0:16
- It's a, I guess Computer Science's attempt
to mimic a real,
the neurons and how our brain actually functions.
So 20, 30 years ago a neural network
would have some inputs that would come in
they would be fed into different processing nodes
that would then do some transformation on them
and aggregate them or something
and then maybe go to another level of nodes
and finally some output would come out.
And I can remember training a neural network
to recognize digits, handwritten digits and stuff.
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1:00
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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4:12
Some of the first ones are speech recognition.
Yann LeCun who teaches the deep learning class at NYU
and is also the Head Data Scientist at Facebook
comes into class with a notebook
and it's a pretty thick notebook.
It looks a little odd because it's like this.
It's that thick because it has
a couple of graphics processing units in it
and then he will ask the class
to start to speak to this thing
and it will train while he's in class,
he will train a neural network to recognize speech.
So recognizing speech, recognizing people, images,
classifying images, almost all of the traditional tasks
that neural nets used to work on in little tiny things,
now they can do really, really large things.
It will learn, on it's own, the difference between
a cat and a dog and different kinds of objects.
It doesn't have to be taught.
It doesn't, it just learns.
That's why they call it deep learning,
and if you hear, he plays this.
If you hear how it recognizes speech and generates speech,
it sounds like a baby learning to talk.
You can just, you're like
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5:55
You need to learn some linear algebra.
A lot of this stuff is based on matrix
and linear algebra, so you need to know how to do,
use linear algebra and do transformations.
Now, on the other hand,
there's now lots of packages out there
that will do deep learning
and they'll do all the linear algebra for you,
but you should have some idea
of what is happening underneath.
Deep learning, in particular,
needs really high powered computational power,
so it's not something that you're going to go out
and do on your notebook for, you could play with it,
but if you really want to do it seriously
you have to have some special computational resources.
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Alex AklsonPh.D., Data Scientist
