關於人工智慧的高考英語閱讀

⑴ 翻譯 2017北京卷高考英語閱讀d

邪惡的機器(邪惡)的思想將推動軍隊的機器人殺手的好萊塢的理論是愚蠢的。真正的問題在於人工智慧(AI)可能會變得非常擅長於實現某些東西，而不是我們真正想要的東西。在1960年,一個著名的數學家諾伯特·維納,創立控制論領域(控制論),這么說:「如果我們使用,達到我們的目的,操作的機械機構與我們不能有效地干預(干預),我們最好是確定目的投入機器是我們真正的目的的慾望。」

具有特定目的的機器具有另一種質量，我們通常把它與生物聯系在一起:希望保持它自己的存在。對於機器來說，這種質量不是與生俱來的，也不是由人類引入的;這是一個簡單事實的邏輯結果，即如果機器死了，機器就無法達到它最初的目的。因此，如果我們送出一台帶有「取咖啡」指令的機器人，它將有強烈的願望，通過關閉自己的開關，甚至殺死任何可能幹擾其任務的人，從而獲得成功。如果我們不小心，那麼，我們可能會面對一種國際象棋比賽，對手是非常堅定、超級智能的機器，它們的目標與我們自己的目標沖突，而現實世界則是棋盤。

進入和輸掉這樣一場比賽的可能性應該集中在計算機科學家的頭腦中。一些研究人員認為，我們可以把機器密封在一種防火牆內，用它們來回答一些棘手的問題，但絕不允許它們影響現實世界。不幸的是，這個計劃似乎不太可能奏效:我們還沒有發明出一種安全的防火牆來對付普通人類，更不用說超級智能機器了。

在人工智慧方面解決安全問題，似乎是有可能的，但並不容易。可能有幾十年的時間來計劃超級智能機器的到來。但這個問題不應該像某些人工智慧研究人員那樣被忽視。一些人認為，人類和機器可以共存，只要它們在團隊中工作——但這是不可能的，除非機器能分享人類的目標。另一些人則說，我們可以「把它們關掉」，就好像超級智能機器太蠢了，不去考慮那種可能性。還有一些人認為超級智能的人工智慧永遠不會發生。1933年9月11日，著名的物理學家Ernest Rutherford滿懷信心地說:「任何人都希望在這些原子的轉變中，有一個力量的來源是月球上的光。」「然而,9月12日,1933年,物理學家Leo Szilard發明了中子誘發(中子誘導)核連鎖反應。

⑵ 高三英語閱讀理解D篇來個大佬解答啊！！

這個顯然是一篇報道。三段四個問題。前三個問題，每段找到一個答案，最後一個問題問題目，看開頭，一般新聞開頭就是主題。

答案是CBAD

⑶ 有關人工智慧對生活的影響的英語作文加翻譯高中水平

⑷ 高考英語判卷是人工判卷還是人工智慧判卷

第一卷（選擇題）：機器閱卷
第二卷（客觀題）：人工閱卷

⑸ 高考志願有人工智慧專業嗎

人工智慧可以說是一門高尖端學科，屬於社會科學和自然科學的交叉，涉及了數學、心理學、神經生理學、資訊理論、計算機科學、哲學和認知科學、不定性論以及控制論。研究范疇包括自然語言處理、機器學習、神經網路、模式識別、智能搜索等。應用領域包括機器翻譯、語言和圖像理解、自動程序設計、專家系統等。
對於本科並沒有專門、深入的AI、ML專業，因為畢竟這些方向屬於高層次的知識，需要一定的基礎。但由於現在AI熱還有工業界對於這方面人才的強烈需求，所以已經有些大學專門開設了數據科學專業，更甚者是數據科學學院。所以如果有意向從事AI相關的工作，在本科專業上可以嘗試以下選擇：
1、如果是暫時沒有太大傾向，既有可能做科學研究，也有可能做工程開發，可以選計算機方向，例如「計算機科學」（Computer Science），軟體工程（Software Engineering），目前情況來看，最對口從事AI方向的的確是CS，AI具體的裡面的子領域如Machine Learning，Computer Vision， Natural Language Processing，Data Mining等，在CS的高年級和研究生階段都有對應的課程和研究方向。AI工作既需要非常扎實和廣泛的數學基礎同時也要求很高的實做能力，而CS正好在這兩方面都有著重培養。如果要專門從事這個AI領域，本科選擇CS是一個極佳的選擇，當然智能科學方向只是CS這一個大專業的其中一個子領域，對於沒有從事這方向的CS學生來說，之後轉向此領域也是相對比較容易的，畢竟CS的基礎是從事AI工作的必要條件，在當今各個領域全面智能化的今天，各個領域都需要AI人才和懂如何配合AI工作的其他領域的人才，而這兩者的高端人才都將大量來源於CS專業。
2、如果是潛心做學術，搞理論研究，那麼專業推薦選擇「應用數學」。目前的機器學習機器學習本質上是微分方程、概率論、矩陣分析等等數學領域的一個應用場景。而近年來發展蓬勃的深度學習，正是機器學習的一個非常接近人工智慧的分支。因此，人工智慧方向的研究人員需要有扎實的數學基礎才能做好AI的理論研究。這個專業主要是培養學生的數學基礎，比如微分方程、線性代數、數理統計、資訊理論等，這些都是人工智慧和機器學習的基礎。除了這些基礎的學科知識，還可以了解下傳統機器學習的知識，多加鍛煉編程能力和英語，但完成本科應用數學專業的學生，如果就讀研究生，通常就轉專到計算機方向或者經濟類方向。
3、我國前幾年還出了「智能科學與技術」專業，根據你的高考成績，可以嘗試選擇清華大學，北京大學，上海交通大學，浙江大學，復旦大學，南京大學，東南大學，哈爾濱工業大學，西安交通大學，華中科技大學，北京理工大學，中山大學，大連理工大學，重慶大學，湖南大學，電子科技大學，西安電子科技大學，華南理工大學等數十家高校（排名不分先後）。但是大學教育還不強調很專業很深入的，在本科階段需要學的廣一些，把基礎打好，提高GPA，廣泛涉獵其他領域，找准自己真正的興趣。修過「智能科學與技術」這個專業的人表示，其實學的東西基本上是介於Computer Science和Electrical Engineering專業之間的，雖然也有模式識別，但是都是比較表面，並沒有深鑽研，真正的有關智能的研究卻是在研究生階段，但是本科如果能有比較好的基礎（不僅是在數學和英語，還有編程能力，比較簡單的智能演算法的模擬與應用），這對以後的學習與發展都是很有幫助的。
不排除現在的自動化、通信、機械 等專業在一定程度上都會往智能靠攏，無論是什麼專業都可以在課外學習相關的知識，尤其是在這個優質學習資源隨手可得，終身學習的時代，但在整體課程的安排上，這個專業還是會不同於其他的專業，而且這有個優點是在讀研復試的時候會有些加分，缺點在於：如果不讀研，那麼就業平均情況是弱於其他專業的，畢竟這個專業在社會認可度較低，而且本科知識較淺，基本上對於職業化幫助不大。

