Traditional deep neural network’s training requires millions of example and so, despite its great success, is immensely inefficient.

Is this a limited advancement in training techniques? Right now I'm working on several types of image classification models. How would this be able to help me?

It's over for us useless eaters, no matter how useful one is we will always be useless compared to what's coming.

Now what if learning of these machines was as fast or faster than a human’s ?

What do you mean? It's already faster than human's. I takes years for a person to learn basic language and decades to gain expert knowledge in any field.

For reference, ICML is one of the most prestigious machine learning conferences alongside ICLR and NeurIPS.

To be clear, the papers at conferences undergo a peer review process as well. There are journal publications in CS, but a lot of publishing is done through conferences. Arxiv, while a great resource, has little to do with the conferences and it is worth noting that the papers on arxiv do not go through a peer review process (but are often published at conferences where the paper has gone under peer review — some papers on arxiv may be preprint versions from before the peer review process).

Good to know, Thanks.
Yours is the type of comment I was really hoping to read here.

You are right : it's the same authors (Brenden M. Lake & Marco Baroni) with mostly the same content.

But, they also write (in nature) that modern systems (GPT-4) do not yet incorporate these abilities :

Preliminary experiments reported in Supplementary Information 3 suggest that systematicity is still a challenge, or at the very least an open question, even for recent large language models such as GPT-4.