[kayo_20211030]

re: all above/below comments. It's still an extraordinary claim.

I'm not claiming it's not possible, nor am I claiming that it's not true, or, at least, honest.

But, there will need to be evidence that using real machines, and using real energy an _equivalent performance_ is achievable. A defense that "there are no suitable chips" is a bit disingenuous. If the 95% savings actually has legs some smart chip manufacturer will do the math and make the chips. If it's correct, that chip making firm will make a fortune. If it's not, they won't.

[throwawaymaths]

> If the 95% savings actually has legs some smart chip manufacturer will do the math and make the chips

Terrible logic. By a similar logic we wouldn't be using python for machine learning at all, for example (or x86 for compute). Yet here we are.

[kayo_20211030]

What's wrong with the logic? A caveat in the paper is that the technique will save 95% energy but that the technique will not run efficiently on current chips. I'm saying that if the new technique needs new chips and saves 95% of energy costs with the same performance, someone will make the chips. I say nothing about how and why we do ML as we do today - the 100% energy usage level.

[throwawaymaths]

It's Terrible logic because it doesn't take into account the way this industry works. We don't do things because they are better. We do things because we can convince investors, because it's hirable, because we don't want to learn something new, because we're afraid our built up knowledge base is going to become obsolete, so we pull more people into our technical debt, etc.