Winoground: Probing Vision and Language Models for Compositionality

> The dataset was carefully hand-curated by expert annotators and is labeled with a rich set of fine-grained tags to assist in analyzing model performance.

It's a real pity that the training data is so expensive to create, otherwise Google could feed all of YouTube into an AI and get it to understand physical reality as well as GPT-3 understands textual reality.

I guess some people would say that GPT-3 doesn't "understand" anything, it merely predicts tokens, but I think that GPT-3 does at least learn "sensible" patterns, so it would be nice if an AI could for example predict how humans (and cameras) move in a scene.

Perhaps being able to translate between textual and visual domains would help speed up machine training/learning, since humans often combine the two domains when learning new concepts too, but my guess is that AIs need to be able to symbolically manipulate abstract ideas to really bridge the gap between prediction and understanding.