夜雨聆风
Does humanity need science? The Nobel prizewinner Max Perutz posed this question in a landmark essay1 in 1989. His conclusion, unsurprisingly, was ‘yes’. Had he lived to see the era of artificial intelligence, he might have inverted his framing of the question: does science need humanity?
Two studies in Nature provide a glimpse of what some are interpreting as humanity’s shrinking role in scientific discovery in Perutz’s own field, molecular biology. Both describe a pivotal step towards truly AI-driven drug discovery, in which a system of connected AI agents is trained to autonomously navigate multi-step workflows. The system trawled scholarly literature, formed hypotheses, interpreted data and engaged in internal debate to arrive at candidate drugs to treat a particular disease.
The results are impressive, but also highlight something else: AI scientists can and should empower human researchers. They cannot and should not replace them.
In one study, a team based at FutureHouse, a non-profit AI research laboratory in San Francisco, California, asked its AI system, called Robin, to find a treatment for the eye disorder dry age-related macular degeneration2. Robin’s agents searched the scientific literature to derive a therapeutic strategy, identified candidate molecules and selected assays to test them. The experiments Robin suggested were then handed over to humans, who conducted the studies and fed the results back to Robin for analysis, interpretation and the design of follow-up studies. The team estimates that Robin reduced the time needed for the project 200-fold compared with a typical human workflow.
A second group led by researchers at Google, based in Mountain View, California, used its AI-agent system, called Co-Scientist, to look for approved drugs that could be repurposed to treat a form of leukaemia, and to discover drug targets to treat liver fibrosis3. Humans provided input throughout the process, helping to prioritize hypotheses and approaches. The team also asked the agents to develop a hypothesis to explain why many species of bacterium share a particular suite of antibiotic-resistance genes. Scientists, including some of the project’s authors, had been investigating this microbial puzzle for around a decade, but had not yet published the results. Co-Scientist arrived at the same hypothesis as the researchers — within days.
These projects represent a significant step forwards. But for all the ‘wow’ factor, it is crucial to bear in mind that the AI systems were not working alone. In both cases, humans framed the initial project, performed experiments, offered guidance and checked the agents’ output along the way. That is a feature, not a bug — and shutting humans out of the loop would not be easy.
For one thing, the creation of a fully autonomous AI researcher would require many more AI agents to be cross-linked, and scientists would need to ensure that those agents could maintain focus and coordinate efficiently with one another during long, complex tasks4,5. It is also not yet clear whether AI agents would be able to master every step of the process of drug development. Even if they could, human oversight would still be needed to catch hallucinations (AI data fabrication) and misinterpretations, and to ensure that certain tasks are done correctly — for example, that the data AI harvests from databases for its analyses are used appropriately.
More broadly, the expertise of researchers, and the accumulated wisdom of teams that have worked on a problem, perhaps for decades, might prove impossible for machines to replicate. Humans are also needed to train future generations, and their knowledge is built, in part, on lessons learnt from failures and inefficient meanderings. Human messiness, curiosity and playfulness have fuelled countless discoveries, and helped to inform society’s ethical frameworks. AI systems might offer greater efficiency in some instances, but we don’t yet know whether greater efficiency equates to greater insight.
These caveats should be borne in mind as society argues over competing visions of how AI should fit into the future. Some researchers see in AI tools a path to accelerated discovery, relief from the tedium that comes with much day-to-day lab work and a faster, cheaper way to create medicines and make other discoveries. Others see a future in which scientific literature is polluted with slop, human scientists cannot find suitable training or work, and research is untempered by human empathy, ethics and wisdom.
Scientists should not allow a negative view of AI to drive them away from exploring the possibilities that AI co- scientists might hold for research. Equally, however, they must rise above the din of AI hype and advocate for their own importance — to remind the wider public, funders and fellow researchers that science still needs humanity, and that not every grant proposal need include an AI project.
Perutz began his essay with a false dialectic that also plagues many modern discussions of AI: “Is scientific research the noblest pursuit of the human mind, from which springs a never-ceasing stream of beneficial discoveries, or is it a sorcerer’s broom that threatens us all with destruction?”. These opposing extremes, both true in their own way, should not be allowed to distort AI’s true potential — or obscure its limitations.
读到这里，请大家做一道考研英语阅读仿真模拟题：
It can be inferred from the passage that AI systems in scientific research _______.
[A] are capable of conducting independent and flawless drug development
[B] fail to match human researchers in experiential and cognitive depth
[C] will eventually replace human labor in tedious laboratory work
[D] have fully unlocked their potential in cross-disciplinary scientific research
第二遍，我带上翻译，形成英中对照译文，大家可以再读一遍，充分查漏补缺：
Does humanity need science? The Nobel prizewinner Max Perutz posed this question in a landmark essay1 in 1989. His conclusion, unsurprisingly, was ‘yes’. Had he lived to see the era of artificial intelligence, he might have inverted his framing of the question: does science need humanity?
