AI Isn’t Magic—It’s a Tool (If You Know How to Use It!)

AI Isn’t Magic—It’s a Tool (If You Know How to Use It!)

Martin Kubicek

We live in an era where AI is often seen as a magical entity—an all-knowing machine that, when given data, will spit out the perfect answer. If only it were that simple! The truth is, AI is only as good as the problem you define for it. And here’s where things get tricky: we often don’t know what we don’t know.

The Importance of Assumptions

Let’s start with a seemingly simple question: What is 1 + 1?

Most people would say 2. But is that always the right answer? Well, that depends. In the decimal system, sure, 1 + 1 = 2. But in binary, 1 + 1 equals 10. If we’re talking about biological reproduction, sometimes 1 + 1 equals 3! A mathematician might ask whether the numbers are real or imaginary. A programmer might wonder if we’re dealing with integers, floats, or booleans. An engineer might ask about tolerances.

See the problem? The answer isn’t always straightforward—it depends on the assumptions we make. And assumptions are the foundation of any AI system.

AI Needs Clear Instructions

I once had a friend who dismissed mathematical software as useless because it failed to simplify an equation for him. He entered a command—simplify—but the equation came back unchanged. The issue wasn’t the software; it was that he hadn’t told it how to simplify. Should it assume only real numbers? Should it allow imaginary numbers? The software wasn’t dumb—it just needed clearer instructions.

AI works the same way. It doesn’t think like humans do. It doesn’t make assumptions unless we explicitly provide them. So when we use AI, we need to be crystal clear about what we’re asking and what constraints we’re working within.

Defining the Problem Correctly

Let’s say we want to predict how a satellite behaves based on temperature data. If we just ask an AI model, “Hey, predict satellite behavior based on temperature,” we might not get useful results. Why? Because we haven’t provided the right context. A better approach would be something like:

“Hey AI, I have temperature data from a satellite, and I want to predict future readings under new conditions. But remember, this is space—some sensors might fail due to the harsh environment. Also, solar radiation plays a huge role, and don’t forget about heat reflecting from Earth’s atmosphere.”

The more context and boundary conditions we provide, the more useful the AI’s answer will be.

AI is Smart—But You Need to Be Smarter

At the end of the day, AI is a tool—an incredibly powerful one, but still just a tool. If it gives an answer that seems stupid, it’s usually because we didn’t frame the problem correctly. AI isn’t here to read our minds; it’s here to help us make sense of data. But to get the best results, we need to understand what we want, why we want it, and the conditions we’re working with.

So the next time you use AI, don’t just expect magic. Take a step back, think through your problem, and set the right expectations. You’ll be surprised how much more effective AI becomes when it actually knows what you’re asking for!