There's this crowd on HN which is very vocal against academia. From what I've seen, the main points are that academia isn't efficient, most of the science coming out of academia is useless and that the whole system is just a waste of taxpayers money. Instead, what is often argued, all good research is done in private labs. Then pointing to SpaceX, Moderna, OpenAI, Google, etc.
And while it is very true that often the research coming out of Academia is useless, what is always neglected are the roots of the research done in private labs.
When Jürgen Schmidhuber and team published their work on Neural Nets back in 1991 it was also useless. Unless you had a supercomputer and very, very deep pockets you were not going to do anything with what came out of their lab.
But still, 30 years later here we are, standing on top of the shoulders of this useless research.
Like half of what Schmidhuber is always complaining about is that (except for LSTMs) people aren't standing on the shoulders of his research very much. They try to solve some of the same problems people have always wanted to solve, try some of the same approaches people always tend to try, and then tinker until it works. At no point do they consult Schmidhuber's decade-old papers where he tried something kind of similar but didn't get very impressive results, and hence they also do not think to cite him. Then he comes out of the woodwork to assert priority.
Shane Legg was in Schmidhuber's lab at IDSIA before being one of the founders of DeepMind, so he probably read the papers personally and knows what influenced him or not...
"if you haven't read them you also shouldn't cite them" -- this is wildly incorrect in an academic context. If I'm using ResNets, I should cite the original ResNet paper, even if I haven't read it. If I'm using Transformers, I should cite the original Transformer paper, even if I haven't read it. If my work is a direct extension of method B, and method B is a direct extension of method A, I should cite the source of A, even if I haven't read it.
You can't claim independence from past work simply because you didn't look directly at it. The job of an academic researcher is to know the landscape of relevant ideas, where they come from, where they're going, and to hopefully contribute a few new good ones.
Citation chains should extend back from your work, along a reasonable line conceptual inheritance, back to a reasonable point of origin. Schmidhuber has different definitions for both of these reasonables than the bulk of the ML research community, to a point that makes him difficult to satisfy.
> Of course, but if you haven't read them you also shouldn't cite them.
But if you build on them you should have read them. I don't know about the specifics and I don't know if Schmidhuber is out of line or not, and citations and impact factors are a terrible mess, but generally speaking, you are responsible for finding and reading and citing any related work that needs to be cited, and if you work on neural networks in an academic context you probably have been forced to read that particular one at some point. Citation obligations don't just disappear because you don't want to do the research.
I do a lot of work that is based on academic research, aka building a proprietary sparse embedding model. My issue with academia is that they don’t bother to solve the practical issues. They tell you how to build a PPMI model, but what about hitting a database that’s 500TB to find co-occurrence numbers? This isn’t even touched so you’d then have to go and invent a bazillion of algorithms yourself to make your life easier. So while the bedrock is based on academic research and we thank them for that, scaling anything requires a lot of work in uncharted territories.
The practical issue of academia is epistemological. It's about learning how a phenomenon came to exists. If you are looking for efficiency the field of academia related to learning how to do so is computational complexity and it works quite well.
The goal of academia isn't to be practical, "only" learning.
But that isn't the purpose of academia -- the purpose of it is to discover new phenomena not to make products. It is true that there is a lot of work to turn a new advance into a product whether it is software or turning biological knowledge into a drug, but without discovery of new phenomena new products will come to a halt. While it is true that some corporate labs, most famously Bell Labs in its heyday, but also for example IBM's T.J. Watson and Xerox's PARC did do basic research besides product-focused work, this is pretty rare because it is hard to justify the cost of something that may only be practical in decades and often help your competitors as much as yourself.
Every western academic nearly systematically ignores eastern science and philosophy: classicism means "western European". Never mind Europe only flourished intellectually post Islam, which imported the science and engineering of China and India, critically including printing and zero[0]. IMHO this is why distaste for academia grows: it's based on appeals to authority which are demonstrably farcically misplaced. Alternatively stated: the emperor has no clothes, much less silk or paper!
