AI Project Cycle Class 9 Questions and Answers

These Class 10 AI Important Questions Chapter 2 AI Project Cycle Class 9 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

AI Project Cycle Class 9 Important Questions

Class 9 AI Project Cycle Important Questions

Important Questions of AI Project Cycle Class 9 – Class 9 AI Project Cycle Important Questions

Problem Scoping

Question 1.
What are the various stages of AI Project Cycle? Can you explain each with an example?
Answer:
Various Stages of AI Project Cycle
(i) Problem Identification This stage involves identifying a problem or opportunity that can be addressed using AI techniques. For example, a company may identify a need to improve customer service response times.

(ii) Data Acquisition Once the problem is identified, relevant data needs to be collected and prepared for analysis. In our customer service example, this could involve gathering historical customer interactions and feedback data.

(iii) Data Exploration Explore the collected data to understand its characteristics, quality, and relationships between variables. This stage helps in identifying patterns and initial insights. Analyzing patient data to discover correlations between specific treatments and readmission rates, or identifying outliers and missing values that need to be addressed.

(iv) Modelling This stage involves selecting appropriate AI models and algorithms, training them on the prepared data, and tuning them for optimal performance. For instance, in our scenario, natural language processing (NLP) models might be used to analyze customer queries.

(v) Evaluation After building the model, it needs to be evaluated to ensure it meets the desired criteria and effectively addresses the problem. This evaluation could involve metrics like accuracy, precision, and recall in our customer service example.

(vi) Deployment Once the model is deemed satisfactory, it’s deployed into the operational environment. In our case, the NLP model might be integrated into the company’s customer service platform.

Question 2.
How is an AI project different from an IT project?
Answer:
Difference between an AI project and an IT project are as follows

Aspect	AI Project	IT Project
Focus	Developing intelligent models and systems	Developing and maintaining information systems
Data Usage	Heavily reliant on large datasets for training	Data management for processing and storage
Development	Iterative, experimental model training and tuning	Structured methodologies like Agile or Waterfall
Outcome	Systems that learn, predict, or automate tasks	Systems that meet business needs and improve efficiency
Skill Set	Algorithms, statistics, data science, programming	Software development, network management, cybersecurity

Question 3.
Explain the 4Ws problem canvas in problem scoping.
Answer:
The 4 W s problem canvas is a tool used in problem scoping to thoroughly understand the problem by addressing four key aspects: What, Who, Where, and When.

• What Describes the problem or opportunity in detail.
• Who Identifies the stakeholders involved or affected by the problem.
• Where Specifies the location or context in which the problem occurs.
• When Indicates the timeframe or frequency of the problem occurrence.

This canvas helps in clarifying the problem scope and understanding its various dimensions before proceeding with problem-solving activities.

Question 4.
Why is there a need to use a Problem Statement Template during problem scoping?
Answer:
Problem Statement Template provides a structured framework for defining the problem clearly and comprehensively. It ensures that all relevant information about the problem, its context, and its stakeholders are captured systematically. This template serves as a guide for the problem scoping process, helping stakeholders align their understanding of the problem and facilitating effective communication throughout the project lifecycle.

Question 5.
What is Problem Scoping? What are the steps of Problem Scoping?
Answer:
Problem scoping is the initial phase of a project where the problem or opportunity is defined, analyzed, and understood in detail. The steps involved in problem scoping typically include:

• Problem Identification Recognizing and articulating the problem or opportunity.
• Problem Understanding Gaining a deep understanding of the problem’s context, causes, and implications.
• Stakeholder Analysis Identifying and engaging with stakeholders who are involved or affected by the problem.
• Scope Definition Clearly defining the boundaries and objectives of the problem-solving effort.
• Data Collection Gathering relevant data and information to inform the problem-solving process.
• Problem Statement Formulation Crafting a concise and precise statement that describes the problem and its key aspects.

Question 6.
Who are the stakeholders in the problem scoping stage?
Answer:
Stakeholders in the problem scoping stage typically include

• Project Sponsor The individual or group funding the project and with a vested interest in its success.
• Subject Matter Experts (SMEs) Individuals with specialized knowledge or experience related to the problem domain.
• End Users Those who will ultimately use or be impacted by the solution.
• Data Owners/Custodians Individuals or departments responsible for providing access to relevant data.
• Project Team Members Those directly involved in scoping and solving the problem.
• Regulatory Bodies If applicable, regulatory agencies that govern the industry or domain related to the problem.

