正文翻譯

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?

關于人工智能的爭論往往集中在它的潛在風險上：算法偏見和歧視、消滅大量的工作崗位，甚至有人說會導致人類的滅絕。雖然一些觀察人士對這些反烏托邦情景感到擔憂，但也有人關注潛在的回報。他們聲稱，人工智能可以幫助人類解決一些最重大、最棘手的問題。他們表示，人工智能將以一種特殊的方式做到這一點：大幅加快科學發(fā)現(xiàn)的步伐，特別是醫(yī)學、氣候科學、綠色技術等領域。該領域的杰出人物德米斯·哈薩比斯、楊立昆等人認為，人工智能可以推動科學進步，引領科學發(fā)現(xiàn)的黃金時代。他們的判斷是對的嗎？

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.

這些說法值得研究，有助于緩解人們對大規(guī)模失業(yè)和殺手機器人的擔憂。當然，以前的許多技術都被錯誤地譽為萬靈丹。19世紀50年代，電報被譽為世界和平的先驅(qū)，20世紀初，飛機也有過這樣的美譽；20 世紀 90 年代的權(quán)威人士認為，互聯(lián)網(wǎng)將會減少不平等現(xiàn)象，根除民族主義。但人工智能解決世界問題的機制有著更堅實的歷史基礎，因為歷史上有幾個時期，新方法和新工具確實有助于帶來一系列變革世界的科學發(fā)現(xiàn)和創(chuàng)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 世紀，顯微鏡和望遠鏡開辟了探索的新視野，鼓勵研究人員偏愛自己的觀察結(jié)果而不是古人的傳統(tǒng)智慧，科學期刊的出現(xiàn)為他們提供了分享和宣傳研究成果的新方式。結(jié)果是天文學、物理學及其他領域迅速發(fā)展，出現(xiàn)了從擺鐘到蒸汽機（工業(yè)革命原動力）的新發(fā)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.

后來從19世紀末開始，科研實驗室以工業(yè)規(guī)模匯集了思想、人、材料，引發(fā)了新一輪的創(chuàng)新，例如人造肥料、藥品、晶體管（計算機的組成部分）。從 20 世紀中葉開始，計算機又催生了基于模擬和建模的新的科研方式，包括對武器和飛機的設計、更準確的天氣預報。

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.

計算機革命可能尚未結(jié)束。正如我們在科學專欄中所報道的，人工智能工具和技術已被應用于幾乎所有的科學領域，只是使用率差異很大：在2022 年發(fā)表的物理學和天文學論文中，人工智能的使用率為7.2%，獸醫(yī)科學為1.4%。人工智能的用途已經(jīng)十分廣泛，它可以發(fā)現(xiàn)有潛力的分析候選物，例如：在藥物研發(fā)中發(fā)現(xiàn)具有特殊性質(zhì)的分子，或者具有蓄電池或太陽能電池所需特性的材料。人工智能可以篩選粒子對撞機或程控望遠鏡產(chǎn)生的大量數(shù)據(jù)，從中尋找范式。人工智能還可以模擬和分析更復雜的系統(tǒng)，例如蛋白質(zhì)折疊、星系形成。人工智能工具已被用于尋找新的抗生素，揭示希格斯玻色子的奧秘，識別狼的地方口音等。

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.

這一切都是可喜的。但科學期刊和實驗室發(fā)揮的作用更大：通過將人和思想以新的方式進行更大規(guī)模的融合，它們使科學實踐發(fā)生了變革，開啟了更強大的研究手段。同樣，人工智能也可能引發(fā)這樣的變革。

Two areas in particular look promising. The first is “l(fā)iterature-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.

有兩個領域看起來特別有希望。第一個是“基于文獻的知識發(fā)現(xiàn)”（LBD），涉及利用Chatgpt式的語言分析對現(xiàn)有的科學文獻進行分析，尋找人類可能忽視的新假設、新聯(lián)系、新觀點。LBD在發(fā)現(xiàn)可嘗試的新試驗、甚至推薦潛在的科研合作者方面表現(xiàn)出了潛力。這可以鼓勵跨學科合作，促進跨領域創(chuàng)新。LBD系統(tǒng)還可以發(fā)現(xiàn)特定領域的“盲點”，甚至可以預測未來的科學發(fā)現(xiàn)以及誰將做出這些發(fā)現(xiàn)。

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.

第二個領域是“機器人科學家”，也稱為“自動駕駛實驗室”。這些機器人系統(tǒng)使用人工智能，根據(jù)對現(xiàn)有數(shù)據(jù)和文獻的分析而提出新假設，然后通過千百次試驗來驗證這些假設，涵蓋的領域包括系統(tǒng)生物學和材料科學。與人類科學家不同，機器人不太執(zhí)著于前人的研究成果，受偏見的影響較小，最重要的是容易重復試驗。它們可以擴大實驗研究的規(guī)模，提出意想不到的理論，探索人類研究人員可能未曾想過的研究途徑。
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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，人工智能可能會改變科學實踐的想法是可行的。但主要障礙是在社會層面：只有人類科學家愿意且有能力使用這種工具，科學實踐才會發(fā)生改變。許多人缺乏技能和培訓；有些人擔心自己會失業(yè)。幸好有了希望的跡象，現(xiàn)在人工智能工具不僅受到人工智能研究人員的推崇，而且在其他領域也得到了專家的認可。

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.

政府和資助機構(gòu)可以通過敦促更多地使用共同標準來提供幫助，以允許人工智能系統(tǒng)共享和解釋實驗室檢查結(jié)果和其他數(shù)據(jù)。他們還可以資助更多的研究，包括將人工智能與實驗室機器人相結(jié)合，以及超出私營部門研究范圍的人工智能形式，私營部門幾乎將所有籌碼都押在了Chatgpt 等基于語言的系統(tǒng)上。不太流行的人工智能形式，例如基于模型的機器學習，可能更適用于提出假設等科學任務。

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年是科學飛速發(fā)展的時期，英國博學多才的羅伯特·胡克將顯微鏡和望遠鏡等新科學儀器的出現(xiàn)描述為“給天生的器官增添了人造器官”。這些儀器使研究人員得以探索以前無法企及的領域，并以新的方式探索事物，“對各種有用的知識都大有裨益”。對于胡克的當代后繼者來說，在未來幾年里，將人工智能添加到科學工具箱中必將起到同樣的作用——同樣會帶來變革世界的科學成果。