As AI Safety Concerns Mount, Pioneers Make the Case for Openness

Three of the World's Most Respected AI Researchers Weigh In

At the Ai4 conference in Las Vegas last week, three of the world's most respected AI researchers spoke out on the issue of open-source models, making a powerful case for keeping AI open. The speakers, Nobel Prize winner Geoffrey Hinton, World Labs CEO and co-founder Fei-Fei Li, and Coursera co-founder Andrew Ng, disagreed on particular tactics, but all three emphasized the importance of allowing a handful of major AI companies to control the pace of progress.

The Risks of Gatekeepers

According to Ng, the core concern is allowing a few companies to control access to AI, which can slow innovation and influence what gets built on the platforms. "I don't want there to be gatekeepers," Ng said. "That limits how all of us can access AI." Companies have an incentive to protect their competitive advantages, including by influencing the rules that govern the industry, which could create a dynamic where only the largest, best-capitalized firms with the resources to build the most advanced AI systems can thrive.

The Dangers of Open-Weight Models

Not everyone agrees that open-weight models would help preserve that state of play. Hinton, in particular, drew a distinction between open-source software, which makes the underlying code available for inspection and modification, and open-weight models, which release the parameters of a trained AI model to the public. "Open source is great. You show people the code, and lots of people look at the lines of code and say, 'Oh, there's a bug.' Open weights means you train a big model and then you give people the weights. That's very different," Hinton said. "I was against open [weights] because it makes it so easy for people to take these big foundation models, which are very expensive to train, and for much less money train them to do bad things like cyber attacks."

But Open-Source Models Are Here to Stay

Yet accepting reality didn't mean ignoring the risks. Hinton's position was clear: AI would continue to advance, and he thought that was largely a good thing. It would boost productivity and improve education and healthcare. "Worrying about the possible bad effects of AI and the things that intelligent beings might do when they're smarter than us. I don't think that's unfair. I think it is unfair to label anybody who thinks like that as a fear-monger," Hinton added.

A Nuanced Approach

Li pushed back on the framing of the debate, saying it's "very dangerous to make this a dichotomy between complete openness all the way to complete closedness." She highlighted collaborations between public and private institutions, such as the Human Genome Project, as an example of how openness can be nuanced. "So I think we have to use [AI] as that kind of infrastructure," Li said. "We need some levels of openness, both in scientific discovery, in education, in global partnership, as well as lucrative business models for entrepreneurs. But we also will accept closed-source systems. This debate, especially at the sweeping level of 'we can only tolerate one,' is a false debate. We need to get to a level of nuance."

Regulation Is Key

But everyone agreed that some level of regulation would be necessary to keep AI on the right track. "What we want to do is develop AI in a direction that helps people, and regulation will help us do that," Hinton said. "You can't leave it to people like Elon Musk and Mark Zuckerberg to decide how AI should be done.