中学英语单词用法总结学习精编trade和tradition和train

中学英语单词用法总结学习精编trade和tradition和train
trade [treɪd]
n.& v 交易；贸易
China does a lot of trade with many countries.中国和许多国家进行多方面贸易。
短语：1. trade (in sth )with sb/sth 做生意，做买卖
2.trade in sth 行业，生意
eg: be in the book trade
3. trade at sth
eg: which store do you trade at? 你常在哪个商店买东西。
4.trade sth for sth 用一物交换另一物

traditional [trə'dɪʃ(ə)n(ə)l]
adj.①传统的，惯例的;②口传的，传说的
It's traditional to eat dumplings on the Spring Festival.在春节吃饺子是一项传统。
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tradition [trə'dɪʃ(ə)n]
n. 惯例，传统；传说
eg: By tradition, people play practical jokes on 1 April.
按传统风俗，在四月一号可以开恶作剧玩笑。

train [treɪn]
n. [C]火车 ‖v.训练；教育
The 6:30 train for Berlin has been canceled. 6点30分开往柏林的列车已经取消。
The country needs to train five thousand doctors a year.这个国家一年需要培养5000名医生。
短语：1.train sb as sth/in sth
eg: She trained for a year as a secretary. 她受过一年做护士的培训。
He was trained as an engineer /in engineering.他受过当工程师的培训

Directions: For each blank in the following passage there are four words or phrases marked A, B, C and D. Fill in each blank with the word or phrase that best fits the context.
Artificial intelligence (AI) has amazing potential to change the world, and we’ve only just begun to scratch the surface. As AI matures and people move further away from distinct programming and monitoring of systems, unidentified bias (偏见) might make decisions continue for a long time that cause 41 harm for individuals and society. This bias might 42 input data or even the algorithms (算法) themselves.
All too often, data sets are incomplete and the sample represented in the data set does not 43
the population that the AI model is making predictions about—this is known as coverage bias. Some other types of bias related to input data include sampling bias, where data is not collected randomly from the target group, and participation bias, where users from certain groups 44 surveys at different rates than users from other groups. Still, another more challenging bias to identify is confirmation bias that occurs when a decision maker or analyst has a strong 45 belief or experience that affects their ability to consider alternatives. This could lead one to more strongly 46
data that confirms a preexisting belief.
Bias resulting from AI algorithms themselves, or algorithmic bias, is equally 47 . One example of algorithmic bias is implicit bias or unconscious bias, where data scientists 48 make associations or assumptions based on their mental models and memories that affect data modeling decisions. Implicit bias can 49 how data is collected and classified, or how systems are designed and developed. As machines learn, their conclusions and decisions affect people. Ethical (道德的) AI must understand these impacts and create governance and testing methods to 50 mistakes and inaccuracies.
To create ethical AI, companies need to put the 51 of the individual at the center of data innovation. This means thinking about 52 rights as human rights and developing a comprehensive approach to data, including how we use AI.
Having 53 data practices for AI means having good AI governance. This governance not only focuses on data and analytics but also understands the impacts of any given analysis and makes sure it’s 54 and accurate. Good AI governance includes data responsibility as well as a commitment to transparency (透明性).
None of this will be easy, but true innovation never is. By coming together and working on the problem of bias now, before it becomes a(n) 55 force, businesses can help bring out the best AI has to offer the world.
41. A. theoretical B. psychological C. disproportionate D. unintended
42. A. arise from B. contribute to C. take over D. make up
43. A. inspire B. match C. protect D. restrict
44. A. quit B. administer C. compare D. analyze
45. A. distinct B. predictable C. original D. widespread
46. A. restore B. imply C. miss D. favor
47. A. embarrassing B. dangerous C. relevant D. ridiculous
48. A. intentionally B. temporarily C. automatically D. appropriately
49. A. influence B. help C. attract D. predict
50. A. admit B. define C. address D. publicize
51. A. belongings B. expressions C. characteristics D. needs
52. A. civil B. digital C. legal D. natural
53. A. frequent B. responsible C. peculiar D. graceful
54. A. fair B. quick C. appealing D. adequate
55. A. leading B. innovative C. cultural D. destructive