Yeah that'd be great.
I also want cars that run on salt water.
I'm not saying that small data ai is equally impossible, but simply saying "we should make this better thing" isn't enough.
I also want cars that run on salt water.
I'm not saying that small data ai is equally impossible, but 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.
And then, someone is using pretrained 500B model, and fine-tuning your few examples, and getting new SOTA.
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.
Already in 2018 SenseTime reported that for face recognition, clean dataset surpasses accuracy of 4x larger raw dataset.
Only, the article seemed to show a very conservative Ng about the algorithms, a focus on data management - so it's still ML.