Connectionists: Annotated History of Modern AI and Deep Learning

Tue Jan 17 13:45:42 EST 2023

Michael,

I agree, looking forward to reading it.

I think the cybernetics book.. with the transcriptions of the discussion is nice bit of time travel..

Best,

Steve

On 1/17/23 13:35, Gary Marcus wrote:
Wow. Chills down spine, in a good way. I did not know that and look forward to reading!

On Jan 17, 2023, at 10:32, Michael Arbib <arbib at usc.edu><mailto:arbib at usc.edu> wrote:

Now that Cybernetics has been brought into the conversation, and since I may be the only person who was both a PhD student of Norbert Wiener (for a while) and an RA for Warren McCulloch, I take the liberty of drawing attention to a memoir I wrote:

Arbib, M. A. (2018). From cybernetics to brain theory, and more: A memoir. Cognitive Systems Research, 50, 83-145.

A preprint is available on ResearchGate – just enter “Arbib ResearchGate Memoir” in your browser. There are ideas in there whose solution I still await ….

************************************

From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu><mailto:connectionists-bounces at mailman.srv.cs.cmu.edu> On Behalf Of Stephen José Hanson
Sent: Tuesday, January 17, 2023 5:13 AM
To: Sean Manion <stmanion at gmail.com><mailto:stmanion at gmail.com>; Gary Marcus <gary.marcus at nyu.edu><mailto:gary.marcus at nyu.edu>
Cc: connectionists at cs.cmu.edu<mailto:connectionists at cs.cmu.edu>
Subject: Re: Connectionists: Annotated History of Modern AI and Deep Learning

Sean,

What a wonderfult find!

I believe this is most likely a precursor to the Macy meetings 1946-1953, which was run by McCulloch.
It included the wonderful list from Wiener of the areas that "have obtained a degree of intimacy".

These meetings became called by the attendees-- CYBERNETICS.. and of course is the precursor to AI and Neural Networks, computational neuroscience etc..

https://press.uchicago.edu/ucp/books/book/distributed/C/bo23348570.html<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fpress.uchicago.edu%2Fucp%2Fbooks%2Fbook%2Fdistributed%2FC%2Fbo23348570.html__%3B!!LIr3w8kk_Xxm!rhOV1_I3SVtEWzbHAOD-b63GOlQ_CnsGhyQR9CixHIagtitUuZEV-Lr8y549an4wJVQrYiLaOuilVTjVXw%24&data=05%7C01%7Cjose%40rubic.rutgers.edu%7C1452a475741143ec71ab08daf8b99414%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C0%7C638095774087135620%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AETyfd%2BtZRq5KTUMUFuu2yKx3Jo%2BVjhIWwltEF0Hmq8%3D&reserved=0>

thanks for sharing!

Steve
On 1/17/23 00:28, Sean Manion wrote:
Thank you all for a great discussion, and of course Jürgen for your work on the annotated history that has kicked it off.

For reasons tangential to all of this, I have been recently reviewing some of the MIT Archives and found this invitation from Wiener, von Neumann, and Aiken to several individuals for a sometimes historically overlooked 2 day meeting that was held at Princeton in January 1945 on a "...field of effort, which as yet is not even named."

I thought some might find this of interest.

Cheers!

Sean

On Mon, Jan 16, 2023 at 11:51 PM Gary Marcus <gary.marcus at nyu.edu<mailto:gary.marcus at nyu.edu>> wrote:
Hi, Juergen,

