Insights & Case Studies
Short write-ups on real projects, practical engineering decisions, and lessons learned while building data, AI, and software systems.
The Risk of Model Collapse in Generative AI
Examining how recursive training, distribution shift, and the loss of original data can affect the quality and diversity of future models.
Training Data Distribution and Generalization Performance in Real-World Computer Vision Applications
Understanding how training conditions, deployment environments, and distribution shift influence computer vision performance.
General-Purpose vs. Domain-Specific Models: Understanding the Trade-offs
Their versatility has led many to believe that a single foundation model can solve virtually any AI problem.
Understanding the English Advantage in Large Language Models
Multilingual does not imply equal performance across all languages. These models consistently demonstrate superior performance in English compared to many other languages.
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