Are We Learning AI the Right Way? My Take as a Student

AI is moving really fast.

As a computer science student, I've been trying to keep up with everything happening around AI, Generative AI, machine learning and cloud computing. And honestly, sometimes it feels like there is too much to learn.

One day you're learning about machine learning models. The next day there's a new AI model, framework or tool that everyone is talking about. It can be difficult to know what you should actually spend your time learning.

I've definitely fallen into the trap of trying to learn everything at once.

Recently, I've started looking at it a little differently.

Instead of trying to learn every new AI tool that comes out, I'm trying to focus more on understanding the fundamentals and actually building things.

For me, that means understanding how models work, how data is handled, how APIs connect different parts of an application, and how cloud platforms like AWS can be used to turn an idea into something people can actually use.

I've also realized that using AI is very different from understanding AI.

It's easy to ask an AI tool to generate some code. The more difficult part is understanding what that code is doing, why it works, and how to fix it when something goes wrong.

That's something I'm still working on.

I don't think students need to know everything about AI right now. There are simply too many things changing too quickly.

I'm starting to think that a better approach is to pick something interesting, build a small project, make mistakes, and learn from them.

That's what I'm trying to do with AI, Generative AI and AWS.

I'm still learning, but I'm enjoying the process of figuring things out along the way.

I'd be interested to hear what other students and developers in the Builder Center community are working on.