Data Acquisition

Question 1.
How will you differentiate between Training-Data and Testing Data? Elaborate with examples.
Answer:

	Training Data	Testing Data
Definition	A dataset of examples used during the learning process to fit the parameters.	A set of examples used solely to assess the performance of the fully specified classifier.
Usage	Used to train the model by adjusting its parameters.	Used to evaluate the performance of the trained model.
Dataset Proportion	Maximum portion of the dataset (usually around 80%).	A smaller portion of the dataset (usually around 20%).
Example	Training a model to recognize cats from images using a dataset of labeled images of, cats and other objects.	After training, using a separate set of images containing cats and othe objects to test the model’s accuracy in corréctly identifying cats.

Question 2.
Name various methods for collecting data. For each method, can you name at least one project in which you may use that method of data collection?
Answer:
The data acquisition stage is crucial in AI project cycles, as it lays the foundation for subsequent analysis and model development. Here are various methods for collecting data along with potential project examples:
Web Scraping

• Method Automated extraction of data from websites.
• Example Project A project aiming to analyze customer reviews for sentiment analysis of a particular product or service.

Surveys and Questionnaires

• Method Gathering data through structured questions.
• Example Project Conducting a survey to gather feedback on user experience for improving a mobile application.

Sensor Data Collection

• Method Gathering data from physical sensors.
• Example Project Using IoT sensors to collect environmental data for predictive maintenance in manufacturing plants.

Database Queries

• Method Retrieving data from databases.
• Example Project Extracting historical sales data from a company’s database for sales forecasting.

Question 3.
What must you keep in mind while collecting data so it is useful?
Answer:
When collecting data, ensure it’s relevant to your objectives, accurate, collected ethically, and of sufficient quantity and quality for meaningful analysis.

Question 4.
Imagine you are responsible to enable farmers from a village to take their produce to the market for sale. Can you draw a system map that encompasses all the steps and factors involved?
Answer:
System Map for Connecting Farmers to Markets
Data Acquisition
Farmer Data

• Names, locations, types & quantities of produce
• Can be collected through surveys, village meetings, or mobile app

Market Data

• Location, types of produce accepted, current prices
• Data from local markets or government sources (agriculture departments)

Logistics Data

• Transportation options (trucks, bikes), costs, travel time
• Gathered from local transport providers or existing services

System Actors

• Farmers Provide data on their produce
• Data Collector Manages farmer data collection (surveys, app)
• Market Aggregator Gathers and maintains market data
• Logistics Coordinator Manages transportation options & costs
• Matching Algorithm (AI) Analyzes data to connect farmers with suitable markets

Data Flow

• Farmer data is collected (surveys, app)
• Market data is obtained (local markets, government sources)
• Logistics data is gathered (transport providers)
• All data is fed into the AI system

AI Model
The AI model analyzes data to

• Find the best market for each farmer’s produce (considering price & distance)
• Recommend the most efficient transportation option

Outputs

• Farmer Reports Suggest markets, transport options, estimated earnings
• Logistics Plan Coordinates pick-up and delivery for produce

Benefits

• Increased income for farmers
• Reduced post-harvest losses
• Improved market access for villagers

Question 5.
Name a few government websites from where you can get open-source data.
Answer:
Here are a few government websites from where you can get open-source data:
Data.gov is the federal government’s open data site in the United States. It provides access to a wide variety of datasets from various government agencies.
Data.gov.in is the Open Government Data Platform for India. It provides access to datasets from the Government of India and its various ministries and departments.

Data Exploration

Question 1.
Which one of the following is the second stage of AI project cycle?
(a) Data Exploration
(b) Data Acquisition
(c) Modelling
(d) Problem Scoping
Answer:
(b) Data Acquisition

Question 2.
Which of the following comes under Problem Scoping?
(a) System Mapping
(b) 4 Ws Canvas
(c) Data Features
(d) Web scraping
Ans.
(b) 4Ws Canvas

Question 3.
Which of the following is not valid for Data Acquisition?
(a) Web scraping
(b) Surveys
(c) Sensors
(d) Announcements
Answer:
(d) Announcements

Question 4.
If an arrow goes from X to Y with a – (minus) sign, it means that
(a) If X increases, Y decreases
(b) The direction of relation is opposite
(c) If X increases, Y increases
(d) It is a bi-directional relationship
Answer:
(a) If X increases, Y decreases

Question 5.
Which of the following is not a part of the 4 Ws Problem Canvas?
(a) Who?
(b) Why?
(c) What?
(d) Which?
Answer:
(d) Which?

Question 6.
What is the significance of Data Exploration after you have acquired the data for the problem scoped? Explain with examples.
Answer:
Data exploration helps in understanding the characteristics and patterns in the data, identifying outliers, and refining hypotheses before analysis. For example, in healthcare, exploring patient data might reveal trends in disease prevalence or demographic patterns.

Question 7.
What do you think is the relevance of Data Visualization in AI?
Answer:
Data visualization in AI is crucial for interpreting complex datasets and communicating insights effectively. It helps analysts and stakeholders understand trends, patterns, and relationships in the data.

