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Unit 7. Cloud Comruting
111
automation technologies will take over the tedious, mortifying and dehumanizing
jobs, and leave us free to pursue things we like. But as we know on the flip side
even though this technology is gaining a lot of momentum, deep inside we are
still scared about the tradeoff that it has to offer. Sounds dangerous, but with so
much of exponential intelligence explosion and upcoming super intelligence, machines will be steering our future. This raises the question: Should we limit it?
With all these technological advancements we have a great future ahead and
humanity holds the dream of reinventing the world.
Artificial intelligence is going to be one of the most competitive benefits in
the business soon and each organization should have a plan, not to just apply
artificial intelligence, but to continuously think, adapt and innovate how artificial
intelligence can help in the journey.
11. Read the article again and say whether the following statements are
true or false.
STATEMENTS
T/F
1
AI has already gone beyond the bounds of the field of Computer
Science.
2
Intelligence is the ability to do things automatically or by instinct.
3
A Turing Test is a classic test.
4
The CAPTCHA test is an example of the Turing test.
5
Training pets and machines have a lot in common.
6
AI and ML can be used interchangeably.
7
NLP is the automatic management of artificial language by software.
8
Computer Vision is a subset of AI applications.
9
Jia Jia can carry out a wide range of human actions.
10
The final aim of artificial intelligence is a technological wonder.
12. Answer the following questions according to the article.
1. What areas does Intelligence cover?
2. Can Artificial Intelligence be described as a machine? If yes, why? If
no, why?
3. Who outlined the term artificial intelligence?
4. What kind of field is AI?
5. What terms are mixed up?
6. What is the main function of Computer Vision?
Unit 7. Cloud Comruting
112
7. What are humanoids robots?
8. What humanoid robot did Hanson robotics develop?
9. What is NLP?
10. Can AI become a threat to mankind?
13. Skim the article and make a list of key words. Then summarize the
article using the chosen keywords.
Go to page 201 to learn how to create a good summary.
VOCABULARY IN USE
14. Follow the link https://rutube.ru/audio/31c922b29808dea096f04f-
3b0ec29d44/ to listen to the article. Then read the article and fill the gaps.
A number of definitions of artificial intelligence (AI) have (1) _____ over
the last few decades. John McCarthy offers the following definition in this 2004
paper: "It is the science and (2) _____ of making intelligent (3) _____, especially
intelligent computer programs. It is related to the similar task of using computers
to understand human (4) _____, but AI does not have to confine itself to methods
that are biologically observable."
However, decades before this definition, the artificial intelligence conversation began with Alan Turing's 1950 work "Computing (5) _____ and Intelligence" (PDF, 89.8 KB) (link resides outside of IBM). In this paper, Turing, often
referred to as the "father of computer science", asks the following question: "Can
machines think?" From there, he offers a test, now famously known as the
"(6) _____ Test", where a human interrogator would try to distinguish between a
computer and human text response. While this test has undergone much (7) _____
since its publication, it remains an important part of the history of AI.
One of the leading AI textbooks is Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. In the book, they (8) _______ into
four (9) _______ goals or definitions of AI, which (10) ______ computer systems
as follows:
Human approach:
Systems that think like humans
Systems that act like humans
Ideal approach:
Systems that think (11) _____
Unit 7. Cloud Comruting
113
Systems that act rationally
In its simplest form, artificial intelligence is a field that combines computer
science and (12) _____ datasets to (13) _____ problem-solving. Expert systems,
an early successful application of AI, aimed to copy a human’s decision-making
process. In the early days, it was (14) _____ to (15) _____ and (16) _____ the
human’s knowledge.
AI today includes the sub-fields of machine learning and (17) _____ learning, which are frequently mentioned in conjunction with artificial intelligence.
These disciplines are comprised of AI (18) _____ that typically make predictions
or classifications based on (19) _____ data. Machine learning has improved the
quality of some (20) _____ systems, and made it easier to create them.
