考研热点话题-AI and Humanity
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”，
一、让 AI 更好地反映人类的深层智能；
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.
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.
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.
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?
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.
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 Hubbard和Torsten 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.
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.
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.
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.
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.
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.
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.
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.
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.
2018年 4月 AI-Spy 的一篇文章
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）
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.
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.”
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.
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.
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.
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）
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