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Contents

Evolution of Technology

CHAPTER ONE Role of Data for Emerging Technology

Introduction to Programmable Devices

Emerging Technology Human to Machine Interaction

Future Trends in Emerging Technologies

Andualem T.

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Emerging Technology What is the root word of Technology and Evolution?

• Is a term generally used to describe a new technology Technology
• It may also refer to the continuing development of existing • 1610s, "discourse or treatise on an art or the arts," from Greek

technology tekhnologia "systematic treatment of an art, craft, or technique,"

• Originally referring to grammar, from tekhno- (see techno-) + -logy.
• The term commonly refers to technologies that are currently
• The meaning "science of the mechanical and industrial arts" is first
developing, or that are expected to be available within the
recorded in 1859.
next five to ten years
Evolution
• The term is usually reserved for technologies that are
• Evolution means the process of developing by gradual changes.
creating or are expected to create significant social or
• This noun is from Latin evolutio, "an unrolling or opening," combined
economic effects. from the prefix e-, "out," plus volvere, "to roll."
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List of some currently available Emerged Technologies Introduction to the Industrial Revolution (IR)
• A period of major industrialization and innovation that took place
• Artificial Intelligence
during the late 1700s and early 1800s.
• Blockchain
• IR occurs when a society shifts from using tools to make products to
• Augmented Reality and Virtual Reality use new sources of energy, such as coal, to power machines in
• Cloud Computing factories.
• Angular and React • The revolution started in England, with a series of innovations to make
• DevOps labor more efficient and productive.
• Internet of Things (IoT) • IR was a time when the manufacturing of goods moved from small
• Intelligent Apps (I-Apps) shops and homes to large factories.
• Big Data • This shift brought about changes in culture as people moved from

• Robotic Processor Automation (RPA) rural areas to big cities in order to work.

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Introduction to the Industrial Revolution (IR) Introduction to the Industrial Revolution (IR)
• The American Industrial Revolution commonly referred to as the
• Generally, the following industrial revolutions
Second Industrial Revolution, started sometime between 1820
and 1870. fundamentally changed and transfer the world around
• Industries such as textile manufacturing, mining, glass making,
us into modern society.
and agriculture all had undergone changes.
• For example, prior to the Industrial Revolution, textiles were • The steam engine,
primarily made of wool and were handspun.
• From the first industrial revolution (mechanization through • The age of science and mass production, and
water and steam power) to the mass production and assembly
lines using electricity in the second, the fourth industrial • The rise of digital technology
revolution will take what was started in the third with the
• Smart and autonomous systems fueled by data and
adoption of computers and automation and enhance it with smart
and autonomous systems fueled by data and machine learning. machine learning.
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Introduction to the Industrial Revolution (IR) Historical Background (IR 1.0, IR 2.0, IR 3.0)
• The Most Important Inventions of the Industrial • The industrial revolution began in Great Britain in the late 1770s
before spreading to the rest of Europe.
Revolution
• The first European countries to be industrialized after England
• Transportation: The Steam Engine, The Railroad, The Diesel
were Belgium, France, and the German states.
Engine, The Airplane.
• The final cause of the Industrial Revolution was the effects
• Communication.: The Telegraph, Transatlantic Cable,
created by the Agricultural Revolution.
Phonograph, the Telephone.
• As previously stated, the Industrial Revolution began in Britain in
• Industry: The Cotton Gin. The Sewing Machine. Electric the 18th century due in part to an increase in food production,
Lights. which was the key outcome of the Agricultural Revolution.

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Historical Background (IR 1.0, IR 2.0, IR 3.0) Historical Background (IR 1.0, IR 2.0, IR 3.0)
• The four types of industries are: • Industrial Revolution (IR 1.0)
• The Industrial Revolution (IR) is described as a transition to new
• Primary industry involves getting raw materials e.g. mining,
manufacturing processes.
farming, and fishing.
• IR was first coined in the 1760s, during the time where this revolution
• Secondary industry involves manufacturing e.g. making cars began.
and steel. • The transitions in the first IR included going from hand production

• Tertiary industries provide a service e.g. teaching and nursing. methods to machines, the increasing use of steam power, the development
of machine tools and the rise of the factory system.
• Quaternary industry involves research and development

industries e.g. IT.

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Historical Background (IR 1.0, IR 2.0, IR 3.0) Historical Background (IR 1.0, IR 2.0, IR 3.0)
• Industrial Revolution (IR 2.0) • Industrial Revolution (IR 3.0)
• Also known as the Technological Revolution, began somewhere in the • Introduced the transition from mechanical and analog electronic
1870s. technology to digital electronics which began from the late 1950s.
• The advancements in IR 2.0 included the development of methods for • Due to the shift towards digitalization, IR 3.0 was given the nickname,
manufacturing interchangeable parts and widespread adoption of pre- “Digital Revolution”.
existing technological systems such as telegraph and railroad networks. • The core factor of this revolution is the mass production and widespread
• This adoption allowed the vast movement of people and ideas, enhancing use of digital logic circuits and its derived technologies such as the
communication. computer, handphones and the Internet.
• Moreover, new technological systems were introduced, such as electrical • These technological innovations have arguably transformed traditional
power and telephones. production and business techniques enabling people to communicate with
another without the need of being physically present.
• Certain practices that were enabled during IR 3.0 is still being practiced
until this current day, for example – the proliferation of digital computers
and digital record.

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Historical Background (IR 1.0, IR 2.0, IR 3.0, IR 4.0) Role of Data for Emerging Technology
• Industrial Revolution (IR 4.0) • Data is regarded as the new oil and strategic asset since we are living in the

• Now, with advancements in various technologies such as robotics, Internet age of big data, and drives or even determines the future of science,
of Things (IoT), additive manufacturing and autonomous vehicles, the term
“Fourth Industrial technology, the economy, and possibly everything in our world today and
• Revolution” or IR 4.0 was coined by Klaus Schwab, the founder and tomorrow.
executive chairman of World Economic Forum, in the year 2016.
• The technologies mentioned above are what you call – cyber physical • Data have not only triggered tremendous hype and buzz but more
systems. importantly, presents enormous challenges that in turn bring incredible
• A cyber-physical system is a mechanism that is controlled or monitored by
computer-based algorithms, tightly integrated with the Internet and its innovation and economic opportunities.
users.
• This reshaping and paradigm-shifting are driven not just by data itself but all
• Examples: Computer Numerical Control (CNC) machines and Artificial
Intelligence (AI). other aspects that could be created, transformed, and/or adjusted by
• CNC Machines: operated by giving it instructions using a computer
understanding, exploring, and utilizing data.
• AI: one of the main elements that give life to Autonomous Vehicles and
Automated Robots.
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Role of Data for Emerging Technology Enabling Devices & Network (Programmable Devices)
• In the world of digital electronic systems, there are four basic kinds of
• The preceding trend and its potential have triggered new debate about data- devices: memory, microprocessors, logic, and networks.
intensive scientific discovery as an emerging technology, the so-called “fourth • Memory devices store random information such as the contents of a
spreadsheet or database.
industrial revolution,” There is no doubt, nevertheless, that the potential of • Microprocessors execute software instructions to perform a wide variety of
tasks such as running a word processing program or video game.
data science and analytics to enable data-driven theory, economy, and • Logic devices provide specific functions, including device-to-device
professional development is increasingly being recognized. interfacing, data communication, signal processing, data display, timing and
control operations, and almost every other function a system must perform.
• This involves not only core disciplines such as computing, informatics, and • Network is a collection of computers, servers, mainframes, network devices,
peripherals, or other devices connected to one another to allow the sharing of
statistics, but also the broad-based fields of business, social science, and data.
• Example of a network is the Internet, which connects millions of people all
health/medical science. over the world Programmable devices usually refer to chips that incorporate
field programmable logic devices (FPLDs), complex programmable logic
devices (CPLD) and programmable logic devices (PLD).
• There are also devices that are the analog equivalent of these called field
programmable analog arrays.

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Enabling Devices & Network (Programmable Devices) Enabling Devices & Network (Programmable Devices)
• Why is a computer referred to as a programmable device?
• Service Enabling Devices (Network Related Equipment)
• Because a computer follows a set of instructions.

