Nvidia’s Stock Rally Sputters Ahead of Quarterly Report

A view of a Nvidia logo at their headquarters in Taipei, Taiwan May 31, 2023. (Reuters)
A view of a Nvidia logo at their headquarters in Taipei, Taiwan May 31, 2023. (Reuters)
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Nvidia’s Stock Rally Sputters Ahead of Quarterly Report

A view of a Nvidia logo at their headquarters in Taipei, Taiwan May 31, 2023. (Reuters)
A view of a Nvidia logo at their headquarters in Taipei, Taiwan May 31, 2023. (Reuters)

A scorching rally in Nvidia's shares this year came to a halt on Tuesday as investors worried if the high-flying chip designer's quarterly results would justify its towering valuation.

The stock fell 5.3% to $687.91. If losses hold, it could be the biggest percentage drop in more than eight months.

Nvidia has been at the heart of the frenzy around artificial intelligence (AI). A more than 40% surge in its stock this year helped it replace Alphabet as the third most valuable US company, behind Microsoft and Apple.

The market capitalization of Nvidia was $1.79 trillion on Friday.

"The market is maybe a little bit hesitant whether they (Nvidia) can deliver a strong enough guidance to reinvigorate the market even higher," said Frank Lee, head of technology research at HSBC.

The company will report quarterly results on Feb. 21. Analysts expect earnings of $4.56 a share and revenue to rise to $20.378 billion from $6.05 billion a year earlier, according to LSEG estimates.

Still, Nvidia's eye-popping run this year that pushed it to new peaks and powered gains in US stock markets could make the stock vulnerable if earnings are less than stunning.

"You can't come out and simply meet or slightly beat for the stock to go higher, Nvidia's going to need to blow it away," said Dennis Dick, a trader at Triple D Trading.

Nvidia options are pricing a swing of about 11% in either direction following results, according to data from options analytics service ORATS.

Other AI-focused stocks such as Super Micro Computer fell 11.6% and Arm Holdings dropped 7.3%.

Advanced Micro Devices was down nearly 6%, having recorded double-digit gains on a year-to-date basis.

Nvidia's shares are trading at 32 times its forward earnings estimates compared with the industry median of 25.4.



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.