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  15. ^ "Gerald Mahan Obituary (1937 - 2021) - New York, NY - The Oregonian". Legacy.com. Retrieved October 13, 2024.
  16. ^ a b c d e f John Joseph Hopfield at the Mathematics Genealogy Project
  17. ^ Li, Zhaoping (1990). A model of the olfactory bulb and beyond (phd thesis). California Institute of Technology.
  18. ^ Hopfield, J. J. (December 1, 1958). "Theory of the Contribution of Excitons to the Complex Dielectric Constant of Crystals". Physical Review. 112 (5): 1555–1567. Bibcode:1958PhRv..112.1555H. doi:10.1103/PhysRev.112.1555. ISSN 0031-899X.
  19. ^ a b Agranovich, Vladimir M. (February 12, 2009). Excitations in Organic Solids. OUP Oxford. ISBN 978-0-19-155291-5. Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  20. ^ Huttner, B.; Barnett, S. M. (1992). "Dispersion and Loss in a Hopfield Dielectric". Europhysics Letters. 18 (6): 487. Bibcode:1992EL.....18..487H. doi:10.1209/0295-5075/18/6/003. ISSN 0295-5075. Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  21. ^ Reynolds, D. C.; Litton, C. W.; Collins, T. C. (1965). "Some Optical Properties of Group II-VI Semiconductors (I)". Physica Status Solidi B. 9 (3): 645–684. Bibcode:1965PSSBR...9..645R. doi:10.1002/pssb.19650090302. ISSN 0370-1972.
  22. ^ Zangwill, Andrew (January 8, 2021). A Mind Over Matter: Philip Anderson and the Physics of the Very Many. Oxford University Press. ISBN 978-0-19-264055-0.
  23. ^ Topp, William C.; Hopfield, John J. (February 15, 1973). "Chemically Motivated Pseudopotential for Sodium". Physical Review B. 7 (4): 1295–1303. Bibcode:1973PhRvB...7.1295T. doi:10.1103/PhysRevB.7.1295. ISSN 0556-2805.
  24. ^ Martin, Richard M. (August 27, 2020). Electronic Structure: Basic Theory and Practical Methods. Cambridge University Press. ISBN 978-1-108-42990-0.
  25. ^ Marx, Dominik; Hutter, Jürg (April 30, 2009). Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods. Cambridge University Press. ISBN 978-1-139-47719-2.
  26. ^ Hopfield, J. J. (1974). "Kinetic Proofreading: A New Mechanism for Reducing Errors in Biosynthetic Processes Requiring High Specificity". Proceedings of the National Academy of Sciences. 71 (10): 4135–4139. Bibcode:1974PNAS...71.4135H. doi:10.1073/pnas.71.10.4135. ISSN 0027-8424. PMC 434344. PMID 4530290.
  27. ^ Flyvbjerg, Henrik; Jülicher, Frank; Ormos, Pal; David, Francois (July 1, 2003). Physics of Bio-Molecules and Cells: Les Houches Session LXXV, 2–27 July 2001. Springer Science & Business Media. ISBN 978-3-540-45701-5. Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  28. ^ Hopfield, J J (April 1982). "Neural networks and physical systems with emergent collective computational abilities". Proceedings of the National Academy of Sciences of the United States of America. 79 (8): 2554–2558. Bibcode:1982PNAS...79.2554H. doi:10.1073/pnas.79.8.2554. ISSN 0027-8424. PMC 346238. PMID 6953413.
  29. ^ Hopfield, J J (1984). "Neurons with graded response have collective computational properties like those of two-state neurons". Proceedings of the National Academy of Sciences of the United States of America. 81 (10): 3088–3092. Bibcode:1984PNAS...81.3088H. doi:10.1073/pnas.81.10.3088. ISSN 0027-8424. PMC 345226. PMID 6587342.
  30. ^ Hopfield, John J. (March 1, 2014). "Whatever Happened to Solid State Physics?". Annual Review of Condensed Matter Physics. 5 (1): 1–13. Bibcode:2014ARCMP...5....1H. doi:10.1146/annurev-conmatphys-031113-133924. ISSN 1947-5454.
  31. ^ Hopfield, J. J.; Tank, D. W. (July 1, 1985). ""Neural" computation of decisions in optimization problems". Biological Cybernetics. 52 (3): 141–152. doi:10.1007/BF00339943. ISSN 1432-0770. PMID 4027280. Archived from the original on October 8, 2024. Retrieved October 8, 2024.
  32. ^ Hopfield, John J.; Tank, David W. (August 8, 1986). "Computing with Neural Circuits: A Model". Science. 233 (4764): 625–633. Bibcode:1986Sci...233..625H. doi:10.1126/science.3755256. ISSN 0036-8075. PMID 3755256. Archived from the original on April 14, 2024. Retrieved October 8, 2024.
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  34. ^ Pruessner, Gunnar (August 30, 2012). Self-Organised Criticality: Theory, Models and Characterisation. Cambridge University Press. ISBN 978-0-521-85335-4.
  35. ^ Hopfield, John J. (February 1, 1994). "Neurons, Dynamics and Computation". Physics Today. 47 (2): 40–46. Bibcode:1994PhT....47b..40H. doi:10.1063/1.881412. ISSN 0031-9228.
  36. ^ Hopfield, J J; Herz, A V (July 18, 1995). "Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons". Proceedings of the National Academy of Sciences. 92 (15): 6655–6662. Bibcode:1995PNAS...92.6655H. doi:10.1073/pnas.92.15.6655. ISSN 0027-8424. PMC 41391. PMID 7624307.
  37. ^ Beggs, John (2007). "Neuronal avalanche". Scholarpedia. 2 (1): 1344. Bibcode:2007SchpJ...2.1344B. doi:10.4249/scholarpedia.1344. ISSN 1941-6016.
  38. ^ Krotov, Dmitry; Hopfield, John J. (2016). "Dense Associative Memory for Pattern Recognition". Advances in Neural Information Processing Systems. 29. Curran Associates, Inc. arXiv:1606.01164. Archived from the original on June 19, 2024. Retrieved October 8, 2024.
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