《人工智能英语》试卷(含答案)

参考试卷

一、写出以下单词的中文意思（每小题0.5分，共10分）

1 accuracy

2 actuator

3 adjust

4 agent

5 algorithm

6 analogy

7 attribute

8 backtrack

9 blockchain

10 cluster

11 customize

12 definition

13 defuzzification

14 deployment

15 effector

16 entity

17 extract

18 feedback

19 finite

20 framework

二、根据给出的中文意思，写出英文单词（每小题0.5分，共10分）

1

v.

收集，搜集

2

adj.

嵌入的，内置的

3

n.

指示器；指标

4

n.

基础设施，基础架构

5

v.

合并；集成

6

n.

解释器，解释程序

7

n.

迭代；循环

8

n.

库

9

n.

元数据

11

n.

神经元；神经细胞

12

n.

节点

13

v.

运转；操作

14

n.

模式

15

v.

察觉，发觉

16

n.

前提

17

adj.

程序的；过程的

18

n.

回归

19

adj.

健壮的，强健的；

结实的

20

v.

筛选

10

v.

监视；控制；监测

三、根据给出的短语，写出中文意思（每小题1分，共10分）

1

2

3

4

5

6

7

8

9

10

data object

cyber security

smart manufacturing

clustered system

data visualization

open source

analyze text

cloud computing

computation power

object recognition

四、根据给出的中文意思，写出英文短语（每小题1分，共10分）

1

2

3

4

5

6

7

8

9

10

数据结构

决策树

演绎推理

贪婪最佳优先搜索

隐藏模式，隐含模式

知识挖掘

逻辑推理

预测性维护

搜索引擎

文本挖掘技术

五、写出以下缩略语的完整形式和中文意思（每小题1分，共10分）

缩略语 完整形式 中文意思

1

2

3

4

5

6

7

8

9

10

ANN

AR

BFS

CV

DFS

ES

IA

KNN

NLP

VR

六、阅读短文，回答问题（每小题2分，共10分）

Artificial Neural Network (ANN)

An artificial neural network (ANN) is the piece of a computing system designed to simulate

the way the human brain analyzes and processes information. It is the foundation of artificial

intelligence (AI) and solves problems that would prove impossible or difficult by human or

statistical standards. ANNs have self-learning capabilities that enable them to produce better

results as more data becomes available.

Artificial neural networks are built like the human brain, with neuron nodes interconnected

like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made

up of a cell body that is responsible for processing information by carrying information towards

(inputs) and away (outputs) from the brain.

An ANN has hundreds or thousands of artificial neurons called processing units, which are

interconnected by nodes. These processing units are made up of input and output units. The input

units receive various forms and structures of information based on an internal weighting system,

and the neural network attempts to learn about the information presented to produce one output

report. Just like humans need rules and guidelines to come up with a result or output, ANNs also

use a set of learning rules called backpropagation, an abbreviation for backward propagation of

error, to perfect their output results.

An ANN initially goes through a training phase where it learns to recognize patterns in data,

whether visually, aurally, or textually. During this supervised phase, the network compares its

actual output produced with what it was meant to produce — the desired output. The difference

between both outcomes is adjusted using backpropagation. This means that the network works

backward, going from the output unit to the input units to adjust the weight of its connections

between the units until the difference between the actual and desired outcome produces the lowest

possible error.

A neural network may contain the following 3 layers:

Input layer – The activity of the input units represents the raw information that can feed into

the network.

Hidden layer – To determine the activity of each hidden unit. The activities of the input units

and the weights on the connections between the input and the hidden units. There may be one or

more hidden layers.

Output layer – The behavior of the output units depends on the activity of the hidden units

and the weights between the hidden and output units.

1. What is an artificial neural network (ANN)?

2. What is each neuron made up of？

3. Wha do the input units do?

4. What does an ANN initially go through?

5. How many layers may a neural network contain? What are they?

七、将下列词填入适当的位置（每词只用一次）。（每小题10分，共20分）

填空题1

供选择的答案:

transactions

unstructured

Deep Learning

1. What Is Deep Learning?

Deep learning is an artificial intelligence (AI) function that imitates the workings of the

human brain in processing data and creating patterns for use in decision making. Deep learning is

a ___1___ of machine learning in artificial intelligence that has networks capable of learning

unsupervised from data that is ___2___ or unlabeled. Also known as deep neural learning or deep

neural network.

2. How Does Deep Learning Work?

Deep learning has evolved hand-in-hand with the digital era, which has brought about an

___3___ of data in all forms and from every region of the world. This data, known simply as big

data, is drawn from sources like social media, internet search engines, e-commerce platforms, and

information

subset

techniques

shared

fraud

automated

nodes

explosion

online cinemas, among others. This enormous amount of data is readily accessible and can be

___4___ through fintech applications like cloud computing.

However, the data, which normally is unstructured, is so vast that it could take decades for

humans to comprehend it and extract relevant ___5___. Companies realize the incredible potential

that can result from unraveling this wealth of information and are increasingly adapting to AI

systems for ___6___ support.

