Both deep networks and the human brain require lots of data, but the kind of data they require is not the same. Humans engage mostly in semi-supervised learning, where supervised data comprises a small fraction of the total.
This is probably a misconception for several reasons. Firstly, given that we don't fully understand the learning mechanisms in the brain yet, it's unlikely that it's mostly one thing. Secondly, we have some pretty good evidence for reinforcement learning in the cortex, hippocampus, and basal ganglia. We have evidence for internally supervised learning in the cerebellum, and unsupervised learning in the cortex.
The point being: these labels aren't all that useful. Efficient learning is multi-objective and doesn't cleanly divide into these narrow categories.
The best current guess for questions like this is almost always to guess that the brain's solution is highly efficient, given it's constraints.
In the situation where a go player experiences/watches a game between two other players far above one's own current skill, the optimal learning update is probably going to be a SL style update. Even if you can't understand the reasons behind the moves yet, it's best to compress them into the cortex for later. If you can do a local search to understand why the move is good, then that is even better and it becomes more like RL, but again, these hard divisions are arbitrary and limiting.
A few hundred TitanX's can muster up perhaps a petaflop of compute.
Could you elaborate? I think this number is too high by roughly one order of magnitude.
The GTX TitanX has a peak perf of 6.1 terraflops, so you'd need only a few hundred to get a petaflop supercomputer (more specifically, around 175).
The high end estimate of the brain is 10 petaflops (100 trillion synapses * 100 hz max firing rate).
Estimating the computational capability of the human brain is very difficult. Among other things, we don't know what the neuroglia cells may be up to, and these are just as numerous as neurons.
It's just a circuit, and it obeys the same physical laws. We have this urge to mystify it for various reasons. Neuroglia can not possibly contribute more to the total compute power than the neurons, based on simple physics/energy arguments. It's another stupid red herring like quantum woo.
These estimates are only validated when you can use them to make predictions. And if you have the right estimates (brain equivalent to 100 terraflops ish, give or take an order of magnitude), you can roughly predict the outcome of many comparisons between brain circuits vs equivalent ANN circuits (more accurately than using the wrong estimates).
This is probably a misconception for several reasons. Firstly, given that we don't fully understand the learning mechanisms in the brain yet, it's unlikely that it's mostly one thing ...
We don't understand the learning mechanisms yet, but we're quite familiar with the data they use as input. "Internally" supervised learning is just another term for semi-supervised learning anyway. Semi-supervised learning is plenty flexible enough to encompass the "multi-objective" features of what occurs in the brain.
...The GTX TitanX has a peak per
DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.