关于人工智能的高考英语阅读

⑴ 翻译 2017北京卷高考英语阅读d

邪恶的机器(邪恶)的思想将推动军队的机器人杀手的好莱坞的理论是愚蠢的。真正的问题在于人工智能(AI)可能会变得非常擅长于实现某些东西，而不是我们真正想要的东西。在1960年,一个著名的数学家诺伯特·维纳,创立控制论领域(控制论),这么说:“如果我们使用,达到我们的目的,操作的机械机构与我们不能有效地干预(干预),我们最好是确定目的投入机器是我们真正的目的的欲望。”

具有特定目的的机器具有另一种质量，我们通常把它与生物联系在一起:希望保持它自己的存在。对于机器来说，这种质量不是与生俱来的，也不是由人类引入的;这是一个简单事实的逻辑结果，即如果机器死了，机器就无法达到它最初的目的。因此，如果我们送出一台带有“取咖啡”指令的机器人，它将有强烈的愿望，通过关闭自己的开关，甚至杀死任何可能干扰其任务的人，从而获得成功。如果我们不小心，那么，我们可能会面对一种国际象棋比赛，对手是非常坚定、超级智能的机器，它们的目标与我们自己的目标冲突，而现实世界则是棋盘。

进入和输掉这样一场比赛的可能性应该集中在计算机科学家的头脑中。一些研究人员认为，我们可以把机器密封在一种防火墙内，用它们来回答一些棘手的问题，但绝不允许它们影响现实世界。不幸的是，这个计划似乎不太可能奏效:我们还没有发明出一种安全的防火墙来对付普通人类，更不用说超级智能机器了。

在人工智能方面解决安全问题，似乎是有可能的，但并不容易。可能有几十年的时间来计划超级智能机器的到来。但这个问题不应该像某些人工智能研究人员那样被忽视。一些人认为，人类和机器可以共存，只要它们在团队中工作——但这是不可能的，除非机器能分享人类的目标。另一些人则说，我们可以“把它们关掉”，就好像超级智能机器太蠢了，不去考虑那种可能性。还有一些人认为超级智能的人工智能永远不会发生。1933年9月11日，著名的物理学家Ernest Rutherford满怀信心地说:“任何人都希望在这些原子的转变中，有一个力量的来源是月球上的光。”“然而,9月12日,1933年,物理学家Leo Szilard发明了中子诱发(中子诱导)核连锁反应。

