中英对照
听力原文
This is Scientific American's 60-second Science, I'm Christopher Intagliata.
这里是《科学美国人》的60秒科学，我是克里斯托弗·因塔利亚塔。
Christopher Intagliata: Artificial intelligence systems have bested humans at chess, poker, Jeopardy, Go, and countless other games. But machines still aren't that great at understanding some basic rules about the physical world.
人工智能系统在国际象棋、扑克、危险游戏、围棋和无数其他游戏中击败了人类。但是在理解物理世界的一些基本规则方面仍然不是很理想。
Susan Hespos: They still can't do what 3-month-olds do. And I'm a champion for babies at the end of the day and this is a clear win for babies. Babies are still slam dunking our most powerful computers when it comes to intuitive physics.
智能机器仍然不能像3个月大的孩子那样。说到底，作为婴儿中的佼佼者，婴儿无疑是胜利者。当涉及到直觉物理学时，婴儿仍然完胜我们最强大的计算机。
Intagliata: Cognitive psychologist Susan Hespos of Northwestern University listed off a few examples of those "intuitive physics" principles. Like "solidity" - your coffee cup does not just fall right through the table. Or "continuity" -- objects don't just blink in and out of existence. And "boundedness" -- when you pick up your coffee cup, it sticks together. You don't end up with just the handle.
西北大学的认知心理学家苏珊·赫斯波斯列举了一些“直觉物理学”的例子。比如“坚固性”一样——咖啡杯不会穿过桌子掉下来，“连续性”——物体不会忽隐忽现，“捆绑性”——当你拿起咖啡杯时，把手和杯子会粘在一起，你不会只拿到一个把手。
Hespos: Babies know all three of these things as early as three months of age. Their visual acuity is lousy, the world is blurry…they could barely grasp this stuff. You know, babies get a lot of things wrong. But it's these initial kernels that get elaborated and refined through experience in the world.
婴儿在三个月大时就知道所有这三件事。他们的视力很差，世界很模糊……他们几乎不能理解这些东西。你知道，婴儿会犯很多错误。但正是这些最初的内核，通过在世界上的经验得到了充实和完善。
Intagliata: Now computer engineers have taken a page from the baby playbook. Researchers at DeepMind -- the AI company that trained computers to beat humans at Go -- have endowed a machine learning system with certain kernels of knowledge about intuitive physics built in… akin to what an infant might be equipped with.
现在计算机工程师已经从婴儿身上得到了启发。DeepMind是一家人工智能公司，该公司训练计算机在围棋中击败人类。这家公司的研究人员为机器学习系统内置了直觉物理学的某些核心知识--类似于婴儿可能具备的东西。
And after watching the equivalent of just 28 hours of training videos, showing things like balls rolling, and blocks dropping -- the AI system actually showed "surprise" when it was shown something physically impossible. Its counterparts not modeled on babies weren't as sharp.
人工智能系统观看了相当于28个小时的训练视频，这个视频展示了球体滚动和砖块掉落，在此之后，当系统看到一些物理上不可能的事情时，它实际上会表现出“惊讶”。其他不以婴儿为模型的人工智能系统就没这么灵敏了。
Hespos: It's really interesting that when you do this direct comparison what you find is learning from experience goes far. But only so far. And the computer that was built based on research on babies, did far better. It's confirming evidence for what baby research has shown for a while, just using something very different from a baby.
非常有趣的是，当你进行这种直接比较时，你发现从经验中学到的东西意义深远。但仅此而已。而基于对婴儿的研究而构建的系统，表现要好得多。这就证实了一段时间以来婴儿研究显示的证据，只是使用了与婴儿截然不同的东西。
Intagliata: The results appear in the journal Nature Human Behavior.
结果发表在《自然人类行为》杂志上。
Hespos wasn't involved in the work, but wrote an editorial accompanying the paper. She says the research is a step towards making machine learning systems more efficient thinkers -- like humans. Even the tiny ones.
赫斯波斯没有参与这项工作，但为该论文撰写了一篇社论。她表示，这项研究朝着让机器学习系统像人类一样更高效地思考迈出了一步。哪怕是很小的一步。
Thanks for listening for Scientific American's 60-second Science. I'm Christopher Intagliata.
感谢收听《科学美国人》的60秒科学。我是克里斯托弗·因塔利亚塔。
This is Scientific American's 60-second Science, I'm Christopher Intagliata.
Christopher Intagliata: Artificial intelligence systems have bested humans at chess, poker, Jeopardy, Go, and countless other games. But machines still aren't that great at understanding some basic rules about the physical world.
Susan Hespos: They still can't do what 3-month-olds do. And I'm a champion for babies at the end of the day and this is a clear win for babies. Babies are still slam dunking our most powerful computers when it comes to intuitive physics.
Intagliata: Cognitive psychologist Susan Hespos of Northwestern University listed off a few examples of those "intuitive physics" principles. Like "solidity" - your coffee cup does not just fall right through the table. Or "continuity" -- objects don't just blink in and out of existence. And "boundedness" -- when you pick up your coffee cup, it sticks together. You don't end up with just the handle.
Hespos: Babies know all three of these things as early as three months of age. Their visual acuity is lousy, the world is blurry…they could barely grasp this stuff. You know, babies get a lot of things wrong. But it's these initial kernels that get elaborated and refined through experience in the world.
Intagliata: Now computer engineers have taken a page from the baby playbook. Researchers at DeepMind -- the AI company that trained computers to beat humans at Go -- have endowed a machine learning system with certain kernels of knowledge about intuitive physics built in… akin to what an infant might be equipped with.
And after watching the equivalent of just 28 hours of training videos, showing things like balls rolling, and blocks dropping -- the AI system actually showed "surprise" when it was shown something physically impossible. Its counterparts not modeled on babies weren't as sharp.
Hespos: It's really interesting that when you do this direct comparison what you find is learning from experience goes far. But only so far. And the computer that was built based on research on babies, did far better. It's confirming evidence for what baby research has shown for a while, just using something very different from a baby.
Intagliata: The results appear in the journal Nature Human Behavior.
Hespos wasn't involved in the work, but wrote an editorial accompanying the paper. She says the research is a step towards making machine learning systems more efficient thinkers -- like humans. Even the tiny ones.
Thanks for listening for Scientific American's 60-second Science. I'm Christopher Intagliata.