Thanks for your reply.  Restricting your title to “modern” AI as you did is a start, but I think still not enough. For example, from what I understand about NNAISANCE, through talking with you and Bas Steunebrink, there’s quite a bit of hybrid AI in what you are doing at your company, not well represented in the review. The related open-access book certainly draws heavily on both traditions (https://link.springer.com/book/10.1007/978-3-031-08020-3<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fnam02.safelinks.protection.outlook.com%2F%3Furl%3Dhttps*3A*2F*2Flink.springer.com*2Fbook*2F10.1007*2F978-3-031-08020-3%26data%3D05*7C01*7Cjose*40rubic.rutgers.edu*7Cb3db76bd41ff4a59071908daf85da95a*7Cb92d2b234d35447093ff69aca6632ffe*7C1*7C0*7C638095378420227407*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C2000*7C*7C*7C%26sdata%3DnzjxkTj5pTpbFiPk8QGz0HvRgVoDGlRJnyo9vGsuALU*3D%26reserved%3D0__%3BJSUlJSUlJSUlJSUlJSUlJSUlJSUlJQ!!LIr3w8kk_Xxm!rhOV1_I3SVtEWzbHAOD-b63GOlQ_CnsGhyQR9CixHIagtitUuZEV-Lr8y549an4wJVQrYiLaOui_qF6Ftw%24&data=05%7C01%7Cjose%40rubic.rutgers.edu%7C1452a475741143ec71ab08daf8b99414%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C0%7C638095774087135620%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=DwiriPTVs6zmAqu9tOHf8yB%2FBbzI%2FkkOUa3sP8N%2F%2BZ0%3D&reserved=0>).

Likewise, there is plenty of eg symbolic planning in modern navigation systems, most robots etc; still plenty of use of symbolic trees in game playing; lots of people still use taxonomies and inheritance, etc., an AFAIK nobody has built a trustworthy virtual assistant, even in a narrow domain, with only deep learning. And so on.

In the end, it’s really a question about balance, which is what I think Andrzej was getting at; you go miles deep on the history of deep learning, which I respect, but just give relatively superficial pointers (not none!) outside that tradition. Definitely better, to be sure, in having at least a few pointers than in having none, and I would agree that the future is uncertain. I think you strike the right note there!

As an aside, saying that everything can be formulated as RL is maybe no more helpful than saying that everything we (currently) know how to do can be formulated in terms of Turing machine. True, but doesn’t carry you far enough in most real world applications. I personally see RL as part of an answer, but most useful in (and here we might partly agree) the context of systems with rich internal models of the world.

My own view is that we will get to more reliable AI only once the field more fully embraces the project of articulating how such models work and how they are developed.

