Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.

[Stephen José Hanson]

Geoffrey.

we should at least get his name right, as his citation proclivities are 
bandied about.

On 1/1/22 5:18 PM, Asim Roy wrote:
> In fairness to Jeffrey Hinton, he did acknowledge the work of Amari in a debate about connectionism at the ICNN’97 (International Conference on Neural Networks) in Houston. He literally said "Amari invented back propagation" and Amari was sitting next to him. I still have a recording of that debate.
>
> Asim Roy
> Professor, Information Systems
> Arizona State University
> https://isearch.asu.edu/profile/9973
> https://lifeboat.com/ex/bios.asim.roy
>
>
>
> -----Original Message-----
> From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> On Behalf Of Schmidhuber Juergen
> Sent: Friday, December 31, 2021 11:00 AM
> To: connectionists at cs.cmu.edu
> Subject: Re: Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.
>
> Sure, Steve, perceptron/Adaline/other similar methods of the 1950s/60s are not quite the same, but the obvious origin and ancestor of all those single-layer  “shallow learning” architectures/methods is indeed linear regression; today’s simplest NNs minimizing mean squared error are exactly what they had 2 centuries ago. And the first working deep learning methods of the 1960s did NOT really require “modern” backprop (published in 1970 by Linnainmaa [BP1-5]). For example, Ivakhnenko & Lapa (1965) [DEEP1-2] incrementally trained and pruned their deep networks layer by layer to learn internal representations, using regression and a separate validation set. Amari (1967-68)[GD1] used stochastic gradient descent [STO51-52] to learn internal representations WITHOUT “modern" backprop in his multilayer perceptrons. Jürgen
>   
>
>> On 31 Dec 2021, at 18:24, Stephen José Hanson <jose at rubic.rutgers.edu> wrote:
>>
>> Well the perceptron is closer to logistic regression... but the heaviside function  of course is <0,1>   so technically not related to linear regression which is using covariance to estimate betas...
>>
>> does that matter?  Yes, if you want to be hyper correct--as this appears to be-- Berkson (1944) coined the logit.. as log odds.. for probabilistic classification.. this was formally developed by Cox in the early 60s, so unlikely even in this case to be a precursor to perceptron.
>>
>> My point was that DL requires both Learning algorithm (BP) and an
>> architecture.. which seems to me much more responsible for the the success of Dl.
>>
>> S
>>
>>
>>
>> On 12/31/21 4:03 AM, Schmidhuber Juergen wrote:
>>> Steve, this is not about machine learning in general, just about deep
>>> learning vs shallow learning. However, I added the Pandemonium -
>>> thanks for that! You ask: how is a linear regressor of 1800
>>> (Gauss/Legendre) related to a linear neural network? It's formally
>>> equivalent, of course! (The only difference is that the weights are
>>> often called beta_i rather than w_i.) Shallow learning: one adaptive
>>> layer. Deep learning: many adaptive layers. Cheers, Jürgen
>>>
>>>
>>>
>>>
>>>> On 31 Dec 2021, at 00:28, Stephen José Hanson
>>>> <jose at rubic.rutgers.edu>
>>>>   wrote:
>>>>
>>>> Despite the comprehensive feel of this it still appears to me to be  too focused on Back-propagation per se.. (except for that pesky Gauss/Legendre ref--which still baffles me at least how this is related to a "neural network"), and at the same time it appears to be missing other more general epoch-conceptually relevant cases, say:
>>>>
>>>> Oliver Selfridge  and his Pandemonium model.. which was a hierarchical feature analysis system.. which certainly was in the air during the Neural network learning heyday...in fact, Minsky cites Selfridge as one of his mentors.
>>>>
>>>> Arthur Samuels:  Checker playing system.. which learned a evaluation function from a hierarchical search.
>>>>
>>>> Rosenblatt's advisor was Egon Brunswick.. who was a gestalt perceptual psychologist who introduced the concept that the world was stochastic and the the organism had to adapt to this variance somehow.. he called it "probabilistic functionalism"  which brought attention to learning, perception and decision theory, certainly all piece parts of what we call neural networks.
>>>>
>>>> There are many other such examples that influenced or provided context for the yeasty mix that was 1940s and 1950s where Neural Networks  first appeared partly due to PItts and McCulloch which entangled the human brain with computation and early computers themselves.
>>>>
>>>> I just don't see this as didactic, in the sense of a conceptual view of the  multidimensional history of the         field, as opposed to  a 1-dimensional exegesis of mathematical threads through various statistical algorithms.
>>>>
>>>> Steve
>>>>
>>>> On 12/30/21 1:03 PM, Schmidhuber Juergen wrote:
>>>>
>>>>> Dear connectionists,
>>>>>
>>>>> in the wake of massive open online peer review, public comments on the connectionists mailing list [CONN21] and many additional private comments (some by well-known deep learning pioneers) helped to update and improve upon version 1 of the report. The essential statements of the text remain unchanged as their accuracy remains unchallenged. I'd like to thank everyone from the bottom of my heart for their feedback up until this point and hope everyone will be satisfied with the changes. Here is the revised version 2 with over 300 references:
