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.
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.
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.
You can be influenced downstream by papers you haven't personally read.
TU Munich and Nipkow, Makarius et.al. are also at the center of the influential Isabelle theorem prover. TU Munich is cool :-)
https://en.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber
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.
Hinton did not invent backpropagation.
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.
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.
Both can be right.
Surely the roots go back to Turing, Gödel, Hilbert, Frege, Leibniz, Aristoteles.