Which is maybe the one place where you (eg https://arxiv.org/pdf/1803.10122.pdf<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fnam02.safelinks.protection.outlook.com%2F%3Furl%3Dhttps*3A*2F*2Farxiv.org*2Fpdf*2F1803.10122.pdf%26data%3D05*7C01*7Cjose*40rubic.rutgers.edu*7Cb3db76bd41ff4a59071908daf85da95a*7Cb92d2b234d35447093ff69aca6632ffe*7C1*7C0*7C638095378420227407*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C2000*7C*7C*7C%26sdata%3DZyJcnldLGH1FWLfLy*2B1OaQogSmVjZdOKu05SVnmQgyo*3D%26reserved%3D0__%3BJSUlJSUlJSUlJSUlJSUlJSUlJSUlJQ!!LIr3w8kk_Xxm!rhOV1_I3SVtEWzbHAOD-b63GOlQ_CnsGhyQR9CixHIagtitUuZEV-Lr8y549an4wJVQrYiLaOujbUxHYPQ%24&data=05%7C01%7Cjose%40rubic.rutgers.edu%7C1452a475741143ec71ab08daf8b99414%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C0%7C638095774087135620%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=3VYrP80rw%2F3QuIjY78iUVpbHcwB92S%2BQt%2FnuunWLlXY%3D&reserved=0>), Yann LeCun (eg https://openreview.net/forum?id=BZ5a1r-kVsf<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fnam02.safelinks.protection.outlook.com%2F%3Furl%3Dhttps*3A*2F*2Fopenreview.net*2Fforum*3Fid*3DBZ5a1r-kVsf%26data%3D05*7C01*7Cjose*40rubic.rutgers.edu*7Cb3db76bd41ff4a59071908daf85da95a*7Cb92d2b234d35447093ff69aca6632ffe*7C1*7C0*7C638095378420227407*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C2000*7C*7C*7C%26sdata%3DNciJImyaIct5w804QCbLcBdvC56Tb8s8oyS9Rn*2BGxhY*3D%26reserved%3D0__%3BJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!LIr3w8kk_Xxm!rhOV1_I3SVtEWzbHAOD-b63GOlQ_CnsGhyQR9CixHIagtitUuZEV-Lr8y549an4wJVQrYiLaOugZ5NfTNg%24&data=05%7C01%7Cjose%40rubic.rutgers.edu%7C1452a475741143ec71ab08daf8b99414%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C0%7C638095774087135620%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=gY2PVwNtOj%2F0VRS21tSZnVgpTNH5PDzrcCCLAJjt48c%3D&reserved=0>), and I (eg  https://arxiv.org/abs/2002.06177<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fnam02.safelinks.protection.outlook.com%2F%3Furl%3Dhttps*3A*2F*2Farxiv.org*2Fabs*2F2002.06177%26data%3D05*7C01*7Cjose*40rubic.rutgers.edu*7Cb3db76bd41ff4a59071908daf85da95a*7Cb92d2b234d35447093ff69aca6632ffe*7C1*7C0*7C638095378420227407*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C2000*7C*7C*7C%26sdata%3Dxk1Vn44bpzaJS*2Bgxa2RrYNREA4i1O*2Bmf2vInHJc76KE*3D%26reserved%3D0__%3BJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!LIr3w8kk_Xxm!rhOV1_I3SVtEWzbHAOD-b63GOlQ_CnsGhyQR9CixHIagtitUuZEV-Lr8y549an4wJVQrYiLaOugdXMd64Q%24&data=05%7C01%7Cjose%40rubic.rutgers.edu%7C1452a475741143ec71ab08daf8b99414%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C0%7C638095774087291850%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=PM5sXpl9WIc3PyU42LHyxeYybQsfSHLDWS33tjjDeVY%3D&reserved=0>) are most in agreement.

Best,
Gary

On Jan 15, 2023, at 23:04, Schmidhuber Juergen <juergen at idsia.ch<mailto:juergen at idsia.ch>> wrote:
Thanks for these thoughts, Gary!

1. Well, the survey is about the roots of “modern AI” (as opposed to all of AI) which is mostly driven by “deep learning.” Hence the focus on the latter and the URL "deep-learning-history.html.” On the other hand, many of the most famous modern AI applications actually combine deep learning and other cited techniques (more on this below).

Any problem of computer science can be formulated in the general reinforcement learning (RL) framework, and the survey points to ancient relevant techniques for search & planning, now often combined with NNs:

"Certain RL problems can be addressed through non-neural techniques invented long before the 1980s: Monte Carlo (tree) search (MC, 1949) [MOC1-5], dynamic programming (DP, 1953) [BEL53], artificial evolution (1954) [EVO1-7][TUR1] (unpublished), alpha-beta-pruning (1959) [S59], control theory and system identification (1950s) [KAL59][GLA85],  stochastic gradient descent (SGD, 1951) [STO51-52], and universal search techniques (1973) [AIT7].

Deep FNNs and RNNs, however, are useful tools for _improving_ certain types of RL. In the 1980s, concepts of function approximation and NNs were combined with system identification [WER87-89][MUN87][NGU89], DP and its online variant called Temporal Differences [TD1-3], artificial evolution [EVONN1-3] and policy gradients [GD1][PG1-3]. Many additional references on this can be found in Sec. 6 of the 2015 survey [DL1].

When there is a Markovian interface [PLAN3] to the environment such that the current input to the RL machine conveys all the information required to determine a next optimal action, RL with DP/TD/MC-based FNNs can be very successful, as shown in 1994 [TD2] (master-level backgammon player) and the 2010s [DM1-2a] (superhuman players for Go, chess, and other games). For more complex cases without Markovian interfaces, …”

Theoretically optimal planners/problem solvers based on algorithmic information theory are mentioned in Sec. 19.

