Isaac Newton may have been one of the finest minds of all time, but he turned out to be a miserable investor. “I can calculate the motions of the heavenly bodies, but not the madness of people,” he lamented after losing a fortune in the South Sea bubble.

艾萨克•牛顿(Isaac Newton)可能是有史以来最聪明的人，然而事实证明他是个糟糕的投资者。“我能计算出天体的运动，却不能计算出人的疯狂，”他在南海股票泡沫中损失了一大笔钱以后哀叹道。

Increasingly, however, technology-savvy investors think they can harness mathematics and bleeding edge computer science to predict the ebb and flow of financial markets. Some of the most advanced asset managers are turning to artificial intelligence techniques, with investment algorithms that can autonomously learn, adapt and scour vast data sets for tradable patterns.

然而，越来越多精通技术的投资者认为他们可以利用数学和计算机尖端科技，来预测金融市场的起起伏伏。一些最先进的资产管理公司现在正求助于人工智能(AI)技术，其中包括能够自动学习、适应和搜索大量数据组以研究出可交易的模式的投资算法。

But some “quantitative” financiers (quants) are sceptical that these tools are any more than a somewhat better mousetrap, and argue that areas such as “machine learning” are overhyped and AI used as a marketing gimmick.

但有些“量化”金融家(quant，即量化分析师)怀疑这类工具可能不过是一种高明一点的陷阱。他们认为“机器学习”这类领域被过度炒作，AI则是一种营销噱头。

“Everyone wants the Holy Grail, something they can invest in and it will make 1 per cent a month forever,” says Ewan Kirk, head of Cantab Capital, a Cambridge-based quantitative hedge fund. “I don’t want to be cynical, but I am sceptical.”

“每个人都想要得到‘圣杯’，某种能够投资并且实现1%恒定月回报率的东西，”位于剑桥(Cambridge)的量化对冲基金Cantab Capital的负责人尤安•柯克(Ewan Kirk)表示，“我不想表现得悲观，但我很怀疑。”

David Harding, head of Winton Capital, one of the biggest quantitative hedge funds in the world, is also doubtful that AI represents a quantum leap for the investment industry. “I’m not a Luddite, we’re always interested in new ways to make money. But I have to be very sceptical because I constantly have world-class people showing me miracle cures that don’t actually work,” he says.

全球最大量化对冲基金之一温顿资本(Winton Capital)负责人戴维•哈丁(David Harding)也怀疑，AI并不能给投资业带来重大飞跃。“我不是卢德分子(Luddite)，我们总是对赚钱的新方式感兴趣。因为总有世界级的人物向我展示实际上并没有效果的灵丹妙药，我不得不对此深表怀疑，”他说。

Dramatic improvements in computing power have revolutionised the investment world, with algorithmic traders and investors increasingly influential across markets. Money is pouring into computer-driven hedge funds that have consistently managed to parse signals amid market noise. As a result many money managers are scrambling to hire computer scientists, often pitting them in direct competition for talent with Silicon Valley’s tech giants and hot start-ups.

计算能力的显著提升彻底改变了投资界，依据算法的交易商和投资者在市场上的影响力越来越大。大量资金涌入持续从市场杂音中分析出风向的计算机驱动对冲基金。这导致许多资金管理公司竞相雇佣计算机专家，直接与硅谷技术巨头和热门初创企业争夺人才。

AI is at the forefront of this. The field has also enjoyed several leaps forward in recent years. Most notably, Google’s DeepMind AI arm has created a programme that recently thrashed a legendary player of Go, an ancient Chinese game that is so complex that most experts previously reckoned it would take at least a decade before a computer could beat a human champion.

AI处于领域的最前沿。近年来AI领域也经历了几次飞跃。最引人注目的是，谷歌(Google)旗下DeepMind的AI部门研发的程序，最近打败了一位著名围棋选手。围棋是一种古老的中国游戏，因为过于复杂，大多数专家此前都认为，计算机至少还需要10年才能打败人类围棋冠军。

The potentially wider applications of techniques used by the likes of DeepMind’s AlphaGo algorithm has fuelled optimism that investment management could be on the cusp of another technological revolution, possibly similar in scale to the electronification of markets in the 1970s and 1980s.

DeepMind的AlphaGo这类算法所运用的技术或许还能得到更广泛的应用，这引发了有关投资管理可能即将迎来另一场技术革命的乐观情绪。在规模上，这场革命可能和上世纪七八十年代的市场电子化革命相仿。

“Machine learning and artificial intelligence is going to play a very large role in quant managers, but also with traditional asset managers that are aggressively expanding in this space,” says Osman Ali, a fund manager at Goldman Sachs Asset Management.

