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Kabosu, the Face of Cryptocurrency Dogecoin, Dies at 18, Owner Says

This picture taken on March 19, 2024 shows Atsuko Sato (L) with her Japanese Shiba Inu dog Kabosu, best known as the logo of cryptocurrency Dogecoin, playing with students at a kindergarten in Narita, Chiba prefecture, east of Tokyo. (AFP)
This picture taken on March 19, 2024 shows Atsuko Sato (L) with her Japanese Shiba Inu dog Kabosu, best known as the logo of cryptocurrency Dogecoin, playing with students at a kindergarten in Narita, Chiba prefecture, east of Tokyo. (AFP)
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Kabosu, the Face of Cryptocurrency Dogecoin, Dies at 18, Owner Says

This picture taken on March 19, 2024 shows Atsuko Sato (L) with her Japanese Shiba Inu dog Kabosu, best known as the logo of cryptocurrency Dogecoin, playing with students at a kindergarten in Narita, Chiba prefecture, east of Tokyo. (AFP)
This picture taken on March 19, 2024 shows Atsuko Sato (L) with her Japanese Shiba Inu dog Kabosu, best known as the logo of cryptocurrency Dogecoin, playing with students at a kindergarten in Narita, Chiba prefecture, east of Tokyo. (AFP)

Kabosu, the Japanese dog that became a global meme and the face of alternative cryptocurrency Dogecoin has died at 18, her owner announced in a blog post on Friday.

The Japanese Shiba Inu passed away while sleeping, her owner Atsuko Sato wrote.

Kabosu became recognizable as the face of Dogecoin, an alternative cryptocurrency that began as a satirical critique of the 2013 crypto frenzy.

But the token jumped in value after Tesla boss Elon Musk, a proponent of cryptocurrencies, began tweeting about it in 2020. Since then the billionaire has repeatedly promoted the coin.

Dogecoin added as much as $4 billion to its market value last year when the billionaire, who bought social media site Twitter in 2022, briefly replaced Twitter's blue bird logo with an image of Kabosu. Musk subsequently renamed Twitter X.

With a market capitalization of around $23.6 billion, Dogecoin is now the ninth biggest cryptocurrency, according to data site Coingecko.com. “The impact this one dog has made across the world is immeasurable,” Dogecoin posted on social media site X on Friday.

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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
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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.