Class 8 AI | Unit 1 Deep Dive: The Stages of the AI Project Lifecycle
Stage 1: Problem Scoping (The 4W Canvas) 🎯
To solve a problem, we must first understand it perfectly. We use the 4W Canvas:
- Who: Who is the "Stakeholder" (the person facing the problem)?
- What: What is the actual problem? What evidence do we have?
- Where: What is the context/location of the problem?
- Why: What is the "Value" or benefit of solving it?
Problem Statement Template:
"Our [Who] has a problem that [What] happens at [Where]. A solution would help them because [Why]."
Stage 2: Data Acquisition (Collecting Clues) 📂
AI needs data to learn. There are two main ways to get it:
- Primary Data: Data you collect yourself (Surveys, experiments, sensors).
- Secondary Data: Data already available (Websites like Kaggle, Government records, Books).
Types of Data: 1. Structured: Organized in tables (like Excel). 2. Unstructured: Raw data (Images, Videos, Voice notes).
Stage 3: Data Exploration (The Power of Graphs) 📊
Before training the AI, we must "see" the data to find patterns or errors. We use:
- Bar Charts: To compare different categories.
- Scatter Plots: To see if two things are related (e.g., Temperature vs. Ice Cream sales).
- Histograms: To see the frequency of data points.
Stage 4: Modelling (Building the Brain) 🧠
We choose a "Learning Model" based on the problem:
- Rule-Based: The developer writes all the rules (If-Then statements).
- Learning-Based (AI): The machine finds its own rules from the data.
- Decision Trees: A flow-chart like structure to reach a conclusion.
- Neural Networks: Inspired by the human brain for complex tasks like image recognition.
Stage 5: Evaluation & Deployment 🚀
Evaluation Metrics: We don't just guess if AI is good; we measure it using Accuracy (How many times was it right?).
Deployment: This is the final step where the AI is actually put into a real-world app or machine (like a chatbot on a website or a sensor in a farm).
🌟 Relatable Example: Smart Traffic Lights
Problem: Long traffic jams at Crossroad A.
Data: Cameras (Computer Vision) counting cars at different times.
Modelling: A system that learns when to stay green longer based on car density.
Deployment: Installing the smart controller at the signal.
Activity Link: Google Teachable Machine
(Go through the full cycle: Acquire images of your hand, Train the model, and Deploy it to see it recognize your gestures!)
Reference Handbook
Refer to Unit 1 of the official Class 8 manual for the complete 4W Canvas worksheets:
Download Class 8 AI Handbook
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