S is for Supervised Learning - ABCs of AI (Elementary+)

We break down supervised learning, explaining how AI learns from labeled examples just like how children learn from teachers identifying objects.

• Supervised learning is when AI learns from labeled examples (like seeing pictures of apples with labels)
• The more examples an AI sees, the better it gets at recognizing patterns
• Real-world applications include Google Lens, spam filters, AI art apps, and medical diagnostic tools
• Bad or confusing data leads to incorrect learning outcomes
• AI can struggle with examples it hasn't seen before (like only recognizing yellow cats but not black ones)
• Try the "Label It Game" at home using pictures from magazines or your own drawings
• Experiment with Google's Teachable Machine website to train your own mini AI model

Join us next time for the letter T in our ABCs of AI series!

Chapters

• 0:00 Podcast Intro and Welcome
• 1:27 S for Supervised Learning Explained
• 2:39 Real-World Uses of Supervised Learning
• 3:49 Limitations and Watching Out
• 5:08 The Label It Game Activity
• 6:34 Closing Thoughts and Next Steps

Transcript

Speaker 1: 0:29

Welcome to the AI for Kids podcast, where playtime, learning and creating collide bit by bit. Ever wonder how your phone recognizes your face. How does a game learn to get harder as you get better? This is AI. This podcast is designed for kids like you and your human parents, making the complex world of AI easy to understand and, most importantly, fun. So are you ready to unlock the mysteries of artificial intelligence? Subscribe and join us on AI for Kids. Hey AI Explorers, welcome back to the ABCs of AI.

Speaker 1: 1:02

Today we're talking about the letter S, and it stands for supervised learning. Don't worry, it's not about someone watching over you all the time. Supervised learning is actually how AI learns from examples, kind of like how you learn with a teacher or a coach. Let's dig in. So what is supervised learning? Supervised learning is a way that AI learns by being shown the right answers first. It's like when your teacher shows you a picture of an apple and says this is an apple. After a few examples or pictures, you can recognize apples all by yourself. The AI looks at lots and lots of examples, like pictures of apples, cats and dogs and learns how to tell them apart, because each example actually has a label of what it is on it. So I have a quick question have you learned to sort your toys by type or color? Who helped you learn how to do it? First, share nearby with your family member or friend and talk to them about how you learned how to sort different toys or understand what different objects were.

Speaker 1: 2:10

So how does it work? First, the AI gets data, like pictures of animals with labels like cat, dog, cow whatever the picture is is labeled. Next, it studies these to find patterns and also match them with the label. Then, finally, the next time it sees a new picture, it tries to guess what it is using what it already learned. And did you know supervised learning is used in a lot of different places. There are apps that can tell you what kind of plant or animal you're looking at just by scanning it, like Google Lens, you can literally use it in your parent's phone to identify a certain animal, identify an object, and it uses supervised learning. There's also spam filters and emails that block junk emails. Ai art apps learn how to color in pictures using supervised learning. And then, last but not least, tools are there that help doctors look at x-rays or test results using supervised learning.

Speaker 1: 3:13

So I want to share a few quick fun facts about it. Better the more examples it sees, just like you. But if the examples are wrong or confusing, the AI can learn the wrong thing, uh-oh. That's why it's important for the data to be clear and correct. Remember DS4Data episode and go back there and listen to it again if you want to be reminded of how data works. And, as always, you have to be cautious about AI, because things can go wrong.

Speaker 1: 3:43

Supervised learning isn't perfect. Here's what to watch out for. Bad data equals bad learning. If AI is given incorrect or unfair examples, it might make unfair or bad decisions. Also, sometimes the AI memorizes examples too well and doesn't do a good job with new ones. So it may see a yellow cat over and over again, but then, if you show it a black cat, it may not realize that that is still a cat. Ai also can be limited in its thinking right. Supervised learning can only learn what it's taught, so it can't figure things out if it hasn't seen examples first. So I want to ask you another question for you to think about what happens if someone teaches you the wrong name for an animal? What would that do to your learning? Share your answer with someone nearby.

Speaker 1: 4:38

Let's play a game. Let's play the label it game. Let's play a game. Let's play the Label it game. Tools needed paper crayons or printed pictures of animals, food or toys. You can get those from old magazines that may be around the house or old coloring books, or you can draw your own animals with paper crayons and pencils. So how do you play?

Speaker 1: 4:58

I want you to pick two categories like dogs versus cats, fruit versus veggies or something like that. Right, and then I want you to mix up the pictures or drawings and go through them one by one. Have a teacher. In this example, you can use a parent or sibling to be the teacher. Label each picture out loud. For example, they hold up a picture and say this is a dog, this is a cat. After a few rounds, have the AI, that's you or a friend try to guess the labels on their own. Then I want you to switch roles so you can be the teacher. Now this is probably most fun if you actually draw your pictures so people don't know exactly what it is, and then you name them and you can change the name from what it actually is to make it hard, but I want you to use this to see how much better your guesses get after learning from examples, just like ai, using supervised learning.

Speaker 1: 5:49

So what's next if you could teach ai to recognize anything in the world? What would it be? Snacks, superheroes, video game characters? I would love to hear your answer. Share it with somebody nearby and talk about what would you want AI to recognize? Just like you need good teachers, ai needs very clear, helpful examples to learn the right way. Maybe one day you will help teach the next generation of AI. And if you want to continue to try some things at home, you can make flashcards with your family to teach each other animal names or objects or topics. You may not know.

Speaker 1: 6:25

There's also the app or the website, google's Teachable Machine, where you can upload your own images and train a mini AI model at home just to see how this works. You can also talk to a grown-up about why it's important for AI to learn from fair and accurate examples. So, as always, thanks for tuning in. We hope you had fun learning about supervised learning. Join us next time for the letter T in our ABCs of AI. Until then, keep learning, ask great questions and always remember, stay curious and keep that creativity Bye-bye. Thank you for joining us as we explore the fascinating world of artificial intelligence. Don't keep this adventure to yourself. Download it, share it with your friends and let everyone else in on the fun. Subscribe wherever you get your podcasts or on YouTube. See you next time on AI for Kids.