COLLEGE OF DISTANCE AND CONTINUIG

DEPARTMENT OF MANAGEMENT

INTRODUCTION TO EMERGING TECHNOLOGIES

INDIVIDUAL ASSIGNMENT

Name ID No_
1.Lami Getachew 5170/16

Submitted to: Mr. Zemzem M

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Submission Date : February 2, 2025
 1. What makes “emerging technologies” happen and what impact will they have on
individuals, society, and the environment?
o Emerging technologies arise due to continuous advancements in scientific
research, increasing market demand for efficiency, economic expansion, and the
necessity to solve real-world problems. Factors such as globalization, digital
transformation, heavy investment in research and development (R&D), and
supportive government policies foster technological innovation.
o Impact on Individuals:
 Improves daily life with smart devices and automation.
 Enhances productivity through AI-driven tools.
 Raises concerns over privacy and digital security.
 Changes job requirements, necessitating new skills.
o Impact on Society:
 Increases connectivity and information access.
 Alters job markets by automating traditional roles.
 Raises ethical considerations regarding AI, data privacy, and surveillance.
o Impact on the Environment:
 Development of sustainable technologies reduces carbon footprint.
 Advances in renewable energy provide eco-friendly alternatives.
 Excessive electronic waste and energy consumption pose challenges.
2. Describe the social, economic, and environmental impact of the Industrial
Revolution and make connections between its impact and ideological/political
responses.
o Social Impact:
 Rapid urbanization led to overcrowding and poor living conditions.
 Shift from agrarian economies to industrialized societies.
 Child labor and exploitation due to high factory demands.
o Economic Impact:
 Expansion of industries and economic growth.
 Creation of new job opportunities and rise in wages.
 Widening income inequality and capitalist dominance.
o Environmental Impact:
 Extensive deforestation and depletion of natural resources.
 Rise in pollution levels due to industrial emissions.
 Negative effects on biodiversity and climate change.
o Ideological & Political Responses:
 Emergence of capitalism, socialism, and labor unions.
 Demand for labor rights, fair wages, and workplace safety.
 Government interventions leading to policies on working conditions and
industrial regulations.
3. List some programmable devices and explain their properties.
1. Computers
General-purpose machines executing software for diverse tasks. They automate processes like
data analysis and system monitoring. Interaction occurs through keyboards, mice, and
touchscreens, while vast storage manages complex computations.
2. Smartphones

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Compact computing devices integrating communication, multimedia, and internet access. They
automate reminders, AI-assisted tasks, and background processes. Equipped with touchscreens,
biometric sensors, and cameras, they store extensive data, including cloud backups.
3. Microcontrollers
Embedded systems designed for specific control tasks. They handle automation in home
appliances and industrial tools, using sensors and actuators for interaction. Though storage is
limited, they efficiently process input data in real-time.
4. Robots
Autonomous or semi-autonomous machines performing repetitive or complex operations. They
use AI and programmed logic to function in manufacturing, healthcare, and exploration,
interacting via cameras, sensors, and voice recognition while processing vast environmental data.
5. ATMs
Self-service banking terminals executing financial transactions. They automate cash
withdrawals, deposits, and account inquiries, interacting via touchscreens, card readers, and
biometric scanners while securely accessing banking databases.
Properties:
 Capable of executing programmed instructions.
 Automates repetitive tasks to enhance efficiency.
 Interacts with users through interfaces and sensors.
 Stores and processes large amounts of data.
4. Discussion on Emerging Technologies and Their Future Impact:
As technology evolves, emerging innovations are reshaping industries and improving efficiency,
security, and user experiences. Below is a detailed discussion of key emerging technologies and
their anticipated impact on various sectors.

1. Chatbots: AI-Driven Virtual Assistants

Chatbots are artificial intelligence (AI)-powered programs designed to simulate human-like
conversations, providing automated responses and interactions across digital platforms. These
virtual assistants enhance customer service, streamline workflows, and optimize user
engagement.
Future Impact:
 Customer Support Automation – Chatbots reduce wait times and improve response
efficiency in industries like e-commerce, banking, and healthcare.
 24/7 Availability – Unlike human agents, chatbots operate round the clock, ensuring
continuous service.
 Personalized User Experience – AI-driven chatbots analyze user behavior to provide
customized responses and recommendations.
 Cost Reduction – Businesses can cut labor costs by using chatbots for routine inquiries
and support.
 Advanced AI Integration – Future chatbots will leverage Natural Language
Processing (NLP) and Machine Learning (ML) to handle complex conversations and
sentiment analysis.
Industries Benefiting from Chatbots:
 Retail & E-Commerce – Automates shopping assistance and order tracking.
 Healthcare – Assists with appointment scheduling and basic medical advice.
 Banking & Finance – Facilitates fraud detection and account inquiries.

