It's a perfect technology for their uses, they get a big chunk of a $100 billion black budget, and they've had access to the research for at least as long as we have.
Anyways, isn’t NSA one of the largest employers of mathematicians in the world? Surely they’re doing something useful.
Here is a banger quote on this by Shannon’s boy Warren Weaver, keeping in mind LLMs came from translation problems:
“One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: 'This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.”
I mean yes, in both deal with information theory.
That's a long way from any practical insight.
Given the evergreen discussion of "are these companies making a profit"*, I think any LLMs that the NSA (or any other government agency worldwide) may be making are quite far from the leading edge.
* Person A: "they are making a loss!" Person B: "Only if you count training, they make a profit on inference, look at what it costs to run comparable open models on generic cloud servers" A: "Sure, but if they don't train new models they'll be left behind, so they're still making a loss"
That and the way compute is now measured in GW, I think even random low budget vloggers just getting started would be able to spot if the NSA was doing anything significant just from the extra heat emissions or power plants getting built.
The rate of inference compute to training compute is ~10:1, for popular frontier models. Models are routinely overtrained past the Chinchilla optimum now because it makes an immense amount of economic sense to do so.
Worse the more niche and unused your models get, but when this "making a loss" fuckery pops up, it's usually about the big guys like Anthropic, OpenAI, GDM and maybe xAI and Meta. Of which only the latter can be accused of not selling enough inference to offset the training runs.
The real money sinks are: R&D and infrastructure buildouts.
I wouldn't count them out.
Especially academia tend to do their work out of interest, their monetary gain isn't their primary goal
Of course, that doesn’t mean nobody will do job B for other, non-financial reasons.
That sort of proves the opposite point, assuming you're referring to Dual EC DRBG, because the flaw was noticed very early on, by people who weren't even involved in its development.
They probably already have access to Sentinel, so they wouldn't need to train their own.
- find "modern AI" to have strategic importance
- have ways to spend loads of money while having a front-facing budget on the record
- could be running a PR program to have Americans think they "buy" access to models like they do, but the AI companies were taken over by these agencies long ago
Look at Google, Microsoft...Apple got away with it by having as much on-device operation as possible so they could wash their hands, honestly saying "We don't have it."
This is the world's largest data gathering operation. Remember after 9/11 when the NSA copied as much Internet back bone traffic as they could?
I'm not for or against, even as a resident, but we certainly shouldn't be naive.
the issue here that is a forgone conclusion, regardless of where the model comes from and which chips it runs on, is that now they can reasonably comb through all the stuff that they've been collecting. that's a pretty huge operational change.
They have at least one pretty vast, largely classified data centre in Utah, with a sizeable chunk of the black budget and they also have pretty large data sets.