Thinking Machines 宣言：构建延伸人类意志与判断的AI

Thinking Machines 于7月10日发布宣言，使命是构建能延伸人类意志与判断的AI。

该公司认为，当前多数AI在少数地方训练后便冻结，无法被服务对象塑造。

其技术方向包括：训练具备多模态交互与可定制能力的强模型；

开发允许用户自定义模型（包括训练模型权重）的工具；

构建拓宽人机沟通渠道的界面，使个人判断持续影响AI工作；

为科学界发表研究。

Thinking Machines强调，AI应服务于分布式知识，让每个组织利用其独特知识微调模型并持续适配，而非用标准化方案取代。

The mission of Thinking Machines is to build AI that extends human will and judgment.

Artificial intelligence can do more every day, but deciding what it should do is up to us: individuals, organizations, humanity as a whole. These decisions require knowledge and judgment that people acquire through continuous contact with the work, increasingly done alongside AI. Shaping the goals of advanced intelligence is also a continuous process of feedback, learning, and realignment.

Most AI in use today is trained in a handful of places and then frozen. It isn’t shaped by the people it serves, and doesn’t learn much from the work they do together. Extending human will and judgment calls for AIs as diverse and distributed as people themselves are. This is the path we have chosen.

To progress on that path, we are pursuing these technical directions:

We believe the future worth building is human — shaped by human knowledge, guided by human will, and decided by human judgment. What follows is the case for that future, and the work we’re doing to bring it about.

AI exists to serve the work that we do. This work runs on knowledge of how things are done and what is worth doing, knowledge that is generated continuously by people engaged in the work.

Think of a chef crafting a new recipe or a shopkeeper rearranging the items and prices on display. They are pursuing a complex set of goals and applying know-how that isn’t immediately legible to outsiders. This knowledge is constantly updated through feedback; it’s not a static repository that can be written into a database. It’s local — a different restaurant or shop pursues different outcomes by different means. The collective knowledge of shops and kitchens is scattered across every shopkeeper and chef. Michael Polanyi, The Tacit Dimension：https://press.uchicago.edu/ucp/books/book/chicago/T/bo6035368.html (1966)

The dispersion of knowledge is a collective strength; it’s the source of variety, adaptability, and resilience of the overall system. It’s the reason that free markets outperform planned economies. Central planning fails not because of insufficient intelligence, but because of the nature of productive knowledge: tacit, local, fleeting, and held privately by those who acquired it through their work. Friedrich Hayek, The Use of Knowledge in Society：https://www.econlib.org/library/Essays/hykKnw.html (1945) Attempting to aggregate knowledge for the use of a centralized intelligence faces the same challenge.

There are domains where intelligence alone is sufficient, and where autonomous AI doesn’t require human participation to race ahead. Two examples are chess, where the strongest engines are trained purely on self-play, and math, where frontier models are solving long-standing problems on their own. These examples share two traits. First, the goal given to AI is static and expressible: to win a chess match, to prove a theorem. Second, these domains don’t contain hidden knowledge. The rules of chess and math are universal; the board is visible to all. Outside the board, intelligence alone is not enough.

For artificial intelligence to benefit from distributed knowledge, it must itself be distributed. Every organization is powered by the expert knowledge of its people, gained and expressed through their work. We believe in AI that helps the organization cultivate that unique knowledge, not AI that extracts a snapshot of it and replaces it with a standard offering. This cultivation is an ongoing process that requires AI to work with people, not in their stead.

In 2014, Toyota, long a master of the automated plant, brought its expert craftsmen back onto the line with the explicit goal of growing craftsmanship and knowledge. The man who led this, Mitsuru Kawai, put the reason this way: “To be the master of the machine, you have to have the knowledge and the skills to teach the machine.” Craig Trudell, Yuki Hagiwara and Ma Jie, Humans Replacing Robots Herald Toyota’s Vision of Future：https://www.bloomberg.com/news/articles/2014-04-06/humans-replacing-robots-herald-toyota-s-vision-of-future (2014) The production of knowledge and application of intelligence lift each other; they are not substitutes.

The work people do may change, and turn toward more of what only people bring, but the best organizations will make the fullest use of both. AI should enable each organization to be excellent in its own way, not to erase the differences between them.

We aim to bring intelligence to where knowledge is made and used. We build tools：https://thinkingmachines.ai/tinker/ that enable everyone to fine-tune models with their unique knowledge, and to keep adapting the models as their knowledge evolves. We publish research：https://thinkingmachines.ai/blog/ and recipes：https://tinker-docs.thinkingmachines.ai/cookbook/ that put this capability within reach of more people. We envision frontier AI as a collective, as diverse as the people it serves because it was shaped by them in each unique location.

Keeping people engaged in setting goals and sharing knowledge with AI doesn’t mean resisting automation for its own sake. What a machine does reliably on its own, it should do. But it should also know when to act alone and when to invite oversight and feedback, as people themselves do when working in teams. The best collaborators anticipate: they learn what someone is reaching for and bring it before being asked, earning over time the right to act on their behalf. These are technical challenges, requiring a new approach to how AI is designed and evaluated.