Name: Supervised Learning: Crash Course AI #2
Uploaded: 2020-03-30T03:31:00.000Z
Duration: 15 min 23 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Hey, I'm Jabril, and this is CrashCourse AI!

Today, we're going to try to teach John Green-bot something.

John Green-bot: “Hello humanoid friend!”

As you can see, he has a lot of learning to do, which is the basic story of all artificial

Humans aren't born with many skills, and we need to learn how to sort mail, land airplanes,

So computer scientists have tried to help computers learn like we do, with a process

The process of learning is how anything can make decisions, like for example humans, animals,

They can adapt their behavior based on their experiences.

In Crash Course AI, we'll talk about three main types of learning: Reinforcement Learning,

Unsupervised Learning, and Supervised Learning.

Reinforcement Learning is the process of learning in an environment, through feedback from an

AI's behavior, it's how kids learn to walk!

No one tells them how, they just practice, stumble, and get better at balancing until

they can put one foot in front of the other.

Unsupervised Learning is the process of learning without training labels.

It could also be called clustering or grouping.

Sites like YouTube use unsupervised learning to find patterns in the frames of a video,

and compress those frames so that videos can be streamed to us quickly.

And Supervised Learning is the process of learning with training labels.

It's the most widely used kind of learning when it comes to AI, and it's what we'll

focus on today and in the next few videos!

Supervised learning is when someone who knows the right answers, called a supervisor, points

out mistakes during the learning process.

You can think of this like when a teacher corrects a student's math.

In one kind of supervised setting, we want an AI to consider some data, like an image

of an animal, and classify it with a label, like “reptile” or “mammal.”

AI needs computing power and data to learn.

And that's especially true for supervised learning, which needs a lot of training examples

After training this hypothetical AI, it should be able to correctly classify images it hasn't

seen before, like a picture of a kitten as a mammal.

That's how we know it's learning instead of just memorizing answers.

And supervised learning is a key part of lots of AI you interact with every day!

It's how email accounts can correctly classify a message from your boss as important, and

It's how Facebook tells your face apart from your friend's face so that it can make

And it's how your bank may decide whether your loan request is approved or not.

Now, to initially create this kind of AI, computer scientists were loosely inspired

They were mostly interested in cells called neurons, because our brains have billions

Each neuron has three basic parts: the cell body, the dendrites, and the axon.

The axon of one neuron is separated from the dendrites of another neuron by a small gap

And neurons talk to each other by passing electric signals through synapses.

As one neuron receives signals from other neurons, the electric energy inside of its

cell body builds up until a threshold is crossed.

Then, an electric signal shoots down the axon, and is passed to another neuron -- where everything

So the goal of early computer scientists wasn't to mimic a whole brain.

Their goal was to create one artificial neuron that worked like a real one.

To see how, let's go to the Thought Bubble.

In 1958, a psychologist named Frank Rosenblatt was inspired by the Dartmouth

Conference and was determined to create an artificial neuron.

His goal was to teach this AI to classify images as “triangles” or “not-triangles”

That's what makes it supervised learning!

The machine he built was about the size of a grand piano, and he called it the Perceptron.

Rosenblatt wired the Perceptron to a 400 pixel camera, which was hi-tech for the time, but

is about a billion times less powerful than the one on the back of your modern cellphone.

He would show the camera a picture of a triangle or a not-triangle, like a circle.

Depending on if the camera saw ink or paper in each spot, each pixel would send a different

Then, the Perceptron would add up all the signals that match the triangle shape.

If the total charge was above its threshold, it would send an electric signal to turn on

That was artificial neuron speak for “yes, that's a triangle!”

But if the electric charge was too weak to hit the threshold,

it wouldn't do anything and the light wouldn't turn on, that meant “not a triangle.”

At first, the Perceptron was basically making random guesses.

So to train it with supervision, Rosenblatt used “yes” and “no” buttons.

If the Perceptron was correct, he would push the “yes” button and nothing would change.

But if the Perceptron was wrong, he would push the “no” button, which set off a

chain of events that adjusted how much electricity crossed the synapses, and adjusted the machine's

So it'd be more likely to get the answer correct next time!

Nowadays, rather than building huge machines with switches and lights, we can use modern

computers to program AI to behave like neurons.

The basic concepts are pretty much the same:

First, the artificial neuron receives inputs multiplied by different weights, which correspond

In our brains, the electric signals between neurons are all the same size, but with computers,

The threshold is represented by a special weight called a bias, which can be adjusted

to raise or lower the neuron's eagerness to fire.

So all the inputs are multiplied by their respective weights, added together, and a

In the simplest AI systems, this function is called a step function, which only outputs

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Supervised Learning: Crash Course AI #2

process

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artificial

bias