• Many electronic devices are computers that perform only one operation, • Traditional channel service unit (CSU) and data service unit (DSU)
but they are still following instructions that reside permanently in the unit. • Modems
• List of some Programmable devices
• Achronix Speedster SPD60 • Routers
• Actel’s
• Altera Stratix IV GT and Arria II GX • Switches
• Atmel‟s AT91CAP7L
• Cypress Semiconductor‟s programmable system-on-chip (PSoC) family • Conferencing equipment
• Lattice Semiconductor‟s ECP3
• Network appliances (NIDs and SIDs)
• Lime Microsystems‟ LMS6002
• Silicon Blue Technologies
• Hosting equipment and servers
• Xilinx Virtex 6 and Spartan 6
• Xmos Semiconductor L series
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Human to Machine Interaction(HMI) Human to Machine Interaction(HMI)

• HMI refers to the communication and interaction between a human and a machine via
3. How important is human-computer interaction?
a user interface.
• Nowadays, natural user interfaces such as gestures have gained increasing attention as • The goal of HCI is to improve the interaction between users and computers
they allow humans to control machines through natural and intuitive behaviors.
by making computers more user-friendly and receptive to the user's needs.
1. What is interaction in human-computer interaction?
• HCI (human-computer interaction) is the study of how people interact with • The main advantages of HCI are
computers and to what extent computers are or are not developed for
successful interaction with human beings. • Simplicity
• As its name implies, HCI consists of three parts: the User, the Computer
• Ease of deployment & operations
itself, and the ways they work together.
2. How do users interact with computers? • Cost savings for smaller set-ups
• The user interacts directly with hardware for the human input and output
• Reduce solution design time and integration complexity.
such as displays, e.g. through a graphical user interface.
• The user interacts with the computer over this software interface using the
given input and output (I/O) hardware.

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Human to Machine Interaction(HMI) Future Trends in Emerging Technologies

• Disciplines Contributing to Human-Computer Interaction (HCI) • Emerging Technology trends in 2019

• Cognitive psychology: Limitations, information processing, performance • 5G Networks

prediction, cooperative working, and capabilities. • Artificial Intelligence (AI)

• Computer science: Including graphics, technology, prototyping tools, user • Autonomous Devices
interface management systems. • Blockchain
• Linguistics
• Augmented Analytics
• Engineering and design
• Digital Twins
• Artificial intelligence
• Enhanced Edge Computing and
• Human factors
• Immersive Experiences in Smart Spaces
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Future Trends in Emerging Technologies

?
• Some emerging technologies that will shape the future of you and
your business
• The future is now or emerging technologies are taking over our minds
more and more each day.
• These are very high-level emerging technologies though.

• Chatbots

• Virtual/augmented reality
END OF CHAPTER ONE
• Blockchain Next: Chapter Two: Data Science
• Ephemeral Apps and

• Artificial Intelligence are already shaping your life whether you like it
or not.

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Contents

Overview of Data Science

CHAPTER TWO Data and Information

Data Processing Cycle

Data Science Data Types and their Representation

Data Value Chain

Basic Concepts of Big Data

Andualem T. Clustered Computing and Hadoop Ecosystem

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Overview of Data Science Data and Information

• Data science is a multi-disciplinary field that uses scientific methods, Data
processes, algorithms, and systems to extract knowledge and insights • Defined as a representation of facts, concepts, or instructions in a formalized manner,

from structured, semi-structured and unstructured data. which should be suitable for communication, interpretation, or processing, by human or
electronic machines.
• Data science is much more than simply analyzing data.
• It can be described as unprocessed facts and figures.
• Let’s consider this idea by thinking about some of the data involved in • Represented with the help of characters such as alphabets (A-Z, a-z), digits (0-9) or special

buying a box of cereal from the store or supermarket: characters (+, -, /, *, <,>, =, etc.).

• Whatever your cereal preferences teff, wheat, or burly you prepare Information
for the purchase by writing “cereal” in your notebook. This planned • Is the processed data on which decisions and actions are based

• It is data that has been processed into a form that is meaningful to the recipient
purchase is a piece of data though it is written by pencil that you can
• Information is interpreted data; created from organized, structured, and processed data in
read.
a particular context.

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Data Processing Cycle Data Processing Cycle

• Data processing is the re-structuring or re-ordering of data by people or • Input
• In this step, the input data is prepared in some convenient form for processing.
machines to increase their usefulness and add values for a particular
• The form will depend on the processing machine.
purpose.
• For example, when electronic computers are used, the input data can be recorded on any one

• Data processing consists of the following basic steps - input, processing, of the several types of storage medium, such as hard disk, CD, flash disk and so on.

and output. • Processing

• In this step, the input data is changed to produce data in a more useful form.
• These three steps constitute the data processing cycle
• For example, interest can be calculated on deposit to a bank, or a summary of sales for the
month can be calculated from the sales orders.
• Output
• At this stage, the result of the proceeding processing step is collected.

• The particular form of the output data depends on the use of the data.

• For example, output data may be payroll for employees.

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Data Types and their Representation Data Types and their Representation
• Data types can be described from diverse perspectives. 2. Data types from Data Analytics perspective
• In computer science and computer programming, for instance, a data type is • From a data analytics point of view, it is important to understand that there
simply an attribute of data that tells the compiler or interpreter how the are three common types of data types or structures:
programmer intends to use the data.
A. Structured
1. Data types from Computer programming perspective
• Almost all programming languages explicitly include the notion of data type.
B. Semi-structured, and

• Common data types include C. Unstructured data types

• Integers(int)- is used to store whole numbers, mathematically known as integers • below describes the three types of data and metadata.
• Booleans(bool)- is used to represent restricted to one of two values: true or false

• Characters(char)- is used to store a single character

• Floating-point numbers(float)- is used to store real numbers

• Alphanumeric strings(string)- used to store a combination of characters and numbers

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Structured Data Semi-structured Data

• Structured data is data that adheres to a pre-defined data model and is • Semi-structured data is a form of structured data that does not conform

therefore straightforward to analyze. with the formal structure of data models associated with relational

• Structured data conforms to a tabular format with a relationship between databases or other forms of data tables, but nonetheless, contains tags or

the different rows and columns. other markers to separate semantic elements and enforce hierarchies of

• Example: Excel files or SQL databases.

records and fields within the data.

• Therefore, it is also known as a self-describing structure.

• Each of these has structured rows and columns that can be sorted.
• Examples: JSON and XML are forms of semi-structured data.
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Unstructured Data Metadata (Data about Data)

• Unstructured data is information that either does not have a predefined • From a technical point of view, this is not a separate data structure, but it

data model or is not organized in a pre-defined manner. is one of the most important elements for Big Data analysis and big data

• Unstructured information is typically text-heavy but may contain data solutions.

such as dates, numbers, and facts as well. • Metadata is data about data.

• This results in irregularities and ambiguities that make it difficult to • It provides additional information about a specific set of data.

understand using traditional programs as compared to data stored in • metadata is frequently used by Big Data solutions for initial analysis.
structured databases. • In a set of photographs, for example, metadata could describe when and
• Example: Audio, video files or NoSQL databases. where the photos were taken. The metadata then provides fields for dates
and locations which, by themselves, can be considered structured data.

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Data Value Chain Data Acquisition

• The Data Value Chain is introduced to describe the information flow • It is the process of gathering, filtering, and cleaning data before it is put in

within a big data system as a series of steps needed to generate value and a data warehouse or any other storage solution on which data analysis can
useful insights from data. be carried out.

• The Big Data Value Chain identifies the following key high-level • Data acquisition is one of the major big data challenges in terms of

activities: infrastructure requirements.

• The infrastructure required to support the acquisition of big data must

deliver low, predictable latency in both capturing data and in executing

queries; be able to handle very high transaction volumes, often in a
distributed environment; and support flexible and dynamic data
structures.
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Data Analysis Data Curation

• It is concerned with making the raw data acquired amenable to use in • It is the active management of data over its life cycle to ensure it meets the
necessary data quality requirements for its effective usage.
decision-making as well as domain-specific usage.
• Data curation processes can be categorized into different activities such as
• Data analysis involves exploring, transforming, and modeling data with
content creation, selection, classification, transformation, validation, and
the goal of highlighting relevant data, synthesizing and extracting useful preservation.
hidden information with high potential from a business point of view. • Data curation is performed by expert curators that are responsible for

• Related areas include data mining, business intelligence, and machine improving the accessibility and quality of data.
• Data curators (also known as scientific curators or data annotators) hold the
learning.
responsibility of ensuring that data are trustworthy, discoverable, accessible,
reusable and fit their purpose.
• A key trend for the duration of big data utilizes community and
crowdsourcing approaches.

