Here is my first article on Generative AI and the use case for which I am using it. Lately, I’ve been delving into Generative AI to develop AI assistants for Observability. While working on this project and presenting its capabilities to our customers, sellers, and partners during Cisco Live a few weeks ago, I realized that AI has become one of the most frequently used terms in the IT industry since the launch of ChatGPT in November 2022.
It’s no surprise that Gartner felt the need to create a Hype Cycle for our industry, as I strongly believe that the tech industry has a way of generating hype around new technologies. By now, Generative AI is referred to as AI/ML, AI, ML, Deep ML, Supervised and Unsupervised Learning, and is sometimes even associated with neural networks, which is not entirely accurate. The definition lies somewhere withing these concepts that work together to transform from programming, to neural networks and now generative.
To demystify these terms, I used Generative AI to create simple definitions for all of these terminologies so that I could explain them to a 10-year-old. I was extremely pleased with the results and thought, why not share them with more people? So without further ado, let me share them. Feel free to modify the questions as you like and ask your favorite Generative AI application.
Explain artificial intelligence to a 10 year old
Sure! Imagine you have a really smart robot friend who can learn things, remember them, and even make decisions based on what it learns. This friend doesn’t get tired or bored and can do some tasks much faster than humans.
Artificial Intelligence, or AI for short, is like giving a computer or a robot a brain that works a bit like ours. It can’t think or feel emotions like we do, but it can be programmed to solve problems, play games, recognize pictures, understand what people say, and even help doctors diagnose diseases.
It’s like when you play a video game and the characters seem to know what to do, or when you ask your phone a question and it talks back to you with an answer. That’s AI at work! It’s not magic; it’s just very clever programming that lets machines act a bit like humans.
Explain machine learning to a 10 year old
Alright! Machine Learning, or ML for short, is like teaching your computer to learn from experience, just like you learn to get better at a video game the more you play it.
Imagine you’re teaching your dog a new trick. You show your dog what to do, give it treats when it does the right thing, and slowly, your dog learns the trick. Machine Learning is similar, but instead of teaching a dog, you’re teaching a computer.
You give the computer lots of examples, like pictures of cats and dogs. Each picture is labeled – this one’s a cat, that one’s a dog. The computer looks at all these pictures and starts to notice differences, like “cats usually have pointy ears and dogs have different shapes of noses.” After seeing lots of pictures, the computer gets really good at figuring out which ones are cats and which ones are dogs, even if it’s a picture it has never seen before.
So, Machine Learning is a way for computers to learn from examples and get better at tasks without being directly programmed with every single rule. It’s like giving the computer a puzzle and clues, and it learns how to solve the puzzle on its own.
Explain supervised learning to a 10 year old
Okay, let’s talk about supervised learning, which is a type of Machine Learning. Remember how I told you that Machine Learning is like teaching a computer to learn from examples? Well, supervised learning is like when you do homework and your teacher gives you a worksheet with questions and the right answers.
Imagine you’re learning about fruits. Your teacher shows you a picture of an apple and says, “This is an apple.” Then she shows you a picture of a banana and says, “This is a banana.” You’re getting examples and being told what each one is. That’s what we call “labeled data” in Machine Learning.
Now, let’s say you’ve seen lots of pictures of apples and bananas with labels, and you’ve learned what they look like. If your teacher shows you a new picture without telling you what fruit it is, you can guess if it’s an apple or a banana because you’ve seen many examples before.
In supervised learning, the computer gets a bunch of data that’s already labeled, like the pictures of fruits with names. The computer studies these examples and learns the patterns. Then, when it gets new data without labels, it can use what it learned to make a good guess about what the label should be. Just like you can guess the fruit in the new picture, the computer can guess the right answer for new data it hasn’t seen before!
Explain unsupervised learning to a 10 year old
Alright, let’s talk about unsupervised learning, which is another type of Machine Learning. If supervised learning is like doing homework with questions and answers given to you, unsupervised learning is like playing with a big box of LEGOs without any instructions. You have to figure out what to build and how to group the pieces by yourself.
