Nvidia CEO Jensen Huang has suggested that a significant milestone in artificial intelligence (AI) could be reached within the next five years, potentially leading to AI systems capable of surpassing human performance in exams and medical assessments. Speaking at an economic forum at Stanford University, Huang remarked on the imminent possibility of AI achieving human-like cognitive abilities.
Addressing queries regarding the trajectory of AI advancements, Huang emphasized the potential for a substantial leap forward within the coming half-decade. He noted that the timeframe for the emergence of artificial general intelligence (AGI), capable of completing human-level tasks, hinges on the objectives set for AI development.
Huang’s remarks underscore the rapid pace of AI innovation and its potential implications for various fields, including education and healthcare.
“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,” he said.
As of now, AI can pass tests such as legal bar exams, but still struggles on specialized medical tests such as gastroenterology. But Huang said that in five years, it should also be able to pass any of them.
However, by other definitions, AGI could still be further away as scientists are still researching how the human brain actually works. “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.
The Nvidia CEO said that still many more chip factories are needed, but the AI chips get better and better with time and research, eventually slowing down manufacturing.”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,” he added.
