我对人工智能的担忧在于：当使用者没有深刻理解机器学习创造的算法所假定的因果关系时，就已接受了它们（甚至根据它们来行动）。在解释原因之前，我想先阐明我的用词。人们经常轻率地使用“人工智能”和“机器学习”，并将其作为同义词，但其实二者大不相同。我把当前的计算机辅助决策技术分为三大类：专家系统、模仿和数据开采（这是我的分类方式，而非科技界常用的分类）。
我们在桥水使用的是专家系统，设计者根据自己对一系列因果关系的逻辑性理解将决策标准表述出来，然后观察不同条件下会出现什么不同情况。但计算机也能发现规律并将其应用于计算机决策，而不需理解这些规律背后的逻辑。我把这种决策技术称为“模仿”。当同样的情况以可靠的方式反复不变地发生时，例如在一场规则极其严格的游戏中，这一做法也许有效。但现实世界中事物不断变化，所以这样的系统很容易与现实脱节。
I worry about the dangers of AI in cases where users accept—or, worse, act upon—the cause-effect relationships presumed in algorithms produced by machine learning without understanding them deeply.
Before I explain why, I want to clarify my terms. “Artificial intelligence” and “machine learning” are words that are thrown around casually and often used as synonyms, even though they are quite different. I categorize what is going on in the world of computer-aided decision making under three broad types: expert systems, mimicking, and data mining (these categories are mine and not the ones in common use in the technology world).
Expert systems are what we use at Bridgewater, where designers specify criteria based on their logical understandings of a set of cause-effect relationships, and then see how different scenarios would emerge under different circumstances.
But computers can also observe patterns and apply them in their decision making without having any understanding of the logic behind them. I call such an approach “mimicking.” This can be effective when the same things happen reliably over and over again and are not subject to change, such as in a game bounded by hard-and-fast rules. But in the real world things do change, so a system can easily fall out of sync with reality.
