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I would find it hard to enter a scientific community if it is not
scholarly.<br>
<br>
Each of us can do our bit to be scholarly, to set an example, if not
a warning, to the next generation. <br>
<br>
Zhaoping<br>
<br>
<div class="moz-cite-prefix">On 28/10/2021 15:27, Stephen José
Hanson wrote:<br>
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cite="mid:2f1d9928-543f-f4a0-feab-5a5a0cc1d4d7@rubic.rutgers.edu">
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<p>Well, to popularize is not to invent. <br>
</p>
<p>Many of Juergen's concerns could be solved with some
scholarship, such that authors look sometime before 2006 for
other relevant references.</p>
<p>This isn't a social issue.. good science writers know they
didn't invent the algorithms they are describing for AI
applications.</p>
<p>OTOH, Dave Rumelhart, who introduction of the backprop learning
methods, often gets confused for gradient descent and <br>
consequently, Newton *should* be referenced for gosh sakes! <br>
</p>
<p> But keep in mind: Context matters. The PDP framework was
pretty exclusively about Cognitive Science not about how to
solve multivariable engineering problems. The real value of
Dave and PDP, was framing associative learning in networks and
how that might provide a foot-hold in understanding cognitive
function in the brain. It was no accident that before Dave
became very ill, he was working in Cognitive Neuroscience and
doing Brain scanning research.</p>
<p>Sure, if we work at it everything is connected to everything,
but other then historical exegesis, this is useless for paradigm
change and scientific forward motion.</p>
<p>Steve<br>
</p>
<div class="moz-cite-prefix">On 10/28/21 8:49 AM, Jonathan D.
Cohen wrote:<br>
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As a friendly amendment to both Randy and Danko’s comments, it
is also worth noting that science is an *intrinsically social*
endeavor, and therefore communication is a fundamental factor.
This may help explain why the *last* person to invent or
discover something is the one who gets the [social] credit.
That is, giving credit to those who disseminate may even have
normative value. After all, if a tree falls in the forrest… As
for those who care more about discovery and invention than
dissemination, well, for them credit assignment may not be as
important ;^).
<div class="">
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<div class="">jdc<br class="">
<div class="">
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<blockquote type="cite" class="">
<div class="">On Oct 28, 2021, at 4:23 AM, Danko
Nikolic <<a
href="mailto:danko.nikolic@gmail.com" class=""
moz-do-not-send="true">danko.nikolic@gmail.com</a>>
wrote:</div>
<br class="Apple-interchange-newline">
<div class="">
<div dir="auto" class="">Yes Randall, sadly so. I
have seen similar examples in neuroscience and
philosophy of mind. Often, (but not always), you
have to be the one who popularizes the thing to
get the credit. Sometimes, you can get away, you
just do the hard conceptual work and others
doing for you the (also hard) marketing work.
The best bet is doing both by yourself. Still no
guarantee.
<div dir="auto" class=""><br class="">
</div>
<div dir="auto" class="">Danko<br class="">
<div dir="auto" class=""><br class="">
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<br class="">
<div class="gmail_quote">
<div dir="ltr" class="gmail_attr">On Thu, 28 Oct
2021, 10:13 Randall O'Reilly <<a
href="mailto:oreilly@ucdavis.edu" class=""
moz-do-not-send="true">oreilly@ucdavis.edu</a>>
wrote:<br class="">
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<blockquote class="gmail_quote" style="margin:0
0 0 .8ex;border-left:1px #ccc
solid;padding-left:1ex"> I vaguely remember
someone making an interesting case a while
back that it is the *last* person to invent
something that gets all the credit. This is
almost by definition: once it is sufficiently
widely known, nobody can successfully reinvent
it; conversely, if it can be successfully
reinvented, then the previous attempts failed
for one reason or another (which may have
nothing to do with the merit of the work in
question).<br class="">
<br class="">
For example, I remember being surprised how
little Einstein added to what was already
established by Lorentz and others, at the
mathematical level, in the theory of special
relativity. But he put those equations into a
conceptual framework that obviously changed
our understanding of basic physical concepts.
Sometimes, it is not the basic equations etc
that matter: it is the big picture vision.<br
class="">
<br class="">
Cheers,<br class="">
- Randy<br class="">
<br class="">
> On Oct 27, 2021, at 12:52 AM, Schmidhuber
Juergen <<a href="mailto:juergen@idsia.ch"
target="_blank" rel="noreferrer" class=""
moz-do-not-send="true">juergen@idsia.ch</a>>
wrote:<br class="">
> <br class="">
> Hi, fellow artificial neural network
enthusiasts!<br class="">
> <br class="">
> 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.<br class="">
> <br class="">
> 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 <a href="mailto:juergen@idsia.ch"
target="_blank" rel="noreferrer" class=""
moz-do-not-send="true">juergen@idsia.ch</a>:<br
class="">
> <br class="">
> <a
href="https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html"
rel="noreferrer noreferrer" target="_blank"
class="" moz-do-not-send="true">
https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html</a><br
class="">
> <br class="">
> 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.<br class="">
> <br class="">
> 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.<br class="">
> <br class="">
> Thank you all in advance for your help! <br
class="">
> <br class="">
> Jürgen Schmidhuber<br class="">
> <br class="">
> <br class="">
> <br class="">
> <br class="">
> <br class="">
> <br class="">
> <br class="">
> <br class="">
<br class="">
<br class="">
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<div class="moz-signature">-- <br>
<img src="cid:part6.6083D158.6CA30F27@tuebingen.mpg.de" class=""
border="0"></div>
</blockquote>
<br>
<pre class="moz-signature" cols="72">--
Li Zhaoping, Ph.D.
Prof. of Cognitive Science, University of Tuebingen
Head of Department of Sensory and Sensorimotor Systems,
Max Planck Institute for Biological Cybernetics
Author of "Understanding vision: theory, models, and data", Oxford University Press, 2014
<a class="moz-txt-link-abbreviated" href="http://www.lizhaoping.org">www.lizhaoping.org</a></pre>
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