Comment by advael
2 months ago
This argument, by now a common refrain from defenders of companies like OpenAI, misses the entire putative point of intellectual property, and the point of law in general. It is a distraction of a common sort - an attempt to reframe a moral and legal question into an abstract ontological one
The question of whether the mechanism of learning in a human brain and that in an artificial neural network is similar is a philosophical and perhaps technical one that is interesting, but not relevant to why intellectual property law was conceived: To economically incentivize human citizens to spend their time producing creative works. I don't actually think property law is a good way to do this. Nonetheless the question when massive capital investments are used to scrape artists' work in order to undercut their ability to make a living from that work for the benefit of private corporations that do not have their consent to do this is whether this should violate this artificial notion of intellectual property that we have constructed for this purpose, and in that sense, it's fairly obvious that the answer is yes
I wasn't responding to a moral and legal question. I was responding to a comment arguing that humans are some magical special case in nature.
If you want to argue it's a distraction, argue that with the person I replied to, who was the person who changed the focus.
Yea I'll give you that. But many people seem to have the argument you've made - which is dubious on its own terms, by the way, as we don't really have a complete picture of human learning and the assumption that it simply follows the mechanisms we understand from machine learning is not a null hypothesis that doesn't demand justification - loaded up for these conversations, and it needs to be addressed wherever possible that the ontological question is not what matters here
> which is dubious on its own terms, by the way, as we don't really have a complete picture of human learning and the assumption that it simply follows the mechanisms we understand from machine learning is not a null hypothesis that doesn't demand justification
The argument I made in no way rests on a "complete picture of human learning". The only thing they rest on is lack of evidence of computation exceeding the Turing computable set. Finding evidence of such computation would upend physics, symbolic logic, maths. It'd be a finding that'd guarantee a Nobel Prize.
I gave the justification. It's a simple one, and it stands on its own. There is no known computable function that exceeds the Turing computable, and all Turing computable functions can be computed on any Turing complete system. Per the extended Church Turing thesis this includes any natural system given the limitations of known physics. In other words: Unless you can show knew, unknown physics, human brains are computers with the same limitations as any electronic computer, and the notion of "something new" arising from humans, other than as a computation over pre-existing state, in a way an electronic computer can't also do, is an entirely unsupportable hypothesis.
> and it needs to be addressed wherever possible that the ontological question is not what matters here
It may not be what matters to you, but to me the question you clearly would prefer to discuss is largely uninteresting.
1 reply →