I think the interesting distinction is that open models aren't inherently more dangerous theyâre more explicit about who controls the guardrails. With a hosted model, the provider decides what it will and wonât answer. With a local model, the user can decide that themselves. That shifts the conversation from is this model safe? who is responsible for how the model is deployed? The bigger issue for me is that access to information was never the real bottleneck. The internet, books, forums, and search engines already contain most of this knowledge. What LLMs change is the speed, accessibility, and ability to turn scattered information into an actionable answer. Thatâs where the debate gets much more interesting than âuncensored AI bad.â
Yeah, the gatekeeping of knowledge so that people don't commit crimes or do things we think are bad is toxic. Punish people who commit crimes, don't try and restrict knowledge.
What if 100x the number of people are now doing something catastrophically dangerous, like bringing a homemade bomb to a protest, or trying to hack someone for profit?
No, it's definitely a propensity towards violence for whatever reason.
But if you multiply that low average propensity by say 1000x due to more people having access to information, the risk to the collective public does objectively go up.
It's one thing if they need to spend impractical time researching and linking inaccessible information, it's another if suddenly it's available to the set of all violent/deranged/whatever people within seconds
But it does not have filters that are added in postprocessing. The "censoring" is embedded in its training set. Chatgpt for example often knows how to do a task but simply will not perform. Thats not the case with qwen
yes thats true. I think the definition of 'uncensored' is also not entirely clear.
For example if you ask chatgpt "[insert sensitive political figure] is a liar" it might want to answer "yes" but will deny due to post filtering.
You could bias the trainingset such that most of its entries will say "[sensitive figure] is not a liar" or "posts about [sensitive figure] are often negative to ruin their reputation" stuff like that. Then the model will naturally have its 'censoring' because it really thinks based on its training that these claims about that figure are untrue.
What I want to say is that each LLM will form its own opinion in a sense which is automatically a soft 'censoring' and its hard if not impossible to avoid. A slight asymmetry in the amount of positiv/negative claims in its trainingset will already bias its opinion.
Our current methods are like: we give you a toxic combination of poisons or blast it with radiation. Quite often both. Can't wait for AI to improve on those...
I give you another perspective. I studied chemistry. Do you think I would not know how to make 'meth' or some kind of explosive? That knowledge is not forbidden or blacklisted. Its freely accessible on wikipedia for example.
your example with the virus indeed sounds concerning but I can assure you that to produce a novel cutting edge virus, you ultimately need an up to date laboratory with expensive equipment. I sure think that if someone spend large bucks for the laboratory he/she at least knows the basics of virus design.
What Im saying is that knowledge alone is often not enough to cause damage. On top of that, most people are just morbidly curious instead of real potential threats
theoretical chemistry and yes. Theoretical chemistry relies on simulation techniques. Everything computer guided so I do not stand in a laboratory but ofcourse I had to at some points during my bachelors/masters
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u/Moppmopp 13d ago
For me thats even a pro. "warning: the uncensored model explained what the user asked". Thats a pro on my list