Nvidia CEO Jensen Huang said Friday that artificial general intelligence could arrive in as little 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 were to give an AI… every test you can imagine, create a list of those tests and put it in front of the computer science industry, and in five years, all the You’re going to do well on the test, that’s the only thing,” Huang said. The company’s closing price on Friday pushed its market capitalization above $2 trillion for the first time.
Currently, AI can pass exams such as the bar exam, but it still struggles 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 each chip will also improve in performance over time, limiting the number of chips needed.
“We’re going to need a lot more fabs. But remember, we’ve also gotten much better at algorithms and (AI) processing 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 over the last 10 years.”
