It’s not Magic, It’s AI!. /
By: Audrey M
Tags: Python, Artificial Intelligence, High School, Machine Learning, Course

When I first heard about AI I thought it was magic. I mean a computer can recognize a face? Predict the future? (add more) After learning more about programming and computers I was even more amazed that computers were able to do these things, but I had no clue how. How does someone program a computer to solve a Rubik’s Cube? What does that code even look like?

After taking this class, I now know that AI isn't magic, but it still is something very special and innovative. Although I might not be able to program an AI to solve a Rubik’s Cube yet, I now understand more about AI. It might seem very complex, and parts of it are, but the fundamental concepts of most algorithms are actually surprisingly easy!

Hello, I’m Audrey, a high school junior. I am from a smallish town in Oregon and I like technology, reading, and of course learning. Fun fact, I recently found out I am a cat person.

Like most people, I’d heard about AI and how it’s used every day in EVERYTHING. Yet I didn’t understand how a computer - which refuses to function, like a toddler, if I forget a semicolon - can identify a human face. It truly sounds like magic. I hadn’t even begun to contemplate the hows and whats. How does it learn? How much time does that take? What does that code look like? How did someone come up with all this?

Like magic, I believed that only programming wizards could harness the power of AI. Yet somehow I -someone who had a minuscule amount of programming experience- became one of those wizards? Through taking this class, I discovered that AI is surprisingly simple. Through this discovery, I can say that I firmly believe that if I can become an AI wizard, you reader, can too. Even if you’ve never touched coding in your life. At the very least you can explain the concepts of it.

I’m going to be honest, learning about AI can be hard. It can get messy and complex, especially if someone has little to no experience with it. There were many times I had to review the lessons, code, and read extra articles online. It is very hard to simplify such complex topics to allow everyone to understand it, yet my instructor tackled this difficult task while infusing his explanations with passion. He is truly one of a kind and I’d like to thank him for that!

After taking this AI fluency course, I now have a basic to complex understanding of how common AI algorithms function. I will also have a headstart in any future course with AI. This course has also given me a glimpse of how college classes function, teaching a specific topic and catering to a group of people with specific interests, rather than high school classes that teach general knowledge to a general audience. Furthermore, I now have the fundamentals needed to implement these algorithms in personal projects, which I am very excited about!

One project I have had in mind for a while now is using reinforcement learning to teach an AI how to play a simple game. Reinforcement learning is like teaching a computer what to do and what not to do. To give a non-technological example of reinforcement learning, a person owns a dog and wants to train them to sit. To train the dog, they scold it if it fails and rewards it if it does sit. In the AI’s case, the program rewards it with a higher number if it does something we like and a lower number if it does something we don’t like.

The image above is a visual representation of reinforcement learning. Say the programmer wants to train the AI to move the blue dot all the way to the yellow star without touching the red square. In the AI’s beginning stages it will most likely fail and get stuck, but as time goes on, the AI will move to other squares as it is encouraged by the higher and higher numbers. In the end, if all goes right, the AI will move the blue dot all the way to the yellow square and be rewarded with the highest number on the board: 1.

Isn’t that interesting!! Reinforcement learning is basically just teaching your pet, the computer, to do fun tricks. I was very happy to finally learn how reinforcement works as I had heard about it in the past. Being able to look back on videos and articles I didn't understand without a background in AI was very satisfying. Before I didn't understand how someone could program an AI how to play Tetris or solve a Rubik's cube. Now when the programmer was explaining how he programmed it, I understood what he was saying! It was also surprisingly simple, in theory at least.

Besides reinforcement learning, I found that image recognition was my favorite AI technology. It still blows my mind that a computer can recognize objects! I can think of so many uses of this AI in the real world. Almost every cutting-edge technology seems to have some form of image recognition. For example, self-driving cars need to keep track of the cars and objects as it drives, facial recognition in phones, captcha, etc. I found this technology so fascinating that I watched many videos and read various articles about it. I am even in the process of trying to learn how to use image recognition to take my robots to the next level!

Audrey has received her graduation certificate

Audrey has also received an award for her project presentation

Join the AI Fluency Bootcamp at: https://aifluency.com/apply