>>>>>
>>>>>
>>>>>
>>>>> https://urldefense.com/v3/__https://people.idsia.ch/*juergen/scient
>>>>> ific-integrity-turing-award-deep-learning.html__;fg!!IKRxdwAv5BmarQ
>>>>> !NsJ4lf4yO2BDIBzlUVfGKvTtf_QXY8dpZaHzCSzHCvEhXGJUTyRTzZybDQg-DZY$
>>>>>
>>>>>
>>>>>
>>>>> In particular, Sec. II has become a brief history of deep learning up to the 1970s:
>>>>>
>>>>> Some of the most powerful NN architectures (i.e., recurrent NNs) were discussed in 1943 by McCulloch and Pitts [MC43] and formally analyzed in 1956 by Kleene [K56] - the closely related prior work in physics by Lenz, Ising, Kramers, and Wannier dates back to the 1920s [L20][I25][K41][W45]. In 1948, Turing wrote up ideas related to artificial evolution [TUR1] and learning NNs. He failed to formally publish his ideas though, which explains the obscurity of his thoughts here. Minsky's simple neural SNARC computer dates back to 1951. Rosenblatt's perceptron with a single adaptive layer learned in 1958 [R58] (Joseph [R61] mentions an earlier perceptron-like device by Farley & Clark); Widrow & Hoff's similar Adaline learned in 1962 [WID62]. Such single-layer "shallow learning" actually started around 1800 when Gauss & Legendre introduced linear regression and the method of least squares [DL1-2] - a famous early example of pattern recognition and generalization from training d!
>   at!
>>>   a through a parameterized predictor is Gauss' rediscovery of the asteroid Ceres based on previous astronomical observations. Deeper multilayer perceptrons (MLPs) were discussed by Steinbuch [ST61-95] (1961), Joseph [R61] (1961), and Rosenblatt [R62] (1962), who wrote about "back-propagating errors" in an MLP with a hidden layer [R62], but did not yet have a general deep learning algorithm for deep MLPs  (what's now called backpropagation is quite different and was first published by Linnainmaa in 1970 [BP1-BP5][BPA-C]). Successful learning in deep architectures started in 1965 when Ivakhnenko & Lapa published the first general, working learning algorithms for deep MLPs with arbitrarily many hidden layers (already containing the now popular multiplicative gates) [DEEP1-2][DL1-2]. A paper of 1971 [DEEP2] already described a deep learning net with 8 layers, trained by their highly cited method which was still popular in the new millennium [DL2], especially in Eastern Europe!
>   , w!
>>>   here much of Machine Learning was born [MIR](Sec. 1)[R8]. LBH !
>>>   failed to
>>>   cite this, just like they failed to cite Amari [GD1], who in 1967 proposed stochastic gradient descent [STO51-52] (SGD) for MLPs and whose implementation [GD2,GD2a] (with Saito) learned internal representations at a time when compute was billions of times more expensive than today (see also Tsypkin's work [GDa-b]). (In 1972, Amari also published what was later sometimes called the Hopfield network or Amari-Hopfield Network [AMH1-3].) Fukushima's now widely used deep convolutional NN architecture was first introduced in the 1970s [CNN1].
>>>
>>>>> Jürgen
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> ******************************
>>>>>
>>>>> On 27 Oct 2021, at 10:52, Schmidhuber Juergen
>>>>>
>>>>> <juergen at idsia.ch>
>>>>>
>>>>>   wrote:
>>>>>
>>>>> Hi, fellow artificial neural network enthusiasts!
>>>>>
>>>>> The connectionists mailing list is perhaps the oldest mailing list on ANNs, and many neural net pioneers are still subscribed to it. I am hoping that some of them - as well as their contemporaries - might be able to provide additional valuable insights into the history of the field.
>>>>>
>>>>> Following the great success of massive open online peer review
>>>>> (MOOR) for my 2015 survey of deep learning (now the most cited
>>>>> article ever published in the journal Neural Networks), I've
>>>>> decided to put forward another piece for MOOR. I want to thank the
>>>>> many experts who have already provided me with comments on it.
>>>>> Please send additional relevant references and suggestions for
>>>>> improvements for the following draft directly to me at
>>>>>
>>>>> juergen at idsia.ch
>>>>>
>>>>> :
>>>>>
>>>>>
>>>>>
>>>>> https://urldefense.com/v3/__https://people.idsia.ch/*juergen/scient
>>>>> ific-integrity-turing-award-deep-learning.html__;fg!!IKRxdwAv5BmarQ
>>>>> !NsJ4lf4yO2BDIBzlUVfGKvTtf_QXY8dpZaHzCSzHCvEhXGJUTyRTzZybDQg-DZY$
>>>>>
>>>>>
>>>>>
>>>>> The above is a point-for-point critique of factual errors in ACM's justification of the ACM A. M. Turing Award for deep learning and a critique of the Turing Lecture published by ACM in July 2021. This work can also be seen as a short history of deep learning, at least as far as ACM's errors and the Turing Lecture are concerned.
>>>>>
>>>>> I know that some view this as a controversial topic. However, it is the very nature of science to resolve controversies through facts. Credit assignment is as core to scientific history as it is to machine learning. My aim is to ensure that the true history of our field is preserved for posterity.
>>>>>
>>>>> Thank you all in advance for your help!
>>>>>
>>>>> Jürgen Schmidhuber
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>> --
>>>> <signature.png>
>>>>
>> --
>> <signature.png>
>
>
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