शैडोइंग अभ्यास: Supervised Learning: Crash Course AI #2 - वीडियो के साथ अंग्रेजी बोलना सीखें
पाठ बनाया जा रहा है...
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Hey, I'm Jabril and this is Crash Course AI.
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Today, we're going to try and teach John Greenbot something.
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Hey, John Greenbot.
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Hello, humanoid friend.
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Are you ready to learn?
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Hello, humanoid friend.
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As you can see, he has a lot of learning to do, which is the basic story of all artificial intelligence.
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But it's also our story.
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Humans aren't born with many skills, and we need to learn how to sort mail, land airplanes, and have friendly conversations.
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So computer scientists have tried to help computers learn like we do, with a process called supervised learning.
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You ready John Greenbot?
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Hello, humanoid friend.
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The process of learning is how anything can make decisions, like for example, humans, animals, or AI systems.
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They can adapt their behavior based on their experiences.
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In Crash Course AI, we'll talk about three main types of learning, reinforcement, unsupervised, and supervised learning.
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Un-learning is the process of learning in an environment, through feedback from an AI's behavior.
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It's how kids learn to walk.
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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.
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Unsupervised learning is the process of learning without training labels.
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It could also be called clustering or grouping.
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Sites like YouTube use unsupervised learning to find patterns
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and frames of video and compress those frames so that videos can be streamed to us quickly.
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And supervised learning is the process of learning with training labels.
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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.
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Supervised learning is when someone who knows the right answer, called a supervisor, points out mistakes during the learning process.
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You can think of this like when a teacher corrects a student's math.
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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.
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AI needs computing power and data to learn.
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And that's especially true for supervised learning, which needs a lot of training examples from a supervisor.
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After training this hypothetical AI, it should be able to correctly classify images it hasn't seen before.
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Like a picture of a kitten as a mammal.
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That's how we know it's learning, instead of just memorizing answers.
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And supervised learning is a key part of lots of AI you interact with every day.
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It's how email accounts can correctly classify a message from your boss as important and ads as spam.
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It's how Facebook tells your face apart from your friend's face
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so that it can make tag suggestions when you upload a photo.
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And it's how your bank may decide whether your loan request is approved or not.
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Now, to initially create this kind of AI, computer scientists were loosely inspired by human brains.
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They were mostly interested in cells called neurons because our brains have billions of them.
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Each neuron has three basic parts, the cell body, the dendrites, and the axon.
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The axon of one neuron is separated from the dendrites of another neuron by a small gap called a synapse,
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and neurons talk to each other by passing electric signals through synapses.
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As one neuron receives signals from another neuron, the electric energy inside of its cell body builds up until a threshold is crossed.
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Then, an electric signal shoots down the axon and is passed to another neuron, where everything repeats.
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The goal of early computer scientists wasn't to mimic a whole brain.
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Their goal was to create one artificial neuron that worked like a real one.
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To see how, let's go to the Thought Bubble.
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In 1958, a psychologist named Frank Rosenblatt was inspired by the Dartmouth Conference and was determined to create an artificial neuron.
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His goal was to teach this AI to classify images as triangles or not triangles with his supervision.
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That's what makes it supervised learning.
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The machine he built was about the size of a grand piano, and he called it the perceptron.
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Rosenblatt wired the perceptron to a 400 pixel camera, which was high-tech for the time,
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but is about a billion times less powerful than the one on the back of your modern cell phone.
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He would show the camera a picture of a triangle or a not triangle, like a circle.
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Depending on if the camera saw ink or paper in each spot, each pixel would send a different electric signal to the perceptron.
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Then, the perceptron would add up all the signals that match the triangle shape.
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If the total charge was above its threshold, it would send an electric signal to turn on a light.
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That was artificial neuron speak for, yes, that's a triangle.
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But if the electric charge was too weak to hit the threshold, it wouldn't do anything and the light wouldn't turn on.
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That meant not a triangle.
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At first, the perceptron was basically making random guesses.
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So to training with supervision, Rosenblatt used yes and no buttons.
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If the perceptron was correct, he would push the yes button and nothing would change.
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But if the perceptron was wrong, he would push the no button,
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which would set off a chain of events that adjusted how much electricity crossed the synapses and adjusted the machine's threshold levels.
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So, it'd be more likely to get the answer correct next time.
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Thanks Thought Bubble.
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Nowadays, rather than building huge machines with switches and lights, we can use modern computers to program AI to behave like neurons.
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The basic concepts are pretty much the same.
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First, the artificial neuron receives inputs multiplied by different weights, which correspond to the strength of each signal.
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In our brains, the electric signals between neurons are all the same size, but with computers, they can vary.
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The threshold is represented by a special weight called the bias, which can be adjusted to raise or lower the neuron's eagerness to fire.
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So, all the inputs are multiplied by their respective weights, added together, and a mathematical function gets a result.
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In the simplest AI systems, this function is called a step function, which can only output a zero or a one.
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If the sum is less than the bias, then the neuron will output a zero, which could indicate not triangle or something different depending on the task.
