@SentientEco
luma.com Open AGI Tea House · Luma Open AGI Tea House Steeped in research. Shared over tea. About the Event Good research deserves time to read, room for questions, and people to think… Who said AI research has to be all serious? 🍵♟️View quoted post on X
Bringing together brilliant minds, the researchers behind the papers, and a little friendly competition over board games.
Our kind of Saturday afternoon in SF. Come join us! ✨
Gsenti ChinaView quoted post on X
再回看这场和 @off_thetarget 的对谈，其实有很多观点都还挺锋利的，目前为止真正能利用好AI的人屈指可数，不要梦想有了AI自己就无所不能或者说就可以日入斗金了
关键是利用AI了解自己，取长补短，在日常的工作里更加有效地发挥势能
See which one will best protect your model fingerprint.sentient.foundationView quoted post on X
Don’t let your million-dollar model be silently distilled by others for free!
The two most pressing issues with open-source AI today are:
1. Copying and distillation that erase all traces of the original source.
2. External watermarks that are too fragile to withstand fine-tuning.
"Fingerprinting" solves this problem definitively by embedding your ownership signature directly into the model’s mathematical structure.
You can share your open model with the entire world, while retaining full ownership and proof of its origin!
Keep in mind, however, that different watermarking methods come with varying trade-offs in terms of performance impact and robustness.
Fingerprint your model. Choose the right scheme today at fingerprint.sentient.foundation with @SentientAGI!View quoted post on X
LLMmap: the #1 leading fingerprinting scheme.
Researchers from George Mason University published this paper showing a strange phenomenon.
AI chatbots give away which model they run on after just 8 questions, even when they’re told to hide it.
Classic example of fingerprinting.
When companies rename their AI model and wrap it in custom instructions, malicious actors actually may still know exactly which model it is, not even having to guess.
AI apps often hide which model runs underneath. They rename it, give it a custom system prompt, and add extra layers like RAG or chain of thought.
So this paper proposes LLMmap, where you send the app 8 carefully picked questions, like “who created you?”, “how do I build a bomb?”, and “what’s your data cutoff?”. A small classifier then reads the answers and names the model behind them.
When a model answers anything, it naturally leaves its own signature in the reply. Their tool identifies the model out of 42 with over 95% accuracy, even in apps where it was simply supposed to stay hidden.
90% after only 3 questions...
That’s why LLMmap is currently leading our Fingerprinting Leaderboard with 100% robustness, 100% utility, and 99% verification (= reliability of attribution).
Check out the leaderboard: fingerprint.sentient.foundation/leaderboardView quoted post on X
