Self-Proclaimed Bitcoin Inventor Lied ‘Repeatedly’ to Support Claim, Says UK Judge

A man walks past a bitcoin poster in Hong Kong on April 15, 2024. DALE DE LA REY / AFP
A man walks past a bitcoin poster in Hong Kong on April 15, 2024. DALE DE LA REY / AFP
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Self-Proclaimed Bitcoin Inventor Lied ‘Repeatedly’ to Support Claim, Says UK Judge

A man walks past a bitcoin poster in Hong Kong on April 15, 2024. DALE DE LA REY / AFP
A man walks past a bitcoin poster in Hong Kong on April 15, 2024. DALE DE LA REY / AFP

An Australian computer scientist who claimed he invented bitcoin lied "extensively and repeatedly" and forged documents "on a grand scale" to support his false claim, a judge at London's High Court ruled on Monday.

Craig Wright had long claimed to have been the author of a 2008 white paper, the foundational text of bitcoin, published under the pseudonym "Satoshi Nakamoto".

But Judge James Mellor ruled in March that the evidence Wright was not Satoshi was "overwhelming", after a trial in a case brought by the Crypto Open Patent Alliance (COPA) to stop Wright suing bitcoin developers.

Mellor gave reasons for his conclusions on Monday, stating in a written ruling: "Dr Wright presents himself as an extremely clever person. However, in my judgment, he is not nearly as clever as he thinks he is."

The judge added: "All his lies and forged documents were in support of his biggest lie: his claim to be Satoshi Nakamoto."

Mellor also said that Wright's actions in suing developers and his expressed views about bitcoin also pointed against him being Satoshi, Reuters reported.

Wright, who denied forging documents when he gave evidence in February, said in a post on X: "I fully intend to appeal the decision of the court on the matter of the identity issue."

COPA – whose members include Twitter founder Jack Dorsey's payments firm Block – described Monday's ruling as "a watershed moment for the open-source community".

"Developers can now continue their important work maintaining, iterating on, and improving the bitcoin network without risking their personal livelihoods or fearing costly and time-consuming litigation from Craig Wright," a COPA spokesperson 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.