Artificial intelligence seems like a very complex thing packaged in a simple box. It's this magic concept of creating intelligence from something that otherwise wouldn't have any intelligence at all. Giving machines, just a bunch of 1s and 0s, electrical impulses with no real knowledge, the ability to make informed, complex decisions just like we do.
But what if we've been looking at AI from the wrong perspective?
Today, we're talking about the new AI perspective. Which is:
Drum roll please…
AI, DL, ML engineers, and all the like are the new teachers.
On a random Tuesday, I realized that the very first people who thought of Artificial Intelligence weren't trying to create magic. They were just trying to take the idea of human intelligence, one of the most fascinating abilities we have, and give it to machines.
And if learning is the goal, then someone has to teach.
The Classroom
Think about what happens when we build a machine learning model, ranging from linear regression models to complex neural networks.
After we have the data and have cleaned it, the very first thing we do is create the "brain." A blank slate walking into a classroom on the first day of school. It doesn't understand house prices. It doesn't know what a cat looks like. It has no idea whether a passenger on the Titanic survived or not.
It simply has the potential to learn anything and everything.
It's the same thing with humans. When a teacher receives a student for the very first time, they see a blank slate that knows almost nothing about the subject this teacher is going to teach. And this teacher's goal is to make sure that by the end of the school year, the student is basically a master at this subject.
In this case, the data is the textbook.
The Teaching Process
Now our classroom has three things:
As the engineer, our job is to feed the textbook to the student over and over again. The student makes mistakes, lots of them, learns from those mistakes, slowly discovers patterns hidden in the data, and eventually gets better at making predictions.
A good teacher doesn't just hand students a textbook and hope for the best. They organize the lessons, remove confusion, provide examples, and make sure the student learns the underlying concepts instead of just memorizing answers. So, the responsibility shifts back to the teacher. If the teacher fails to frame the knowledge in a way the student can understand, or lets the student learn shortcuts, then the teacher has failed at their objective.
Machine learning engineers do exactly the same thing. We can't have models with leakage, or models that can't capture anything from the data. So it's up to us to take the data and use the best techniques we know to make sure the model captures actual relationships and learns from it. In engineering terms, this means encoding data, removing unnecessary columns, engineering new features, resizing images, handling missing values, and countless other techniques that help the model focus on what actually matters.
If a student memorizes answers instead of understanding the material, we question the teaching. If a model memorizes the training data, suffers from leakage, or fails to generalize to new examples, we should question the training process the exact same way.
Exam Time
Every student eventually sits for a TEST. A teacher gauges how well a student performs by how well they do on it.
Now, you might ask, "Ruva, how does this equate to my Titanic Survival Rate Prediction model?" Well, the same way we have those stressful exams, models are tested on a small portion of unseen test data, data that's locked away and only brought out when it's time to test the model. One common way to do this is by using scikit-learn's train_test_split() function.
The model is then "graded" using evaluation metrics such as:
R² score
Mean squared error (MSE)
ROC AUC
Accuracy
Precision and recall
All of these different metrics let us evaluate the model's performance and figure out whether it performed well, is just guessing, or is as confused as it can possibly get.
A Different Way to See AI
Thinking about AI this way changed how I see machine learning.
We aren't creating intelligence out of thin air. We're creating students. And our responsibility isn't just to build models, it's to teach them well. Just like in a real classroom, the quality of the student depends on the quality of the teaching, the lessons, and the textbook.
Maybe AI was never about building intelligent machines. Maybe it's always been about learning how to teach them.
That's all for today. Next time, we'll talk about the magic behind Artificial Intelligence, and how AI is just the child of math and programming.
See you next time!