this post was submitted on 06 Mar 2026
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234
We messed up with the Windows 12 article. What we got wrong and how it happened (www.pcworld.com)
submitted 12 hours ago* (last edited 11 hours ago) by Brkdncr@lemmy.world to c/technology@lemmy.world
17 comments fedilink hide all child comments
 

Earlier this week, PCWorld published a roundup of Windows 12 rumors translated from PCWelt that does not meet our editorial standards. We’re deeply embarrassed by it, and I personally apologize that the article was published. It should not have been, but we’re keeping the article live (with an editor’s note at the top) so it remains in the public record.

Windows Central published a response detailing its errors. Thanks for keeping us accountable, guys — genuinely. In the same spirit of accountability, I want to explain how this happened, and what we’re doing to ensure a mistake like this never occurs again.

Let’s start by discussing how PCWorld handles translated articles, and then I’ll dive into the issues with the article itself.

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[–] panda_abyss@lemmy.ca 7 points 10 hours ago (1 children)

Translation is what the transformer architecture was designed for. It is the state of the art, and translation software has been using ML for a long long time.

This feels like an appropriate use of AI, but failure of editing.

[–] wewbull@feddit.uk 10 points 8 hours ago (1 children)

Not with general purpose LLMs. They start off ok, but become much more interested in continuing the text they've already translated, rather than looking back to what it is they're meant to translate. So they drift off course as the translation gets longer.

[–] XLE@piefed.social 2 points 7 hours ago* (last edited 7 hours ago)

General purpose LLMs' failure to do a task like translation must be very funny for their investors. Even the more translation-gocused ones seem to have issues.

[DeepL] translation is said to be generated using a supercomputer that reaches 5.1 petaflops and is operated in Iceland with hydropower.

In general, [convolutional neural network]s are slightly more suitable for long coherent word sequences, but they have so far not been used by the competition because of their weaknesses compared to recurrent neural networks.

The weaknesses of DeepL are compensated for by supplemental techniques, some of which are publicly known.

(ETA I need to edit my comments to federate them?)