Emerging Technologies Notes

Chapter 1: Introduction to Emerging Technologies

  • This module was prepared by the Ministry of Science and Higher Education (MOSHE) in collaboration with Bahir Dar University (Since 1963), Adama Science & Technology University (Since 1993), and Addis Ababa Science & Technology University (Since 2011).

  • The module provides learning resources and teaching ideas related to emerging technologies.

  • Key topics include the evolution of technologies, the role of data, programmable devices, human to machine interaction, and future trends.

1.1 Evolution of Technologies

  • Emerging technology describes new or developing technologies expected to have significant social or economic effects within 5-10 years.

  • Technological evolution is the radical transformation of society through technological development.

  • Examples of emerging technologies:

    • Artificial Intelligence

    • Blockchain

    • Augmented and Virtual Reality

    • Cloud Computing

    • Internet of Things (IoT)

    • Big Data

    • Robotic Process Automation (RPA)

1.1.1 Introduction to the Industrial Revolution (IR)

  • The Industrial Revolution involved a shift from tools to energy sources like coal to power machines in factories, starting in England in the late 1700s.

  • The American Industrial Revolution, or Second Industrial Revolution, began between 1820 and 1870.

  • The Fourth Industrial Revolution enhances the third (computers and automation) with smart and autonomous systems fueled by data and machine learning.

  • Industrial Revolutions:

    • The steam engine

    • The age of science and mass production

    • The rise of digital technology

    • Smart and autonomous systems fueled by data and machine learning

1.1.2 The Most Important Inventions of the Industrial Revolution

  • Transportation: Steam Engine, Railroad, Diesel Engine, Airplane.

  • Communication: Telegraph, Transatlantic Cable, Phonograph, Telephone.

  • Industry: Cotton Gin, Sewing Machine, Electric Lights.

1.1.3 Historical Background (IR 1.0, IR 2.0, IR 3.0)

  • The Industrial Revolution began in Great Britain in the late 1770s and spread to Europe.

  • Four types of industries:

    • Primary: Raw materials (mining, farming, fishing).

    • Secondary: Manufacturing (cars, steel).

    • Tertiary: Service (teaching, nursing).

    • Quaternary: Research and development (IT).

1.1.3.1 Industrial Revolution (IR 1.0)

  • Transition to new manufacturing processes using machines, steam power, machine tools, and factories beginning in the 1760s.

1.1.3.2 Industrial Revolution (IR 2.0)

  • Began in the 1870s, including interchangeable parts, telegraph and railroad networks, electrical power, and telephones.

1.1.3.3 Industrial Revolution (IR 3.0)

  • Transition from mechanical and analog electronic technology to digital electronics from the late 1950s resulting in mass production and widespread use of digital logic circuits like computers, handphones, and the Internet.

1.1.3.4 Fourth Industrial Revolution (IR 4.0)

  • Coined by Klaus Schwab in 2016 involving robotics, Internet of Things (IoT), additive manufacturing, autonomous vehicles, and cyber-physical systems.

  • These systems are controlled or monitored by computer-based algorithms and integrated with the Internet and users.

  • CNC machines and Artificial Intelligence (AI) are examples.

1.2 Role of Data for Emerging Technologies

  • Data is considered strategic and drives the future of science, technology, and the economy.

  • Data science and analytics enable data-driven theory, economy, and professional development, involving computing, informatics, statistics, business, social science, and health/medical science.

1.3 Enabling Devices and Network (Programmable Devices)

  • Four basic kinds of devices: memory, microprocessors, logic, and networks.

  • Programmable devices include field programmable logic devices (FPGAs), complex programmable logic devices (CPLD), and programmable logic devices (PLD).

  • Service Enabling Devices (SEDs): channel service units (CSU), data service units (DSU), modems, routers, switches, conferencing equipment, network appliances, hosting equipment, and servers.

1.4 Human to Machine Interaction

  • Human-machine interaction (HMI) refers to communication and interaction between a human and a machine via a user interface.

  • HCI (human-computer interaction) is the study of how people interact with computers and how computers are developed for successful interaction.

  • Disciplines contributing to HCI: Cognitive psychology, Computer science, Linguistics, Engineering and design, Artificial intelligence, Human factors.

