Artificial Intelligence (AI)
Latest about Artificial Intelligence
Nvidia partners indirectly confirm Blackwell B200 GPU delay
By Anton Shilov published
Supermicro says Blackwell got 'pushed out a little bit,' but demand for AI servers is so high that it will not have any meaningful impact on its financial performance.
Intel reportedly gave up a chance to buy a stake in OpenAI in 2017
By Andrew E. Freedman published
Reuters reports that Intel had a chance to buy a stake in OpenAI in 2017 and 2018, but CEO Bob Swan didn't think AI models would make a splash anytime soon.
Nvidia accused of scraping ‘A Human Lifetime’ of videos per day to train AI
By Jowi Morales published
A 404 Media report sourced through an anonymous former employee accuses Nvidia of scraping millions of videos without the proper permission to train its AI.
OpenAI has built a text watermarking method to detect ChatGPT-written content
By Jowi Morales published
OpenAI built a text watermarking tool to detect whether a piece of content was written by ChatGPT. However, internal debates rage over whether it should be released.
Firm says Nvidia's skyrocketing AI valuation is in a 'bubble' and 'overhyped'
By Anton Shilov published
Elliot Management follows Sequoia Capital's assessment that AI is overhyped, which makes Nvidia's stock a bubble.
U.S. DoJ launches Nvidia antitrust investigation — investigating potential strong-arm tactics related to AI GPU supply
By Anton Shilov published
U.S. Department of Justice investigates whether Nvidia forced its partners to buy multiple products to maximize its earnings.
Nvidia to deliver Blackwell engineering samples this week — chips on track for fourth quarter launch
By Anton Shilov published
Nvidia is about to start sampling of Blackwell GPUs for AI applications.
Apple skips Nvidia's GPUs for its AI models, uses thousands of Google TPUs instead
By Mark Tyson published
Recently released research paper reveals the details.
New memory tech unveiled that reduces AI processing energy requirements by 1,000 times or more
By Jeff Butts published
Seeing the need to improve the energy-efficiency of AI applications, one research team from Minnesota may have cracked the code to lowering energy consumption by a huge amount.
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