In the early days of generative modeling, researchers often had to justify it as a way to learn features from unlabeled data that might improve supervised learning.

“We were still like building little generative models over MNIST and it was like a big success if you could generate these grainy images of digits and, you know, it was even hard to publish papers back then on that topic and you had to kind of like justify training a generative model as a way to learn features from unlabeled data that then could maybe help you do better at supervised learning because”

“because that was the thing that everybody cared about but then you know the things took over of course and so it was like a i was at the right place at the right time working on the right thing and so i've been doing research in in that space since since the beginning basically yeah”