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  Education – Provides tutoring and automated grading services.

2. Virtual, Augmented & Mixed Reality (VR, AR, MR)

 Virtual Reality (VR): Immersive technology that creates a completely digital
environment (e.g., Oculus, HTC Vive).
 Augmented Reality (AR): Overlays digital content onto the real world using devices
like smartphones and AR glasses (e.g., Pokémon GO).
 Mixed Reality (MR): Merges real and virtual environments for interactive experiences
(e.g., Microsoft HoloLens).
Future Impact:
 Gaming & Entertainment – Provides immersive experiences, revolutionizing the
gaming industry.
 Healthcare & Surgery Training – Enables medical professionals to simulate surgeries
and patient treatments.
 Education & Training – AR/VR enhances learning by creating interactive virtual
classrooms.
 Retail & E-Commerce – Virtual try-ons for clothing, furniture, and accessories improve
online shopping experiences.
 Remote Work & Collaboration – Mixed reality creates virtual workspaces, enabling
real-time interaction and collaboration.
Challenges:
 High Cost of Equipment – VR and MR devices remain expensive for widespread
adoption.
 Motion Sickness Issues – Some users experience discomfort when using VR headsets.
 Privacy & Data Security – AR applications raise concerns about personal data
collection and surveillance.

3. Blockchain Technology
Blockchain is a decentralized and distributed ledger technology that ensures secure,
transparent, and tamper-proof transactions. It eliminates the need for intermediaries in
financial transactions, supply chains, and digital identities.
Future Impact:
 Finance & Cryptocurrency – Blockchain underpins cryptocurrencies like Bitcoin and
Ethereum, providing decentralized financial systems.
 Supply Chain Transparency – Ensures traceability of goods in industries like food,
pharmaceuticals, and luxury goods.
 Healthcare Data Security – Protects patient records from unauthorized access and
ensures secure medical transactions.
 Smart Contracts – Automates agreements between parties without intermediaries,
reducing legal and operational costs.
 Decentralized Applications (DApps) – Enables secure peer-to-peer applications for file
sharing, cloud storage, and voting systems.
Challenges:
 Regulatory Uncertainty – Governments are still working on legal frameworks for
blockchain applications.

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  Energy Consumption – Cryptocurrency mining requires significant energy, impacting
sustainability.
 Scalability Issues – Processing speed and transaction validation remain challenges in
large-scale blockchain networks.

