Humans have had a good run2. But with the most recent breakthrough in robotics， it is clear that their time as masters of planet Earth has come to an end. 人类虽已拔得头筹，占得先机，但近年来机器人領域科学技术的重大突破，清楚地表明人类作为地球统治者的时代已宣告结束。
Computers have already proved better than people at playing chess and diagnosing diseases. But now a group of artificial-intelligence researchers in Singapore have managed to teach industrial robots to assemble an IKEA chair—for the first time uniting the worlds of Allen keys and Alan Turing3. Now that machines have mastered one of the most baffling4 ways of spending a Saturday afternoon， can it be long before AIs rise up and enslave5 human beings in the silicon mines6？
The research also holds a serious message7. It highlights a deep truth about the limitations of automation. Machines excel at the sorts of abstract， cognitive tasks that， to people， signify intelligence—complex board games8， say， or differential calculus9. But they struggle with physical jobs， such as navigating10 a cluttered11 room， which are so simple that they hardly seem to count as12 intelligence at all. The IKEA-bots are a case in point13. It took a pair of them， pre-programmed by humans， more than 20 minutes to assemble a chair that a person could knock together14 in a fraction of15 the time.
AI researchers call that observation Moravec’s paradox16， and have known about it for decades. It does not seem to be the sort of problem that could be cured with a bit more research. Instead， it seems to be a fundamental truth： physical dexterity17 is computationally harder than playing Go18. That humans do not grasp this is a side-effect19 of evolution. Natural selection has had billions of years to attack the problem of manipulating the physical world， to the point where it feels effortless. Chess， by contrast， is less than 2，000 years old. People find it hard because their brains are not wired for20 it.
That is something to bear in mind when thinking about the much-hyped21 effects of AI and automation， especially as AI moves out of the abstract world of data and information and into the real world of things you can drop on your foot22. Machines may soon be able to drive delivery vans. But， at least for now， they could well fail to carry a parcel to a flat at the top of a flight of slippery stairs， especially if the garden was patrolled by a dangerous dog.
Today’s AI systems are limited in other ways， too. They are pattern-recognition engines23， trained on thousands of examples in the hope that the rules they infer24 will continue to apply in the wider world. But they apply those rules blindly， without a human-like understanding of what they are doing or an ability to improvise25 a solution on the spot26. Makers of self-driving cars， for instance， worry constantly about how their machines will perform in “edge cases27”—complicated and unusual situations that cannot be foreseen during training.
计算机已证明自己在棋类游戏和疾病诊断方面要优于人类。但一个人工智能研究团队在新加坡进行了一场实验，让工业机器人组装宜家的椅子，这是第一次将现实世界与人工智能连接起来。既然机器已掌握一种最令人困惑的方式来消磨一个周六下午的时间，那么是不是在不久的将来，人工智能便可在硅谷奴役人类？
这项研究还释放出另一个重要的信号。它揭示了有关自动化局限性的一个深刻真相。机器擅长处理一些抽象、认知类任务，例如复杂的棋类游戏或微积分，对人类来说完成这类任务意味着智商够高。但是机器处理现实世界的问题就很难，比如穿过一个杂乱房间这种根本算不上智能的简单任务。宜家家具组装机器人就是一个很好的例子。预设定程序的两个机器人组装一把椅子需要20多分钟，而一般人很短时间就能装好。
人工智能研究者把这一现象称为莫拉维克悖论，该悖论已提出了几十年。这类问题看起来并不能通过更进一步的研究去解决。相反，这似乎更像是一个基本法则，物理上的灵巧性对计算机而言远远比玩围棋难得多。人类没能掌握这一技能是进化的意外结果。人类历经数十亿年物竞天择的演化，不断应付“如何摆平现实世界”这个问题，操控现实世界已易如反掌。相反，国际象棋的历史不到2000年。人类之所以觉得国际象棋难，是因为人脑还没有将其内化为本能。
在思考被大肆炒作的人工智能和自动化的影响时，尤其是人工智能从抽象的数据和信息世界进入真实的世界时，应将上述结论铭记于心。机器不久便能驾驶送快递的小货车了。但是，至少现在看来，机器恐怕没法顺着一段很滑的楼梯爬到公寓顶楼送包裹，尤其是这栋公寓的花园里还有恶狗看门的话。
现今的人工智能系统在其他方面也有局限性。它们是会识别模式的机器，经过成千上万案例的训练推论出各种规则，期待这些规则能继续运用到更广阔的领域里。但是机器对这些规则的运用有盲目性，它们不像人类明白自己在做什么，也缺乏现场解决问题的能力。例如，无人驾驶汽车的研制者就担心机器如何处理一些“极端状况”，即一些不能在平时训练中预见的复杂的非常规情况。