The speaker says that in machine learning, understanding deep learning is valued as an instrumental goal toward practical outcomes such as improved sample efficiency or pretraining loss.

“incredibly meaningful in ML because in ML you're not you don't care about like better understanding the nature of deep learning or you only care about that as a”

“instrumental goal towards the broader sense of like just achieve the result just solve this like well-scoped problem of improve the sample efficiency of our models like improve the free training loss so the kind of progress that we're just seeing arrive like an avalanche in mathematics is structurally actually very similar to again i'm curious if this is the case i'm just total outsider i'm wondering if it's the case that it's like structurally very similar to the direct”