Microsoft Offers Cloud Customers AMD Alternative to Nvidia AI Processors 

A view shows a Microsoft logo at Microsoft offices in Issy-les-Moulineaux near Paris, France, March 25, 2024. (Reuters)
A view shows a Microsoft logo at Microsoft offices in Issy-les-Moulineaux near Paris, France, March 25, 2024. (Reuters)
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Microsoft Offers Cloud Customers AMD Alternative to Nvidia AI Processors 

A view shows a Microsoft logo at Microsoft offices in Issy-les-Moulineaux near Paris, France, March 25, 2024. (Reuters)
A view shows a Microsoft logo at Microsoft offices in Issy-les-Moulineaux near Paris, France, March 25, 2024. (Reuters)

Microsoft said on Thursday it plans to offer its cloud computing customers a platform of AMD artificial intelligence chips that will compete with components made by Nvidia, with details to be given at its Build developer conference next week.

It will also launch a preview of new Cobalt 100 custom processors at the conference.

Microsoft's clusters of Advanced Micro Devices' flagship MI300X AI chips will be sold through its Azure cloud computing service. They will give its customers an alternative to Nvidia's H100 family of powerful graphics processing units (GPUs) which dominate the data center chip market for AI but can be hard to obtain due to high demand.

To build AI models or run applications, companies typically must string together - or cluster - multiple GPUs because the data and computation will not fit on a single processor.

AMD, which expects $4 billion in AI chip revenue this year, has said the chips are powerful enough to train and run large AI models.

As well as Nvidia's top-shelf AI chips, Microsoft's cloud computing unit sells access to its own in-house AI chips called Maia.

Separately, the Cobalt 100 processors Microsoft plans to preview next week offer 40% better performance over other processors based on Arm Holdings' technology, the company said. Snowflake and others have begun to use them.

The Cobalt chips, which were announced in November, are being tested to power Teams, Microsoft's messaging tool for businesses, and positioned to compete with the in-house Graviton CPUs made by Amazon.com.



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.