4 ms·
There's no doubt that LLMs massively expand the ability of agencies like the NSA to perform large-scale surveillance at a higher quality. I wonder if Anthropic
by alach11 2y ago
There's no doubt that LLMs massively expand the ability of agencies like the NSA to perform large-scale surveillance at a higher quality. I wonder if Anthropic (or other LLM providers) ever push back or restrict these kinds of use cases? Or is that too risky for them?
- wkat4242 2y agoWill it really though? So far I've seen most of the "revolutionise" claims to be mainly hot air and marketing. It's possible that LLMs will suddenly make a leap in reliability and usability (e.g. much higher context window without corresponding massive increases in memory usage). But I have yet to see it. So far it's great at some specific usecases. Interacting with humans, rewriting or making up text. Summarising. A hit & miss at everything else. Don't get me wrong, I love AI tech and I'm heavily experimenting with it (both at work and at home with local models). But as with most hyped technologies I find the benefits far overblown in marketing stories. Our leadership jumped on Microsoft Copilot (the one for Office 365 because they have tens of different copilots :) ) like a pack of hungry wolves afraid to miss the boat. And the result was.... kinda meh. It's kinda promising and impresses with simple play school stuff ("make me a presentation about home safety") and totally and utterly fails when you try to do anything serious work related. Sooo many times I get "Sorry I can't do this right now", "Sorry I need more training for this", "I can't do this for you but this is how you can do it yourself!" or it does something but like totally wrong. Meanwhile we have a bunch of MS training people running around evangelising and telling us how great everything is and making excuses for everything that goes wrong :) You can almost see them breathe a sigh of relief every time something works as it should. That's not what we were promised. Maybe it will get there, but I don't see it happening tomorrow to be honest. LLMs were an impressive leap but their achilles heels have become clear and it's proving difficult to overcome them. I'm really enjoying surfing the knife's edge of technology (as I was and still am with metaverse) but I don't yet see this as a game changer except in a few specific industries. People editing text for a living certainly have a need to worry. I also wonder what will happen with future AI training. Now that more and more websites are filled with AI-generated content that is often at best "mediocre", and considering future AI models will be trained on that, will they be able to improve their accuracy or struggle to maintain it?
- alach11 2y agoI use LLMs extensively in my field to automate all sorts of tasks. Need to classify a million PDF documents for cheap? Write a prompt and submit a batch job. Need to read 30,000 drilling reports to automatically scan for hazards? Done in 60 minutes. These are tasks that would have taken months of development or millions of dollars in manual effort before. It's not just hype.
- JBorrow 2y agoBoy, I can’t wait for the foundation of my house to disappear because the LLM mis-classified a drilling report as non-hazardous. What’s the deal here with liability and accountability? That’s a serious problem when considering using these for anything other than toy problems.
- bongodongobob 2y agoYou don't actually think the LLM is reviewing those 30k documents do you? You tell it to write a program (which is easy to audit) to pull the info from the PDFs or whatever. I don't get why this crowd is so goddamn unimaginative with LLMs.
- goatlover 2y agoBecause I've heard of enough lazy uses of LLMs to be suspicious. Auditing the program means being sure that the info pulled from those documents is reviewed properly. Also, a complete lack of regard for other people's privacy.
- bongodongobob 2y agoNo idea where privacy enters in here.
- svieira 2y ago> You tell it to write a program (which is easy to audit) to pull the info from the PDFs Wherein you discover that unless you ask it to consider the fact that PDFs are ... very hard to parse [1] [2] you get something that misses whole blocks of text or turns them into something they aren't and the rest of the program misses chunks of the document. [1]: https://news.ycombinator.com/item?id=22473263 https://news.ycombinator.com/item?id=22473263 [2]: https://web.archive.org/web/20200303102734/https://www.filingdb.com/pdf-text-extraction https://web.archive.org/web/20200303102734/https://www.filin...
