Interesting thoughts about Software 2.0 IDEs at the end. A desirable property of such a tool (I think) is having a short feedback loop. However, it seems to me

Interesting thoughts about Software 2.0 IDEs at the end. A desirable property of such a tool (I think) is having a short feedback loop. However, it seems to me that common changes would include re-labeling a significant portion of your data set (e.g. because you realize you need a new class that you didn't think of when you began labeling), and retraining the model. It seems like transfer learning/fine-tuning can alleviate the latter problem some. Also an interesting take on how complexity has shifted from architecture selection to labeling data. It makes me wonder if there won't be a "Software 3.0" where most of the complexity shifts from creating a good labeling schema to, say, deciding on a good evaluation metric (I think the buck stops here as I can't imagine an AI silver-bullet automatically determining the evaluation metric). Perhaps unsupervised learning will come to the rescue and free us from the complexities of label schema design.