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

[Danko Nikolic]

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.

Danko


On Thu, 28 Oct 2021, 10:13 Randall O'Reilly <oreilly at ucdavis.edu> wrote:

> 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).
>
> 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.
>
> Cheers,
> - Randy
>
> > On Oct 27, 2021, at 12:52 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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