Elon Musk fulfills his announcement and publishes the X algorithm in open source

The tycoon and owner of X (formerly Twitter), Elon Musk has published the social network’s recommendation algorithm in open source, as announced on January 10.

After the growing series of controversies that have involved the platform related to the generation of explicit images with artificial intelligence At the request of users, including nudes of minors, Musk took the step of publishing the X algorithm in open source in an attempt to make it more transparent.

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Although it announced that it would publish the algorithm last week, it was finally made public this Tuesday on the developer platform Github, where the open source can be redistributed, modified and used.

In a message on the social network, Musk announced the publication of the algorithm, which he described as “dumb” and stated that it needs “massive improvements.” “At least you can see that we are striving to improve it in real time and with transparency. No social network does this,” he said.

However, it is not the first time that the tycoon has tried to make the X code public, since in 2023, months after taking command of the platform, he published the code for the ‘For you’ ‘feed’ on GitHub.

The ‘For you’ feed recommends content using two main sources: the accounts followed by the user (Thunder) and other publications found on the platform (Phoenix). The algorithm then processes, filters and sorts by relevance.

In this way, the algorithm filters the content and eliminates duplicates, blocked accounts, topics in which the user is not explicitly interested, silenced words or illegal and inappropriate publications.

The algorithm uses the same Transformer architecture as Grok, the social network’s artificial intelligence, to rate each post based on the history of user behavior (interactions). Posts with higher scores are more likely to be recommended to the user.

This design eliminates the traditional method of manual feature extraction and instead uses an end-to-end learning approach to predict user interests.

By Editor

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