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But if the sum is greater than the bias, then the neuron will output a 1, which indicates the opposite result.
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An AI can be trained to make simple decisions about anything where you have enough data and supervised labels, like triangles, junk mail, languages,
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movie genres, or even similar looking foods, like donuts and bagels.
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Hey John Green bot!
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You want to learn how to sort some disgusting bagels from delicious donuts?
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Hello, humanoid friend.
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John Greenbot still has the Talk Like a Human program.
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Remember, we don't have generalized AI yet.
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That program is pretty limited.
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So I need to swap it out for the Perception program.
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Now that John Greenbot 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.
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How about you hold onto these for me?
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Right now, he doesn't know anything about bagels or donuts or what their masses and diameters might be.
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So his program is initially using random weights for mass, diameter, and the bias to help make a decision.
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But as he learns, those weights will be updated.
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Now we can use different mathematical functions to account for how close or far an AI is from the correct decision.
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But we're going to keep it simple.
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John Greenbot's Perception program is using a step function, so it's an either-or choice.
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Zero or one.
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Bagel or donut.
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Completely right or completely wrong.
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Let's do this.
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This here is a mixed batch of bagels and donuts.
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This first item has a mass of 34 grams and a diameter of 7.8 centimeters.
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The perceptron takes these inputs, multiplies them by their respective weights, then adds them together.
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If the sum is greater than the bias, which, remember, is the threshold for the neuron firing, John Greenbot will say, bagel.
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So, if it helps to think of it this way, the bias is like a bagel threshold.
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If the sum is less than the bias, it hasn't crossed the bagel threshold and John Greenbot will say, donut.
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All this math can be tricky to picture.
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So to visualize what's going on, we can think of John Greenbot's perceptron program as a graph, with the mass on one axis and the diameter on the other.
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The weights and bias are used to calculate a line called a decision boundary on the graph, which separates bagels from donuts.
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And if we represent this same donut as a data point, we'd graph it at 34 grams and 7.8 centimeters.
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This data point is above the decision boundary, in the bagel zone.
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So all this means is when I ask John Greenbott what this food is, he'll say, BAGEL And he got it wrong,
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because this is a donut.
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No big deal though.
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With a brand new program, he's like a baby that made a random guess.
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he's using random weights right now.
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But we can help him learn by updating his weights.
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So we take an old weight and add a number calculated by an equation called the update rule.
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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.
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Now because our perceptron can only be completely right or completely wrong, the update rule ends up being pretty simple.
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If John Greenbott made the right choice, like labeling a donut as a donut, the update rule works out to be zero, so he adds zero to the weight and the weight stays the same.
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But, if John Greenbot made the wrong choice, like labeling a donut as a bagel, the update rule will have a value, a small positive or negative number.
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He'll add that value to the weight and the weight will change.
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Conceptually, this means John Greenbot learns from failure, but not from success.
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So he called this donut a bagel and got the label wrong.
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By pressing this no button, I'm supervising his learning and letting him know that he made the wrong choice, so his weights update.
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If we look back at the graph, we can see that when the weights update, the decision boundary changes.
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That data point we added is now below the line, in the donut zone.
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Now his perception will classify another item with this mass and diameter as a donut.
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This next item has a mass of 26 grams and a diameter of 6.1 centimeters.
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What do you think, John Greenbot?
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Doughnut.
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He got it right!
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When he took those inputs into the same calculation, the sum was less than the bias.
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That data point appeared below the decision boundary, in the donut zone.
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And so, I'm gonna push the yes button.
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In this case, the update rule equation works out to be zero.
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So the weights are the same, and so does the decision boundary.
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Now we do this 48 more times to train his perceptron.
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After we're done training John Greenbot's perceptron, we have to test it on new data to see how well he's learned.
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So I got a hundred new bagels and donuts for him to classify.
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Oh, whoa.
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This is a big what.
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What is this?
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Bagel.
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All right.
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All right.
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Just let me write that down.
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Write down your answer.
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Alright, so overall, he's classified 25 donuts and 75 bagels.
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We can visualize the results on a graph with a decision boundary like this.
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But we can also put the results in a table called a confusion matrix
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because it tells us where John Greenbot was confused.
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He got 8 donuts correct and 73 bagels correct, but he said that a bagel was a donut twice, and that a donut was a bagel 17 times.
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Using these numbers, we can calculate his overall accuracy by adding together what he got right, which were 8 donuts and 73 bagels,
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and dividing by the total 100 to get 81%.
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But to really understand what's wrong, we need to take a look at his precision and his recall.
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We can calculate these percentages for both foods, but we'll focus on donuts right now.
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Precision tells us how much you should trust your program when it says it's found something.
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If John Greenbot tells me something's a donut, I'm expecting to eat a donut.
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I don't want to bite into a bagel because that would be a gross surprise.
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Of the 10 items he said were donuts, 8 were actually donuts.
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So he was 80% precise, and I can be 80% sure he's only handing me donuts when he says he is.
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Recall tells you how much your program can find of the thing you're looking for.
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I'm really hungry, so I want as many donuts as possible.
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But, of the 25 items that were donuts, he correctly labeled 8 of them.
