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
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.
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.
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.
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.
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.
Except ... while machine learning is great, has made important and significant strides, it's not a yet science. It involves essentially a series of sophisticated, mathematically informed recipes for feeding data to giant algorithms and having them create something useful (maybe very useful but still).
An analogy from a couple years ago is bridge building before physics. You accumulate rules of thumb, you get a vague understand what works. You get better. But you aren't producing a systematic field.
And that implies merely advancement isn't necessarily progress (which isn't to say there's no progress but building larger SOTA isn't that as the article notes).
What we should be careful about is to not be too strict when defining "science". In my view the goal of any scientific field is to build understanding that is useful for predicting outcomes of experiments. Now, this understanding could be defined mathematically, but it doesn't have to be. Don't see why building heuristics can't be a part of this, assuming such heuristics reliably predict outcomes of experiments.
So... scientists need to do scienceing until it is. This is what happened with Biology over the last 50 years after 2000 years of pinning things on cards and putting them in draws.
So I'd claim we're really at the situation right now, at the exploring new ideas, the "crisis of science" phase where essentially people have to start brainstorming (and not all ideas are good here either but they need to be somewhat original).
All this is using Thomas Kuhn's Structure Of Scientific Revolutions model very roughly.
This would be more like Kuhn's pre-paradigmatic (pre-scientific) activity, than moving from one scientific paradigm to another ?
https://samzdat.com/2018/05/19/science-under-high-modernism/
(I don't know which one is more appropriate here.)
Sounds like science to me; systematic recording of (perceived) cause and effect.
Arguably, deep learning and cell biology both appear like equal parts pure wizardry and flailing in the dark, but maybe that’s just because we haven’t gathered enough pieces yet, and not necessarily because people are doing the wrong things, thus failing to advance?
- It's easy to recognize scientific / technological revolutions in hindsight, but at the time they're anything but. I think most recognize the importance of persistence in the progenitor of an idea. What's often missed in these discussions is how important the subsequent incremental progress is: working out consequences of a theoretical insight, figuring out what you can build on top of a new tech, etc.
- I don't have a strong opinion about the optimal amount of "risk," but I do think making risk existential (as in one would starve without a research breakthrough) would have the opposite effect, because while we all say we like people to take risks, what we really mean is like people to take risks and suceeed. And yes, too little risk can breed complacency.
And yes, convolutions are a specific kind of linear transformation, whereas matrices represent linear transformations on vector spaces. The specific structure is that convolutions represent linear transformations that are translation-invariant, a property that many types of data (e.g., images) have, at least approximately.
() My perspective as an academic but not a computer scientist.
The author cites no compelling trend that ML is stagnating, esp relative to other disciplines.
I’d also add: is it BAD if universities are churning out highly skilled workers when there is high demand for them?
This seems to be more a rant of ML becoming more mainstream and accessible to a wider variety of students than anything.
They admit that some of the most “innovative and challenging” problems are still around, but many do not focus on that. Ok, so what, there are maybe more tiers and splitting of work into subdomains, some more “vocational” and some more “research-y” in nature. Is this a BAD thing?
So if you want to credit success to dumb luck, or to stubbornness, or insanity (as Einstein said: "doing the same thing over and over again and expecting different results." then LeCon is your man. I don't know of any quote where LeCun says something to the effect of: "Well, I knew the hardware would speed up and then my neural net software would work exactly as I designed it to and manifest AI. So I waited twenty years."
IOW It wasn't the LeCunn's surfboard(his software) that handed him success, it was the wave(advancing hardware speeds):
https://www.wired.com/2017/04/race-make-ai-chips-everything-...
You're talking like we only discovered Moore's law now, instead of it being a good bet for the last half-century...
He speaks French after all...
> A nitpick: CNNs are most definitely not a generalization of matrix multiplication. In fact, the opposite: you can view CNNs as a matrix multiplication with a particular matrix structure.
Neither is really a generalization. Any matrix multiplication can be implemented with a convolution function, and yet any convolution can be represented as a matrix multiplication via im2col.
[1] https://en.wikipedia.org/wiki/Toeplitz_matrix#Discrete_convo...
Is the deep learning really the result of incremental research? The SOTA chasing frenzy comes after the discovery of deep learning. The motivation of incremental research can hardly be justified as to discover the next deep learning, although they might do.
It's not some dude disappearing into the forest for a few years and coming back with a revolutionary idea (maybe in movies). Everything that helped shape the scientist's thinking has contributed to the idea, even if sometimes the idea is so revolutionary that it's hard to see the direct link. Besides, show me a scientific paper with no references. :-)
It's a shame that this is rarely acknowledged and more often than not we talk about "that guy who invented X and got a Nobel prize for it". But us mere mortals can take solace in knowing that even if we didn't change the world with our own ideas, it's possible that we've influenced someone who has.
> we’ve rewarded and lauded incremental researchers as innovators, increased their budgets so they can do even more incremental research
There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning). In no way is machine learning unique in this. Sounds like the author is simply disappointed that, like in any other profession, day-to-day of a researcher is a slog and not a perennial intellectual festival. 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.
> Machine Learning Researchers can now engage in risk-free, high-income, high-prestige work
Not sure what author means by "risk-free". Yes, if you're not publishing enough you're most likely not going to starve. Is that a bad thing? Is the survival instinct the only good motivator for doing good research?
I would argue that there's plenty of risk, in that people who don't publish good research don't get very far in their academic careers, which in my view is good enough motivation. "They must do good research or starve" is a rather cynical take, especially from someone who seems to not be doing too badly for themselves.
I'd rather more fields provided similar benefits. Maybe then going into science wouldn't be associated with so much sacrifice, so more smart people would choose science over investment banking or such, and we'd make more scientific progress faster.
> CNNs use convolutions which are a generalization of matrix multiplication.
A nitpick: CNNs are most definitely not a generalization of matrix multiplication. In fact, the opposite: you can view CNNs as a matrix multiplication with a particular matrix structure.