Connectionists: 2024 Nobel Prize in Physics goes to Hopfield and Hinton

[Anand Ramamoorthy]

Dear Axel,                           Yes, Poincare, Lorentz et al were influences (just as Mach was for GR) and even remarkable developments such as theories of relativity do not occur in vacuo.  
Einstein was awarded the Physics Nobel for his work on the photoelectric effect and his contributions to theoretical physics (The Nobel Prize in Physics 1921)

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The Nobel Prize in Physics 1921

The Nobel Prize in Physics 1921 was awarded to Albert Einstein "for his services to Theoretical Physics, and esp...
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.I believe the special and general theories were a tad controversial at the time and this is perhaps reflected in the *services to theoretical physics* bit but it is not a direct acknowledgment of said body of work. Odd considering how General Relativity gave more accurate estimates for the precession of the perihelion of Mercury and had some empirical support following Eddington's famous expedition ( a few years prior to the date of the award). 
Best, 





Anand Ramamoorthy
 

    On Wednesday 9 October 2024 at 10:37:00 BST, Axel Hutt <axel.hutt at inria.fr> wrote:  
 
 Dear all,
essentially all research is based on the current knowledge and the work of previous generations.

For instance, Albert Einstein knew the Lorentz equations and their meaning 
from the work of Hendrik Lorentz developed at the end of the 19th century.
Henri Poincaré, Oliver Heaviside and many others have worked on them and their meaning assuming a world of aether. Then, 1905 Einstein derived them without assuming aether in his special relativity theory (surely motivated and heavily influenced by these previous studies) and won the Nobel Prize for it in 1921. Even Einstein's own wife (also a physicist) contributed much in scientific discussions with him.

Axel

----- On 9 Oct, 2024, at 01:55, Brad Wyble <bwyble at gmail.com> wrote:

We really can trace the current AI boom back to John Carmack who wrote Doom, which ushered in the era of GPU-hungry computing.  Credit where it's due please.  
On Tue, Oct 8, 2024 at 4:10 PM Stephen José Hanson <jose at rubic.rutgers.edu> wrote:


Hi Steve,



The problem every writer encounters is what can be concluded as resolved knowledge rather then new/novel knowledge. In the law this is of course “legal precedence”, so does the reference refer to a recent precedent, or does the one for the 17th century hold precedence? In the present case, I agree that calculating gradients of functions using the chain rule was invented (Legendre -- Least squares) far before Rumelhart and Hinton applied it to error gradients in acyclic/cyclic networks, and of course there were others as you say, in the 20th century that also applied error gradient to networks (Parker, Le cun et al). Schmidhuber says all that matters is the “math” not the applied context. However, I seriously doubt that Legendre could have imagined using gradients of function error through succesive application in a acylic network would have produced a hierarchical kinship relationship (distinguishing between an italian and english family mother, fathers, sons, aunts, grandparents etc.) in the hidden units of a network, simply by observing individuals with fixed feature relations. I think any reasonable person would  maintain that this application is completely novel and could not be predicted in or out of context from the “math” and certainly not from the 18th century. Hidden units were new in this context and their representational nature was novel, in this context.   Scope of reference is also based on logical or causal proximity to the reference. In this case, referencing Darwin or Newton in all biological or physics papers should be based on the outcome of the metaphorical test of whether the recent results tie back to original source in some direct line, for example, was Oswald’s grandfather responsible for the death of President John F. Kennedy? Failing this test, suggests that the older reference may not have scope. But of course this can be subjective.

Steve



On 10/8/24 2:38 PM, Grossberg, Stephen wrote:


Actually, Paul Werbos developed back propagation into its modern form, and worked out computational examples, for his 1974 Harvard PhD thesis.

 

Then David Parker rediscovered it in 1982, etc.

 

Schmidhuber provides an excellent and wide-ranging history of many contributors to Deep Learning and its antecedents:

 

https://www.sciencedirect.com/science/article/pii/S0893608014002135?casa_token=k47YCzFwcFEAAAAA:me_ZGF5brDqjRihq5kHyeQBzyUMYBypJ3neSinZ-cPn1pnyi69DGyM9eKSyLsdiRf759I77c7w

 

This article has been cited over 23,000 times.

 

From:Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of Stephen José Hanson<jose at rubic.rutgers.edu>
Date: Tuesday, October 8, 2024 at 2:25 PM
To: Jonathan D. Cohen <jdc at princeton.edu>, Connectionists <connectionists at cs.cmu.edu>
Subject: Re: Connectionists: 2024 Nobel Prize in Physics goes to Hopfield and Hinton

Yes, Jon good point here, and  although there is a through line from Hopfield to Hinton and Sejnowski.. Ie boltzmann machines and onto DL and LLMs

Dave of course invented BP, Geoff would always say.. his contribution was to try and talk Dave out of it as it had so many computational problems and could be in no way considered biologically plausible.

Steve

On 10/8/24 8:47 AM, Jonathan D. Cohen wrote:

I’d like to add, in this context, a note in memoriam of David Rumelhart, who was an integral contributor to the work honored by today’s Nobel Prize. jdc 
-- Stephen José HansonProfessor, Psychology DepartmentDirector, RUBIC (Rutgers University Brain Imaging Center)Member, Executive Committee, RUCCS
-- 
Stephen José Hanson
Professor, Psychology Department
Director, RUBIC (Rutgers University Brain Imaging Center)
Member, Executive Committee, RUCCS


-- 
Brad Wyble (he/him)


-- 
Axel Hutt
Directeur de Recherche
Equipe MIMESIS - INRIA Nancy Grand Est 
Equipe MLMS - iCube Strasbourg
Bâtiment NextMed
2, rue Marie Hamm
67000 Strasbourg, France
https://mimesis.inria.fr/members/axel-hutt/
  
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