人工智能是一种“全新生产要素”

Talk to a bunch of economists and they will doubtless tell you that poor productivity growth is the scourge of our age.

与一些经济学家交谈，他们几乎肯定会告诉你，疲弱的生产率增长是我们这个时代的灾难。

Lounge in the back of a limo with some chief executives, on the other hand, and they will enthuse about how new technologies are transforming corporate productivity.

另一方面，舒服地靠在一些首席执行官的豪华轿车的后座上，他们会热情洋溢地诉说新技术正如何改变企业生产率。

Track down some experts in artificial intelligence and they may well babble on about standing on the brink of a productivity revolution. If we ever reach the point of technological singularity — when computers outsmart humans — productivity growth will accelerate exponentially.

与人工智能领域的一些专家谈话，他们很有可能会喋喋不休地说着我们正濒临一场生产率革命。如果我们达到技术奇点（当电脑智慧超过人类智慧时），生产率增速将呈指数式加快。

From that moment, a computer superintelligence will rapidly discover everything left to discover. This Master Algorithm, as the author — a computer science professor at the University of Washington — Pedro Domingos calls it, will be the last invention that man makes. It will be able to derive all knowledge in the world — past, present, and future — from data.

从那一刻起，电脑超级智能将迅速发现留待发现的一切。正如华盛顿大学(University of Washington)计算机学教授、《主算法》(Master Algorithm)一书作者佩德罗?多明戈斯(Pedro Domingos)所说，这个主算法将成为人类的最后一个发明。这个主算法将能够从数据中获得世界上的一切知识——过去、现在和未来。

There does appear to be, to put it mildly, something of a “productivity paradox”. Can all three stories be true? Quite possibly, yes.

说得婉转些，其中似乎确实存在某种“生产率悖论”。这3个故事有可能全部为真吗？很有可能，是的。

Hype, of course, is not an alien phenomenon in the tech industry. At present, we are a very, very long way from technological singularity and opinion is divided about whether we will ever reach it. It is worth noting, though, that some (younger) researchers in the field are convinced they will achieve it in their lifetimes.

当然，在科技行业，天花乱坠的宣传并不新鲜。目前，我们距离技术奇点还相当遥远，关于我们达到这个奇点的那一天会不会到来，人们还没有达成一致。然而，我们有必要注意到，该领域有些（较年轻）的研究人员相信，他们将在他们的有生之年迎来这一刻。

Yet even the application of narrow, domain-specific AI that exists today is producing startling results as the big tech companies — Google, Microsoft and IBM — pour money into the field. For a glimpse of what is possible, it is worth checking in with BenevolentAI, a London start-up attempting to revolutionise medical research.

然而，即便是目前存在的狭窄、针对特定领域的人工智能应用也在产生惊人的结果——大型科技公司（谷歌(Google)、微软(Microsoft)和IBM）正在该领域投入资金。要了解未来可能发生的事情，我们有必要关注一下伦敦初创企业BenevolentAI，该公司试图实现医学研究的革命。

Kenneth Mulvany, Benevolent’s founder, argues that drug discovery is in large part an information and data challenge that can be effectively addressed by AI. PubMed, the online medical research site, holds 26m citations and is adding about 1m new publications a year. That is clearly more than any team of researchers could ingest in a lifetime.

BenevolentAI创始人肯尼思?梅尔文(Kenneth Mulvany)认为，药品的发现在很大程度上是一项信息和数据挑战，这些挑战能够由人工智能有效解决。在线医学研究网站PubMed拥有2600万篇文献，并每年新增约100万篇文献。这显然是任何一个研究团队所有成员一辈子都无法完全吸收的。

Benevolent has built a computer “engine” capable of reading and mapping such data and extracting relevant information, highlighting “conceptual hypotheses” in one field that can be applied to another. “You can look at things on a scale that was unimaginable before,” Mr Mulvany says. “This AI-assessed component can augment human intelligence.”