2. Here a few relevant paragraphs from the intro:

"A history of AI written in the 1980s would have emphasized topics such as theorem proving [GOD][GOD34][ZU48][NS56], logic programming, expert systems, and heuristic search [FEI63,83][LEN83]. This would be in line with topics of a 1956 conference in Dartmouth, where the term "AI" was coined by John McCarthy as a way of describing an old area of research seeing renewed interest.

Practical AI dates back at least to 1914, when Leonardo Torres y Quevedo built the first working chess end game player [BRU1-4] (back then chess was considered as an activity restricted to the realms of intelligent creatures). AI theory dates back at least to 1931-34 when Kurt Gödel identified fundamental limits of any type of computation-based AI [GOD][BIB3][GOD21,a,b].

A history of AI written in the early 2000s would have put more emphasis on topics such as support vector machines and kernel methods [SVM1-4], Bayesian (actually Laplacian or possibly Saundersonian [STI83-85]) reasoning [BAY1-8][FI22] and other concepts of probability theory and statistics [MM1-5][NIL98][RUS95], decision trees, e.g. [MIT97], ensemble methods [ENS1-4], swarm intelligence [SW1], and evolutionary computation [EVO1-7][TUR1]. Why? Because back then such techniques drove many successful AI applications.

A history of AI written in the 2020s must emphasize concepts such as the even older chain rule [LEI07] and deep nonlinear artificial neural networks (NNs) trained by gradient descent [GD’], in particular, feedback-based recurrent networks, which are general computers whose programs are weight matrices [AC90]. Why? Because many of the most famous and most commercial recent AI applications depend on them [DL4]."

3. Regarding the future, you mentioned your hunch on neurosymbolic integration. While the survey speculates a bit about the future, it also says: "But who knows what kind of AI history will prevail 20 years from now?”

Juergen

On 14. Jan 2023, at 15:04, Gary Marcus <gary.marcus at nyu.edu<mailto:gary.marcus at nyu.edu>> wrote:

Dear Juergen,

You have made a good case that the history of deep learning is often misrepresented. But, by parity of reasoning, a few pointers to a tiny fraction of the work done in symbolic AI does not in any way make this a thorough and balanced exercise with respect to the field as a whole.

I am 100% with Andrzej Wichert, in thinking that vast areas of AI such as planning, reasoning, natural language understanding, robotics and knowledge representation are treated very superficially here. A few pointers to theorem proving and the like does not solve that.

Your essay is a fine if opinionated history of deep learning, with a special emphasis on your own work, but of somewhat limited value beyond a few terse references in explicating other approaches to AI. This would be ok if the title and aspiration didn’t aim for as a whole; if you really want the paper to reflect the field as a whole, and the ambitions of the title, you have more work to do.

My own hunch is that in a decade, maybe much sooner, a major emphasis of the field will be on neurosymbolic integration. Your own startup is heading in that direction, and the commericial desire to make LLMs reliable and truthful will also push in that direction.
Historians looking back on this paper will see too little about that roots of that trend documented here.

Gary

On Jan 14, 2023, at 12:42 AM, Schmidhuber Juergen <juergen at idsia.ch<mailto:juergen at idsia.ch>> wrote:

Dear Andrzej, thanks, but come on, the report cites lots of “symbolic” AI from theorem proving (e.g., Zuse 1948) to later surveys of expert systems and “traditional" AI. Note that Sec. 18 and Sec. 19 go back even much further in time (not even speaking of Sec. 20). The survey also explains why AI histories written in the 1980s/2000s/2020s differ. Here again the table of contents:

