当今全球经济的技术变化步伐，似乎似乎接管了一个手工工作的算法，可能会导致一个巨大的失业阶层;正如yuval noah harari在2015年的书籍所建议的那样，Homo Deus。但如果金融服务专业人士认为他们是免疫和埋在沙滩上的头脑上，却被遗憾地弄错了。正如机器学习系统正在彻底改变医学，分析数百万数据以改善癌症诊断，可能导致医生在不太遥远的未来无关的作用 - 更不用说出租车司机，教师和律师 - 所以他们也是他们塑造投资管理进行的方式。
很多已经采取了复杂量子资金的兴起，其中一些使用神经网络来产生贸易指标。然而，在过去，算法用于测试和验证前台团队产生的投资假设，最近他们已经进化了使用大型非结构化数据集，以识别市场上的主题/因素。然后交易团队使用这些因素来磨练他们的投资策略。
关键差异是，否则输入的输入被送入交易代码以创建交易信号，现在量子经理正在开发使用机器学习来思考的算法。
这是一个关键的转变，并由谷歌深度队展示如此美妙地展示的转变。
几年来，DeepMind建造了alphago，看它是否可以掌握古老的比赛 - 更复杂的国际象棋版本 - 并击败人类。Alpha Go是喂食了比赛的规则，它播放了数百万次。这是2016年3月的亚光歌曲李塞托，韩国盛大大师并赢得了四场比赛。该算法已经变得如此复杂，在一场比赛中，它提出了这种创新和意外的动作，它完全竹制塞米尔 - 这是机器思考远远超出人类思想的极限。
This prompted the DeepMind team to see if AlphaGo could still beat the human if the rules of the game were removed – could it learn for itself? In 2017, AlphaGo Zero did precisely that, writing its own moves to master the game by learning from scratch, and in doing so it set a new frontier.
alphago零击败它的前身100游戏不仅要为零，但在播放的40天内，通过自我播放，成为有史以来最伟大的球员。这不再是人工智能，这是“加强学习”，机器在思考自己（自主学习）。
在一些最大的最聪明的对冲基金中应用了类似的校长，他们认识到AI和机器学习工具代表竞标中的下一个战场，以筹集资产并成为世界包。像Philippe Laffont的Coatue Management和Paul Tudor Jones'Tudor Investment Corp的经理一直在招聘数据科学家，以便在技术改变游戏规则的情况下装饰他们的自由投资团队。
是否algorithms completely take over from the discretionary trader remains to be seen. However, hedge funds, as we currently know them, will be markedly different in 10 years’ time. Yes, there will still be portfolio managers overlooking the strategy and providing input, but most of the trading will be done by algorithms. And rather than be regarded as abstract systems that operate in the background, these highly prized proprietary algorithms could end up having a seat on investment committees. They will have a voice.
How far hedge fund managers embrace this technological revolution is the $64,000 question but if past history is anything to go by, those who sit on the sidelines and adopt a ‘wait and see’ attitude will lose out. They will disappear. Discretionary long/short equity funds will become a novelty, a throw back to past decades; they will be analog managers in a digital, increasingly interconnected world.
但这并不是说机器将完全接管。事实上，这可能最终是灾难性的。一些，就像埃龙麝香一样，这会争辩说，这可能导致算法“流氓”和故意开始第三次世界大战，以帮助投资组合的防守股增加;算法不关心我们的人类。
Rather, hedge fund managers will see their roles change. After all, machines are superior at number crunching but presently they still lack the ability to see into the future. They have no concept of time or how to think about future market conditions.
已经在进行的角色，将看到男人和机器的更大融合，并将到我的思想，定义明天的对冲基金经理的内容。
Also referred to as centaurs, a new breed of managers running autonomous learning investment strategies is coming through. Often younger millennials who have grown up with the Internet and who are adept at coding, these ALIS managers represent the next frontier of investing. Just as AlphaGo Zero has confounded us with its ability to play Go at a level no human can ever achieve, it is not inconceivable that similar deep learning algorithms will increasingly exert their influence in the financial markets, harvesting ephemeral alpha in ways we have never seen.
ALIS managers are disrupting the old order. Rather than solely relying on market analysts, they are building teams of data scientists and engineers to build advanced quantitative models, with AI and machine learning at the heart of the mission.
去年，在一个名为的迷人白纸自主学习时代的智能投资者- 基于纽约投资公司MOV37的Jeffrey Tarrant，CEO和创始人，详细介绍了AI功能的快速弧。Alis背后的想法是，人类，机器和数据科学的结合被设定为创造投资管理的“第三波”。
Tarrant说，第一波是基本的自由裁量权投资者。第二波是系统的定量管理人员，使用假设驱动的编程和结构化财务数据。
第三波是使用机器学习的Alis经理，这通常是使用非结构化和非财务数据驱动的数据（而不是假设）。
不是每个人都被ai诱惑。
Last week, I was speaking on the subject to Dr. Savvas Savouri, Chief Economist and Partner, ToscaFund Asset Management. He used a dietary analogy to explain his viewpoint. Investing is akin to a healthy balanced diet that consists of food we’ve eaten very recently, and some a while ago. The same applies when digesting information. “Consider the 2016 referendum to leave the EU. It was so similar to 1992 when the UK suddenly left the EMU it was uncanny yet it is very unlikely AI would consider this historical retrospective relevant,” he says.
作出的论点是，由于市场将会做到这一点，不需要返回太远，而且AI将在阅读正在读取历史回顾的信号时迅速利用。嗯，假设大多数市场参与者没有1992年的工作记忆，更不用说1971年？简单的真相，Savouri博士说，“携带经验的伤疤是在任何职业中更好的专业人士;外科医生，一个律师，也是一个投资者。“
替代资金行业确实觉得它矗立在一个令人兴奋的新时代的尖端;一个将看到使用机器学习和AI技术的利用增加了平凡的任务。
In turn, this will free up managers to hone their investment strategies and build investment solutions that more closely match investors' needs.
