Bruce Upbin

Artificial intelligence and machine learning are revolutionizing chromatography by enabling more efficient method development, real-time instrument control, and deeper data interpretation. However, rather than replacing scientists, AI should be seen as an augmentation tool—enhancing human decision-making while preserving the theoretical and mechanistic understanding that underpins separation science.

Imagine there’s still no baseball a year from now and one team owner takes matters into his own hands to save the game from ruin. A note from the fictional future. By Bruce Sherman March 23, 2021 Tomorrow would have been baseball’s Opening Day. Derek Jeter and I would have done our ritual final inspection of Marlins Park before the fans arrived. The freshly cut grass, crack of the practice bats, and bright chalk lines always quicken my pulse and make me feel like a Queens kid again. Not this year.

Lenders looking to switch to machine learning from the old ways of doing underwriting often bump up against a tough question: These ML models are incredibly powerful but incredibly complex. How do I know why I’m getting the results I’m getting? Great question, because a lot of ML is black-box decision-making, gleaning insight from an infinite number of correlations among many pieces of information that may seem completely unrelated.