Very interesting read.
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.
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
What if you copy it into word or notepad and then open another program and post as text only?
The watermark is in the word choice, it’s not in hidden characters or Unicode characters.
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.
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.
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.
I wonder what would happen if some start to watermark document they doesn’t want in Claude training
The goal is to ensure that they don’t inbreed their models, not fix the problems they’ve caused.
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.
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.
Yeah, still, I wouldn’t claim “as no human would end up falling into that”, given that it may not be that unlikely to find at least one human who displays similar writing the more humans you involve.
Until a formal analysis is presented and an experimental study is published, which covers the most important influencing factors, the reliability of this concept is limited.
Yeah, still, I wouldn’t claim “as no human would end up falling into that”, given that it may not be that unlikely to find at least one human who displays similar writing the more humans you involve.
No, it is actually statistically impossible for a human to replicate this on sufficiently long runs of text.
This is not about replicating writing like a model. This is basically about guessing which words to pick from the list of suitable words based on a rule that you don’t know (because the key is secret).
To reduce this to the simplest possible example, imagine you are writing a “text” from just two letters: “a” and “b”. Let’s say for convenience that the text is supposed to be random. So the text would look something like “ababaaabbababbbabababaabbbabaababbaaabbabaabbaaaaabaaabbbaabaabababbabbbbbbbbabbabaabbbbbbbaabbabaab”
(generated with
'''.join(random.choice(['a', 'b']) for i in range(0, 50)))The watermarking works as follows: the model owner holds a key, and then uses that key to influence the random choices between “a” and “b” somehow, in a context-dependent way. The actual algorithm is quite complicated, but for simplicity let’s just say we have a secret pattern which biases the random choice towards it. In order to see the exaggerated results, let’s say the secret key is “aaaabbbb” (of course this is a bad secret key, once again just an example), and that the bias is strong (let’s say 80%). So this would mean that the first four letters in our text are more likely to be “a”, the next four letters are more likely to be “b”, then the next four letters are more likely to be “a”, and so on.
Then the text would look something like “aaaabaabaabaabbbabaaaabbaaaababbbaaaabbbabaabbbbaaabaabbaaaaabbbabababbaaaaabbbbaaabbbbbaaaaababaaba”.
(generated with
''.join(random.choice(['a', 'b'] + ([key[i % len(key)]] * 3)) for i in range(0, 100)))You can see visually that the secret key has affected the text. Of course in this example even if you didn’t know the secret key you could probably figure it out, in reality the algorithm is way more complicated than that, relying on cryptography, so you wouldn’t be able to know the secret key or see that the string has been biased at all.
If the text is long enough, and you know the secret key, you can guarantee that the text was generated with it. In our examples, the letters in the text match our key 77% of the time. The probability of an actual random algorithm generating a text like that is already very low, despite the base entropy being only 100 bits. If my math is correct, for our example the p-value is 2.7 * 10⁻⁸, or about 0.00000027%. I would bet a hungy that the text was generated by our watermarking algorithm, with odds like these!
Of course we did exaggerate the bias and our base algorithm was random. In reality the bias is smaller, the algorithm for determining the likelihoods of possible next tokens is very complicated (it’s the LLM itself), and the algorithm for determining which token to bias is also way more complicated (involving cryptography and real secret keys). That said, hopefully it should help you understand why, for sufficiently long texts, this fingerprinting is just not possible to be replicated by humans.
I thought the article explained that pretty reasonably on a scale of probability and weight. The longer the text, the more reliable the scoring.
But it does not show a sufficient formal proof and no experimental validation. Many important questions to evaluate the concept are left unanswered, which limits the interpretability and condenses it to “just trust me, bro, it’s a good idea, because I say so”.
I’m not sure we’ve read the same article. There are literally interactive demonstrations within the page to demonstrate how the concept works.
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.
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.
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.
- 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.
with enough text, you will see that certain words are used more often
Which is also a thing humans do.
Absolutely! We all basically do fingerprinting. We’re just not really conscious about the key we are using. But with a bit of statistics, you could identify people.
But how reliable? With which guarantees? What are the prerequisites for this to work at all? Telling people from each other apart is one thing, the other is telling them reliably apart from a machine generated text.
What happens if you cut and paste the text.
They’re invisible, they survive copying, and they work because they don’t live in the characters at all. They live in the choices between words.
So it is yet another way for AI to falsely hurt neurodivergent people over their writing styles and word choices.
Fuck AI.
Only if the neurospicy people happen to make all of the same choices, which seems extremely unlikely to me.
Certainly as someone quite neurospicy, I am not personally worried by this at all.
This is not a “watermark,” it is a minor shift of statistical probability. Whether content contains that “shift” will be “measured” using more AI-based tools that will flag sentence structures that hew toward faintly less common word selections.
We need AI companies to shut the fuck down. We do not need theatrics designed to deceive the populace into thinking this is not akshually a great filter moment while false positives and ponzy-scheme AI buildout harm those of us who want nothing to do with these tech-bro beatified lorem ipsum regurgitators.
Don’t fall for the theatrics. This is not at all what they are trying to bill it.
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.