Critics like to point out that DL requires tons of data, but so does the human brain.
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. They also manage feats of "one-shot learning" (making critically-important generalizations from single datapoints) that are simply not feasible for neural networks or indeed other 'machine learning' methods.
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 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.
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 internall...
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.