1.5 Future Trends in Emerging Technologies

1.5.1 Emerging technology trends in 2019

  • 5G Networks

  • Artificial Intelligence (AI)

  • Autonomous Devices

  • Blockchain

  • Augmented Analytics

  • Digital Twins

  • Enhanced Edge Computing

  • Immersive Experiences in Smart Spaces

1.5.2 Some emerging technologies that will shape the future of you and your business

  • Chatbots

  • Virtual, Augmented, and Mixed Reality

  • Blockchain

  • Ephemeral Apps

  • Artificial Intelligence

Chapter 2: Data Science

  • This chapter covers data science, data vs. information, data types and representation, data value chain, and basic concepts of big data.

2.1 An Overview of Data Science

  • Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data.

2.1.1 What are data and information?

  • Data is a representation of facts, concepts, or instructions in a formalized manner.

  • Information is the processed data on which decisions and actions are based.

2.1.2 Data Processing Cycle

  • Data processing is the re-structuring or re-ordering of data to increase its usefulness.

  • Basic steps: input, processing, and output.

2.3 Data types and their representation

  • Data types are attributes of data that tell the compiler or interpreter how the programmer intends to use the data.

2.3.1 Data types from Computer programming perspective

  • Common data types: integers (int), booleans (bool), characters (char), floating-point numbers (float), alphanumeric strings (string).

2.3.2 Data types from Data Analytics perspective

  • Common types or structures: Structured, Semi-structured, and Unstructured data types.

    • Structured data adheres to a pre-defined data model.

    • Semi-structured data does not conform to a formal structure but contains tags or markers.

    • Unstructured data does not have a predefined data model.

  • Metadata provides additional information about a specific set of data.

2.4 Data value Chain

  • The Data Value Chain describes the information flow within a big data system.

2.4.1 Data Acquisition

  • Process of gathering, filtering, and cleaning data.

2.4.2 Data Analysis

  • Exploring, transforming, and modeling data.

2.4.3 Data Curation

  • Active management of data over its life cycle.

2.4.4 Data Storage

  • Persistence and management of data in a scalable way.

2.4.5 Data Usage

  • Data-driven business activities that need access to data.

2.5 Basic concepts of big data

  • Big data is a blanket term for the strategies and technologies needed to gather, organize, and process large datasets.

2.5.1 What Is Big Data?

  • Big data is characterized by volume, velocity, variety, and veracity.

2.5.2 Clustered Computing and Hadoop Ecosystem
2.5.2.1 Clustered Computing

  • Combines the resources of many smaller machines, providing resource pooling, high availability, and easy scalability.

2.5.2.2 Hadoop and its Ecosystem

  • Hadoop is an open-source framework for distributed processing of large datasets across clusters of computers.

  • Key characteristics: Economical, Reliable, Scalable, Flexible.

  • Hadoop Ecosystem components: HDFS, YARN, MapReduce, Spark, PIG, HIVE, HBase, Mahout, Spark MLLib, Solar, Lucene, Zookeeper, Oozie.

2.5.3 Big Data Life Cycle with Hadoop

  • Ingesting data

  • Processing the data

  • Computing and analyzing data

  • Visualizing the results

Chapter 3: Artificial Intelligence (AI)

3.1. What is Artificial Intelligence (AI)

  • Artificial Intelligence (AI) is a branch of computer science focused on creating intelligent machines that can behave and think like humans.

  • Intelligence encompasses:

    • Reasoning

    • Learning

    • Problem Solving

    • Perception

    • Linguistic Intelligence

  • AI deals with developing computing systems capable of performing tasks that humans excel at, such as object recognition, speech understanding, and decision-making.

3.1.1. Need for Artificial Intelligence

  • To create expert systems that exhibit intelligent behavior with the capacity to learn, demonstrate, explain, and advise its users.

  • To help machines find solutions to complex problems like humans and apply them as algorithms.

