ironSkillet

Doesn't this entirely depend on the latent embeddings of strategies and game space in the AI model, which may not be so concrete and explicit as you've described? That's kind of the magic of LLMs with coding, they can generalize because the abstract patterns are encoded in latent space, not the specifics.

That is one of the core strategies in the private equity playbook.

Something cool and interesting for its own sake. A rare find.

I am not familiar with the standards of publishing in machine learning, but as someone trained in a mathematics background, this paper seems relatively light on details and heavy on exposition. Is that typical? Is this a really novel idea? Not trying to be snarky, just trying to understand how meaningful this is.

The entire thing is so generic and full of wise sounding things with no deep concrete content. It screams "an AI wrote this and a human thought it looked ok and pushed it".

I think we can agree to disagree here. I don't see how a company needs to have an $8000 increase in revenue to justify a $240 software purchase. You are assuming that the current operating cost for every dollar of revenue is also applied to every incremental dollar in revenue gained from software efficiency, and that is just not true.

I understand that. The AI software is meant to be a productivity enhancer for the employees using it. Other than the licenses, some training etc, there are no operating costs associated with it. Just by using the software, I don't suddenly have to pay more for salaries, retirement plans, etc, which are things that in aggregate produce the 3% margin. Maybe I have to pay more in logistics because I'm moving more product now, but I think the point stands.

I'm not an MBA over here, but this math seems wrong. If they are spending $240 in increased costs, then they only have to make about $247 in additional revenue from that spend to preserve a 3% margin. That seems much more reasonable if it increases the probability that customers find the product they are looking for and have a good experience.

I interviewed someone recently who worked at Meta a couple years ago. He was a software engineer, was paid a bunch of money to mostly up dashboards all day, and eventually quit because it was neither interesting nor challenging.

No that's not true either. A quick Google will reveal many examples, in particular the "Cantor set".

It is not determined by the derivative, it's the antiderivative, as someone else mentioned. The derivative is the rate of change of a function. The "area under a curve" of the graph of a function measures how much the function is "accumulating", which is intuitively a sum of rates of change (taken to an infinitesimal limit).

This is some high quality content. Love the visual animations to go along with the mathematical ideas. Did a great job helping to tie the algebra to geometric intuition, but I think the importance of commutators could have gotten a little bit more exposition.

The disk model of hyberolic geometry is made to map hyperbolic 2 space (which is infinite in area) into the finite interior of the disk. In order to capture this, the normal euclidean notion of distance is distorted by a function which allows "distances" to go to infinity as a curve approaches the boundary of the disk.

Binary search minimizes the number of expected moves until you find the target. If you are already ahead, this is a natural thing to want to do. The reason why this doesn't work when you're behind is that your opponent can also do that and probabilistically maintain their lead.

I think the user means that bullies in school face little consequences, but a bully at work may get called by HR and potentially disciplined.