Give a Man a Fish, Teach a Machine to Learn
The other night, I was having a conversation with my wife about AI. She knows absolutely nothing about artificial intelligence — no technical background, no exposure to machine learning jargon — and was asking very simple, honest questions that challenged the way I normally think about explaining these ideas. As I was describing how machine learning works, she paused and asked a question that stuck with me:
"But someone still has to create the model, right?"
It’s such a simple question — and yet it cuts to the very core of what AI really is. Yes, AI is a man-made creation. But what separates it from traditional programming is how it learns. And the best way I’ve found to explain that is through the old saying:
"Give a man a fish, and you feed him for a day. Teach a man to fish, and you feed him for a lifetime."
Traditional Programming: Giving the Fish
In traditional programming, we write explicit instructions — rules the computer must follow step by step. If this happens, do that. If that happens, do this. The computer does exactly what it’s told, nothing more, nothing less. We're giving it the fish.
Imagine you’re building a calculator app. You write the rules for addition, subtraction, multiplication, and division. Every edge case, every step-by-step instruction is manually coded. The computer is powerful — but only as powerful as the rules you’ve written.
There’s no adaptation. No learning. No change. The program will behave the exact same way no matter how long it runs. That’s traditional software.
Machine Learning: Teaching to Fish
With machine learning, the process looks very different. Instead of writing rules, we show the model examples and allow it to learn patterns from those examples. We might define the architecture — the type of model, the inputs and outputs — but the behavior is shaped by the data.
Let’s say we want to teach a model to identify cats in photos. We don’t write code like “If it has whiskers and pointy ears, it’s a cat.” Instead, we feed it thousands of labeled photos of cats and non-cats. The model analyzes these examples, identifies subtle patterns, and creates its own internal rules for making predictions.
So yes, we build the fishing rod — but we don’t cast it or tell the model where the fish are. It learns those skills by seeing others do it (i.e., from the data).
This is why AI has felt so transformational. For the first time, we can build systems that generalize — systems that figure out solutions to problems they weren’t explicitly programmed to solve. That’s a big leap.
Why This Matters
This shift — from programming rules to teaching behaviors — is what makes AI feel so powerful. It can generalize. It can adapt. It can spot things we might not even notice.
But it also introduces a new kind of fragility. The model’s performance depends entirely on the data it sees. If that data is biased, incomplete, or misleading, the model will reflect those flaws. It learns what it’s shown — no more, no less.
And that’s where the human element remains critical. We design the systems. We curate the data. We define the goals. Machine learning isn’t about removing humans from the equation — it’s about changing our role from coder to coach.
That’s what my wife’s question reminded me. Yes, someone builds the model. But once it’s built, the learning comes from experience. It’s not about giving instructions anymore. It’s about creating the conditions for growth.
We’re no longer just giving machines fish. We’re teaching them how to fish — and watching what they do with the rod.
By: Travis Fleisher