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Data Storage Data Usage

• It is the persistence and management of data in a scalable way that satisfies • It covers the data-driven business activities that need access to
the needs of applications that require fast access to the data.
data, its analysis, and the tools needed to integrate the data
• Relational Database Management Systems (RDBMS) have been the main,
analysis within the business activity.
and almost unique, a solution to the storage paradigm for nearly 40 years.
• Data usage in business decision-making can enhance
• However, the ACID (Atomicity, Consistency, Isolation, and Durability)

properties that guarantee database transactions lack flexibility with regard to competitiveness through the reduction of costs, increased added
schema changes and the performance and fault tolerance when data volumes value, or any other parameter that can be measured against
and complexity grow, making them unsuitable for big data scenarios. existing performance criteria.
• NoSQL technologies have been designed with the scalability goal in mind

and present a wide range of solutions based on alternative data models.

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Basic Concepts of Big Data What is Big Data?

• Big data is a blanket term for the non-traditional strategies and • Big data is the term for a collection of data sets so large and complex

technologies needed to gather, organize, process, and gather that it becomes difficult to process using on-hand database

insights from large datasets. management tools or traditional data processing applications.

• In this context, a “large dataset” means a dataset too large to

• While the problem of working with data that exceeds the
reasonably process or store with traditional tooling or on a single
computing power or storage of a single computer is not new, the
computer.
pervasiveness, scale, and value of this type of computing have
• This means that the common scale of big datasets is constantly shifting
greatly expanded in recent years.
and may vary significantly from organization to organization.

• Big data is characterized by 3V and more: Volume, Velocity, Variety

and Veracity

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What is Big Data? Clustered Computing and Hadoop Ecosystem

• Characteristics of Big Data • Clustered Computing

• Volume: large amounts of data Zeta bytes/Massive datasets • Because of the qualities of big data, individual computers are often
inadequate for handling the data at most stages.
• Velocity: Data is live streaming or in motion
• To better address the high storage and computational needs of big
• Variety: data comes in many different forms from diverse sources
data, computer clusters are a better fit.
• Veracity: can we trust the data? How accurate is it? etc. • Big data clustering software combines the resources of many
smaller machines, seeking to provide a number of benefits:
• Resource Pooling

• High Availability

• Easy Scalability
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Clustered Computing and Hadoop Ecosystem Clustered Computing and Hadoop Ecosystem
• Resource Pooling • Using clusters requires a solution for managing cluster membership,
• Combining the available storage space to hold data is a clear benefit, but coordinating resource sharing, and scheduling actual work on
CPU and memory pooling are also extremely important.
• Processing large datasets requires large amounts of all three of these individual nodes.
resources.
• Cluster membership and resource allocation can be handled by
• High Availability software like Hadoop’s YARN (which stands for Yet Another
• Clusters can provide varying levels of fault tolerance and availability Resource Negotiator).
guarantees to prevent hardware or software failures from affecting access
to data and processing. • The assembled computing cluster often acts as a foundation that other
• This becomes increasingly important as we continue to emphasize the
software interfaces with to process the data.
importance of real-time analytics.
• The machines involved in the computing cluster are also typically
• Easy Scalability
involved with the management of a distributed storage system, which
• Clusters make it easy to scale horizontally by adding additional machines
to the group. This means the system can react to changes in resource we will talk about when we discuss data persistence.
requirements without expanding the physical resources on a machine.

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Clustered Computing and Hadoop Ecosystem Hadoop and its Ecosystem

• Hadoop and its Ecosystem
• The four key characteristics of Hadoop are:
• Hadoop is an open-source framework intended to make interaction with
big data easier. • Economical: Its systems are highly economical as ordinary

• It is a framework that allows for the distributed processing of large datasets computers can be used for data processing.
across clusters of computers using simple programming models. • Reliable: It is reliable as it stores copies of the data on different
• It is inspired by a technical document published by Google.
machines and is resistant to hardware failure.
• The four key characteristics of Hadoop are:
• Scalable: It is easily scalable both, horizontally and vertically. A
• Economical
few extra nodes help in scaling up the framework
• Reliable
• Flexible: It is flexible and you can store as much structured and
• Scalable
unstructured data as you need to and decide to use them later.
• Flexible
 53 54

Hadoop and its Ecosystem Hadoop and its Ecosystem

• Hadoop has an ecosystem that has evolved from its four core
components: data management, access, processing, and storage.
• It is continuously growing to meet the needs of Big Data.

• It comprises the following components and many others:

• HDFS: Hadoop Distributed File System
• YARN: Yet Another Resource Negotiator
• MapReduce: Programming based Data Processing
• Spark: In-Memory data processing
• PIG, HIVE: Query-based processing of data services
• HBase: NoSQL Database
• Mahout, Spark MLLib: Machine Learning algorithm libraries
• Solar, Lucene: Searching and Indexing
• Zookeeper: Managing cluster
• Oozie: Job Scheduling

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Big Data Life Cycle with Hadoop Big Data Life Cycle with Hadoop
• Ingesting data into the system • Computing and analyzing data
• The first stage of Big Data processing is Ingest. • The third stage is to Analyze.
• The data is ingested or transferred to Hadoop from various sources • Here, the data is analyzed by processing frameworks such as Pig,
such as relational databases, systems, or local files. Hive, and Impala.
• Sqoop transfers data from RDBMS to HDFS, whereas Flume • Pig converts the data using a map and reduce and then analyzes it.
transfers event data. • Hive is also based on the map and reduce programming and is most
• Processing the data in storage suitable for structured data.
• The second stage is Processing. • Visualizing the results
• In this stage, the data is stored and processed. • The fourth stage is Access, which is performed by tools such as Hue
• The data is stored in the distributed file system, HDFS, and the
NoSQL distributed data, HBase. and Cloudera Search.
• Spark and MapReduce perform data processing. • In this stage, the analyzed data can be accessed by users.
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? CHAPTER THREE
END OF CHAPTER TWO Artificial Intelligence
Next: Chapter Three: Artificial Intelligence

Andualem T.

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Contents Artificial Intelligence (AI)

• Artificial Intelligence is composed of two words Artificial and

Artificial Intelligence
Intelligence.

• Artificial defines "man-made," and intelligence defines "thinking power",

History of AI
or “the ability to learn and solve problems”

Levels of AI • Artificial Intelligence means "a man-made thinking power."

• Artificial Intelligence (AI) as the branch of computer science by which we

Types of AI
can create intelligent machines which can behave like a human, think like
How humans think
humans, and able to make decisions.
Application of AI • Intelligence is the ability to acquire and apply knowledge.
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Artificial Intelligence (AI) Artificial Intelligence (AI)

• Knowledge is the information acquired through experience. • Intelligence is composed of:

• Experience is the knowledge gained through exposure (training). • Reasoning

• Therefore, artificial intelligence as the “copy of something natural (i.e., • Learning

human beings) „WHO‟ is capable of acquiring and applying the
• Problem Solving
information it has gained through exposure.”
• Perception and
• Artificial Intelligence exists when a machine can have human-based
• Linguistic Intelligence
skills such as learning, reasoning, and solving problems.
• Artificial Intelligence system is composed of
• In Artificial Intelligence you do not need to preprogram a machine to
• Agent and
do some work, despite that you can create a machine with programmed
algorithms which can work with own intelligence. • Environment.

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Artificial Intelligence (AI) Artificial Intelligence (AI)

• Artificial Intelligence system is composed of Agent and its • High-profile Examples of AI include
• Autonomous vehicles (such as drones and self-driving cars)
Environment.
• Medical diagnosis
• An agent (e.g., human or robot) is anything that can perceive its • Creating art (such as poetry)
environment through sensors and acts upon that environment • Proving mathematical theorems
• Playing games (such as Chess or Go)
through effectors.
• Search engines (such as Google search)
• Intelligent agents must be able to set goals and achieve them. • Online assistants (such as Siri)

• Machine perception is the ability to use input from sensors (such • Image recognition in photographs
• Spam filtering
as cameras, microphones, sensors, etc.) to deduce aspects of the
• Prediction of judicial decisions and
world. e.g., Computer Vision. • Online advertisements
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Artificial Intelligence (AI) Artificial Intelligence (AI)

• AI deals with the area of developing computing systems that are • The term machine learning was introduced by Arthur Samuel in 1959.

capable of performing tasks that humans are very good at. • Neural networks are biologically inspired networks that extract

• Example: recognizing objects, recognizing and making sense of features from the data in a hierarchical fashion.
speech, and decision making in a constrained environment. • The field of neural networks with several hidden layers is called deep

• Machine Learning is an advanced form of AI where the machine can learning.

learn as it goes rather than having every action programmed by
humans.

• Machine learning, a fundamental concept of AI research since the

field‟s inception, is the study of computer algorithms that improve

automatically through experience.