⑵ 高三英语阅读理解D篇来个大佬解答啊！！

这个显然是一篇报道。三段四个问题。前三个问题，每段找到一个答案，最后一个问题问题目，看开头，一般新闻开头就是主题。

答案是CBAD

⑶ 有关人工智能对生活的影响的英语作文加翻译高中水平

⑷ 高考英语判卷是人工判卷还是人工智能判卷

第一卷（选择题）：机器阅卷
第二卷（客观题）：人工阅卷

⑸ 高考志愿有人工智能专业吗

人工智能可以说是一门高尖端学科，属于社会科学和自然科学的交叉，涉及了数学、心理学、神经生理学、信息论、计算机科学、哲学和认知科学、不定性论以及控制论。研究范畴包括自然语言处理、机器学习、神经网络、模式识别、智能搜索等。应用领域包括机器翻译、语言和图像理解、自动程序设计、专家系统等。
对于本科并没有专门、深入的AI、ML专业，因为毕竟这些方向属于高层次的知识，需要一定的基础。但由于现在AI热还有工业界对于这方面人才的强烈需求，所以已经有些大学专门开设了数据科学专业，更甚者是数据科学学院。所以如果有意向从事AI相关的工作，在本科专业上可以尝试以下选择：
1、如果是暂时没有太大倾向，既有可能做科学研究，也有可能做工程开发，可以选计算机方向，例如“计算机科学”（Computer Science），软件工程（Software Engineering），目前情况来看，最对口从事AI方向的的确是CS，AI具体的里面的子领域如Machine Learning，Computer Vision， Natural Language Processing，Data Mining等，在CS的高年级和研究生阶段都有对应的课程和研究方向。AI工作既需要非常扎实和广泛的数学基础同时也要求很高的实做能力，而CS正好在这两方面都有着重培养。如果要专门从事这个AI领域，本科选择CS是一个极佳的选择，当然智能科学方向只是CS这一个大专业的其中一个子领域，对于没有从事这方向的CS学生来说，之后转向此领域也是相对比较容易的，毕竟CS的基础是从事AI工作的必要条件，在当今各个领域全面智能化的今天，各个领域都需要AI人才和懂如何配合AI工作的其他领域的人才，而这两者的高端人才都将大量来源于CS专业。
2、如果是潜心做学术，搞理论研究，那么专业推荐选择“应用数学”。目前的机器学习机器学习本质上是微分方程、概率论、矩阵分析等等数学领域的一个应用场景。而近年来发展蓬勃的深度学习，正是机器学习的一个非常接近人工智能的分支。因此，人工智能方向的研究人员需要有扎实的数学基础才能做好AI的理论研究。这个专业主要是培养学生的数学基础，比如微分方程、线性代数、数理统计、信息论等，这些都是人工智能和机器学习的基础。除了这些基础的学科知识，还可以了解下传统机器学习的知识，多加锻炼编程能力和英语，但完成本科应用数学专业的学生，如果就读研究生，通常就转专到计算机方向或者经济类方向。
3、我国前几年还出了“智能科学与技术”专业，根据你的高考成绩，可以尝试选择清华大学，北京大学，上海交通大学，浙江大学，复旦大学，南京大学，东南大学，哈尔滨工业大学，西安交通大学，华中科技大学，北京理工大学，中山大学，大连理工大学，重庆大学，湖南大学，电子科技大学，西安电子科技大学，华南理工大学等数十家高校（排名不分先后）。但是大学教育还不强调很专业很深入的，在本科阶段需要学的广一些，把基础打好，提高GPA，广泛涉猎其他领域，找准自己真正的兴趣。修过“智能科学与技术”这个专业的人表示，其实学的东西基本上是介于Computer Science和Electrical Engineering专业之间的，虽然也有模式识别，但是都是比较表面，并没有深钻研，真正的有关智能的研究却是在研究生阶段，但是本科如果能有比较好的基础（不仅是在数学和英语，还有编程能力，比较简单的智能算法的仿真与应用），这对以后的学习与发展都是很有帮助的。
不排除现在的自动化、通信、机械 等专业在一定程度上都会往智能靠拢，无论是什么专业都可以在课外学习相关的知识，尤其是在这个优质学习资源随手可得，终身学习的时代，但在整体课程的安排上，这个专业还是会不同于其他的专业，而且这有个优点是在读研复试的时候会有些加分，缺点在于：如果不读研，那么就业平均情况是弱于其他专业的，毕竟这个专业在社会认可度较低，而且本科知识较浅，基本上对于职业化帮助不大。

⑹ 高三时一篇英语课文有关于机器人的文章，有关于它的电影，电影叫啥名字

科幻小说改编的《人工智能》 (2001)

导演: 史蒂文·斯皮尔伯格
主演: 海利·乔·奥斯蒙版 / 弗兰西丝·奥康纳 / 山姆·洛权巴兹 / 杰克·托马斯 / 裘德·洛
类型: 剧情 / 科幻 / 冒险
制片国家/地区: 美国
语言: 英语
上映日期: 2001-06-26

⑺ 英语阅读理解文章内容有固定的模型吗

有，不过不同的英语阶段，阅读来源不一样，比如研究生就是外国的杂志原文，高中也是外报回外答刊(一度以为高考英语是考研究生或者大学四级英语)，初中的就比较简单一些了
一方面你要了解现在发生的事情，因为报刊也不会写没意义的文章，都是最近或者比较火的事情，多看多读多练就可以
你不要把它想的非常难，就像语文阅读一样，外国人考中文也有阅读理解，这么想，想的太多太复杂往往就会进入死胡同的

⑻ 求英文演讲稿 主题 人工智能 （高中水平） 10分钟演讲的样子

本文 仅供参考， 请自行修改

10 Examples of Artificial Intelligence You’re Using in Daily Life

Internet Tech
Artificial intelligence (AI) might seem like the realm of
science fiction, but you might be surprised to find out that you’re
already using it. AI has a huge effect on your life, whether you’re
aware of it or not, and its influence is likely to grow in the coming
years. Here are 10 examples of artificial intelligence that you’re
already using every day.

Virtual Personal Assistants
Siri, Google Now, and Cortana
are all intelligent digital personal assistants on various platforms
(iOS, Android, and Windows Mobile). In short, they help find useful
information when you ask for it using your voice; you can say “Where’s
the nearest Chinese restaurant?”, “What’s on my schele today?”,
“Remind me to call Jerry at eight o’clock,” and the assistant will
respond by finding information, relaying information from your phone, or
sending commands to other apps.