人类离不开科学吗？诺贝尔奖得主马克斯·佩鲁茨在1989年一篇里程碑式的论文中提出了这个问题。不出所料，他给出的答案是肯定的。倘若他能活到人工智能时代，或许会调转问题的发问角度：科学离不开人类吗？
Two studies in Nature provide a glimpse of what some are interpreting as humanity’s shrinking role in scientific discovery in Perutz’s own field, molecular biology. Both describe a pivotal step towards truly AI-driven drug discovery, in which a system of connected AI agents is trained to autonomously navigate multi-step workflows. The system trawled scholarly literature, formed hypotheses, interpreted data and engaged in internal debate to arrive at candidate drugs to treat a particular disease.
《自然》刊发的两项研究，让人们窥见在佩鲁茨深耕的分子生物学领域，人类在科研发现中的作用正日渐弱化。两项研究均展现了全人工智能驱动药物研发的关键进展：一套互联互通的智能AI程序经过训练后，可自主完成多环节研发流程。该系统检索学术文献、提出研究假说、解析实验数据，并通过内部推演论证，筛选出用于特定疾病治疗的候选药物。
The results are impressive, but also highlight something else: AI scientists can and should empower human researchers. They cannot and should not replace them.
相关成果亮眼，但也点明了关键事实：人工智能科研工具能够、也应当赋能科研人员，却无法、也不该取代人类研究者。
In one study, a team based at FutureHouse, a non-profit AI research laboratory in San Francisco, California, asked its AI system, called Robin, to find a treatment for the eye disorder dry age-related macular degeneration2. Robin’s agents searched the scientific literature to derive a therapeutic strategy, identified candidate molecules and selected assays to test them. The experiments Robin suggested were then handed over to humans, who conducted the studies and fed the results back to Robin for analysis, interpretation and the design of follow-up studies. The team estimates that Robin reduced the time needed for the project 200-fold compared with a typical human workflow.
其中一项研究由美国加州旧金山非营利人工智能实验室未来之家团队完成，研究人员依托名为罗宾的人工智能系统，寻找干性老年性黄斑病变的治疗方案。罗宾智能程序查阅海量科研文献，敲定治疗思路、筛选候选分子、选定配套体外检测方案。罗宾给出的实验方案交由科研人员落地实操，实验结果再回传给人工智能，由其完成数据分析、结论研判以及后续实验规划。研究团队测算，相较传统全人工研发模式，罗宾将项目研发周期缩短至原先的二百分之一。
A second group led by researchers at Google, based in Mountain View, California, used its AI-agent system, called Co-Scientist, to look for approved drugs that could be repurposed to treat a form of leukaemia, and to discover drug targets to treat liver fibrosis3. Humans provided input throughout the process, helping to prioritize hypotheses and approaches. The team also asked the agents to develop a hypothesis to explain why many species of bacterium share a particular suite of antibiotic-resistance genes. Scientists, including some of the project’s authors, had been investigating this microbial puzzle for around a decade, but had not yet published the results. Co-Scientist arrived at the same hypothesis as the researchers — within days.
另一项研究由加州山景城的谷歌科研团队牵头，团队使用协同科学家人工智能系统，筛选已上市老药的新用途以治疗某类白血病，同时挖掘抗肝纤维化的新药靶点。全研发流程中人类持续参与，协助筛选优先级更高的研究假说与实验路径。科研人员还委托该人工智能破解一道微生物难题：为何多种细菌携带同一组耐药基因。包括部分论文作者在内的科学家耗时近十年钻研该问题，始终未能发表定论，而协同科学家仅用数日，就得出了和人类研究者完全一致的假说。
These projects represent a significant step forwards. But for all the ‘wow’ factor, it is crucial to bear in mind that the AI systems were not working alone. In both cases, humans framed the initial project, performed experiments, offered guidance and checked the agents’ output along the way. That is a feature, not a bug — and shutting humans out of the loop would not be easy.
上述两项研究堪称重大突破。尽管成果令人惊叹，但我们必须认清：人工智能从未独立完成全部科研工作。两项实验中，从项目立项、落地实验，到全程指导、核验人工智能输出结果，均离不开人类参与。人类介入是这套研发体系的固有优势，而非设计缺陷，想要彻底把人类排除在科研链路之外并不现实。
For one thing, the creation of a fully autonomous AI researcher would require many more AI agents to be cross-linked, and scientists would need to ensure that those agents could maintain focus and coordinate efficiently with one another during long, complex tasks4,5. It is also not yet clear whether AI agents would be able to master every step of the process of drug development. Even if they could, human oversight would still be needed to catch hallucinations (AI data fabrication) and misinterpretations, and to ensure that certain tasks are done correctly — for example, that the data AI harvests from databases for its analyses are used appropriately.
