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银河国际登录:硅脑战胜人脑 Silicon brainpower on track for victory against human

银河国际登录-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与生俱来公平。上周在釜山展出的人工智能,就比如今用在在线引荐引擎和客户支持系统中的普通机器智能更加有意思。如果它真为能超过所撒谎的水平,它或许不会让真实世界中的大量应用于上一个台阶——尽管历史经验指出,人工智能领域那些更有眼球的突破,未构建人们在它们最疯狂时对它们的希望。

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公司在对棋士世界冠军李世石(Lee Se-dol)的第二局比赛中取得胜利,这令其它距离获得这场五局对战的胜利仅有一步之遥。此前,DeepMind的AlphaGo程序已在人工智能领域引起了注目。如今,它就要为“硅脑”获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年前就建构了这种抹黑模式。当时,该公司的深蓝(Deep Blue)国际象棋计算机击败了世界冠军加里卡斯帕罗夫(Garry Kasparov)。

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那时候,或许人类智力的一个堡垒已被计算机科学攻陷。不过,深蓝更好地是强劲硬件的胜利,而不是一般来说被视作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,国际象棋电脑程序仍然在平稳变革,运用强劲的计算能力,企图预测未来所有有可能的下法,并计算出来当前拟合的一步。由于摩尔定律(Moores Law)不能挡住的行进步伐为计算能力带给了指数式快速增长,深蓝在人机大战中最后大获全胜完全是定局——这只是个时间问题。

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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,深蓝的胜利仍伴着在人们的脑海中,然而它对增进人工智能应用于却没有起着什么起到。尽管该系统可以在狭小的棋盘上生产奇迹,这种奇迹却未传送到纷繁复杂、“没什么章法”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还尝试过一种几乎有所不同的噱头。

当时,依照其创始人名字命名的电脑沃森(Watson),在美国电视智力解说竞赛节目《危险性边缘》(Jeopardy!)中,与几名人类的最佳运动员对战。这一次,IBM让自己面临的挑战是解决问题“自然语言处置”的知名难题,即解读语言的含义,即使这种含义隐蔽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. 沃森的顺利是一次人工创造性的胜利。

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IBM采行了研究人员已知悉多年的一系列推理小说策略,通过调整这些策略创建了一个系统,该系统在处置语言时的灵活性多达了此前的想象。这一顺利启动了IBM最有前途的新业务:沃森部门沦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已集中力量将这种技术用作真实世界的商业问题,但对于它原本指出有能力解决问题的确实艰难的问题,该公司到目前为止依然难以解决。

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则是几乎有所不同的一类技术。与国际象棋有所不同,棋士的有可能下法过于多了,计算机无法计算出来。

因此,机器可以采行的唯一办法是通过模式识别“解读”棋局的进展,再行设计出有一种策略并动态调整。因此,这样的系统必需依赖所谓深度自学技术——人工智能领域近期最难以置信进展的幕后技术——运用由人工神经元构成的网络,分析大量数据,找寻模式和“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让两个棋士程序彼此对局,用于一种被称作“增强自学”的技术,协助该技术重复递归和进化。在对局中,两台电脑分解了自己未曾习过的策略。 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. 人工智能专家依然不确认否该称作新的智能的问世,但似乎,这代表着机器学习演化过程中的某种新的东西。

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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. 谷歌积极开展人工智能研究的目标,一直是为了重塑其核心的互联网业务:它某种程度要通过其现有的搜索引擎展示出涉及信息,还要解读并预测用户的市场需求并获取建议。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. 至于谷歌究竟能在这次弈棋胜利的基础上回头多近,还很难辨别。不过,李世石似乎遭遇了一次活生生的展出。在赛前拒绝接受英国《金融时报》专访时,他对电脑获得胜利的可能性不屑一顾。

最少,刻薄仍然是人类没受到挑战的一种能力。。

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