5. Artificial Intelligence (AI): Driving Innovation Across Industries

Artificial Intelligence (AI) refers to systems that can learn, adapt, and make decisions without
human intervention. AI is transforming various industries by automating complex tasks,
improving data analytics, and enhancing productivity.
Future Impact:
 Healthcare
o AI-powered diagnostics detect diseases earlier and improve accuracy.
o Drug discovery accelerates with AI-driven simulations and analysis.
o Robotic surgery enhances precision and reduces human error.
 Finance & Banking
o AI detects fraudulent transactions in real time.
o Automated trading systems optimize stock market investments.
o AI chatbots provide financial advice and personalized banking solutions.
 Retail & E-Commerce
o Personalized product recommendations enhance customer experience.
o AI-driven demand forecasting improves inventory management.
o Automated checkout systems reduce waiting times in physical stores.
 Manufacturing & Logistics
o AI-powered robots streamline production lines, reducing operational costs.
o Predictive maintenance prevents equipment failures.
o AI optimizes supply chains by analyzing market trends.
 Autonomous Vehicles & Smart Transportation
o AI enables self-driving cars to navigate traffic safely.
o AI-powered traffic control systems improve urban mobility.
o Drone deliveries reduce logistics costs and increase efficiency.
Challenges:
 Job Displacement – Automation may replace traditional jobs, requiring workforce
reskilling.
 Bias in AI Models – AI can inherit biases from training data, leading to unfair outcomes.
 Regulatory & Ethical Concerns – Governments must establish policies to ensure
responsible AI deployment.
5. Describe the main disciplines that contribute to data science.
o Mathematics & Statistics: Provides the foundation for data analysis and machine
learning models.
o Computer Science: Covers programming, algorithms, and data structures
necessary for handling large datasets.
o Domain Knowledge: Understanding specific industries to apply data science
effectively in fields like healthcare, finance, and marketing.
o Machine Learning & AI: Develops predictive models and automates data
analysis.
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 o Big Data Technologies: Manages vast amounts of structured and unstructured
data efficiently.
Data science is an interdisciplinary field that integrates techniques from multiple domains to
extract insights from data. Below are the key disciplines that contribute to data science and their
roles in shaping this field:
6. Can we process data manually using a pencil and paper? Discuss the differences
with data processing using a computer.
Yes, we can process data manually using pencil and paper, but the approach and efficiency are
significantly different when compared to using a computer. Let's break down the key differences:
Manual Data Processing (Pencil and Paper):
 Speed: Manually processing data with pencil and paper is slow. Each calculation, data
entry, or manipulation requires time, and accuracy depends on the individual’s focus and
skill.
 Error Prone: Human error is common in manual data processing, especially when
working with large datasets. Mistakes like miswriting numbers or missing steps in a
calculation can lead to incorrect results.
 Limited to Small Data: With pencil and paper, the amount of data that can be processed
is limited by physical space and the time available. Handling large datasets is impractical.
 Physical Effort: Manual data processing involves a lot of manual writing, erasing, and
organizing. It’s physically demanding for tasks like record-keeping or data entry.
 No Automation: Every step must be done manually, which means there is no ability to
automate repetitive tasks or perform advanced calculations without redoing everything by
hand.
Data Processing with a Computer:
 Speed: Computers can process large amounts of data much faster than a human.
Operations that would take hours or days manually can be completed in seconds or
minutes.
 Accuracy: Computers execute operations with high precision. The likelihood of errors
due to manual mistakes is minimized, and automated checks can ensure correctness.
 Scalability: With computers, you can process vast datasets, making it possible to handle
millions of data points that would be impossible manually.
 Automation: Computers allow for automation, meaning repetitive tasks like sorting,
filtering, or calculations can be done automatically without additional input.
Programming languages and software can handle complex tasks with ease.
 Accessibility and Sharing: Data can be easily shared, edited, and accessed by multiple
people remotely, whereas manual data would require physical presence or copying.
7. What are the different data types and their value chain?
In the context of data processing and analysis, data types refer to the classification of data based
on their characteristics and how they are used in computations. Below, we’ll explore different
data types and discuss their value chain.
Common Data Types:
 Numerical Data:
o Integer: Whole numbers without any decimal point (e.g., 5, -3, 100).
o Floating-point: Numbers with decimal points (e.g., 3.14, -0.001, 100.23).
o Use: Used for calculations, measurements, and counting.
 Categorical Data:

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 o Nominal: Data that represents categories without a particular order (e.g., colors,
brands, or names).
o Ordinal: Data that has a meaningful order or ranking but the intervals between
the values are not consistent (e.g., ratings like “Low”, “Medium”, “High”).
o Use: Classification, grouping, and analysis based on categories or rankings.
 Boolean Data:
o True/False: Represents binary data (e.g., Yes/No, 1/0, True/False).
o Use: Logical operations, decision-making processes, or binary classification.
 Text Data:
o String: A sequence of characters (e.g., names, addresses, descriptions).
o Use: Textual information storage, processing, and analysis, including natural
language processing (NLP) tasks.
 Date/Time Data:
o Date: Data that represents calendar dates (e.g., 2025-01-30).
o Time: Data that represents time values (e.g., 14:30:00).
o DateTime: Combines both date and time (e.g., 2025-01-30 14:30:00).
o Use: Timestamping, tracking events, and temporal analysis.
 Binary Data:
o Binary: Data represented as a sequence of bits (0s and 1s). Often used for image
files, multimedia, or encrypted data.
o Use: Storage of images, videos, and files that need to be encoded for use in
systems.
 Geospatial Data:
o Coordinates (Latitude/Longitude): Used for representing locations on the
Earth’s surface (e.g., (40.748817, -73.985428) for the Empire State Building).
o Use: Mapping, navigation, geographic analysis.
Value Chain of Data:
The value chain of data refers to the stages through which data moves and is transformed from
raw data into valuable insights or information. The following are the key stages in the data value
chain:
 Data Generation:
o Raw data is collected through various sources such as sensors, human input,
online platforms, or transactions.
o Example: A sensor in a car collects speed data, or an online purchase generates
transaction data.
 Data Collection:
o Data is gathered from various sources into a structured format that can be
processed. It may be stored in databases, data lakes, or files.
o Example: Customer feedback from surveys, sales data from transactions.
 Data Storage:
o Data is stored in databases, warehouses, or cloud systems, and is organized for
easy access, retrieval, and analysis.
o Example: Storing transactional data in a relational database (SQL), large datasets
in data lakes, or cloud platforms like AWS, Google Cloud.
 Data Processing:

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 o Raw data is cleaned, transformed, and organized. It may be processed using
algorithms, and inconsistencies or missing values are addressed.
o Example: Removing duplicates from data, standardizing date formats, or
converting text data to lowercase.
 Data Analysis:
o Processed data is analyzed to extract patterns, trends, correlations, or insights
using statistical techniques, machine learning models, or business intelligence
tools.
o Example: Analyzing customer purchasing behavior to predict future sales,
running a regression model to forecast demand.
 Data Visualization:
o Insights derived from data are presented in visual formats (charts, graphs,
dashboards) to make the data comprehensible for decision-makers.
o Example: Using a dashboard to show key performance indicators (KPIs) or
presenting sales trends over time in a bar chart.
 Data Interpretation:
o Data is interpreted and contextualized by stakeholders or decision-makers to make
informed decisions.
o Example: A manager interprets customer behavior analysis to develop targeted
marketing strategies.
 Data Action:
o Insights are acted upon to achieve business objectives or operational goals.
Actions are taken based on the interpreted data to drive changes or improvements.
o Example: A company launches a new product based on customer feedback and
market trends identified through data analysis.
 Data Maintenance and Governance:
o Continuous monitoring of data for quality assurance, privacy, security, and
compliance with regulations.
o Example: Regular audits of databases, implementing data protection policies,
ensuring adherence to data protection laws (e.g., GDPR).
8. Disciplines required for AI to learn and make decisions like humans.
To build AI systems capable of learning and making decisions like humans, several disciplines
work together to create algorithms, models, and frameworks that mimic human intelligence.
Below are the key disciplines involved:
1. Machine Learning (ML)
 Definition: A core subset of AI, machine learning involves teaching machines to learn
from data and improve their performance over time without being explicitly programmed.
 Role in AI: Machine learning algorithms allow AI systems to recognize patterns, classify
data, make predictions, and learn from past experiences—just like humans improve their
skills with practice.
 Key Focus: Supervised learning (learning from labeled data), unsupervised learning
(identifying patterns in unlabeled data), and reinforcement learning (learning through
interaction with an environment).
2. Neural Networks

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  Definition: Neural networks are computational models inspired by the structure of the
human brain, consisting of layers of interconnected nodes (neurons) that process
information.
 Role in AI: Neural networks are the backbone of deep learning, a powerful subfield of
machine learning. These models are used to simulate human-like perception, such as
image and speech recognition, and are crucial for AI systems to recognize complex
patterns in large datasets.
 Key Focus: Artificial neural networks, deep learning, backpropagation (error correction),
and optimization to refine predictions.
3. Data Science
 Definition: Data science involves extracting knowledge and insights from structured and
unstructured data using various techniques, algorithms, and statistical models.
 Role in AI: Data science is vital for preparing, cleaning, and analyzing large datasets that
AI systems learn from. Data scientists also help in feature engineering, ensuring that the
right data is used to train machine learning models.
 Key Focus: Data cleaning, statistical analysis, data visualization, and model building to
help AI systems make informed decisions based on data.
4. Cognitive Science
 Definition: Cognitive science is the interdisciplinary study of the mind and intelligence,
involving psychology, neuroscience, philosophy, linguistics, and artificial intelligence.
 Role in AI: Cognitive science provides insights into human cognition and decision-
making processes. This understanding helps AI systems replicate human-like thinking,
reasoning, learning, and problem-solving.
 Key Focus: Modeling human cognitive functions, such as memory, perception, learning,
and decision-making, in AI systems.
5. Linguistics
 Definition: Linguistics is the scientific study of language, including its structure,
meaning, and use.
 Role in AI: Linguistics plays a crucial role in enabling AI systems to understand,
process, and generate human language through Natural Language Processing (NLP). This
allows AI to interact with humans, interpret text or speech, and make decisions based on
linguistic data.
 Key Focus: Syntax (structure), semantics (meaning), pragmatics (context), and language
generation (creating natural-sounding responses).
6. Psychology
 Definition: Psychology is the scientific study of behavior and mental processes,
including emotions, perception, learning, and decision-making.
 Role in AI: Insights from psychology help AI systems model human-like decision-
making, adapt to new information, and simulate emotions or behaviors. This helps AI to
make more human-like judgments, especially in areas like customer service or personal
assistants.
 Key Focus: Cognitive psychology (mental processes), behavioral psychology (actions
and responses), and emotional intelligence (understanding and simulating human
emotions).
7. Computer Vision

