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With Nvidia at the forefront of producing AI chips, crucial for developing AI systems like OpenAI's ChatGPT, Huang's insights carry significant weight in the tech industry.
He proposed that measuring AGI by a computer's ability to pass a comprehensive array of human tests could lead to reaching this milestone relatively soon, Reuters reports.
Currently, AI systems can succeed in exams like the legal bar but face challenges in more specialized fields such as gastroenterology. However, Huang is optimistic that AI could also excel in these areas within five years.
Despite this optimism, Huang acknowledged that the broader definition of AGI, which encompasses a deeper understanding and replication of human cognitive processes, remains elusive.
This complexity is partly because there is still no consensus among scientists on precisely defining the workings of the human mind, making it a more challenging target for engineers who thrive on clear objectives.
Regarding the infrastructure required to support the burgeoning AI industry, Huang responded to queries about the necessity for more chip manufacturing facilities.
While agreeing on the need for additional fabs, he highlighted simultaneous improvements in chip efficiency and AI processing algorithms.
These advancements, he suggested, could amplify computing capabilities by a million times over the next decade, potentially moderating the sheer number of chips needed as each becomes more powerful and efficient.
Analysts have vouched for Nvidia's dominance in the $85 billion+ accelerator market, particularly in data center sales, which are likely to exceed 85% of its total sales, marking a significant growth.
Investors can gain exposure to Nvidia via VanEck Semiconductor ETF
Price Actions: NVDA shares traded higher by 2.11% at $840.19 premarket on the last check Monday.
