财富杂志 AI如何让老药新用?(4)（在线收听）

GNS has raised a total of $38 million over the years—

这些年来，GNS共筹集了3800万美元——

from Amgen Ventures, the venture capital arm of the drug giant, along with Celgene and a variety of other investors—

资金来自制药巨头的风险投资机构Amgen Ventures以及赛尔基因和各种各样的投资者——

to build and fine-tune its models of disease. And in a recent series of trials, first published in 2017 in the medical journal The Lancet,

建立并调整其疾病模型。并且近期的一系列试验于2017年首次发表于医学期刊《柳叶刀》，

GNS has detailed REFS's potential when applied to a disease such as Parkinson's—

在试验中，GNS详细说明了REFS应用于一种疾病，如帕金森氏综合症时的潜能，

an ailment in which pleiotropic factors render existing treatments wildly hit-or-miss in their effectiveness.

在治疗这种疾病时，多效性因素致使现有治疗方法的效果参差不齐。

With Parkinson's, the network of interactions set in motion by defective genes has a particular shape to it,

对于帕金森氏综合症，有缺陷基因调动的交互网络具有特定的形状，

and the breakdown of motor functioning is the most reliable indication of its progression.

并且运动机能的中断是其进展中最可靠的指示。

Feeding the genetic data of Parkinson's sufferers and a control group into REFS

将帕金森氏综合症患者的基因数据和一个对照组的基因数据输入REFS中

helped GNS generate over 100 computer models depicting what might be going on as motor function deteriorates.

可以帮助GNS生成了100多个计算机模型，用以描绘运动机能恶化时可能发生的情况。

The models can uncover previously unknown genetic mutations that may contribute to the speedup of deterioration.

这些模型可以揭露之前未知的基因突变，这些突变可能导致疾病恶化的加速。

But that's just the first part. GNS has used those findings to create 5,000 different computer simulations of randomized control trials,

但这仅仅是第一部分。GNS利用这些发现创造了5000个随机对照试验的不同计算机模拟

each aiming to predict how fast the disease would progress with varying approaches to treatment.

每一个的目的都是预测，在不同治疗方式下，这种疾病的进展速度会有多快。

Such speed--testing can be vastly more economical than seeking the same result through controlled human trials.

这样的速度测试比通过控制人体试验寻求同样的结果要经济得多。

And GNS, in partnership with other drugmakers, is now applying similar approaches to treatments for diabetes,

并且GNS正在和其他制药公司合作，将类似的治疗方法应用于糖尿病、

ALS, multiple myeloma, and breast cancer, among other diseases.

肌萎缩侧所硬化症、多发性骨髓瘤和乳腺癌等疾病。

"We now have the ability to create and construct, on the computer, representations of human patients and their diseases

“现在我们有能力在电脑上创建人类患者以及他们所患疾病的表现，

such that we can now probe, drug by drug, care management intervention by care management intervention,

这样一来，我们就可以对每一种药物以及每一种护理管理干预进行探究，

and say what treatments work for which patient," says Colin Hill, CEO of GNS.

并得知哪一种治疗方法对哪种病人是有效的，”GNS的CEO Colin Hill说到。

The simulation, in other words, is not just finding correlations: It is answering What if questions.

换句话说，这种模拟并不仅仅是寻找关联性：它是在回答‘假使将会怎么样’的问题。

What if we had given drug A instead of drug B to patient X?

如果我们给病人X服用药物A而不是药物B会怎么样？

That ability to simulate and answer counterfactuals is a recent arrival in the practice of A.I.

在AI实践中，这种模拟和回答反设事实的能力是最近才出现的。

It owes its growing importance in large part to GNS's technology adviser,

其日益增长的重要性很大程度上要归功于GNS的技术顾问，

Judea Pearl, a longtime A.I. researcher and professor of computer science at UCLA.

Judea Pearl，AI研究人员，兼UCLA计算机科学部教授。

In a popular volume published last year called The Book of Why,

在去年出版的畅销书《The Book of Why》中，

Pearl describes how true intelligence ascends from merely noticing patterns, which machine learning does in spades,

Pearl描述了真正的智能是如何从仅注意机器学习的模式

to being able to express counterfactual reasoning about what would have happened, based on those patterns.

提升到能够根据这些模式，对可能要发生的事情进行反事实推理的。

Data alone, disconnected from any idea of a mechanism, doesn't provide real insight.

仅凭数据，不与任何机制的想法相联系，就无法提供真正的洞察力。

"Data is profoundly dumb about causality," claims Pearl. Hill puts it more bluntly: "Deep learning is not that deep."

“数据对因果关系是极其愚蠢的，”Pearl声称。Hill则说得更直接：“深度学习没那么深奥。”

Daniel Cohen, now 67, spent his childhood in Tunisia's heterogeneous society of Jews, Christians, Muslims,

丹尼尔·科恩现年67岁，他的童年是在突尼斯犹太人、基督教徒以及穆斯林组成的混杂社会中度过，

"living all together in a very elegant and pacific way."

“他们以一种非常优雅而平和的方式生活在一起。”

He credits that experience for his taste for "things that are not complicated, but complex."

他将这一段经历归功于自己对“并不难懂却很复杂事情”的喜好”。

When he was 9, Cohen's family immigrated to Paris, where he pursued the piano avidly.

9岁时，科恩的家人移民到了巴黎，在那里他对钢琴有着强烈的追求。

He switched to medicine once he realized he might have a greater impact as a scientist than a musician, but the passion has not left him.

当他意识到作为一名科学家可能比音乐家更有影响力时，他转向了医学，但激情并没有离去。

He has been a guest conductor at the Royal Philharmonic in London and dreams of leading that ensemble in Tchaikovsky's Symphony Pathetique.

他是伦敦皇家爱乐乐团的客座指挥，并梦想着在柴可夫斯基的《悲怆交响曲》演奏中带领着这个乐团。

"The predisposition to orchestra conductor, CEO, and scientist are all controlled by the same genes," he jokes.

他开玩笑说到，“对交响乐指挥、CEO和科学家的倾向都是由同一种基因所控制的。”