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  Definition: Computer vision is the field of AI that enables machines to interpret and
understand visual information from the world, such as images and videos.
 Role in AI: Computer vision allows AI systems to "see" and interpret their environment,
similar to how humans use their vision to make decisions. It is crucial for tasks like
object recognition, facial recognition, and autonomous navigation.
 Key Focus: Image processing, object detection, segmentation, feature extraction, and the
application of machine learning to interpret visual data.
9. Is it possible to map human thinking to AI components?
Mapping human thinking to AI components is complex, but certain AI systems can simulate
aspects of human cognition. Here’s a simplified comparison:
1. Perception:
 Human Thinking: Humans process sensory data (sight, sound, etc.) to understand the
environment.
 AI Component: AI uses sensors (e.g., cameras, microphones) to gather data and
interpret it via computer vision or speech recognition.
 Challenge: Human perception integrates emotion and context, while AI lacks this depth.
2. Memory:
 Human Thinking: Human memory stores and recalls information flexibly.
 AI Component: AI uses databases and machine learning models to store and retrieve
information.
 Challenge: AI lacks the nuanced, emotional, and selective recall that humans have.
3. Reasoning:
 Human Thinking: Humans use logical, deductive, inductive, and intuitive reasoning.
 AI Component: AI uses algorithms and probabilistic reasoning for problem-solving.
 Challenge: Human reasoning includes biases and intuition, which AI doesn’t replicate
fully.
4. Learning:
 Human Thinking: Humans learn from experience and adapt flexibly.
 AI Component: Machine learning enables AI to learn from data and improve over time.
 Challenge: AI needs large datasets and lacks the flexibility to learn from minimal
examples, unlike humans.
5. Decision-Making:
 Human Thinking: Human decisions involve weighing options, emotions, and risks.
 AI Component: AI uses optimization and decision trees to make choices.
 Challenge: AI lacks emotional depth and cannot make decisions based on human values
or ethics.
6. Creativity:
 Human Thinking: Humans generate novel ideas through creativity.
 AI Component: AI can generate content (e.g., art, music) using generative models.
 Challenge: AI’s creativity is pattern-based, while humans can think outside the box and
innovate.
7. Emotions:
 Human Thinking: Emotions deeply influence human thinking and actions.
 AI Component: AI simulates emotions through affective computing.
 Challenge: AI does not experience real emotions, it only mimics emotional responses.

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 10. Structured vs. Unstructured Data and Its Role in AI.
 Structured Data is well-organized and easy to analyze, making it ideal for tasks
like business analysis and predictive modeling in AI.
 Unstructured Data is complex and less organized, requiring specialized
techniques like NLP and computer vision to derive insights for tasks like
sentiment analysis and image classification. Both types of data are critical for
training AI systems, with structured data being more straightforward, and
unstructured data offering more depth and complexity.
Structured vs. Unstructured Data and Its Role in AI
Aspect Structured Data Unstructured Data
Definition Data organized in rows, columns, Data without a predefined structure
and tables (e.g., spreadsheets, (e.g., text, images, videos, audio).
databases).
Format Tabular format with defined fields No predefined format or organization
(e.g., SQL databases). (e.g., raw text, multimedia).
Data Type Numbers, dates, categories (e.g., Text, images, audio, video, social media
names, addresses). posts.
Ease of Easy to process, analyze, and store Requires advanced techniques like
Processing using traditional database systems. Natural Language Processing (NLP),
Computer Vision, and Speech
Recognition.
Example Customer data (name, age, income, Email content, social media feeds,
etc.), sales records. medical imaging.
Role in AI Used for training machine learning Provides rich, complex data for AI
models that require clearly defined models in tasks like image recognition,
data (e.g., regression, classification). sentiment analysis, and language
translation.
AI Structured data is commonly used in Unstructured data is used in image
Application predictive modeling, business classification, speech recognition, text
analytics, and financial forecasting. mining, and chatbots.
Processing Traditional databases, Excel, SQL Machine learning algorithms (e.g., deep
Tools queries, data warehousing. learning), image recognition tools, NLP
libraries.

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 REFERENCES
1. INTRODUCTION TO EMERGING TECHNOLOGIES COURSE MODULE
(EMTE1011/1012)
2. Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd
ed.). Pearson Education.
3. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer
4. Briney, K. (2015). Data Management for Researchers: Organizing, Sharing, and
Publishing Your Data. Elsevier
5. Wikipedia

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