As AI safety concerns mount, three pioneers make the case for staying open
As AI safety concerns mount, three pioneers make the case for staying open
随着人工智能安全担忧加剧，三位先驱力挺开源路径
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. 随着“Pacing the Frontier”等项目寄望于大型实验室来保障人工智能研究的安全，开源模型已成为行业内的痛点。由于可以自由分发且难以控制使用方式，开放权重（open-weight）模型并不容易被监管，这导致一些实验室将其视为洪水猛兽。
But at the Ai4 conference in Las Vegas last week, three of the world’s most respected AI researchers — Nobel Prize winner Geoffrey Hinton, World Labs CEO and co-founder Fei-Fei Li, 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. 但在上周于拉斯维加斯举行的 Ai4 大会上，三位全球最受尊敬的人工智能研究人员——诺贝尔奖得主杰弗里·辛顿（Geoffrey Hinton）、World Labs 首席执行官兼联合创始人李飞飞，以及 Coursera 联合创始人吴恩达（Andrew Ng）——就此问题发表了看法。尽管他们在具体策略上存在分歧，但三人均有力地论证了保持人工智能开放的必要性。
For the three speakers, the core concern was allowing a handful of major AI companies to control the pace of progress. 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. 对于这三位发言者而言，核心担忧在于让少数几家大型人工智能公司掌控技术进步的步伐。当少数公司控制了对某项技术的访问权限（正如苹果和谷歌在移动操作系统领域所做的那样），创新可能会放缓，而控制平台的公司则能左右其上构建的内容。
Andrew Ng said that he worried about a similar dynamic emerging in AI. “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. That could create a dynamic where only the largest, best-capitalized firms have the resources to build the most advanced AI systems. 吴恩达表示，他担心人工智能领域会出现类似的动态。“我不希望出现‘守门人’，”吴恩达说，“这限制了我们所有人获取人工智能的方式。”企业有动力保护其竞争优势，包括通过影响行业监管规则。这可能会导致一种局面：只有规模最大、资金最雄厚的公司才有资源构建最先进的人工智能系统。
Ng’s solution was to maintain multiple providers, with models and companies competing rather than allowing a handful of players to dominate the field. “If I were to try to give one prescription, it would be to promote openness,” Ng said, “because AI is amazing technology and I want it to be in everyone’s hands.” 吴恩达的解决方案是维持多个供应商，通过模型和公司之间的竞争，而不是让少数参与者主导该领域。“如果非要我给出一个处方，那就是促进开放，”吴恩达说，“因为人工智能是一项了不起的技术，我希望它能掌握在每个人手中。”
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 release the parameters of a trained AI model to the public. 但并非所有人都认同开放权重模型有助于维持这种竞争格局。辛顿特别区分了开源软件（提供底层代码供检查和修改）与开放权重模型（向公众发布已训练人工智能模型的参数）之间的区别。
“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.” “开源很好。你向人们展示代码，很多人查看代码行并说：‘哦，这里有个漏洞。’而开放权重意味着你训练了一个大模型，然后把权重交给人们。这完全不同，”辛顿说，“我曾反对开放（权重），因为它让人们太容易获取这些训练成本极其昂贵的基础模型，并以低得多的成本对其进行训练，从而从事网络攻击等坏事。”
But whatever his reservations, Hinton acknowledged that open-weight models are already a permanent fixture of AI. “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.” 但无论他有何保留意见，辛顿承认开放权重模型已成为人工智能领域不可逆转的现实。“我认为那场战斗已经输了。我们现在已经有了开放权重模型，所以很多人获取这些大模型的门槛——即训练基础模型的成本——已经消失了。现在为时已晚。”
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. He said 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. 然而，接受现实并不意味着忽视风险。辛顿的立场很明确：人工智能将继续进步，他认为这在很大程度上是件好事。他说，这将提高生产力，改善教育和医疗。“担心人工智能可能带来的负面影响，以及当智能生物比我们更聪明时可能做出的事情，我认为这并不过分。我认为给任何有这种想法的人贴上‘散布恐惧者’的标签是不公平的，”辛顿补充道。
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. 吴恩达则持不同观点。他认为，问题不在于开放模型是否有风险，而在于谁控制了访问权限，以及谁将赢得市场。谁能构建出更便宜的模型，谁就拥有优势。他警告称，如果中国的开放权重模型在亚洲、非洲和/或发展中世界得到广泛采用，它们可能会影响数十亿人接触有关民主、自由和人权观念的方式。
“One thing I hope we do is encourage American competitiveness and open source AI. It turns out that AI is a tremendous source of soft power. You can see the way China’s model has tremendous accomplishment with Africa, for example,” Ng said. “But my worry is because of all the lobbying in the U.S. and the fear-mongering, building open source AI in America is struggling to compete with open-weight models coming out of China, and my worry is that if China figures out a fundamentally more cost-efficient way to build AI, then things that are more cost-efficient have a fundamental business adoption advantage.” “我希望我们能做的一件事是鼓励美国的竞争力和开源人工智能。事实证明，人工智能是软实力的巨大来源。例如，你可以看到中国的模式在非洲取得了巨大成就，”吴恩达说，“但我担心的是，由于美国国内的游说和散布恐惧，在美国构建开源人工智能正难以与来自中国的开放权重模型竞争。我担心的是，如果中国找到了从根本上更具成本效益的人工智能构建方式，那么更具成本效益的事物将拥有根本性的商业采用优势。”
Li pushed back on that framing. “It’s very dangerous to make this a dichotomy between complete openness all the way to complete closedness,” she said. “In complex software systems as well as scientific systems it’s much more nuanced.” 李飞飞反驳了这种框架。“将问题二元对立为‘完全开放’与‘完全封闭’是非常危险的，”她说，“在复杂的软件系统和科学系统中，情况要微妙得多。”
Li used nuclear physics as an example: Scientific papers are published openly, but uranium is regulated, while laboratory work falls somewhere in between. The lesson, she explained, was that openness doesn’t have to be an all-or-nothing choice. Different layers of the ecosystem can operate at different levels of openness. 李飞飞以核物理为例：科学论文是公开出版的，但铀是受管制的，而实验室工作则介于两者之间。她解释说，其中的教训是，开放不必是一个非黑即白的选择。生态系统的不同层面可以在不同的开放程度上运行。
She also highlighted collaborations between public and private institutions, such as the Human Genome Project. The resulting knowledge became a platform that others could build on, she said, allowing pharmaceutical companies to profit, scientists to advance their work and society to benefit. 她还强调了公共和私营机构之间的合作，例如人类基因组计划。她说，由此产生的知识成为了一个他人可以构建的平台，使制药公司能够获利，科学家能够推进工作，社会也能从中受益。
“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.” “所以我认为我们必须将（人工智能）视为那种基础设施，”李飞飞说，“我们需要一定程度的开放，无论是在科学发现、教育、全球伙伴关系方面，还是为企业家提供有利可图的商业模式。但我们也会接受闭源系统。这场辩论，特别是‘我们只能容忍一种’这种一概而论的层面，是一个伪命题。我们需要达到一个更微妙的层面。”
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.” 但所有人都同意，为了让人工智能保持在正确的轨道上，一定程度的监管是必要的。“我们想要做的是让人工智能朝着帮助人类的方向发展，而监管将帮助我们实现这一目标，”辛顿说，“你不能把人工智能该如何发展的问题留给埃隆·马斯克（Elon Musk）和马克·扎克伯格（Mark Zuckerberg）这样的人来决定。”