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

[Tsvi Achler]

Since the title of the thread is Scientific Integrity, I want to point out
some issues about trends in academia and then especially focusing on the
connectionist community.

In general analyzing impact factors etc the most important progress gets
silenced until the mainstream picks it up Impact Factiors in novel research
www.nber.org/.../working_papers/w22180/w22180.pdf
<https://www.nber.org/system/files/working_papers/w22180/w22180.pdf?fbclid=IwAR1zHhU4wmkrHASTaE-6zwIs6gI9-FxZcCED3BETxUJlMsbN_2hNbmJAmOA>
 and
often this may take a generation
https://www.nber.org/.../does-science-advance-one-funeral...
<https://www.nber.org/digest/mar16/does-science-advance-one-funeral-time?fbclid=IwAR1Lodsf1bzje-yQU9DvoZE2__S6R7UPEgY1_LxZCSLdoAYnj-uco0JuyVk>
  .

The connectionist field is stuck on feedforward networks and variants such
as with inhibition of competitors (e.g. lateral inhibition), or other
variants that are sometimes labeled as recurrent networks for learning time
where the feedforward networks can be rewound in time.

This stasis is specifically occuring with the popularity of deep learning.
This is often portrayed as neurally plausible connectionism but requires an
implausible amount of rehearsal and is not connectionist if this rehearsal
is not implemented with neurons (see video link for further clarification).

Models which have true feedback (e.g. back to their own inputs) cannot
learn by backpropagation but there is plenty of evidence these types of
connections exist in the brain and are used during recognition. Thus they
get ignored: no talks in universities, no featuring in "premier" journals
and no funding.

But they are important and may negate the need for rehearsal as needed in
feedforward methods.  Thus may be essential for moving connectionism
forward.

If the community is truly dedicated to brain motivated algorithms, I
recommend giving more time to networks other than feedforward networks.

Video:
https://www.youtube.com/watch?v=m2qee6j5eew&list=PL4nMP8F3B7bg3cNWWwLG8BX-wER2PeB-3&index=2

Sincerely,
Tsvi Achler



On Wed, Oct 27, 2021 at 2:24 AM 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://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html
>
> 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
>
>
>
>
>
>
>
>
>
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