3.1.2. Goals of Artificial Intelligence

  • Replicate human intelligence

  • Solve Knowledge-intensive tasks

  • Create an intelligent connection of perception and action

  • Build machines that can perform tasks requiring human intelligence

3.1.3. What Comprises to Artificial Intelligence?

  • Artificial Intelligence requires knowledge from:

    • Mathematics

    • Biology

    • Psychology

    • Sociology

    • Computer Science

    • Neurons Study

    • Statistics

3.1.4. Advantages of Artificial Intelligence

  • High Accuracy with fewer errors

  • High-Speed

  • High reliability

  • Useful for risky areas

  • Digital Assistant

  • Useful as a public utility

3.1.5. Disadvantages of Artificial Intelligence

  • High Cost

  • Can't think out of the box

  • No feelings and emotions

  • Increase dependence on machines

  • No Original Creativity

3.2. History of AI

  • 1943: Artificial neurons model by Warren McCulloch and Walter Pitts.

  • 1950: Alan Turing proposes the Turing test.

  • 1956: John McCarthy coins the term "Artificial Intelligence" at the Dartmouth Conference.

B. The birth of Artificial Intelligence (1952-1956)

  • 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program" Which was named "Logic Theorist".

C. The golden years-Early enthusiasm (1956-1974)

  • 1966: Joseph Weizenbaum created the first chatbot named ELIZA.

  • 1972: The first intelligent humanoid robot WABOT-1 built in Japan.

D. The first AI winter (1974-1980)

  • The years 1974 to 1980 was the first AI winter duration due to shortage of funding from the government for AI researches.

E. A boom of AI (1980-1987)

  • 1980: AI came back with "Expert System" programmed that emulate the decision-making ability of a human expert.
    ### F. The second AI winter (1987-1993)

  • The duration between the years 1987 to 1993 was the second AI Winter duration.

  • Again, Investors and government stopped in funding for AI research due to high cost but not efficient results.
    G. The emergence of intelligent agents (1993-2011)

  • 1997: IBM Deep Blue beats world chess champion, Gary Kasparov

H. Deep learning, big data and artificial general intelligence (2011-present)

  • 2011: IBM's Watson won jeopardy, a quiz show

  • 2018: “Project Debater” from IBM debated on complex topics with two master debaters and performs extremely well.

3.3. Levels of AI

  • Stage 1 – Rule-Based Systems

  • Stage 2 – Context Awareness and Retention

  • Stage 3 – Domain-Specific Expertise

  • Stage 4 – Reasoning Machines

  • Stage 5 – Self Aware Systems / Artificial General Intelligence (AGI)

  • Stage 6 – Artificial Superintelligence (ASI)

  • Stage 7 – Singularity and Transcendence

3.4. Types of AI

A. Based on Capabilities

  • Weak AI or Narrow AI

  • General AI

  • Super AI

B. Based on the functionality

  • Reactive Machines

  • Limited Memory

  • Theory of Mind AI

  • Self-Awareness AI

3.4.1. How humans think

  • Humans thinking stages: Observe and input information, interpret and evaluate input, make decisions as a reaction towards received input which can be mapped to the main stages of AI systems.

3.4.2. Mapping human thinking to artificial intelligence components

  • AI model stages:

    • Sensing layer captures data like human senses.

    • Interpretation layer reasons and thinks about the data.

    • Interacting layer takes action or makes decisions.

3.5. Influencers of artificial intelligence

  • Big data: Structured data versus unstructured data

  • Advancements in computer processing speed and new chip architectures

  • Cloud computing and APIs

  • The emergence of data science

3.5.1. Big Data

  • Big data requires innovative information processing as it has huge amounts of data.
    Traditionally, computers primarily process structured data, that is, information with an organized structure, such as a relational database that is searchable by simple and straightforward search engine algorithms or SQL statements.

3.5.1.2. Advancements in computer processing speed, new chip architectures, and big data file systems

  • Significant advancements in computer processing and memory speeds enable us to make sense of the information that is generated by big data more quickly, processing large amount of data at high speeds.

3.5.2. Cloud computing and application programming interfaces

  • Cloud computing is a general term that describes the delivery of on-demand services, usually through the internet, on a pay-per-use basis.

