4.6 考研英语外刊阅读模拟试题【难度9.0】

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Section II Reading Comprehension 

Part A 

Directions: Read the following four texts. Answer the questions below each text by choosing A, B, C or D. Mark your answers on the ANSWER SHEET. 

Text 4

In February, Cortical Labs, an Australian startup, announced that a programmer had taught one of its “biological computers”—made of 200,000 human brain cells mounted on a silicon chip—to play “Doom”, a classic first-person shooter game. The firm had previously taught a collection of brain cells to play the much simpler “Pong”. Its ambitions are much bigger than video games, however. It hopes that neurons, packaged into super-efficient “biological computers” and slotted into racks at conventional data centres, might one day take their place alongside the transistor-packed chips of silicon that have defined conventional computing for the past half-century.

At the heart of Cortical’s system is an array of thousands of tiny electrodes, upon which sit neurons grown from stem cells taken from a human donor. The array allows a conventional computer to both pick up the electrical activity generated by those neurons and to stimulate them with electrical activity of its own. The neurons are kept alive for up to six months by tubes and pumps that supply oxygen and nutrients, and remove cellular waste products. The whole thing is packaged into a box designed to fit in the standard server racks used in commercial data centres.

Neurons offer several possible advantages over electronics when it comes to computing. Efficiency is one. Modern artificial-intelligence models gulp power by the millions of watts, and that demand for energy has become one of the biggest barriers to the industry’s growth. Neurons, by contrast, sip power: a typical human brain, made up of almost 90 billion of them, consumes something in the region of 20 watts.

Sophistication is another. The transistors from which electronic computers are built are tiny switches that can be in one of two states: on or off. Neurons are more complicated. Their behaviour depends on all sorts of variables, including the voltage across the cell’s membrane. Existing computer architectures also store information far from where the actual processing happens. Micron, a big maker of memory chips, estimates that up to half the energy budget of a conventional AI processor is spent shifting data around. It also causes traffic jams as data is shuttled back and forth. Brains mix data and processing side-by-side, minimising such logistical issues.

Brett Kagan, a neuroscientist and Cortical’s chief scientific officer, speculates that all this may make neurons better suited than electronics to some types of computational work, especially those involving the interpretation of messy, analogue signals common in the real world. He cites “Moravec’s paradox”, a long-standing observation in AI research that suggests that abstract reasoning—playing high-level chess or multiplying huge numbers—is computationally easier than the trivial-seeming motor skills needed to navigate the physical world. While modern AI models can excel at abstract maths, they cannot do something simple like making a cup of tea.

Realising this vision will be tricky. Cortical is still experimenting with how best to translate signals between electronic computers and living cells. Furthermore, it is swimming against a powerful tide. Big tech firms and AI labs are betting hundreds of billions of dollars that the future of computing involves doubling down on standard electronics.

To build momentum, Cortical has opened its technology to anyone who fancies playing with it, connecting some of its computers to the internet. Around 5,500 people have already experimented with it. And it will have allies in high places, too. DARPA, an agency of the American government that funds high-risk technologies, recently announced a research funding programme into biological computing, hoping to produce “biological processing units” that might one day prove useful for autonomously flying drones and tackling the messy real world.

附注：根据历年考研英语真题阅读题源外刊等，摘选最新文章，模拟仿真出题。

参考答案见以下。

Quick look: CCABC

36.【正确答案】C【解析】题型：事实细节题定位： 第一段第三、四句“Its ambitions are much bigger than video games, however. It hopes that neurons, packaged into super-efficient ‘biological computers’ and slotted into racks at conventional data centres, might one day take their place alongside the transistor-packed chips...”分析： 原文指出，Cortical Labs的野心远不止于电子游戏，它希望将神经元包装成高效的“生物计算机”，并插入传统数据中心的机架中（slotted into racks at conventional data centres），与传统的硅芯片并驾齐驱。这表明其最终目标是将生物处理器整合到现有的计算基础设施中。选项 C 准确概括了这一意图。干扰项：[A] 彻底改变经典第一人称射击游戏的设计，文章明确表示其野心“远不止电子游戏（much bigger than video games）”；[B] 在高级医学研究中完全取代人类捐献者，无中生有；[D] 延长实验室中孤立人类干细胞的寿命，维持细胞存活只是第二段提到的技术手段，并非公司的“最终野心（ultimate ambition）”。

