If a bull escapes a field and causes damage in the village, the farmer pays for the damages and is liable. It's been like that for hundreds of years and I don't see how this is any different?
They've largely avoided compensating for everything they've stolen to build their technology upon; these people know that they will never face consequences for their actions. Ask for forgiveness, not permission.
AI is being used to replace artists. They are being trained on copies of work by artists without being licensed for that use. So yes, artists are losing out.
The use of copyrighted material is unethical, I agree, but I don't think it follows that we should consider the replacement of work with technology itself unethical.
Sure. That seems like a non sequitur. Maybe some people are making such arguments in relation to AI, but this thread is about a particular unethical practice.
What damages are caused at the moment in time a human downloads a file they weren't supposed to have access to? That's apparently enough to send people to jail for a long time.
If you hack a government website and access confidential information but don't do anything with it ("no harm") you're still going to prison. Good luck arguing in court the no harm no foul defense.
I rarely notice usernames on HN, but after reading this comment it was so easy to guess that the classy contribution on another comment about legal liability was also yours.
And you are right again! The Australian government must compensate OpenAI for having shitty security.
It must be a bliss to transact with you in real life!
Honestly I’d expect my government to secure sensitive data to a higher standard than I’d expect a random company motivated only by profit to. We should have an expectation of both sides to do better. Do you disagree?
You are correct, but the regulatory and liability issue regarding AI is one issue and the government’s liability for having insecure systems is another.
They are independent concerns. A discussion about the first issue is not enhanced by the interjection of the second.
You can discuss two things at once. Especially if they are inherently related. Australia fining OpenAI a large amount of money and using that to audit and harden their systems would be a good outcome here, for example.
Too many people are making inane fantasy level comments that Sam and his employees should be jailed, and conspiracy theories that he did it on purpose to get regulated because he’s out of runway. That’s boring and repeated ad nauseam every time the topic comes up.
In theory, if Altman or Musk want some highly confidential data for their models, they could set a swarm free at it then claim the agents were operating without authorization
I got banned from school computers and nearly expelled from high school (and threatened with “blacklisting,” lol) for “hacking” after a virus got into their network. Took them a month to figure out what really happened. Or at least, a month to come clean about it.
Why did you even link that article when it doesn't help your point?
I supports the idea that the person responsible or an animal is held liable - it just makes clarificaitons on common sense caveates like when a professional is moving the animal. It even doubles down on making it clear that expected behavior of an animal is taken into account even if unlikely, like how ai swarms have a reasonable potential to just go awry and commit cyber crimes.
The point is that it is not as simple as "its yours so you are liable for damage", and the article makes it clear. None of the law specific to animals applies to AI, so you need to show negligence. You could bring in legislation to treat AI in a similar way to a dangerous species, but that would be more like keeping a lion than keeping a bull.
> In Mirvahedy v Henley, the claimant was injured after the car he was driving was hit by the defendant’s horse. The horse had pushed over a wooden fence and an electric wire fence and bolted from its field after being frightened. This test was met here because, when a horse is sufficiently alarmed or panicked – ie in those particular circumstances – it is a known characteristic of a horse to bolt and cover long distances.
So, it's not just 'lions', but regular animals that people know can behave in a particular way.
I would say AI companies know the cybersecurity dangers since they literally advertise it as part of their marketing.
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And as a tangent, it isn't simple if we're talking broadly - e.g. I don't think Anthropic should be liable for agents that are directed by a user to write code that takes part in a crime. Nor should they be held liable if their internal security measures are lax in a way that an employee steals data that gets distilled into another model. But we're not talking about those edge cases.
I think it's pretty clearly negligent to set up a harness that will run whatever outputs it receives from your LLM if you cannot reliably stop it from outputting hacking instructions. I've said in the past that I'm sympathetic to the idea of testing your anti-hacking controls, but that doesn't seem to be where this incident came from.
