Critique of 2018 Turing Award for Drs. Bengio and Hinton and LeCun
people.idsia.ch
people.idsia.ch
At NIPS 2016, Schmidhuber interrupted a widely-attended workshop on GANs given by Ian Goodfellow, to make his argument (see [1] at 1 hour, 2 minutes). The community has generally taken Goodfellow's side. Just because Schmidhuber "had the idea" doesn't mean he should get the credit for other peoples' work decades later.
As any entrepreneur knows, just having an idea isn't enough. You have to put the work in, do the experiments and get results.
[1] https://channel9.msdn.com/Events/Neural-Information-Processi...
The crazy thing is ... he is not entirely wrong. Only mostly.
I recommend having a stroll through his website, for anyone interested in AI (or even art, creativity, etc.):
https://people.idsia.ch/~juergen/
Special Notes:
Godel Machine : While clearly absolutely impractical, this is a nice theoretical construction on what we could mean by "self-improvement" in a real, definite way.
Speed Priors
Super Omegas (and various thoughts on algorithmic information)
Always keep in mind a grain of salt when reading his results, but they are genuinely great.
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I think establishing causation and giving credit does have a role in research, namely giving additional opportunity to those researchers to do even better research, elucidating the history of science, and to an extent giving a monetary incentive and morale recognition for researchers w.r.t. prizes, grants, etc.
For example isn't the story of Einstein, or von Neuman, or Feynman, etc. part of a narrative that makes science compelling? They had very interesting and lively lives and opinions that sometimes drove their research tastes. There is value in preserving individual history (as well as disregarding it when not particularly relevant).
That said, a scientists (or a good citizen in general) should not be driven primarily by ego, and in science this becomes clearly bad, impeding open collaboration, exchange of ideas that are essential for good functioning and good progress of science; I'd say Schmidhuber does let that through sometimes, to the detriment of his work.
Reminds of Dave Ungar haunting OOPSLA talks for some time. There were only two possibilities:
1. The idea you presented was something SELF already did and therefore uninteresting.
2. The idea your presented was not like what SELF already did, and therefore wrong.
¯\_(ツ)_/¯
I hear he has mellowed.
Well, did you really read it? In what respect do you consider it to be "obscure"? Because they used other terms to name the concepts?
> As any entrepreneur knows, just having an idea isn't enough.
You mean he should have patented it? Is this really the way to go: scientists in machine learning have to patent their work to make sure it's properly attributed in future?
That's quite far fetched given the sentence starts with "As any entrepreneur knows". Society can consider itself fortunate that every valuable scientific finding eventually prevails; but unfortunately - as many cases show - it often takes several publications of the same idea, and the respective earlier authors often fall into oblivion (if known at all).
The problem is that, in his original Artificial Curiosity paper he talks so specifically about world-simulating reinforcement learning. He even uses the terms "World Model" and "Controller" instead of the more general "Discriminator" and "Generator." In this paper, he says the GAN is a more specific application of his Artificial Curiosity paper, but I see it the other way around.
Goodfellow should have cited the Artificial Curiosity paper though, as training two networks at odds with each other in parallel is explicitly mentioned by Schmindhuber in his original paper.
It also doesn't hurt that Bengio and others came out with research when you could actually run the damn thing. Schmindhuber's Artificial Curiosity paper doesn't list a single experimental result.
Schmindhuber perhaps should have shared in the Turing award, but at some point you have to cut it off. We stand on the shoulders of giants, and so do those giants. At some point, a giant is getting left out of the award.
[1] Schmindhuber's original Artificial Curiosity paper: https://people.idsia.ch/~juergen/FKI-126-90ocr.pdf [2] Ian Goodfellow's original GAN paper: https://arxiv.org/abs/1406.2661
He discovered it in 1930, decades before Prim (and also before computers). If he were British or American, and not Czech, my guess is his discovery would never have to have been "rediscovered." This is not to put down Prim, it's just let's give credit where credit is due.
https://en.wikipedia.org/wiki/Vojtěch_Jarn%C3%ADk#Combinator...
Is it the +30 people that he quotes in the text? Himself? No one?
ACM just acknowledged that DL was being used more and more than was allowing computers to do tasks that were out of reach before. They picked Bengio, Hinton and LeCun because they were are the forefront of that new movement which to me sounds valid?
When a Turing award was given for the Goldwasser-Micali encryption scheme the same argument could've been made with different names, that it was not the "first" public key encryption scheme that was provably secure because it was just the result of Diffie-Hellman? Should Merkle be credited?
It's not an argument without merit. His work on LSTM and related ideas were really important to the field. And, as with all heroes in science, the deep learning triumvirate of Bengio, Hinton, and LeCunn get most of the credit but were building on lots of existing work from researchers who don't get their due. But, as with all awards, you need to draw the line somewhere.
On top of that (although I understand why a researcher might not consider this to be important), Theano really helped democratize model development and deployment and it was developed at MILA under Yoshua Bengio's. You can't just hand wave the tools that actually sparked the new movement.
Google sponsors the Turing Award:
https://awards.acm.org/sponsors
I don't know who is right, but Schmidhuber certainly comes across as someone who is interested in an accurate history.
For example the backpropagation algorithm is often misattributed. After the Perelman story my faith in proper attribution in science is fairly low in general.