IT 231: IT and Application (BBM)
By the end of this introductory chapter, you will be able to:
Emerging trends are technologies and concepts that are gaining momentum and are poised to have a significant impact in the near future.
Key Idea: They are not just theoretical; they are actively shaping industries, creating new business models, and changing how we live and work.
In this unit, we'll explore the "what," "why," and "how" of these powerful forces.
This unit covers several interconnected domains that are driving innovation:
Definition: The simulation of human intelligence in machines, enabling them to learn, reason, problem-solve, and understand language.
AI is the broad concept of creating "smart" machines.
It encompasses sub-fields like:
🎯 Goal: To automate tasks that typically require human cognitive abilities.
A subset of AI where systems learn and improve from data without being explicitly programmed.
Example: A spam filter that learns to identify junk mail based on the emails you mark as spam.
An interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data.
Example: Analyzing customer purchase history to recommend new products.
Definition: Extremely large and complex datasets that cannot be easily managed or processed with traditional data-processing application software.
Often characterized by the "3 Vs":
Massive quantities of data.
Data is generated at high speed.
Data comes in many forms (structured, unstructured).
The challenge isn't just storing the data, but analyzing it to find valuable patterns.
Definition: The delivery of on-demand computing services—including servers, storage, databases, networking, and software—over the Internet ("the cloud").
Instead of owning your own computing infrastructure, you can rent access from a cloud provider like Amazon Web Services (AWS) or Microsoft Azure.
Definition: A network of physical objects ("things") embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.
🔍 Think of it as: Giving everyday objects a digital voice.
The evolution of the web into a platform for communication and commerce.
Platforms that allow users to create and share content or to participate in social networking.
(Facebook, Instagram, TikTok, etc.)
Using online channels to promote or market products and services to consumers.
(SEO, Content Marketing, Social Media Ads)
These have fundamentally changed how businesses connect with their customers, moving from broadcasting to engagement.
These trends don't exist in isolation. They feed into and empower one another.
These global trends are having a local impact:
Companies like Fonepay and eSewa use AI/ML for fraud detection and analyzing transaction patterns to improve services.
Platforms like Daraz rely on cloud infrastructure to handle massive traffic during sales events, scaling resources on demand.
Pilot projects are using IoT sensors to monitor soil moisture and weather conditions, helping farmers optimize irrigation and improve crop yields.
Countless Nepali businesses, from small cafes to large corporations, use social media for customer engagement and targeted advertising.
AI is a tool inside an information system. Data quality and accountability still sit with managers.
A quick summary of our introduction:
Revise the chapter note before writing these answers.
Read the full note: AI and Its Impact on Business and Society
Next deck: Unit 10.2: AI Concepts: Data Science, Machine Learning, and Neural Networks
By the end of this chapter, you will be able to:
Artificial Intelligence is a broad field. Let's see how our key concepts fit together.
Artificial Intelligence (AI)
The overall concept of creating intelligent machines.
Machine Learning (ML)
A subset of AI where machines learn from data.
Neural Networks / Deep Learning
A powerful type of ML inspired by the human brain.
It's an interdisciplinary field focused on extracting knowledge from data.
Data Science is the practice of using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Think of it as a combination of:
Instead of being explicitly programmed with rules, ML systems learn patterns directly from data.
Machine Learning (ML) is a subset of AI that focuses on building systems that can learn from data, identify patterns, and make decisions with minimal human intervention.
Key Idea: The system is "trained," not "programmed."
They are related, but not the same. ML is a tool often used in Data Science.
This powerful type of Machine Learning is behind many recent AI breakthroughs like ChatGPT and image recognition.
An Artificial Neural Network (ANN) is a machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes ("neurons") organized in layers.
Neural Networks are the foundation of Deep Learning.
Data flows through layers, with each layer recognizing more complex patterns.
Receives raw data (e.g., pixels of an image, words in a sentence).
Performs computations and finds patterns. The "deep" in deep learning refers to many hidden layers.
Produces the final result (e.g., "This is a cat," a predicted stock price).
AI isn't just a global concept; it's being applied here at home.
Let's recap the core concepts from this chapter.
Revise the chapter note before writing these answers.
Any Questions?
Read the full note: AI Concepts: Data Science, Machine Learning, and Neural Networks
Next deck: Unit 10.3: Cloud, Green, and Virtual Computing Trends