Choosing Between Different Types of AI Models
Explore how to evaluate and choose between generative and discriminative AI models for a handwriting recognition task. Understand the importance of asking clarifying questions about dataset size, resource constraints, and future goals. Learn to align model selection with interview priorities such as accuracy, data generation, and scalability for optimal decision-making.
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: ...