Нобелевские премии 2024 и искусственный интеллект. Физика: Джон Хопфилд и нейросети имени его

По прошествии недели стали появляться различные точки зрения, которые отличаются от мнения Нобелевского комитета. Со своей стороны я должен обязательно упомянуть про критическое мнение Юргена Шмитхубера, изобретателя LSTM. Цитирую: 

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To reach a broader audience, I made a popular tweet (see below) on the 2024 Physics Nobel Prize, which unfortunately isn't really about physics, but rewards plagiarism and misattribution in Artificial Intelligence (AI). In fact, AI has a major problem that's now even invading other fields (such as physics): the wrong people are credited for inventions of others. Some people have lost their titles or jobs due to plagiarism, e.g., Harvard's former president, but how can advisors now continue to tell their students that they should avoid plagiarism at all costs?

Of course, it is well known that plagiarism can be either "unintentional" or "intentional or reckless" [PLAG1-6], and the more innocent of the two may very well be partially the case here. But science has a well-established way of dealing with "multiple discovery" and plagiarism, be it unintentional [PLAG1-6][CONN21] or not [FAKE2], based on facts such as time stamps of publications and patents. The deontology of science requires that unintentional plagiarists correct their publications through errata and then credit the original sources properly in the future. The awardees didn't; instead they kept collecting citations for inventions of other researchers [DLP].
Original Nobel Foundation tweet:
https://x.com/NobelPrize/status/1843589140455272810

My response (now 1/7th as popular as the original):
https://x.com/SchmidhuberAI/status/1844022724328394780

Here is the text of the tweet:

The Nobel Prize in Physics 2024 for Hopfield & Hinton rewards plagiarism and incorrect attribution in computer science. It's mostly about Amari's "Hopfield network" and the "Boltzmann Machine."

1. The Lenz-Ising recurrent architecture with neuron-like elements was published in 1925 [L20][I24][I25]. In 1972, Shun-Ichi Amari made it adaptive such that it could learn to associate input patterns with output patterns by changing its connection weights [AMH1]. However, Amari is only briefly cited in the "Scientific Background to the Nobel Prize in Physics 2024." Unfortunately, Amari's net was later called the "Hopfield network." Hopfield republished it 10 years later [AMH2], without citing Amari, not even in later papers.

2. The related Boltzmann Machine paper by Ackley, Hinton, and Sejnowski (1985)[BM] was about learning internal representations in hidden units of neural networks (NNs)[S20]. It didn't cite the first working algorithm for deep learning of internal representations by Ivakhnenko & Lapa (Ukraine, 1965)[DEEP1-2][HIN]. It didn't cite Amari's separate work (1967-68)[GD1-2] on learning internal representations in deep NNs end-to-end through stochastic gradient descent (SGD). Not even the later surveys by the authors [S20][DL3][DLP] nor the "Scientific Background to the Nobel Prize in Physics 2024" mention these origins of deep learning. ([BM] also did not cite relevant prior work by Sherrington & Kirkpatrick [SK75] & Glauber [G63].)

3. The Nobel Committee also lauds Hinton et al.'s 2006 method for layer-wise pretraining of deep NNs (2006)[UN4]. However, this work neither cited the original layer-wise training of deep NNs by Ivakhnenko & Lapa (1965)[DEEP1-2] nor the original work on unsupervised pretraining of deep NNs (1991)[UN0-1][DLP].
4. The "Popular information" says: “At the end of the 1960s, some discouraging theoretical results caused many researchers to suspect that these neural networks would never be of any real use." However, deep learning research was obviously alive and kicking in the 1960s-70s, especially outside of the Anglosphere [DEEP1-2][GD1-3][CNN1][DL1-2][DLP][DLH].
5. Many additional cases of plagiarism and incorrect attribution can be found in the following reference [DLP], which also contains the other references above. One can start with Sec. 3:

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