15. Read the following article and fill the gaps with one of the following
words:
artificial, data, efficiencies, human, intelligence, learning,
machines, performs, processing, technologies
(1) _____ intelligence is a constellation of many different (2) _____ working
together to enable machines to sense, comprehend, act, and learn with humanlike levels of (3) _____. Maybe that’s why it seems as though everyone’s defini-
tion of artificial intelligence is different: AI isn’t just one thing.
Technologies like machine (4) _____ and natural language (5) _____ are all
part of the AI landscape. Each one is evolving along its own path and, when applied in combination with (6) _____, analytics and automation, can help businesses achieve their goals, be it improving customer service or optimizing the
supply chain.
Some go even further to define artificial intelligence as “narrow” and “general” AI. Most of what we experience in our day-to-day lives is narrow AI, which
(7) _____ a single task or a set of closely related tasks. Examples include:
Weather apps, Digital assistants
These systems are powerful, but the playing field is narrow: They tend to be
focused on driving (8) _____. But, with the right application, narrow AI has immense transformational power — and it continues to influence how we work and
live on a global scale.
General AI is more like what you see in sci-fi films, where sentient machines
emulate human intelligence, thinking strategically, abstractly and creatively, with
the ability to handle a range of complex tasks. While (9) _____ can perform some
Unit 7. Cloud Comruting
114
tasks better than humans (e.g. data processing), this fully realized vision of general AI does not yet exist outside the silver screen. That’s why human-machine
collaboration is crucial—in today’s world, artificial intelligence remains an ex-
tension of (10) _____ capabilities, not a replacement.
SPEAKING
Discussing advantages and disadvantages
LANGUAGE WORK
16. The following phrases can be useful while discussing advantages and
disadvantages. Complete the phrases with one of the following words:
added, benefit, downside, drawbacks, main, outweigh, plus, pros.
1. …. for me, it’s a _____ / minus
2. … the upside / _____ of … is ….
3. … the advantages _____ the disadvantages
4. … has the _____ bonus of …
5. …. one major _____/ drawback of … is …
6. … weighing up the ____ and cons, I’d say…
7. … has some additional benefits / _____ such as
8. … the _____ advantage of … is
17. Choose the word that best completes each sentence.
1. The committee pointed out the advantages and ____ of the new security
policy.
a) disadvantage b) disadvantages c) advantage
2. We should review the positive and ______ aspects of installing software.
a) minus b) negative c) bad
3. There are always positive and negative ______ to any city.
a) parts b) ways c) aspects
4. The article focuses on the ______ points and minus points of the device.
a) positive b) plus c) pro
5. The professor asked us to highlight the pros and _____ of AI in our
presentation.
a) downsides b) disadvantages c) cons
6. Could you please elaborate on the _______ and drawbacks of the new
computer system?
Unit 7. Cloud Comruting
115
a) benefits b) advantages c) minuses
7. In your essay, please compare the ____ and minuses of the recent changes
in the education system.
a) pluses b) pros c) benefits
8. I think you should consider all benefits and _____ of buying a new com-
puter at this time.
a) drawbacks b) cons c) downsides
9. The report pointed out the pluses and _____ of the new security system.
a) negative aspects b) minuses c) downsides
10. Why don’t you list ____ and cons of installing the new security system?
a) positive b) pros c) pro
18. Get ready to discuss advantages and disadvantage of one of sub-
fields encountered by artificial intelligence in today’s world.
• Machine Learning (ML)
• Neural Networks
• Natural Language Processing (NLP
• Computer Vision
• Robots
WRITING
Writing an advantages and disadvantages essay
19. Write the essay “Artificial Intelligence – Boon or Curse?”
While writing an advantages and disadvantages essay follow this plan:
Paragraph 1: introduction
Paragraph 2: discuss what you think are the advantages
Paragraph 3: discuss what you think are the disadvantages
Paragraph 4: explain if you think the advantages outweigh the disadvantages
Paragraph 5: summarise your views
Go to page 199 to learn how to write an advantages and disadvantages
essay.
Unit 8. Big Data
116
UNIT 8
BIG DATA
LEAD-IN
1.Follow the link https://www.youtube.com/watch?v=GEfsltXnCo4 to
watch “Introduction to big data” and answer the questions.
