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考研热点话题-人工智能与人类

考研热点话题-AI and Humanity

 

随着大数据的发展,神经网络算法和计算机技术的巨大进步,AI已经从学术研究化身为引领制造业、医疗保健、运输、零售业等众多行业的赋能力量。

 我们看一下李飞飞发表的文章

 AI 正在对人类社会各行各业带来的巨大影响,如果我们的时代确实在经历许多人所说的工业革命,那么 AI 无疑是其中一个推动力

在李飞飞看来,以人为中心的 AI”包含三大主旨,分别是:

 

一、让 AI 更好地反映人类的深层智能;

 

二、AI 应帮助人类变得更强,而不是替代人类;

 

三、确保 AI 在发展过程中对人类的影响得到正确的引导。

 

 

 

纽约时报-观点:How to Make A.I. Human-Friendly

 

 

 

 

 

1. For a field that was not well known outside of academia a decade ago, artificial intelligence has grown dizzyingly fast. Tech companies from Silicon Valley to Beijing are betting everything on it, venture capitalists are pouring billions into research and development, and start-ups are being created on what seems like a daily basis. If our era is the next Industrial Revolution, as many claim, A.I. is surely one of its driving forces.

第一段直接开题,举出科技公司和初创公司在AI 上的投入。

 

It is an especially exciting time for a researcher like me. When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face. But thanks to the growth of big data, advances in algorithms like neural networks and an abundance of powerful computer hardware, something momentous has occurred: A.I. has gone from an academic niche to the leading differentiator in a wide range of industries, including manufacturing, health care, transportation and retail.

 

 

I worry, however, that enthusiasm for A.I. is preventing us from reckoning with its looming effects on society. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. So if we want it to play a positive role in tomorrow’s world, it must be guided by human concerns.

 

I call this approach “human-centered A.I.” It consists of three goals that can help responsibly guide the development of intelligent machines.

 

First, A.I. needs to reflect more of the depth that characterizes our own intelligence. Consider the richness of human visual perception. It’s complex and deeply contextual, and naturally balances our awareness of the obvious with a sensitivity to nuance. By comparison, machine perception remains strikingly narrow.

 

首先,AI需要更多地反映我们智能的深度。以人类视觉的丰富感知为例,它是如此复杂、深层次,并且能在明确地觉知前景和灵敏地捕获背景中取得自然平衡。相比之下,机器感知仍然非常狭窄。

 

Sometimes this difference is trivial. For instance, in my lab, an image-captioning algorithm once fairly summarized a photo as “a man riding a horse” but failed to note the fact that both were bronze sculptures. Other times, the difference is more profound, as when the same algorithm described an image of zebras grazing on a savanna beneath a rainbow. While the summary was technically correct, it was entirely devoid of aesthetic awareness, failing to detect any of the vibrancy or depth a human would naturally appreciate.

有时候这种差异微不足道,例如,在我的实验室里,图像字幕算法可以识别出骑马的人,而完全没有注意到两个都是铜像。同样的算法用来识别彩虹之下草原之上的斑马时差异更明显。虽然识别和描述实现了技术上的正确性,但完全没有审美意识,没有任何人类可以自然感受到的活力或深度。

 

That may seem like a subjective or inconsequential critique, but it points to a major aspect of human perception beyond the grasp of our algorithms. How can we expect machines to anticipate our needs — much less contribute to our well-being — without insight into these “fuzzier” dimensions of our experience?

这听起来有点吹毛求疵,但是这也指出了我们人类感知超越机器算法的一个主要方面。如果我们不能洞察人类体验中这些模糊的维度,又如何期待机器能预测我们的需求,何谈为人类的福祉做贡献?

 

 

Making A.I. more sensitive to the full scope of human thought is no simple task. The solutions are likely to require insights derived from fields beyond computer science, which means programmers will have to learn to collaborate more often with experts in other domains.