“机器学习和人工智能将在量化资产管理中起到极大作用，但传统资产管理公司也会在这个领域大举扩张，”高盛(Goldman Sachs)资产管理部门的基金经理奥斯曼•阿里(Osman Ali)表示。

Popular AI approaches such as machine learning can be used by computers to learn and develop autonomously. For example, a machine learning algorithm can learn to play and master a computer game such as Super Mario independently, at first playing the arcade classic randomly but quickly figuring out how the controls work and how to get to the end of the level.

计算机可以利用机器学习等流行的AI策略自主学习和发展。比如，一种机器学习算法可以独立上手和掌握如何玩《超级马里奥》(Super Mario)这样的游戏。一开始算法会随机地玩这款经典街机游戏，但很快算法就能摸清如何操作和通关。

There is therefore widespread enthusiasm over the potential of unleashing machine learning algos to find fleeting but profitable patterns in the vast sea of data.

因此，自由的机器学习算法在海量数据中寻找稍纵即逝的可盈利模式的潜能，引起人们的广泛兴趣。

“I think of algos as little children that can scale tremendously. And you can teach them to read millions of books at the same time,” says Brad Betts, a former Nasa computer scientist working in BlackRock’s San Francisco-based Scientific Active Equity arm.

“我认为算法就相当于拥有巨大潜力的幼童。你可以教它们同时阅读数百万本书，”美国国家航空航天局(NASA)前计算机科学家、现在供职于贝莱德(BlackRock)位于旧金山的“科学主动股票投资”部门的布拉德•贝茨(Brad Betts)表示。

Yet scepticism, even among many quants, is still pervasive. They see areas such as machine learning and deep learning — the latter underpinned DeepMind’s Go exploits — merely as extensions or enhancements of techniques that have for long been in use.

然而，甚至是在很多量化分析师中，怀疑情绪依然普遍。在他们看来，机器学习和深度学习——后者支撑了DeepMind的AlphaGo引人注目的成功——只不过是对已经投入使用很长时间的技术的扩展或加强。

“Lots of people use techniques that could be called machine learning for decades,” argues Robert Hillman, head of Neuron Capital. “There’s a huge difference between image recognition and using AI in markets. Will this be a paradigm change for investing? I don’t think so … It’s not a fundamental change, it’s an efficiency improvement.”

“很多人使用了数十年的一些技术，都可以被称为机器学习技术，”Neuron Capital负责人罗伯特•希尔曼(Robert Hillman)表示，“图片识别和把AI运用到市场之中存在巨大差异。这是否将带来投资的范式转变？我不这么认为……这不是根本性的变化，这是一种效率的提升。”

Mr Kirk points out that most common AI approaches are focused on pattern recognition, such as telling the difference between a cat and a dog in an image. But markets are dominated by noise and chaos, the patterns are harder to find.

柯克指出，最常见的AI策略着重于模式识别，比如区分出图片中的一只猫和一只狗。但市场上充斥着杂音和乱流，要找到模式更为困难。

“As a geek I’m super-excited about AlphaGo, but it’s a big leap from beating a game with clearly defined rules and objectives and investing,” he says.

“作为一名极客，AlphaGo让我超级兴奋，但从打赢一个有清晰规则和目标的游戏、到进行投资，中间还有巨大的跨度，”他说。

Even quants that are cautiously optimistic on the future of AI in investing warn of many pitfalls. Algorithms that may look ingenious and backrest superbly against historical data have a nasty habit of unravelling when confronted with unforgivingly fickle financial markets.

即使是对AI在投资界的应用前景抱谨慎乐观态度的量化分析师，也警告这个领域存在许多陷阱。一些看起来可能很巧妙、与历史数据完美契合的算法，在面对金融市场的反复无常时却常常出毛病。

“Playing Super Mario might not necessarily work for markets. If you hit the button you always know what will happen, but you don’t in markets,” says another quant at a large hedge fund. “It can take time for it to find the good trades and to optimise them. It can go through a lot of bad trades.”

“能玩《超级马里奥》未必能驾驭市场。当你按下按键的时候，你总是知道会发生什么，但在市场上就不是这样了。”一家大型对冲基金中的另一名量化分析师表示，“算法可能需要时间才能找到好的交易机会并进行优化，可能先要经历很多糟糕的交易。”