Apple is hoping to recreate the success of a deal it struck with Google back in 2005 by announcing a partnership with OpenAI fit for the AI age.
Category: robotics/AI – Page 373
Raspberry PI unveils its first AI-enabled board that will let developers and creators use AI in their projects straight out of the box.
Chinese state-owned automaker Dongfeng Motor is partnering with robotics firm UBTech to introduce the latter’s humanoid into its manufacturing process.
The industrial version of the Walker S humanoid robot from Ubtech will be used on the production line of Dongfeng Motor to carry out various manufacturing duties.
According to reports, it will involve conducting safety belt inspection, door lock testing, body quality checks, oil filling, and label application. The robot will integrate with traditional automated machinery to handle complex scenarios in unmanned manufacturing.
Advanced Micro Devices (AMD) has introduced its newest artificial intelligence (AI) processor, the MI325X. This advanced version of the MI300 series is expected to be available in the fourth quarter.
At the Computex technology trade show in Taipei, AMD also announced that it will develop AI chips over the next two years to challenge Nvidia’s dominance.
It should be noted that Nvidia has an almost 80% share in the market of AI semiconductors.
Nvidia CEO Jensen Huang has unveiled plans to build a next-generation AI platform called Rubin — named after astronomer Vera Rubin.
Huang made the announcement at an address ahead of the COMPUTEX technology convention in Taipei, which starts on June 4.
According to the company’s blog, Huang spoke to nearly 6,500 industry executives, reporters, entrepreneurs, gamers, inventors, and AI fans who had congregated at the glass-domed National Taiwan University Sports Center in Taipei.
NVIDIA takes to the stage at COMPUTEX, with a slew of AI-powered tools to enable game assistants, digital humans, and improve generative AI.
Scientists are calculating earthquake risk using an ISI-created system that automates and manages data-and compute-intensive research.
Quantum simulators are now addressing complex physics problems, such as the dynamics of 1D quantum magnets and their potential similarities to classical phenomena like snow accumulation. Recent research confirms some aspects of this theory, but also highlights challenges in fully validating the KPZ universality class in quantum systems. Credit: Google LLC
Quantum simulators are advancing quickly and can now tackle issues previously confined to theoretical physics and numerical simulation. Researchers at Google Quantum AI and their collaborators demonstrated this new potential by exploring dynamics in one-dimensional quantum magnets, specifically focusing on chains of spin-1/2 particles.
They investigated a statistical mechanics problem that has been the focus of attention in recent years: Could such a 1D quantum magnet be described by the same equations as snow falling and clumping together? It seems strange that the two systems would be connected, but in 2019, researchers at the University of Ljubljana found striking numerical evidence that led them to conjecture that the spin dynamics in the spin-1⁄2 Heisenberg model are in the Kardar-Parisi-Zhang (KPZ) universality class, based on the scaling of the infinite-temperature spin-spin correlation function.
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AI technology is spreading quickly throughout many different industries, and its integration depends on users’ trust and safety concerns. This matter becomes complicated when the algorithms powering AI-based tools are vulnerable to cyberattacks that could have detrimental results.
Dr. David P. Woodruff from Carnegie Mellon University and Dr. Samson Zhou from Texas A&M University are working to strengthen the algorithms used by big data AI models against attacks.
Much like the invigorating passage of a strong cold front, major changes are afoot in the weather forecasting community. And the end game is nothing short of revolutionary: an entirely new way to forecast weather based on artificial intelligence that can run on a desktop computer.
Today’s artificial intelligence systems require one resource more than any other to operate—data. For example, large language models such as ChatGPT voraciously consume data to improve answers to queries. The more and higher quality data, the better their training, and the sharper the results.