硅腦戰勝人腦 Silicon brainpower on track for victory against human mind

Not all artificial intelligence is created equal. The variant that has been on display in Seoul this week is of a more intriguing kind than the run-of-the-mill machine intelligence used in today’s online recommendation engines and customer support systems. If it can live up to the hype, it may bring a step-change in a wide range of real-world applications — though history suggests that eye-catching breakthroughs in AI fail to deliver as much as hoped for at their moment of maximum prominence.

不是所有的人工智能都生來平等。上週在首爾展示的人工智能，就比如今用在在線推薦引擎和客戶支持系統中的普通機器智能更有趣。如果它真能達到所吹噓的水平，它也許會讓真實世界中的大量應用上一個臺階——儘管歷史經驗表明，人工智能領域那些吸引眼球的突破，並未實現人們在它們最火爆時對它們的期望。

Yesterday, Google’s DeepMind subsidiary won its second game of Go against Lee Se-dol, world champion of the ancient board game, putting it on the brink of victory in a five-game series. DeepMind’s program, AlphaGo, had already turned heads in the AI world. Now, it is on track to notch up a landmark victory for silicon brainpower.

上週四，谷歌(Google)旗下的DeepMind公司在對圍棋世界冠軍李世石(Lee Se-dol)的第二局比賽中取勝，這令它距離取得這場五局對戰的勝利僅一步之遙。此前，DeepMind的AlphaGo程序已在人工智能領域引發了關注。如今，它就要爲“硅腦”取得里程碑式的勝利了。

Publicity stunts that pit man against machine are nothing new. IBM set the pattern 19 years ago, when it’s Deep Blue chess-playing computer beat world champion Garry Kasparov. At the time, it seemed that a citadel of human intelligence had fallen to computer science. But Deep Blue was more a victory for powerful hardware than the algorithms normally thought of as the basis of intelligence.

人機對戰的噱頭並不是什麼新事物。IBM在19年前就創造了這種炒作模式。當時，該公司的深藍(Deep Blue)國際象棋計算機打敗了世界冠軍加里•卡斯帕羅夫(Garry Kasparov)。那時候，似乎人類智力的一個堡壘已被計算機科學攻破。不過，深藍更多地是強大硬件的勝利，而不是通常被視爲智能基礎的算法的勝利。

Computer chess programs had been making steady progress for years, using brute number-crunching to try to anticipate all possible future moves and calculate the best one available. Thanks to the inexorable advance of Moore’s law — bringing exponential increases in computing capacity — it was almost inevitable that Deep Blue would crush the human competition in the end: it was just a matter of time.

多年來，國際象棋電腦程序一直在穩定進步，運用強大的計算能力，試圖預測未來所有可能的下法，並計算當前最優的一步。由於摩爾定律(Moore's Law)不可阻擋的前進步伐爲計算能力帶來了指數式增長，深藍在人機大戰中最終大獲全勝幾乎是定局——這只是個時間問題。

Two decades later, the Deep Blue victory still reverberates but it did little to advance the uses of AI. While the system could perform miracles in the narrow grid of a chessboard, that didn’t translate to the messy, “unstructured” nature of real-world phenomena.

二十年後，深藍的勝利仍迴盪在人們的腦海中，然而它對促進人工智能應用卻沒起到什麼作用。儘管該系統可以在狹小的棋盤上製造奇蹟，這種奇蹟卻並未傳遞到紛繁複雜、“毫無章法”的現實世界現象。

IBM tried an altogether different stunt in 2011, when Watson — a computer named after its founder — took on the best human champions in the US TV quiz show Jeopardy. This time, IBM had set itself the challenge of cracking the notoriously difficult task of “natural language processing” — understanding the meaning of language, even when it is veiled in puns and word games.

2011年，IBM還嘗試過一種完全不同的噱頭。當時，依照其創始人名字命名的電腦沃森(Watson)，在美國電視智力問答競賽節目《危險邊緣》(Jeopardy!)中，與幾名人類的最佳選手對戰。這一次，IBM讓自己面對的挑戰是解決“自然語言處理”的著名難題，即理解語言的含義，即使這種含義隱藏在雙關語和文字遊戲中。

Watson’s success was a victory for engineering ingenuity. IBM had taken a collection of reasoning strategies known to researchers for years, and tuned them to create a system more supple in its handling of language than previously thought possible. It launched IBM’s most promising new business: the Watson division became the flagship of the company’s data analytics operation.

沃森的成功是一次人工創造性的勝利。IBM採取了研究人員已知曉多年的一系列推理策略，通過調整這些策略建立了一個系統，該系統在處理語言時的靈活性超過了此前的想象。這一成功啓動了IBM最有前途的新業務：沃森部門成爲該公司數據分析業務的旗艦部門。

But while IBM has raced to apply the technology to real-world business problems, it has struggled so far to pull off the really difficult tasks it hoped were within its grasp.

不過，儘管IBM已加緊將這種技術用於真實世界的商業問題，但對於它原本認爲有能力解決的真正困難的問題，該公司到目前爲止仍然難以解決。

DeepMind, by contrast, is a different class of technology altogether. Unlike chess, Go permits too many possible moves for a computer to calculate. As a result, the only approach a machine can take is to use pattern-recognition to “understand” how a game is developing, then devise a strategy, and adapt it on the fly. A system must therefore rely on so-called deep learning — the technology behind the most startling recent advances in AI — applying networks of artificial neurons to sort through masses of data in the search for patterns and “meaning”.

相比之下，DeepMind則是完全不同的一類技術。與國際象棋不同，圍棋的可能下法太多了，計算機難以計算。因此，機器可以採取的唯一辦法是通過模式識別“理解”棋局的進展，再設計出一種策略並實時調整。因此，這樣的系統必須依賴於所謂深度學習技術——人工智能領域近期最驚人進展的幕後技術——運用由人工神經元組成的網絡，分析大量數據，尋找模式和“背後含義”。

To teach its system, DeepMind set two Go-playing programs against each other, using a technique known as “reinforcement learning” to help the technology iterate and adapt. In competition, the two computers came up with strategies that neither on its own had learnt.

爲了教會該系統，DeepMind讓兩個圍棋程序彼此對弈，使用一種被稱爲“強化學習”的技術，幫助該技術反覆迭代和演化。在對弈中，兩臺電腦生成了自己從未學過的策略。

AI experts are hesitant about calling this the birth of a new intelligence, but suggest it represents something new in the evolution of computer learning.

人工智能專家仍然不確定是否該稱之爲新智能的誕生，但暗示，這代表着機器學習演化過程中的某種新東西。

Google’s goal for its AI research has been nothing less than a remaking of its core internet business: not just to present relevant information through its existing search engine, but to understand and anticipate its users’ needs and present advice. This technology could also be applied in new markets, such as healthcare.

谷歌開展人工智能研究的目標，始終是爲了重塑其核心的互聯網業務：它不僅僅要通過其現有的搜索引擎展示出相關信息，還要理解並預測用戶的需求並提供建議。這種技術還可以用在醫療保健等新的市場中。

Quite how well Google can build on its board game success remains hard to judge. But Mr Lee has clearly been on the receiving end of a highly visible demonstration. Speaking to the Financial Times in advance of the contest, he was dismissive about the chance of a computer victory. At least hubris remains an unchallenged human capability.

至於谷歌到底能在這次弈棋勝利的基礎上走多遠，還很難判斷。不過，李世石顯然遭遇了一次活生生的展示。在賽前接受英國《金融時報》採訪時，他對電腦獲勝的可能性不屑一顧。至少，傲慢依然是人類沒有受到挑戰的一種能力。