经济学人：AI能否改变科学的研究方式？

纤竹无泪、拒泪

"By amplifying human intelligence, AI may cause a new Renaissance, perhaps a new phase of the Enlightenment," Yann LeCun,

“通过增强人类智慧，AI 可能会引发一场新的文艺复兴，也许是启蒙运动的新阶段，”杨立昆（音），

one of the godfathers of modern artificial intelligence (AI), suggested earlier this year.

现代人工智能教父之一，今年早些时候提出的。

ai can already make some existing scientific processes faster and more efficient, but can it do more, by transforming the way science itself is done?

如今，AI 已经可以使某些现有的科学流程变得更快、更高效，但它 能否通过改变科学本身的工作方式来做得更多？

Such transformations have happened before.

这样的转变以前也发生过。

With the emergence of the scientific method in the 17th century, researchers came to trust experimental observations,

17 世纪，随着科学方法的出现，研究人员开始相信实验观察

and the theories they derived from them, over the received wisdom of antiquity.

和从中得出的理论，而不再信奉古代的传统智慧。

This process was, crucially, supported by the advent of scientific journals, which let researchers share their findings,

至关重要的是，这一过程得到了科学期刊的支持，这些期刊让研究人员得以分享他们的发现，

both to claim priority and to encourage others to replicate and build on their results.

这既是为了声明优先，也是为了鼓励其他人复制和发展他们的成果。

Journals created an international scientific community around a shared body of knowledge,

期刊围绕着一个共享的知识体系创建了一个国际科学共同体，

causing a surge in discovery known today as the scientific revolution.

导致了今天被称为科学革命的发现激增。

A further transformation began in the late 19th century, with the establishment of research laboratories—factories of innovation where ideas,

19世纪末开始了进一步的变革，建立了研究实验室--创新工厂，

people and materials could be combined on an industrial scale.

人和材料可以在工业规模上结合在一起。

This led to a further outpouring of innovation, from chemicals and semiconductors to pharmaceuticals.

这带来了进一步涌现的创新，从化学品和半导体到制药业。

These shifts did more than just increase scientific productivity.

这些转变不仅仅是提高了科学生产率。

They also transformed science itself, opening up new realms of research and discovery.

它们还改变了科学本身，开辟了新的研究和发现领域。

How might AI do something similar, not just generating new results, but new ways to generate new results?

人工智能可能会如何做类似的变革，不仅产生新的结果，而且还会以新的方式产生新的结果？

A promising approach is "literature-based discovery" (lbd) which, as its name suggests,

一种很有前途的方法是“基于文献的发现”，顾名思义，

aims to make new discoveries by analysing scientific literature.

旨在通过分析科学文献来取得新的发现。

The first lbd system, built by Don Swanson at the University of Chicago in the 1980s, looked for novel connections in medline,

第一个LBD系统是由芝加哥大学的Don Swanson在20世纪80年代建造的，它在Medline上寻找新的联系，

a database of medical journals.

它是一个医学期刊数据库。

In an early success, it put together two separate observations—that Raynaud's disease, a circulatory disorder, was related to blood viscosity,

在早期的成功中，它结合了两个独立的观察结果——雷诺氏病，一种循环障碍，与血液粘度有关，

and that fish oil reduced blood viscosity—and suggested that fish oil might therefore be a useful treatment.

而且鱼油降低了血液粘度——这表明鱼油可能是一种有用的治疗方法。

This hypothesis was then experimentally verified.

这一猜想后来得到实验证实。