人工智能是一種“全新生產(chǎn)要素”

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

與一些經(jīng)濟(jì)學(xué)家交談，他們幾乎肯定會(huì)告訴你，疲弱的生產(chǎn)率增長(zhǎng)是我們這個(gè)時(shí)代的災(zāi)難。

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執(zhí)行官的豪華轎車的后座上，他們會(huì)熱情洋溢地訴說(shuō)新技術(shù)正如何改變企業(yè)生產(chǎn)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.

與人工智能領(lǐng)域的一些專家談話，他們很有可能會(huì)喋喋不休地說(shuō)著我們正瀕臨一場(chǎng)生產(chǎn)率革命。如果我們達(dá)到技術(shù)奇點(diǎn)(當(dāng)電腦智慧超過(guò)人類智慧時(shí))，生產(chǎn)率增速將呈指數(shù)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.

從那一刻起，電腦超級(jí)智能將迅速發(fā)現(xiàn)留待發(fā)現(xiàn)的一切。正如華盛頓大學(xué)(University of Washington)計(jì)算機(jī)學(xué)教授、《主算法》(Master Algorithm)一書作者佩德羅•多明戈斯(Pedro Domingos)所說(shuō)，這個(gè)主算法將成為人類的最后一個(gè)發(fā)明。這個(gè)主算法將能夠從數(shù)據(jù)中獲得世界上的一切知識(shí)——過(guò)去、現(xiàn)在和未來(lái)。

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

說(shuō)得婉轉(zhuǎn)些，其中似乎確實(shí)存在某種“生產(chǎn)率悖論”。這3個(gè)故事有可能全部為真嗎?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.

當(dāng)然，在科技行業(yè)，天花亂墜的宣傳并不新鮮。目前，我們距離技術(shù)奇點(diǎn)還相當(dāng)遙遠(yuǎn)，關(guān)于我們達(dá)到這個(gè)奇點(diǎn)的那一天會(huì)不會(huì)到來(lái)，人們還沒(méi)有達(dá)成一致。然而，我們有必要注意到，該領(lǐng)域有些(較年輕)的研究人員相信，他們將在他們的有生之年迎來(lái)這一刻。

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，即便是目前存在的狹窄、針對(duì)特定領(lǐng)域的人工智能應(yīng)用也在產(chǎn)生驚人的結(jié)果——大型科技公司(谷歌(Google)、微軟(Microsoft)和IBM)正在該領(lǐng)域投入資金。要了解未來(lái)可能發(fā)生的事情，我們有必要關(guān)注一下倫敦初創(chuàng)企業(yè)BenevolentAI，該公司試圖實(shí)現(xiàn)醫(yī)學(xué)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創(chuàng)始人肯尼思•梅爾文(Kenneth Mulvany)認(rèn)為，藥品的發(fā)現(xiàn)在很大程度上是一項(xiàng)信息和數(shù)據(jù)挑戰(zhàn)，這些挑戰(zhàn)能夠由人工智能有效解決。在線醫(yī)學(xué)研究網(wǎng)站PubMed擁有2600萬(wàn)篇文獻(xiàn)，并每年新增約100萬(wàn)篇文獻(xiàn)。這顯然是任何一個(gè)研究團(tuán)隊(duì)所有成員一輩子都無(wú)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搭建了一個(gè)電腦“引擎”，能夠閱讀這些數(shù)據(jù)、對(duì)其整理歸類并提取相關(guān)信息，突出顯示一個(gè)領(lǐng)域中能夠應(yīng)用于另一個(gè)領(lǐng)域的“概念假說(shuō)”。“你可以用以前想象不到的規(guī)模來(lái)看事情，”馬爾瓦尼表示，“這種由人工智能評(píng)估的組件可以增強(qiáng)人類智慧。”

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正與謝菲爾德大學(xué)(Sheffield university)的研究人員合作，以研究治療運(yùn)動(dòng)神經(jīng)元疾病和肌萎縮性側(cè)索硬化癥(ALS)的新方法。初步結(jié)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.”

神經(jīng)學(xué)講師理查德•米德(Richard Mead)表示，BenevolentAI已確認(rèn)一種藥物發(fā)現(xiàn)的途徑并開啟了一種驚人的新途徑。“他們的引擎可以瀏覽大量信息，以發(fā)現(xiàn)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.

它還可以幫助根據(jù)基因構(gòu)成來(lái)制定個(gè)性化的個(gè)人解決方案。轉(zhuǎn)化神經(jīng)生物學(xué)講師勞拉•費(fèi)拉約洛(Laura Ferraiuolo)表示：“我們確實(shí)對(duì)此感到興奮。潛力是驚人的。”

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.

一些經(jīng)濟(jì)學(xué)家認(rèn)為，迅速擴(kuò)大的數(shù)據(jù)集、機(jī)器學(xué)習(xí)和日益提高的計(jì)算能力，這些都應(yīng)被列為除資本和勞動(dòng)力之外的一種全新的生產(chǎn)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.

人工智能正締造一種新的“虛擬勞動(dòng)力”，提高人類智慧的生產(chǎn)率并推動(dòng)新的創(chuàng)新。另外，與其他生產(chǎn)要素不同，人工智能不會(huì)隨著時(shí)間的流逝而貶值。它將受益于網(wǎng)絡(luò)和規(guī)模效應(yīng)。例如所有自動(dòng)駕駛汽車都能從其他此類汽車身上學(xué)習(xí)。

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.

來(lái)自埃森哲(Accenture)與經(jīng)濟(jì)學(xué)前沿公司(Frontier Economics)最近的一份報(bào)告大膽提出，到2035年，基于人工智能的技術(shù)的普遍采用，可能會(huì)將很多發(fā)達(dá)國(guó)家的經(jīng)濟(jì)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.

報(bào)告估計(jì)，人工智能有可能將美國(guó)、英國(guó)和日本的總增加值(與國(guó)內(nèi)生產(chǎn)總值(GDP)近似)年度增速分別提高到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.”

這些研究屬于學(xué)術(shù)猜測(cè)?？萍嫉倪M(jìn)步是不可預(yù)測(cè)的。但一些人工智能先驅(qū)相信，它可以“改變一切”，從材料科學(xué)到能源。“我們正處在一個(gè)新的創(chuàng)新時(shí)代的開端，”馬爾瓦尼表示，“我們已擁有由人類增強(qiáng)的創(chuàng)新。我們將最終擁有機(jī)器創(chuàng)新。”

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

甚至連目光最犀利的經(jīng)濟(jì)學(xué)家可能也很快不得不承認(rèn)，人工智能將以深遠(yuǎn)且可能非同一般的方式影響生產(chǎn)率。

[email protected] 譯者/梁艷裳