by Andrew Batson @andrewbatson.com
智庫 Gavekal Dragonomics 的中國專家 Andrew Batson 對媒體和投資人投以大量關注的 “中美 AI 競賽” 提出了他的分析。他認爲將其和冷戰時期美蘇 “太空競賽” 做類比不太恰當，因爲不像冷戰時期有發射衛星、登陸月球等具體目標，兩國現在對 AI 未來能做到什麼程度都還沒什麼概念。 不過至少以目前的 AI 發展階段來看，中國的特殊環境的確有一定優勢。
One of the most intense areas of focus is artificial intelligence, where recent rapid breakthroughs have captivated investors and the media—and where China has emerged as the main US rival.
In brief, I think China will do well in artificial intelligence, in part because the technology is now in a phase that plays to its strengths. But it does not make sense to think of the US and China being engaged in an artificial-intelligence “race” along the lines of the US-Soviet space race.
對 AI 的定義 —— 能執行某些認知任務的電腦系統：
A more precise definition of artificial intelligence, closer to that used in the industry, would be: the development of computer systems that can perform tasks associated with human intelligence, such as understanding speech, playing games, or driving cars.
Machine learning is essentially a way of building better algorithms. That means it could be applied to almost any process that already uses software—which, in today’s world, is quite a lot—as well as many new processes that could not be effectively automated before.
機器學習走出象牙塔，開始在現實生活中發揮作用。通常是跟隨同一套模式 —— 準備大量的標記數據，用機器學習將其辨識自動化，有明確的成敗標準：
The key point is that machine learning has now moved from a pure research phase into a practical development phase. According to Oren Etzioni of the Allen Institute for Artificial Intelligence, all of the major recent successes in machine learning have followed the same template: apply machine-learning algorithms to a large set of carefully categorized data to solve problems in which there is a clear definition of success and failure.
Rather than say there is a competition between the artificial intelligence sectors in the US and China, it might be more accurate to say that there is a single, global field of machine-learning research that has a significant presence in both North America (Canada also has some top people) and China.
科幻小說最喜歡的主題 —— 通用人工智慧其實還沒有人知道怎麼做出來：
There is a vague goal of “general purpose artificial intelligence,” which means the kind of thinking, talking computers that are familiar from decades of portrayal in science fiction. But there is no race to make one, since no one knows how.
What we can say is that there are economies of scale and scope in machine-learning research: teams of experts who have successfully developed one machine-learning application themselves learn things that will make them better at developing other machine-learning applications (see this recent paper by Avi Goldfarb and Daniel Trefler for more).
At the moment, it is far from clear what the most commercially important use of machine learning will be. In a way, it is a solution in search of problems.
中國對 AI 的計劃其實不過是堆疊一堆流行詞匯的清單。中國不斷進步的機器學習研究能力最大的受害者不是美國，而是較小的國家們：
China’s development plan for artificial intelligence is mostly a laundry list of buzzwords and hoped-for technical breakthroughs.
China’s current strength in machine learning is the result of a convergence between its own capabilities and the needs of the technology; since both are evolving, this convergence may not be a permanent one.
The biggest loser from this trend, however, is not the US, which already has well-established clusters of machine-learning research, but smaller nations who would also like to become home to such clusters.
The rising tensions between the US and China pose the question of whether a global artificial-intelligence field structured in this way is sustainable, or will be forced to split into national communities. The loss of those exchanges would slow progress in both countries.