[Datenstrom]

> simply saying "we should make this better thing" isn't enough.

Besides the references to his company which has customers and a product that already works on these principles the literature currently shows that this is very much possible if you dig into the correct niches. Besides the SOTA in few-shot and meta-learning it is possible to smartly choose the correct few samples for the network that yield the same results.

It has also been my primary focus for the past 5 years and the core of the company I founded.

[riku_iki]

> it is possible to smartly choose the correct few samples for the network that yield the same results.

And then, someone is using pretrained 500B model, and fine-tuning your few examples, and getting new SOTA.

[YeGoblynQueenne]

They might get new SOTA because the metric is accuracy, but if the metric was accuracy weighted by sample efficiency, then SOTA would look a lot less impressive.

Simplest way to weigh by sample efficiency: multiply accuracy by ratio of test set to training set sizes. Everyone's training/testing on 80/20 splits, so everybody's SOTA would go down by 3/4s.