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The Kamprad test坎普拉德测试
本文英文部分选自经济学人Leaders版块
IKEA furniture and the limits of AI
宜家家具与人工智能的局限性
Humans have had a good run. But with the most recent breakthrough in robotics, it is clear that their time as masters of planet Earth has come to an end
人类一直把地球经营得很好。但是随着机器人技术取得新突破,显而易见,人类掌控地球的时代已经结束了。
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?
在下棋和诊断疾病方面,计算机已经超过人类。但是现在,新加坡的一群人工智能研究员第一次把艾伦图灵的智慧和艾伦扳手的技术相结合,成功教会机器人组装宜家家具。既然机器人已经解决了如何度过星期六下午这种最令人头疼的事之一,那距离人工智能崛起并奴役人类,让他们开采硅矿还会远吗?
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 (see article).
这项研究也包含了一条重要的信息,它突出了关于自动化的局限性这一深刻的真相。机器擅长各种抽象的、认知的工作,这些工作对人类来说意味着是智能的,比如复杂的棋牌游戏,或微积分。但是他们对体力工作,比如在一间杂乱的房间中穿行,却无能为力,因为这些事情十分简单,根本谈不上智力工作。宜家机器人的例子也正好说明了这点,一个人可以在非常短的时间内完成组装的工作,却要花费由人类提前进行编程的两组机器人20多分钟。
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.
AI研究人员把上述观察称为莫拉维克悖论,并且在几十年前就对其就有所了解。而该悖论也似乎不像是其他难题稍微多做点研究便能够解决。相反,它似乎反映出一个基本的事实:提高体能灵敏度所涉及的电脑计算难度比下棋所涉及的电脑计算难度更大。人类并没领会到这种技能是伴随进化过程所产生的。在自然选择的过程中,人类用了几十亿年的时间来解决如何操纵物质世界这个大难题,以至于到今日人类控制外界显得毫不费力。相反,象棋的历史不足2000年。人们觉得下棋不容易是因为人脑的发展并非以下棋为目的。
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.
考虑到AI和自动化的夸张的广告宣传效应,这点(人工智能的局限)需要我们时刻警惕,尤其在AI 跨出了抽象的数据信息世界并进入了我们的现实生活的时候。4月13号,电动汽车公司特斯拉(Tesla)的老总伊隆·马斯克(Elon Musk)说,困扰他公司高科技工厂的生产问题的部分原因,就是由于过度依赖机器人和自动化。 “人类被低估了。”他曾发推特说道。许多工作都有让机器人棘手的体力部分。例如,机器人可能很快就能开运货车了。但是,至少就现在而言,它们很可能无法将包裹送达有着光滑楼梯的公寓顶上,尤其当花园里还有一只危险的狗在巡逻的情况下。
Not such a silly Billy
并没那么愚蠢
Today’s AI systems are limited in other ways, too. They are pattern-recognition engines, trained on thousands 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 to improvise 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.
现在AI系统在其他方面也有局限性。它们是模式识别引擎,演练数以千计的样例,人们希望他们推断出的规则将继续适用于更广阔的世界。但是,它们盲目应用规则,不能像人类一样思考自己在干什么也不能当场提出新问题的解决方案。例如,自动驾驶的制造者时常担心他们的车辆会在边缘性情况下会如何应对,毕竟演练无法预测实际运行中遇到的复杂的、不寻常的情况。
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.
纠正人们对AI的激进态度十分棘手。研究人员抱怨说,伟大的进步常被快速遗忘:一旦计算机能够做些什么,它就不再被视为“人工智能”。但这些研究人员也比许多权威人士对未来更加谨慎。原则上,没有理由否认计算机在将来某一天能做到人类所能做的一切,甚至更多。但那至少需要几十年的努力。家具组装能解释原因。
翻译组:
Cece,女,消防工作者,CATTI三笔
Doris,女,法律学习者 经济学人粉丝
Aileen,女,大四数学狗 经济学人粉丝
Wesley,男,自由职业,经济学人铁粉
Cyrus,男, 口译民工,经济学人爱好者
Xiaofeng, 女,好奇心重的医疗民工,经济学人粉丝
校核组:
Eva , 女,经贸翻译学生,经济学粉丝
Samantha,女,滑冰狂人,邓伦未婚妻
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观点 |评论|思考
评论一:Samantha
Samantha,女,滑冰狂人,邓伦未婚妻
读书的时候喜欢去宜家,因为好吃又便宜的三文鱼;工作了之后也喜欢去宜家,因为家具好看轻便又容易组装。前些天宜家创始人过世,让我对这个经常去但又没过多关注的地方重新认识了一遍。就像某些优秀的明星,颜值只是最不值得一提的优点。宜家也一样,经典大方的设计使人们在家动手组装时也不会太费力。
不是AI从业者,对高科技产业的态度常是不明觉厉。一会全说人工智能比人还智能,一会又纷纷表示其实人工智能也挺笨的。这次文章的观点就倾向于后者。AI目前还不能对非计算类产品进行思考,所以它赢过了李世石却不能装好一个椅子。这就印证了所谓诗集也是通过高速运转排列组合的产物。
宜家每年要用掉世界上所有森林木材的1%,这些制造出来的家具服务于世界上60%的地区和70%的人口,已在36个国家设有超过300家门店,这些门店每年要接待约7亿人次光顾。我觉得,如果暂时还不能学会组装家具,何不在宜家工厂或者门店的某些环节上设立AI职位,如此庞大的商业帝国,必然能找到新科技试水的一席之地。
数据来源:
https://www.zhihu.com/question/37675775
评论二:一粒粒
一粒粒,女,未知,xx未婚妻
关于文中说的机器擅长抽象、认知工作,不擅长体力工作,这个不太认同哎。
AI 前段时间大火是因为在围棋领域战胜了李世石,还有在医学案例检索中快速准确的定位方案。
这两点其实有个共同点,就是快速“分析大量数据”的能力,这本来就是计算机一直以来的优势,无非是运用方式和范围update了,一旦决策涉及到人类情感等抽象因素,我觉得机器在这方面还是达不到人类的复杂程度。
最近看了一个有意思的美剧,叫“西部世界”,讲的就是一家设计机器人小社会,供客人体验新人生的公司,因领导层观念不一致,底下的机器人在长期受虐待和折磨中逐渐苏醒,产生人类意识的故事。
他们原先所有的情绪,行为都是一行代码决定的,也就是人类控制。后面慢慢失控其实也是人类导致的,加入了“thought”代码,让机器人产生探索的能力。
科幻故事毕竟是故事,但是这也说明真正的类“人”的AI是AI的终极目标,前期往往做的是不太需要复杂思考的体力或运算行为。
如果体力工作没有做好,那一定是这个AI设计还没到合格水平。
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