Artificial intelligence/training: Difference between revisions

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==Why it is a problem==

=== Intellectual property laundering ===

===Intellectual property laundering===

Most, if not all, of the data used for training is copied indiscriminately, without even checking licenses or any copyright terms.{{Citation needed}} This is very controversial. Some people argue that it is "fair use" because AI systems learn in ways similar to animal and human brains, others claim it's more like [[wikt:parroting|a parrot learning phrases]], others claim that it's "transformative" so it's still fair-use, others say it's akin to [[wikipedia:Tracing_(art)|tracing images]] (this applies mostly to image models, though the analogy can work for text models).{{Citation needed|reason=too many opinions}}

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Some people request that, at the very least, the sources of the training data must be publicly disclosed, for the sake of [[wikipedia:Transparency_(behavior)|transparency]] and [[wikipedia:Attribution_(copyright)|attribution]].<ref>{{Cite web |last=Tunney |first=Justine |date=2024-08-23 |title=AI Training Shouldn't Erase Authorship |url=https://justine.lol/history/ |access-date=2026-04-26}}</ref>

=== ~~Ecosystem damage~~ ===

===Energy use===

TO-DO

While [[Self-hosting|self-hosted]] models can be trained with a single consumer-grade GPU, data-centers with hundreds or thousands of GPUs (known for being more power-hungry than CPUs) are used to train corporate-grade (or "enterprise") models. This can worsen [[wikipedia:Climate_change|climate change]].

=== Bandwidth abuse ===

Massive data needs massive bandwidth. Scraping web-pages across the entire internet requires sending millions of requests to all known servers. Some AI companies go as far as to ''repeatedly'' send requests for the same content (or several revisions of the same content) as frequent bursts in short intervals, which is indistinguishable from [[wikipedia:Denial-of-service_attack|DDoS attacks]].

==Examples==

@@ Line 7: / Line 7: @@
 ==Why it is a problem==
-=== Intellectual property laundering ===
+===Intellectual property laundering===
 Most, if not all, of the data used for training is copied indiscriminately, without even checking licenses or any copyright terms.{{Citation needed}} This is very controversial. Some people argue that it is "fair use" because AI systems learn in ways similar to animal and human brains, others claim it's more like [[wikt:parroting|a parrot learning phrases]], others claim that it's "transformative" so it's still fair-use, others say it's akin to [[wikipedia:Tracing_(art)|tracing images]] (this applies mostly to image models, though the analogy can work for text models).{{Citation needed|reason=too many opinions}}
@@ Line 14: / Line 14: @@
 Some people request that, at the very least, the sources of the training data must be publicly disclosed, for the sake of [[wikipedia:Transparency_(behavior)|transparency]] and [[wikipedia:Attribution_(copyright)|attribution]].<ref>{{Cite web |last=Tunney |first=Justine |date=2024-08-23 |title=AI Training Shouldn't Erase Authorship |url=https://justine.lol/history/ |access-date=2026-04-26}}</ref>
-=== Ecosystem damage ===
+===Energy use===
-TO-DO
+While [[Self-hosting|self-hosted]] models can be trained with a single consumer-grade GPU, data-centers with hundreds or thousands of GPUs (known for being more power-hungry than CPUs) are used to train corporate-grade (or "enterprise") models. This can worsen [[wikipedia:Climate_change|climate change]].
+=== Bandwidth abuse ===
+Massive data needs massive bandwidth. Scraping web-pages across the entire internet requires sending millions of requests to all known servers. Some AI companies go as far as to ''repeatedly'' send requests for the same content (or several revisions of the same content) as frequent bursts in short intervals, which is indistinguishable from [[wikipedia:Denial-of-service_attack|DDoS attacks]].
 ==Examples==