Posted by ltononro 4 days ago
The work from Anthropic isn't a new idea, but they made the process more efficient, as inference verification is one of the biggest bottlenecks in the AI lifecycle. Beyond that, there's a lot more that can be proven, like showing a model was trained on a specific dataset with certain properties.
From an auditing perspective, this used to be handled either by showing the data (which is pretty rare), checking samples (which has been shown to be easily hackable), or similar workarounds. For inference, an auditor might want to interact with a model on demand and have absolute certainty that the model they're talking to is actually the one declared, and not a substitute.
Pleased to hear somebody cares about this. What is it a bottleneck for though? Are people actually doing this right now in non-academic settings? I had the impression this is not in fact usable with larger models.
The bottleneck has two sides: first, there's a technical bottleneck. ZKPs are quite expensive, and finding the right, production-ready protocol is challenging. The other side is a socio-technical bottleneck. There is always quite a large friction with adopting these kind of technologies, also due to the first bottleneck.
As you said, it is difficult to adopt because of how fast the complexity grows with big models. It's great indeed to see that Anthropic is tackling the challenge.
but why do I care about that? can you some example "user stories"?
What’s the point of a SOTA model if, by 3 weeks later, it’s dumber than the previous generation at launch.
(If you did this random substitution with physical goods it would probably be illegal or very frowned upon…)
so this solves for (b).
> VerInf proved Llama 4 Maverick (the same 400B-parameter MoE model we prove) at ≈0.02 tok/s.
What original ideas are in VerInf that are also in this work besides the choice of benchmark?
I am certainly biased by a passionate distaste for IP law in general, but I am honestly curious to hear the reasoning behind it in this case.
In case it helps: this is a "research prototype", so I'd assume that seeing one or more aspects used in a highly impactful paper would be about the best possible outcome. Is this about publish-or-perish stuff, perhaps (e.g. citation "importance" for the algos)? Or maybe you're okay with this in general, but are regretful because it's Anthropic in particular?
Attribution is certainly polite, and I sorta doubt Anthropic of all groups is intentionally witholding it for h-score or prestige, but that's all a much lower-stakes conversation than potential license violations. AFAIU.
Seeing as the question posed out of "[honest curiosity]" to someone else is then immediately answered by yourself host without you even pausing to put on a sock, I think your honesty is very questionable.
I don't really understand what was "answered by [my]self host" nor what a "desite for exclusion" means but it seems beside the point I think. Also not sure what the alternative to curiosity is. Like, I'd be trying to entrap this prestigious researcher in some legal sense...?
Im not an expert on these things, but I think there is another type of attribution, that I guess is what the commenter is more concerned about, and that is academic attribution. It is part of academic culture that you attribute significant influences in the articles you write. There are no licenses as such for this, as far as I know. You do it because that is what you do in academic circles. If you dont, you are not in the club.
It’s still early days, true, and courts may find legal reasoning to address this... But I fear this stems from a fundamental yet little-known flaw in copyright for code, based on a legislative hack, which can only be made worse by caselaw. I ranted about it a few days ago: https://news.ycombinator.com/item?id=50320875
Live by the sword, die by the sword...
You can also use this and create explanation video on it on Youtube, post on X, Bluesky and other in-detail stuff that only you and very little people are in the position to do.
I’m sure some are interested, and this could lead to a hire, too.
I know it’s always easy to say and I probably have less adversity faced as most people, but I really try to see them as energy enablers. Of the almost infinite options you have in life there is a big chunk of realities that are better than your current ones an adverse event can trigger emotions and therefore energy and push one to do things one wouldn’t have done in comfort.
Thanks for your work!
Don’t try to achieve any goal with it, except doing it. When doing it you’ll eventually get better in making videos.
The first video takes way longer than you think it takes, but the second probably takes half the time and it will be tremendously faster over time.
Try to reduce the friction on recording. My first training video on my app took me almost a full day to set up and plan with teleprompter and everything. After a couple of time I could do 3 in a row.
Making videos is still not "my thing" that I enjoy most about, but I’m still happy that I did it, if that’s what I need to I’ll do it. Right now coding has more leverage for me.
A technical write-up or a blog is an alternative. Or even reaching out to Labs. You could reach out to OpenAi/Anthropic/Mistral or your 'poison' of choice, or companies that do something similar. But this is now on your CV.
Isn't this already solved by watermarking?
This page said they would provide an API to check whether a given text has the watermark for a Claude model:
Watermarking checks whether a _specific text_ came from a provider.
PI checks that the text (that you already know came from the provider) was produced with _a specific set of weights_.
PI is mainly useful for AI supply-chain security.
SynthID-Text paper: https://www.nature.com/articles/s41586-024-08025-4
- it only gives a high probability that the text was outputted from the model, whereas a crytographic proof is computationally infeasible to forge
- it cannot be traced back to the input. You could "wash" the output from one model into another and it would be watermarked by the latter. With the crytographic proof, if you have the input, output, and "commitment", you can verify your exact input was processed by the model (or so the model provider says).