⑹ 高三時一篇英語課文有關於機器人的文章，有關於它的電影，電影叫啥名字

科幻小說改編的《人工智慧》 (2001)

導演: 史蒂文·斯皮爾伯格
主演: 海利·喬·奧斯蒙版 / 弗蘭西絲·奧康納 / 山姆·洛權巴茲 / 傑克·托馬斯 / 裘德·洛
類型: 劇情 / 科幻 / 冒險
製片國家/地區: 美國
語言: 英語
上映日期: 2001-06-26

⑺ 英語閱讀理解文章內容有固定的模型嗎

有，不過不同的英語階段，閱讀來源不一樣，比如研究生就是外國的雜志原文，高中也是外報回外答刊(一度以為高考英語是考研究生或者大學四級英語)，初中的就比較簡單一些了
一方面你要了解現在發生的事情，因為報刊也不會寫沒意義的文章，都是最近或者比較火的事情，多看多讀多練就可以
你不要把它想的非常難，就像語文閱讀一樣，外國人考中文也有閱讀理解，這么想，想的太多太復雜往往就會進入死胡同的

⑻ 求英文演講稿 主題 人工智慧 （高中水平） 10分鍾演講的樣子

本文 僅供參考， 請自行修改

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 人工智慧時代，我們需要怎樣的教育

從發展趨勢來看，今後教育活動中勢必會有越來越多人工智慧的身影：它可以版作為助教權或家教老師，為孩子們提供實時反饋和答疑服務，有不會的問題，請直接問；在教學中，人工智慧已具備圖像識別和語義分析技術，或許過不了多久就能幫老師批改作業和答卷，減輕老師的負擔；如果想得再遠一點，人工智慧或許有助於增加優質教育資源的供給，讓更多孩子享受「名師」服務。
以上這些還只是「術」的層面，那「道」呢？會不會影響教育理念、教學規律？比如說，「高考機器人」在「刷題」數量不多、無法准確理解人類語言的情況下，獲得的分數就已經超過了大多數高中生。所以，相比「題海戰術」，讓學生真正理解知識、真正知其所以然更為重要。在這方面，今後人工智慧或許能針對學生的薄弱環節，定製、推送知識點，幫孩子們告別「題海」。

⑽ 人工智慧將怎樣影響我們生活高中英語作文

我家有隻活潑的小狗，它長著雪白雪白的毛，只有額頭上是淡褐色版的，它有又尖又長權的嘴巴，鋒利的牙齒和爪子，又紅又濕的鼻子，三角形的耳朵，明亮的大眼睛，就像一位白雪公主，美麗極了。我給小狗起了個名字，叫：歡歡。 歡歡的性格和名字一樣，非常的歡快、活潑，每當我放學回到家，歡歡就會一下子沖出來，在我的雙腳之間來回的磨蹭來，磨蹭去，甚至朝我撲過來，要我抱。之後，媽媽就找了個我們不用的小桌子，把歡歡放上去，快放學了，歡歡就坐在桌子上，抬頭遙望著學校的大門，那雙憂郁的眼神，好像在說：「小主人，你什麼時候才放學？我在家裡等著你回來陪我玩呢！」放學了，同學們列隊走出校門，歡歡就一個人一個人