The speaker considers solving well-scoped problems—such as improving model sample efficiency or pretraining loss—an important kind of machine-learning progress, while treating deeper understanding of deep learning as instrumental to achieving results.

“I think that that kind of progress would be incredible.”

“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”