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Need for Artificial Intelligence Goals of Artificial Intelligence

• Following are the main goals of Artificial Intelligence:
• Why we need AI at this time?
1. Replicate human intelligence
1. To create expert systems that exhibit intelligent behavior with the 2. Solve Knowledge-intensive tasks
capability to learn, demonstrate, explain and advice its users. 3. An intelligent connection of perception and action
4. Building a machine which can perform tasks that requires human
2. Helping machines find solutions to complex problems like humans
intelligence such as:
do and applying them as algorithms in a computer-friendly • Proving a theorem
manner. • Playing chess
• Plan some surgical operation
• Driving a car in traffic
5. Creating some system which can exhibit intelligent behavior, learn
new things by itself, demonstrate, explain, and can advise to its
user.
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What Comprises to Artificial Intelligence? Advantages of Artificial Intelligence

• High Accuracy with fewer errors: AI machines or systems are prone to fewer
• To create the AI-first we should know that how intelligence is
errors and high accuracy as it takes decisions as per pre-experience or
composed, so Intelligence is an intangible part of our brain which is a information.
• High-Speed: AI systems can be of very high-speed and fast-decision making,
combination of Reasoning, learning, problem-solving, perception,
because of that AI systems can beat a chess champion in the Chess game.
language understanding, etc • High reliability: AI machines are highly reliable and can perform the same
action multiple times with high accuracy.
• To achieve the above factors for a machine or software Artificial • Useful for risky areas: AI machines can be helpful in situations such as defusing
Intelligence requires the following disciplines a bomb, exploring the ocean floor, where to employ a human can be risky.
• Digital Assistant: AI can be very useful to provide digital assistant to users such
as AI technology is currently used by various E-commerce websites to show the
products as per customer requirements.
• Useful as a public utility: AI can be very useful for public utilities such as a self
driving car which can make our journey safer and hassle-free, facial recognition
for security purposes, Natural language processing (for search engines, for
spelling checker, for assistant like Siri, for translation like google translate ), etc.

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Disadvantages of Artificial Intelligence History of AI [Reading Assignment]

• High Cost: The hardware and software requirement of AI is very costly
as it requires lots of maintenance to meet current world requirements.
• Can't think out of the box: Even we are making smarter machines with
AI, but still they cannot work out of the box, as the robot will only do that
work for which they are trained, or programmed.
• No feelings and emotions: AI machines can be an outstanding performer,
but still it does not have the feeling so it cannot make any kind of
emotional attachment with humans, and may sometime be harmful for
users if the proper care is not taken.
• Increase dependence on machines: With the increment of technology,
people are getting more dependent on devices and hence they are losing
their mental capabilities.
• No Original Creativity: As humans are so creative and can imagine
some new ideas but still AI machines cannot beat this power of human
intelligence and cannot be creative and imaginative.
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History of AI History of AI
A. Maturation of Artificial Intelligence (1943-1952) B. The birth of Artificial Intelligence (1952-1956)

• The year 1943: The first work which is now recognized as AI was done by • The year 1955: An Allen Newell and Herbert A. Simon created the
Warren McCulloch and Walter pits in 1943. They proposed a model of "first artificial intelligence program" Which was named "Logic
artificial neurons.
Theorist". This program had proved 38 of 52 Mathematics theorems,
• The year 1949: Donald Hebb demonstrated an updating rule for modifying
and find new and more elegant proofs for some theorems.
the connection strength between neurons. His rule is now called Hebbian
learning. • The year 1956: The word "Artificial Intelligence" first adopted by

• The year 1950: The Alan Turing who was an English mathematician and American Computer scientist John McCarthy at the Dartmouth
pioneered Machine learning in 1950. Conference. For the first time, AI coined as an academic field. At that
• Alan Turing publishes "Computing Machinery and Intelligence" in time high-level computer languages such as FORTRAN, LISP, or
which he proposed a test. The test can check the machine's ability to exhibit COBOL were invented. And the enthusiasm for AI was very high at
intelligent behavior equivalent to human intelligence, called a Turing test.
that time.

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History of AI History of AI
C. The golden years-Early enthusiasm (1956-1974) E. A boom of AI (1980-1987)
• The year 1980: After AI winter duration, AI came back with "Expert System".
• The year 1966: The researchers emphasized developing algorithms that can
Expert systems were programmed that emulate the decision-making ability of a
solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966,
human expert.
which was named as ELIZA.
• In the Year 1980, the first national conference of the American Association of
• The year 1972: The first intelligent humanoid robot was built in Japan which Artificial Intelligence was held at Stanford University.
was named WABOT-1. F. The second AI winter (1987-1993)
D. The first AI winter (1974-1980) • The duration between the years 1987 to 1993 was the second AI Winter

• The duration between the years 1974 to 1980 was the first AI winter duration. AI winter duration.
refers to the time period where computer scientists dealt with a severe shortage of • Again, Investors and government stopped in funding for AI research due to
funding from the government for AI researches. high cost but not efficient results. The expert system such as XCON was very
• During AI winters, an interest in publicity on artificial intelligence was decreased. cost-effective.
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History of AI History of AI
G. The emergence of intelligent agents (1993-2011) H. Deep learning, big data and artificial general intelligence (2011-
present)
• The year 1997: In the year 1997, IBM Deep Blue beats
• The year 2011: In the year 2011, IBM's Watson won jeopardy, a
world chess champion, Gary Kasparov, and became the
quiz show, where it had to solve complex questions as well as
first computer to beat a world chess champion
riddles.
• The year 2002: for the first time, AI entered the home in
• Watson had proved that it could understand natural language and
the form of Roomba, a vacuum cleaner.
can solve tricky questions quickly.
• The year 2006: AI came into the Business world until the
• The year 2012: Google has launched an Android app feature
year 2006. Companies like Facebook, Twitter, and Netflix
"Google now", which was able to provide information to the user
also started using AI.
as a prediction.

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History of AI Levels of AI
• Stage 1 – Rule-Based Systems
H. Deep learning, big data and artificial general intelligence (2011-present)
• Uses rules as the knowledge representation
• The year 2014: In the year 2014, Chatbot "Eugene Goostman" won a
• Is a system that applies human-made rules to store, sort and
competition in the infamous "Turing test" manipulate data. In doing so, it mimics human intelligence.
• The year 2018: The "Project Debater" from IBM debated on complex • It‟s a logical program that uses pre-defined rules to make deductions
and choices to perform automated actions.
topics with two master debaters and also performed extremely well.
• Stage 2 – Context Awareness and Retention
• Google has demonstrated an AI program "Duplex" which was a virtual
• Algorithms that develop information about the specific domain they
assistant and which had taken hair dresser appointment on call, and the are being applied in.
lady on the other side didn't notice that she was talking with the • They are trained on the knowledge and experience of the best
humans, and their knowledge base can be updated as new situations
machine.
and queries arise.
• Eg. chatbots and “robo advisors”
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Levels of AI Levels of AI
• Stage 3 – Domain-Specific Expertise • Stage 5 – Self Aware Systems / Artificial General Intelligence (AGI)
• Expertise and Domain Specific Knowledge. • These systems have human-like intelligence
• Going beyond the capability of humans • AGI is the intelligence of a machine that has the capacity to understand or
• These systems build up expertise in a specific context taking in learn any intellectual task that a human being can.
massive volumes of information which they can use for decision
making. • Stage 6 – Artificial Superintelligence (ASI)
• Eg. AlphaGo • AI algorithms can outsmart even the most intelligent humans in every
• Stage 4 – Reasoning Machines domain.
• These algorithms have some ability to attribute mental states to themselves • Logically it is difficult for humans to articulate what the capabilities might
and others – they have a sense of beliefs, intentions, knowledge, and how be, yet we would hope examples would include solving problems we have
their own logic works. failed to so far, such as world hunger and dangerous environmental change.
• This means they could reason or negotiate with humans and other • A few experts who claim it can be realized by 2029.
machines. • Fiction has tackled this idea for a long time, for example in the film Ex
• At the moment these algorithms are still in development, however, Machina or Terminator.
commercial applications are expected within the next few years

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Levels of AI Levels of AI
• Stage 7 –Singularity and Transcendence
• This leads to a massive expansion in human capability.
• Human augmentation could connect our brains to each other and to a future
successor of the current internet, creating a “hive mind” that shares ideas,
solves problems collectively, and even gives others access to our dreams as
observers or participants.
• Pushing this idea further, we might go beyond the limits of the human
body and connect to other forms of intelligence on the planet – animals,
plants, weather systems, and the natural environment.
• Some proponents of singularity such as Ray Kurzweil, Google‟s Director
of Engineering, suggest we could see it happen by 2045 as a result of
exponential rates of progress across a range of science and technology
disciplines.
• The other side of the fence argues that singularity is impossible and human
consciousness could never be digitized
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Types of AI Based on Capabilities

• Artificial Intelligence can be divided into various types, these are 1. Weak AI or Narrow AI:
• Based on Capabilities and • Is a type of AI which is able to perform a dedicated task with intelligence.

