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LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

people.idsia.ch

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Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#81

Earlier quoted context omitted.

Funny ... According to [1], the papers [42,43] showed that plain backprop was capable of solving complex tasks without pretraining on a GPU. From [1]: > Our team then showed that good old backpropagation [A1] on GPUs (with training pattern distortions [42,43] but without any unsupervised pre-training) I can't verify since I have a barely stable connection and little battery, but somebody else should be able to. [1] h…

Sorry what's your contention? or do you have one? Backprop is backprop on CPU or GPU - it just runs (way) faster on GPU. So while you can do some stuff with NN's on CPU, you can do more stuff with NN's on GPU. Further, the sources you reference are from before advances made in software and hardware for massively parallel GPGPU architectures (CUDA), so it's kind of a moot point anyways. Those researchers may have had…

> Sorry what's your contention?

Time of publication. I merely pointed that Schmidhuber et al's results were published long before Hinton et al's work (I included year of publication for a reason), ergo the comment that was in support of Hinton was refuted and my comment gives further validity to Schmidhuber's claims of misattribution and plagiarism, while pointing at yet another paper tiger in the claims that the three pioneered the field.

If anything, Schmidhuber may have deserved a Turing award on his own, and what we see is the result of politics.

This is pure speculation, but to me the whole ordeal hints at manipulation from the companies where 2 of the 3 worked at, which would essentially increase the pool of candidates the respective companies have only by association with their employee.

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#82
post #11

Earlier quoted context omitted.

We are scientists and can use names. 'him' is Jürgen Schmidhuber. He's a luminary of ML, but he has entered into several attribution controversies. He famously accused Ian Goodfellow of appropriating his work to create generative adversarial networks (GANs) in a public venue (a NeurIPS workshop). I agree with the parent comment. Ultimately, Science is about advancing the human condition more than celebrating great in…

Well said. Attribution is important but it's impossible to know all the literature that is out there and even when you've tried your best to seek out prior arts there is always a possibility that you've missed something or not understood the connection with something previously read. Heck even when something is similar you may not cite it since it's so old the relevance is no longer there for a working research paper…

This was not the attitude of top guys in mathematics when I was at university.

Instead, one guy, who won a bunch of major scientific prizes in mathematics was of the view that people when they get scooped by people publishing in 'obscure' languages or non-English journals, were still scooped and that they can just shut up about proving things. Another guy of similar caliber was happy to read and figure out the ideas of papers in Russian and French maths journals, when it was relevant to his work, even though he couldn't read Russian.

Meanwhile, I've heard people speak of Schmidhuber's stuff as obscure because some stuff was in German, which is of course much easier than Russian for English speakers.

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#83

Earlier quoted context omitted.

Yes, like Watson & Crick and lots of others have shown over the years by lighting the way with proper accreditation. /s Science is a means to an end, the 'giving proper credit' bit has been under fire since the day that someone figured out that citations can bestow fame (hardly every fortune) and given the low stakes it is to be expected that that is the battleground. But the number of PhD students that have been scr…

>Yes, like Watson & Crick and lots of others have shown over the years by lighting the way with proper accreditation. /s This just in: A wrong thing become right when famous people (allegedly) do it. More at 9. And that's aside from the fact that you're wrong on the particular case of W&C.

> This just in: A wrong thing become right when famous people (allegedly) do it. More at 9.

I'm pretty sure the claim was that it's not so "fundamental", not about right or wrong.

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#84
post #61

Earlier quoted context omitted.

I'm not sure you know what Strawman means. Are you a researcher? Have you published research papers? >> The options are: cite him, slight him, or prove him wrong. Again, are you a researcher? These are not the options. Also what even is "slight him" or "prove him wrong"? My comments above are about citation practices not this one researcher in particular. >> I think he's made it clear that he won't accepts slights wi…

> A straw man (sometimes written as strawman) is a form of argument and an informal fallacy of having the impression of refuting an argument, whereas the real subject of the argument was not addressed or refuted, but instead replaced with a false one. https://en.wikipedia.org/wiki/Straw_man Good day sir.

That's my point. I didn't replace the argument with any other. My original comment concerned citation practices whereas you're arguing about something else.

So it's not a straw man, but rather that we're having different discussions.

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#85

Earlier quoted context omitted.

Sorry what's your contention? or do you have one? Backprop is backprop on CPU or GPU - it just runs (way) faster on GPU. So while you can do some stuff with NN's on CPU, you can do more stuff with NN's on GPU. Further, the sources you reference are from before advances made in software and hardware for massively parallel GPGPU architectures (CUDA), so it's kind of a moot point anyways. Those researchers may have had…

> Sorry what's your contention? Time of publication. I merely pointed that Schmidhuber et al's results were published long before Hinton et al's work (I included year of publication for a reason), ergo the comment that was in support of Hinton was refuted and my comment gives further validity to Schmidhuber's claims of misattribution and plagiarism, while pointing at yet another paper tiger in the claims that the thr…

Ah, that makes sense. Thanks for clarifying.

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#86
post #45

Earlier quoted context omitted.

