Twitter Makes Some of its Source Code Public

Twitter app logo is seen in this illustration taken, August 22, 2022. REUTERS/Dado Ruvic/Illustration/File Photo
Twitter app logo is seen in this illustration taken, August 22, 2022. REUTERS/Dado Ruvic/Illustration/File Photo
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Twitter Makes Some of its Source Code Public

Twitter app logo is seen in this illustration taken, August 22, 2022. REUTERS/Dado Ruvic/Illustration/File Photo
Twitter app logo is seen in this illustration taken, August 22, 2022. REUTERS/Dado Ruvic/Illustration/File Photo

Twitter on Friday made public parts of the computer code that decides how the social media site recommends content, with its owner Elon Musk adding that the entirety of the code will be available in the next few weeks.

The announcement will allow users and programmers a peek into its workings and the ability to suggest modifications to the algorithm.

"In the coming weeks, we will open source literally everything that contributes to showing a tweet," Musk said in a tweet on Saturday.

The company said in a blog post it had uploaded the code in two repositories on code-sharing platform Github. They include the source code for many parts of Twitter, including the recommendations algorithm which controls the tweets that users see on their timeline.

The move comes at the behest of Musk, its billionaire owner, who has said code transparency would lead to higher trust among users and rapid improvements to the product.

It also serves to address common concerns among users and lawmakers, who are increasingly scrutinizing social media platforms over how algorithms select the content that users see.

Musk tweeted on Friday that third parties should be able to analyze the open-sourced code and "determine, with reasonable accuracy, what will probably be shown to users."

"No doubt, many embarrassing issues will be discovered, but we will fix them fast!" he tweeted.

According to Reuters, Musk also said Twitter will update its recommendation algorithm based on user suggestions every 24 to 48 hours.

On Friday, Musk and some Twitter employees held a session on Spaces, Twitter's audio chat feature, asking users to bring recommendations and questions about how the platform's code works.

One person questioned why Twitter's code appeared to classify users as Republicans or Democrats. A Twitter employee responded that it was an old feature that was not important to the platform's recommendation system, and the company was looking to remove it.

The repositories on Github do not include the code that powers Twitter's ad recommendations, the company said.

It also said it excluded code that would compromise user safety or privacy, as well as details that would undermine efforts to prevent child sexual abuse material on the platform.

The news also comes after parts of Twitter's source code were leaked on Github, which took down the code last week at Twitter's request.

Twitter asked the US District Court for the Northern District of California to order Github to produce "all identifying information" associated with the Github account that had posted the leaked code, according to a legal filing.



SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI
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SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

The Center of Excellence for Data Science and Artificial Intelligence at the Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have introduced the MiniGPT-Med model.

The large multi-modal language model is designed to help doctors quickly and accurately diagnose medical radiology using artificial intelligence techniques.

Dr. Ahmed Alsinan, the Artificial Intelligence Advisor at the National Center for Artificial Intelligence and head of the scientific team at SDAIA, explained that the MiniGPT-Med model is capable of performing various tasks such as generating medical reports, answering medical visual questions, describing diseases, locating diseases, identifying diseases, and documenting medical descriptions based on entered medical images.

The model was trained on different medical images, including X-rays, CT scans, and MRIs.

The MiniGPT-Med model, derived from large-scale language models, is specifically tailored for medical applications and demonstrates significant versatility across different imaging methods, including X-rays, CT scans, and MRI. This enhances its utility in medical diagnosis.

Dr. Alsinan highlighted that the MiniGPT-Med model was developed collaboratively by artificial intelligence specialists from SDAIA and KAUST.

The model exhibits advanced performance in generating medical reports, achieving 19% higher efficiency than previous models. It serves as a general interface for radiology diagnosis, enhancing diagnostic efficiency across various medical imaging applications.