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

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

#41

Post by [deleted] »

IfOnlyYouKnew wrote: post #36
seydor wrote: post #12

Lecun's post is not even a paper as stated in the foreward of the paper. But thanks for bringing it up, i will read it > This document is not a technical nor scholarly paper in the traditional sense, but a position paper expressing my vision for a path towards intelligent machines that learn more like animals and humans, that can reason and plan, and whose behavior is driven by intrinsic objectives, rather than by ha…

Well you can’t just state “this isn’t a paper” at a beginning of a paper to get an exemption from the rules and traditions of scientific discourse. It’s like the people believing it’s not a crime to pay with counterfeit money if the signature reads “Donald Duck”.

[deleted]

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

#42

Post by dekhn »

jacquesm wrote: post #26
logicchains wrote: post #18

Earlier quoted context omitted.

>Ultimately, Science is about advancing the human condition more than celebrating great individuals. Giving proper credit to prior work is a fundamental component of scientific progress. Just look at the Chinese academic system to see what happens to science when people are given free reign to plagiarise.

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…

Watson and Crick gave all necessary and proper attributions.

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

#43

Post by jollybean »

How can there not seem to be a kind of consensus around this?

There were tons of citations, why does a simple idea written down need to cite work from 20 years ago?

The threshold of tolerance here is basically ridiculous, authors will be more predisposed to research and citations than to actual thinking.

I suggest if someone feels left out, they can make a claim and the paper can be 'amended or not' and that's that.

How on earth is someone supposed to have an 'idea' without a team of grad students ...

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

#44

Post by bsedlm »

swayvil wrote: post #24
seydor wrote: post #12

Lecun's post is not even a paper as stated in the foreward of the paper. But thanks for bringing it up, i will read it > This document is not a technical nor scholarly paper in the traditional sense, but a position paper expressing my vision for a path towards intelligent machines that learn more like animals and humans, that can reason and plan, and whose behavior is driven by intrinsic objectives, rather than by ha…

"intrinsic objectives" and "hard-wired programs" sound alike. Given that both must be designed.

declarative vs procedural?

i.e. saying "what to" instead of saying "how to"

???

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

#45

Post by catgary »

ekleraki wrote: post #23
dchichkov wrote: post #20

Earlier quoted context omitted.

In my opinion the difference that Hinton students made was using GPUs to run deep learning models. First in 2010, when publishing Theano at NIPS, making deep learning on a single computer node with a large GPUs very approachable ( using a Python framework, precursor to TensorFlow). Then in 2012, with Alex Krizhevsky masterfully using two GPUs to build the landmark model AlexNet, which outperformed traditional compute…

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.

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

#46

Post by aeontech »

dekhn wrote: post #42
jacquesm wrote: post #26

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…

Watson and Crick gave all necessary and proper attributions.

The parent is referring to the fact that Watson and Crick relied on work of Rosalind Franklin without crediting her.

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

#47

Post by jfim »

IfOnlyYouKnew wrote: post #35
harveywi wrote: post #32

lecuna (noun): An empty space in a list of citations where the works of Jürgen Schmidhuber should appear.

This is somewhat funny and very smart and can only be downvoted by people who don’t speak fluent Latin.

For those who never had a Latin class, could you explain the joke? Thanks!

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

#48

Post by angrais »

carapace wrote: post #31

I recall watching a video of Schmidhuber presenting. He comes onstage, introduces himself, and then makes a joke. ("You again!") It's a funny joke. And it's self-deprecating. I remember thinking to myself, "This is a formidable Austrian." That and the Gödelmachine: I'm a fan. There's a simple and obvious way to solve this: cite the papers. It's traditional, it's noble, it's not hard, and it makes you look good. I men…

You do not have to cite every paper under a specific topic nor do you have to cite the first paper that introduces a paper. Why? Maybe those papers are not well written. Maybe a recent paper covers the idea better, etc.

So no, he does not have to be cited just because.

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

#49

Post by harveywi »

jfim wrote: post #47
IfOnlyYouKnew wrote: post #35

Earlier quoted context omitted.

This is somewhat funny and very smart and can only be downvoted by people who don’t speak fluent Latin.

For those who never had a Latin class, could you explain the joke? Thanks!

https://en.wikipedia.org/wiki/Lacuna_(manuscripts)

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

#50

Post by ShamelessC »

ekleraki wrote: post #23
dchichkov wrote: post #20

Earlier quoted context omitted.

In my opinion the difference that Hinton students made was using GPUs to run deep learning models. First in 2010, when publishing Theano at NIPS, making deep learning on a single computer node with a large GPUs very approachable ( using a Python framework, precursor to TensorFlow). Then in 2012, with Alex Krizhevsky masterfully using two GPUs to build the landmark model AlexNet, which outperformed traditional compute…

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 access to such a system, but you definitely couldn't just `pip install pytorch` on your gaming GPU in the 90's...

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