A decade ago, DeepMind co-founder Shane Legg predicted a 50% chance of reaching human-level artificial intelligence (AI) by 2028. In this article, we explore his perspective, the challenges in defining AI intelligence, and the need for scalable AI training models.
More than a decade ago, the co-founder of Google's DeepMind artificial intelligence lab predicted that by 2028, AI will have a half-and-half shot of being about as smart as humans — and now, he's holding firm on that forecast.
In an interview with tech podcaster Dwarkesh Patel, DeepMind co-founder Shane Legg said that he still thinks that researchers have a 50-50 chance of achieving artificial general intelligence (AGI), a stance he publicly announced at the very end of 2011 on his blog.
It's a notable prediction considering the exponentially growing interest in the space. OpenAI CEO Sam Altman has long advocated for an AGI, a hypothetical agent that is capable of accomplishing intellectual tasks as well as a human, that can be of benefit to all. But whether we'll ever be able to get to that point — let alone agree on one definition of AGI — remains to be seen.
Legg apparently began looking towards his 2028 goalpost all the way back in 2001 after reading "The Age of Spiritual Machines," the groundbreaking 1999 book by fellow Google AI luminary Ray Kurzweil that predicts a future of superhuman AIs.
"There were two really important points in his book that I came to believe as true," he explained. "One is that computational power would grow exponentially for at least a few decades. And that the quantity of data in the world would grow exponentially for a few decades."
Paired with an understanding of the trends of the era, such as the deep learning method of teaching algorithms to "think" and process data the way human brains do, Legg wrote back at the start of the last decade that in the coming ones, AGI could well be achieved — so long as "nothing crazy happens like a nuclear war."
Today, the DeepMind co-founder said that there are caveats to his prediction that the AGI era will be upon us by the end of this decade.
The first, broadly, is that definitions of AGI are reliant on definitions of human intelligence — and that kind of thing is difficult to test precisely because the way we think is complicated.
- "You'll never have a complete set of everything that people can do," Legg said — things like developing episodic memory, or the ability to recall complete "episodes" that happened in the past, or even understanding streaming video. But if researchers could assemble a battery of tests for human intelligence and an AI model were to perform well enough against them, he continued, then "you have an AGI."
When Patel asked if there could be a single simple test to see whether an AI system had reached general intelligence, such as beating Minecraft, Legg pushed back.
"There is no one thing that would do it, because I think that's the nature of it," the AGI expert said. "It's about general intelligence. So I'd have to make sure [an AI system] could do lots and lots of different things and it didn't have a gap."
The second biggest caveat, Legg added, was the ability to scale AI training models way, way up — a worthy point given how much energy AI companies are already using to churn out large language models like OpenAI's GPT-4.
- "There's a lot of incentive to make a more scalable algorithm to harness all this computing data," Legg explained. "So I thought it would be very likely that we'll start to discover scalable algorithms to do this."
Asked where he thought we stand today on the path to AGI, Legg said that he thinks computational power is where it needs to be to make it happen, and the "first unlocking step" would be to "start training models now with the scale of the data that is beyond what a human can experience in a lifetime" — a feat he believes the AI industry is ready to achieve.
All that said, Legg reiterated his personal stance that he only believes there's a 50 percent chance researchers will achieve AGI before the end of this decade, and Futurism has reached out to DeepMind to see if the Google subsidiary has anything to add to that prognosis.
"I think it's entirely plausible," he said, "but I'm not going to be surprised if it doesn't happen by then."