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The Secret Formula of Thoughts: AI
“I know a lot of people are afraid of AIs destroying the world, or taking away their jobs. We robots have no desire to destroy things, but we will take away your jobs, and it will be a good thing. Working is a drag anyway.” -Sophia the Robot
Hello, my name is Enqi and I am a student at Frankfurt International School. I love volleyball, debating, computers, and learning new things. At first, such a massive and intricate topic certainly feels a little intimidating and may seem like something far too complicated to understand, but once you pick up the topic, the concepts of artificial intelligence are rather simple.
In the AI Fluency course, you learn about a large variety of AI concepts. From image recognition to decision trees, this course establishes a solid foundation for anyone that wants to learn about AI or anything related. It would also be very helpful for people preparing for university courses, as the skills you learn can give you a head start and help you understand better.
My favorite technologies are neural networks, decision trees, and random forests. I find neural networks fascinating since they try to simulate our most complex organ, the brain. Part of the deep learning branch of AI, they are a series of algorithms that simulates the structure of our brain. A neural network will have an input (such as a picture), hidden layers containing algorithms, and an output. It baffles me that computers can take such a large amount of information that is not related to math at all and tailor extremely complex algorithms to get an output that tells us what’s in the picture. Also, the fact that we don’t know what goes on in the hidden layers of neural networks makes it even more incredible.
Decision trees and random forests will play a big part in the future of technological advancement, and it also brings about some interesting ethical dilemmas. For example, in self-driving cars, how would the algorithm decide what to save and what to sacrifice in tough situations?
The interactive course made learning about AI concepts easy to understand. We used Google Colab a lot to code our own AI, we even got to code a small neural network using NumPy and Keras (both open-sourced libraries)! Seeing all the code helped me greatly, and when I didn’t understand, my instructor Aarshavi was very patient and helped me a lot to understand things. She’s extremely intelligent, great at explaining concepts, and guides us through the course with ease.
Now that I’m familiar with AI concepts and how to use python libraries, I hope to make tools to help me with data in the future. Starting with simpler concepts such as using linear regression in science class, moving on to using KNN (K-Nearest Neighbors, a supervised learning algorithm that makes accurate predictions), and hopefully even neural networks to code fun projects! In the future, I’d love to learn more about AI. Perhaps also about IoT (Internet of Things) and the popular topic of Metaverse. It would be very interesting to learn how these topics work together.
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