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Comment Re:Why did we need LHC? (Score 1) 292

It's kinda a welfare program for high end scientists. How else could billions be spent on ``science'' and benefit thousands of contractors. The liberal scientists get something fun to play with, and a bunch of special interest groups get billions of easy money to build and operate the thing.

With that thought, perhaps U.S. should build one that's perhaps 150 miles, somewhere in mid west (nice and flat). Locate it such that it spans at least 4-5 states to get a bunch of senators for it. There are worse ways to prop up the economy... and this make work project might actually do something cool.

Comment Re:Deep learning? (Score 2) 162

Advances are in ways of learning hidden layers that are slightly more clever than backpropagation. For example, lets say you have an image, apply some transform to it (dct, wavelet, whatever, neural net layer, etc.) and save all the important features, but at say 10x less space. Then do the same to those features. Every time reducing the amount of data by 10x. After a few such layers, lets say you're left with 10 bits worth of information---the ``most important'' (according to your benchmark used) ten bits of the whole image.

The ten bits could be anything, such as `this image is a car' or `this image is a face', or ``this face looks angry', etc.

The trick is applying the benchmark on the hidden layers---e.g. how do you pick out which features are important after applying a transform. For that, you train another (inverse) transform that recovers original data from the features---the one that gets you closest to the original wins (e.g. lets say you feed 1000 bits into a neural net to get 100 bits out, and then via inverse transform turn those 100 bits into the *original* 1000 bits... that would mean that your 100 bits represented all the information in the input 1000 bits---obviously more often than not you won't get a perfect match but something close---repeat for any number of layers you want).

Comment Re:Sources of improvements? (Score 5, Informative) 162

The ``new'' (e.g. last decade or so) advances are in training hidden layers of neural networks. Kinda like peeling an onion, each layer getting progressively coarser representation of the problem. e.g. if you have 1000000 inputs, and after a few layers, only have 100 hidden nodes, those 100 nodes are in essence representing all the ``important'' (some benchmark you choose) information of those 1000000 inputs.

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