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Is a self-driving car smarter than a seven-month-old?
自动驾驶汽车和7个月大的婴儿，谁更聪明?
By the age of seven months, most children have learned that objects still exist even when they are out of sight. Put a toy under a blanket and a child that old will know it is still there, and that he can reach underneath the blanket to get it back. This understanding, of “object permanence”, is a normal developmental milestone, as well as a basic tenet of reality. It is also something that self-driving cars do not have. And that is a problem. Autonomous vehicles are getting better, but they still don’t understand the world in the way that a human being does. For a self-driving car, a bicycle that is momentarily hidden by a passing van is a bicycle that has ceased to exist.
对于大多数婴儿来说，到了七个月大的时候，他们已经可以意识到：有些东西即使是在看不见的情况下仍然存在。例如把玩具放在毯子下面，那么大点的孩子依然知道它还在那里，而且可以把手伸进毯子下面拿出来。这种对“客体永恒性”的理解是一个孩子正常发育的过程，也是现实存在的基本原则之一。然而，这却是当前自动驾驶汽车所不具备的，这是个问题。自动驾驶汽车正在变得越来越好，但它们仍然不能像人类那样理解世界。对于自动驾驶汽车来说，一辆被路过的面包车暂时遮挡起来的自行车就是一辆已经不复存在的自行车。
This failing is basic to the now-widespread computing discipline that has arrogated to itself the slightly misleading moniker of artificial intelligence (AI). Current AI works by building up complex statistical models of the world, but it lacks a deeper understanding of reality. How to give AI at least some semblance of that understanding—the reasoning ability of a seven-month-old child, perhaps—is now a matter of active research.
这一弱项是如今被广泛使用的计算机学科面临的挑战之一，该学科自诩为人工智能(AI)，但实际上这是个略带误导性的名称。目前的人工智能是通过建立复杂的世界统计模型来工作的，但它缺乏对现实的更深层次理解。如何让人工智能至少具备一些类似的理解能力——也许是一个7个月大的孩子的推理能力——现在是一个积极的研究课题。
Modern AI is based on the idea of machine learning. If an engineer wants a computer to recognise a stop sign, he does not try to write thousands of lines of code that describe every pattern of pixels which could possibly indicate such a sign. Instead, he writes a program that can learn for itself, and then shows that program thousands of pictures of stop signs. Over many repetitions, the program gradually works out what features all of these pictures have in common.
现代人工智能是基于机器学习的理念。如果工程师想让计算机识别停车标志，他不会试图编写数千行代码来描述可能指示这种标志的每一种像素模式。取而代之的是，他会编写可以自我学习的程序，然后向该程序展示了数千张停车标志的图片。经过多次重复训练，该程序就能够逐渐找出所有这些图片的共同特征。