Just as the Dewey Decimal System really only served the purpose of providing the facetious nominal linearization of an arbitrary depth ontological oversimplification, so too humans are much more like random pattern matching machines than festidious sense-makers glued to absolutes derived from false appeals to static mono-perspective ontological hierarchies. The same is becoming lived experience in the LLM age, although the tiktokked youth apparently cannot string ten words together or focus longer than three seconds to attest, I'd wager they can feel it. Are we losing something by rejecting the habit of rigorously manually tending to spurious and temporary ontologies? Yes. Is it necessarily a loss in the long term? Probably not, in the same way we no longer write long-form letters or leave calling cards. Are we gaining something in response? Yes, at a minimum much stronger cross-pollination between ivory towers by fearless exploratory pragmatists who disrespect the would-be scope of nominal professions in favor of holistic thinking... both AI and human.
The closest to that that I've seen is that traditional academia approaches are too far removed from practical applications for highly applied fields like software engineering, or too slow for fast-moving fields like modern day ML (thus, all the preprints).
Practically no one is against hard science research, properly conducted. The issues are rampant fraud / p-hacking / unreproducible garbage mixed with an unhealthy dose of ideological monoculture and indoctrination, garnished with rising tuition prices while sitting on huge endowments in case of the Ivy Leagues.
I feel like you're constructing a strawman to argue against. I visit this site almost daily and the prevailing sentiment is usually the polar opposite of what you're suggesting.
If sentiment on HN were as you say, how could your pro-academia and anti-big tech comment be sitting at the top as the most upvoted comment?
It's sad that he is the only one speaking out about Hinton. This whole Hinton glorification seems like it's being pushed by an agenda. I'm not sure if he would receive this much attention if he held a different view (closer to LeCun or Ng), rather than these Effective Altruism takes on current AI.
No, the research was made in USSR, however much Scmidhuber likes to think of "occupied Ukraine".
I mean, if one thinks it is his mission to establish the truth..
The truth, Scmidhuber, was never in your fuhrer's hands. Nor it is in the hands of the western fuhrers of today.
Just for the context, today is Russia's Commemoration Day of the victims of the Velikaya Otechestvennaya Voyna (your translations always feel wrong, sorry) of 1941-1945. (Yes, 1941, when the western fascist coalition of 5 million soldiers, invaded Soviet Union.)
26 and more million people of USSR perished, 13 million civilians. Of all nationalities.
Which work has more value: the abstract description of a catalogue of potential model architectures or their validated application trained on real data?
In the Schmidhuber case their is 20 years and a chain of countless other works in between the two.
Surely the roots, if we skip over the early preceptron work', are in backpropagation and Hinton, and the work going on at Edinburgh and elsewhere in the 80s.
Indeed I remember buying a set of three conference-papers-as-books around that time, titled Artificial Neural Networks .. proceedings of the whatever the conference was.
No doubt Schmidhuber made important contributions, but I see him pop up claiming to be the 'root' of it all every couple of years.
Modern backpropagation was first published by Seppo Linnainmaa as "reverse mode of automatic differentiation" (1970)[26] for discrete connected networks of nested differentiable functions.[27][28][29]
In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard.
Paul Werbos did not apply backprop to MLPs as cleanly described in Hinton's paper, but rather to some kind of autoregressive non-linear parametrized functions with a much more specific application scope.
Both papers are direct applications of the chain rule applied to estimate the gradient of a multivariate function.
That's what bugs me about him. So much work has gone into today's models that calling his contributions "the root" isn't really warranted. He's always complaining that Hinton, LeCun, and Bengio get more credit than they deserve, and now he's over-claiming himself.
It's crazy to think that if Elon Musk hadn't mentioned Schmidhuber, most people would have no idea.
It's nauseating how all the researchers who happened to work for big tech got tons of media coverage but Schmidhuber and his team were getting zero coverage yet they made massive contributions. I bet there are many others not mentioned.
Nobody even knows about Frank Rosenblatt. It's insane how distorted our perception of innovation is.
Even science has been corrupted. It makes one doubt every story we're told about who invented what.
I believe invention of Transformers and especially Attention mechanism do have influence from past research but its not definitely only the Schmidhuber's work. Said that, if we remove the papers mentioned by Schmidhuber from history, I am quite certain that there will be no influence in the discovery of Transformers, hence his works can not be the root. He has to grow up and accept that work and equations can appear similar, looking at inverse squared law and saying Newton stole that from someone is being dishonest.
And while it is very true that often the research coming out of Academia is useless, what is always neglected are the roots of the research done in private labs.
When Jürgen Schmidhuber and team published their work on Neural Nets back in 1991 it was also useless. Unless you had a supercomputer and very, very deep pockets you were not going to do anything with what came out of their lab.