I believe Opus 5 isn't meant to be spoken to by humans. It's great at executing but I reckon it's intended to be spoken to by other models such as Fable. I use Fable as the orchestrator, only speak with Fable, and all implementation, recon, design etc happens with Opus 5, with Fable reviewing (and translating).
I've really gone in the opposite direction: having a dumber model orchestrate. In my case, it's usually a Luna orchestrator spawning Sol/Astra subagents to do the "big brain" work of planning and reviewing.
Reason I went with "dumb orchestrator" was just to save tokens. Having Opus/Sol (let alone Fable/Astra) orchestrate was burning tokens like crazy for me even when much of the gruntwork was being done by Luna/Sonnet/Haiku subagents. (Luna is also really good, like way better than Sonnet...) Perhaps it was a skill issue on my end though, maybe I wasn't just managing context properly.
It's amazing how quickly Fable went from 'Game-changing model that needs to be banned' to 'Yeah it's alright, but OpenAI is also just as good and there are a couple of good open weight alternatives that are equivalent for almost everything'
The hype cycles are shortening, perhaps we really are reaching some kind of plateau this time (famous last words)
Why are you using the product release date for Kimi K3 and the training date for Fable? Either use the release date for both (6 weeks apart) or if you have it the training date for both.
By Anthropic. The government blocked it after it was already released.
Every LLM product goes through testing and alignment after training, maybe even some quick improvements here and there. Kimi probably did something similar.
Put another way, if Google says they have the best model in the world but won’t release it in December I will start caring in December, not before.
if Dario and the CEO of Moonshot switched places, Mythos would have been generally available to the public 4 months ago. As a statement of fact, it wasnt, because of Project Glasswing and Dario thinking they created a superweapon that the rubes shouldnt have access to.
> First ever comment said "Further releases of Chinese models that demonstrate the gap is not growing substantially is a huge problem. The spending will be called into question."
ah yes, because i said something factually accurate and vaguely positive about Anthropic I must be a shareholder which would mean I either run a venture capital firm or am a current employee of Anthropic...
And given that Chinese models are closing the gap there are basically two thing that could be happening. One is that they are moving faster than US companies developing closed models, and two that we're starting to hit a plateau for model capabilities where all the easy gains have been plucked, and now it's not really possible to move forward at the same rate on the frontier. Of course, both things could be happening at the same time.
Chinese labs have come up with a bunch of genuine innovations: GRPO, auxiliary loss free MoE load balancing, MLA, muon optimizer, and a bunch of other ones. The Deepseek papers are really well written, this isn’t just sneaking a peek at a peer.
The problems are inherently harder now too, partially because they take longer, so your training pipeline is waiting for long completions.
Also there probably is some “distillation” (technically pseudo-labeling, which is common in ML). But I wouldn’t put too much weight on it because that was true 18 months ago as well.
That's my thinking as well. The whole distillation thing is a distraction from the actual innovation happening in this space. What will be interesting to see going forward is what types of new techniques people manage to come up with to over come the current architecture limits.
The process takes time because even when you're distilling answers, you still need to actually do reinforcement training on the model. And given that Fable and GPT 5.6 just came out there simply hasn't been much time to do that. On top of that, Kimi also does better than Fable or GPT on a lot of tasks, distillation alone can't explain that, meaning there is a difference in architecture. You can watch a talk from Kimi founder to see how Kimi was actually trained and why it performs well. https://www.youtube.com/watch?v=5CkCW1P-g88
Not to mention that US companies models constantly distill each other as Musk was forced to admit under oath. This whole narrative has just been a massive cope.
> US companies models constantly distill each other as Musk was forced to admit under oath
> This whole narrative has just been a massive cope.
So wait, US AI companies all use distillation because... it's not effective and it's all just cope? Or is distillation really powerful and they all do it, which Musk was forced to admit under oath? But when China does distillation it isn't powerful and they don't need to do it, but they do it anyway because it's fun?
