US and Japan Announce Joint Partnership to Accelerate Nuclear Fusion

US President Joe Biden and Japanese Prime Minister Fumio Kishida listen to translation during their meeting in the Oval Office of the White House in Washington, US, April 10, 2024. (Reuters)
US President Joe Biden and Japanese Prime Minister Fumio Kishida listen to translation during their meeting in the Oval Office of the White House in Washington, US, April 10, 2024. (Reuters)
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US and Japan Announce Joint Partnership to Accelerate Nuclear Fusion

US President Joe Biden and Japanese Prime Minister Fumio Kishida listen to translation during their meeting in the Oval Office of the White House in Washington, US, April 10, 2024. (Reuters)
US President Joe Biden and Japanese Prime Minister Fumio Kishida listen to translation during their meeting in the Oval Office of the White House in Washington, US, April 10, 2024. (Reuters)

The United States and Japan announced a joint partnership to accelerate development and commercialization of nuclear fusion, the US Department of Energy said on Wednesday.

The partnership was announced as Japanese Prime Minister Fumio Kishida was in Washington for a summit with President Joe Biden.

US Deputy Secretary of Energy David Turk and the Japan Minister of Education, Sports, Science and Technology Masahito Moriyama, met in Washington on Tuesday to discuss fusion.

The partnership is intended to focus on addressing scientific and technical challenges of delivering commercially viable fusion.

Scientists, governments, and companies have been trying for decades to harness fusion, the nuclear reaction that powers the sun, to provide carbon-free electricity. It can be replicated on Earth with heat and pressure using lasers or magnets to fuse two light atoms into a denser one, releasing large amounts of energy.

Unlike plants that run on fission, or splitting atoms, commercial fusion plants, if ever built, would produce little long-lasting radioactive waste.

The two countries will also agree to support sustainable aviation fuel in a statement from the summit, two sources with knowledge of the talks between the countries said.



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.