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2021-04-07 13:47:02
本文摘要:Talk to a bunch of economists and they will doubtless tell you that poor productivity growth is the scourge of our age.与一些经济学家聊天,他们完全认同不会告诉他你,不振的生产率快速增长是我们这个时代的灾难。

Talk to a bunch of economists and they will doubtless tell you that poor productivity growth is the scourge of our age.与一些经济学家聊天,他们完全认同不会告诉他你,不振的生产率快速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,难受地靠在一些首席执行官的豪华轿车的后座上,他们不会热情洋溢地述说新技术于是以如何转变企业生产率。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道着我们于是以深陷一场生产率革命。如果我们超过技术奇点(当电脑智慧多达人类智慧时),生产率增长速度将呈圆形指数式减缓。

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,电脑超级智能将很快找到再行找到的一切。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,这个主算法将沦为人类的最后一个发明者。这个主算法将需要从数据中取得世界上的一切科学知识——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.说道得含蓄些,其中或许显然不存在某种“生产率悖论”。这3个故事有可能全部为真为吗?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非常很远,关于我们超过这个奇点的那一天不会会来临,人们还没达成协议完全一致。然而,我们有适当注意到,该领域有些(较年长)的研究人员坚信,他们将在他们的有生之年步入这一刻。

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,即便是目前不存在的狭小、针对特定领域的人工智能应用于也在产生难以置信的结果——大型科技公司(谷歌(Google)、微软公司(Microsoft)和IBM)正在该领域投放资金。要理解未来有可能再次发生的事情,我们有适当注目一下伦敦初创企业BenevolentAI,该公司企图构建医学研究的革命。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)指出,药品的找到在相当大程度上是一项信息和数据挑战,这些挑战需要由人工智能有效地解决问题。


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搭起了一个电脑“引擎”,需要读者这些数据、对其整理归类并萃取涉及信息,引人注目表明一个领域中需要应用于另一个领域的“概念假说”。“你可以用以前想象将近的规模来看事情,”马尔瓦尼回应,“这种由人工智能评估的组件可以强化人类智慧。”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于是以与谢菲尔德大学(Sheffield university)的研究人员合作,以研究化疗运动神经元疾病和肌萎缩性侧索硬化症(ALS)的新方法。可行性结果大有希望。

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)回应,BenevolentAI已证实一种药物找到的途径并打开了一种难以置信的新途径。“他们的引擎可以网页大量信息,以找到新的点子新的利用。”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.它还可以协助根据基因包含来制订个性化的个人解决方案。转化成神经生物学讲师劳拉?费拉约洛(Laura Ferraiuolo)回应:“我们显然回应深感激动。

潜力是难以置信的。”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.一些经济学家指出,很快不断扩大的数据集、机器学习和日益提升的计算能力,这些都不应被列入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.人工智能于是以创下一种新的“虚拟世界劳动力”,提升人类智慧的生产率并推展新的创意。另外,与其他生产要素有所不同,人工智能会随着时间的推移而升值。

它将获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自学。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明确提出,到2035年,基于人工智能的技术的广泛使用,可能会将很多发达国家的经济增长速度提升一倍。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.报告估算,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)近似于)年度增长速度分别提升到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.”这些研究归属于学术猜测。科技的变革是不能预测的。但一些人工智能先驱坚信,它可以“转变一切”,从材料科学到能源。“我们于是以处在一个新的创意时代的开端,”马尔瓦尼回应,“我们已享有由人类强化的创意。

我们将最后享有机器创意。”Even the most gimlet-eyed of economists may soon have to accept that AI is affecting productivity in profound and possibly extraordinary ways.甚至连目光最诙谐的经济学家有可能也迅速被迫否认,人工智能将以深远影响且有可能非同一般的方式影响生产率。