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

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

  • Hey John Green-bot!

  • John Green-bot: “Hello humanoid friend!”

  • Are you ready to learn?

  • 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

  • intelligence.

  • But it's also our story.

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

  • and have friendly conversations.

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

  • called supervised learning.

  • You ready, John Green-bot?

  • John Green-bot: “Hello humanoid friend!”

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

  • or AI systems.

  • 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, likereptileormammal.”

  • AI needs computing power and data to learn.

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

  • from a supervisor.

  • 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

  • ads as spam.

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

  • tag suggestions when you upload a photo.

  • 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

  • by human brains.

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

  • of them.

  • 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

  • called a synapse.

  • 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

  • repeats.

  • 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 astrianglesornot-triangles

  • with his supervision.

  • 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

  • electric signal to the Perceptron.

  • 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

  • a light.

  • That was artificial neuron speak foryes, 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 meantnot a triangle.”

  • At first, the Perceptron was basically making random guesses.

  • So to train it with supervision, Rosenblatt usedyesandnobuttons.

  • If the Perceptron was correct, he would push theyesbutton and nothing would change.

  • But if the Perceptron was wrong, he would push thenobutton, which set off a

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

  • threshold levels.

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

  • Thanks, Thought Bubble.

  • 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

  • to the strength of each signal.

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

  • they can vary.

  • 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

  • mathematical function gets a result.

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

  • a 0 or a 1.

  • If the sum is less than the bias, then the neuron will output a 0, which could indicate

  • not-triangle or something else depending on the task.

  • But If the sum is greater than the bias, then the neuron will output a 1, which indicates

  • the opposite result!

  • An AI can be trained to make simple decisions about anything where you have enough data

  • and supervised labels: triangles, junk mail, languages, movie genres, or even similar looking

  • foods.

  • Like donuts and bagels.

  • Hey John Green-bot!

  • You want to learn how to sort some disgusting bagels from delicious donuts?”

  • John Green-bot: “Hello humanoid friend!”

  • John Green-bot still has the talk-like-a-human program!

  • Remember that we don't have generalized AI yetthat program is pretty limited.

  • So I need to swap this out for a perceptron program.

  • Now that John Green-bot is ready to learn, we'll measure the mass and diameter of some

  • bagels and donuts, and supervise him so he gets better at labeling them.

  • How about you hold on to these for me?

  • Right now, he doesn't know anything about bagels or donuts or what their masses and

  • diameters might be.

  • So his program is initially using random weights for mass, diameter, and the bias to help make

  • a decision.

  • But as he learns, those weights will be updated!

  • Now, we can use different mathematical functions to account for how close or far an AI is from

  • the correct decision, but we're going to keep it simple.

  • John Green-bot's perceptron program is using a step function, so it's an either-or choice.

  • 0 or 1.

  • Bagel or donut.

  • Completely right or completely wrong.

  • Let's do it. This here is a mixed batch of bagels and donuts.

  • This first item has a mass of 34 grams and a diameter of 7.8 centimeters.

  • The perceptron takes these inputs (mass and diameter), multiplies them by their respective

  • weights, then adds them together.

  • If the sum is greater than the bias -- which, remember, is the threshold for the neuron

  • firing -- John Green-bot will saybagel.”

  • So if it helps to think of it this way, the bias is like a bagel threshold.

  • If the sum is less than the bias, it hasn't crossed the bagel threshold, and John Green-bot

  • will saydonut.”

  • All this math can be tricky to picture.

  • So to visualize what's going on, we can think of John Green-bot's perceptron program

  • as a graph, with mass on one axis and diameter on the other.

  • The weights and bias are used to calculate a line called a decision boundary on the graph,

  • which separates bagels from donuts.

  • And if we represent this same item as a data point, we'd graph it at 34 grams and 7.8

  • centimeters.

  • This data point is above the decision boundary, in the bagel zone!

  • So all this means is that when I ask John Green-bot what this food ishe'll say:

  • John Green-bot: “Bagel!”

  • And... he got it wrong, because this is a donut.

  • No big deal!

  • With a brand new program, he's like a baby that made a random guess!

  • Because he's using random weights right now.

  • But we can help him learn by updating-- his weights.

  • So we take an old weight and add a number calculated by an equation called the update

  • rule.

  • We're going to keep this conceptual, but if you want more information about this equation,

  • we've linked to a resource in the description.

  • Now because our perceptron can only be completely right or completely wrong, the update rule

  • ends up being pretty simple.

  • If John Green-bot made the right choice, like labeling a donut as a donut, the update rule

  • works out to be 0.

  • So he adds 0 to the weight, and the weight stays the same.

  • But if John Green-bot made the wrong choice, like labeling a donut as a bagel, the update

  • rule will have a value -- a small positive or negative number.

  • He'll add that value to the weight, and the weight will change.

  • Conceptually, this means John Green-bot learns from failure but not from success.

  • So he called this donut a bagel, and got the label wrong.

  • By pressing thisnobutton, I'm supervising his learning and letting him know he made