Notes - Class 10 - AI - 417 - Unit 2 - 2023-24

 Unit 2: AI PROJECT CYCLE

AI project cycle is a step-by-step process that a person should follow to develop an AI Project to solve a problem. AI Project Cycle provides us with an appropriate framework which can lead us to achieve our goal.

Components of AI Project Cycle

Problem Scoping – Understanding the Problem
Data Acquisition – Collecting accurate and reliable data
Data Exploration – Arranging the data uniformly
Modelling – Creating Models from the data
Evaluation – Evaluating the project

1. Problem Scoping:

In Problem Scoping, we set the goal for our AI project by stating the problem which we wish to solve with it. Under problem scoping, we look at various parameters which affect the problem we wish to solve so that the picture becomes clearer.

To find the problems around us, we can take the help of 17 Sustainable Development Goals announced by the United nations.

GOAL 1: No Poverty

GOAL 2: Zero Hunger

GOAL 3: Good Health and Well-being

GOAL 4: Quality Education

GOAL 5: Gender Equality

GOAL 6: Clean Water and Sanitation

GOAL 7: Affordable and Clean Energy

GOAL 8: Decent Work and Economic Growth

GOAL 9: Industry, Innovation and Infrastructure

GOAL 10: Reduced Inequality

GOAL 11: Sustainable Cities and Communities

GOAL 12: Responsible Consumption and Production

GOAL 13: Climate Action

GOAL 14: Life Below Water

GOAL 15: Life on Land

GOAL 16: Peace and Justice Strong Institutions

GOAL 17: Partnerships to achieve the Goal

For scoping of a problem, we need to take the help of 4Ws problem canvas.

4Ws: 

1. Who – “Who” part helps us in comprehending and categorizing who all are affected directly and indirectly with the problem and who are called the Stake Holders.

2. What – “What” part helps us in understanding and identifying the nature of the problem and under this block, we also gather evidence to prove that the problem we have selected exists.

3. Where – “Where” does the problem arise, situation, and location.

4. Why – “Why” is the given problem worth solving.

Problem Statement Template (PST) : The Problem Statement Template helps us to summarize all the key points into one single Template so that in the future, whenever there is a need to look back at the basis of the problem, we can take a look at the Problem Statement Template and understand the key elements of it.

2. Data Acquisition: Data Acquisition is the process of collecting accurate and reliable data to work with. Data Can be in the format of the text, video, images, audio, and so on and it can be collected from various sources like interest, journals, newspapers, and so on.

Data Sources:

1. Surveys

2. Web Scrapping

3. Sensor

4. Cameras

5. Observations

6. API (Application Programming Interface)

1. Surveys: Survey is a method of gathering specific information from a sample of people. for Example a census survey is conducted every year for analyzing the population.

2. Web Scraping: Web Scraping means collecting data from web using some technologies. 

3. Sensors : Sensors senses the physical parameters like temperature, pressure, light, sound etc. They are part of IOT. Example of IOT is smart watches or smart fire alarm which automatically detects wire and starts the alarm.

4. Cameras: Camera captures the visual information and then that information which is called image is used as a source of data.

5. Observations : When we observe something carefully we get some information. For example, Scientists take insects in observation for years and that data will be used by them .

6. API (Application Programming Interface) : API is like a messenger which takes requests from us and then tells the system what we want and then it gives us a response.

3. Data Exploration: Data Exploration is the process of arranging the gathered data uniformly for a better understanding. Data can be arranged in the form of a table, plotting a chart, or making a database.

In Data Exploration, we need to arrange the data which we collected in Data Acquisition, for example if we have data of 50 students in a class, we have to arrange them in a table e.g. their Mobile Number, Date of Birth, Class, etc.

Visualization Tools: To explore the data, we need to visualize them. Thus, to analyse the data, we need to visualise it in some user-friendly format so that we can:

● Quickly get a sense of the trends, relationships and patterns contained within the data.
● Define strategy for which model to use at a later stage.
● Communicate the same to others effectively. To visualize data, we can use various types of visual representations.

There are several visualization tools available. Few of them are:  

1. Google Charts

2. Tableau

3. Fusion Charts

4. High charts

 

4. Modelling: Modelling is the process in which different models based on the visualized data can be created and even checked for the advantages and disadvantages of the model.

There are two types of approaches to design an AI model:

1. Rule Based approach

2. Learning Based approach

1. Rule based approach: Rule Based Approach Refers to the AI modelling where the relationship or patterns in data are defined by the developer. That means the machine works on the rules and information given by the developer and performs the task accordingly.

For example: Suppose we have a dataset containing 100 images of apples and bananas each. Now we created a machine using Computer-Vision and trained it with the labeled images of apples and bananas. If we test our machine with an image of an apple it will give us the output by comparing the images in its datasets. This is known as the Rule-Based Approach.

2. Learning based approach: It refers to the AI modelling where the machine learns by itself. Under the Learning Based approach, the AI model gets trained on the data fed to it and then is able to design a model which is adaptive to the change in data.