吴恩达 2025-06-03 12:02 浙江
亲爱的朋友们：我对美国拟议削减基础研究经费的计划感到震惊，这一举措将严重影响美国在人工智能及其他领域的竞争力。资助公开共享的研究成果造福的是全世界，但受益最大的，仍是研究本身发生的那个国家。如果没有美国国家科学基金会（NSF）和国防高级研究计划局（DARPA）对我早期深度学习工作的资助——这两个机构承担了美国大部分科研资金的分配工作——我就不可能开展那些关于规模化的经验，进而提出创立 Google Brain，以推进深度学习的发展。我担心，削减基础科学的经费将导致美国——乃至全世界——错失下一波重要的创新机遇。事实上，这类资助对美国本国的益处远大于其他国家。科学研究最直接的益处通常归属于其进行地，因为：(i) 新知识在这个国传播得更快；(ii) 研究过程本身也培养了大量新人才。为什么生成式人工智能的大部分创新仍然出现在硅谷？因为有两个总部位于硅谷的团队——发明 Transformer 网络的 Google Brain 和将其规模化的 OpenAI——做了很多早期的奠基性工作。随后，这些团队成员流向了其他本地公司、发展了竞争对手，或与本地大学合作。此外，知识还通过日常咖啡聊天、本地会议，甚至孩子们的玩耍时间——家长趁机交流技术想法——在本地社交网络中迅速传播。就这样，这些知识在硅谷内部扩散的速度远远快于其他地区。类似地，美国本土进行的研究成果在美国国内传播的速度也远快于全球其他地区。尤其是当研究以论文或开源形式公开时：研究人员可以畅所欲言，分享更多信息，比如某个算法真正奏效的诀窍与技巧；其他人也能更快找到答案的提供者。学术界的知识扩散尤为迅速，因为学术环境高度开放，教授与学生不像很多公司员工那样受限，可以自由地讨论自己的工作。因此，资助美国的基础研究首先利好美国本国，同时也会惠及盟友。的确，这种开放也可能让对手国家受益。但正如美国众议院科学、空间与技术委员会的一个小组指出的那样：“……基础研究的开放共享并非没有风险。然而……研究开放性对于国家竞争力与安全而言至关重要，其价值足以抵消对手可能因此受益的风险。”此外，生成式人工智能发展迅速，关键在于始终站在技术最前沿。例如，现在许多团队都能训练出 GPT-3.5 乃至 GPT-4 水平的模型，但这似乎并没有对 OpenAI 造成多大影响，他们正专注于发展前沿技术如 o4、Codex、GPT-4.1 等。技术的首创者拥有率先商业化的机会，在快速变化的世界中，领先的技术才是最具价值的。尽管是在互联网尚未普及的年代完成的，但类似这类研究也表明，知识在本地的扩散远快于全球范围。2022 年 ChatGPT 发布时，中国在生成式人工智能领域明显落后于美国。然而，中国的科技生态系统在内部非常开放，这使得它在过去两年里迅速追赶：● 中国在学术界为开放研究提供了充足的资助；● 企业如 DeepSeek 和阿里巴巴也发布了前沿的开源模型，这种企业层级的开放大大加快了知识扩散；● 中国的劳动法使竞业限制协议（防止员工跳槽至竞争对手）较难执行，加之职场文化鼓励跨公司间员工的想法交流，这也使知识流动更加高效。尽管有些方面不建议效仿，但中国在其科技生态系统的开放性，确实帮助其实现了快速发展。1945 年，Vannevar Bush在其具有里程碑意义的报告《科学：无尽的前沿》中，为美国科研与人才培养的公共资助制定了基本原则。正是这些原则，使美国在此后几十年中引领了全球科技进步。美国联邦对科学的资助带来了无数突破，不仅极大造福了美国，也惠及全世界，同时培养了一代代美国本土以及受益于美国的移民科学家。好消息是，这套成功的模式已经广为人知。我希望更多国家能效仿美国的做法，大力投资于科学与人才。我也希望，美国作为这一成功模式的开创者，能够坚守初心，不要通过大幅削减科研经费而自毁根基。吴恩达
原文：
Dear friends,
I am alarmed by the proposed cuts to U.S. funding for basic research, analyzed here, and the impact this would have for U.S. competitiveness in AI and other areas. Funding research that is openly shared benefits the whole world, but the nation it benefits most is the one where the research is done.