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So his recall is just 32%.
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And he just handed me 32% of all the donuts.
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The precision and recall depend on the criteria John Greenbot is using to make a decision.
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Diameter and mass.
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And as you can see from this graph, he thinks that donuts generally have smaller diameters and masses than bagels.
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They're small, fluffy treats.
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So when it comes to classifying donuts, he has a high precision, because if he says something's a donut, we're pretty sure it's a donut and not a disgusting bagel.
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But John Greenbot has a low recall, because this criteria didn't account for the fact
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that some donuts can be way bigger than the donuts we use to train his perceptron.
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They have a bigger diameter and mass, and they fall in the current bagel zone.
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So he missed a lot of donuts when he was classifying.
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Thanks John Greenbot!
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Figuring out what criteria to use is the key to most AI challenges.
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If we wanted better accuracy for this donut bagel problem, maybe we should have used inputs besides mass and diameter, like checking for seeds or sprinkles.
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Generally, more inputs are better for accuracy, but the AI will need more processing power and time to make decisions.
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An ideal AI system would be small, powerful, and have perfect precision and perfect recall.
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But in the real world, mistakes happen, so we have to prioritize based on our goals.
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The AI filtering our inboxes needs to make sure we get all the important emails, so it needs a high recall.
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But it's okay if it isn't very precise, because we can deal with some spam getting through and don't need only good emails.
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Most AIs handle more complicated problems than sorting something into one of two categories, though.
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The world isn't all just donuts and bagels.
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So to answer more complicated questions, we need more complicated AI.
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Next time, we'll combine artificial neurons to create an artificial neural network.
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See you then.
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Crash Course is produced in association with PBS Digital Studios.
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If you want to help keep Crash Course free for everyone forever, you can join our community on Patreon.
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And if you want to learn more about how the brain and nervous system works, check out our anatomy and physiology videos about them.
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इस पाठ की शब्दावली और बोलने के सुझाव
यह C1 स्तर का स्पीकिंग पाठ वीडियो “Supervised Learning: Crash Course AI” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: donut, bagel, learning, neuron, weight। इस वीडियो में शैडोइंग के लिए 190 वाक्य और 2579 शब्द हैं। बोला गया हिस्सा 15:18 लंबा है। वक्ता लगभग 169 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। केवल 77% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| doughnut संज्ञा | /ˈdəʊnʌt/ | डोनट |
| neuron संज्ञा | /ˈn(j)ʊɹɑn/ | तंत्रिकोशिका |
| diameter संज्ञा | /daɪˈæmɪdɚ/ | व्यास |
| triangle संज्ञा | /ˈtɹaɪˌæŋɡəl/ | त्रिकोण, त्रिभुज |
| threshold संज्ञा | /ˈθɹɛʃ(h)oʊld/ | डेवढ़ी |
| graph संज्ञा | /ɡɹæf/ | लेखाचित्र, ग्राफ़ |
| calculate क्रिया | /ˈkælkjʊleɪt/ | गणना करना, हिसाब रखना |
| axon संज्ञा | /ˈæksɒn/ | अक्षतंतु |
| criterion संज्ञा | /kɹaɪˈtɪɹ.i.ən/ | मानदंड, मापदंड |
| multiply क्रिया | /ˈmʌltɪplaɪ/ | गुणा करना |
| gram संज्ञा | /ˈɡɹæm/ | ग्राम |
| equation संज्ञा | /ɪˈkweɪ.ʒən/ | समीकरण |
| mammal संज्ञा | /ˈmæməl/ | स्तनपायी, स्तनधारी |
| supervision संज्ञा | /ˌs(j)uːpəˈvɪʒən/ | पर्यवेक्षण |
| supervisor संज्ञा | /ˈsupɚˌvaɪzɚ/ | पर्यवेक्षक |
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| शर्त वाले वाक्य if + उपवाक्य, will/would + क्रिया — शर्त और उसका नतीजा | If the perceptron was correct, he would push · if the perceptron was wrong, he would push |
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | aren't born · be called · can be streamed |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | have tried · hasn't seen · have generalized |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | someone who knows · camera, which was · bias, which can |
ध्यान देने योग्य उच्चारण
वक्ता 36 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे I'm, we'll, we're। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: threshold /ˈθɹɛʃ(h)oʊld/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, hypothetical /ˌhaɪpəˈθɛtɪkəl/
- “sh” और “zh” की ध्वनियाँ: threshold /ˈθɹɛʃ(h)oʊld/, precision /pɹɪˈsɪʒ.ən/, equation /ɪˈkweɪ.ʒən/, visualize /ˈvɪʒuəˌlaɪz/, supervision /ˌs(j)uːpəˈvɪʒən/
- लंबे शब्द — बलाघात सही रखें: diameter /daɪˈæmɪdɚ/, criterion /kɹaɪˈtɪɹ.i.ən/, supervision /ˌs(j)uːpəˈvɪʒən/, supervisor /ˈsupɚˌvaɪzɚ/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर doughnut, neuron, diameter जैसे शब्दों पर ध्यान दें।
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।