Funny ... According to [1], the papers [42,43] showed that plain backprop was capable of solving complex tasks without pretraining on a GPU. From [1]: > Our team then showed that good old backpropagation [A1] on GPUs (with training pattern distortions [42,43] but without any unsupervised pre-training) I can't verify since I have a barely stable connection and little battery, but somebody else should be able to. [1] h…

Isn’t backprop just reverse-mode AD, which has been around since the 60’s? I get the impression that a lot of physicists and scientific computing researchers have been rolling their eyes at various ML “breakthroughs” over the years.

My point was not backprop, but time of publication.

However, "reverse-mode" AD is not as simple as just caching results.

> BP’s continuous form was derived in the early 1960s (Kelley, 1960; Bryson, 1961; Bryson and Ho, 1969). Dreyfus (1962) published the elegant derivation of BP based on the chain rule only.

> explicit, efficient error backpropagation (BP) in arbitrary, discrete, possibly sparsely connected, NN-like networks

But that wasn't close enough to current BP

> BP’s modern efficient version for discrete sparse networks (including FORTRAN code) was published by Linnainmaa (1970). Here the complexity of computing the derivatives of the output error with respect to each weight is proportional to the number of weights. That’s the method still used today.

See [1] for more details.

I do not know the exact implementation details of Linnainmaa's BP, but there are all sorts of improvements to plain reverse mode auto-grad through stuff like Jacobian-Vector products that save an enormous amount of memory.

Nocedal's "Numerical Optimization" covers a great deal on how to implement autograd, and I can easily recommend it to anyone interested.

[1] https://www.reddit.com/r/MachineLearning/comments/e5vzun/d_j...

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#87

Earlier quoted context omitted.

> At the same time Schmidhuber's tone and anal-retentive attitude explains why he is considered so annoying. The people calling him annoying are generally hangers-on trying to display that they're part of the group - people who took a class once 20 years ago and think they need now to come explain. He's a lovely man, and he's 100% correct to be angry that people are getting rich and famous while not citing him. It's…

> Hinton is being a plagiarist and he knows it . What are some of the papers he has plagiarized?

They are listed here: https://people.idsia.ch/~juergen/scientific-integrity-turing...

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#88

Earlier quoted context omitted.

Sorry what's your contention? or do you have one? Backprop is backprop on CPU or GPU - it just runs (way) faster on GPU. So while you can do some stuff with NN's on CPU, you can do more stuff with NN's on GPU. Further, the sources you reference are from before advances made in software and hardware for massively parallel GPGPU architectures (CUDA), so it's kind of a moot point anyways. Those researchers may have had…

Dan Ciresan’s DanNet ran on GPUs before Hinton’s students

Schmidhuber's DanNet pages:

https://people.idsia.ch/~juergen/DanNet-triggers-deep-CNN-re...

https://people.idsia.ch/~juergen/2010-breakthrough-supervise...

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#89
post #11

It's him again. That must be enough to give context on who I am talking about. I do not know what to tell about him. Sure he has explored a lot of topic and never got the limelight like others. But I think it's time for him to stop gaslighting about other researchers just because they didn't cite his papers. Sure, they could have never read it. He can make his work shout by publishing better papers and better design.…

We are scientists and can use names. 'him' is Jürgen Schmidhuber. He's a luminary of ML, but he has entered into several attribution controversies. He famously accused Ian Goodfellow of appropriating his work to create generative adversarial networks (GANs) in a public venue (a NeurIPS workshop). I agree with the parent comment. Ultimately, Science is about advancing the human condition more than celebrating great in…

>He famously accused Ian Goodfellow of appropriating his work to create generative adversarial networks (GANs) in a public venue (a NeurIPS workshop).

Here is his take on what happened: https://people.idsia.ch/~juergen/scientific-integrity-turing...

Re: LeCun's 2022 paper on autonomous machine intelligence does not cite prior work

#90
post #87

Earlier quoted context omitted.

> Hinton is being a plagiarist and he knows it . What are some of the papers he has plagiarized?

They are listed here: https://people.idsia.ch/~juergen/scientific-integrity-turing...

These are some examples of non citations in talks (not papers), not plagiarism. I will discuss a representative example here.

> 9. LBH claim ReLUs enabled deep learning to outperform previous methods for object recognition, referring to their GPU-based ImageNet 2012 winner called AlexNet,[GPUCNN4] without mentioning that our earlier groundbreaking deep GPU-based DanNet[GPUCNN1-3,5-8][DAN] did not need ReLUs at all to win 4 earlier object recognition competitions and to achieve superhuman results already in 2011[GPUCNN1-8][R5-6] (see Sec. XIV).

If we click and look into the details, Alexnet won imagenet - a general purpose image recognition dataset. Whereas Dannet worked on specific domains- Chinese handwriting recognition, mitosis etc. So Dannet is not comparable in impact to Alexnet at all. ReLUs are in all complex DNNs now - wouldn't have happened if ReLUs are redundant as implied by Schmidhuber.

https://people.idsia.ch/~juergen/computer-vision-contests-wo...

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