EDIT: changed a set of neurons to neural network per andbbergers comments
Sorry to be that guy, but the AI hype is getting out hand. COSYNE this year was packed with papers comparing deep learning to the brain... it drives me nutty. Convnets can be reasonably put into analogy with the visual system.... because they were inspired from it. But that's about it.
To address your actual comment: I would argue that this is not really interesting or surprising (at least to the ML practitioner), it is very well known that neural nets are incredibly sensitive to initialization. Think of it like this: as training progresses, parameters of neural nets move along manifolds in parameter space, but they can get nudged off of the "right" manifold and will never be able to recover.
Sorry for the rant, the AI hype is just getting really out of hand recently.
Machine learning is specifically not magic. Blackboxes are not useful. Convnets work so well because they build the symmetries of natural scenes directly into the model - natural scenes are translation invariant (as well as a couple of other symmetries), anything that models them sure as hell better have those symmetries too, or you're just crippling your model with extra superfluous parameters.
Makes neuroscience seem kinda bleak doesn't it?
There has been a lot of great work lately building up a theory of how these things work, but it is very much still in the early stage. Jascha Sohl-Dickstein in particular has been doing some great work on this.
We don't even have answers to the most basic questions.
For instance (pedagogically), how the hell is it possible to train these things at all? They have ridiculously non-convex loss landscapes and we optimize in the dumbest conceivable way, first-order stochastic gradient descent. This should not work. But it does, all too often.
Not a great example because there are easy hand wavy arguments as to why it should work, but as far as proofs go...
The hand wavy argument goes as follows: - we're in like a 10000 dimensional space, for the stationary point we're at to be a true local minima that means each one of those 10000 dimensions goes uphill in either direction. It's overwhelming likely that there's at least one way out - there are many many different ways to set the params of the net for each function. Permutation is a simple example.
We really have no idea how these things work.
Anyone who tells you otherwise is lying to you...
Of course we do. It's matching a smooth multi-dimensional curve to sample data.
What I found interesting was mainly that it was us who nudged the parameter space you talked about into the "wrong" one manifold, especially given how old and complicated Go is. The sheer amount of human brain power that has been put into getting good at a game wasn't able to find certain aspects of it, and in 60 hours of training a neural network was able to.
We absolutely should and do look to the brain for inspiration.
I'm taking issue with the rather ham-fisted series of papers that have come out in recent years aggressively pushing the agenda of connections between ML and neuro that just aren't there.
Are you sure that humans have done more net compute on Go than Deepmind just did? The Go game tree is _enormous_, humans are bias. We don't invent strategies from scratch, we use heuristics handed down to us from the pros (who in turn were handed down the heuristics from their mentors).
To me, it's not so interesting or surprising that the human initialized net performed worse. We just built the same biases and heuristics we have into that net.
The question is whether it can do so efficiently. As far as I know, alternating applications of affine transforms and non-linearities are not so useful for some computations that are known to occur in the brain such as routing, spatio-temporal clustering, frequency filtering, high-dimensional temporal states per neuron etc.
When you're a big scientific figure, I think that you have some extra responsibility to the public to only say things you're very confident about. Or otherwise very clearly communicate your uncertainty!!
It's not like neuroscientists know that either.
I'm not an expert, but my impression is that this is not really a reasonable claim, unless you're only considering very small function-like subsystems of the brain (e.g. visual cortex). Neural nets (of the nonrecurrent sort) are strict feed-forward function approximators, whereas the brain appears to be a big mess of feedback loops that is capable of (sloppily, and with much grumbling) modeling any algorithm you could want, and, importantly, adding small recursions/loops to the architecture as needed rather than a) unrolling them all into nonrecursive operations (like a feedforward net) or b) building them all into one central singly-nested loop (like an RNN).
The brain definitely seems to be using something backprop-like (in that it identifies pathways responsible for negative outcomes and penalizes them). But brains also seem to make efficiency improvements really aggressively (see: muscle memory, chunking, and other markers of proficiency), even in the absence of any external reward signal, which seems like something we don't really have a good analogue for in ANNs.
The constraints you mention aren't deal breakers. We can make RNNs without maintaining a global state and fully unrolling the loop. See synthetic gradients for instance. NNs can do unsupervised learning as well, through things like autoencoders.
"These search probabilities usually select much stronger moves than the raw move probabilities of the neural network; MCTS may therefore be viewed as a powerful policy improvement operator. Self-play with search – using the improved MCTS-based policy to select each move, then using the game winner as a sample of the value – may be viewed as a powerful policy evaluation operator. The main idea of our reinforcement learning algorithm is to use these search operators repeatedly in a policy iteration procedure ..."
The fact that this reinforcement training is unsupervised from the very beginning is quite exciting and may lead to better heuristics for other kinds of combinatorial optimization problems.