If not for funding for my early work in deep learning from the National Science Foundation (NSF) and Defense Advanced Research Projects Agency (DARPA), which disburse much of U.S. research funding, I would not have discovered lessons about scaling that led me to pitch starting Google Brain to scale up deep learning. I am worried that cuts to funding for basic science will lead the U.S. — and also the world — to miss out on the next set of ideas.
In fact, such funding benefits the U.S. more than any other nation. Scientific research brings the greatest benefit to the country where the work happens because (i) the new knowledge diffuses fastest within that country, and (ii) the process of doing research creates new talent for that nation.
Why does most innovation in generative AI still happen in Silicon Valley? Because two teams based in this area — Google Brain, which invented the transformer network, and OpenAI, which scaled it up — did a lot of the early work. Subsequently, team members moved to other nearby businesses, started competitors, or worked with local universities. Further, local social networks rapidly diffused the knowledge through casual coffee meetings, local conferences, and even children’s play dates, where parents of like-aged kids meet and discuss technical ideas. In this way, the knowledge spread faster within Silicon Valley than to other geographies.
In a similar vein, research done in the U.S. diffuses to others in the U.S. much faster than to other geographic areas. This is particularly true when the research is openly shared through papers and/or open source: If researchers have permission to talk about an idea, they can share much more information, such as tips and tricks for how to really make an algorithm work, more quickly. It also lets others figure out faster who can answer their questions. Diffusion of knowledge created in academic environments is especially fast. Academia tends to be completely open, and students and professors, unlike employees of many companies, have full permission to talk about their work.
Thus funding basic research in the U.S. benefits the U.S. most, and also benefits our allies. It is true that openness benefits our adversaries, too. But as a subcommittee of the U.S. House of Representatives committee on science, space, and technology points out, “... open sharing of fundamental research is [not] without risk. Rather, ... openness in research is so important to competitiveness and security that it warrants the risk that adversaries may benefit from scientific openness as well.”
Further, generative AI is evolving so rapidly that staying on the cutting edge is what’s really critical. For example, the fact that many teams can now train a model with GPT-3.5- or even GPT-4-level capability does not seem to be hurting OpenAI much, which is busy growing its business by developing the cutting-edge o4, Codex, GPT-4.1, and so on. Those who invent a technology get to commercialize it first, and in a fast-moving world, the cutting-edge technology is what’s most valuable. Studies like this one (albeit done while the internet was not as prevalent as it is today) also show how knowledge diffuses locally much faster than globally.
China was decisively behind the U.S. in generative AI when ChatGPT was first launched in 2022. However, China’s tech ecosystem is very open internally, and this has helped it to catch up over the past two years:
There is ample funding for open academic research in China.
China’s businesses such as DeepSeek and Alibaba have released cutting-edge, open-weights models. This openness at the corporate level accelerates diffusion of knowledge.
China’s labor laws make non-compete agreements (which stop an employee from jumping ship to a competitor) relatively hard to enforce, and the work culture supports significant idea sharing among employees of different companies; this has made circulation of ideas relatively efficient.
While there’s also much about China that I would not seek to emulate, the openness of its tech ecosystem has helped it accelerate.
In 1945, Vannevar Bush’s landmark report “Science, The Endless Frontier” laid down key principles for public funding of U.S. research and talent development. Those principles enabled the U.S. to dominate scientific progress for decades. U.S. federal funding for science created numerous breakthroughs that have benefited the U.S. tremendously, and also the world, while training generations of domestic scientists, as well as immigrants who likewise benefit the U.S.
The good news is that this playbook is now well known. I hope many more nations will imitate it and invest heavily in science and talent. And I hope that, having pioneered this very successful model, the U.S. will not pull back from it by enacting drastic cuts to funding scientific research.
Andrew
