原文標(biāo)題：
When robots do research
How artificial intelligence can revolutionise science
Consider the historical precedents
當(dāng)機(jī)器人做研究
人工智能如何徹底改變科學(xué)
參考?xì)v史先例

[Paragraph 1]
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.
關(guān)于人工智能 (AI) 的爭(zhēng)論往往都集中在潛在危險(xiǎn)上：算法偏見(jiàn)與歧視、大規(guī)模失業(yè)，甚至有人擔(dān)心人類(lèi)滅絕。

As some observers fret about these dystopian scenarios, however, others are focusing on the potential rewards.
然而，一些人擔(dān)心這些反烏托邦的場(chǎng)景，而另一些人關(guān)注的是潛在的回報(bào)。

AI could, they claim, help humanity solve some of its biggest and thorniest problems.
他們稱(chēng)，人工智能可以幫助人類(lèi)解決一些最大最棘手的問(wèn)題。

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.
他們稱(chēng)，人工智能將以特定的方式實(shí)現(xiàn)這一目標(biāo)：從根本上加快科學(xué)研究的步伐，尤其對(duì)醫(yī)學(xué)、氣候科學(xué)和綠色技術(shù)等領(lǐng)域有幫助。

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.
戴密斯·哈薩比斯和楊立昆等該領(lǐng)域的杰出人士認(rèn)為，人工智能可以加速科學(xué)進(jìn)步，并引領(lǐng)研究發(fā)現(xiàn)的黃金時(shí)代。

Could they be right?
他們是對(duì)的嗎？

[Paragraph 2]
Such claims are worth examining, and may provide a useful counterbalance to fears about large-scale unemployment and killer robots.
這種說(shuō)法值得研究，并且可能抵消掉人們對(duì)大規(guī)模失業(yè)和機(jī)器人殺手的擔(dān)憂。

Many previous technologies have, of course, been falsely hailed as panaceas.
當(dāng)然，歷史上許多技術(shù)也曾被錯(cuò)誤地譽(yù)為靈丹妙藥。

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.
1850年代，電報(bào)被譽(yù)為世界和平的先驅(qū)，1900年代的飛機(jī)也是如此。1990年代的專(zhuān)家稱(chēng)互聯(lián)網(wǎng)將減少不平等并消除民族主義。

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.
但人工智能解決世界問(wèn)題的機(jī)制有著堅(jiān)實(shí)的歷史基礎(chǔ)，因?yàn)闅v史上有幾個(gè)時(shí)期，新方法和新工具確確實(shí)實(shí)幫助推動(dòng)了世界性的科學(xué)發(fā)現(xiàn)和創(chuàng)新爆發(fā)。

[Paragraph 3]
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.
17世紀(jì)的顯微鏡和望遠(yuǎn)鏡開(kāi)辟了研究新視野，促使研究者更加重視自己的觀察結(jié)果，而非古代的傳統(tǒng)智慧。科學(xué)期刊的出現(xiàn)則為科學(xué)家們提供了分享和宣傳研究成果的新途徑。

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.
因此，天文學(xué)、物理學(xué)以及其他領(lǐng)域都迅速發(fā)展，同時(shí)，擺鐘、蒸汽機(jī)等新發(fā)明也應(yīng)運(yùn)而生——蒸汽機(jī)推動(dòng)了工業(yè)革命。

[Paragraph 4]
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.
然后，從 19 世紀(jì)末開(kāi)始，研究實(shí)驗(yàn)室的建立將思想、人員和材料以工業(yè)化的規(guī)模聚集在一起，催生了更多創(chuàng)新，如人工肥料、藥品和晶體管（計(jì)算機(jī)的組成部分）。

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.
從 20 世紀(jì)中葉開(kāi)始，計(jì)算機(jī)反過(guò)來(lái)又帶來(lái)了基于模擬和模型的新型科學(xué)，如武器和飛機(jī)設(shè)計(jì)、更準(zhǔn)確的天氣預(yù)報(bào)等等。

[Paragraph 5]
And the computer revolution may not be finished yet. AI is being employed in many ways.
計(jì)算機(jī)革命可能還沒(méi)有結(jié)束。人工智能應(yīng)用于許多領(lǐng)域。

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.
它可以識(shí)別出潛在的分析候選物，例如識(shí)別藥物發(fā)現(xiàn)中具有特殊性質(zhì)的分子，或識(shí)別具有電池或太陽(yáng)能電池所需特性的材料。

It can sift through piles of data such as those produced by particle colliders or robotic telescopes, looking for patterns.
它可以篩選大量數(shù)據(jù)，例如粒子對(duì)撞機(jī)或機(jī)器人望遠(yuǎn)鏡產(chǎn)生的數(shù)據(jù)，尋找其中的規(guī)律。

And AI can model and analyse even more complex systems, such as the folding of proteins and the formation of galaxies.
人工智能可以建模和分析更復(fù)雜的系統(tǒng)，例如蛋白質(zhì)折疊和星系形成。

