Consider the historical precedents 考虑历史先例

2023年9月14日

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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 (ai) 的争论往往集中在其潜在的危险上：算法偏见和歧视、大规模就业岗位的破坏，甚至有人说，人类的灭绝。然而，当一些观察家对这些反乌托邦情景感到担忧时，其他人则关注潜在的回报。他们声称，人工智能可以帮助人类解决一些最大、最棘手的问题。他们表示，人工智能将以一种非常具体的方式做到这一点：从根本上加快科学发现的步伐，特别是在医学、气候科学和绿色技术等领域。 Demis Hassabis 和 Yann LeCun 等该领域的杰出人物相信人工智能可以推动科学进步并引领发现的黄金时代。他们可能是对的吗？

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，并且可能为人们对大规模失业和杀手机器人的担忧提供有用的平衡。当然，之前的许多技术都被错误地誉为万能药。电报在 1850 年代被誉为世界和平的先驱，飞机在 1900 年代也是如此。 20 世纪 90 年代的专家表示，互联网将减少不平等并消除民族主义。但人工智能解决世界问题的机制有着更强大的历史基础，因为历史上有几个时期，新方法和新工具确实有助于带来改变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新的发现前景，并鼓励研究人员将自己的观察结果置于古代公认的智慧之上，而科学期刊的推出则为他们提供了分享和宣传其发现的新方式。其结果是天文学、物理学和其他领域的快速进步，以及从摆钟到工业革命原动力蒸汽机的新发明。

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.

然后，从 19 世纪末开始，研究实验室的建立在工业规模上汇集了思想、人员和材料，引发了进一步的创新，例如人造肥料、药品和晶体管（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科学形式，从武器和飞机的设计到更准确的天气预报。

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.

计算机革命可能还没有结束。正如我们在科学特别版块中报道的那样，人工智能工具和技术现在几乎应用于所有科学领域，尽管采用程度差异很大：例如，2022 年发表的物理学和天文学论文中有 7.2% 涉及人工智能，而兽医科学占 1.4%。人工智能正在以多种方式得到应用。它可以识别有前途的分析候选物，例如药物发现中具有特定特性的分子，或具有电池或太阳能电池所需特性的材料。它可以筛选大量数据，例如粒子对撞机或机器人望远镜产生的数据，寻找模式。人工智能可以模拟和分析更复杂的系统，例如蛋白质的折叠和星系的形成。人工智能工具已被用来识别新的抗生素、揭示希格斯玻色子以及识别狼的区域口音等。

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.

这一切都是值得欢迎的。但该杂志和实验室走得更远：它们改变了科学实践本身，并通过允许人和思想以新的方式和更大规模的融合，开启了更强大的发现手段。人工智能也有潜力引发这样的转变。

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.

有两个领域看起来尤其有希望。第一个是“基于文献的发现”（lbd），它涉及使用 Chatgpt 式的语言分析来分析现有的科学文献，以寻找人类可能错过的新假设、联系或想法。 LBD 在确定新的实验尝试方面表现出了希望，甚至还推荐了潜在的研究合作者。这可以刺激跨学科工作并促进领域之间的创新。 lbd 系统还可以识别特定领域的“盲点”，甚至可以预测未来的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，形成新的假设，然后通过在系统生物学和材料科学等领域进行数百或数千次实验来测试这些假设。与人类科学家不同，机器人不太执着于先前的结果，较少受到偏见的驱动，而且最重要的是，它很容易复制。他们可以扩大实验研究，发展出意想不到的理论，并探索人类研究人员可能没有考虑到的途径。

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，人工智能可能改变科学实践的想法是可行的。但主要障碍是社会学的：只有人类科学家愿意并且能够使用这些工具，这种情况才可能发生。许多人缺乏技能和培训；有些人担心自己会失业。幸运的是，出现了充满希望的迹象。人工智能工具现在正从人工智能研究人员的推动转向被其他领域的专家所接受。

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.

各国政府和资助机构可以通过敦促更多地使用通用标准来提供帮助，以允许人工智能系统交换和解释实验室结果和其他数据。他们还可以资助更多研究，研究人工智能与实验室机器人的整合，以及私营部门所追求的人工智能形式之外的研究，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，可能更适合科学任务，例如形成假设。

The adding of the artificial人工添加

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，在科学飞速进步的时期，英国博学者罗伯特·胡克将显微镜和望远镜等新科学仪器的出现描述为“在自然中添加了人造器官”。它们让研究人员探索以前无法进入的领域，并以新的方式发现事物，“对各种有用的知识都有巨大的好处”。对于胡克的现代继任者来说，将人工智能添加到科学工具包中有望在未来几年实现同样的效果，并产生类似的改变世界的结果。 ■