LAURENCE MORONEY: In the previous episode,
we looked at the concept of a hype cycle,
and where AI is on the hype cycle right now.
And that's namely here, where we have
to go past the peak of inflated expectations
through the trough of disillusionment
up the slope of enlightenment to get
to the plateau of productivity.
So we'll start with understanding what
machine learning is all about.
And if you have any questions, please leave a comment for us
down below.
If you're not familiar with it, the name
evokes images of computers being able to "learn."
And you might think that it's some kind of magical technology
that allows a machine to act like a living being
and learn from its experience and mistakes.
And maybe there's a mechanical brain somewhere that
works like a biological one.
Well, that's not really true.
Machine learning is just a new way of programming a computer.
You might be familiar with how computers
are programmed using code.
And that code is a way that the programmer
breaks down the solution to a problem into simple steps
that a computer can be told to follow.
And computers are really good at doing that really,
really quickly.
So for example, consider a game like pong,
where a programmer wants to code a computer
to play pong against you.
They might create rules like this
to project where the ball will intersect
the edge of the screen.
And if you're above that spot, you should move down.
If you're below that spot, you probably want to move up.
And then you want to express those rules in code.
But what's important here is that the computer
isn't really smart.
It's just following the rules that the programmer figured
out.
It doesn't really know how to play the game.
I like to represent that with boxes like this.
There are rules and data in, where
the rules are figured out by the programmer,
and expressed in code.
And data is things like the location of the ball, its path,
and the location of the bats.
And out of this come answers.
In this case, the answer is to move the bat
in a particular direction.
Machine learning is very similar to this,
but I just like to flip the diagram around a little bit.
Instead of the rules being figured out
by the programmer, what if we gave a computer the answers
along with the data, and we have the computer
to figure out what the rules should be?
So for example, the data could be
that the ball is in this position,
and the bat is in this one.
And then we tell the computer in this case,
that the bats should move up, because the ball's going
to end up over here.
Or in this case, based on the location and trajectory
of the ball, the bat should move down.
And we get lots, and lots, and lots,
and lots of examples like this.
And we have the computer figure out the patterns
that determine the movement of the bats instead of us
explicitly telling it by putting those rules into code.
That pattern matching is what we call machine learning.
So we change the diagram to this.
Instead of rules and data in, we give the answers.
So we have the answers, and we have the data coming in.
And it's up to the computer to figure out the rules.
And that's what machine learning is all about.
It's really that simple.
But I want to tell you a little secret.
When you have a computer spotting patterns in data,
you open up new possibilities that weren't previously
possible or feasible.
I mean, pong is great and all that, but what about this?
If you're an experienced ophthalmologist,
you could probably read this to get a diagnosis of
whether or not this eye was diseased.
But could you tell the age of the person,
or could you tell their birth-assigned gender?
What if I told you that machine learning could do that.
All you have to do is give it lots of images of eyes,
and the age of the person who owns that eye,
and then have it figure out the patterns that
match one to the other.
You give it the answers.
You give it the data, and it can figure out the rules.
Now, no programmer can do that.
Nobody can understand the rules that
can determine how old somebody is just from their eye scan.
So like I said earlier, when you get down into the trough,
and you realize how these things actually work,
then you can begin to climb.
Just like a scientist at Google who
used machine learning to build a model that predicts somebody's
age to within three and 1/4 years using only a retina scan.
Now, that's why I'm really excited about this stuff.
So now, you've seen the idea behind machine learning.
You're getting a computer to spot
the patterns between answers and data
in order to figure out the rules.
In the next episode, we're going to go
through how it can do that using artificial neurons.
So please like, share, and subscribe
to the Google Developers channel, and of course,
most of all, enjoy this series.
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