要让AI对人类思维的全方位更敏感不是一件容易的事。这需要计算机科学之外其它领域的专业知识,这意味着程序员必须与其他领域的专家合作。

Such collaboration would represent a return to the roots of our field, not a departure from it. Younger A.I. enthusiasts may be surprised to learn that the principles of today’s deep-learning algorithms stretch back more than 60 years to the neuroscientific researchers David Hubel and Torsten Wiesel, who discovered how the hierarchy of neurons in a cat’s visual cortex responds to stimuli.

这种合作代表着回归,而非背离我们这个领域的起源,年轻AI学生们可能会惊讶于今天深度学习算法原理,起源于 David HubbardTorsten Wiesel发现的猫视觉皮层中神经元的层次结构对刺激的反应机制。

 

Likewise, ImageNet, a data set of millions of training photographs that helped to advance computer vision, is based on a project called WordNet, created in 1995 by the cognitive scientist and linguist George Miller. WordNet was intended to organize the semantic concepts of English.

同样,包含数百万张训练图片的ImageNet,帮助发展了计算机视觉。这个项目,是基于认知科学家和语言学家George Miller1995年创建的WordNet数据集。WordNet旨在组织英语的语义概念。

 

 

Reconnecting A.I. with fields like cognitive science, psychology and even sociology will give us a far richer foundation on which to base the development of machine intelligence. And we can expect the resulting technology to collaborate and communicate more naturally, which will help us approach the second goal of human-centered A.I.: enhancing us, not replacing us.

重新连接AI与认知科学、心理学甚至社会学,将给人工智能一个更加强大的发展基础。而且我们可以期待这样发展出来的技术,会让合作和交流更加自然,从而实现以人为本的第二个目标:强化人类,而不是取代人类。

Imagine the role that A.I. might play during surgery. The goal need not be to automate the process entirely. Instead, a combination of smart software and specialized hardware could help surgeons focus on their strengths — traits like dexterity and adaptability — while keeping tabs on more mundane tasks and protecting against human error, fatigue and distraction.

想象一下AI在手术中的作用。它的目标不是把整个过程完全自动化,相反,智能软件和专用硬件的结合可以帮助外科医生专注于自己的优势——如灵活性和适应性——而让机器从事更加常规性的工作, 以避免人类容易发生的失误、疲劳和被干扰。

Or consider senior care. Robots may never be the ideal custodians of the elderly, but intelligent sensors are already showing promise in helping human caretakers focus more on their relationships with those they provide care for by automatically monitoring drug dosages and going through safety checklists.

或者考虑老人护理的情景。机器人可能并不是老人看护的最佳人选,但智能感应器在帮助人类护理员方面前景很好。通过自动监测药物剂量和自动核对安全检查清单,人类护理员可以将更多的精力放在建设与被护理者之间的关系上。

These are examples of a trend toward automating those elements of jobs that are repetitive, error-prone and even dangerous. What’s left are the creative, intellectual and emotional roles for which humans are still best suited.

这些都是自动化取代那些重复的、容易出错的甚至是危险工作的例子。而剩下的创造性的,需要智力和情感的工作,由人类来完成仍然是最适合的。

No amount of ingenuity, however, will fully eliminate the threat of job displacement. Addressing this concern is the third goal of human-centered A.I.: ensuring that the development of this technology is guided, at each step, by concern for its effect on humans.

然而,没有任何聪明才智会完全消除工作流失的威胁。解决这个问题是以人为本的AI的第三个目标:确保这项技术的每一步发展都关注其对人类的影响。

Today’s anxieties over labor are just the start. Additional pitfalls include bias against underrepresented communities in machine learning, the tension between A.I.’s appetite for data and the privacy rights of individuals and the geopolitical implications of a global intelligence race.

今天对工作流失的焦虑只是一个开始。其他问题还包括弱势群体中机器学习从业人数的偏倚,AI对数据的高需求与保护个人隐私之间的关系,以及全球智能竞赛的地缘政治影响。

Adequately facing these challenges will require commitments from many of our largest institutions. Universities are uniquely positioned to foster connections between computer science and traditionally unrelated departments like the social sciences and even humanities, through interdisciplinary projects, courses and seminars. Governments can make a greater effort to encourage computer science education, especially among young girls, racial minorities and other groups whose perspectives have been underrepresented in A.I. And corporations should combine their aggressive investment in intelligent algorithms with ethical A.I. policies that temper ambition with responsibility.

