Amazon Makes First Investment in Direct Air Capture Climate Technology 

The logo of Amazon is seen at the company logistics center in Lauwin-Planque, northern France, November 15, 2022. (Reuters)
The logo of Amazon is seen at the company logistics center in Lauwin-Planque, northern France, November 15, 2022. (Reuters)
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Amazon Makes First Investment in Direct Air Capture Climate Technology 

The logo of Amazon is seen at the company logistics center in Lauwin-Planque, northern France, November 15, 2022. (Reuters)
The logo of Amazon is seen at the company logistics center in Lauwin-Planque, northern France, November 15, 2022. (Reuters)

E-commerce giant Amazon.com is making its first investment in direct air capture technology, which removes emissions from the atmosphere, by committing to purchase 250,000 tons of removal credits over 10 years, it said on Tuesday.

Amazon will purchase the credits from the 1PointFive direct air capture (DAC) plant in Texas, which is being developed by oil company Occidental’s Oxy Low Carbon Ventures subsidiary and will use them to help meet its climate target of net zero carbon emissions by 2040.

The company did not reveal any financial details of the deal, but developers of DAC technology have said removal credits currently cost in the mid-to-high-triple digits in dollars per metric ton.

Many scientists believe extracting billions of tons of carbon dioxide from the atmosphere annually, by using nature or technology, is the only way to meet goals set under the UN Paris climate agreement to curb climate change because so many emissions are still being generated by the use of fossil fuels.

Projects that suck carbon dioxide (CO2) out of the air can generate removal credits that can then be bought and used by companies to help offset emissions they are unable to cut from their business.

Although the technological solutions are still far from proven at a cost and scale that could allow a global roll-out, tech giants have increasingly backed DAC. Microsoft last week signed a multi-year deal for the purchase of 315,000 metric tons with U.S. project developer Heirloom.

Amazon's carbon footprint for 2022 was 71.27 million metric tons of carbon dioxide equivalent, including Scope 3 emissions which are those generated indirectly from sources the company does not control or own, such as the emissions generated by staff flying for work.

Jamey Mulligan, head of carbon neutralization science and strategy at Amazon said an “all hands on deck approach” was needed to scale up the technology.

“We have to have massive scale very quickly, 1PointFive and Occidental have significant knowledge, expertise and workforce and experience that’s needed to scale industrial plants like this,” he said.

Some green groups have criticized the role of oil companies in developing plants to remove carbon dioxide.

The 1PointFive project was one of two large-scale DAC "hubs" last month selected for the largest US Department of Energy grants available for the technology.

Mulligan said Amazon is focused on cutting its own emissions and scaling up use of renewable energy but will also likely use a portfolio of carbon offsets, including those from nature-based projects, to help reach its net zero target.



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.