But just 2 months ago there was an article on HN that stated:
The database is a key value store prepopulated with the english names of countries and their capital cities from the continent of Europe. Selecting the country will perform a search of the matching capital. On a 2019 Macbook Pro laptop, the example searches take under 80 seconds.
I think the 40x overhead is a case of comparing throughput overhead (from what I know, FHE based secure inference protocols have poor latency, but can process many predictions in parallel, improving throughput)
Doing a exact string match on 200-ish rows in 80 seconds on a modern computer is so inefficient that I have a hard time seeing any less complex but useful operations whatsoever. Perhaps I'm just not clever enough, but for now homomorphic encryption seems like it isn't useful for common, real world usecases to me.
The database is a key value store prepopulated with the english names of countries and their capital cities from the continent of Europe. Selecting the country will perform a search of the matching capital. On a 2019 Macbook Pro laptop, the example searches take under 80 seconds.
https://news.ycombinator.com/item?id=23435305
And today this article claims only a 40x compute cost for "machine learning"?
What is the cause of the disparity?