• cannot perform beyond its field or limitations, as it is only trained for one
• Based on Functionality
specific task.

• Also called as Weak AI.

• Is the most common and currently available AI in the world

• Can fail in unpredictable ways if it goes beyond its limits.

• E.g. Apple Siri , IBM's Watson supercomputer ,Google translate, playing

chess, purchasing suggestions on e-commerce sites, self-driving cars, speech
recognition, and image recognition.

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Based on Capabilities Based on Capabilities

2. General AI
3. Super AI
• Is a level of Intelligence of Systems at which machines could surpass human
• Is a type of intelligence that could perform any intellectual task with
intelligence, and can perform any task better than a human with cognitive
efficiency like a human.
properties.
• The idea behind the general AI to make such a system that could be
• This refers to aspects like general wisdom, problem solving and creativity.
smarter and think like a human on its own.
• It is an outcome of general AI.
• Currently, there is no such system exists which could come under general
• Some key characteristics of strong AI include capability include the ability to
AI and can perform any task as perfect as a human.
think, to reason solve the puzzle, make judgments, plan, learn, and
• It may arrive within the next 20 years communicate on its own.
• As systems with general AI are still under research, and it will take lots of • Super AI is still a hypothetical concept of Artificial Intelligence.

effort and time to develop such systems. • The development of such systems in real is still a world-changing task.
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Based on Functionality Based on Functionality

1. Reactive Machines: 2. Limited Memory

• The most basic types of Artificial Intelligence • Can store past experiences or some data for a short period of time.

• Do not store memories or past experiences for future actions • These machines can use stored data for a limited time period only.

• Only focus on current scenarios and react on it as per possible best • As the name suggests they have limited memory or short-lived

action. memory
• Eg. IBM's Deep Blue system and Google's AlphaGo • Eg. Self-driving cars : can store the recent speed of nearby cars, the

distance of other cars, speed limits, and other information to navigate

the road.

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Based on Functionality How Humans Think

3. Theory of Mind • Intelligence or the cognitive process is composed of three main

• Understand human emotions, people, beliefs, and be able to interact stages:

socially like humans. • Observe and input the information or data in the brain.

• Still not developed • Interpret and evaluate the input that is received from the

4. Self Awareness surrounding environment.

• is the future of Artificial Intelligence • Make decisions as a reaction towards what you received as input
• will be super intelligent and will have their own consciousness,
and interpreted and evaluated.
sentiments, and self-awareness.
• These machines will be smarter than the human mind.
• does not exist in reality still and it is a hypothetical concept.
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Mapping Human Thinking to AI Components Influencers of Artificial Intelligence

• Because AI is the science of simulating human thinking, it is possible • Influencers of AI includes:

to map the human thinking stages to the layers or components of AI • Big data: Structured data versus unstructured data
systems. • Advancements in computer processing speed and New chip
• In the first stage, humans acquire information from their surrounding architectures
environments through human senses, such as sight, hearing, smell, • Cloud computing and Application Program Interfaces
taste, and touch, through human organs, such as eyes, ears, and other • Cloud computing is a general term that describes the delivery of on-demand
sensing organs, for example, the hands. services, usually through the internet, on a pay-per-use basis.

• In AI models, this stage is represented by the sensing layer, which • The emergence of data science
perceives information from the surrounding environment. • The goal of data science is to extract knowledge or insights from data in
various forms, either structured or unstructured, which is like data mining.
• Eg: Sensors like voice recognition and visual imaging recognition

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Applications of Artificial Intelligence AI Tools and Platforms

• AI has developed a large number of tools to solve the most difficult
• AI in agriculture • AI in Social Media
• Very helpful for farmers • To organize and manage massive problems in computer science, like:
• As agriculture robotics, solid and amounts of data
crop monitoring • Search and optimization
• AI in Data Security
• Logic
• AI in Healthcare • used to make your data more
• to make a better and faster
safe and secure. • Probabilistic methods for uncertain reasoning
diagnosis than humans. • AI in Travel &Transport • Classifiers and statistical learning methods
• AI in education • Use chatbots which can make • Neural networks
human-like interaction with
• as a teaching assistant customers • Control theory
• Eg: chatbot
• AI in Robotics • Languages
• AI in Finance and E-commerce
• AI in Entertainment • Most common artificial intelligence platforms include Microsoft
• AI in Gaming : eg chess AZURE Machine Learning, Google Cloud Prediction API, IBM
• AI in the Automotive Industry
Watson, TensorFlow, Infosys Nia, Wipro HOLMES, API.AI,
Premonition, Rainbird, Ayasdi, MindMeld, and Meya.
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Sample AI Application

?
• Commuting • Online Shopping
• Googles AI-powered predictions
• Search (Amazon)
• Ridesharing Apps like Uber

• E-mail • recommendations for products

• Spam filters
• Mobile Use
• Smart e-mail categorization
• Voice-to-text
• AI in education
• As a teaching assistant • Smart personal Assistants (Siri)
END OF CHAPTER THREE
• Eg: chatbot Next: Chapter Four: IoT
• Social Networking

• Facebook

• Instagram

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Contents

Definition of the term IOT

CHAPTER FOUR History of IOT

Internet of Things Advantage and disadvantage of IOT

Explain how IOT works

Architecture of IOT

Andualem T. Application areas of IOT

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Overview of Internet of Things Overview of Internet of Things

• Artificial intelligence
• The most important features of internet of things (IoT) include:
• IoT makes anything virtually “smart”, meaning it enhances every aspect
• Artificial intelligence
of life with the power of data collection, artificial intelligence algorithms,
• Connectivity and networks
• Connectivity
• Sensors
• new enabling technologies for networking and specially IOT networking ,
• Active engagement and mean networks are no longer exclusively tied to major provides therefore
IOT creates these small networks between its system devices.
• Small device
• Sensors
• IOT loses its distinction without sensors

• They act as defining instruments that transform from standard passive network
of device in to an active system cabling of real world integration.

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Overview of Internet of Things Definitions of IOT

• Active engagement • Several groups defined IOT using different definitions

• IOT introduce a new paradigm for active content , product ,or 1. According to the internet architecture boards definition :-

service engagement rather than passive engagement. • IOT is networking of smart objects, means a huge number of devices
intelligently communicating in the presence of internet protocol that cannot
• Small device be directly operated by human beings but exist as components in building

• Those devices has become smaller, cheaper and more vehicles or the environment.

powerful over time so IOT exploits purpose built small 2. According to the internet engineering task force (IETF) organizations

• IOT is the networking of smart objects in which smart objects have some
devices to deliver its precision, scalability, and versatility.
constraints such as limited bandwidth ,power, and processing accessibility for
achieving interoperability among smart objects.
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Definitions of IOT Definitions of IOT

3. According to the IEEE communications category magazines definition:-
• Generally, IOT is the network of physical objects or “things”
• IOT is framework of all things that have a representation of the
embedded with electronics , software and network connectivity ,
presence of the internet in such a way that new applications and
services enable the interaction in the physical and virtual world in the which enables these to collect and exchange data.

form of machine to machine (M2M)communication in the cloud. • Is also a system of interrelated computing devices, mechanical and

4. According to the oxfords definition digital machines, objects, animals or people that are provided with
• IOT is the interaction of everyday objects computing devices through unique identifiers and the ability to transfer data over a network
the internet that enables the sending and receiving of useful data. without requiring human-to-human or human-to-computer interaction
• The term Internet of things defines according to the 2020 conceptual
• IOT is a network of devices that can sense, accumulate and transfer
frame work is expressed through simple formula such as:-
data over the internet without any human intervention.
IOT=services + data +networks + sensors

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Definitions of IOT Areas where IOT is applicable

• Simply stated, the Internet of Things consists of any device with an on/off • In connected industry
switch connected to the Internet including anything such as:-
• In smart-city
• Ranging from cellphones to building maintenance to the jet engine of an
airplane • In using smart-home

• Medical devices, such as a heart monitor implant or a biochip transponder in • In smart-energy

a farm animal can transfer data over a network and are members of the IOT.
• In connected car
• The IOT consists of a gigantic network of internet-connected “things” and
devices. • In the smart agriculture

• E.g. Ring, a doorbell that links to your smartphone, provides an excellent • In connected building and campus
example of a recent addition to the Internet of Things means Ring signals you
• In the health care
when the doorbell is pressed and lets you see who it is and to speak with
them. • In Logistics and other domains
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• IOT systems allow users to achieve deeper automation, analysis,

and integration within a system.
• It also improve the reach of these areas and their accuracy.

