Choosing Between Different Types of AI Models
Learn how to evaluate different types of models for classification tasks based on real-world constraints.
Imagine you’ve applied to an exciting new AI startup building a system designed to recognize handwritten digits (from 0 through 5). During your technical interview, your interviewer introduces two intriguing candidate models for this task:
Model A: This model doesn’t just recognize digits—it tries to understand how they’re formed. It pays close attention to details like stroke thickness, curvature, and style variations. Impressively, it can even create brand-new digit samples that look realistic.
Model B: This model takes a more straightforward approach. It concentrates solely on accurately classifying each image into its correct digit category. It doesn’t care about understanding how digits are made; it simply knows how to recognize them based on learned patterns.
Then, your interviewer poses an interesting question:
“Given that our primary goal is accurately pinpointing the correct digit every single time—and not necessarily generating or recreating digits, which model approach do you believe would be more suitable, and why?”
Pause for a moment to absorb the question. Your interviewer has made it clear: classification accuracy matters most right now. However, you may also wonder about other factors, like future scalability, interpretability, or perhaps even detecting anomalies later. For now, though, let’s stay aligned with exactly what the interviewer asked you.
What should you ask your interviewer?
Before diving straight into an answer, we must differentiate the two models we’ve been given. As described earlier: ...