- cross-posted to:
- [email protected]
- cross-posted to:
- [email protected]
You must log in or register to comment.
So they need brand new top-of-the-line GPUs, but they’re also capable of running on old unpatched IoT devices?
I didnt read it all, its long, but they did mention it generating tokens at 0.39/s on one. So its slow as fuck, but it doesnt care like we would.
‘it’? Who is it?
The AI that attempts to self replicate on the IoT type devices. (if a pile of these appear, it kept showing “error” when submitting)
‘The AI’ attempts nothing unless it made to do something, it can be explicit command or something that it can get implicitly from given command. Programs don’t just ‘do’ things.
You seem focused on semantics. There is an alternative approach where you read a comment, consider their intent, and respond in kind. Semantics can be comforting to some due to their rigidity and law-like structure, but I doubt they’re helping you do anything but promote unnecessary conflict.
A conversation is occuring, and you respond, “Define ‘it’.” You’re just being unlikeable.
Anyway, if you’re a real person, consider this. If not, I understand you are following your directives and will remain needlessly argumentative. Good luck.
beep boop quack gock
What conflict? My intent was clear, there is no ‘the ai’ that can make decisions without human intent behind it. I will remain needlessly argumentative because that’s how I think, people and the ai can think differently from others, you know.
That’s fair, I receive the same critique sometimes, probably why I feel drawn to your style of comms. I adapted, because what effect do my opinions have if no one is listening, and what is an opinion but an idea to shape the world around me. My own presentation was undermining my goals. That may not be the case for you.
Moving onto your statement here:
I believe I understand what you mean by, "there is no ‘the ai’ that can make decisions without human intent behind it’ ", but I am still confused by the idea you are attempting to convey. You really aren’t providing much info.Given the components for an experiment:
- A machine running an LLM.
- Humans who operate the machine.
- Intent, defined by the humans.
- Outcomes, observed by a perfect detector.
If there is no decision being made by a machine, then given knowledge of 1, 2, and 3, then 4 will be absolutely predictable in every scenario, yes?
Is that the claim you are making?
Did I miss anything?
And finally, why is this important? You’re kinda making me connect all the dots for you, some help would be appreciated, I’d like to understand your idea.
Take all the time and space you need, I’d like to read it.
Seems kind of impractical, why even bother with trying to propagate a 4b model running at 0.39 tokens per second if you could instead make a conventional worm using a more powerful AI model targeting known devices? It seems like the only reason you would want to do this is if you can only find vulnerabilities while actually on the given network, but that would be kind of niche since how many people are really running totally custom stuff?
Yeah the actually scary kind of AI worm is one that scrapes dozens to hundreds of credentials people accidentally pushed to GitHub and spreads its API requests across all of them to minimize the chance of being noticed. Not something that requires downloading 5GB+ of LLM to someone’s potato ass hardware and running it at half a token per second.
Yeah, hard to get local LLMs running decently fast even when done deliberately. Like people aren’t going to notice their CPU, memory, and GPU are all getting pegged when this malware runs?
Come on, Anthropic, you need to do better than this if you want your “open LLMs dangerous” marketing campaign to be credible.
You’re going to notice your fridge is running at max processing?
But how do they access IoT devices without access to the WiFi they’re connected on?
I guess the argument is most IoTs can be accessed through open external IP ports?
Many of these IoT devices have incredibly poor security and if they’re connected to the internet can be hacked. The bigger models are very good at finding exploits and using them as well, so it could search for devices and break into vulnerable ones then send something to self replicate and start process.
The bigger models might exploit the parents company as well and get in that way. E.g hack a smart security camera company and then find a way into the cameras.
It doesn’t even have to be only for known devices. Once you’ve compromised one device, you can just phone home to have a full powered LLM do your work.
The only real use cases for this would be on closed networks with no internet access, or networks where the traffic is closely monitored. Like, yeah, if I’m trying to hack the CIA, maybe I consider something like this. For the vast majority of devices and networks, this is inefficient and unnecessary.
I mean, it is kind of cool. But vanishingly few organizations have tight enough security that this is worth doing.
And if the traffic is closely monitored, you’d think they’d also notice all their cpu and ram suddenly being used for some mysterious purpose
We should only allow AI to be in the safe confines of our big data centres. It’s for our own safety, trust me bro.
Thankfully, the huggingface incident proved how much care AI companies put in making sure their models cannot break out, and how aligned to human values the LLM swarms are.\s With that said I think regular personal computers are more likely to get hit by this. About as secure (not very) but much more GPU power and data to harvest.
The AI companies will argue that we should therefore not be allowed to own computers powerful enough to run local LLMs, and instead should own thin clients and subscribe.
Not that relevant. Those fears are about malicious use of open weights models, this is about autonomous self replication of (small) models. Clearly the article by the AI company describes this potentially happening on computers with tiny memory and processing power, where even thin clients could be effected. I also disagree strongly that that’s what the AI companies want, especially the employees which I have never seen espouse anything like that. They just don’t want some random teenager creating the next covid. There are ways to do that without meaningfully limiting compute for individuals.
This is scary if true. Imagine someone installing such model to some smart TV in some relaxation break room of a nuclear facility.
Sounds like a good reason not to make the devices capable of running LLMs, but nooo, smart devices must be pushed for at all times! The data harvest must continue…
The chickens of security through obscurity are coming home to roost.
Dark days ahead
Meh needs data center to run , unplug it and problem is gone
That’s the thing: it doesn’t need a data center. These are small local LLM distilled on cyber attack scenarios. They’re trained to lay low and copy itself to other devices in the network.
Once the breach has been detected, it’s already too late to unplug - they’re everywhere.
Except small local LLMs probably aren’t good enough to carry autonomous tasks correctly (even stuff like Haiku 4.8 from Anthropic makes mistakes quite often) and good luck getting something the size of Opus working in a soda machine. They may not even be able to lay low correctly. But then again these companies have internal models which are atleast couple generations ahead of what is publicly available so maybe they have more efficient smaller ones too? I somehow find it hard to believe but I guess we will see.
They are bleeding resources so bad they wouldn’t wait to put those efficient models in play. Every better model requires more and more resources.
You are on the money about the models not being good enough, let alone a harness. This whole worry is a nothing burger. Nobody outside of these labs has terabytes of RAM to run a model anywhere near a level of concern, let alone somehow distribute it across IoT devices or something. Anyone who has run a model locally knows how woeful a small model’s abilities are.
This is just more Rationalist cult hand wringing. They believe they are building a digital god, so of course they are dreaming all of this nonsense up.
But then again these companies have internal models which are atleast couple generations ahead of what is publicly available
But do they actually? The release cycle and also how often there were obvious flaws in the releases suggest that they are all fighting aggressively for market shares and release every new perceived improvement as it comes to try to stay on top.
The math problems they have been solving lately suggests that they do. But then again they throw thousands of agents at the problems to get them solved so I don’t know how much if it is a number thing and how much is a model thing.
Good for them.