AI is important in these apps, as they collect information on your
requests and use that information to better recognize your speech and
serve you results that are tailored to your preferences. Microsoft says
that Cortana “continually learns about its user” and that it will
eventually develop the ability to anticipate users’ needs. Virtual
personal assistants process a huge amount of data from a variety of
sources to learn about users and be more effective in helping them
organize and track their information.

Video Games

One of the instances of AI that most people are probably familiar
with, video game AI has been used for a very long time—since the very
first video games, in fact. But the complexity and effectiveness of that
AI has increased exponentially over the past several decades, resulting
in video game characters that learn your behaviors, respond to stimuli,
and react in unpredictable ways. 2014’s Middle Earth: Shadow of Mordor
is especially notable for the indivial personalities given to each
non-player character, their memories of past interaction, and their
variable objectives.

First-person shooters like Far Cry and Call of Duty
also make significant use of AI, with enemies that can analyze their
environments to find objects or actions that might be beneficial to
their survival; they’ll take cover, investigate sounds, use flanking
maneuvers, and communicate with other AIs to increase their chances of
victory. As far as AI goes, video games are somewhat simplistic, but
because of the instry’s huge market, a great deal of effort and money
are invested every year in perfecting this type of AI.

Smart Cars

You probably haven’t seen someone reading the newspaper while driving
to work yet, but self-driving cars are moving closer and closer to
reality; Google’s self-driving car project and Tesla’s “autopilot”
feature are two examples that have been in the news lately. Earlier this
year, the Washington Post reported
on an algorithm developed by Google that could potentially let
self-driving cars learn to drive in the same way that humans do: through
experience.

The AI detailed in this article learned to play simple video games,
and Google will be testing that same intelligence in driving
games before moving onto the road. The idea is that, eventually, the car
will be able to “look” at the road ahead of it and make decisions based
on what it sees, helping it learn in the process. While Tesla’s
autopilot feature isn’t quite this advanced, it’s already being used on
the road, indicating that these technologies are certainly on their way
in.

Purchase Prediction

Large retailers like Target and Amazon stand to make a lot of money
if they can anticipate your needs. Amazon’s anticipatory shipping
project hopes to send you items before you need them,
completely obviating the need for a last-minute trip to the online
store. While that technology isn’t yet in place, brick-and-mortar
retailers are using the same ideas with coupons; when you go to the
store, you’re often given a number of coupons that have been selected by
a predictive analytics algorithm.

This can be used in a wide variety of ways, whether it’s sending you
coupons, offering you discounts, targeting advertisements, or stocking
warehouses that are close to your home with procts that you’re likely
to buy. As you can imagine, this is a rather controversial use of AI,
and it makes many people nervous about potential privacy violations from
the use of predictive analytics.

Fraud Detection

Have you ever gotten an email or a letter asking you if you made a
specific purchase on your credit card? Many banks send these types of
communications if they think there’s a chance that fraud may have been
committed on your account, and want to make sure that you approve the
purchase before sending money over to another company. Artificial
intelligence is often the technology deployed to monitor for this type
of fraud.

In many cases, computers are given a very large sample of fraulent
and non-fraulent purchases and asked to learn to look for signs that a
transaction falls into one category or another. After enough training,
the system will be able to spot a fraulent transaction based on the
signs and indications that it learned through the training exercise.

Online Customer Support

Many websites now offer customers the opportunity to chat with a
customer support representative while they’re browsing—but not every
site actually has a live person on the other end of the line. In many
cases, you’re talking to a rudimentary AI. Many of these chat support
bots amount to little more than automated responders, but some of them
are actually able to extract knowledge from the website and present it
to customers when they ask for it.

Perhaps most interestingly, these chat bots need to be adept at
understanding natural language, which is a rather difficult proposition;
the way in which customers talk and the way in which computers talk is
very different, and teaching a machine to translate between the two
isn’t easy. But with rapid advances in natural language processing
(NLP), these bots are getting better all the time.

News Generation

Did you know that artificial intelligence programs can write news stories? According to Wired,
the AP, Fox, and Yahoo! all use AI to write simple stories like
financial summaries, sports recaps, and fantasy sports reports. AI isn’t
writing in-depth investigative articles, but it has no problem with
very simple articles that don’t require a lot of synthesis. Automated
Insights, the company behind the Wordsmith software,
says that e-commerce, financial services, real estate, and other
“data-driven” instries are already benefitting from the app.

