The nice thing with digital models is they can get data from everywhere and continue to improve slowly over time. It's the lean startup applied to ML: there's nothing wrong with launching with a one-shot ML product to test a hypothesis, and as time goes on keep adding to it. That's the core concept there: start with a working but basic model and continue to extrapolate from that as time goes on.
"I would challenge you to show proof of X" in ML is an empty challenge when the technology is in its infancy. You might as well say "I challenge you to stream HD video over the internet" in 1989. Theoretically possible but technically impossible for the time. We proved it possible as tech improved, though.
The problem with cynicism is it costs nothing but still makes you look like an expert.
> That's the core concept there: start with a working but basic model and continue to extrapolate from that as time goes on.
This has literally nothing to do with few-shot learning though. You can always make a crappy model with a small amount of data and then improve it as you get more. And my point is that if you have a competitor with several orders of magnitude more data, their models are almost certainly still going to be better than yours.
Few-shot learning will probably be able to improve the baseline of what you can do on certain tasks, but you're not magically going to learn a sufficiently accurate cancer diagnosis algorithm from 5 radiology images.
The best case for few-shot learning is that you go from "worse than the alternatives" to "good enough for some people to get value", which will probably happen a few times, but is going to be a minor phenomenon.
"I would challenge you to show proof of X" in ML is an empty challenge when the technology is in its infancy. You might as well say "I challenge you to stream HD video over the internet" in 1989. Theoretically possible but technically impossible for the time. We proved it possible as tech improved, though.
The problem with cynicism is it costs nothing but still makes you look like an expert.