The AI Buzz at IITK: Learning Without Understanding?
Image: GDJ / Pixabay
Walk into any common room at IIT Kanpur today, and you're likely to hear someone mention EE708 or CS771 —the beloved courses on Data Science and Machine Learning. They’re the new dream, the golden ticket to FAANG, and an entry pass into the high temple of Artificial Intelligence.
But there's an elephant in the room. For many, the hype is real, but the foundation is weak. Students are jumping headfirst into machine learning libraries, tweaking models, and discussing ChatGPT prompts—without a real grasp of the math that powers it all.
“Everyone's running PyTorch. Few know what a Jacobian is.”
Linear Algebra is the language of deep learning. Differential equations explain how weights evolve during backpropagation. Yet ask students about eigenvalues, vector spaces, or gradient flows—and you might get a blank stare. Many can fine-tune BERT, but stumble at interpreting a simple matrix decomposition.
It’s not a problem unique to IITK, but here—where excellence is the baseline—it's a disservice to ourselves. Relying on code over concepts turns us into API engineers, not innovators. We shouldn’t just consume models. We should know how to build them from scratch.
The solution? A recalibration. Before sprinting through transformer architectures, maybe we revisit MTH201 and MSO201. Real AI fluency isn’t built in Google Colab—it’s built on chalkboards, solving partial differential equations, and proving matrix theorems.
The future belongs not just to those who use AI—but to those who truly understand it.