2024考研英语：《经济学人》中英读译Day258

英语阅读能力依靠什么？必然是词汇的积累，但，仅仅是词汇的积累是不够了，更应该了解英语句式，不同句型所表达意义。如何培养自己的阅读语感呢？那么就跟着聚创考研网小编每天学一点英语，一起为考研筑起坚实的地基。阅读是需要积累的，就像我们需要经常和人沟通才会促进我们的表达能力一样~小伙伴跟着小编一起，每天坚持打卡，培养英语阅读能力及语感，了解文章一些经典词汇的用法。

经济学人：这种人工智能可以像婴儿一样学习

This is Scientific American's 60-second Science, I'm 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.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 even our most powerful computers when it comes to intuitive physics.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.

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这里是科学美国人——60秒科学系列，我是克里斯托弗·因塔利亚塔。人工智能系统已经在国际象棋、扑克、危险游戏、围棋和无数游戏中击败了人类。但机器在理解物理世界的一些基本规则方面仍然不够好。机器仍然做不到像3个月大的婴儿那样。说到底，我只能当婴儿中的佼佼者，婴儿明显是胜利者。当涉及到直观物理学时，婴儿仍然会碾压我们最强大的计算机。西北大学的认知心理学家苏珊·赫斯波斯列举了几个“直觉物理学”原理的例子。比如“固体性”，咖啡杯不会穿过桌子掉下去。比如“连续性”——物体不会在眨眼之间消失。比如“捆绑性”——当拿起咖啡杯时，会粘连在一起拿过来。不会只拿过来杯子的把手。

 

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.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.

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婴儿早在三个月大的时候就知道这三件事。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，婴儿会做很多错事。这些最初的内核，通过世界上的经验得到了充实和完善。现在，计算机工程师们已经从婴儿的剧本中借鉴了一页。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。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那么灵敏了。

 

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.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.

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很有趣的是，当你做这个直接的比较时，你会发现从经验中学习是很有意义的。但也仅仅到这里而已。基于对婴儿的研究而制造的计算机，表现要好得多。这证实了婴儿研究一段时间以来所显示的证据。该研究结果发表在《自然人类行为》期刊上。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。即使是很小的一步。谢谢大家收听科学美国人——60秒科学。我是克里斯托弗·因塔利亚塔。