Eli Zaretskii <e...@gnu.org> writes:
>> Date: Sat, 8 Mar 2025 20:31:44 +0300
>> From: Jean Louis <bugs@gnu.support>
>> Cc: van...@sdf.org, emacs-tangents@gnu.org
>>
>> * Eli Zaretskii <e...@gnu.org> [2025-03-08 13:09]:
>> > > Date: Sat, 8 Mar 2025 00:51:23 +0300
>> > > From: Jean Louis <bugs@gnu.support>
>> > > Cc: emacs-tangents@gnu.org
>> > >
>>
>> > When Large Language Model (LLM) analyzes 130,000 documents it
>> > means it is getting the information, thus uses that information. If
>> > you call it learning or absorbing, whatever, it took the information,
>> > it did not invent new information.
>>
>> > That is inaccurate. Machine learning does produce information not
>> > explicitly included in the learning set.
>>
>> > You mentioned "new original insight in the data" -- that is matter
>> > of viewpoint and consciousness of the observer.
>>
>> > No, it isn't. The model data includes data generated by analyzing the
>> > learning set, and that fits the definition of "new insight".
>>
>> Let us then ask Microsoft Phi-4, fully free "smart" one:
>> https://pageassist.xyz/share/cm80d751k00718c5qil4pwyo0
>>
>> And look what it says, surprisingly matches my view point:
>
> No, it says that both your view and my view are possible
> interpretations ("from a strict data-centric view ... however, from a
> functional ...").
>
>> In essence, whether machine learning produces "new information" can
>> depend on how one defines newness and insight. From a strict
>> data-centric view, all information is derived from the input data.
>> However, from a functional or application-oriented perspective, the
>> insights and predictions generated by the model can be seen as new
>> information, as they provide value and understanding beyond the
>> explicit content of the training set.
>>
>> I didn't ask the LLM before I made my own opinion.
>
> No, you just read half of its answer and ignored the rest.
>
>> You said machine learning produce information not included in the
>> learning it. And I asked for example. I can understand people's
>> opinions, but it is not an opinion I am looking for, I am genuinely
>> interested to find such examples, which are beyond personal biased
>> perspective of how it may look like.
>
> If you ever used ML in actual applications, you would have known that
> learning generates new information not explicitly present in the
> learning set. Claiming that this information was in the data is like
> saying that a solution of an equation was in the equation. If you
> never had any practical experience with ML, then I understand how you
> could be so wrong about this.
>
>> If you have examples, some links, send me.
>
What is the `Microsoft Phi-4' model response to the prompt:
What about Roy Herbert's dilation theory coordinate space and
holography, restricted to Michael Levin's bioelectric signal field
in Stuart Hameroff's microtubule tubelin that is believed to relate
to consciousness in the Penrose Hameroff Orchestrated Objective
Reduction concept ???
--
vl
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