充分面对这些挑战要求各大机构的共同付出。大学的独特定位是通过跨学科项目、课程和研讨会来促进计算机科学与传统上不相关的学科,如社会科学甚至人文科学之间的联系。各国政府可以作出更大的努力,鼓励计算机科学教育,特别是在AI中代表性不足的年轻女孩、少数种族和其他群体。公司应该将积极投资智能算法与伦理道德结合,兼顾抱负与责任。

 

 

No technology is more reflective of its creators than A.I. It has been said that there are no “machine” values at all, in fact; machine values are human values. A human-centered approach to A.I. means these machines don’t have to be our competitors, but partners in securing our well-being. However autonomous our technology becomes, its impact on the world — for better or worse — will always be our responsibility.

 

没有哪项技术比AI更能反映它的创造者。实际上,虽然有人认为机器没有价值观,但事实是:机器的价值观是其创造者的价值观。AI以人为本的方法意味着这些机器不是人类的竞争对手,而是保证我们福祉的伙伴。无论我们的技术自动化到什么程度,它对世界的影响——无论好坏——始终是我们的责任。

 

 

 

 

 

 

 

我们看一下经济学人等外刊有关AI 的平行文本

 

20184AI-Spy 的一篇文章

 

先看看开头段该怎么借鉴

 

1. ARTIFICIAL intelligence (AI) is barging its way(横冲直撞) into business. Firms of all types are harnessing(利用) AI to forecast demand, hire workers and deal with customers. In 2017 companies spent around $22bn on AI-related mergers and acquisitions, about 26 times more than in 2015. The McKinsey Global Institute, a think-tank within a consultancy, reckons that just applying AI to marketing, sales and supply chains could create economic value, including profits and efficiencies, of $2.7trn over the next 20 years. Google’s boss has gone so far as to declare that AI will do more for humanity than fire or electricity.

 

人工智能(AI)横冲直撞,闯入了商业领域。各种各样的公司都在利用人工智能来预测需求、雇用员工、与客户打交道。2017年,企业在AI方面的并购支出达220亿美元上下,大约是2015年的26倍。咨询公司麦肯锡的内部智库麦肯锡全球研究院(McKinsey Global Institute)认为,仅仅是将人工智能应用到营销、销售和供应链上,未来20年就能创造2.7万亿美元的经济价值,包括利润和效率。谷歌的老板甚至宣称对人类而言,人工智能比火和电的用处更大。

 

第一段商业领域各种各样的公司都在利用人工智能来预测需求(harness AI to forcast demand

1. 企业在AI方面的并购 (M&A)

2. 咨询公司麦肯锡预测AI 带来的经济价值(economic value

 

我们还可以借鉴2018年纽约时报:《为何机器人抢不走你的工作——至少现在还不行》 一段进行开头

 Today it’s widely accepted that brainy computers are coming for our jobs. They’ll have finished your entire weekly workload before you’ve had your morning toast – and they don’t need coffee breaks, pension funds, or even sleep. Although many jobs will be automated in the future, in the short term at least, this new breed of super-machines is more likely to be working alongside us.

今天,人们普遍认为智能计算机会抢走我们的工作,在你早餐还没吃完以前,它就已经完成了你一周的工作量,而且他们还不休息,不喝咖啡,也不要退休金,甚至不用睡觉。但事实上,虽然很多工作未来都会自动化,但至少短期内,这种新品种智能机器更有可能是与我们一起工作(而不是取代人类)。

 

我们再看一下20186月经济学人 《谷歌在人工智能方面遭遇更多抨击》开头段

DISCOVERING and harnessing fire unlocked more nutrition from food, feeding the bigger brains and bodies that are the hallmarks(标志) of modern humans.Google’s chief executive, Sundar Pichai, thinks his company’s development of artificial intelligence trumps(胜过) that. “AI is one of the most important things that humanity is working on,” he told an event in California earlier this year. “It’s more profound than, I don’t know, electricity or fire.”