1. Why won’t Big Data run on our laptop’s hardware?
2. Is the work typically distributed across several physical computers?
3. What are the 3Vs of Big Data?
4. What volume of data can be qualified as Big Data?
5. What does variety mean?
6. Why do we need to think about our goals as we handle Big Data?
7. Why has Big Data become much easier?
8. What does cloud computing provide?
USEFUL VOCABULARY
2. To be able to discuss the main features, peculiarities and types of
Big Data we need to study the following terminology and professional
vocabulary.
Big data [ˌbɪɡ ˈdeɪ.tə] − data that exceeds the processing capacity of con-
ventional database systems. The data is too big, moves too fast, or doesn't fit the
strictures of your database architectures. To gain value from this data, you must
choose an alternative way to process it.
The 3 Vs of BIG Data − characteristics that make big data different from
traditional data. Today big data is streaming at us with increasing velocity, variety, and volume.
Volume (n) [ˈvɒl.juːm] refers to the amount of data that is larger than ever
before (and constantly growing).
Unit 8. Big Data
117
Velocity (n) [vəˈlɒs.ə.ti] refers to the increasing rate at which data is gener-
ated and processed.
Variety (n) [vəˈraɪ.ə.ti] refers to different types and formats of big data com-
ing from different sources.
Structured data [ˈstrʌk.tʃədˈdeɪ.tə] − quantitative data that consists of num-
bers and values. Structured data has a pre-defined data model.
Unstructured data [ˌʌn.ˈstrʌk.tʃədˈdeɪ.tə] − qualitative data that does not
have a pre-defined data model, so it is best managed in non-relational (NoSQL)
databases.
Semi-structured data [ˌsem.i.ˈstrʌk.tʃədˈdeɪ.tə] − data that lies midway be-
tween structured and unstructured data. It doesn't have a specific data model but
may contain some data tables, tags or other structural elements.
Metadata [ˈmet.əˌdeɪ.tə] − data about data. It provides additional infor-
mation about a specific set of data.
Raw data [ˈrɔːˌdeɪ.tə] − the data that is collected from a source, but in its
initial state. It has not yet been processed.
Algorithm (n) [ˈæl.ɡə.rɪ.ðəm] − a mathematical formula or a set of instruc-
tions that we provide to the computer which describes how to process the given
data in order to obtain needed information.
Schema (n) [ˈskiː.mə] − the logical representation of a database, which
shows how the data is stored logically in the entire database.
Query (n) [ˈkwɪə.ri] − a request to access data from a database to manipulate
it or retrieve it.
Schema-on-Write approach − approach, where you write data into a pre-
defined schema and read data by querying it.
Schema On-Read approach −approach, where the schema is created only
when the data is read and not before data ingestion. This enables raw, unstructured data to be stored in the database.
Database (n) [ˈdeɪ.tə.beɪs] − a digital collection of data that is organized in
a specific way.
Database Management System (DBMS) − a software which is used to
manage the database.
Relational databases [rɪˌleɪ.ʃən.əl ˈdeɪ.tə.beɪs] store data in tables that are
related to each other.
Table (n) [ˈteɪ.bəl] − a logical structure made up of rows and columns.
Unit 8. Big Data
118
Non-relational (NoSQL) database − a type of database that does not
store data in tables. Instead, this type of database uses a hierarchical structure to
store data.
Data warehouse (n) [ˈweə.haʊs] − a data management system which aggre-
gates large volumes of data from multiple sources into a single repository of highly
structured and unified historical data.
Data Lake (n) [leɪk] − a repository that stores a huge amount of raw data in
its original format. It employs a flat architecture which allows you to store raw data
at any scale without the need to structure it first.
Big Data Technology [tekˈnɒl.ə.dʒi] − a Software-Utility that is designed to
analyse, process and extract the information from an extremely complex and large
data sets which the Traditional Data Processing Software could never deal with.
Operational [ˌɒp.ərˈeɪ.ʃən.əl] Big Data Technologies − technologies re-
sponsible for generating the big data and storing them in an efficient manner that
is not responsible for providing any solutions.
