Thus far, machines have not done well in scenarios of low stationarity. Those are scenarios where the past is not so good a predictor of the future or where the data manifold is rapidly changing. This occurs for example, when the act of predicting changes what is being predicted. The related notion of Antipredictable Sequences is the most interesting consequence of the oft misconstrued notion of the No free lunch theorem.
Humans are far from the ideal machine for this scenario but they vastly outmatch current algorithms. This is because humans are much better in the kind of generalization that is based off theory building. While not perfect, it does well enough at generalizing from observations (think Newton deriving the laws of gravitation from observations and Brahe's data) to scenarios that are not so close to the data.
This means that for now and in complex environments, simpler models that are adaptable to change online are better. Additionally, their inspectability means they are more easily modified to better match any very changed dynamics.
So your examples of: `speech recognition, face recognition, object classification and detection, speech`
Are of the type where a learner, such as a deep network, whose generalization ability mostly hails from interpolating to fill in missing structure based on data (implying poor out of sample performance) does well when you can give lots of examples which provide good coverage.
Language translation is sort of inbetween (the space is not as smooth but is still fairly stationary at the scale of years) and so we also find that the advantages of deep learning have not been as pronounced and dramatic as has been the case for image and speech.
The digital machine advantage is two. Their memory capacity is outmatched. Second, and related to the first, energy needs are not so pressing a concern.
A hypothetical "robot-cat-AI" could for example have access to a really precise physics engine, and also could run at a much higher "frame-rate", so it could see the world in slow-motion and execute really exquisite moves as a result.
When it comes to comparing the sophistication, adaptivity and functions of life to the in-silico mashines we produce and use very, very primitive.
Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking. How often do most human beings come to exhibiting the level of insight and reasoning power required to construct the calculus and discover the laws of classical mechanics? Inventing supernatural explanations for natural phenomena, burning sacrifices / witches / books (on Evolution or other heresies) seem more par for the course than Newtonian level revelations.
Having said that, learning the laws of classical physics from observational data strikes me as a pretty natural task for the right kinds of machine architectures, since they represent essentially geometric symmetries which hold over a vast range of scales, space and time.
> Citing Newton as a typical example of the superiority of human inferential abilities perhaps represents a case of cherry picking
Are hyperparameter searches across a sea of AWS instances cherry picking?
Anyways, I don't think I cherry picked. There is not a single machine that can do the same yet. And more importantly, a learning algorithm that notices model failure and works out the surgery required to correct or even completely replace it.
Newton is an example of the human brain at its best. But he was far from the only: Maxwell, Green, Einstein, Noether, Archimedes and so on the list goes. But we don't need to go that far since Crows are already capable of generalization that out matches what machines can do for the near future.
If our mathamechanical minions ever start musing about 'The Supreme Eigenvector' that would be the time to start worrying about machines taking over.
This self-defeating problem is not present in the other AI applications you mentioned.
Bear in mind that in the 90's only a handful of supercomputers had teraflops of computing power, where now you can get an 11 TFLOPS Titan X Ultimate for $1200. Compute power continues to grow exponentially, yet it has only recently reached a level where certain kinds of approaches are truly practical. As Heinlein said, "When it's time to go railroading, people go railroading."
It's interesting that you should talk about antagonistic systems, since Actor-Critic Models, dueling architectures, Generative Adversarial Networks (GANs) are an extremely hot area of AI/ML research at the moment.
Frankly, rules based AI is basically a bust compared to learning from data approaches.
The hive was pretty amazing at destabilizing the entire global economy within a brief span following fundamental financial de-regulation.
People flatter themselves in ways which satisfies their egos and self-interest. Of course they want to believe in, and have everyone else credit, their magical superpowers. No government (backstop) wires necessary.
Machine learning is rule based. The difference is just that it creates many more rules for more and more granular cases. But it will never be intelligent, it will always be a dumb digital bureaucrat.
Beating a human when there is no discoverability to the problem, and when building an exhaustive dataset is a problem in itself is not on the horizon yet.
You may have missed various dramatic improvements over the past year:
https://scholar.google.com/scholar?as_ylo=2016&q=%22image+ca...
Anyway, this paper demonstrates some impressive results:
https://en.m.wikipedia.org/wiki/Quantitative_easing
Google is getting really good.
And even if you could, acting on that knowledge would change the future.
Casinos don't try to predict the future of every spin of the wheel or roll of the dice. Every individual game is random. But over a large enough sample size (number of games played), the odds are not random.
The same principle is at play in trading the markets, except the "odds" are determined by the traders' skill - their "edge." Which is roughly some combination of access to the right people, access to better information (where better might but not necessarily mean quicker), and experience (gut feelings, hunches, what-have-you).
So the real question is, can machine learning algorithms develop an "edge", and if so, can they stay solvent long enough to do so.
The stock market isn't a game; it isn't designed and it continuously evolves.
That's exactly what I meant by odds; they always win because the odds of the games are in their favor.
> The stock market isn't a game
It's a game in the same sense that life itself is a game.
> it isn't designed
The mechanisms that fit all the pieces together are very much designed by humans. True the behavior of the market isn't designed; it's an emergent property of the complex system we call "the markets."
> it continuously evolves
Yes, but change is also the constant. What underlies that change is supply and demand.
Of course you're free to disagree. But if you'd like to learn more about the not-100%-accurate analogy I described in abbreviated form, the source is here: http://www.goodreads.com/book/show/253516.Trading_in_the_Zon...
Edit: Good reading on "life as a game": http://www.goodreads.com/book/show/189989.Finite_and_Infinit...
Life isn't a game.
In a sense, it's all about what information you have access to.
GS is very well connected, and they are constantly out there asking people for their opinions. Ten years ago they probably had some sort of database where people could share experiences, and that is most likely what led to them guessing the financial crash.
But it's not like they had some machine that one day told them to short mortgages.
What do speech recognition, language translation, face recognition, object classification/detection have in common that is not true about predicting the future price of a security?
The reality is that speech recognition, language translation, face recognition, object classification and detection, semantic segmentation, speech and image synthesis have improved by extraordinary leaps and bounds in recent years. If we used the logic that past failures inform a confident belief that future success in a challenge will inevitably fail, then we should've bet heavily against Alpha Go defeating one of the most accomplished human Go champions in the world. Self driving cars seem like a sci-fi fantasy until they become a mundane reality.
There's an irrational arrogance to human beings in general, and Wall Street types in particular, regarding the specialness / non-reproducibility of their intelligence. It's not unlike the belief that people had that organic molecules were somehow special, "vital," and not synthesizable from base elements.
Certainly, there's a long way to go to replicate the capabilities of a human brain, but I don't think we should exaggerate or fetishisize the human power to estimate and mitigate risk. We've seen many spectacular failures of that in recent years.