AI tools have been used to identify new antibiotics, reveal the Higgs boson and spot regional accents in wolves, among other things.
人工智能工具已被用來(lái)識(shí)別新的抗生素、揭示希格斯玻色子、識(shí)別狼的地方口音等。

[Paragraph 6]
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.
所有這些都是值得歡迎的。但期刊和實(shí)驗(yàn)室產(chǎn)生了更深遠(yuǎn)的影響：它們改變了科學(xué)實(shí)踐本身，通過(guò)讓人和思想以新的方式和更大的規(guī)模相互融合，解鎖了更強(qiáng)大的科研手段。

AI, too, has the potential to set off such a transformation.
人工智能也有潛在的能力引起這樣的變革。

[Paragraph 7]
Two areas in particular look promising.
有兩個(gè)領(lǐng)域特別有前景。

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.
第一個(gè)領(lǐng)域是“基于文獻(xiàn)的發(fā)現(xiàn)”（LBD），即利用 ChatGPT 式的語(yǔ)言分析來(lái)分析現(xiàn)有的科學(xué)文獻(xiàn)，尋找人類(lèi)可能忽略的新假設(shè)、聯(lián)系或觀點(diǎn)。

LBD is showing promise in identifying new experiments to try—and even suggesting potential research collaborators.
LBD 在確定要嘗試的新實(shí)驗(yàn)方面大有可為，甚至還能推薦潛在的研究合作者。

This could stimulate interdisciplinary work and foster innovation at the boundaries between fields.
這可以激勵(lì)跨學(xué)科研究，促進(jìn)各領(lǐng)域之間的創(chuàng)新。

LBD systems can also identify “blind spots” in a given field, and even predict future discoveries and who will make them.
LBD 系統(tǒng)還能識(shí)別特定領(lǐng)域的 "盲點(diǎn)"，甚至可以預(yù)測(cè)未來(lái)的科學(xué)成果及其發(fā)現(xiàn)者。

[Paragraph 8]
The second area is “robot scientists”, also known as “self-driving labs”.
第二個(gè)領(lǐng)域是“機(jī)器人科學(xué)家”，也稱(chēng)為“自動(dòng)駕駛實(shí)驗(yàn)室”。

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.
這些機(jī)器人系統(tǒng)利用人工智能，在分析現(xiàn)有數(shù)據(jù)和文獻(xiàn)的基礎(chǔ)上形成新的假設(shè)，然后進(jìn)行數(shù)百次或數(shù)千次實(shí)驗(yàn)來(lái)測(cè)試這些假設(shè)，可應(yīng)用于系統(tǒng)生物學(xué)和材料科學(xué)等領(lǐng)域。

Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate.
與人類(lèi)科學(xué)家不同，機(jī)器人不那么執(zhí)著于先前的結(jié)果，也不太會(huì)受到偏見(jiàn)的影響--而且最關(guān)鍵的是，它很容易復(fù)制。

They could scale up experimental research, develop unexpected theories and explore avenues that human investigators might not have considered.
機(jī)器人可以擴(kuò)大實(shí)驗(yàn)研究的規(guī)模，發(fā)展出意想不到的理論，探索人類(lèi)研究者可能未曾考慮過(guò)的途徑。

[Paragraph 9]
The idea that AI might transform scientific practice is therefore feasible.
因此，人工智能可能改變科學(xué)實(shí)踐的想法是可行的。

But the main barrier is sociological: it can happen only if human scientists are willing and able to use such tools.
但主要障礙在于社會(huì)學(xué)方面：只有人類(lèi)科學(xué)家愿意并且能夠使用這些AI工具，以上設(shè)想情況才可能發(fā)生。

Many lack skills and training; some worry about being put out of a job.
有些人缺乏技能和培訓(xùn)；有些人擔(dān)心自己會(huì)失業(yè)。

Fortunately, there are hopeful signs. AI tools are now moving from being pushed by AI researchers to being embraced by specialists in other fields.
幸運(yùn)的是，出現(xiàn)了一些令人振奮的跡象。由于AI研究人員的推動(dòng)，現(xiàn)在AI工具逐漸被其他領(lǐng)域的專(zhuān)家所接受。

[Paragraph 10]
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.
各國(guó)政府和資助機(jī)構(gòu)可以通過(guò)推動(dòng)使用通用標(biāo)準(zhǔn)，為AI系統(tǒng)之間交流和解釋實(shí)驗(yàn)室結(jié)果和其他數(shù)據(jù)方面提供幫助。

Less fashionable forms of AI, such as model-based machine learning, may be better suited to scientific tasks such as forming hypotheses.
不太流行的AI形式（例如基于模型的機(jī)器學(xué)習(xí)）可能更適合進(jìn)行形成假設(shè)等科學(xué)任務(wù)。

[Paragraph 11]
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”.
1665年，在科學(xué)飛速進(jìn)步的時(shí)期，英國(guó)博學(xué)者羅伯特·胡克將顯微鏡和望遠(yuǎn)鏡等新科學(xué)儀器的出現(xiàn)描述為“在自然界中添加了人工器官”。

