The latest evidence that the robots are coming for your job emerged this March when Google’s DeepMind artificial intelligencesoftware beat the world champion in Go, an ancient Chinese board game similar to chess but with much more complex movements. The exhibition showed that the software not only had mathematical processing power but also was capable of humanlike intuition to make decisions. If artificial intelligence software is now capable of consistently winning a game that has more possible positions than there are atoms in the universe, surely it could replace the job of a Wall Street analyst, right?
有一个健康数量的故事，说机器人正在为华尔街工作而来。魔法猎犬arnold schwarzenegger的图像 - 尽管有一个由科学计算器的一支武装，并授权扼杀分析师的军队 - 这些故事涂了一张不太遥远的未来的图片华尔街被自动化软件取代。Although that makes for a sensational headline, it won’t be the case.
From where I sit on the front lines of data science evolution, it is clear that automation is indeed coming to Wall Street in a big way. But the future of how it will be implemented is a lot lessTerminator比它Iron Man。The central difference between the two, of course, is thatThe Terminatoris about robots taking over and an apocalyptic future, whereasIron Manrepresents what is possible when smart people are empowered by amazing technology.
Big data,algorithmic trading, machine learning and all of the other buzzwords currently shaking up the financial services industry are more about building our future than they are about knocking us down.
当然，这种进化会对华尔街产生影响，提出了许多传统的交易业务方式，同时定义新人的蓬勃发展。然而，最终，该过程将激发金融服务业的增长。
The amount of new information introduced to the world every day is staggering. IBM estimates that 2.5 quintillion bytes of data are created every day. It’s further projected that 1.7 megabytes of new information will be created every second for every human being on the planet by 2020. The task of sorting through that information, reconciling disparate sources, filtering out noise and finding a signal that can support an investment decision has surpassed the capabilities of mere mortals.
在传统的金融研究世界中，通过使用关键字和其他基本搜索技术通过人类过滤，平均研究人员将阅读十个文件来查找一个相关的信息。这根本无法与每天创建的信息山一起扩展。
Thanks to innovations in data science, the way we interact with data is also evolving rapidly. At the most basic level, this type of technology is being used today in online businesses such asNetflixand Amazon.com, which have been able to learn what users like and adjust their offerings based on customer behaviors. In the financial services world, this behavioral customization is making an impact on everything from the design of new research platforms to the process of integrating regulatory and compliance measures directly into the financial professional’s workflow.
随着数据技术的不断发展，我们希望看到超出老式数据终端方法以及完全定制的解决方案，以便最大限度地定制效用和生产力。
Best of all, we see data science having a huge impact on the ability to generate investment insights drawn from seemingly disparate events around the world. Unstructured text in everything from breaking news articles to text messages to tweets can have dramatic andunpredictable impacts on financial markets。想象一下，如果我们能够快速理解这些类型的非结构化信息和市场表现之间存在的相关性。
We can. Companies have built search applications to interrogate vast quantities of unstructured data, allowing for enhanced predictive capabilities at what had been thought unimaginable speed, providing even the most traditional Wall Street analyst with the equivalent ofTony Stark’s arc reactorto power research.
Data science is changing and improving the world of financial markets research and analytics. Those embracing this evolution will soon realize opportunities that were once the stuff of science fiction.
Mike Chinn是总统S&P Global Market Intelligencein Charlottesville, Virginia.
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