So a neural network is trying to use

a computer program that will mimic

how neurons, how our brains use neurons to process things,

brains to synapse, neurons to synapses

and building these complex networks that can be trained.

So a neural network starts out

with some inputs and some outputs

and you keep feeding these inputs in

to try to see what kinds of transformations

will get to these outputs,

and you keep doing this over and over and over again

in a way that this network should converge

so these input, the transformations

will eventually get these outputs.

The problem with neural networks was that

even though the theory was there

and they did work on small problems

like recognizing handwritten digits and things like that,

they were computationally very intensive,

and so they went out of favor.

I stopped teaching them,

well, probably 15 years ago.

Then all of a sudden we started hearing about deep learning.

I heard the term deep learning.

This is another term that

when did you first hear it?

Fours years ago, five years ago.

So I finally said,

"What the hell is deep learning?

It's really doing all this great stuff.

What is it?"

I Google it and I find this is neural networks on steroids.

What they did was they just had more

multiple layers of neural networks

and they use lots and lots and lots

of computing power to solve them.

Just before this interview

I had a young faculty member in the marketing department

whose research is partially based on deep learning.

She needs a computer that has

a graphics processing unit in it

because it takes an enormous amount of matrix

and linear algebra calculations

to actually do all of the mathematics

that you need in neural networks,

but they are now quite capable.

We now have neural networks and deep learning

that can recognize speech, can recognize people.

If you're out there and getting your face recognized

I guarantee that NSA has a lot of work

going on in neural networks.

The University, right now,

as Director of Research Computing,

I have some small set of machines

down at our South Data Center

and I went in there last week

and there were just piles and piles and piles

of cardboard boxes all from Dell with a GPU on the side.

Well, a GPU is a graphics processing unit.

There is only one application in this University

that needs 200 servers,

each with graphics processing units in it,

and each graphics processing unit

has the equivalent of 600 cores of processing,

so this is tens of thousands of processing cores.

That is for deep learning.

I guarantee.

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