37.【正确答案】C【解析】题型：因果细节题定位： 第四段第四、五、六句“Existing computer architectures also store information far from where the actual processing happens. Micron... estimates that up to half the energy budget... is spent shifting data around. It also causes traffic jams as data is shuttled back and forth.”分析： 原文在解释神经元的复杂性和优势时，对比了现有的计算机架构，指出传统架构将信息存储在远离实际处理发生的地方，导致高达一半的能源预算都花在“转移数据（shifting data around）”上，并且数据来回穿梭会造成“交通拥堵（traffic jams）”。选项 C“数据存储和处理的物理分离导致了物流拥堵”完美对应了原文的解释。干扰项：[A] 晶体管在不可预测的状态之间频繁交替，第四段第二句提到晶体管只有开或关两种状态（in one of two states: on or off），并非不可预测；[B] 努力维持细胞膜两端的最佳电压，这是第四段提到的“神经元（Neurons）”的特征，而非传统AI处理器；[D] 需要大型冷却泵来清除积累的细胞废物，这是第二段描述“生物计算机（保持神经元存活）”的装置，属于张冠李戴。

38.【正确答案】A【解析】题型：概念理解题定位： 第五段第二、三句“He cites ‘Moravec’s paradox’... suggests that abstract reasoning—playing high-level chess or multiplying huge numbers—is computationally easier than the trivial-seeming motor skills needed to navigate the physical world. While modern AI models can excel at abstract maths, they cannot do something simple like making a cup of tea.”分析： 莫拉维克悖论指出，对于人工智能来说，抽象推理（如高级国际象棋或巨大的数字乘法）在计算上比在物理世界中导航所需看似微不足道的运动技能要容易得多（computationally easier than... motor skills）。现代AI擅长抽象数学，却连泡茶这样简单的事都做不到。选项 A“它们擅长抽象推理但在基本物理交互上失败”精准传达了该悖论的内涵。干扰项：[B] 在执行复杂数学计算时消耗更少的能量，悖论讨论的是“计算难度”而非“能耗”；[C] 解释混乱模拟信号比人脑更有效率，原文第一句指出“神经元（人脑）”比电子设备更适合解释混乱的模拟信号；[D] 完全依赖人类程序员来导航真实的物理世界，原文未提及依赖人类程序员导航物理世界。

39.【正确答案】B【解析】题型：句意推断题定位： 第六段第二、三句“Furthermore, it is swimming against a powerful tide. Big tech firms and AI labs are betting hundreds of billions of dollars that the future of computing involves doubling down on standard electronics.”分析： 作者指出Cortical Labs正在“逆流而上（swimming against a powerful tide）”，紧接着在下一句解释了这股“强大的潮流”是什么：大型科技公司和AI实验室正押注数千亿美元，在标准电子产品（standard electronics，即硅基芯片）上加倍下注。这说明科技界的巨大投资都集中在传统路线上，生物计算属于逆势而为。选项 B“科技行业在传统的硅基电子产品上投入了巨资”是对这股“潮流”的准确解读。干扰项：[A] 电信号的翻译在理论上仍然是不可能的，前一句说正在实验“如何最好地翻译（how best to translate）”，并未说理论上不可能；[C] 主要大学限制学生参与开源黑客马拉松，与第一段提及的斯坦福大学黑客马拉松事实相悖；[D] 政府机构拒绝资助高风险的生物计算项目，最后一段明确指出DARPA（美国政府机构）宣布了相关的研究资助计划。

