I'm not a mathematician, but I don't think that's true..? It's just that some problems are considered "hard" or known to have been "open" for a long time or that involve some clever/pioneering new technique. There's tons of math papers out there that are in some technical sense a novel contribution but in practice just languish without much attention except maybe from like two other people working in the same subfield.
> Emojis don't even render the same on every platform
This is a feature, not a bug. That it's using your platform-specific emojis makes it easier to scan, since it's the ideograms you are already accustomed to. Much faster to scan familiar symbols than to read each section heading serially.
I'm not convinced this is a problem. For example, all the folks who developed convex analysis for its pure geometric and mathematical beauty in the land of pure Platonic forms, well their work was still useful downstream for all of us doing convex optimization and dealing with log-concave probability distributions. So no harm, no foul.
And I would further add: In addition to performance, Julia's language and semantics are much more ergonomic and natural for mathematical and algorithmic code. Even linear algebra in Python is syntactically painful. (Yes, they added the "@" operator for matmul, but this is still true).
I wonder if there's a law+econ analysis of comparing the current framework (regulations and upfront permitting) vs having the regulations but then enforcement via combination of randomized gov't inspections and private lawsuits. The motivation would be to allow things to move faster while also requiring the same degree of compliance, but without the massive red tape upfront with administrators having no real incentive to approve projects or move fast. One obvious downside is that it effectively creates an economic incentive to try and skirt the law and/or find loopholes, but that arguably exists to the same degree in the existing system.
I don't think the point was to say "look, AI can just take care of writing a browser now". I think it was to show just how far the tools have come. It's not meant to be production quality, it's meant to be an impressive demo of the state of AI coding. Showing how far it can be taken without completely falling over.
EDIT: I retract my claim. I didn't realize this had servo as a dependency.
That makes sense, but how do you efficiently evaluate the Gaussian kernel based approach (“operator-based data structures (OBDS)”)? Presumably you want to do it in a way that keeps a dynamically updating data structure instead of computing a low rank approximation to the kernel etc? In my understanding the upside of the kNN based approaches are fast querying and ability to dynamically insert additional vectors..?
Berkeley, California
Email: jey#kottalam!net