• IOT utilizes existing and emerging technology for sensing,

networking, and robotics.
• IOT exploits recent advances in software, falling hardware
prices, and modern attitudes towards technology
• Its new and advanced elements bring major changes in the
delivery of products, goods, and services; and the social,
economic, and political impact of those changes.

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History of IOT
History of IOT • The traditional fields of automation (including the automation of
• The Internet of Things has not been around for very long.
buildings and homes), wireless sensor networks, GPS, control systems,
• since the early 1800s there have been visions of machines communicating with one
and others, all support the IoT.
another.
• In 1830s and 1840s Machines have been providing direct communications since the • Kevin Ashton, the Executive Director of Auto-ID Labs at MIT, was the
telegraph (the first landline) was developed. first to describe the Internet of Things, during his 1999 speech.
• In June 3, 1900, Described as “wireless telegraphy,” the first radio voice transmission
• Kevin Ashton stated that Radio Frequency Identification (RFID) was a
took place, providing another necessary component for developing the Internet of
Things. prerequisite for the Internet of Things. He concluded if all devices were
• In 1950s The development of computers began . “tagged,” computers could manage, track, and inventory them.
• In 1962 The Internet, itself a significant component of the IOT, started out as part of
• To some extent, the tagging of things has been achieved through
DARPA (Defense Advanced Research Projects Agency).
technologies such as digital watermarking, barcodes, and QR codes.
• In 1969 evolved into ARPANET.
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Advantages of IOT Advantages of IOT

• Advantages of IoT span across every area of lifestyle and business Here • Reduced Waste − IoT makes areas of improvement clear. Current
is a list of some of the advantages that IoT has to offer: analytics give us superficial insight, but IoT provides real-world
• Improved Customer Engagement − Current analytics suffer from information leading to the more effective management of resources.
blind-spots and significant flaws inaccuracy; and as noted, engagement
• Enhanced Data Collection − Modern data collection suffers from its
remains passive. IoT completely transforms this to achieve richer and
limitations and its design for passive use. IoT breaks it out of those
more effective engagement with audiences
spaces and places it exactly where humans really want to go to analyze
• Technology Optimization − The same technologies and data which
our world. It allows an accurate picture of everything.
improve the customer experience also improve device use, and aid in
more potent improvements to technology. IoT unlocks a world of
critical functional and field data.

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Disadvantage of IOT Challenges of IOT

• As the number of connected devices increases and more information is shared • Security − IoT creates an ecosystem of constantly connected
between devices, the potential that a hacker could steal confidential
devices communicating over networks. The system offers little
information also increases.
control despite any security measures. This leaves users exposed
• If there‟s a bug in the system, it‟s likely that every connected device will
to various kinds of attackers.
become corrupted.

• Since there‟s no international standard of compatibility for IoT, it‟s difficult • Privacy − The sophistication of IoT provides substantial personal

for devices from different manufacturers to communicate with each other. data in extreme detail without the user's active participation.
• Enterprises may eventually have to deal with massive numbers maybe even
• Flexibility − Many are concerned about the flexibility of an IoT
millions of IoT devices and collecting and managing the data from all those
system to integrate easily with another. They worry about finding
devices will be challenging.
themselves with several conflicting or locking systems.
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Challenges of IOT How does it work

• An IoT ecosystem consists of web-enabled smart devices that use embedded
• Complexity − Some find IoT systems complicated in terms of
processors, sensors and communication hardware to collect, send and act on
design, deployment, and maintenance given their use of multiple
data they acquire from their environments.
technologies and a large set of new enabling technologies.
• IoT devices share the sensor data they collect by connecting to an IoT gateway
• Compliance − IoT, like any other technology in the realm of or another edge device where data is either sent to the cloud to be analyzed or
business, must comply with regulations. Its complexity makes the analyzed locally.

issue of compliance seem incredibly challenging when many • Sometimes, these devices communicate with other related devices and act on

consider standard software compliance a battle. the information they get from one another Those devices do most of the work
without human intervention, although people can interact with the devices.
• For instance, to set them up, give them instructions or access the data.

• The connectivity, networking and communication protocols used with these

web-enabled devices largely depend on the specific IoT applications deployed.

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Architecture of IoT
• In general, an IoT device can be explained as a network of things

that consists of hardware, software, network connectivity, and

sensors.

• The architecture of IoT devices comprises four major components

• Sensing

• Network

• Data processing and

• Application layers
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• There are three kinds of sensors in IOT device they are

1. Sensing Layer
A. Motion Sensors
• The main purpose of the sensing layer is
• They can measure the change in motion as well as the orientation of the devices.
• To identify any phenomena in the devices
• There are two types of motions one can observe in a device: linear and angular
• To obtain data from the real world.
• Linear motion refers to the linear displacement of an IoT device
• This layer consists of several sensors.
• Angular motion refers to the rotational displacement of the device.
• Sensors in IoT devices are usually integrated through sensor hubs.
B. Environmental Sensors
• A sensor hub is a common connection point for multiple sensors that • Sensors such as light sensors, pressure sensors etc are embedded in IoT devices
accumulate and forward sensor data to the processing unit of a device. to sense the change in environmental parameters in the devices peripheral.
• Actuators can also interfere to change the physical conditions that • Its primary purpose is to help the devices to take autonomous decisions
generate the data. according to the changes of a device‟s peripheral

• Example on actuator is shut off a power supply, adjust an airflow valve, • For instance, environment sensors are used in many applications to improve user

or move a robotic gripper in an assembly process. experience, home automation systems, smart locks, smart lights,

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C. Position sensors
3. Data Processing Layer
• Deal with the physical position and location of the device.

• The most common position sensors used in IoT devices are Magnetic sensors and • Consists of the main data processing unit of IoT devices
Global Positioning System (GPS) sensors.
• It takes data collected in the sensing layer and analyses the data to
• Magnetic sensors are usually used as digital compass and help to fix the
make decisions based on the result.
orientation of the device display
• Global Positioning System is used for navigation purposes in IoT devices. • In some IoT devices (e.g., smartwatch, smart home hub, etc.), the data

processing layer also saves the result of the previous analysis to

2. Network Layer
• Acts as a communication channel to transfer data, collected in the sensing layer,
improve the user experience.
to other connected devices. • This layer may share the result of data processing with other
• In IoT devices, the network layer is implemented by using diverse
connected devices via the network layer.
communication technologies to allow data flow between other devices within the
same network.
• e.g. Wi-Fi, Bluetooth, Zigbee, ZWAVE, LoRa, cellular network, etc.
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4. Application Layer Device and Network

• Implements and presents the results of the data processing layer to • Connected devices are part of a scenario in which every device talks to other
related devices in an environment to automate home and industrial tasks, and to
accomplish different applications of IoT devices.
communicate usable sensor data to users, businesses and other interested parties.
• It‟s a user-centric layer that executes various tasks for the users.
• IoT devices are meant to work in concert for people at home, in industry or in the
• There exist diverse IoT applications, which includes enterprise.
• The devices can be categorized into three main groups: Consumer, Enterprise
• Smart transportation
and Industrial.
• Smart home
• Consumer connected devices include smart TVs, smart speakers, toys,
• Personal care wearables, and smart appliances.

• Healthcare, etc. • Industrial and Enterprise IoT devices include smart meters, commercial security
systems and smart city technologies such as those used to monitor traffic and
weather conditions

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IOT tools and platforms Applications of IOT

• There are many vendors in the industrial IoT platform marketplace, offering • Agriculture
remarkably similar capabilities and methods of deployment
• Consumer Use
• These IoT platform solutions are based on IoT and cloud technology.
• Insurance
• They can be used in areas of smart home, city, enterprise, home automation,
healthcare or automotive. • Healthcare

• IoT Platform includes: • Manufacturing

• KAA: Manage unlimited number of connected devices • Retail

• SiteWhere: run any number of IoT applications on single SiteWhere
• Transportation
instance
• Utilities
• ThingSpeak: collect data in private channels

• Zetta: supports a wide rage of hacker boards

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1. IoT Based Smart Home • Smart Home Appliances: Refrigerators with LCD screen telling

• Smart Home initiative allows subscribers to remotely manage and what‟s inside, food that‟s about to expire, ingredients you need to buy
monitor different home devices from anywhere via smartphones or over and with all the information available on a smartphone app and also
the web with no physical distance limitations.
washing machine.
• These “smart” devices have the potential to share information with each
• Safety Monitoring: cameras, and home alarm systems making people
other given the permanent availability to access the broadband internet
connection. feel safe in their daily life at home.
• Components those are included in smart home development • Intrusion Detection Systems: Detection of window and door
• Remote Control Appliances: Switching on and off remotely openings and violations to prevent intruders.
appliances to avoid accidents and save energy.
• Energy and Water Use: Energy and water supply consumption
• Weather: Displays outdoor weather conditions such as humidity,
monitoring to obtain advice on how to save cost and resources, &
temperature, pressure, wind speed and rain levels with the ability to
transmit data over long distances. many more.