They let researchers explore previously inaccessible realms and discover things in new ways, “with prodigious benefit to all sorts of useful knowledge”.
這些器具使研究人員可以探索以前無(wú)法進(jìn)入的領(lǐng)域，以前所未有的方式發(fā)現(xiàn)事物，“對(duì)所有實(shí)用性知識(shí)都有巨大益處”。

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.
對(duì)于胡克的現(xiàn)代繼承者來(lái)說(shuō)，將AI添加到科學(xué)工具箱中將在未來(lái)有同樣的效果，并產(chǎn)生類(lèi)似的改變世界的結(jié)果。

（恭喜讀完，本篇英語(yǔ)詞匯量1036左右）
原文出自：2023年9月16日《The Economist》Leaders版塊

精讀筆記來(lái)源于：自由英語(yǔ)之路

本文翻譯整理: Irene本文編輯校對(duì): Irene
僅供個(gè)人英語(yǔ)學(xué)習(xí)交流使用。

【補(bǔ)充資料】（來(lái)自于網(wǎng)絡(luò)）
戴密斯·哈薩比斯（Demis Hassabis），畢業(yè)于倫敦大學(xué)學(xué)院，游戲開(kāi)發(fā)者、神經(jīng)學(xué)家和人工智能企業(yè)家，掌握的先進(jìn)人工智能技術(shù)，幫助谷歌展開(kāi)一場(chǎng)全新的人工智能革命。2010年，哈薩比斯和他的幾位合作伙伴共同創(chuàng)立了DeepMind科技公司，該公司的目標(biāo)是利用人工智能技術(shù)解決復(fù)雜的現(xiàn)實(shí)世界問(wèn)題。2016年，DeepMind的AlphaGo人工智能系統(tǒng)成功擊敗了國(guó)際象棋冠軍李世石，這一事件轟動(dòng)全球，標(biāo)志著人工智能技術(shù)實(shí)現(xiàn)了重大突破。

楊立昆（Yann LeCun）是一位法國(guó)的計(jì)算機(jī)科學(xué)家，也是一位在人工智能領(lǐng)域的領(lǐng)軍人物。他被廣泛認(rèn)為是深度學(xué)習(xí)的奠基人之一，并且在計(jì)算機(jī)視覺(jué)和自然語(yǔ)言處理方面的研究領(lǐng)域貢獻(xiàn)頗多。擔(dān)任Facebook首席人工智能科學(xué)家和紐約大學(xué)教授，2018年圖靈獎(jiǎng)（Turing Award）得主。

羅伯特·胡克Robert Hooke（1635年-1703年）是一位英國(guó)科學(xué)家，也是17世紀(jì)著名的物理學(xué)家、數(shù)學(xué)家、天文學(xué)家、化學(xué)家和工程師。他是一位才華橫溢的“萬(wàn)事通”，并且在多個(gè)領(lǐng)域都有杰出的貢獻(xiàn)。他是現(xiàn)代微觀世界的奠基人。他通過(guò)自己的實(shí)驗(yàn)，發(fā)現(xiàn)了彈性力學(xué)定律，即著名的“胡克定律”，該定律描述了彈簧的伸長(zhǎng)程度和加力之間的關(guān)系。胡克對(duì)萬(wàn)有引力定律的發(fā)現(xiàn)起了重要作用，1679年他寫(xiě)信給牛頓，信中認(rèn)為天體的運(yùn)動(dòng)是由于有中心引力拉住的結(jié)果，而且認(rèn)為引力與距離平方應(yīng)成反比。牛頓對(duì)此沒(méi)有復(fù)信，但接受了胡克的觀點(diǎn)。胡克希望牛頓能對(duì)他的勞動(dòng)成果“提一下”，但遭到牛頓的斷然拒絕。這是后來(lái)胡克控告牛頓剽竊他的成果的來(lái)由。1703年3月3日，胡克在落寞中去世了。在他死后不久，牛頓就當(dāng)上了英國(guó)皇家學(xué)會(huì)的主席。隨后，英國(guó)皇家學(xué)會(huì)中的胡克實(shí)驗(yàn)室和胡克圖書(shū)館就被解散，胡克的所有研究成果、研究資料和實(shí)驗(yàn)器材或被分散或被銷(xiāo)毀，沒(méi)多久，這些屬于胡克的東西就全都消失了。

【重點(diǎn)句子】（3個(gè)）
As some observers fret about these dystopian scenarios, however, others are focusing on the potential rewards.
然而，一些人擔(dān)心這些反烏托邦的場(chǎng)景，而另一些人關(guān)注的是潛在的回報(bào)。

And the computer revolution may not be finished yet. AI is being employed in many ways.
計(jì)算機(jī)革命可能還沒(méi)有結(jié)束。人工智能應(yīng)用于許多領(lǐng)域。

Fortunately, there are hopeful signs. AI tools are now moving from being pushed by AI researchers to being embraced by specialists in other fields.
幸運(yùn)的是，出現(xiàn)了一些令人振奮的跡象。由于AI研究人員的推動(dòng)，現(xiàn)在AI工具逐漸被其他領(lǐng)域的專(zhuān)家所接受。