This isn't really what is responsible for the success of deep learning. Lots and lots of machine learning algorithms existed before deep learning which are essentially optimizing a (sub-)differentiable objective function, most notably the LASSO. Rather, it's that recursive / hierarchical representation utilized by DL is somehow a lot better at representing complicated functions than things like e.g. kernel methods. I say "somehow" because exactly why and to what extent this is true is still an active subject of research within theoretical ML. It happen in many areas of math that "working in the right basis" can dramatically improve one's ability to solve certain problems. This seems to be what is happening here, but our understanding of the phenomenon is still quite poor.
>We have very little idea how to make really complicated problems differentiable.
All of the problems that deep learning solves were once called "really complicated" and "unndifferentiable". There's nothing inherently differentiable about image recognition, or go playing, or predicting the next word in a sentence, or playing an Atari game. NNs can excel at these tasks any way, because they are really good at pattern recognition. Amazingly good. And this is an extremely general ability that can be used as a building block for more complex things.
So Bostrom is no more an expert in that field than a corner-store psychic is an expert on predicting her clients' love lives. But both are very comfortable speculating, and have a knack for framing the discussion in a way that appeals to their listeners.
I don't find that too hard to believe, personally, but it's unfair to single out Bostrom as a charlatan if you really believe that there's no way of knowing what will happen post-development of human-level AI.
>Bostrom is basically dealing in the outcomes of very long joint probabilities, which, multiplied by the probability of each individual variable, is very low.
That's not how probability works. You can't just assume Bostrom is wrong by default. That might work for futurists like Hanson or Kurzweil, which do that. But Bostrom isn't making a huge number of assumptions. His book presents a large number of possible futures, and the dangers of each.
Bostrom is also using the tools of human philosophy assuming they are general enough to apply to superintelligence. So he comes off as inherently anthropomorphizing even as he warns against doing just that.
He said Superintelligence was a very difficult book to write and that's probably part of what he meant by "difficult."
There is plenty to doubt. One big doubt about the danger of AI is that AI is not an animal. It is not alive like an animal. It has no death like an animal has. It doesn't need a "self." It doesn't propagate genes. It did not evolve through natural selection. So, except for Bostrom's use of whole brain emulation as a yardstick, there isn't much of the commonplace things that makes humans dangerous that needs to be in an AI.
But if the ideas of "strategic advantage" are in general correct, in the way Bostrom uses them, then Bostrom is right to say we are like a child playing with a bomb.
Just don't enhance the chimps. That would be bad.
So yes he absolutely is more informed than a psychic, or even an expert in today's machine learning. And he isn't even making very specific predictions. He's not giving an exact timetable of when we will have AI, or what algorithms it will use. Just a very general prediction that we will get superintelligence someday, and that it will be very dangerous if we don't solve the control problem.
These seem like very reasonable statements, and have strong arguments for them IMO.
LeCunn cites photo recognition, Ng cites autonomous trucks, Nosek cites auto-scaling difficulty for online courses and some kind of magnetic brain implant.
Seems to me that these are all fringe/isolated use-cases - like learning to use your fingers one at a time without learning the concept of how to use them together to grasp an object. Perhaps once we get better with each of these fringe "senses" we'll be able to create some higher level intelligence that can leverage learnings across dimensions that we haven't even considered yet.
However, in the short term, I think the areas where AI has the potential to make the biggest impact are where we have huge amounts of data (and gathering data is cheap/getting cheaper) and we have an obvious objective. I am really excited to see what Deep Mind will do with their collaboration with the NHS, and I think some incredibly exciting thing will happen when people with a deep understanding of biology collaborate with people who really understand deep learning.
The difficulty is that in industry the people that often have an easy time raising funds are impressive bullshit artists that are great at selling stuff (Theranos, Verily) while in academia people often spend too much time in their own comfortable silos, and straddling multiple fields is a very difficult act to pull off if you want to ever get tenure.
Most academic people tend to spend all their time in their own comfortable silos for the same reason most industry people send time in their (MUCH more comfortable!) siloes -- there's enough to keep everyone busy for 40-60+ hours/wk without leaving the silos, and families/eating/sleeping are all important too.
I suspect there's a promising future in detecting and potentially correcting for out of sample data, even if the methods for doing so are non-differentiable.
But this only defines under which circumstances Deep Learning will produce a solution, this doesn't tell us why DL has been so effective. There is little point in an algorithm guaranteed to converge if that algorithm has little applicability.
To me, DL's triumph was being able to make advances in fields that were at a standstill with traditional methods (CV is an obvious one, but natural language processing is a good one as well). This in turn has attracted enough attention that DL is now being considered for a very wide variety of problems. Obviously, DL won't be successful on all of them but that's science as usual.
What comes to mind for me is quantum annealing as seen in D-Wave's concept videos.
What is, I think, is that the error function is continuous and there is a monotonic path of improvement from most of the space to good solutions in it. Then you can just descend in the error function until you reach it.
We don't really understand why that happens in deep learning, that is, why the error functions of deep neural networks tend to not have complex shapes with local minima that you can get stuck in. This is currently being investigated.
This also isn't a problem limited to deep learning. In evolution, the same is true - we are limited to small local improvements, but over large amounts of time nature arrives at amazingly efficient solutions. That suggests that the fitness function space over genomes is also, to some extent, continuous and allows monotonic improvement. Again, we don't really know why, yet.
It's an absolute non-starter.
The point here is that we are descending in a continuous and monotonic loss function. You don't need an accurate derivative or even an estimate of the derivative to do so, although it definitely helps a great deal.
- edit used the wrong word, used derivative meant differentiable.
A lot of interesting advances have come from making operations that seems like they aren't differentiable, into differentiable operations that can be trained by backpropagation. See LSTMs and neural turing machines
The huge triumph of DL has been figuring out that as long as you can pose a problem in a differentiable way and you can obtain a sufficient amount of data, you can efficiently tackle it with a function approximator that can be optimized with first order methods - from that, flows everything.
We have very little idea how to make really complicated problems differentiable. Maybe we will - but right now the toughest problems that we can put in a differentiable framework are those tackled by reinforcement learning, and the current approaches are incredibly inefficient.