If an AI won the Nobel prize for medicine

如果有朝一日，人工智能獲得諾貝爾醫(yī)學(xué)獎(jiǎng)......

Rage against the machine

橫眉冷對(duì)計(jì)算機(jī)

Should the greatest prize in medical research really be awarded to a non-human? An imagined scenario from 2036

醫(yī)學(xué)研究的最高獎(jiǎng)?wù)娴膽?yīng)該頒發(fā)給非人類么？一段對(duì)2036年的設(shè)想

Editor’s note: This year What If?, our annual collection of scenarios, considers the future of health. Each of these stories is fiction, but grounded in historical fact, current speculation and real science. They do not present a unified narrative but are set in different possible futures

編者按:年度欄目“如果……會(huì)怎樣?”旨在探討一些假設(shè)場(chǎng)景，今年我們聚焦健康領(lǐng)域的未來(lái)。這里面的每一個(gè)故事雖為虛構(gòu)，但都建立在歷史事實(shí)、當(dāng)前猜測(cè)以及真實(shí)科學(xué)的基礎(chǔ)之上。這些故事各自成篇，設(shè)定于不同的未來(lái)世界。

IT WAS A scene that the Nobel committee had dearly hoped to avoid. As the recipients of this year’s prizes filed into the Stockholm Concert Hall to take their seats, dozens of protesters, including several former laureates, clashed with police in the streets outside. They had gathered to express their opposition to the unprecedented decision to award the Nobel prize in physiology or medicine to an artificial intelligence.

注釋：

file in(to something or some place) To move or process into a place or thing in a line.

The committee’s citation recognised YULYA—the nickname of a machine-learning system officially known as System for Automated Lymphoma Diagnosis—as the discoverer of ancillary vulnerability, a mechanism whereby specific pairs of antibiotics, working in tandem, can prove effective against bacteria that are otherwise resistant. The committee estimates that in the 18 months since the discovery, which occurred when the death rate associated with the failure of existing antibiotics had risen to around 2.5m a year, YULYA’s work has saved around 4m lives, both through direct treatment of infections and by allowing the resumption of surgical procedures, including caesarean sections, that were considered too dangerous without antibiotics.

注釋：

1. citation：A formal statement of the accomplishments of one being honored with an academic degree.

2. Lymphoma: 淋巴瘤

3. caesarean sections剖宮產(chǎn)

Bringing to an end the greatest global public-health crisis since the coronavirus pandemic of 2020-22 would, you might have thought, be considered qualification enough for anyone, whether human or machine, to win the Nobel prize. But the decision has proved hugely controversial. Though the statutes of the Nobel Foundation have historically been interpreted as implying that only a human can win the award, another of its dictates was deemed to take precedence: recognition for having “conferred the greatest benefit to humankind” in the preceding year. Another factor behind the break with tradition was a demographic shift in the prize committee. When two of the committee’s five members succumbed to bacterial infections last year, younger replacements were elected, both of whom happened to have used machine-learning systems in their doctoral research.

YULYA was originally built to tackle a different problem: finding more effective cancer treatments. One of the world’s most advanced causal nets, it is one of a new generation of artificial-intelligence systems combining the pattern-recognition skills of conventional “deep” neural networks with the ability to distinguish causation from mere correlation. By examining records from patient databases, in conjunction with a corpus of papers from medical journals and historical data from pharmaceutical companies, it sought to identify the patterns of symptoms that led to the most severe outcomes, in order to diagnose them earlier. It was also programmed to evaluate the effectiveness of different treatments, including combinations of treatments, in order to suggest new therapeutic regimens that could be tested in patients.

Its focus shifted, however, when a software upgrade in 2034 accidentally gave it access to all recent papers in medical journals, rather than just those associated with cancer. YULYA duly began to crunch data relating to antimicrobial resistance, which accounted for a steadily growing proportion of medical-research papers as the crisis intensified. At first, its requests for more data in specific areas, and suggestions for new approaches to treatment, were thought to be errors, because they did not relate to cancer. Then YULYA’s operators realised what had happened, and saw that it had used its reasoning capabilities to build a testable hypothesis: the forerunner of what would become ancillary vulnerability. It highlighted the data that would be needed to validate the hypothesis, including specific guidelines as to how it should be collected. “It amounted to a full-scale programme of research,” says Anisha Rai, one of YULYA’s creators.

