According to some definitions, artificial general intelligence could exist in as little as five years, according to Nvidia Chief Executive Jensen Huang’s comments on Friday.
Huang was answering a question about how long it would take to realize one of Silicon Valley’s long-standing dreams of creating computers that can think like humans at an economic forum held at Stanford University. Huang is the head of the world’s top manufacturer of artificial intelligence chips used to create systems like OpenAI’s ChatGPT.
According to Huang, a lot relies on how the objective is stated. Artificial general intelligence (AGI) will be here soon, according to Huang, if the definition is the capacity to pass human tests.
Huang, whose company saw its market value reach $2 trillion on Friday, said, “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.”
AI is currently able to pass exams like the legal bar exam, but it still has difficulty with specialized medical exams like gastroenterology. However, Huang stated that it ought to be able to pass any of them in five years as well.
However, according to Huang, different definitions could put AGI much further off since researchers are still unable to agree on a common understanding of how minds function.
As a result, Huang claimed, “it’s hard to achieve as an engineer” because engineers require clear objectives.
The question of how many more chip factories—referred to as “fabs” in the industry—are required to support the growth of the AI sector was also addressed by Huang. According to media reports, OpenAI CEO Sam Altman believes a significant number of additional fabs are required.
Although Huang stated that more will be required, the number of chips required will be constrained because each chip will improve with time.
We will require additional fabs. But keep in mind that over time, we’re also making significant advancements in (AI) processing and algorithms,” Huang added. “It’s not like the demand is this high because computing efficiency is what it is today. In ten years, I will have improved computing a million times.
