>We've been in an exciting deep learning craze for a while, but it's silly to expect it to last forever. Back to the grind now.
This was effectively my response to hardmaru when this topic came up on reddit [1]
Basically 2010-2018 was an open field for ML/DL research with old(ish) methods being rapidly applied to low hanging fruit and large datasets with newly cheap compute.
Deepmind and others are actually making new methods but by and large are remixes of those same old approaches.
The majority of different research out there trying other approaches (Numenta, OpenCog, Causal Calculus, anything Schmidhuber etc...) don't really get any love because it doesn't fit within the mass tensorflow/torch framework.
> Basically 2010-2018 was an open field for ML/DL research with old(ish) methods being rapidly applied to low hanging fruit and large datasets with newly cheap compute.
Absolutely! And what many folks need to continue to remember is that many scientific disciplines and domains are really just starting to wrestle with the utility and implications of this first generation of deep learning tools and applications. I graduated with my PhD in atmospheric science from an R1 just over 4 years ago; at that time, very few people were looking at how DL provided useful tools for their work. These days, the field is inundated with folks playing with these tools and knocking tons of low-hanging fruit off the tree - it might not be "deep", revolutionary research, but it's fomenting a mini-revolution with respect to R2O and real applications of what had previously been somewhat niche science.
There's no reason to think this trend won't continue. New tools let new generations of scientists take new stabs at their discipline, and of course the low-hanging fruit drops first as folks get their bearing, build skills/experience, and - most importantly - prove efficacy so that they can get funding for more ambitious work.
This seems to be the trend in every computational science field. Take something compute intensive, apply DL magic and get results in a fraction of the CPU time as the old approach.
Similarly on the commercial side, plenty of opportunity to solve existing problems with DL.
So while it may be true that DL research progress has slowed, still plenty to do in applying existing DL.
Those other approaches you mentioned don’t get much love because they don’t work. Their advocates worked on them for many years and have nothing to show for it.
In the same way that ANNs didn't work for quite some time, until we've had the compute and the data to train them successfully?
I get that it's important to prove that an idea is worthwhile, and the easiest way to do that is to use it to solve a practical problem. At the same time, I am conscious that we shouldn't put all our eggs in the deep learning basket: who knows where the ceiling is going to be.
Don't get me wrong, I like deep learning, and you have to be silly not to admit how successful it has been. But the field would be so much more boring if not for the people with alternative views and ideas.
No, not in the same way. Convnets and lstms worked great when they were invented in the 90s. Yes, they were limited by the available compute power but they did work well right away.
When I was in grad school in 1999 studying AI the general consensus was that neural networks didn't work very well, and that you were better off with the more mathematically grounded methods like support vector machines. TD-Gammon was just about the only success story for neural networks, there hadn't been much else since 1992.
I don't know where that "general consensus" came from, because in 1998 Lecun clearly showed [1] convnets beating all other models, including SVMs, at image recognition.
It like other neural network research was ignored because neural networks were considered a dead-end at the time. In the early 2000s I recreated his LeNet-5 implementation, and no one was interested despite the great results I was getting in OCR and medical image processing with tumor detection.
Younger people don't realize there was strong bias against using neural networks in the late 90s up until Hinton's talk on NNs around 2007. I get the feeling we're going through the same thing where novel research is becoming ignored because everything must fit the deep learning paradigm to be noticed.
Most research will be ignored if it does not have good results, regardless of whether it's novel, or whether it's DL. In fact, a lot of recently published DL research is ignored for exactly this reason. Top DL conferences are so competitive currently that the quality bar is pretty high.
There are lots of ideas floating around in AI field. Some of them might be good, most are not. If you have an idea and want others to look at it you better demonstrate how it outperforms every other method when applied to some task.
OP's point is that research is/was being ignored despite having good results. But this is normal, it just takes time, and a critical mass of good results for most researchers to switch to the new paradigm. (A decade is a very short time in the history of science.)
The first convnet that is similar to what we use today was described in 1989 [1], and immediately became the best method to do a handwritten digit recognition.
This was effectively my response to hardmaru when this topic came up on reddit [1]
Basically 2010-2018 was an open field for ML/DL research with old(ish) methods being rapidly applied to low hanging fruit and large datasets with newly cheap compute.
Deepmind and others are actually making new methods but by and large are remixes of those same old approaches.
The majority of different research out there trying other approaches (Numenta, OpenCog, Causal Calculus, anything Schmidhuber etc...) don't really get any love because it doesn't fit within the mass tensorflow/torch framework.
[1]https://twitter.com/AndrewKemendo/status/1349387455552745473