Analytical [ˌæn.əˈlɪt.ɪ.kəl] Big Data Technologies − technologies that help
find business solutions by analyzing a large amount of data.
ETL stands for Extract, Transform and Load a process of extracting the data
from various sources, transforming it to fit operational needs and loading it into
the database.
Data pipeline (n) [ˈpaɪp.laɪn] − a set of tools and processes used to automate
the movement and transformation of data between a source system and a target
repository.
Machine Learning [məˌʃiːn ˈlɜː.nɪŋ] − an approach to data analysis that in-
volves building and adapting models, which allow programs to "learn" through
experience and to improve their ability to make predictions.
Structured query language (SQL) is the programming language used to
manage structured data in relational databases.
NoSQL − an abbreviation of “not only SQL”. It describes databases or
database management systems, which deal with non-relational or non-structu-
red data.
Cluster computing [ˈklʌs.tər.kəmˈpjuː.tɪŋ] refers to the process of sharing
the computation task to multiple computers of the cluster.
Hadoop − an open source framework or software platform that allows for
storing and analyzing vast quantities of data.
Unit 8. Big Data
119
Access (v) [ˈæk.ses] data − obtain or retrieve data stored within a database
or other repository.
Aggregate (v) [ˈæɡ.rɪ.ɡət] − to combine into a single group or total.
Analyze (v) [ˈæn.əl.aɪz] data − clean, transform, and model data to discover
useful information for decision-making.
Cleanse (v) [klenz] − remove incorrect, duplicate, or otherwise erroneous
data from a dataset.
Extract (v) [ɪkˈstrækt] data − retrieve data out of data sources for further
processing or storage.
Ingest (v) [ɪnˈdʒest] data − collect data from various data sources into a
target data warehouse.
Load (v) [loʊd] data − send data to a designated data warehouse.
Mine (v) [maɪn] − analyze dense volumes of data to find patterns, discover
trends, and gain insight into how that data can be used.
Process (v) [ˈprəʊ.ses] data − perform mathematical and logical operations
on data according to programmed instructions to obtain the required information.
Retrieve (v) [rɪˈtriːv] data − select and extract data from a database, based
on a query provided by the user or application.
Store (v) [stɔːr] data − keep data so that it can be accessed again later.
3. Think about the Russian equivalents of the terms given above.
4. Match the following definitions with the terms.
DEFINITIONS
TERMS
1
Data that exceeds the processing capacity of conventional database systems because it is too big, moves too fast, or doesn't fit
the strictures of your database architectures.
variety
2
This term refers to the amount of data that is larger than ever
before and constantly growing.
data pipeline
3
This term refers to different types and formats of big data coming from different sources.
volume
4
A mathematical formula or a set of instructions that we provide
to the computer which describes how to process the given data
in order to obtain needed information.
ingest data
5
A digital collection of data that is organized in a specific way.
database
schema
6
A logical structure made up of rows and columns.
database
Unit 8. Big Data
120
DEFINITIONS
TERMS
7
A repository that stores a huge amount of raw data in its
original format. It employs a flat architecture which allows you to store raw data at any scale without the need to
structure it first.
machine learning
8
A set of tools and processes used to automate the movement and transformation of data between a source system
and a target repository.
table
9
An approach to data analysis that involves building and
adapting models, which allow programs to "learn"
through experience and to improve their ability to make
predictions.
data lake
10
The logical representation of a database, which shows
how the data is stored logically in the entire database.
query
11
Analyze dense volumes of data to find patterns, discover
trends, and gain insight into how that data can be used.
aggregate data
12
A request to access data from a database to manipulate it
or retrieve it.
cleanse data
13
Remove incorrect, duplicate, or otherwise erroneous data
from a dataset.
algorithm
14
To combine into a single group or total.
big data
15
Collect data from various data sources into a target data
warehouse.
mine data
5. Match the words in A with their synonyms in B.
A B
1
access
a
amount
2
query
b
combine
3
volume
c
model
4
velocity
d
unprocessed
5
raw e request
6
aggregate
f
clean
7
store
g
rate 8 cleanse
h
formula
9
algorithm
i
obtain
10
schema
j
keep
6. Read the definition and write the term.
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