- nuz 2y agoThey're pretty clear about being pro safety to the extreme, and mass surveillance to protect american interests and abuse of LLM tech (e.g. open source misuses) are probably within the umbrella of ends justifying the means logic anthropic employs.
- jsheard 2y agoWhen you see the kinds of things that are developed in the name of "defense" it's easy to see how AI "safety" could become a similar sort of doublespeak.
- CuriouslyC 2y agoAI safety already is double speak. The primary meaning is "safety" for investors who don't want to be associated with something distasteful. The other meaning is basically a thin cover.
- jsheard 2y agoWell you can look forward to worse, give it another decade and Lockheed Martin will be extolling their commitment to AI safety while announcing their new generation of fully autonomous kill drones. For defense, of course.
- dinglestepup 2y agoThat ship has probably sailed. If Llama3 is performing on par with GPT-3.5, then there is no real benefit for companies to restrict access to slightly better proprietary models.
- hmottestad 2y agoGPT-4 is “holy shit, this actually works, could be better but it’s so good I almost can’t believe it” while GPT-3.5 is “when it works it’s pretty great, just a pity it almost never does”. So I would assume that three letter agencies would love to take something like GPT-4 and fine tune it based on all the data they have about existing terrorists.
- brink 2y agoI'm still dealing with hallucinations nearly every time I use it.
- baq 2y agoNSA should be training their own GPT-4 or better model as we speak and should have been doing it for a long while now. Anything else is borderline incompetence.
- Jerrrrry 2y ago[dead]
- causal 2y agoAnd given the volume of data they likely sift through, I'd also expect them to want very small, high-throughput models for identifying targets for larger models to examine. On the flip side, LLMs must give the NSA a new challenge: a flood of garbage text generated by no-one in particular. Perhaps there will be more effort to put surveillance directly on-device as tapping networks yields more noise.
- TeMPOraL 2y agoOn the grasping side, they are probably in the best position to train a GPT-5, given the amount and type of data they're presumed to have.
- ttyprintk 2y agoI’d expect they’re using huge models to train many small ones, one for each threat actor. Those small models could decide whether their actor is detected, or it’s time to slot in a different one.
- kridsdale3 2y agoNSA can't hire the right talent capable of producing that product for the same reason they have trouble finding white-hat security people to hire: You can't work for the government and do drugs in your personal time. Enough of the pie of elite researchers are in to wacky mind-bending that it's a real recruitment problem.
- jakderrida 2y ago
- jp42 2y agodumb question. I can understand LLM can be used for disinformation as it can generate text/image at scale. can you explain how it can do large scale surveillance?
- spidersouris 2y agoI wouldn't say that they can be used to do large-scale surveillance, but they can definitely facilitate it, especially with CV integration. I think one can easily imagine the following scenario: you fill a LLM with photos from people (taken from a public camera for instance), it finds the closest matches (via a web search for instance, as Gemini does). From then, you can easily gather the most essential information: first and last name, age, usernames... And then use this information to structure even more precise prompts and find even more potentially interesting data: posts on forums, relatives... And with this data, you can create an exhaustive database with a plethora of information and data about these people. That's what any good stalker or person experienced with social engineering is able to do right now, but it takes a lot of time and energy. Resorting to LLMs would considerably decrease both. And it gets easier the more people you have information about.
- ttyprintk 2y agoSpecifically, vision transformers (ViT) outperforming established CNN.
- causal 2y agoLLMs can be fed a conversation and understand the intent of its participants, even if no particular keywords are used. Before this, surveillance was limited by how many human agents you could have sifting through recorded data. Put another way: most people only get charged with a crime if it's worth a law-enforcement officer's time to catch you, but many small violations are ignored in favor of higher priorities. We may have to contemplate a future where AI is clever enough to notice everything that can be construed as a violation of some law and put on a prosecutor's backlog. Schneier talks about this as well: https://www.schneier.com/blog/archives/2023/12/ai-and-mass-spying.html https://www.schneier.com/blog/archives/2023/12/ai-and-mass-s...