火的发现和利用使人们可以从食物中汲取更多营养,让变大的大脑和身躯有营养,这是现代人的两大特征。谷歌首席执行官桑达尔·皮查伊(Sundar Pichai)则认为,谷歌在人工智能方面的发展超越了这一点。今年早些时候,他在加利福尼亚举行的一场活动上说道:人工智能是人类最重要的研究之一,或许比火和电更有意义。

 

 

我们再看一下2016年经济学人《机械扭曲》开头

 

EXPERTS warn that “the substitution of machinery for human labour” may “render the population redundant”. They worry that “the discovery of this mighty power” has come “before we knew how to employ it rightly”. Such fears are expressed today by those who worry that advances in artificial intelligence (AI) could destroy millions of jobs and pose a “Terminator”-style threat to humanity. But these are in fact the words of commentators discussing mechanisation and steam power two centuries ago. Back then the controversy over the dangers posed by machines was known as the “machinery question”. Now a very similar debate is under way.

专家们警告称,以机械化替代人力操作有可能会带来人口过剩。他们担心,尽管这种强大的力量已经被发明了,可目前我们却还不清楚该如何正确地利用它。 之所以在今天出现了这些担忧,是因为他们害怕人工智能AI)的发展有可能令数百万个就业机会丧失,并给人类带来终结者式威胁。而实际上早在200年前,当评论家们讨论机械化及蒸汽动力时,他们就说过同样的话。当年,关于机械化所带来的危害性的争论被称为机械质疑。而眼下,极其类似的争论正在进行。

 

我们还可以进行对AI 比喻定义等,我们来看《经济学人》——AI的远大前程

 

Computers have been able to read text and numbers for decades, but have only recently learned to see, hear and speak. AI is an omnibus(综合性的)term for a “salad bowl” of different segments and disciplines(逻辑), says Fei-Fei Li, director of Stanford’s AI Lab and an executive at Google’s cloud-computing unit. Subsections of AI include robotics, which is changing factories and assembly lines(组装线), and computer vision, used in applications from identifying something or someone in a photo to self-driving-car(无人驾驶汽车) technology. Computer vision is AI’s “killer app”, says Ms Li, because it can be used in so many settings, but AI has also become more adept at recognising speech. It underlies voice assistants(语音助手) on phones and home speakers(家庭音箱) and allows algorithms to listen to calls and take in the speaker’s tone(语气、语调) and content.

官方译文计算机能阅读文本和数字已经有几十年了,但直到最近才学会了看、听、说。AI是一个综合性术语,就像是涵盖了不同领域和学科的一碗色拉,斯坦福大学人工智能实验室主管、谷歌云计算部门负责人李飞飞说。它的下属分支包括正在改变工厂和组装线的机器人技术,以及部署在各种应用程序中的计算机视觉——从识别照片中的人或物到无人驾驶汽车技术等。李飞飞说,计算机视觉是AI杀手级应用,因为运用场合是如此之多,但AI在语音识别方面也已变得更加娴熟。它是配备在手机和家用音箱上的语音助理的技术基础,还让算法能够监听来电并识别说话者的语调和内容。

 

 

 

 

 

我们继续看第二段

 

2. Such grandiose forecasts kindle anxiety as well as hope. Many fret that AI could destroy jobs faster than it creates them. Barriers to entry from owning and generating data could lead to a handful of dominant firms in every industry.

 

这些前景光明的预测不仅点燃了人们的希望,同时也引发了焦虑。很多人担忧AI抢夺工作的速度要比创造岗位的速度更快。拥有和产生数据的壁垒将使得各个领域最终只有少数公司能够占据主导地位。

 

  第二段 过渡段,对AI 的担忧

  1. 抢夺工作岗位(destroy job

  2. 垄断公司产生 (a handful of dominant firms

 

 在写作中我们如果借鉴该段想写长一点的文章起到承上启下的作用。

 

 

3. Less familiar, but just as important, is how AI will transform the workplace. Using AI, managers can gain extraordinary control over(严格监管) their employees. Amazon has patented a wristband that tracks the hand movements of warehouse workers and uses vibrations to