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.
The goal of academia isn't to be practical, "only" learning.
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).
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...
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?
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.
More specifically, it was really AlexNet, the 2012 ImageNet entry, running on two NVIDIA GTX 580's, that highlighted the practicality and utility of running large scale neural nets on affordable hardware. CUDA had been released in 2006, but cuDNN (the CUDA library for neural nets) didn't come out until 2014 - after AlexNet had already kickstarted the demand.
What followed from AlexNet was a few years of intense competition on the ImageNet benchmark, and larger and larger/deeper neural nets (CNNs), which gave rise to a lot of the algorithms and concepts still used today such as residual connections (originally from ResNet), ADAM (training algorithm), ReLU/etc, normalization, dropout, etc... all the fundamentals that made building large neural nets possible.
Schmidhuber's continual reminding everyone that he was working on neural nets back in the 1990s is beyond tiresome. Yes, he should have been recognized alongside Hinton/Bengio/LeCun as one of the pioneers, but time for him to get over it.
In the Schmidhuber case their is 20 years and a chain of countless other works in between the two.
Yes is very easy to forget, cause the trillion is not being made in Europe. If it was really conceived in Munich (like the maps that got stolen also), it show how incompetent is Europe to keep it´s technology and protect European companies.
It is painful to read this article.
The real root of the current AI boom is a master thesis from university of Toronto.
The thesis demonstrated that neural networks much longer than before could be trained by simply having a random fraction of the neurons excluded during forward and back propagation.
That's how we got practical deep neural networks. Without that we would still be in AI winter.
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.
