中國漸成全球人工智慧領頭羊

最新 10-01

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人工智慧

演算法的王國

中國數據資源豐富，有機會在人工智慧領域佔據領先地位

Artificial intelligenceThe algorithm kingdomChina』s deep pool of data means it has a chance to lead in artificial intelligence

今年年初，關注全球人工智慧（AI）發展動向的人注意到兩件小事。先是微軟高管之一陸奇在1月表示，自己從自行車事故中恢復後不會回歸這家世界最大的軟體公司，而是將加入中國領先的搜索引擎百度，擔任該公司的首席運營官。當月晚些時候，國際人工智慧協會推遲了年會舉辦的時間。會議原計劃在1月舉行，但時間與中國新年發生了衝突。

AT THE start of this year, two straws in the wind caught the attention of those who follow the development of artificial intelligence (AI) globally. First, Qi Lu, one of the bosses of Microsoft, said in January that he would not return to the world』s largest software firm after recovering from a cycling accident, but instead would become chief operating officer at Baidu, China』s leading search engine. Later that month, the Association for the Advancement of Artificial Intelligence postponed its annual meeting. The planned date for the event in January conflicted with the Chinese new year.

這兩件事再度顯現了一種可能性：在人工智慧的某些領域，中國將緊隨美國而位居第二，甚至可能趕超美國。人們普遍認為，從數字助手到自動駕駛汽車，人工智慧對一切事物都至關重要。陸奇解釋說，中國無疑是未來的熱點所在，而百度是這個國家最重要的玩家。「我們有機會引領人工智慧的未來。」他說。

These were the latest signals that China could be a close second to America—and perhaps even ahead of it—in some areas of AI, widely considered vital to everything from digital assistants to self-driving cars. China is simply the place to be, explains Mr Lu, and Baidu the country』s most important player. 「We have an opportunity to lead in the future of AI,」 he says.

其他證據支持他的這一說法。2016年10月，白宮在一份報告中提到，中國在深度學習（人工智慧的一個分支）領域的期刊論文發表數量已超過美國。諮詢公司普華永道預測，到2030年，與人工智慧有關的增長將令全球GDP增長16萬億美元。它估計這筆「橫財」有將近一半都將流向中國。近年來，中國研究人員提交的與人工智慧相關的專利申請數量已增加近200%，儘管美國在絕對數量上仍然領先（見圖表）。

Other evidence supports the claim. In October 2016 the White House noted in a report that China had overtaken America in the number of published journal articles on deep learning, a branch of AI. PwC, a consultancy, predicts that AI-related growth will boost global GDP by $16trn by 2030; nearly half of that bonanza will accrue to China, it reckons. The number of AI-related patent submissions by Chinese researchers has increased by nearly 200% in recent years, although America is still ahead in absolute numbers (see chart).

要了解中國為何佔據優勢，不妨試想一下人工智慧所需的投入。其中最基本的兩種投入——計算能力和資本，在中國十分充裕。從阿里巴巴和騰訊這樣的巨頭到興業數金和UCloud等創業公司都在儘可能快地打造數據中心。諮詢公司高德納（Gartner）稱，近年中國雲計算市場保持了超過30%的增速，而且這一勢頭還將繼續。據烏鎮智庫的數字，2012年至2016年，中國的人工智慧公司獲得了26億美元的資金投入。這一數字少於美國同行獲得的179億美元，但總額正在迅速增長。

To understand why China is so well placed, consider the inputs needed for AI. Of the two most basic, computing power and capital, it has an abundance. Chinese firms, from giants such as Alibaba and Tencent to startups such as CIB FinTech and UCloud, are building data centres as fast as they can. The market for cloud computing has been growing by more than 30% in recent years and will continue to do so, according to Gartner, a consultancy. In 2012-16 Chinese AI firms received $2.6bn in funding, according to the Wuzhen Institute, a think-tank. That is less than the $17.9bn that poured into their American peers, but the total is growing quickly.

然而，真正讓中國成為人工智慧福地的其實是另外兩大資源。一是研究人才。微軟人工智慧事業部門的負責人沈向陽說，中國除了數學很強，還有語言及翻譯研究的傳統。阿里巴巴的閔萬裏手下有150名數據科學家。他說，在中國，尋找頂尖的人工智慧專家要比在美國難。不過他預測這種局面在兩三年里就會發生改變，因為多數大型高校都開設了人工智慧項目。據一些估計數字，全世界培養的人工智慧科學家中，超過五分之二在中國。

Yet it is two other resources that truly make China a promised land for AI. One is research talent. As well as strong skills in maths, the country has a tradition in language and translation research, says Harry Shum, who leads Microsoft』s AI efforts. Finding top-notch AI experts is harder in China than in America, says Wanli Min, who oversees 150 data scientists at Alibaba. But this will change over the next couple of years, he predicts, because most big universities have launched AI programmes. According to some estimates, China has more than two-fifths of the world』s trained AI scientists.

中國的第二個優勢是數據，這也是人工智慧最重要的「原料」。過去，軟體和數字產品主要是按代碼編寫的規則行事，因而擁有最優秀的編程人員的國家佔據優勢。隨著深度學習演算法的出現，這些規則日益建立於從大量數據中提取的各種規律之上。供試用的數據越多，演算法能學到的東西就越多，人工智慧所提供的服務也就越智能。

The second advantage for China is data, AI』s most important ingredient. In the past, software and digital products mostly obeyed rules laid down in code, giving an edge to those countries with the best coders. With the advent of deep-learning algorithms, such rules are increasingly based on patterns extracted from reams of data. The more data are available, the more algorithms can learn and the smarter AI offerings will be.

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