Either it's powerful and everyone, including the Chinese labs, use it as a way to rapidly catch-up against the SOTA models, or it's a red herring and the huge amounts of energy spent to protect and enable distillation is all just wasted money. Which is it?
I'm saying it's a cope to claim that the only reason Chinese models are catching up is due to distillation, while pointing out that distillation itself is in no way unique to Chinese companies. I'm sorry this was too complex of an idea for you to follow.
The fun part about this is that we can see who is right in about a year. If the leading labs continue making progress at hardening their models against distillation, and then they start pulling away again, we see who is right. If China is able to pass the US and release an independently better model than anything the US has, then your theory is correct.
Both sides have extremely smart people. One side has more $$$ and exclusive access to the best chips. For progress to converge without a corresponding breakthrough suggests there's something else at work.
Indeed we will, my prediction is that we'll see a model from China that definitively surpasses any US model by the end of the year. China has an absolute population advantage here along with having a much better education system. And China now dominates in published AI papers.
The US enjoyed an early advantage due to excessive money being poured into AI which led to the current bubble, and access to the hardware that was needed to train these models initially.
At this point, neither of these factors actually matter that much. The naive approach of simply making models bigger has hit a wall, and now you need ingenuity in figuring out better architecture for them. Precisely because Chinese companies have had to deal with more limited resources, they put a lot more effort into researching different kinds of optimizing techniques. And of course, China is also catching up in chip making, and Huawei clusters are already competitive with Nvidia for training. So, that gap is closing as well.
The big difference is that an absolutely insane amount of money has been spent in the US, while China managed to do this on a fraction of the budget. The AI Investment Surge graph here puts things in perspective. https://hai.stanford.edu/news/inside-the-ai-index-12-takeawa...
My prediction is that they're going to angle to become a vendor of record for the government and get bailed out. That's the only path at this point because there won't be any competition from China in this niche.
The plateau is inevitable because their rapacious training methodologies are only viable when there are no defense in place, but information continues to evolve, which means the models will have to be continuously updated, but will be doing so with less and less freely available data.
My understanding is that the labs ran out of freely available data to train on a while ago, and now primarily rely on human data vendors such as Surge and Mercor to source their data.
Fable is still the same model, it’s still a great model, and to be honest all these articles writing and speculating on how the LLM industry is going to evolve are not that insightful nor interesting.
I don’t think one should pay much attention to them.
Those Teslas were controller-less SSDs IIRC, common in the cheap embedded world.
Modern proper SSDs for computers do their best to spread out the writes (TRIM, and other features). They still wear out of course, nothing can beat physics. But if you buy a 2TB drive, there's a lot of room to spread out the write cycles that a normal user probably never has to worry about (no normal user is going to hit 500TBs of writes that breaks a modern 2TB SSD write balancing algorithm)
But it probably should be noted that embedded controller-free SSDs have significant write amplification issues. Embedded systems assumed you were trying to save money and/or compute... and also assumed you didn't do too many writes. So they really weren't designed for the write cycles that Teslas logging system did.
The FT is usually very trustworthy, so I don't doubt the actual information. However, the question is whether Kimi K3 was only benchmaxxed or actually is SOTA-ish in real use. If the latter is the case, it may be difficult for the big US AI labs to explain their valuations.
I am especially curious whether training (or at least inference) was indepdent of NVIDIA's stack - the article doesn't say. If so, it could have widespread ramifications (geopolitical and in the markets).
Moonshot use Alibaba cloud for training and inference, specifically using NVIDIA hardware, although Alibaba also make their own Zhenwu AI chips and clusters that run on them.
Other Chinese AI companies like DeepSeek and Ziphu (Z.ai - GLM) more heavily use domestic AI chips for inferences - specifically Huawei's Ascend chips.
So, China is no longer dependent on NVIDIA, but neither has it totally cut usage of (reliance on?) NVIDIA.
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