As AI safety concerns mount, three pioneers make the case for staying open
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Why are open‑weight AI models considered a safety risk?
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As projects like pacing the frontier look to major labs as a way to keep AI research safe, open source models have become a sore spot for the industry. With free distribution and little control over how they're used, open weight models aren't easily controlled, leading some labs to treat them as downright scary. But at the AI four conference in Las Vegas last week, three of the world's most respected AI researchers, Nobel Prize winner Jeffrey Hinton, World Lab's CEO and co founder Feife Lee, and Coursera co founder Andrew Ng, spoke out on the issue. And while they disagreed on particular tactics, all three made a powerful case for keeping AI open. For the three speakers, the core concern was allowing a handful of major AI companies to control the pace of progress.
How does Andrew Ng view the threat of AI gatekeepers and market dominance?
You know, when a few companies control access to a technology, as Apple and Google do with mobile operating systems, innovation can slow, and the companies that control the platforms can influence what gets built on them. Andrew Ng said that he worried about a similar dynamic emerging in AI. I don't want there to be gatekeepers, he 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. That could create a dynamic where only the largest, best capitalized firms with the resources to build the most advanced AI systems. Ing's solution was to maintain multiple providers, with models and companies competing rather than allowing a handful of players to dominate the field.
He said If I were to try to give one prescription, it would be to promote openness because AI is amazing technology. And I want it to be in everyone's hands. But not everyone agreed 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 released the parameters of a trained AI model to the public. Hinton said, 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.
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 whatever his reservations, Hinton acknowledged that open weight models are already a permanent fixture of AI, saying
What distinction does Geoffrey Hinton make between open‑source code and open‑weight models?
I think that battle's been lost. We now have open weight models, so the barrier to lots of people getting these big models, which was the cost of training foundation models, that barrier has disappeared. It's too late. Yet accepting reality is 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. He said it would boost productivity and improve education and health care. He added, 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 fearmonger. Ng took a different view.
The question, he argued, wasn't whether open models were risky, but who controlled access and who would win the market. Whoever built the cheaper model would have the advantage. If China's open weight models gained widespread adoption across Asia, Africa, And or the developing world, he warned. They could influence how billions of people encountered ideas about democracy, freedom, and human rights. Ng went on to say, one thing I hope we do is encourage American competitiveness and open source AI.
Why does Fei‑Fei Li argue for a nuanced, layered approach to AI openness?
It turns out that AI is a tremendous source of soft power.
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Chapters
5 chapters
1
Why are open‑weight AI models considered a safety risk?
0:02–1:19
2
How does Andrew Ng view the threat of AI gatekeepers and market dominance?
1:19–3:21
3
What distinction does Geoffrey Hinton make between open‑source code and open‑weight models?
3:21–4:41
4
Why does Fei‑Fei Li argue for a nuanced, layered approach to AI openness?
4:41–6:38
5
How could China’s open‑weight AI strategy affect global competitiveness and soft power?
6:38–7:09
Speakers
1 identifiedMore from TechCrunch Industry News
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