一方面，打造完全自主运行的人工智能科研主体，需要海量智能程序互联互通，科研人员还要保障各类程序在漫长繁复的科研任务中保持研究方向、高效协同。现阶段尚无法证实人工智能可以吃透药物研发全流程的所有环节。即便未来实现，也仍需人类全程监管，排查人工智能产生的信息臆造与数据误读问题，保障各项操作合规，比如规范人工智能调取数据库数据开展分析的使用边界。
More broadly, the expertise of researchers, and the accumulated wisdom of teams that have worked on a problem, perhaps for decades, might prove impossible for machines to replicate. Humans are also needed to train future generations, and their knowledge is built, in part, on lessons learnt from failures and inefficient meanderings. Human messiness, curiosity and playfulness have fuelled countless discoveries, and helped to inform society’s ethical frameworks. AI systems might offer greater efficiency in some instances, but we don’t yet know whether greater efficiency equates to greater insight.
从更广的层面来看，科研人员的专业积淀、科研团队耗费数十年攻关积攒的经验智慧，是机器难以复刻的。科研传承同样依托人类实现，人类的知识体系，一部分源自一次次失败与曲折摸索总结的教训。人类不拘一格的思维、与生俱来的好奇心与探索欲，催生了不计其数的科研突破，也助力搭建起社会伦理准则。人工智能固然能在部分场景提升效率，但高效率未必等同于更深邃的科研洞见。
These caveats should be borne in mind as society argues over competing visions of how AI should fit into the future. Some researchers see in AI tools a path to accelerated discovery, relief from the tedium that comes with much day-to-day lab work and a faster, cheaper way to create medicines and make other discoveries. Others see a future in which scientific literature is polluted with slop, human scientists cannot find suitable training or work, and research is untempered by human empathy, ethics and wisdom.
当下社会围绕人工智能的未来定位争论不休，上述短板值得各方谨记。一部分科研工作者认为人工智能可以提速科研进程，把科研人员从枯燥重复的日常实验里解放出来，以更低成本、更快速度研发新药与新成果；另一部分人则担忧未来科研乱象丛生：劣质AI文稿泛滥污染学术期刊、科研人员失业断档，科研项目失去人性温度、伦理约束与人文智慧的把控。
Scientists should not allow a negative view of AI to drive them away from exploring the possibilities that AI co- scientists might hold for research. Equally, however, they must rise above the din of AI hype and advocate for their own importance — to remind the wider public, funders and fellow researchers that science still needs humanity, and that not every grant proposal need include an AI project.
科研人员不必因片面否定人工智能，就放弃探索人工智能协同科研的潜力；但与此同时，科研从业者也要跳出人工智能炒作热潮，立足自身不可替代的价值发声，向公众、科研资助方和同行阐明：科学发展依旧离不开人类，科研基金项目不必强行捆绑人工智能课题。
Perutz began his essay with a false dialectic that also plagues many modern discussions of AI: “Is scientific research the noblest pursuit of the human mind, from which springs a never-ceasing stream of beneficial discoveries, or is it a sorcerer’s broom that threatens us all with destruction?”. These opposing extremes, both true in their own way, should not be allowed to distort AI’s true potential — or obscure its limitations.
佩鲁茨在论文开篇抛出一组非黑即白的二元论调，如今这类偏激观点也裹挟着人工智能相关讨论：“科学研究究竟是人类心智最高贵的追求，源源不断催生造福人类的发明，还是巫师的扫帚，终将给全人类带来灭顶之灾？”两种极端看法各有依据，但不能用它们歪曲人工智能的真实潜力，或是无视其与生俱来的局限。
最后，让我们一起学习一个语言点：shut sb out of the loop
loop = 圆环、闭环，科研里引申为「工作闭环、流程链路」
shut out = 隔绝、排除在外
直译：把某人关在闭环外面
考研释义：将某人排除在核心工作环节之外，不让参与关键流程
原句：That is a feature, not a bug — and shutting humans out of the loop would not be easy.
翻译：人类参与是体系优势而非缺陷，想要把人类彻底剔除出科研全流程很难。
文章通篇观点：AI只能辅助科研，没法把人类踢出科研闭环。
好啦，今天的文章就到这里啦。
明天，我们不见不散！
