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Accelerated Computing: A Game-Changer in Artificial Intelligence, Says NVIDIA CEO at WGS 24

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At the World Government Summit 2024, Jensen Huang, CEO of NVIDIA Corporation, discussed the groundbreaking impact of accelerated computing on artificial intelligence development in a conversation with Omar Sultan Al Olama, UAE Minister of Artificial Intelligence. Huang highlighted accelerated computing as a transformative shift from traditional central processing units (CPUs) to specialized domain-specific acceleration. This transition, he emphasized, is essential for achieving sustainable, energy-efficient, and high-performance computing, paving the way for groundbreaking AI applications.

Speaking to concerns about fair access to technology, Huang emphasized how accelerated computing has democratized access to high-performance computing. By enabling researchers worldwide to leverage this technology, NVIDIA has fueled innovation and propelled global advancements in AI research and development.

Additionally, Huang championed the concept of sovereign AI, stressing the significance of data ownership and national investment in AI infrastructure. He urged countries to safeguard their data to preserve cultural heritage and ensure equitable distribution of AI capabilities across societies. Acknowledging the ethical implications of AI’s societal impact, Huang underscored the need for targeted regulation focused on specific AI use cases. By navigating ethical considerations prudently, he emphasized the potential of AI to drive positive societal change while mitigating disruptive effects.

Jensen Huang, co-founder, president, and CEO of NVIDIA, is a prominent figure in the world of technology. He has spearheaded the company’s rise as a leader in accelerated computing, particularly with their Graphics Processing Units  NVIDIA, known for their high-performance graphics processing units (GPUs), has become a key player in the rapidly evolving field of artificial intelligence and . Their GPUs, originally designed for graphics processing, have proven to be ideal for parallel processing tasks involved in machine learning, the foundation of AI and holds a monopoly in the development of AI Chips.
 

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