Connectionists: Scientific Integrity, the 2021 Turing Lecture, etc.
Stephen José Hanson
jose at rubic.rutgers.edu
Thu Oct 28 09:27:10 EDT 2021
Well, to popularize is not to invent.
Many of Juergen's concerns could be solved with some scholarship, such
that authors look sometime before 2006 for other relevant references.
This isn't a social issue.. good science writers know they didn't invent
the algorithms they are describing for AI applications.
OTOH, Dave Rumelhart, who introduction of the backprop learning methods,
often gets confused for gradient descent and
consequently, Newton *should* be referenced for gosh sakes!
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.
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.
Steve
On 10/28/21 8:49 AM, Jonathan D. Cohen wrote:
> 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 ;^).
>
> jdc
>
>> On Oct 28, 2021, at 4:23 AM, Danko Nikolic <danko.nikolic at gmail.com
>> <mailto:danko.nikolic at gmail.com>> wrote:
>>
>> 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
>> <mailto: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 <mailto: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 <mailto:juergen at idsia.ch>:
>> >
>> >
>> https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html
>> <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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