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Category: robotics/AI – Page 130

Nvidia CEO Jensen Huang unveils new Rubin AI chips at GTC 2025

Nvidia founder Jensen Huang kicked off the company’s artificial intelligence developer conference on Tuesday by telling a crowd of thousands that AI is going through “an inflection point.”

At GTC 2025—dubbed the “Super Bowl of AI”—Huang focused his keynote on the company’s advancements in AI and his predictions for how the industry will move over the next few years. Demand for GPUs from the top four cloud service providers is surging, he said, adding that he expects Nvidia’s data center infrastructure revenue to hit $1 trillion by 2028.

Huang’s highly anticipated announcement revealed more details around Nvidia’s next-generation graphics architectures: Blackwell Ultra and Vera Rubin—named for the famous astronomer. Blackwell Ultra is slated for the second half of 2025, while its successor, the Rubin AI chip, is expected to launch in late 2026. Rubin Ultra will take the stage in 2027.

A Roadmap for AI Governance: Lessons from G20 National Strategies

The rapid evolution of artificial intelligence (AI) is poised to create societal transformations. Indeed, AI is already emerging as a factor in geopolitics, with malicious non-state actors exploiting its capabilities to spread misinformation and potentially develop autonomous weapons. To be sure, not all countries are equal in AI, and bridging the “AI divide” between the Global North and South is vital to ensuring equal representation while addressing regulatory concerns and the equitable distribution of benefits that can be derived from the technology.

Most G20 members have established comprehensive national AI strategies, notably technology giants like the United States, United Kingdom, China, and countries of the European Union. Global South nations such as Brazil, Argentina, and India, despite economic constraints, are demonstrating progress in leveraging AI in areas like social services and agriculture. Future strategies must anticipate emerging threats like Generative AI (GenAI) and Quantum AI, prioritising responsible governance to mitigate biases, inequalities, and cybersecurity risks.

How an organelle evolves in symbiosis with a cell: Intermediate stage sheds light on the assimilation process

Organelles in cells were originally often independent cells, which were incorporated by host cells and lost their independence in the course of evolution. A team of biologists headed by Professor Dr. Eva Nowack at Heinrich Heine University Düsseldorf (HHU) are examining the way in which this assimilation process occurs and how quickly. They now describe their findings about an intermediate stage in this process in Science Advances.

Eukaryotic cells contain a large number of functional sub-units, so-called organelles. They perform important functions within the cell. Some organelles were independent, at some point in the past. They were then taken up by a cell and have evolved over time in symbiosis with the .

These “endosymbionts” lost their ability to function autonomously in the process. One well-known example of this type of is the mitochondrion, which evolved from a bacterium.

AI-driven software is 96% accurate at diagnosing Parkinson’s

Existing research indicates that the accuracy of a Parkinson’s disease diagnosis hovers between 55% and 78% in the first five years of assessment. That’s partly because Parkinson’s sibling movement disorders share similarities, sometimes making a definitive diagnosis initially difficult.

Although Parkinson’s disease is a well-recognized illness, the term can refer to a variety of conditions, ranging from idiopathic Parkinson’s, the most common type, to other like multiple system atrophy, a Parkinsonian variant; and progressive supranuclear palsy. Each shares motor and nonmotor features, like changes in gait, but possesses a distinct pathology and prognosis.

Roughly one in four patients, or even one in two patients, is misdiagnosed.

AI-driven MRI analysis improves accuracy in distinguishing Parkinsonian disorders

University of Florida researchers have led a multicenter study demonstrating that Automated Imaging Differentiation for Parkinsonism (AIDP), a machine-learning method using magnetic resonance imaging (MRI), accurately distinguishes Parkinson’s disease (PD) from atypical parkinsonian disorders. Findings suggest this approach could significantly improve diagnostic precision and clinical care.