Trending Now Mashable Selects Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Getting Technical with Mashable Gift Ideas For Everyone On Your List Say More Versus In My Bag Mashable @Home Top creators, ranked Creator Playbook All Series Wednesday morning was not a good one for the Internet Archive. Users are also reporting problems with Wikipedia. Alex Perry is a tech reporter at Mashable who primarily covers video games and consumer tech. Alex has spent most of the last decade reviewing games, smartphones, headphones, and laptops, and he doesn't plan on stopping anytime soon. He is also a Pisces, a cat lover, and a Kansas City sports fan.

Trending Now Mashable Selects Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Getting Technical with Mashable Gift Ideas For Everyone On Your List Say More Versus In My Bag Mashable @Home Top creators, ranked Creator Playbook All Series Two serious flaws in software that powers a large share of the web leaked ahead of their fix. Now attacks have reportedly started. Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat father. His writing has also appeared in PC Mag and .

The wonder of the online world was built by a community of eccentric volunteers. Can it survive the A.I. models it helped train? In a world where state policy is shaped by A.I. hallucination, it's cute to remember that consulting Wikipedia was once seen as irresponsible. Schools banned its use, and journalists were shamed for credulously repeating its hoaxes. "Reads like Wikipedia" was a literary insult.

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Wikipedians Believed AI Would Come and Go. But the real loss may be far more existential than website traffic. Long before the storm of AI struck Wikipedia, the Wikimedia Foundation was making preparations. Wikipedians battened down the hatches and made confident-sounding predictions that it would break and wash over them. Large language models might even improve Wikipedia, it was said, automating more of the housekeeping--formatting, sources, citations, missing links--and so freeing up humans to do creative work. It was not so simple. In 2019, then-Wikimedia Foundation head Katherine Maher wrote an op-ed in The New York Times under the headline " Without Humans, A.I. Can Wreak Havoc ." "Too often," she wrote, "artificial intelligence is presented as an all-powerful solution to our problems, a scalable replacement for people."

More than 200 people in roles such as engineering, finance, and communications will now be represented by the Communications Workers of America. More than 200 staffers at the Wikimedia Foundation, the nonprofit behind the encyclopedia Wikipedia, have voted 158-14 to join the Communications Workers of America labor union. The US National Labor Relations Board counted ballots on Thursday. Three Wikimedia employees tell WIRED they will now have a mechanism to challenge management as the organization faces growing competition from AI chatbots and a drop in volunteer editors. The 25-year-old, San Francisco-based foundation employs nearly 700 people across 39 countries, most of them working remotely.

To create a passage representation, the passage title and text are concatenated ([CLS]title [SEP]passage [SEP]), following common practice (Karpukhin et al., 2020). We retrieve top 10 passages and use them as input to mGEN. We differentiate those paragraphs from the question using special tokens (

vs. He graduated with a B.S. degree in Biology in 1957. As in the case of machine translation, we found that the language code does not need to be specified during inference as our model learns the question language automatically. Yet, we found that training with language codes is particularly useful to augment training data for Ltarget without any question data in Ltarget.

The dataset and the baseline code will be made publicly available in a dedicated GitHub repository upon acceptance. License TempEL is distributed under Creative Commons Attribution-ShareAlike 4.0 International license (CCBY-SA 4.0).1 Maintenance The maintenance and extension to further temporal snapshots of TempEL will be carried out by the authors of the paper. Additionally, we will make the code public to create potential new variations and extensions of TempEL using a number of hyperparameters (see Sections A.4 and A.5 for further details). A.2 Datasheet for TempEL In this section we provide a more detailed documentation of the dataset with the intended uses. We base ourselves on the datasheet proposed by [1]. A.2.1 Motivation For what purpose was the dataset created? The TempEL dataset was created to evaluate how the temporal change of anchor mentions and that of target Knowledge Base (KB; i.e., modification or creation of new entities) affects the entity linking (EL) task. This contrasts with the currently existing datasets [9, 7, 8, 6], which are associated with a single version of the target KB such as the Wikipedia 2010 for the widely adopted CoNLL-AIDA[2] dataset. We expect that TempEL will encourage research in devising new models and architectures that are robust to temporal changes both in mentions as well as in the target KBs. Who created the dataset and on behalf of which entity?