This is a most excellent place for technology news and articles.
Assuming a checker tool is public it's enough for a teacher to verify classwork, but AI providers having the sole ability to identify generated content with no way to independently verify is not a real solution.
Plus this site advertises a tool to remove this watermarking, so it can't be that hard to scrub out if you're aware of it.
The goal is to ensure that they don’t inbreed their models, not fix the problems they’ve caused.
This won't fix the inbreeding issue, anyway. The bias is extremely slight, but random, and orthogonal to Claude's own "slop patterns" and tendencies. And theres tons of other LLM content that will end up in their dataset outside their control.
Besides, as much as Claude accusess others of it, everyone's training on everyone else's output and they know it.
I wonder what would happen if some start to watermark document they doesn't want in Claude training
There's a key involved that we don't have, so people can't do this on their own. It's pretty fascinating. Training models would have to be told to check for watermarks and ignore them, but yeah that would be an effective way for the providers to avoid ingesting their own AI output.
I thought declaude would be like degoogle.
Nope, turns out what they do is precisely the opposite. They try to remove those described markers. Real scummy.
Ah, that explains why the article smells like an LLM wrote it.
I think the thing that frustrates me most about AI is that so many people seem to have forgotten that writers exist and wrote huge amounts of text - books, news articles, magazines - for centuries. But now, anytime someone writes a few cogent paragraphs, you get people screaming "AI!!! IT'S AI!!!!!!!".
I know, because it has happened to me.
The article, to me, sounds like a competent author wrote it. Which is representative of a lot of the text LLMs were trained on.
I can't tell you about the text, but the whole site is vibe coded. The CSS, the js, everything. Which doesn't really inspire much confidence on the text, especially as it's explaining how to bypass the claude detector.
I'm against trying to "censor" LLMs, but yeah. What's even the ostensible benevolent scenario for stripping invisible watermarking from Claude?
I can't think of one, playing devil's advocate.
It's pretty scummy, indeed.
I am against AI and LLMs but the reason is quite simple, a lot of modern writing is offical bullshit to get stuff approved. Research grants, medical therapy approval, offical work mail that has to sound professional, job applications (insofar that noone cares what you write, they want a standard template and pick a candidate along qualifications anyways.). All these texts cost a lot of time and it makes no difference if a human or some copy machine writes it. And tbh. were it not for all the disadvantages of modern LLMs i would use it for the exact same reason, because sitting at a grant application for two hours while others do it in 5min is fucking useless and exhausting.
If it's stuff that no one cares what you write, then they also won't care if it's proveably written by AI or not. So removing the watermark still does nothing beneficial.
Well shit, that was fuckin’ interesting.
Yes, AI is evil. So is facebook. But the engineering is still interesting.
That's one of the things that frustrates me most about this whole AI thing. I fucking hate it and I want it to die, I wish it were never created in the first place. But from a tech enthusiast and a maths nerd point of view, it is super interesting.
Like the performance of these models is shit compared to a real person doing actual work. But if we think about what we are doing on a basic level, the performance is way beyond what I would expect it to be. I wouldn't expect it to be able to form a coherent sentence or scale as well as it does (even though the resources required to run these is still very high).
It could have been really cool shit people did studies on and played around with to explore the math. Cool little play models we could let go on a bunch of data and see what it did and how. Something for a small group of nerds and experts who are into that kind of thing, for the sake of learning and nothing else.
But no, somehow it got transmorphed into "AI". And marketed like this actual learning almost sentient computer system that can replace all workers. You can ask it anything and it will give PhD level expert answers. Oh and it's run by a handful of the most vile men imaginable who pour all of the world's money and resources into it, all so they get to be god emperor of the world. Fucking terrible.
Yeah, machine learning is incomprehensibly impressive! But it's the implementation where corporations have slurped up everyone's work and turned it into private profit while also wrecking every kind of media that fucking sucks.
Like if a stock photo company wanted to train and use a model for describing stuff in their library, great! Tagging, describing, and enabling discovery is a difficult task. But using it to slop out some low-quality images? You should reconsider what you're doing with your life.
AI isn't evil. Generative AI isn't evil. AI has existed for 20+ years now, I studied it back in my uni days.
Corporations, how they trained it, how they use it now, how they are willing to pave the planet to force it down our throats is evil.
This is one of those things as tech people we have to come to terms with and understand. No technology is inherently good or evil, it's what people do with it.
That’s a weird way to agree, but ok.