40.【正确答案】C【解析】题型：主旨大意题定位： 全文逻辑结构。分析： 文章开篇通过“培养人类脑细胞打游戏”引出核心话题：“生物计算机”的研发。随后在二、三、四、五段详细论述了由神经元构成的生物计算系统相较于传统硅基芯片在能效、复杂性以及处理现实世界模拟信号（破解莫拉维克悖论）方面的巨大优势。最后两段探讨了该技术面临的商业挑战及未来的开放前景与政府资助。全文紧紧围绕“生物计算（脑细胞与硅芯片的结合）”展开。选项 C“生物计算：当人类脑细胞遇上硅芯片”最完美地概括了全文主旨。干扰项：[A] 现代AI时代硅芯片的消亡，文章第六段明确指出科技巨头正在硅芯片上“加倍下注（doubling down）”，硅芯片并未消亡；[B] 莫拉维克悖论：机器学习的最终极限，这只是第五段用来论证神经元优势的一个论据，以偏概全；[D] 复古电子游戏如何塑造神经科学的未来，打游戏只是第一段引出话题的引子，并非文章探讨的核心。

【词汇注释】

【参考译文】

今年2月，澳大利亚初创公司Cortical Labs宣布，一名程序员教会了其一台“生物计算机”玩经典的第一人称射击游戏《毁灭战士》（Doom）。这台计算机由安装在硅芯片上的20万个人类脑细胞组成。该公司此前曾教会一组脑细胞玩简单得多的《乒乓》（Pong）。然而，它的野心远不止于电子游戏。它希望这些被包装成超级高效的“生物计算机”并插在传统数据中心机架上的神经元，有朝一日能与过去半个世纪定义了传统计算的、密布晶体管的硅芯片并驾齐驱。

Cortical系统的核心是一个由数千个微小电极组成的阵列，上面放置着由人类捐献者的干细胞培育而成的神经元。这个阵列允许传统计算机既能接收这些神经元产生的电活动，又能用自身的电活动去刺激它们。这些神经元通过提供氧气和营养物质、并清除细胞废物的管道和泵来维持生命，存活时间可达六个月。整个装置被打包在一个盒子里，其设计可以完美适配商业数据中心使用的标准服务器机架。

在计算方面，神经元相比电子设备提供了几个潜在的优势。效率是其一。现代人工智能模型消耗数百万瓦的电力，这种对能源的需求已成为该行业增长的最大障碍之一。相比之下，神经元的耗电量极低：一个典型的人类大脑由近900亿个神经元组成，其能耗仅在20瓦左右。

精密复杂性是其二。构成电子计算机的晶体管是微小的开关，只能处于两种状态之一：开或关。而神经元则要复杂得多。它们的行为取决于各种变量，包括细胞膜两端的电压。现有的计算机架构也将信息的存储位置与实际发生处理的位置分离开来。大型存储芯片制造商美光（Micron）估计，传统AI处理器高达一半的能源预算都花在了到处转移数据上。数据来回穿梭也会造成“交通拥堵”。而大脑将数据存储与处理并排混合，从而将这类物流问题降至最低。

神经科学家、Cortical首席科学官布雷特·卡根（Brett Kagan）推测，这一切可能使得神经元在某些类型的计算工作上比电子设备更合适，特别是那些涉及解释现实世界中常见的混乱、模拟信号的工作。他引用了AI研究中长期存在的“莫拉维克悖论”，该悖论指出，从某种深刻的根本意义上讲，抽象推理（如下高级国际象棋或将巨大的数字相乘）在计算上要比在物理世界中导航所需的那种看似微不足道的运动技能容易得多。虽然现代AI模型可以擅长抽象数学，但它们却做不了像泡一杯茶这样简单的事情。

要实现这一愿景将会很棘手。Cortical仍在实验如何最好地在电子计算机和活细胞之间翻译信号。此外，它正在逆流而上。大型科技公司和AI实验室正押注数千亿美元，认为计算的未来在于对标准电子设备（硅芯片）加倍下注。

为了造势，Cortical决定向任何想体验其技术的人开放，将其部分计算机连接到互联网。目前已有约5500人进行了实验。此外，它在高层也会有盟友。资助高风险技术的美国政府机构DARPA最近宣布了一项针对生物计算的研究资助计划，希望能生产出“生物处理单元”，这些处理单元有朝一日可能被证明对自主飞行的无人机以及应对更为混乱的真实世界非常有用。

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