You've probably heard of AI that can do really cool and interesting things
like recognize objects in an image,
or write stories, or play computer games.
And you're probably wondering how scientists got computers to think in the way that we do.
And you're probably wondering, SHOULD scientists let computers think the way that we do?
Well, I can't answer that second question,
but I want to talk about the first part of that question.
One of the major concepts behind getting AI to think in the way that we do is the multilayer perceptron.
It's a pretty long word.
Don't get scared.
I'll explain it using just the perceptron at first.
The perceptron is heavily inspired by our own brain's most basic unit of thinking, which is the neuron.
Th neuron looks something like this.
It has a nucleus.
It takes in inputs from other neurons.
And it gives out outputs to other neurons.
So this forms the output.
And this forms the inputs.
Neurons don't like to be alone and like to be densely connected in big groups.
The neurons that are responsible for your eyes
and your ability to recognize colors and objects and images in depth
are a neural network that are formed of about 140 million neurons,
all working together in concert to get you the images and things that you see.
In the same way, a perceptron is formed of three basic components.
There's the function, which is the thinking part of the perceptron.
There are the inputs that come in from other perceptrons.
And just like the neuron, there's also a set of outputs that go out from the perceptron.
You won't be able to find a perceptron, it's just a concept.
But the way that the perceptron is organized
is also very similar to the way that our neurons are physically organized.
Perceptron are organized in layers.
And this is where the multiplayer part of "multilayer perceptron" comes in.
They're all connected and they all feed off of each other's inputs and outputs.
So this is just the basic concepts behind the multilayer perceptron.
You're probably wondering how we get computers to think
and how we get multilayer perceptrons to learn.
Well, there's three basic parts of learning.
First of all is you make an educated guess.
For example, when you were learning four legged animals,
you would have seen a bear and you might have called it a dog.
Why would you call it a dog?
Why would you guess a dog?
Well, dogs have four legs and a tail,
and this particular bear has four legs and a tail.
Well, you were wrong,
so what you have to do now is the second step of learning,
which is to change.
So you change your mind about what's the difference between a dog and a bear.
But what about the next time when there's a horse?
Neither of those answers really work,
so what needs to happen is you need to repeat this process.
This is basically the same way that scientists are able to train multilayer perceptrons.
First of all, the multilayer perceptron gives an output
based on the function, based on the inputs, it gives out an output.
Very often that output is wrong and sometimes it's right,
but based on that feedback, it has to change.
The changing process is something called Back Propagation.
Long word, I know, but very simply,
it just means that the multilayer perceptron has to go back through its layers
and improve itself all the way down to the input
so that the next output is better.
Speaking of the next output,
the process of repeating is called an Epoch.
Every epoch that a multilayer perceptron goes through
brings it closer to the perfect output.
I hope this has helped in your understanding of how AI can think
and do some of the things that our brains naturally do.
Thank you.
Thanks so much.
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