Re: Proposal -- Interpretation of DFSG on Artificial Intelligence (AI) Models

Wouter Verhelst <[email protected]>
Newsgroups gmane.linux.debian.devel.vote
Organization The Debian Project, http://www.debian.org/
Message-ID <[email protected]>
On Sun, May 04, 2025 at 07:08:00PM +0200, Aigars Mahinovs wrote:
>    On Sun, 4 May 2025 at 17:30, Wouter Verhelst <[1][email protected]> wrote:
> 
>      >    Wikipedia definition is a layman's simplification.
>      It may be a simplification, but that in and of itself does not make
>      it
>      incorrect.
> 
>    I have specifically addressed this point with examples in my reply.
>    Copyright very clearly does not survive learning and then generation of
>    new solutions. In humans that is a given.

Indeed.

>    For software I would assume the equivalence, unless proven
>    differently.

This is not a fact; this is your opinion. You base the rest of your
argument on it, so I'll call it an axiom: something to accept in order
for the rest of the argument to hold.

The problem is, I disagree with your axiom.

To me, software and humans are two very different things. We know how
computers work; we can therefore reason what the output of a software
program is going to be based on the input that you give it. Whether that
program is a compiler or a trainer program for a deep learning model is
just a detail in that context. One computer chip of a given model and
stepping is 100% equivalent to another, and so any process that runs on
one of these chips will produce the same output on another.

The same is not true for human brains; we do not fully understand how
they work, we cannot predict what the resulting experience of a given
person is going to render based on the training that person has
received, and therefore we cannot predict how a given person is going to
write a particular piece of software. Different brains will result in
different programming styles given the same training. In fact, I may
write a solution to the same problem differently on two different days.
An LLM will not do that; when given the same inputs, it will produce the
same output (as long as we consider "the internal state of its
randomizer" as part of its inputs).

Given that I don't agree with your initial axiom, and given that the
rest of your argument is based on that axiom, I'm not surprised that I
didn't agree with your full argument. That is also why I did not see the
need to reply to that part of your argumentation.
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