The speaker says machine-learning engineers often optimize proxy metrics such as loss, even though real-world users do not care about loss itself.

“also there's a lot of reasoning there's a lot of medical reasoning that's kind of difficult and complex for just you know some of the off-the-shelf pre-trained or SFT models and I also think like in machine learning we're often optimizing these proxy metrics right like loss in the real world nobody cares about loss so as a machine learning engineer you're you're always looking for ways and techniques to kind of optimize for the end objective like what do you really want like in basketball like it doesn't matter your form what matters is you get”