But still, 30 years later here we are, standing on top of the shoulders of this useless research.
And that's where Schmidhuber goes off the rails: publicly shaming published papers into citing you isn't good academic practice. It's bullying.
You can't claim independence from past work simply because you didn't look directly at it. The job of an academic researcher is to know the landscape of relevant ideas, where they come from, where they're going, and to hopefully contribute a few new good ones.
Citation chains should extend back from your work, along a reasonable line conceptual inheritance, back to a reasonable point of origin. Schmidhuber has different definitions for both of these reasonables than the bulk of the ML research community, to a point that makes him difficult to satisfy.
But if you build on them you should have read them. I don't know about the specifics and I don't know if Schmidhuber is out of line or not, and citations and impact factors are a terrible mess, but generally speaking, you are responsible for finding and reading and citing any related work that needs to be cited, and if you work on neural networks in an academic context you probably have been forced to read that particular one at some point. Citation obligations don't just disappear because you don't want to do the research.
The goal of academia isn't to be practical, "only" learning.
Just as the Dewey Decimal System really only served the purpose of providing the facetious nominal linearization of an arbitrary depth ontological oversimplification, so too humans are much more like random pattern matching machines than festidious sense-makers glued to absolutes derived from false appeals to static mono-perspective ontological hierarchies. The same is becoming lived experience in the LLM age, although the tiktokked youth apparently cannot string ten words together or focus longer than three seconds to attest, I'd wager they can feel it. Are we losing something by rejecting the habit of rigorously manually tending to spurious and temporary ontologies? Yes. Is it necessarily a loss in the long term? Probably not, in the same way we no longer write long-form letters or leave calling cards. Are we gaining something in response? Yes, at a minimum much stronger cross-pollination between ivory towers by fearless exploratory pragmatists who disrespect the would-be scope of nominal professions in favor of holistic thinking... both AI and human.
[0] https://en.wikipedia.org/wiki/Science_and_Civilisation_in_Ch...
The closest to that that I've seen is that traditional academia approaches are too far removed from practical applications for highly applied fields like software engineering, or too slow for fast-moving fields like modern day ML (thus, all the preprints).
Practically no one is against hard science research, properly conducted. The issues are rampant fraud / p-hacking / unreproducible garbage mixed with an unhealthy dose of ideological monoculture and indoctrination, garnished with rising tuition prices while sitting on huge endowments in case of the Ivy Leagues.
As long as you do that with your own money (or money got freely given from other people), sure.
If you use taxpayer money, that's a different game.
However I often see this going from "there's issues" to discounting academia altogether and positioning private labs as a good or only alternative.
After all, most people in the open science collaboration which published the seminal paper kicking off the replication crisis were from academia.
If sentiment on HN were as you say, how could your pro-academia and anti-big tech comment be sitting at the top as the most upvoted comment?
No, the research was made in USSR, however much Scmidhuber likes to think of "occupied Ukraine".
I mean, if one thinks it is his mission to establish the truth..
The truth, Scmidhuber, was never in your fuhrer's hands. Nor it is in the hands of the western fuhrers of today.
Just for the context, today is Russia's Commemoration Day of the victims of the Velikaya Otechestvennaya Voyna (your translations always feel wrong, sorry) of 1941-1945. (Yes, 1941, when the western fascist coalition of 5 million soldiers, invaded Soviet Union.)
26 and more million people of USSR perished, 13 million civilians. Of all nationalities.
In the Schmidhuber case their is 20 years and a chain of countless other works in between the two.
Indeed I remember buying a set of three conference-papers-as-books around that time, titled Artificial Neural Networks .. proceedings of the whatever the conference was.
No doubt Schmidhuber made important contributions, but I see him pop up claiming to be the 'root' of it all every couple of years.
related paragraph from Wikipedia:
Modern backpropagation was first published by Seppo Linnainmaa as "reverse mode of automatic differentiation" (1970)[26] for discrete connected networks of nested differentiable functions.[27][28][29]
In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard.
Both papers are direct applications of the chain rule applied to estimate the gradient of a multivariate function.
It's nauseating how all the researchers who happened to work for big tech got tons of media coverage but Schmidhuber and his team were getting zero coverage yet they made massive contributions. I bet there are many others not mentioned.
Nobody even knows about Frank Rosenblatt. It's insane how distorted our perception of innovation is.
Even science has been corrupted. It makes one doubt every story we're told about who invented what.