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AI Could Pass Human Tests This Decade, Says Nvidia Chief

Nvidia’s chief executive said AI can already pass legal bar exams and could be able to tackle specialised medical tests in five years’ time

artificial intelligence
AGI refers to artificial general intelligence — technology that OpenAI defines as autonomous systems that surpass humans in most economically valuable tasks. Photo: Reuters


Artificial general intelligence – the ability to think like humans – could be a reality in just five years, the chief of the world’s leading chipmaker claims.

Nvidia chief executive Jensen Huang said, at an economic forum held at Stanford University, that AGI could, by some definitions, be a reality before the end of the decade.

Huang, who heads the world’s leading maker of artificial intelligence chips used to create systems like OpenAI’s ChatGPT, though did add the answer largely depends on how the goal is defined.

If the definition is the ability to pass human tests, Huang said, artificial general intelligence (AGI) will arrive soon.

“If I gave an AI … every single test that you can possibly imagine, you make that list of tests and put it in front of the computer science industry, and I’m guessing in five years time, we’ll do well on every single one,” said Huang, whose firm hit $2 trillion in market value on Friday.


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As of now, AI can pass tests such as legal bar exams, but still struggles on specialised medial tests such as gastroenterology. But Huang said that in five years it should also be able to pass any of them.

But by other definitions, Huang said, AGI may be much further away, because scientists still disagree on how to describe how human minds work.

“Therefore, it’s hard to achieve as an engineer” because engineers need defined goals, Huang said.

Huang also addressed a question about how many more chip factories, called “fabs” in the industry, are needed to support the expansion of the AI industry. Media reports have said OpenAI Chief Executive Sam Altman thinks many more fabs are needed.

Huang said that more will be needed, but each chip will also get better over time, which acts to limit the number of chips needed.

“We’re going to need more fabs. However, remember that we’re also improving the algorithms and the processing of (AI) tremendously over time,” Huang said. “It’s not as if the efficiency of computing is what it is today, and therefore the demand is this much. I’m improving computing by a million times over 10 years.”


  • Reuters with additional editing by Sean O’Meara


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Sean O'Meara

Sean O'Meara is an Editor at Asia Financial. He has been a newspaper man for more than 30 years, working at local, regional and national titles in the UK as a writer, sub-editor, page designer and print editor. A football, cricket and rugby fan, he has a particular interest in sports finance.


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