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2. IOT based smart city 3. IOT based smart Farming

• The development of smart grids, data analytics, and autonomous vehicles
will provide an intelligent platform to deliver innovations in energy • Green Houses: Control micro-climate conditions to maximize the

management, traffic management, and security, sharing the benefits of this production of fruits and vegetables and its quality.
technology throughout society. • Compost: Control of humidity and temperature levels in alfalfa, hay, straw,

• Components those are included in smart city development etc. to prevent fungus and other microbial contaminants.
• Structural Health: Monitoring of vibrations and material conditions in • Offspring Care: Control of growing conditions of the offspring in animal
buildings, bridges and historical monuments. farms to ensure its survival and health.
• Lightning: intelligent and weather adaptive lighting in street lights. • Field Monitoring: Reducing spoilage and crop waste with better monitoring,
• Smart Parking: Real-time monitoring of parking spaces available in the city accurate ongoing data obtaining, and management of the agriculture fields,
making residents able to identify and reserve the closest available spaces including better control of fertilizing, electricity and watering.
• Waste Management: Detection of rubbish levels in containers to optimize the trash • Animal Farming/Tracking: Location and identification of animals grazing in open
collection routes. Garbage cans and recycle bins with RFID tags allow the sanitation pastures or location in big stables, Study of ventilation and air quality in farms and
staff to see when garbage has put out. detection of harmful gases from excrements.
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CHAPTER 5
AUGMENTED REALITY (AR)
?
END OF CHAPTER FOUR
Next: Chapter Five: Augmented Reality

Andualem T.

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Introduction to AR Introduction to AR
¨ The idea of AR is to combine or mix the view of the real ¨ Through augmented vision, the information about the
environment with additional virtual content that is presented surrounding real world helps the user can digitally interact
through computer graphics. with and adjust information about their surrounding
¨ Augmented reality (AR) is a form of emerging technology that environment.
allows users to overlay computer generated virtual graphical
¨ Augmented reality is the integration of digital
content in the real world.
information with the user's environment in real time.
¨ AR refers to a live view of a physical real-world environment
¨ Example: Snapchat
whose elements are merged with augmented computer-
generated images creating a mixed reality.
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And what is VR?

¨ VR tricks your senses into thinking you are in different
environment or world apart from real world
¨ VR is a general term for immersive experiences and content
via a VR headset or HMD (head-mounted display).
¨ It refers to computer technologies using reality headsets to
generate realistic sounds, images and other sensations
that replicate a real environment or create an imaginary
world.
¨ The content is 100% digital and computer-generated. It is
also called a computer-simulated reality.

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And what is VR? And what is VR?

¨ Advanced VR environment engage all five senses (taste,
¨ VR technology creates an environment in which
sight, smell, touch, sound), but it is important to say that
this is not always possible the user feels and seems to be moving inside a
¨ Most VR Devices tethered which are connected to a computer-created virtual world in the same way
computer and some of them are standalone devices like
people move inside the natural environment.
Google Cardboard is among the most popular) and the
remaining's are Smartphone VR. ¨ Example: dragons
¨ Using VR devices such as HTC Vive, Oculus Rift or Google
Cardboard, users can be transported into a number of real-
world and imagined environments
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VR devices Mixed Reality

¨ Mixed Reality (MR), referred to as hybrid reality, is the
merging of real and virtual worlds to produce new
environments and visualizations where physical and digital
objects co-exist and interact in real-time.

¨ It allows for digital content to integrate, enrich and interact

Standalone smartphone
with the user‟s real-world environment.

¨ MR lenses or headsets present an overlay of digital content

that interacts with objects in the real world in real-time.

tethered

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Mixed Reality Mixed Reality Examples

¨ The key characteristic of MR is that the synthetic content and the
real-world content are able to react to each other in real-time.

¨ The products are, in most cases, in the research and development

phase, but MR is viewed through transparent wearable glasses.

¨ Mixed Reality removes the boundaries between real and virtual

worlds using occlusion: the computer-generated objects can be
visibly obscured by objects in the physical environment from the
user‟s point of view.
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AR vs VR vs MR Architecture of AR Systems
¨ Virtual Reality: VR is content which is 100% digital ¨ The first Augmented Reality Systems (ARS) were

usually designed with a basis on three main blocks.

and can be enjoyed in a fully immersive environment.

¨ Augmented Reality: AR overlays digital content on ¨ Infrastructure Tracker Unit

top of the real-world. ¨ Processing Unit

¨ Mixed Reality: MR is a digital overlay that allows ¨ Visual Unit

interactive virtual elements to integrate and interact

with the real-world environment.

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Architecture of AR Systems Architecture of AR Systems

¨ Infrastructure Tracker Unit: is responsible for collecting
data from the real world, sending them to the Processing
Unit.
¨ Processing Unit: which mix the virtual content with the real
content and sent the result to the Video Out module of the
Visual Unit.
¨ Visual Unit: the Visual Unit can be classified into two types
of system, depending on the followed visualization
technology: video see through and optical see through
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Architecture of AR Systems Architecture of AR Systems

1. Video see-through: It uses a Head-Mounted Display 2. Optical see-through: It uses an HMD that employs
(HMD) that employs a video-mixing and displays the optical combiners to merge the images within an open-view
merged images on a closed-view HMD. HMD.

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Applications of AR Systems Applications of AR Systems

1. Educational Purpose: AR supplements educational 2. Medical Purpose: AR provides Health and medical
sector the following benefits: sector the following benefits:
¨ Affordable learning materials ¨ Describing symptoms
¨ Interactive lessons ¨ Nursing care
¨ Higher engagement ¨ Surgery
¨ Higher retention ¨ Ultrasounds
¨ Boost intellectual curiosity ¨ Diabetes management
¨ Navigation
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Applications of AR Systems

?
3. AR in Entertainment: AR could be used in various
entertainment activities.

¨ Games

¨ Music

¨ Tv
END OF CHAPTER FIVE
Next: Chapter Six:
¨ esports

¨ theater

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Outline
qTechnology and ethics
qNew ethical questions

CHAPTER 6 qGeneral ethical principles

qProfessional responsibilities.
qProfessional leadership principles.
qDigital privacy
ETHICS AND PROFESSIONALISM qAccountability and trust
qEthical and regulatory challenge
OF EMERGING TECHNOLOGIES qTreats
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Technology and ethics New ethical questions

vThe increasing use of big data, algorithmic decision-making, and
vTechnology can serve to promote or restrict human rights. The
artificial intelligence can enable more consistent, evidence-based
Information Society should foster the use of emerging and accurate judgments or decisions, often more quickly and
technologies in such a way as to maximize the benefits that they efficiently. However, these strengths can potentially have a darker
provide while minimizing the harms. side too, throwing up questions around the ethical use of these fairly
new technologies.
vEthics is particularly important for the accountancy profession,
vquestions are being asked regarding the interaction between
with a code for professional ethics based on five basic principles computers and humans.
– ü How much reliance can we place on data and models?
ü Integrity ü What is the role of human judgment ?
ü Objectivity üHow do we ensure that we understand the decision-making
ü Competence and due care
process?
vWhatever the power of the machine, humans will still need to be
ü Confidentiality involved, so that people can be held accountable, or explain the
ü Professional behavior reasons behind a decision.
vHowever, the emergence of new technologies raises some new
challenges for the profession to address.