3.5.3. The emergence of data science

  • 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

3.6. Applications of AI

  • AI applications in:

    • Agriculture

    • Healthcare

    • Education

    • Finance and E-commerce

    • Gaming

    • Data Security

    • Social Media

    • Travel & Transport

    • Automotive Industry

    • Robotics

    • Entertainment

3.7. AI tools and platforms

  • AI platforms are defined as some sort of hardware architecture or software framework (including application frameworks), that allows the software to run. It involves the use of machines to perform the tasks that are performed by human beings. such as Microsoft AZURE Machine Learning, Google Cloud Prediction API, IBM Watson, TensorFlow

Chapter 4: Internet of Things (IoT)

4.1. Overview of IoT

  • Artificial intelligence, connectivity, sensors, active engagement, and small device use

4.1.1 What is IoT?

  • General term the Internet of Things (IoT) is the network of physical objects or "things" embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. 4.1.2 History of IoT

  • Machines have been providing direct communications since the telegraph (the first landline) was developed in the 1830s and 1840s.Described as “wireless telegraphy,” the first radio voice transmission took place on June 3 , 1900, providing another necessary component for developing the Internet of Things.

4.1.3 IoT − Advantages

  • Improved Customer Engagement IoT transforms communication with audiences enriching costumer experience

  • Technology Optimization Technologies and data improve device use, and aid in more potent improvements to technology.

4.1.4 IoT – Disadvantages

  • As the number of connected devices increases and more information is shared between devices, the potential that a hacker could steal confidential information also increases.

  • If there’s a bug in the system, it’s likely that every connected device will become corrupted.

4.1.5 Challenges of IoT

  • Security − IoT creates an ecosystem of constantly connected devices communicating over networks. The system offers little control despite any security measures. This leaves users exposed to various kinds of attackers.

  • Privacy − The sophistication of IoT provides substantial personal data in extreme detail without the user's active participation.

  • Complexity − Some find IoT systems complicated in terms of design, deployment, and maintenance given their use of multiple technologies and a large set of new enabling technologies.

4.2 How does it work?

  • An IoT ecosystem consists of web-enabled smart devices that use embedded processors, sensors and communication hardware to collect, send and act on data they acquire from their environments.

4.2.1 Architecture of IoT

General components of IoT architecture:

  • Sensing Layer - The main purpose of the sensing layer is to identify any phenomena in the devices’ peripheral and obtain data from the real world.

  • Network Layer - The network layer acts as a communication channel to transfer data, collected in the sensing layer, to other connected devices.

  • Data Processing Layer - The data processing layer consists of the main data processing unit, and it analyses the data for decision making.
    *Application Layer - The application layer implements and presents the results of the data processing layer to accomplish disparate applications of IoT devices.

4.2.2. Devices and Networks

  • Enterprise connected devices include smart TVs, smart speakers, toys, wearables, and smart appliances.
    *Enterprise technologies, including smart air conditioning, smart thermostats, smart lighting, and smart security, span home, enterprise, and industrial uses

4.3. IoT Tools and Platforms

Key IoT feautures include:

  • Manage an unlimited number of connected devices

  • Set up cross-device interoperability

  • Perform real-time device monitoring
    *Perform remote device provisioning and configuration

4.4 Applications of IoT

  • The versatile nature of IoT makes it an attractive option for so many businesses, organizations, and government branches such as Agriculture, Consumer Use, Healthcare, Insurance, Manufacturing

4.3.1. IoT Based Smart Home

*Smart Home initiative allows subscribers to remotely manage and monitor different home devices as well as for Remote Control Appliances, Weather, Smart Home Appliances, Safety Monitoring.

4.3.2 IoT Based Smart City

  • In cities, the development of smart grids, data analytics, and autonomous vehicles will provide benefits in energy management, traffic management, and structural Health, Lightning and Safety.
    *Smart Parking: Real-time monitoring of parking spaces available in the city Trash Management: Detection of rubbish levels in containers to optimize the trash collection routes

4.3.3 IoT Based Smart Farming

  • The Control micro-climate conditions to maximize the production of fruits and vegetables and its quality, compost with moisture and temperature control. Animal farming location and identification. Field Monitoring: Reduction crop waist with better monitoring, and better managment.

Chapter 5: Augmented Reality (AR)

5.1. Overview of augmented reality

  • Augmented reality (AR) is a form of emerging technology that allows users to overlay computer- generated content in the real world.

