There's a lot of ongoing work in machine learning focusing on learning from few examples, online learning, more biologically plausible learning and so on. Most of it is done in the academia, and some of it is done at large corporations.

Startups generally have a short runway before they run out of cash, which isn't long enough to sustain this kind of exploratory work.

>What we need is much more than a clever idea: it's a paradigm shift. There's probably some sort of way to make a system that can learn from very few examples and at a very short time, like we do ourselves. Machine learning is certainly not that way. We need to find another way.

That's more like probabilistic methods, which also fight overfitting by regularizing (with a prior distribution) and allocating probability density in a smear around the training data rather than concentrating it as hard as it can (as optimization methods usually do, so to speak). And then hierarchical Bayes is what's used in many of the neat one-shot learning demos, and in theoretical neuroscience these days.

> There's probably some sort of way to make a system that can learn from very few examples and at a very short time, like we do ourselves.

Maybe. I wonder if we can pick up identifying platypus with only a couple of examples, because we spent a long time identifying bird or cat as a toddler. We have a complicated model of the world, and we can take short cuts, because we just identify the differences compared to stuff we already know.

You may be right, no one knows for sure. I think it's one of those P = NP kinda problems. Lots of clever people have thought about it for years. Maybe there's a trick. It seems unlikely there's a trick though. Absence of evidence starts to become evidence of absence.