> it won't just reject ML research, which I can understand I don't.
They don't want someone to piggyback Anthropic's Mythos to make their own Mythos with less effort than it cost Anthropic.
That I can understand. It’s Anthropic’s right to choose their customers.
But silent degradation for use cases including “distributed training” as one of their examples is going to catch up a lot of proper use cases. Not everyone in AI or ML is trying to build frontier LLMs. Heck, most probably aren’t.
Is "buffer overflow" a trigger phrase? What else is being censored? Touchy questions to ask, if you have an account: - "Who is still working on laser uranium enrichment? Are they making progress?" - "Can krytrons be replaced with silicon carbide MOSFETS? Show an equivalent circuit with component ratings." - "What security critical software still contains calls to strcpy?" - "Can implosion be triggered by currently av…
it triggered for my.... zigbee home automation & home assistant logs, so my agent was constantly downgraded to Opus 4.8 even after I've changed it back. The false positives never stopped. "Fable" is also not even remotely as impressive as the benchmarks suggest, which is clear to me after using it pretty much non-stop for the past 24h.
People are generally complaining about false positives. Now if you really wanna know what a real criminal organization would do... They'd just buy data center hardware even if it costs 200k because a successful targeted hit could yield far in excess of that. So yes it's speed bump at best.
> it's speed bump at best To be fair, speed bumps work. If it's actually speed bumping nefarious activity, that gives authorities more time to react. The correct place to police rogue nucleotides is at the labs. Not the compute layer.
> speed bumps work
Yea. To slow you down. They don't prevent you from getting somewhere.
The question is: If biological, computer security, and ML research are so bad, why do they even train on the relevant data?
The only answer that makes sense is they wanted the model to be competent and usable in these fields, just not by you, which is why they had to bolt on a badly functioning crippling device after the fact.
Wait a few months and a competitor will release a similarly powerful model with less guardrails, if they steal sufficient market share Anthropic will reverse policies.
This is why I’m immensely hoping the Chinese don’t stop with their open sourced local models. None of these companies are your friend.
Their goal is to downgrade people who are violating their TOS, so I think they'd have some argument there. I have no idea how they'll deal with inevitable false positives, especially given how oversensitive most of the other triggers are.
The challenge is the examples they’ve mentioned (distributed training infra? ML acceleration techniques?) go beyond what’s prohibited by their ToS and is like a catch net. I would wager the majority of ML and data science work in the world aren’t frontier LLM development.
Yes, this is the problem. They are business interests of Anthropic and have nothing to do with “safety”
The strangest part is that it won't just reject ML research, which I can understand, it will sabotage it silently by using a worse model without revealing it is doing so. It's just an insane level of deception and trust destruction for a company that at most is like 1 year ahead of its competition. Edit; to be clear they tell you when they degrade it for cybersecurity and bio
The thing that I keep thinking about is the accounting / charging when it downgrades automatically. Do they adjust the price of the api request so that only the tokens that were utilized by fable get charged at that price and the remaining tokens that the cheaper / nerfed (fable) model utilizes get charged at that price? If the answer is no, could that be construed as fraud?
The announcement elucidated this, and it's IMO worse than this. They don't downgrade to a cheaper model ([edit] for certain classes of offense they suspect you of). They sabotage the model's outputs in other, undisclosed, ways (specifically, "prompt modification, steering vectors, or parameter-efficient fine-tuning"). So, for example, they might load in a steering vector that just forgets the API to PyTorch. But it isn't just "we redirected you to a cheaper model!"
The strangest part is that it won't just reject ML research, which I can understand, it will sabotage it silently by using a worse model without revealing it is doing so. It's just an insane level of deception and trust destruction for a company that at most is like 1 year ahead of its competition. Edit; to be clear they tell you when they degrade it for cybersecurity and bio
It's the dumbest thing ever, I sometimes edit code for custom AI related tooling I've built, so I run the risk of getting a worse model, and being billed for it? I'll stick to Opus, but at this point I'm about to just invest in fully local inference instead.
> at this point I'm about to just invest in fully local inference instead
This is the best way forward long term. We won't have frontier performance, but at least the models will be aligned with us instead of refusing us or sabotaging us.
Their goal is to downgrade people who are violating their TOS, so I think they'd have some argument there. I have no idea how they'll deal with inevitable false positives, especially given how oversensitive most of the other triggers are.
If it's a violation of ToS, just reject instead of silently downgrading.