5.2. Virtual reality (VR), Augmented Reality (AR) vs Mixed reality (MR)

5.2.1. Virtual Reality (VR)

VR is fully immersive, which tricks your senses into thinking you’re in a different environment or world apart from the real world

5.2.2. Augmented Reality (AR)

In augmented reality, users see and interact with the real world while digital content is added to it.
Direct and Indirect Augmentation of Objects

5.2.3. Mixed Reality (MR)

Mixed Reality (MR), sometimes 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

5.3. The architecture of AR Systems

Architectur components of AR

  • Infrastructure Tracker Unit responsible for collecting data from the real world,
    Processing Unit which mixed the virtual content with the real content sent to the Video Out module, Visual Unit can be classified into two types of system Video see-through and Optical see-through.

5.4. Applications of AR Systems

  • Augmented Reality (AR), which can be applied to many different disciplines such as education, medicine, entertainment, military, etc. 5.4.1 AR In education Augmented reality allows flexibility in use that is attractive to education used to enhance content and instruction within the traditional classroom, supplement instruction in the special education classroom, extend content into the world outside the classroom and be combined with other technologies to enrich their individual applications

5.4.2. AR In Medicine

  • Augmented reality is one of the current technologies changing all industries, including healthcare and medical education where The tools aid. 1) Describing symptoms nursing care 3) Surgery 4) Ultrasounds 5) Diabetes management 6) Navigation, In the medicine with AR has the following applications:

5.4.3. AR In Entertainment

  • Augmented reality can be used in various “entertainment” industries as entertainment covers quite a number of different industries – music, movies, live shows, games can benefit from using augmented reality with AR in games AR and music.

Chapter 6: ETHICS AND PROFESSIONALISM OF EMERGING TECHNOLOGIES

6.1. Technology and ethics

*Ethics is particularly important for the accountancy profession, with a code for professional ethics based on five basic principles – integrity, objectivity, competence and due care, confidentiality, and professional behavior.

6.2. New ethical questions

*Ethics can then become a tool to clean up a mess that might have been avoidable. It is probably not contentious to say it would be desirable to have ethical input at the earlier stages of technology design and development

6.2.1. General ethical principles

*Contribute to society and to human well-being *Avoid harm *Be honest and trustworthy *Be fair and take action not to discriminate *Respect privacy *Honor confidentiality

6.2.2. Professional responsibilities

*Strive to achieve high quality *Maintain high standards of professional competence *Know and respect existing rules *Accept and provide appropriate professional review, including analysis of possible risks, by Accessing computing and communication resources

6.2.3. Professional leadership principles

*Ensure that the public is the central concern during all professional computing work *Articulate processes that reflect the principles of the Code *Use care when modifying or retiring systems and integrate special care of systems that become integrated into the infrastructure of society

6.3. Digital privacy

*Digital Privacy is the protection of personally identifiable or business identifiable information, and is a collective definition that encompasses three sub-related categories, including:

6.3.1. Information Privacy

*individuals should have the freedom, or right, to determine how their digital information, mainly that pertaining to personally identifiable information, is collected and used.

6.3.2. Communication Privacy

*individuals should have the freedom, or right, to communicate information digitally with the expectation that their communications are secure.

6.3.3. Individual Privacy

*individuals have a right to exist freely on the internet, in that they can choose what types of information they are exposed to, and more importantly that unwanted information should not interrupt them

6.3.4. Some digital privacy principles

*Data Minimization: collect the minimal amount of information necessary. *Transparency: Notice covering the purpose of the collection and use. *Accuracy: Information collected will be maintained in a sufficiently accurate. *Security: Adequate physical and IT security measures.

6.4. Accountability and trust

*Accountability and trust are important, because often legal and regulatory frameworks haven’t kept pace with digital transformation, and organizations seek guidance and provide better management of risk

6.5. Treats and challenges

Emerging technologies impact how we live and work, so its essential to plan plan and integrate security operations, in order to keep pace with the exponential technological, real time threats.

6.5.1. Ethical and regulatory challenges

*Cyber Security related Ethical and Regulatory consideration *AI Technology, *Robotics, *IOT, *Big Data. 6.5.2. Treats New and emerging technologies pose some risks of: Driverless car, Wearables: Google glass, Fitbit and other wearables, Turbulence is in the offing for manufacturers Drones, Internet of things.

