硅脑战胜人脑 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来平等。上周在首尔展示的人工智能，就比如今用在在线推荐引擎和客户支持系统中的普通机器智能更有趣。如果它真能达到所吹嘘的水平，它也许会让真实世界中的大量应用上一个台阶——尽管历史经验表明，人工智能领域那些吸引眼球的突破，并未实现人们在它们最火爆时对它们的期望。

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.

上周四，谷歌(Google)旗下的DeepMind公司在对围棋世界冠军李世石(Lee Se-dol)的第二局比赛中取胜，这令它距离取得这场五局对战的胜利仅一步之遥。此前，DeepMind的AlphaGo程序已在人工智能领域引发了关注。如今，它就要为“硅脑”取得里程碑式的胜利了。

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.

人机对战的噱头并不是什么新事物。IBM在19年前就创造了这种炒作模式。当时，该公司的深蓝(Deep Blue)国际象棋计算机打败了世界冠军加里•卡斯帕罗夫(Garry Kasparov)。那时候，似乎人类智力的一个堡垒已被计算机科学攻破。不过，深蓝更多地是强大硬件的胜利，而不是通常被视为智能基础的算法的胜利。

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.

多年来，国际象棋电脑程序一直在稳定进步，运用强大的计算能力，试图预测未来所有可能的下法，并计算当前最优的一步。由于摩尔定律(Moore's Law)不可阻挡的前进步伐为计算能力带来了指数式增长，深蓝在人机大战中最终大获全胜几乎是定局——这只是个时间问题。

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.

二十年后，深蓝的胜利仍回荡在人们的脑海中，然而它对促进人工智能应用却没起到什么作用。尽管该系统可以在狭小的棋盘上制造奇迹，这种奇迹却并未传递到纷繁复杂、“毫无章法”的现实世界现象。

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还尝试过一种完全不同的噱头。当时，依照其创始人名字命名的电脑沃森(Watson)，在美国电视智力问答竞赛节目《危险边缘》(Jeopardy!)中，与几名人类的最佳选手对战。这一次，IBM让自己面对的挑战是解决“自然语言处理”的著名难题，即理解语言的含义，即使这种含义隐藏在双关语和文字游戏中。

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创造性的胜利。IBM采取了研究人员已知晓多年的一系列推理策略，通过调整这些策略建立了一个系统，该系统在处理语言时的灵活性超过了此前的想象。这一成功启动了IBM最有前途的新业务：沃森部门成为该公司数据分析业务的旗舰部门。

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.

不过，尽管IBM已加紧将这种技术用于真实世界的商业问题，但对于它原本认为有能力解决的真正困难的问题，该公司到目前为止仍然难以解决。

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则是完全不同的一类技术。与国际象棋不同，围棋的可能下法太多了，计算机难以计算。因此，机器可以采取的唯一办法是通过模式识别“理解”棋局的进展，再设计出一种策略并实时调整。因此，这样的系统必须依赖于所谓深度学习技术——人工智能领域近期最惊人进展的幕后技术——运用由人工神经元组成的网络，分析大量数据，寻找模式和“背后含义”。

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.

为了教会该系统，DeepMind让两个围棋程序彼此对弈，使用一种被称为“强化学习”的技术，帮助该技术反复迭代和演化。在对弈中，两台电脑生成了自己从未学过的策略。

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.

人工智能专家仍然不确定是否该称之为新智能的诞生，但暗示，这代表着机器学习演化过程中的某种新东西。

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.

谷歌开展人工智能研究的目标，始终是为了重塑其核心的互联网业务：它不仅仅要通过其现有的搜索引擎展示出相关信息，还要理解并预测用户的需求并提供建议。这种技术还可以用在医疗保健等新的市场中。

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.

至于谷歌到底能在这次弈棋胜利的基础上走多远，还很难判断。不过，李世石显然遭遇了一次活生生的展示。在赛前接受英国《金融时报》采访时，他对电脑获胜的可能性不屑一顾。至少，傲慢依然是人类没有受到挑战的一种能力。