外刊 | 這次，機器人終於有智能了？

來自專欄 達達萬事屋

# 經濟學人 #

April 21st-27th 2018

專欄 | Leaders

Artificial intelligence: The Kamprad test

人工智慧：宜家的新嘗試

IKEA furniture and the limits of AI

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 Turing. Now that machines have mastered one of the most baffling ways of spending a Saturday afternoon, can it be long before AIs rise up and enslave human beings in the silicon mines?

在棋類對弈和診斷疾病方面，計算機已經證明了自己有強於人類的優勢。世界沒有止步於此，一組來自新加坡的研究團隊已經成功教會機器人組裝宜家的椅子——這是首次用計算機把體能和智能結合了起來（工具「內六角扳手"Allen Keys代表體力活，人工智慧之父"艾倫·圖靈"Alan Turing代表AI）。這確實是「手殘」星人的福利，但是否就意味著不久的將來人工智慧的崛起和奴役人類時代的到來呢？

之前看過斯坦福大學的《機器人學》公開課後才知道原來要讓機器實現類似人類的動作是這般不容易。此課簡直良心課程，涵蓋機器人運動學、動力學、運動規劃、編程及設計等（授課人： Oussama Khatib教授）。

* baffling: making great mental demands; hard to comprehend or solve or believe

The research also holds a serious message. 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 games, say, or differential calculus. But they struggle with physical jobs, such as navigating a cluttered room, which are so simple that they hardly seem to count as intelligence at all. The IKEAbots are a case in point. It took a pair of them, pre-programmed by humans, more than 20 minutes to assemble a chair that a person could knock together in a fraction of the time.

相比計算機的智能，計算機控制的機器在體能方面乏善可陳。機器可以在很多抽象和認知領域(如複雜的桌游、微分學等)大放異彩，但要讓機器能做出一些動作(如在雜亂的房間中導航)就非常難，大部分時候它們處理起來宛如智障，而不是智能。宜家機器人也是這樣，需要兩個預先編好程序的機器協同工作，要花費二十分鐘組裝好一把椅子；而同樣的任務，人類可以在頃刻間完成。

* differential calculus: 微積分中的微分學(無窮、極限)

* knock together: to produce something quickly and easily

AI researchers call that observation Moravec』s paradox, 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 dexterity is computationally harder than playing Go. That humans do not grasp this is a side-effect 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 for it.

在人工智慧研究學者看來，這並不稀奇，早在幾十年前就提出了莫拉維克悖論。而再多的研究，似乎也無法改變這個事實。要提高機器身體靈活性的計算難度遠超計算下圍棋的難度。其實人之所以會現在這番模樣的和功能，得來額也不輕鬆，經過了上億年的進化。而棋類的歷史只有短短兩千年，會讓人類覺得難，是因為沒有聯網的大腦(反正不擅長也不會危及生命)。

莫拉維克悖論(Moravec』s paradox)

由人工智慧和機器人學者所發現的一個和常識相佐的現象。和傳統假設不同，人類所獨有的高階智慧能力只需要非常少的計算能力，例如推理，但是無意識的技能和直覺卻需要極大的運算能力 (high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources)。

* dexterity: n. great skill in using your hands or your mind

That is something to bear in mind when thinking about the much-hyped 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 foot. On April 13th Elon Musk, the boss of Tesla, an electric-car firm, said that the production problems which have dogged his company』s high-tech factory were partly the result of an overreliance on robots and automation. 「Humans are underrated,」 he tweeted. Lots of jobs have physical aspects that robots struggle with. Machines may soon be able to drive delivery vans, for instance. 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.

在鋪天蓋地對人工智慧和自動化的宣傳下，堅守不畏浮雲遮望眼可以讓視界更清晰，特別是在AI穿越了數據和信息世界到現實中來，成為你可以拿捏的物品之時。特斯拉老闆馬斯克在4月13日就說特斯拉的生產進度慢有部分原因就是過度依賴機器人和自動化，他還在推特中寫了「人類被低估了」(所以願景是讓「機器製造機器」的馬斯克也開始挺人類了么)。在機器吃癟的地方，人類上。機器也許很快就能自己開著貨車去送貨，但至少現在還不能像人類那樣，為了要去送包裹先與屋門口的惡狗周旋一番，再爬上一段滑溜的樓梯，最後才能把包裹送到。

* dog: (often passive) to cause trouble for someone over a long period of time

Not such a silly Billy

* silly Billy: [British spoken] a name for someone, especially a child, who is behaving in a silly way

Today』s AI systems are limited in other ways, too. They are pattern-recognition engines, trained onthousands of examples in the hope that the rules they infer 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 toimprovise a solution on the spot. Makers of self-driving cars, for instance, worry constantly about how their machines will perform in 「edge cases」—complicated and unusual situations that cannot be foreseen during training.

今天的AI在其他方面也有極限。它們遵循模式識別，在成千上萬的例子中受訓。但其實在運用這些習得的規則的時候是盲目的，沒有像人類一樣真的理解自己在做什麼，也沒有能力在不能調用數據的時候，在現場經過思考提出解決方案。例如，作為自動駕駛汽車的製造商，就會擔心汽車在「極端情況」會如何反應——通過訓練無法預見複雜和違反常規的情況。

* improvise: to make something from whatever is available, although it is not what you normally use

* edge case: an edge case is a problem or situation that occurs only at an extreme (maximum or minimum) operating parameter.

Calibrating excitement about AI is tricky. Researchers complain that great progress is quickly forgotten: as soon as a computer can do something, it ceases to count as 「AI」. But those same researchers also tend to be more cautious about the future than many pundits. There is no reason, in principle, why a computer could not one day do everything a human can and more. But that will be the work of decades at least. Furniture-assembly helps explain why.

很難去衡量AI的成果。在研究者看來，AI的每一次大的進步都會很快被遺忘：一旦電腦也可以做同樣的事情，人們就不認為這屬於「人工智慧」。這些研究者對未來的看法也比專家們更謹慎。那些說計算機不能做人類能做到的事，或不能比人類更能幹，這樣的說法是沒有根據的。但確實它們也需要時間去「進化」。就像這些能組裝傢具的機器人一樣，它們還需要迭代。

* pundit: n. someone who is an expert in a subject, and is often asked to talk to the public about that subject