I’m not saying binary computing is the tool of the devil. Well, maybe Windows.
Well, yes. You just said AI is evil because capitalism made it evil.
I'm not convinced that they even know 100% how Anthropic is doing it. I can think of an easier way that doesn't corrupt the text: just find a bunch of tokens where there is a good spread of token possibilities, and the more often the most likely one is chosen, the more likely it's AI.
That being said, it doesn't seem much different from what any of us do to identify AI text — it has lots of tells anyway.
That's how AI testers work and its why they don't. Most forms of formal writing are predictable by design. If the AI can predict predictable formulaic writing, it doesn't mean its AI, its probably just any form of professional writing other than fiction.
Famous public domain works will always be considered AI by those tests, because of course your LLM knows the american national constitution. It was in the training data, so it can predict it with 100% accuracy, therefore your test wrongly calls it AI.
Testing for AI writing that way does not work.
The difference between what you describe and what I describe, is that a 100% match isn't a hit. Nor is a 90/7/2/1. You need something with meaningful variability. Even within formal papers there are places where word choice is arbitrary as the article explains.
Of course, you're lacking the context of the full prompt and just feeding in the raw text. Again it gets way more reliable the more text you have.
But it's moot because the more text you have the more tells will sneak in and you probably don't even need an AI checker. Those phrases that AI loves but humans use comparatively rarely. It's not a tell — it's the whole game!
do to identify AI text — it has lots of tells anyway
I see what you did there.
But it doesn't work. It looks like only the owner of the text generator is able to check if some text is written with this concrete generator (with some probability). I see no use of this technique.
It works for them not scraping their own slop back into training data. I assume that is actually the real purpose of the system. They don't want to share the key with the public. But they probably will with other llm companies in exchange for theirs.
Hmm, yes. I just thought about outside usage, somehow haven't thought about it as an inner LLM maintenance tool.
It's practically not of any use to end-users. It's a tool made by the AI company for themselves, to be able to claim a specific text was generated from their model.
Probability becomes a non-problem the longer the scanned text is.
I was not sure how any of this worked, and those interactive demos along with the explanation are quite helpful.
Also a very important point made about this not being a generic AI detector at all, and only being available to the model creator.
Interesting stuff. My own far less scientific reading of the article itself seems to fittingly suggest it too is largely if not entirely AI generated, which I guess would make sense.
I wonder how many people will be identified as AI because they used AI so much they started constructing sentences like AI.
This particular watermarking would be effectively impossible for a person to end up replicating.
This will only catch (literally) zero effort abuse.
That's like 98% of abuse, though. The vast majority of AI abuse is thoughtless, utter laziness.
Bad actors who actually care about "evading detection" wouldn't use Claude, anyway.
Which, in fairness, there are plenty of. I have seen 2 presentations this week at work where they were 1. Obviously AI-generated and 2. Obviously not proofread or changed at all.
Although this looks like a clever approach, a kind of stochastic key, I do not see how this guarantees to distinguish text written by big babble machines versus humans. Humans also have a certain pattern of writing, a given distribution of how some words are more likely to appear than others. How can one tell them really apart?
As an indicator, yeah, might be usable. But I wouldn't read too much into it before seeing results of a study that runs actual tests.
I thought the article explained that pretty reasonably on a scale of probability and weight. The longer the text, the more reliable the scoring.
It's not about the variation of the words, it's about the variation of the words from the model baseline.
Like if your word choice was almost the exact same as Claude's normally, maybe you just talked to them a lot and picked up their phrases like it's not nothing.
But if you managed to be almost exactly like Claude and yet varied the possible words exactly according to a hidden entropy key, they'd know it was actually Claude with the SymthID-Text watermarking applied, as no human would end up falling into that statistical bucket.
Nicely written piece and pretty clever technique.
- Only the key-holder can check. Your teacher, editor, or favourite "AI detector" website cannot run this test; a genuine check needs the provider's secret key, or a checking service the provider runs. Google runs an early-access detector portal for SynthID; Anthropic says detection tooling is forthcoming.
I am not so sure about that. The amounts of words is finite and with enough text, you will see that certain words are used more often, especially in certain combinations. I believe people will brute force this and then create a way to destroy the watermark again.
Great article. I wonder if the same markers can be used to detect AI generated code (if you suppressed comments).
As, code requires a much more rigid syntax, compared to free flowing docs.
I wonder if they did this to appease the EU or just to have a way to prove in court that a specific competitor distilled their model using claude