Sec. 1: Introduction
Sec. 2: 1676: The Chain Rule For Backward Credit Assignment
Sec. 3: Circa 1800: First Neural Net (NN) / Linear Regression / Shallow Learning
Sec. 4: 1920-1925: First Recurrent NN (RNN) Architecture. ~1972: First Learning RNNs
Sec. 5: 1958: Multilayer Feedforward NN (without Deep Learning)
Sec. 6: 1965: First Deep Learning
Sec. 7: 1967-68: Deep Learning by Stochastic Gradient Descent
Sec. 8: 1970: Backpropagation. 1982: For NNs. 1960: Precursor.
Sec. 9: 1979: First Deep Convolutional NN (1969: Rectified Linear Units)
Sec. 10: 1980s-90s: Graph NNs / Stochastic Delta Rule (Dropout) / More RNNs / Etc
Sec. 11: Feb 1990: Generative Adversarial Networks / Artificial Curiosity / NN Online Planners
Sec. 12: April 1990: NNs Learn to Generate Subgoals / Work on Command
Sec. 13: March 1991: NNs Learn to Program NNs. Transformers with Linearized Self-Attention
Sec. 14: April 1991: Deep Learning by Self-Supervised Pre-Training. Distilling NNs
Sec. 15: June 1991: Fundamental Deep Learning Problem: Vanishing/Exploding Gradients
Sec. 16: June 1991: Roots of Long Short-Term Memory / Highway Nets / ResNets
Sec. 17: 1980s-: NNs for Learning to Act Without a Teacher
Sec. 18: It's the Hardware, Stupid!
Sec. 19: But Don't Neglect the Theory of AI (Since 1931) and Computer Science
Sec. 20: The Broader Historic Context from Big Bang to Far Future
Sec. 21: Acknowledgments
Sec. 22: 555+ Partially Annotated References (many more in the award-winning survey [DL1])

Tweet: https://urldefense.proofpoint.com/v2/url?u=https-3A__twitter.com_SchmidhuberAI_status_1606333832956973060-3Fcxt-3DHHwWiMC8gYiH7MosAAAA&d=DwIDaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=oGn-OID5YOewbgo3j_HjFjI3I2N3hx-w0hoIfLR_JJsn8q5UZDYAl5HOHPY-87N5&s=nWCXLKazOjmixYrJVR0CMlR12PasGbAd8bsS6VZ10bk&e=<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fnam02.safelinks.protection.outlook.com%2F%3Furl%3Dhttps*3A*2F*2Furldefense.proofpoint.com*2Fv2*2Furl*3Fu*3Dhttps-3A__twitter.com_SchmidhuberAI_status_1606333832956973060-3Fcxt-3DHHwWiMC8gYiH7MosAAAA*26d*3DDwIDaQ*26c*3DslrrB7dE8n7gBJbeO0g-IQ*26r*3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ*26m*3DoGn-OID5YOewbgo3j_HjFjI3I2N3hx-w0hoIfLR_JJsn8q5UZDYAl5HOHPY-87N5*26s*3DnWCXLKazOjmixYrJVR0CMlR12PasGbAd8bsS6VZ10bk*26e*3D%26data%3D05*7C01*7Cjose*40rubic.rutgers.edu*7Cb3db76bd41ff4a59071908daf85da95a*7Cb92d2b234d35447093ff69aca6632ffe*7C1*7C0*7C638095378420227407*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C2000*7C*7C*7C%26sdata%3DbZfxmr1oj5alCicvKzzhXDN4q6855smPHdbBVbvcqSw*3D%26reserved%3D0__%3BJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSU!!LIr3w8kk_Xxm!rhOV1_I3SVtEWzbHAOD-b63GOlQ_CnsGhyQR9CixHIagtitUuZEV-Lr8y549an4wJVQrYiLaOugHyPKUQw%24&data=05%7C01%7Cjose%40rubic.rutgers.edu%7C1452a475741143ec71ab08daf8b99414%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C0%7C638095774087291850%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=fw09NaOz8sdYRihYVXwRMPPtE8fXHLGMvKSE3%2FF6smA%3D&reserved=0>

