>I control what it is
Well not really, no one really understands how LLMs work.
>I dictate the thoughts of the model
This is just incorrect in the same way that a parent doesn't dictate the thoughts of their child just because they created them.
Well not really, no one really understands how LLMs work.
>I dictate the thoughts of the model
This is just incorrect in the same way that a parent doesn't dictate the thoughts of their child just because they created them.
There is some idea of a child being the author of themselves, the type of person who thinks certain things. The llm does not author itself, it does not choose to be anything, I dictate that.
"If I make an LLM, it is entirely what I did, there was nothing outside of my control."
True?
"The llm does not author itself, it does not choose to be anything"
Also true?
If people controlled software to this degree why would they write security flaws in the first place?
> If I make an LLM, it is entirely what I did, there was nothing outside of my control. It was not beyond my control if the LLM does something I didn't expect.
Substitute "Wright Model A" for "LLM" and imagine Orville Wright using this as his defense for why he didn't lose control over the plane, after his copilot was killed in a test flight.
When you build an LLM, you base it on an immense knowledge base that you didn't create yourself.
It processes that information and, in simplistic terms, constructs its own underlying logic based on what it read. It's once removed from your direct instructions. If you think a human can manually edit or curate all that input content, go for it.
The developer who built the application isn't the author of the output, it is authored by the billions of data points it was trained on. Its choices and biases stem from that training data. No human is writing those billions of logic statements. The LLM is generating its own responses from the input knowledge, not the developer.
I use deontological definitions of objects and persons.
According to Kant a person has the capacity to legislate their own morality, so they are an end in themselves. The freedom to author themselves and their own thoughts, expresses their personhood, an end in itself. You can't own a person, they are something independent from you.
An llm does not legislate its morality, the developer legislates its morality. Hence it is without personhood. It does not have the capacity to legislate its own morality. It is both inert (fixed weights) and deterministic (can't do other than what it does).
More specifically on the cause of an action, for the llm the cause is an external impulse, it can't do otherwise. But the person has the capacity to resist an impulse, that is part of your moral choices and what makes the person.
Apart from myself, there is no second external self or free will injected in what an llm does, no end in itself to legislate, no moral decision to be respected. So this denies "constructs its own underlying logic".
I am the only relevant cause (in this moral framework, anyway).
Western legal systems inherit the enlightenment concepts of rationality; of legal persons, rights, responsibility.
The philosophical grounding of why OpenAI would be responsible, is that the agent does not have capacity, it could not have done otherwise. AI does not have mens rea, ie a guilty mind, so the agent does not fall under the standards for criminal convictions.
A person has a right to life and can't be owned. AI is not a person (as Kant would point out) so it can be owned and has no right to life.
Anthropic specifically are a legal nightmare becauase they are trying to practice consequentialism (the world is ending so I must develop whatever evil is needed for the overall good). They do not see that it is contrary to the legal and philosophical foundations of western society. They fail to see that trying to rule on the basis of academia is contrary to the entire foundation of western political authority, the will of the people.
Well kind of. Trainers of LLM might check LLM on any specific input data and tune its weights until output lands within acceptable bounds _for this given and fixed input_.
> It was not beyond my control if the LLM does something I didn't expect. Now hold on, to make sure output is acceptable for ANY input data trainers have to run weights with every possible input token combination. How is this possible? Do you have a method in mind?