Under less exceptional circumstances, such trials might never have been authorised. Many funding bodies require scientists to lay bare the reasoning process of AI systems, in order to be sure that their recommendations do not lead to deadly conclusions. Dr Rai and her colleagues got funding for YULYA’s trial by playing down its role in suggesting the hypothesis. Only when the results showed promise did they publish YULYA’s original proposals.

That, in turn, led to a heated debate about whether YULYA, or its creators, deserved credit for the breakthrough. Dr Rai continues to insist that YULYA deserves sole credit, a position that has prompted the departure of several members of her original team in the past year. She even refused to go to Stockholm to receive the award on YULYA’s behalf from the queen of Sweden. “It’s not my prize,” she says.

AIs are commonly used to predict the onset of diseases like Alzheimer’s, make personalised treatment recommendations and enhance the diagnostic abilities of physicians. And the use of AI in drug discovery, in particular to help pharmaceutical companies wade through databases, is not new. In 2020 an algorithm developed at the Massachusetts Institute of Technology made headlines when it identified a new antibiotic. Dubbed halicin, after the computer in the film “2001: A Space Odyssey”, it proved to be effective against some resistant bacteria, but was limited in its scope. “Ancillary vulnerability makes halicin look like a homeopathic treatment, like a placebo,” says Una Científica, a researcher at the Houssay Institute in Buenos Aires.

注：

順勢(shì)療法：是替代醫(yī)學(xué)的一種。順勢(shì)療法的理論基礎(chǔ)是“同樣的制劑治療同類疾病”，意思是為了治療某種疾病，需要使用一種能夠在健康人中產(chǎn)生相同癥狀的藥劑

Even so, the Nobel committee’s reference to YULYA’s “discovery” has angered those who see it as little more than a clever tool. “YULYA is an AI capable of winning a Nobel. That is not the same thing as an AI that’s capable of discovery,” says Hars Kritik of the European Robotics Institute in Prague. He argues that even the best AIs are only useful in specialised areas like drug design, where large quantities of data are married to well-defined metrics of success. Saying that they can make discoveries, he says, waving a placard outside the concert hall, is “flawed anthropomorphism”.

Rightly or wrongly, YULYA is unlikely to be the last artificial intelligence to win a Nobel prize. Sources within the Nobel Foundation say that similar nominations have been received for prizes in physics and chemistry, as AI systems are used to search for new materials and chemical compounds suitable for use in batteries, solar panels and carbon-capture membranes. Given the chaos that erupted in Stockholm this week, however, the chances of an AI winning the Nobel peace prize seem rather more remote.

翻譯組：

Cathy，女，春都MTIer,專業(yè)翻譯愛(ài)好者

Frank，男，帝都MTIer,愛(ài)看球，愛(ài)踢球，愛(ài)讀經(jīng)濟(jì)學(xué)人

Diamond,男，搬磚工，理工科永久腦損傷，學(xué)習(xí)翻譯的小學(xué)生

校對(duì)組：

奧莉，女，擼貓狂魔，強(qiáng)迫癥患者

Rachel，學(xué)理工科，愛(ài)跳芭蕾，熱愛(ài)文藝的非典型翻譯

Mia，女，期待快點(diǎn)畢業(yè)的MTIer,有志從事翻譯工作的小文青

感謝其他平臺(tái)編輯作者：

B站地址：

https://space.bilibili.com/522030734

編輯：Daisy，勤勤懇懇工具人，B站客服的好朋友

知乎地址：

zhihu.com/people/damon0922 

編輯：jill，會(huì)展經(jīng)濟(jì)，想跨界英專的英語(yǔ)學(xué)渣

微博地址：

https://weibo.com/u/2375202237

編輯：Erin，在校大學(xué)生一枚，TE愛(ài)好者

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