Samsung Electronics Picks Veteran Executive to Tackle 'Chip Crisis' amid AI Boom

A Samsung sign is displayed, during the GSMA's 2023 Mobile World Congress (MWC) in Barcelona, Spain March 1, 2023. (Reuters)
A Samsung sign is displayed, during the GSMA's 2023 Mobile World Congress (MWC) in Barcelona, Spain March 1, 2023. (Reuters)
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Samsung Electronics Picks Veteran Executive to Tackle 'Chip Crisis' amid AI Boom

A Samsung sign is displayed, during the GSMA's 2023 Mobile World Congress (MWC) in Barcelona, Spain March 1, 2023. (Reuters)
A Samsung sign is displayed, during the GSMA's 2023 Mobile World Congress (MWC) in Barcelona, Spain March 1, 2023. (Reuters)

Samsung Electronics has replaced the chief of its semiconductor division to help the group overcome a "chip crisis", amid a booming market for AI chips where analyst say the world's biggest memory chipmaker lags peers.

The South Korean manufacturer on Tuesday said it has appointed Young Hyun Jun, effective immediately, moving him from the role as head of its future business planning unit.

Jun previously led Samsung's memory chip department after working on the development of DRAM and flash memory chips.

The move is likely aimed at catching up in the market for top-end chips used in artificial intelligence (AI) such as high bandwidth memory (HBM) chips where Samsung has fallen behind rivals such as SK Hynix, analysts said, Reuters reported.

"This is a preemptive measure to strengthen future competitiveness by renewing the atmosphere internally and externally," Samsung Electronics said in a statement.

The firm said Jun, a former chief executive at battery arm Samsung SDI and former executive at Samsung Electronics' memory chip business, would help overcome the "chip crisis" with his management know-how.

Replacing such a high-ranking position in the middle of the year is unusual, given most personnel changes at Samsung normally take place in the beginning of the year, analysts said.

Current chip division chief Kye Hyun Kyung will succeed Jun as head of the future business unit.

"The chip division has been lagging in competitiveness on various fronts. It also missed a lot of the global AI upward trend," said analyst Lee Min-hee at BNK Investment & Securities.



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.