Question 8.
List any five graphs used for data visualization.
Answer:
Five common graphs for data visualization are

• Line graphs
• Bar charts
• Scatter plots
• Histograms
• Pie charts

Question 9.
How is Data Exploration different from Data Acquisition?
Answer:
Data acquisition involves obtaining raw data, while data exploration involves analyzing and understanding the acquired data to derive insights and patterns.

Question 10.
Use an example to explain at least one Data Visualization technique.
Answer:
An example of data visualization technique is a scatter plot, which displays the relationship between two variables. For example, plotting the relationship between study hours and exam scores for a group of students can help visualize if there’s a correlation between the two.

Modelling

Question 1.
What are the various stages of the AI Project Cycle? Explain each with examples.
Answer:
Refer to solution on page no. 136 Question no. 1.

Question 2.
What is Artificial Intelligence? Give an example where Al is used in day-to-day life.
Answer:
When a machine can mimic human intelligence, solve real-world problems, improve on its own from past experiences, and predict and make decisions on its own, it can be called Artificially Intelligent (AI).

One common example of AI in day-to-day life is virtual assistants like Siri, Google Assistant, or Amazon Alexa. These AI-powered assistants use natural language processing (NLP) to understand and respond to user commands or questions. They can perform various tasks such as setting reminders, answering queries, sending messages, playing music, and controlling smart home devices.

Question 3.
How is Machine Learning related to Artificial Intelligence?
Answer:
Machine learning is a subset of artificial intelligence that focuses on creating algorithms and models that enable computers to learn from and make predictions or decisions based on data. In essence, ML empowers AI systems to improve their performance over time without being explicitly programmed for every task.

Artificial intelligence, on the other hand, encompasses a broader spectrum of techniques and methodologies aimed at creating machines or systems that can mimic human intelligence, including reasoning, problem-solving, perception, and learning.

While AI encompasses various disciplines like natural language processing, computer vision, robotics, and expert systems, machine learning stands out as a fundamental approach within AI for enabling systems to learn from data and improve their performance iteratively.

Question 4.
Compare and contrast Rule-based and Learning-based approach in Al modelling indicating clearly when each of these may be used.
Answer:
Rule-based approach relies on predefined rules and logic to make decisions or perform tasks. It’s suitable when the problem domain is well-understood and the rules are clear and stable. It’s often used in applications where transparency and interpretability are important, such as expert systems or legal reasoning. Learning-based approach, on the other hand, is training algorithms on data to recognize patterns and make decisions.

It’s ideal when the problem is complex and there’s a lot of data available for training. Learning-based methods excel in tasks like image recognition, natural language processing, and recommendation systems, where the underlying patterns might be too intricate for rule-based systems to capture effectively.

Question 5.
Identify which of the following are examples of classification/regression/clustering.
(i) Making a diagnosis for a patient on the basis of their symptoms
(ii) Price prediction for a house coming up on sale
(iii) HR shortlisting applications for interview based on information provided in candidates’ resume
(iv) Credit Card Fraud prevention
(v) SPAM filters
Answer:
(i) Classification
(ii) Regression
(iii) Classification
(iv) Classification
(v) Classification

Evaluation

Question 1.
What is Evaluation?
Answer:
Evaluation is the process of understanding the reliability of any AI model, based on outputs by feeding test dataset into the model and comparing with actual answers. There can be different Evaluation techniques, depending of the type and purpose of the model.

Question 2.
What are various model evaluation techniques?
Answer:
There are various techniques for model evaluation are:

• Accuracy
• Precision
• Recall
• F1-Score

Question 3.
Why is model evaluation important in Al projects?
Answer:
Model evaluation is essential for several reasons:

• Ensures model effectiveness
• Identifies biases and errors
• Improves modelyperformance
• Provides insights for real-world deployment

Question 4.
What do you understand by the terms True Positive and False Positive?
Answer:
These terms are used in classification problems where the model predicts a binary outcome (positive or negative).

• True Positive (TP) A case where the model correctly predicts the positive. For example, a spam filter correctly identifying a spam email.
• False Positive (FP) A case where the model incorrectly predicts the positive. For example, a spam filter mistakenly marking a legitimate email as spam.

Deployment

Question 1.
Does modelling mean creating an AI model?
(a) YES
(b) NO
Ans.
(b) NO

Question 2.
Can we use AI on mobile phones?
(a) YES
(b) NO
Answer:
(a) YES

Question 3.
What is deployment in the context of an AI project cycle?
Answer:
Deployment as the final stage in the AI project cycle where the AI model or solution is implemented in a real-world scenario.

Question 4.
Why is deployment an important phase in the Al project cycle?
Answer:
Deployment is crucial because it’s where the AI project delivers real value. After all the development and testing, deployment is where the rubber meets the road – the AI system starts impacting the business or solving the problem it was designed for.

