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Debate about artificial intelligence (ai) tends to focus on its potential dangers: algorithmic bias and discrimination, the mass destruction of jobs and even, some say, the extinction of humanity. As some observers fret about these dystopian scenarios, however, others are focusing on the potential rewards. ai could, they claim, help humanity solve some of its biggest and thorniest problems. And, they say, ai will do this in a very specific way: by radically accelerating the pace of scientific discovery, especially in areas such as medicine, climate science and green technology. Luminaries in the field such as Demis Hassabis and Yann LeCun believe that ai can turbocharge scientific progress and lead to a golden age of discovery. Could they be right?

关于人工智能的争论往往集中在它的潜在风险上：算法偏见和歧视、消灭大量的工作岗位，甚至有人说会导致人类的灭绝。虽然一些观察人士对这些反乌托邦情景感到担忧，但也有人关注潜在的回报。他们声称，人工智能可以帮助人类解决一些最重大、最棘手的问题。他们表示，人工智能将以一种特殊的方式做到这一点：大幅加快科学发现的步伐，特别是医学、气候科学、绿色技术等领域。该领域的杰出人物德米斯·哈萨比斯、杨立昆等人认为，人工智能可以推动科学进步，引领科学发现的黄金时代。他们的判断是对的吗？

Such claims are worth examining, and may provide a useful counterbalance to fears about large-scale unemployment and killer robots. Many previous technologies have, of course, been falsely hailed as panaceas. The electric telegraph was lauded in the 1850s as a herald of world peace, as were aircraft in the 1900s; pundits in the 1990s said the internet would reduce inequality and eradicate nationalism. But the mechanism by which ai will supposedly solve the world’s problems has a stronger historical basis, because there have been several periods in history when new approaches and new tools did indeed help bring about bursts of world-changing scientific discovery and innovation.

这些说法值得研究，有助于缓解人们对大规模失业和杀手机器人的担忧。当然，以前的许多技术都被错误地誉为万灵丹。19世纪50年代，电报被誉为世界和平的先驱，20世纪初，飞机也有过这样的美誉；20 世纪 90 年代的权威人士认为，互联网将会减少不平等现象，根除民族主义。但人工智能解决世界问题的机制有着更坚实的历史基础，因为历史上有几个时期，新方法和新工具确实有助于带来一系列变革世界的科学发现和创新。

In the 17th century microscopes and telescopes opened up new vistas of discovery and encouraged researchers to favour their own observations over the received wisdom of antiquity, while the introduction of scientific journals gave them new ways to share and publicise their findings. The result was rapid progress in astronomy, physics and other fields, and new inventions from the pendulum clock to the steam engine—the prime mover of the Industrial Revolution.

17 世纪，显微镜和望远镜开辟了探索的新视野，鼓励研究人员偏爱自己的观察结果而不是古人的传统智慧，科学期刊的出现为他们提供了分享和宣传研究成果的新方式。结果是天文学、物理学及其他领域迅速发展，出现了从摆钟到蒸汽机（工业革命原动力）的新发明。

Then, starting in the late 19th century, the establishment of research laboratories, which brought together ideas, people and materials on an industrial scale, gave rise to further innovations such as artificial fertiliser, pharmaceuticals and the transistor, the building block of the computer. From the mid-20th century, computers in turn enabled new forms of science based on simulation and modelling, from the design of weapons and aircraft to more accurate weather forecasting.

后来从19世纪末开始，科研实验室以工业规模汇集了思想、人、材料，引发了新一轮的创新，例如人造肥料、药品、晶体管（计算机的组成部分）。从 20 世纪中叶开始，计算机又催生了基于模拟和建模的新的科研方式，包括对武器和飞机的设计、更准确的天气预报。

And the computer revolution may not be finished yet. As we report in a special Science section, ai tools and techniques are now being applied in almost every field of science, though the degree of adoption varies widely: 7.2% of physics and astronomy papers published in 2022 involved ai, for example, compared with 1.4% in veterinary science. ai is being employed in many ways. It can identify promising candidates for analysis, such as molecules with particular properties in drug discovery, or materials with the characteristics needed in batteries or solar cells. It can sift through piles of data such as those produced by particle colliders or robotic telescopes, looking for patterns. And ai can model and analyse even more complex systems, such as the folding of proteins and the formation of galaxies. ai tools have been used to identify new antibiotics, reveal the Higgs boson and spot regional accents in wolves, among other things.

计算机革命可能尚未结束。正如我们在科学专栏中所报道的，人工智能工具和技术已被应用于几乎所有的科学领域，只是使用率差异很大：在2022 年发表的物理学和天文学论文中，人工智能的使用率为7.2%，兽医科学为1.4%。人工智能的用途已经十分广泛，它可以发现有潜力的分析候选物，例如：在药物研发中发现具有特殊性质的分子，或者具有蓄电池或太阳能电池所需特性的材料。人工智能可以筛选粒子对撞机或程控望远镜产生的大量数据，从中寻找范式。人工智能还可以模拟和分析更复杂的系统，例如蛋白质折叠、星系形成。人工智能工具已被用于寻找新的抗生素，揭示希格斯玻色子的奥秘，识别狼的地方口音等。

All this is to be welcomed. But the journal and the laboratory went further still: they altered scientific practice itself and unlocked more powerful means of making discoveries, by allowing people and ideas to mingle in new ways and on a larger scale. ai, too, has the potential to set off such a transformation.

