Machine Learning Isn't Intelligence (And That's Fine)

We need to stop calling it artificial "intelligence."

I think we can all agree machine learning is impressive. A model can analyze a million medical images and spot tumors with 99% accuracy. That's incredible. Life-saving, even.
But here's the thing—it has no idea what a tumor actually is.
It doesn't understand cancer. It doesn't know what mortality means. It's not thinking about the person behind the scan. It's just matching pixels to patterns it's seen before.
A human doctor looks at one scan and gets it: this is a person, this is a disease, this will cause suffering, here's how we help. That's not pattern matching—that's understanding.
Machine learning:

• Optimizes whatever objective function you give it
• Finds correlations in mountains of data
• Gets better with more examples
• Has zero concept of "why"

Human intelligence:

• Questions whether you're even optimizing for the right thing
• Looks for causation, not just correlation
• Sometimes learns from a single example
• Can't stop asking "why"

When your ML model tells you the best way to maximize engagement is rage-bait content, it's not being evil. It's doing exactly what you asked. It just doesn't understand what "engagement" means for actual humans.

We're not building artificial intelligence. We're building really sophisticated calculators.

And honestly? That's okay. Calculators are useful. But we wouldn't expect a calculator to understand mathematics. So why do we expect ML models to understand the problems they're solving?
The danger isn't that machines will become too intelligent.
It's that we'll forget what intelligence actually means.