[deep_etcetera]

Imo these are researchers. Their job was to validate their algorithm for doing generative super-resolution on a dataset, they chose the largest and most well-known dataset, it worked reasonably well on their dataset. The model itself is not productive ready for at least the reason that the dataset is not representative. This is ubiquitous in ML papers, they validate their idea on not completely realistic but widely available datasets. The outcome is a piece of knowledge about the behavior on that dataset not a product.

[joshuamorton]

The point that many are making is that this is a myopic view of research. Why is the generic goal to optimize against some particular dataset? That ends up being a narrow and unhelpful goal.

As someone put on Twitter : we should be rethinking the meta learning algorithm of the ML academic sphere, and a leader like Yann is the kind of person who should be spearheading that.

[Decker87]

It is true that better datasets are sorely needed, but what would you suggest Yann and other researchers do about it in this situation?

[joshuamorton]

Proactively doing the things that the ethicists are calling for. Use Model cards for any model. Actively think about and discuss the limitations the model, intended uses, etc. That goes for everyone.

For someone like Yann specifically? Publicly state that the ML scene is optimizing in a myopic way, and invest in doing so less myopically. For example, I think the translation space has a clear goal and the right goal and is making strides in improving language models in many useful ways.

Ultimately, if there aren't ethical and useful ways to apply facial recognition, leaders should be steering people away from those research topics.