Imagine you have a bunch of different toys mixed together—some are cars, some are action figures, and some are building blocks. No one tells you which is which, but you start sorting them into groups because you notice that cars have wheels, action figures have arms and legs, and building blocks are shaped like cubes or rectangles.
In unsupervised learning, the computer is given a lot of data, but this time, there are no labels or correct answers. The computer looks at all the data and tries to find patterns or things that are similar. It’s like the computer is trying to sort the toys into groups without knowing the names of the groups. It might group things based on size, color, shape, or any other feature it finds interesting.
So, unsupervised learning is when the computer tries to learn about the data by finding patterns and organizing the data in a way that makes sense, all on its own, without any help or instructions on what to look for. It’s like making sense of a pile of toys or LEGOs without being told what to build; you just start creating groups and structures that seem to fit together nicely.
Explain deep learning to a 10 year old
Okay, imagine you have a huge stack of building blocks, and you want to build a really tall tower. To make sure your tower is strong and doesn’t fall over, you need to build it layer by layer, with each layer supporting the ones above it.
Deep Learning is a bit like building that tall tower with layers, but instead of blocks, we’re using a special kind of Machine Learning called “neural networks.” These neural networks are like a bunch of tiny brains connected together, and they’re really good at learning from lots of data.
Each layer in a neural network can learn different things. The first layer might learn something simple, like finding the edges in a picture. The next layer learns something a bit more complicated, like the shapes or textures. As you go higher in the tower, each layer builds on what the last one learned, and the very top layers can understand really complex stuff, like recognizing faces or understanding what someone is saying.
Deep Learning uses many layers—sometimes hundreds!—which is why we call it “deep.” It’s like having a tower with so many layers that it can learn super complicated things. This is how some computers can recognize what’s in a picture or beat humans at really complex games like chess or Go.
So, Deep Learning is like building a really smart tower of tiny brains, where each layer helps the computer learn more and more until it can understand and do really amazing things!
Explain neural networks to a 10 year old
Alright, let’s talk about neural networks! You know how your brain helps you think, remember things, and solve problems? Your brain is made up of lots and lots of tiny cells called neurons, and they all work together to help you understand the world.
A neural network is like a simple computer version of a brain. It’s not made of cells, but of little computer parts that are designed to work together and learn things, just like neurons do in your brain.
Imagine you have a bunch of friends holding hands in a big group. Some friends are at the front, some are in the middle, and some are at the back. You give the friends at the front some information, like telling them about different animals. They whisper what they know to the friends in the middle, and those friends whisper to the ones at the back, and so on.
Each friend is like a tiny computer neuron, and they each add a little bit of their own knowledge. By the time the whisper gets to your friends at the back, they’ve figured out what animal you’re describing because each friend added a little clue.
In a neural network, the “friends” at the front are called the input layer—they get the information first, like pictures or sounds. The “friends” in the middle are called hidden layers—they do a lot of thinking and passing on information. And the “friends” at the back are the output layer—they give the final answer, like telling you what’s in a picture.
The cool thing about neural networks is that they can learn from mistakes. If the friends at the back guess the wrong animal, you can tell them, and they’ll pass the message back so that next time, they can guess better. That’s how neural networks get smarter—they learn from experience, just like you do!
And Finally….
Explain generative AI to a 10 year old
Imagine you have a super smart robot that’s an artist. You can ask this robot to draw or create almost anything you can think of, like a purple cat with wings or a car that can fly. This robot is special because it can make these things without ever seeing them before—it uses its robot imagination!
Generative AI is like this robot artist. It’s a type of artificial intelligence that can create new things all by itself. First, you show it lots and lots of stuff, like pictures, music, or words. The AI looks at all these things and learns what they are like—what makes a cat look like a cat, or what makes a song sound happy.
After it learns a lot, you can ask the AI to make something new. For example, you could say, “Make me a picture of a cat,” and it will create a brand new picture of a cat that no one has ever seen before. It’s not just copying a picture it’s seen; it’s making up its own picture by using what it learned from all the other cat pictures.
So, generative AI is like having a robot that can use its learned skills to make up new and unique things, kind of like having a magic art friend who can draw anything you dream up!
Hope you found it useful. Share it with your friends and colleagues who might need the 10 year old explanation.
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