While growth may be accelerating, this is simply the result of one big paradigm shift in deep learning/NNs. Once we've learned to milk it for all its worth, we'll have to wait for the next epiphany.
In fact looking at the rate of change in applications over an "epiphany" period is probably the least useful estimate of progress & rate of change in progress.
I believe hmate9 is correct. If this paradigm is exploited to the full, unless we've missed something fundamental about how the brain works, we don't need to bother ourselves with inventing the next paradigm (of which there will no doubt be many), because one of the results of the current paradigm will be either an AGI (Artificial General Intelligence) that runs faster and better than human intelligence, or, more likely, an ASI (Artificial Super Intelligence). Either of those is more capable than we are for the purpose of inventing the next paradigm.
You have missed something fundamental about how the brain works. Namely, neuroscientists don't really know how it works. Neuroscientists do not fully understand how neurons in our brain learn.
According to Andrew Ng (https://www.quora.com/What-does-Andrew-Ng-think-about-Deep-L...):
"Because we fundamentally don't know how the brain works, attempts to blindly replicate what little we know in a computer also has not resulted in particularly useful AI systems. Instead, the most effective deep learning work today has made its progress by drawing from CS and engineering principles and at most a touch of biological inspiration, rather than try to blindly copy biology.
Concretely, if you hear someone say "The brain does X. My system also does X. Thus we're on a path to building the brain," my advice is to run away!"
Recently, we also introduced activation functions in our neural nets, like rectified linear and maxout just for their nice mathematical properties without any regards to biological plausibility. And they do work better than what we had before.
But we don't know how the brain works. I think you extrapolate too far. Just because a machine learning technique is inspired by our squishy connectome it does not mean it's anything like it.
I'm willing to bet there are isomorphisms of dynamics between an organic brain and a neural net programmed on silicon but as far as I know, there are still none found - or at least none are named specifically (please correct me).
Our current assertion is that neural networks basically replicate the brain's function
come on, that's hyperbole
I mean, come on- "the art of creating AI paradigms"? What is that even? You're going to find data on this, where, and train on it, how, exactly?
Sorry to take this out on you but the level of hand-waving and magical thinking is reaching critical mass lately, and it's starting to obscure the significance of the AlphaGo achievement.
Edit: not to mention, the crazy hype surrounding ANNs in the popular press (not least because it's the subject of SF stories, like someone notes above) risks killing nascent ideas and technologies that may well have the potential to be the next big breakthrough. If we end up to the point where everyone thinks all our AI problems are solved, if we just throw a few more neural layers to them, then we're in trouble. Hint: because they're not.
As others have pointed out, we don't really know how the brain works. Neural nets represent one of our best attempts to model brains. Whether or not it's good enough to create real intelligence is completely unknown. Maybe it is, maybe it's not.
Intelligence appears to be an emergent property and we don't know the circumstances under which it emerges. It could come out of a neural network. Or maybe it could not. The only way we'll find out is by trying to make it happen.
Taking a position that neural networks cannot ever result in strong AI is as blind as taking a position that they must.
This is Hacker News, not a mass newspaper, so I think we can take the more nuanced and complex view here.
See now that's one of the misconceptions. ANNs are not modelled on the brain,
not anymore and not ever since the poor single-layer Perceptron which itself was
modelled after an early model of neuronal activation. What ANNs really are is
algorithms for optimising systems of functions. And that includes things like
Support Vector Machines and Radial Basis Function networks that don't even fit
in the usual multi-layer network diagram particularly well.
It's unfortunate that this sort of language and imagery is still used
abundantly, by people who should know better no less, but I guess "it's an
artificial brain" sounds more magical than "it's function optimisation". You
shouldn't let it mislead you though.
>> Taking a position that neural networks cannot ever result in strong AI is as blind as taking a position that they must.
I don't agree. It's a subject that's informed by a solid understanding of the
fundamental concepts - function optimisation, again. There's uncertainty because
there's theoretical limits that are hard to test, frex the fact that multi-layer
perceptrons with three neural layers can learn any function given a sufficient
number of inputs, or on the opposite side, that non-finite languages are _not_
learnable in the limit (not ANN-specific but limiting what any algorithm can
learn) etc. But the arguments on either side are, well, arguments. Nobody is
being "blind". People defend their ideas, is all.
>Taking a position that neural networks cannot ever result in strong AI is as blind as taking a position that they must.
Not really. Right now it's taking the position that there is no practical path that anyone can imagine from a go-bot, which is working in a very restricted problem space, to a magical self-improving AI-squared god-bot, which would be working in a problem space with a completely unknown shape, boundaries, and inner properties.
Meta-AI isn't even a thing yet. There are some obvious things that could be tried - like trying to evolve a god-bot out of a gigantic pre-Cambrian soup of micro-bots where each bot is a variation on one of the many possible AI implementations - but at the moment basic AI is too resource intensive to make those kinds of experiments a possibility.
And there's no guarantee anything we can think of today will work.