And here I thought FB was going to try a more open approach than OpenAI. Silly me.
603 karma · joined September 24, 2019
And who knows. If a plugin is successful enough, you might even swap out the OpenAI backend for an open source alternative before OpenAI clones you.
Solid state physics problems of that sort are used in the process of engineering new materials (e.g. a more efficient dielectric for capacitors, a magnetic material that can store magnetic memory data at higher temperatures without destablizing, a higher temperature superconductor, etc). Idk if this particular result has such an application, but I wouldn't be surprised if it did.
In addition to solid state physics, "special functions" show up in many areas of applied math and physics, including the design, calibration, and data analysis of/from MRI machines and antennas. Many special functions including the hypergeometric functions can be approximated more efficiently using fancy number theory algorithms.
There is also a broad category of physics and chemistry problems that are best solved by Monte Carlo simulations, and sometimes Monte Carlo approximations can be made using many fewer iterations by choosing a set of points within the sample space that satisfy number theoretic constraints.
Also, physics models often inspire machine learning models so it's not impossible that some niche ML algorithm could eventually benefit from a faster algorithm that uses this result or a derivative of it.
None of these directly answer your question about a definitive application for this particular number theory result, but I hope it gives you an idea of what it could potentially be used for. The closest analogy I know if is that certain multi-dimensional Monte Carlo integral approximations can be done orders of magnitude more quickly if you choose lattice points whose coordinates satisfy some similar-ish constraints to the ones described for this problem.
Essentially, you can try to break down the benefits into more understandable pieces. E.g. seconds of stadium dome signage shown on TV, seconds of press conference backdrop with stadium name shown on TV, number of eyeballs passing a sign during a game (classic billboard problem), etc.
If you run through these estimates (which clearly not all companies do before buying), it almost always looks like a bad deal. The stock ROI analysis confirms this, and is also a bit more compelling imo because it's harder to argue that something was left out. However, there are exceptions (such as Target) where the advertising components of a stadium deal can actually appear to be quite favorable, and interestingly the stock performance backs that exception up. My next Market Intelligence newsletter mentioned at the end of the article will go into more detail on how to do that analysis for an arbitrary deal.