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General ethical principles Professional responsibilities
1. Contribute to society and to human well-being, 1. Strive to achieve high quality in both the processes and
acknowledging that all people are stakeholders in computing. products of professional work.
2. Avoid harm. 2. Maintain high standards of professional competence, conduct,
3. Be honest and trustworthy. and ethical practice.
4. Be fair and take action not to discriminate 3. Know and respect existing rules pertaining to professional
5. Respect the work required to produce new ideas, inventions, work.
creative works, and computing artifacts. 4. Accept and provide appropriate professional review.
6. Respect privacy. 5.Give comprehensive and thorough evaluations of computer
7. Honor confidentiality systems and their impacts, including analysis of possible risks.
6. Perform work only in areas of competence.
7. Foster public awareness and understanding of computing,
related technologies, and their consequences.
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Cont... Professional leadership principles

1. Ensure that the public good is the central concern during all
8.Access computing and communication resources only when
professional computing work.
authorized or when compelled by the public good.
2. Articulate, encourage acceptance of and evaluate fulfillment of
9. Design and implement systems that are robustly and usably
social responsibilities by members of the organization or group.
secure.
3. Manage personnel and resources to enhance the quality of
working life.
4. Articulate, apply, and support policies and processes that
reflect the principles of the Code.
5. Create opportunities for members of the organization or group
to grow as professionals.
6. Use care when modifying or retiring systems. Interface
changes, the removal of features, and even software updates
have an impact on the productivity of users and the quality of
their work.
7. Recognize and take special care of systems that become
integrated into the infrastructure of society

Digital privacy
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Information Privacy
vDigital Privacy is the protection of personally identifiable or
vInformation privacy is the notion that individuals should have
business identifiable information that is collected from
respondents through information collection activities or from the freedom, or right, to determine how their digital information,
other sources. mainly that pertaining to personally identifiable information, is
collected and used.
vIt is a collective definition that encompasses three sub-related
v Every country has various laws that dictate how information
categories; information privacy, communication privacy, and
individual privacy. may be collected and used by companies. Some of those laws
are written to give agency to the preferences of
vIt is often used in contexts that promote advocacy on behalf of
individuals/consumers in how their data is used.
individual and consumer privacy rights in digital spheres, and is
v In other places, like in the United States, privacy law is argued
typically used in opposition to the business practices of many e-
marketers/businesses/companies to collect and use such by some to be less developed in this regard,
information and data. vFor example, some legislation, or lack of, allows companies to
self-regulate their collection and dissemination practices of
consumer information.
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Communication Privacy Cont.…

vCommunication privacy is the notion that individuals should vAn example of this is a failure to read a company's privacy
have the freedom, or right, to communicate information policy regarding communications on their platform could lead
digitally with the expectation that their communications are one to assume their communication is protected when it is in
secure; meaning that messages and communications will only fact not.
be accessible to the sender's original intended recipient. vAdditionally, companies frequently have been known to lack
v However, communications can be intercepted or delivered to transparency in how they use information, this can be both
other recipients without the sender's knowledge, in a multitude intentional and unintentional.
of ways. vDiscussion of communication privacy necessarily requires
vCommunications can be intercepted directly through various consideration of technological methods of protecting
hacking methods; this is expanded upon further below. information/communication in digital mediums, the
vCommunications can also be delivered to recipients effectiveness, and ineffectiveness of such methods/systems,
unbeknownst to the sender due to false assumptions made and the development/advancement of new and current
regarding the platform or medium which was used to send technologies.
information.

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Individual Privacy Some digital privacy principles
vIndividual privacy is the notion that individuals have a right to v Data Minimization: collect the minimal amount of
exist freely on the internet, in that they can choose what types information necessary from individuals and businesses
of information they are exposed to, and more importantly that consistent with the Department‟s mission and legal
unwanted information should not interrupt them. requirements.
vAn example of a digital breach of individual privacy would be v Transparency: Notice covering the purpose of the collection
an internet user receiving unwanted ads and emails/spam, or a and use of identifiable information will be provided in a clear
computer virus that forces the user to take actions they manner. Information collected will not be used for any other
otherwise wouldn't. purpose unless authorized or mandated by law.
vIn such cases the individual, during that moment, doesn't exist vAccuracy: Information collected will be maintained in a
digitally without interruption from unwanted information; thus, sufficiently accurate, timely, and complete manner to ensure
their individual privacy has been infringed upon. that the interests of the individuals and businesses are protected.
vSecurity: Adequate physical and IT security measures will be
implemented to ensure that the collection, use, and maintenance
of identifiable information are properly safeguarded and the
information is promptly destroyed in accordance with approved
records control schedules.
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Accountability and trust Treats and challenges
vEmerging technologies can provide improved accuracy, Ethical and regulatory challenges
better quality and cost efficiencies for businesses in vAs security professionals, we need to keep pace with ever-
every sector. changing technology and be aware of the AI, IoT, Big Data,
v They can enhance trust in the organization‟s operations Machine Learning, etc.
and financial processes, which is crucial for sustainable vGrowing needs Cyber & Data Security is getting prominence
success. But this can produce a paradox: the very that requires security practitioners to focus on the business
solutions that can be used to better manage risk, increase need for securing data, understanding security and risk from a
transparency and build confidence are often themselves business perspective by extensively interacting with the
the source of new risks, which may go unnoticed. business community in understanding their requirements or
vThe obligation of an individual or organization to what they want.
account for its activities, accept responsibility for them, vEmerging technologies are already impacting how we live and
and to disclose the results in a transparent manner. It work.
also includes the responsibility for money or other v They're also changing how we approach, plan, and integrate
entrusted property. security operations.
vFor security, both physical and cyber, the equation is the same
catalyzing many new potential applications for emerging

Cont.… Cont.…
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Emerging technologies are making an impact include: 8. Situational awareness capabilities via GPS for disaster
1. Counter-terrorism and law enforcement informatics via response and crisis response scenarios
predictive analytics and artificial intelligence. 9. Biometrics: assured identity security screening solutions by
2. Real-time horizon scanning and data mining for threats and bio-signature: (every aspect of your physiology can be used as a
information sharing bio-signature. Measure unique heart/pulse rates,
3. Automated cyber security and information assurance electrocardiogram sensor, blood oximetry, skin temperature)
4. Enhanced Surveillance (chemical and bio-detection sensors, 10. Robotic Policing (already happening in Dubai!)
cameras, drones, facial recognition, license plate readers)
5. Simulation and augmented reality technologies for training and
modeling
6. Safety and security equipment (including bullet and bomb
proof) made with lighter and stronger materials
7. Advanced forensics enabled by enhanced computing
capabilities (including future quantum computing)
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Challenges in using Artificial Intelligence Challenges in using Robotics in manufacturing
vAI is only as good as the data it is exposed to, which is where
vWith automation and robotics moving from production lines
certain challenges may present themselves.
out into other areas of work and business, the potential for
vAlternatively, AI also has the potential to take the burden of humans losing jobs is great here too.
laborious and time-consuming tasks from these people, freeing
vAs automation technologies become more advanced, there will
up their time and brainpower for other things e.g. doctors using
be a greater capability for automation to take over more and
diagnostic AI to help them diagnose patients will analyze the
more complex jobs.
data presented by the AI and make the ultimate decision.
vAs robots learn to teach each other and themselves, there is the
vManaging the challenges posed by AI will require careful
potential for much greater productivity but this also raises
planning to ensure that the full benefits are realized and risks
ethical and cyber security concerns.
are mitigated.

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Challenges in using the Internet of Things Challenges in Big Data
vAlmost all the technologies mentioned above have some
vAs more and more connected devices (such as smart watches
and fitness trackers) join the Internet of Things (IoT) the relation to Big Data.
amount of data being generated is increasing. vThe huge amount of data being generated on a daily basis has

vCompanies will have to plan carefully how this will affect the
the potential to provide businesses with better insight into their
customers as well as their own business operations.
customer-facing application and how to best utilize the masses
of data being produced. vAlthough data can be incredibly useful for spotting trends and
analyzing impacts, surfacing all this data to humans in a way
vThere are also severe security implications of mass
that they can understand can be challenging. AI will play a role
connectivity that need to be addressed.
here.
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Treats Some risks of emerging technology are:
vNew and emerging technologies pose significant opportunities vDriverless car:
for businesses if they utilize them well and understand their true
üwhile a compelling option for future fleer cars, companies could
value early on. crash and burn from claims related to bodily injury and property
v They also pose risks and questions not only to business but to damage.
society as a whole. vWearables:
v Planning for how to deal with these emerging technologies and üGoogle glass, Fitbit and other wearables can expose companies
where value can be derived while assessing potential risks to the invasion of privacy claims that may not be covered by
before they become a fully-fledged reality is essential for general liability or personal injury claims that weren‟t foreseen.
businesses that want to thrive in the world of AI, Big Data and vDrones:
IoT. üTurbulence is in the offing for manufacturers and organizations
that fail to protect themselves for property damage and bodily
injury, as well as errors and omissions.
vInternet of things:
üThe proliferation of sensors and cross-platform integration
creates potential exposure from privacy invasion, bodily injury
and property damage that may connect an organization to huge
liabilities.
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Questions