Chapter 7: Other emerging technologies

7.1. Nanotechnology

  • Nanotechnology is science, engineering, and technology conducted at the nanoscale, which is about 1 to 100 nanometers.

7.1.1. How it started

  • In 1981, with the development of the scanning tunneling microscope that could "see" individual atoms, that modern nanotechnology began.

7.1.2. Fundamental concepts in nanoscience and nanotechnology

*Nanoscience and nanotechnology involve the ability to see and to control individual atoms and molecules.
The properties of materials can be different at the nanoscale for two main reasons

  • First, nanomaterials have a relatively larger surface area,
    *Second, quantum effects can begin to dominate the behavior of matter at the nanoscale.

7.1.3. Applications of nanotechnology:

  • Customized medicine using nanoparticles, electronics with increased capabilities, food with improved taste, safety, and health benefits, agriculture with improved yield, vehicle manufacturers need lighter and stronger materials

7.2. Biotechnology

  • Biotechnology is technology based on biology - biotechnology harnesses cellular and biomolecular processes to develop technologies and products that help improve our lives and the health of our planet by using the living organisms.
    The researcher's modify DNA and proteins to shape the capabilities of living cells, plants, and animals into something useful for humans such as using techniques such as vaccines, genetics, biochemistry, molecular biology

  • Agriculture (Green Biotechnology

  • Medicine (Medicinal Biotechnology

  • Aquaculture Fisheries

  • Environment (Environmental biotechnology.

7.3. Blockchain technology

Blockchain is a growing list of records bound by distributed computers peer to peer networks in public setting. Information held on a blockchain exists as a shared and continually reconciled database. This is a way of using the network that has obvious benefits. The blockchain database isn’t stored in any single location, meaning the records it keeps are truly public and easily verifiable. No centralized version of this information exists for a hacker to corrupt
Key Property * Decentalization * Transparency *Immutability.

7.4. Applications of blockchain A. The sharing economy With companies like Uber and Airbnb flourishing, the sharing economy is already a proven success. B. Crowdfunding. C. Governance. D. Supply chain auditing E. File storage

7.4.Cloud and quantum computing

7.4.1. Cloud computing

  • Cloud computing is a means of networking remote servers that are hosted on the Internet through a public, private infrastructure.. The cloud allows you and multiple users to access your data from any location

7.4.3. Quantum computing

*Quantum computers truly do represent the next generation of computing as their compute power out perform the classical compute algorithms.
They would be able to make complex calculations that would only overwhelm classic computers. With its ability to handle more complex numbers, data could be transferred over the internet with much safer encryption

7.5.Autonomic computing (AC)

Autonomic computing (AC) is an approach to address the complexity and evolution problems in software systems such as *Self-Awareness, *Self-Configuring, *Self-Optimizing: *Self-Healing: *Self-Protecting *Context-Aware, *Open, *Anticipatory

7.6.Computer vision

  • It is an interdisciplinary scientific field that deals with how computers can be made to gain a high- level understanding of digital images or videos for understanding object and images.
    Processing and understanding images in interpretitve sytems *Image segmentation *recognition recognition *Detection
    *Applications Optical Machine imaging Retail Automotive safety fingerprint analysis

7.7.Embedded systems

  • It is a controller with a dedicated function within a larger mechanical or electrical system, often *Components Sensor − A-D Converter − D-A Converter − Actuator. ## Advantages and disadvantages of embedded system Advantages Easily Customizable Enhanced performance Low power consumption Low cost. Disadvantages High development effort Larger time to market

7.8Cybersecurity

  • It is the protection of computer systems from the theft of or damage to their hardware, software, or electronic data, as well as from the disruption or misdirection of the services they provide, there for its is essential to prevent from hacking and spamming the digital equipment.

  • Cyber Security mesures Staff awareness training Application Security password management.
    *Cyber Security Threats Ransomware:-Malware; Social Engineering: -Phishing

  • benefits Prevention of unauthorized users *Improves recovery time after a breach.

7.9.Additive manufacturing (3D Printing)

*Used to create three-dimensional structures out of plastics, metals, polymers and other materials that can be sprayed through a nozzle or aggregated in a vat in the boardroom than the factory