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Meta aims to solve one of the biggest problems of Wikipedia, humanity’s most important collaborative website: fact checking. With over 100,000 volunteer editors reviewing the content, it is likely that the information indexed in articles and profiles could be misrepresented by someone. For this matrix facebook represents algorithm that you can review these facts, and do so with the thousands of data added to the site.
FROM Target AIR&D Lab AI in the Matrix facebook as well as Instagramcollaboration with Fundación Wikipedia for the first machine learning model Wikipediaan algorithm capable of automatically scanning thousands of quotes added to the texts on the site and checking their compatibility with the topic.
“We developed a neural network based system called Side to identify Wikipedia quotes that are unlikely to support their claims and then recommend the best ones from across the web. We train this model on existing Wikipedia links, so we learn from the input and combined wisdom of thousands of Wikipedia editors.” command indicates.
Meta tries to check Wikipedia
This algorithm, trained on 4 million Wikipedia citations, can parse the information associated with a citation in a Wikipedia article and verify its authenticity beyond text comparison.
“In order to test the applicability of our system, we created a demo to interact with the English Wikipedia community.” explained in the document. “We found that Sid’s first citation recommendation gets 60% more likes than existing Wikipedia citations for the same 10% of Sid’s most likely unverifiable claims.”
In addition to validation, the team claims that the tool is intended to be used as a model for better dating recommendations. This, however, is achieved in collaboration with human editors who can make good use of Side’s recommendations.
Meta AI and its validation algorithm on Wikipedia
The project, available to anyone who wants to check it out via this GitHub link, aims to limit the use of fraudulent quotes in all sorts of contexts, allowing information published on Wikipedia to have more support in terms of indexed notes.
With this first attempt, the Wikimedia Foundation may already have had a more robust system for reviewing and correcting records, which is part of the difficult job of manually checking information.
Source: RPP
I’m Liza Grey, an experienced news writer and author at the Buna Times. I specialize in writing about economic issues, with a focus on uncovering stories that have a positive impact on society. With over seven years of experience in the news industry, I am highly knowledgeable about current events and the ways in which they affect our daily lives.