* Re: Guidelines for pre-trained ML model weight binaries
2023-04-07 5:50 Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?) Nathan Dehnel
@ 2023-09-06 14:28 ` Andreas Enge
0 siblings, 0 replies; 2+ messages in thread
From: Andreas Enge @ 2023-09-06 14:28 UTC (permalink / raw)
To: Nathan Dehnel; +Cc: rprior, guix-devel
Hello,
related to this thread, I just came across an entry in Cory Doctorow's blog:
https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means
It is already interesting in its disection of the terms "open" vs. "free",
which is quite relevant to us (but just echoes the sentiment I had anyway).
The end can be seen as an invitation to *not* package neurol network
related software at all: by packaging "big corporation X"'s free software,
but which is untrainable on anything but big corporations' hardware, we
actually help big corporation X to entrap users into its "ecosystem".
Andreas
^ permalink raw reply [flat|nested] 2+ messages in thread
* Re: Guidelines for pre-trained ML model weight binaries
@ 2023-09-12 7:36 Nathan Dehnel
0 siblings, 0 replies; 2+ messages in thread
From: Nathan Dehnel @ 2023-09-12 7:36 UTC (permalink / raw)
To: Andreas Enge, guix-devel
That was fascinating, thanks for sharing.
^ permalink raw reply [flat|nested] 2+ messages in thread
end of thread, other threads:[~2023-09-12 7:37 UTC | newest]
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2023-04-07 5:50 Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?) Nathan Dehnel
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