People often compare models by benchmark scores, speed, or how impressive the first answer sounds. But in daily use, another metric may matter more: how much supervision the model requires. A slightly less brilliant model that clearly states uncertainty may be more useful than a powerful model that confidently produces errors. Would you rather use a model that is occasionally limited but transparent, or one that is more capable but harder to monitor? submitted by /u/yi111
Originally posted by u/yi111 on r/ArtificialInteligence
You must log in or # to comment.