BenevolentAI搭建了一个电脑“引擎”，能够阅读这些数据、对其整理归类并提取相关信息，突出显示一个领域中能够应用于另一个领域的“概念假说”。“你可以用以前想象不到的规模来看事情，”马尔瓦尼表示，“这种由人工智能评估的组件可以增强人类智慧。”

Benevolent is working with researchers at Sheffield university to investigate new pathways to treat motor neurone disease and amyotrophic lateral sclerosis (ALS). Early results are promising.

BenevolentAI正与谢菲尔德大学(Sheffield university)的研究人员合作，以研究治疗运动神经元疾病和肌萎缩性侧索硬化症(ALS)的新方法。初步结果大有希望。

Richard Mead, lecturer in neuroscience, says that Benevolent has already validated one pathway for drug discovery and opened up a surprising new one. “What their engine can do is look across vast swaths of information to pick novel ideas to repurpose.”

神经学讲师理查德?米德(Richard Mead)表示，BenevolentAI已确认一种药物发现的途径并开启了一种惊人的新途径。“他们的引擎可以浏览大量信息，以发现新的想法重新利用。”

It can also help personalise solutions for individuals according to their genetic make up. “We are really excited about it. The potential is incredible,” says Laura Ferraiuolo, lecturer in translational neurobiology.

它还可以帮助根据基因构成来制定个性化的个人解决方案。转化神经生物学讲师劳拉?费拉约洛(Laura Ferraiuolo)表示：“我们确实对此感到兴奋。潜力是惊人的。”

Some economists argue this combination of fast-expanding data sets, machine learning and ever-increasing computing power should be classified as an entirely new factor of production, alongside capital and labour.

一些经济学家认为，迅速扩大的数据集、机器学习和日益提高的计算能力，这些都应被列为除资本和劳动力之外的一种全新的生产要素。

AI is creating a new “virtual workforce”, enhancing the productivity of human intelligence and driving new innovation. Moreover, unlike other factors of production, AI does not degrade over time. Rather, it benefits from network and scale effects. Every self-driving car can “learn” from every other such vehicle, for example.

人工智能正缔造一种新的“虚拟劳动力”，提高人类智慧的生产率并推动新的创新。另外，与其他生产要素不同，人工智能不会随着时间的流逝而贬值。它将受益于网络和规模效应。例如所有自动驾驶汽车都能从其他此类汽车身上学习。

A recent report from Accenture and Frontier Economics made the bold claim that the widespread adoption of AI-enabled technologies could double the economic growth rates of many advanced countries by 2035.

来自埃森哲(Accenture)与经济学前沿公司(Frontier Economics)最近的一份报告大胆提出，到2035年，基于人工智能的技术的普遍采用，可能会将很多发达国家的经济增速提高一倍。

It estimated that AI had the potential to raise the annual growth rate of gross value added (a close approximation of GDP) to 4.6 per cent in the US, 3.9 per cent in the UK and 2.7 per cent in Japan.

报告估计，人工智能有可能将美国、英国和日本的总增加值（与国内生产总值(GDP)近似）年度增速分别提高到4.6%、3.9%和2.7%。

Such studies are educated guesswork. Advances in technology are unpredictable. But some AI pioneers are convinced it could “change everything”, from material science to energy. “We are at the dawn of a new age of innovation,” says Mr Mulvany. “We already have human-augmented innovation. We will eventually have machine innovation.”

这些研究属于学术猜测。科技的进步是不可预测的。但一些人工智能先驱相信，它可以“改变一切”，从材料科学到能源。“我们正处在一个新的创新时代的开端，”马尔瓦尼表示，“我们已拥有由人类增强的创新。我们将最终拥有机器创新。”

Even the most gimlet-eyed of economists may soon have to accept that AI is affecting productivity in profound and possibly extraordinary ways.

甚至连目光最犀利的经济学家可能也很快不得不承认，人工智能将以深远且可能非同一般的方式影响生产率。