From coding to hardware, LLMs are speeding up research progress in artificial intelligence. It could be the most important trend in AI today.
Last week, Mark Zuckerberg declared that Meta is aiming to achieve smarter-than-human AI. He seems to have a recipe for achieving that goal, and the first ingredient is human talent: Zuckerberg has reportedly tried to lure top researchers to Meta Superintelligence Labs with nine-figure offers. The second ingredient is AI itself. Zuckerberg recently said on an earnings call that Meta Superintelligence Labs will be focused on building self-improving AI—systems that can bootstrap themselves to higher and higher levels of performance.
The possibility of self-improvement distinguishes AI from other revolutionary technologies. CRISPR can’t improve its own targeting of DNA sequences, and fusion reactors can’t figure out how to make the technology commercially viable. But LLMs can optimize the computer chips they run on, train other LLMs cheaply and efficiently, and perhaps even come up with original ideas for AI research. And they’ve already made some progress in all these domains.
According to Zuckerberg, AI self-improvement could bring about a world in which humans are liberated from workaday drudgery and can pursue their highest goals with the support of brilliant, hypereffective artificial companions. But self-improvement also creates a fundamental risk, according to Chris Painter, the policy director at the AI research nonprofit METR. If AI accelerates the development of its own capabilities, he says, it could rapidly get better at hacking, designing weapons, and manipulating people. Some researchers even speculate that this positive feedback cycle could lead to an “intelligence explosion,” in which AI rapidly launches itself far beyond the level of human capabilities.