这一切都是可喜的。但科学期刊和实验室发挥的作用更大：通过将人和思想以新的方式进行更大规模的融合，它们使科学实践发生了变革，开启了更强大的研究手段。同样，人工智能也可能引发这样的变革。

Two areas in particular look promising. The first is “literature-based discovery” (lbd), which involves analysing existing scientific literature, using Chatgpt-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed. lbd is showing promise in identifying new experiments to try—and even suggesting potential research collaborators. This could stimulate interdisciplinary work and foster innovation at the boundaries between fields. lbd systems can also identify “blind spots” in a given field, and even predict future discoveries and who will make them.

有两个领域看起来特别有希望。第一个是“基于文献的知识发现”（LBD），涉及利用Chatgpt式的语言分析对现有的科学文献进行分析，寻找人类可能忽视的新假设、新联系、新观点。LBD在发现可尝试的新试验、甚至推荐潜在的科研合作者方面表现出了潜力。这可以鼓励跨学科合作，促进跨领域创新。LBD系统还可以发现特定领域的“盲点”，甚至可以预测未来的科学发现以及谁将做出这些发现。

The second area is “robot scientists”, also known as “self-driving labs”. These are robotic systems that use ai to form new hypotheses, based on analysis of existing data and literature, and then test those hypotheses by performing hundreds or thousands of experiments, in fields including systems biology and materials science. Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate. They could scale up experimental research, develop unexpected theories and explore avenues that human investigators might not have considered.

第二个领域是“机器人科学家”，也称为“自动驾驶实验室”。这些机器人系统使用人工智能，根据对现有数据和文献的分析而提出新假设，然后通过千百次试验来验证这些假设，涵盖的领域包括系统生物学和材料科学。与人类科学家不同，机器人不太执着于前人的研究成果，受偏见的影响较小，最重要的是容易重复试验。它们可以扩大实验研究的规模，提出意想不到的理论，探索人类研究人员可能未曾想过的研究途径。

The idea that ai might transform scientific practice is therefore feasible. But the main barrier is sociological: it can happen only if human scientists are willing and able to use such tools. Many lack skills and training; some worry about being put out of a job. Fortunately, there are hopeful signs. ai tools are now moving from being pushed by ai researchers to being embraced by specialists in other fields.

因此，人工智能可能会改变科学实践的想法是可行的。但主要障碍是在社会层面：只有人类科学家愿意且有能力使用这种工具，科学实践才会发生改变。许多人缺乏技能和培训；有些人担心自己会失业。幸好有了希望的迹象，现在人工智能工具不仅受到人工智能研究人员的推崇，而且在其他领域也得到了专家的认可。

Governments and funding bodies could help by pressing for greater use of common standards to allow ai systems to exchange and interpret laboratory results and other data. They could also fund more research into the integration of ai smarts with laboratory robotics, and into forms of ai beyond those being pursued in the private sector, which has bet nearly all its chips on language-based systems like Chatgpt. Less fashionable forms of ai, such as model-based machine learning, may be better suited to scientific tasks such as forming hypotheses.

政府和资助机构可以通过敦促更多地使用共同标准来提供帮助，以允许人工智能系统共享和解释实验室检查结果和其他数据。他们还可以资助更多的研究，包括将人工智能与实验室机器人相结合，以及超出私营部门研究范围的人工智能形式，私营部门几乎将所有筹码都押在了Chatgpt 等基于语言的系统上。不太流行的人工智能形式，例如基于模型的机器学习，可能更适用于提出假设等科学任务。

The adding of the artificial

增添人造工具

In 1665, during a period of rapid scientific progress, Robert Hooke, an English polymath, described the advent of new scientific instruments such as the microscope and telescope as “the adding of artificial organs to the natural”. They let researchers explore previously inaccessible realms and discover things in new ways, “with prodigious benefit to all sorts of useful knowledge”. For Hooke’s modern-day successors, the adding of artificial intelligence to the scientific toolkit is poised to do the same in the coming years—with similarly world-changing results.

1665年是科学飞速发展的时期，英国博学多才的罗伯特·胡克将显微镜和望远镜等新科学仪器的出现描述为“给天生的器官增添了人造器官”。这些仪器使研究人员得以探索以前无法企及的领域，并以新的方式探索事物，“对各种有用的知识都大有裨益”。对于胡克的当代后继者来说，在未来几年里，将人工智能添加到科学工具箱中必将起到同样的作用——同样会带来变革世界的科学成果。