3. Deep Learning vs. Machine Learning

One of the most common AI ___7___ used for processing big data is machine learning, a

self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with

newly added data.

If a digital payments company wanted to detect the occurrence or potential ___8___ in its

system, it could employ machine learning tools for this purpose. The computational algorithm

built into a computer model will process all ___9___ happening on the digital platform, find

patterns in the data set, and point out any anomaly detected by the pattern.

Deep learning utilizes a hierarchical level of artificial neural networks to carry out the

process of machine learning. The artificial neural networks are built like the human brain, with

neuron ___10___ connected together like a web. While traditional programs build analysis with

data in a linear way, the hierarchical function of deep learning systems enables machines to

process data with a nonlinear approach.

填空题2

供选择的答案:

stored

database

Face Recognition

Face recognition systems use computer algorithms to pick out specific, distinctive details

about a person’s face. These details, such as distance between the ___1___ or shape of the chin,

are then converted into a mathematical representation and compared to data on other faces

collected in a face recognition database. The data about a particular face is often called a face

template and is distinct from a ___2___ because it’s designed to only include certain details that

can be used to distinguish one face from another.

Some face recognition systems, instead of positively ___3___ an unknown person, are

designed to calculate a probability match score between the unknown person and specific face

templates ___4___ in the database. These systems will offer up several potential matches, ranked

in order of likelihood of correct identification, instead of just returning a single result.

Face recognition systems vary in their ability to identify people under challenging conditions

such as poor lighting, low quality image ___5___, and suboptimal angle of view (such as in a

photograph taken from above looking down on an unknown person).

When it comes to errors, there are two key concepts to understand:

A “false negative” is when the face recognition system fails to ___6___ match a person’s

face to an image that is, in fact, contained in a database. In other words, the system will

erroneously ___7___ zero results in response to a query.

resolution

photograph

match

eyes

look

return,

unlock

identifying

A “false positive” is when the face recognition system does match a person’s face to an

image in a ___8___, but that match is actually incorrect. This is when a police officer submits an

image of “Joe,” but the system erroneously tells the officer that the photo is of “Jack.”

When researching a face recognition system, it is important to ___9___ closely at the “false

positive” rate and the “false negative” rate, since there is almost always a trade-off. For example,

if you are using face recognition to ___10___ your phone, it is better if the system fails to identify

you a few times (false negative) than it is for the system to misidentify other people as you and

lets those people unlock your phone (false positive). If the result of a misidentification is that an

innocent person goes to jail (like a misidentification in a mugshot database), then the system

should be designed to have as few false positives as possible.

六、将下面两篇短文翻译成中文（每小题10分，共20分）

短文1

Differences between Strong AI and Weak AI

1. Meaning

Strong AI is a theoretical form of artificial intelligence which supports the view that

machines can really develop human intelligence and consciousness in the same way that a human

in conscious. Strong AI refers to a hypothetical machine that exhibits human cognitive abilities.

Weak AI (also known as narrow AI), on the other hand, is a form of artificial intelligence that

refers to the use of advanced algorithms to accomplish specific problem solving or reasoning tasks

that do not encompass the full range of human cognitive abilities.

2. Functionality

Functions are limited in weak AI as compared to strong AI. Weak AI does not achieve

self-awareness or demonstrate a wide range of human cognitive abilities that a human may have.

Weak AI refers to systems that are programmed to accomplish a wide range problems but operate

within a pre-determined or pre-defined range of functions. Strong AI, on the other hand, refers to

machines that exhibit human intelligence. The idea is to develop artificial intelligence to the point

where human interact with machines that are conscious, intelligent and driven by emotions and

self-awareness.

The goal of weak AI is to create a technology that allows allows machines and computers to

to accomplish specific problem solving or reasoning tasks at a significantly quicker pace than a

human can. But it does not necessarily incorporate any real world knowledge about the world of

the problem that is being solved. The goal of strong AI is to develop artificial intelligence to the

point where it can be considered true human intelligence. Strong AI is a type of which does not

exist yet in its true form.

短文2

Pattern Recognition

Pattern Recognition is defined as the process of identifying the trends (global or local) in the

given pattern. A pattern can be defined as anything that follows a trend and exhibits some kind of

regularity. The recognition of patterns can be done physically, mathematically or by the use of

algorithms. When we talk about pattern recognition in machine learning, it indicates the use of

powerful algorithms for identifying the regularities in the given data. Pattern recognition is widely