Why do they want the jokes back?

Or while waiting for the bus: if it's late, when should you start walking instead?

Looks like @gowld is agreeing with you.

As a researcher in theoretical computer science, I love PDFs more than any other format when it comes to mathematics.

There simply is no contender to LaTeX and PDFs.

Lucky for you, almost all research in math, cs, and physics, are put on arxiv, where you can download the source code (.tex) as well as get an HTML render.

> the "rigorous defense" part

In my country, that's exactly how it is.

Yes, you need to have a thesis to defend, but ultimately it all comes down to the (oral and live) defense/disputation.

If you replace "pdf" with "abs" the link sends you to the arXiv landing site.

Like so: https://arxiv.org/abs/2609.05341

> If we have a bioweapon close call, would you consider AI an existential risk at that point? If not, how close would we need to get?

The thing is, this doesn't really mean anything.

What is a bioweapon close call? What is the process in which a bioweapon close call happens? An AI that hacks all cell phones to emitt anthrax?

It can be subjective, because it's internal to you and not something that gets litigated or whatever. A close call is anything that you personally consider to be a close call.

I'm not asking you to post it here, I'm asking you to think about it and then remember it in the future, should the event ever occur.

Is this HN crowd in the room now?

It's understandable, at this point, to be weary of pelicans.

> To put it differently, saying that a hash table is O(1) or constant time is a model

Nobody really says that, nor is it a model. It is the expected time complexity.

People … say that all the time. *I* say that all the time. It’s true enough to be accurate in 99.9% of the cases; and we put barriers in place when implementing code (like configuring the hashing algorithm) to keep it that way.

I think people do say a hash table is O(1). It's the average time complexity (for some value of average) though, not the worst case.

O(1) insertion is the amortized worst-case time complexity, actually. (Amortized in the sense that the O(n) cost of copying is paid only during the n-th insertion). Average complexity is a slightly different thing.

It is not “worst-case” (as the post demonstrates, you can get worse results by using specifically crafted data that exploits hash collisions). There are algorithms that can get you O(logN) instead of O(N) even on such data.

Θ does not usually mean average, but simultaneously upper and lower asymptotic bounds.

you might want to read that chapter of CLRS again

You're right.

It isn't the average bound, it is the upper and lower bound stated together ( as long as thats the same function )

Expected

Nowadays I expected an opaque dictionary to be amortized O(1).

Granted, one can technically call that O(log(n)), but that's not a helpful categorization.

You cannot guarantee that from a hash map since an adversary who knows the hash function (unless it's cryptographic) could game the data structure to their advantage.