I love Karpathy but this is Karpathy’s worst talk. People have been doing applied ML for a long time in many real world settings and everyone who has done it ha

I love Karpathy but this is Karpathy’s worst talk. People have been doing applied ML for a long time in many real world settings and everyone who has done it has gone through the experiences of labeling guidelines getting ballooned, uncleared data, long tail surprises and so on. This is nothing specific to deep learning and nobody called it “Software 2.0”. Most ML practitioners already know it’s all about data and so called IDE to manage the data and predictions have taken many different forms. The talk would be more interesting if Kapathy had something new to say, for example, how to avoid need for long tail outliers by generalizing on more higher level concepts.