Written by Stephen Nellis
PALO ALTO, Calif. – (Reuters) – Nvidia Chief Executive Jensen Huang said on Friday that artificial general intelligence could arrive as early as five years, depending on the definition.
Huang, head of one of the world's leading manufacturers of artificial intelligence chips used to build systems such as OpenAI's ChatGPT, spoke at an economic forum at Stanford University about one of Silicon Valley's long-standing goals: ” in response to a question about how long it would take to achieve this goal. It's about creating computers that can think like humans.
Huang said the answer largely depends on how you define your goals. If the definition is the ability to pass human tests, then artificial general intelligence (AGI) will soon be here, Huang said.
“If I gave an AI… to run every test imaginable and create a list of those tests and publish them to the computer science industry, I think it would be good in five years.” On Friday, the company's market capitalization It reached $2 trillion, Huang said.
Currently, AI can pass exams such as the bar exam, but it still has difficulty with specialized medical exams such as gastroenterology. But Huang said it should be possible to pass one of them within five years.
But according to other definitions, AGI may be a long way off because scientists still don't agree on how to explain how the human mind works, Huang said. Stated.
Engineers need clear goals, “so it's difficult to achieve as an engineer,” Huang said.
Huang also addressed the question of how many more chip factories, known in the industry as “fabs,” are needed to support the expansion of the AI industry. According to media reports, OpenAI CEO Sam Altman believes more fabs are needed.
Huang said more chips will be needed, but the performance of each chip will also improve over time, limiting the number of chips needed.
“We're going to need a lot more fabs, but don't forget that the algorithms and (AI) processing are also improving significantly over time,” Huang said. “There's such a demand because the efficiency of computing is not what it is today. I've improved computing a million times in 10 years.”
(Reporting by Stephen Nellis in Palo Alto, California; Editing by David Gregorio)