4min snip

AI Research at Capital One with Bayan Bruss

Software Engineering Daily

NOTE

Adapt and Detect: Embrace New Data Challenges

When developing machine learning models, it is crucial to anticipate variations in incoming data versus training data, whether due to changed contexts (like dogs on different surfaces), the emergence of new categories (such as parakeets alongside cats and dogs), or entirely new data regimes (like infections from a novel virus). Robust models should not only adapt to these shifts but also minimize failure rates when faced with unfamiliar data types. Moreover, effective detection mechanisms for new classes or intents are essential, especially in critical applications like self-driving cars and healthcare, where new symptoms or customer queries may arise. This capability enables systems to revert to cautious protocols or trigger human intervention, thus maintaining operational integrity as data dynamics evolve.

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