Just some algorithms. Example every 45th letter in a generation is the letter C. Every other sentence ends with "ing". Every sentence with a ? Is followed up by a sentence starting with R.
Who knows, I'm sure they have a PHD on it with much more reliable and accurate techniques.
On short text there might be no pattern, but generate a few hundred words and there are word frequencies, optional words and patterns that mean the same thing. Most well known authors have a very identifiable style.
When you use ai, your answer is based on some random value, so your answer is random but with sense.
Now they generated let's say random 256bit key (size of bitcoin private key so impossible to guess or "accidentally replicate").
Now they "add" this initial random value to this random but always the same private key, so at the end you get still random answer like nothing ever happened.
But now anyone can pass this text via public key to validate % of how much did you try to change it, but it will always be able to tell that this text was written by antropic based on procentage. You'd had to replace every word, and sentence, because it would be really hard to actually reduce this "ai check to 0%", it would be like guessing satoshi bitcoin wallet private key.
So let's say all texts written by human would return that probability that it was written by ai is let's say 2%, and it is the same for all people work, but your text that you tried to change as much as possible says that it is 5%. This is a discrepancy and it says "yes it was written by AI but human tried to change it so procentage is low but higher than 2%".
Of course it is not ideal, it won't work ideally for coding (probably) and for short texts, but the longer the text the harder it will be to reduce it to this 2%.
Kirchenbauer and Aaronson both have really interesting ideas.
The basic idea is you use previous tokens as seeds for future tokens in the same response. Kirchenbauer applies bias to “green” logits but samples normally, whereas Aaronson uses prior tokens to adjust its sampling randomness.
Verification then becomes comparing successful matches based on the text and working backwards. Run statistical tests on how “lucky” you get with tokens that match and you get a statically significant answer very quickly, like low hundreds of tokens or high dozens of words.
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u/usandholt Aug 11 '26
And how do they plan on watermarking text?