Of course, Wordsmith still needs quite a bit of help from an actual
author to get setup and give it the matrix article that data is placed
into. However, the concept has been proven, and it’s likely that we’ll
see more and more reports generated by these means. Moving beyond
data-driven fields will require major leaps in technology, but the
groundwork has been laid, and it seems like it’s only a matter of time
until fully automated reporters become a reality.

Security Surveillance

A single person monitoring a number of video cameras isn’t a very
secure system; people get bored easily, and keeping track of multiple
monitors can be difficult even in the best of circumstances. Which is
why training computers to monitor those cameras makes a great deal of
sense. With supervised training exercises, security algorithms can take
input from security cameras and determine whether there may be a
threat—if it “sees” a warning sign, it will alert human security
officers.

Of course, the number of things that these computers can catch is currently pretty limited—Wired talks about
seeing flashes of color that may indicate an intruder or someone
loitering around a schoolyard. Identifying actions that might imply a
thief in a store are likely beyond the current technological
limitations, but don’t be surprised if this sort of technology debuts in
the near future.

Music and Movie Recommendation Services

While they’re rather simple when compared to other AI systems, apps like Spotify,
Pandora, and Netflix accomplish a useful task: recommending music and
movies based on the interests you’ve expressed and judgments you’ve made
in the past. By monitoring the choices you make and inserting them into
a learning algorithm, these apps make recommendations that you’re
likely to be interested in.

Much of this functionality is dependent on human-assigned factors.
For example, a song might have “driving bass,” “dynamic vocals,” and
“guitar riffs” listed as characteristics; if you like that song, you’ll
probably like other songs that include the same characteristics. This is
the basis of many recommendation services; and while it’s not
futuristically advanced, it does do a pretty good job of helping you
discover new music and movies.

Smart Home Devices

Many smart home devices now include the ability to learn your
behavior patterns and help you save money by adjusting the settings on
your thermostat or other appliances in an effort to increase convenience
and save energy. For example, turning your oven on when you leave work
instead of waiting to get home is a very convenient ability. A
thermostat that knows when you’re home and adjusts the temperature
accordingly can help you save money by not heating the house when you’re
out.

Lighting is another place where you might see basic artificial
intelligence; by setting defaults and preferences, the lights around
your house (both inside and outside) might adjust based on where you are
and what you’re doing; dimmer for watching TV, brighter for cooking,
and somewhere in the middle for eating, for example. The uses of AI in
smart homes are limited only by our imagination.

⑼ 人工智能时代，我们需要怎样的教育

从发展趋势来看，今后教育活动中势必会有越来越多人工智能的身影：它可以版作为助教权或家教老师，为孩子们提供实时反馈和答疑服务，有不会的问题，请直接问；在教学中，人工智能已具备图像识别和语义分析技术，或许过不了多久就能帮老师批改作业和答卷，减轻老师的负担；如果想得再远一点，人工智能或许有助于增加优质教育资源的供给，让更多孩子享受“名师”服务。
以上这些还只是“术”的层面，那“道”呢？会不会影响教育理念、教学规律？比如说，“高考机器人”在“刷题”数量不多、无法准确理解人类语言的情况下，获得的分数就已经超过了大多数高中生。所以，相比“题海战术”，让学生真正理解知识、真正知其所以然更为重要。在这方面，今后人工智能或许能针对学生的薄弱环节，定制、推送知识点，帮孩子们告别“题海”。

⑽ 人工智能将怎样影响我们生活高中英语作文

我家有只活泼的小狗，它长着雪白雪白的毛，只有额头上是淡褐色版的，它有又尖又长权的嘴巴，锋利的牙齿和爪子，又红又湿的鼻子，三角形的耳朵，明亮的大眼睛，就像一位白雪公主，美丽极了。我给小狗起了个名字，叫：欢欢。 欢欢的性格和名字一样，非常的欢快、活泼，每当我放学回到家，欢欢就会一下子冲出来，在我的双脚之间来回的磨蹭来，磨蹭去，甚至朝我扑过来，要我抱。之后，妈妈就找了个我们不用的小桌子，把欢欢放上去，快放学了，欢欢就坐在桌子上，抬头遥望着学校的大门，那双忧郁的眼神，好像在说：“小主人，你什么时候才放学？我在家里等着你回来陪我玩呢！”放学了，同学们列队走出校门，欢欢就一个人一个人