Jürgen

On 13. Jan 2023, at 14:40, Andrzej Wichert <andreas.wichert at tecnico.ulisboa.pt<mailto:andreas.wichert at tecnico.ulisboa.pt>> wrote:
Dear Juergen,
You make the same mistake at it was done in the earlier 1970. You identify deep learning with modern AI, the paper should be called instead "Annotated History of Deep Learning”
Otherwise, you ignore symbolical AI, like search, production systems, knowledge representation, search, planning etc., as if is not part of AI anymore (suggested by your title).
Best,
Andreas
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Prof. Auxiliar Andreas Wichert
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Instituto Superior Técnico - Universidade de Lisboa
Campus IST-Taguspark
Avenida Professor Cavaco Silva                 Phone: +351  214233231
2744-016 Porto Salvo, Portugal
On 13 Jan 2023, at 08:13, Schmidhuber Juergen <juergen at idsia.ch<mailto:juergen at idsia.ch>> wrote:
Machine learning is the science of credit assignment. My new survey credits the pioneers of deep learning and modern AI (supplementing my award-winning 2015 survey):
https://urldefense.proofpoint.com/v2/url?u=https-3A__arxiv.org_abs_2212.11279&d=DwIDaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=oGn-OID5YOewbgo3j_HjFjI3I2N3hx-w0hoIfLR_JJsn8q5UZDYAl5HOHPY-87N5&s=6E5_tonSfNtoMPw1fvFOm8UFm7tDVH7un_kbogNG_1w&e=<https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Fnam02.safelinks.protection.outlook.com%2F%3Furl%3Dhttps*3A*2F*2Furldefense.proofpoint.com*2Fv2*2Furl*3Fu*3Dhttps-3A__arxiv.org_abs_2212.11279*26d*3DDwIDaQ*26c*3DslrrB7dE8n7gBJbeO0g-IQ*26r*3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ*26m*3DoGn-OID5YOewbgo3j_HjFjI3I2N3hx-w0hoIfLR_JJsn8q5UZDYAl5HOHPY-87N5*26s*3D6E5_tonSfNtoMPw1fvFOm8UFm7tDVH7un_kbogNG_1w*26e*3D%26data%3D05*7C01*7Cjose*40rubic.rutgers.edu*7Cb3db76bd41ff4a59071908daf85da95a*7Cb92d2b234d35447093ff69aca6632ffe*7C1*7C0*7C638095378420227407*7CUnknown*7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0*3D*7C2000*7C*7C*7C%26sdata%3De*2Bf09tNf3tO8tY2jANTGEUnAipIJum92za3z*2FNZMbaw*3D%26reserved%3D0__%3BJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJQ!!LIr3w8kk_Xxm!rhOV1_I3SVtEWzbHAOD-b63GOlQ_CnsGhyQR9CixHIagtitUuZEV-Lr8y549an4wJVQrYiLaOuhyBWAubA%24&data=05%7C01%7Cjose%40rubic.rutgers.edu%7C1452a475741143ec71ab08daf8b99414%7Cb92d2b234d35447093ff69aca6632ffe%7C1%7C0%7C638095774087291850%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=4QTHlyK4AA0OFVtU%2F8p0dH2RBs2gbykjFiP%2FQwgwdl0%3D&reserved=0>
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This was already reviewed by several deep learning pioneers and other experts. Nevertheless, let me know under juergen at idsia.ch<mailto:juergen at idsia.ch> if you can spot any remaining error or have suggestions for improvements.
Happy New Year!
Jürgen

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Stephen José Hanson

Professor, Psychology Department

Director, RUBIC (Rutgers University Brain Imaging Center)

Member, Executive Committee, RUCCS

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Stephen José Hanson
Professor, Psychology Department
Director, RUBIC (Rutgers University Brain Imaging Center)
Member, Executive Committee, RUCCS
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