Question 5.
What are some common challenges in deploying AI models?
Answer:
Deployment is crucial because it’s where the AI project delivers real value. After all the development and testing, deployment is where the rubber meets the road – the AI system starts impacting the business or solving the problem it was designed for. Deploying AI models can be tricky. Here are a few common challenges

• Integration You need to seamlessly connect the AI model with existing systems and data flows.
• Monitoring Keeping an eye on the model’s performance in the real world is essential. This way you can identify and address any issues that arise.
• Maintaining Data Quality AI models rely on good data. In deployment, you need to ensure a consistent flow of high-quality data to the model.

Revision Time

Question 1.
Rearrange the steps of AI project cycle in correct order:
(a) Data Acquisition
(b) Problem Scoping
(c) Modelling
(d) Data Exploration
(e) Deployment
(f) Evaluation
Answer:
(b), (a), (d), (c), (f), (e)

Question 2.
The process of breaking down the big problem into a series of simple steps is known as
(a) Efficiency
(b) Modularity
(c) Both (a) and (b)
(d) None of these
Answer:
(b) Modularity

Question 3.
The primary purpose of data exploration in AI project * cycle is \qquad
(a) To make data more complicated
(b) To simplify complex data
(c) To discover patterns and insights in data
(d) To visualize data
Answer:
(c) To discover patterns and insights in data

Question 4.
Deployment is the final stage in the AI project cycle where the Al model or solution is implemented in a real-world scenario. (True/False)
Answer:
True

Question 5.
Identify A, B and C in the following diagram (Hint: How AI, ML &DL related to each other)

Answer:
A → Arfificial Intelligence (AI)
B → Machine Learning (ML)
C → Deep Learning (DL)

AI Project Cycle Class 9 Very Short Answer Type Questions

Question 1.
What is the AI Project Cycle?
Answer:
The AI Project Cycle is a step-by-step process that a company must follow in order to derive value from an AI project and to solve the problem.

Question 2.
What are the different AI Project stages?
Answer:
The different AI Project stages are:

• Problem Scoping
• Data Acquisition
• Data Exploration
• Modelling
• Evaluation
• Deployment

Question 3.
What is Problem Scoping?
Answer:
Problem scoping is the process of understanding a problem, determining the various factors that affect the problem, and defining the project’s purpose.

Question 4.
In the AI project cycle, what is the first phase, and what does it involve?
Answer:
The first phase of the AI project cycle is “Problem Scoping,” which involves identifying and finalizing the problem that the AI project aims to solve.

Question 5.
What are some methods of data acquisition in the context of AI projects?
Answer:
Methods of data acquisition include surveys, web scraping, sensors, cameras, observations, and API (Application Programming Interface).

Question 6.
In the Al project cycle, what is the purpose of the “Evaluation” stage?
Answer:
The “Evaluation” stage assesses the efficiency of the AI model by testing it with a separate dataset, considering factors such as precision, accuracy, F1 score, and recall.

Question 7.
What role does training data play in machine learning?
Answer:
Training data is essential for teaching algorithms and ensuring accuracy.

Question 8.
How does testing data differ from training data in AI?
Answer:
Training data is used to teach the AI model, helping it learn patterns. Testing data evaluates the model’s performance on unseen data, ensuring it generalizes well.

AI Project Cycle Class 9 Short Answer Type Questions

Question 1.
What is a project, and why is it important to break it down into individual tasks?
Answer:
A project is a series of tasks aimed at achieving a specific goal. Breaking down a project into individual tasks is essential for better management, accountability, and utilizing team strengths. It helps in clear delegation, understanding expectations, and meeting deadlines effectively.

Question 2.
Explain the significance of problem scoping in the AI project cycle and provide an example scenario where problem scoping is essential.
Answer:
Problem scoping is vital in the AI project cycle as it involves defining the scope and aim of the problem to be solved. For example, in developing a facial recognition system for enhanced security, problem scoping includes identifying authorized personnel, obtaining their data, and creating a system to prevent unauthorized access.

Question 3.
What is the role of data exploration in the AI project cycle, and how does data visualization contribute to understanding complex data?
Answer:
Data exploration in the AI project cycle involves finding hidden patterns in complex data. Data visualization, through methods like charts and graphs, helps in spotting trends, choosing the right tools for analysis, and sharing insights. It makes data more understandable and aids in decision-making.

Question 4.
What is the first step in scoping a problem for an Al project?
Answer:
The first step in scoping a problem for an AI project is Problem Identification. This involves creating a concise and specific description of the current situation, the desired situation, and the gap between them. It includes clear identification of the problem, finding the root cause, and framing the problem correctly.

Question 5.
What is Data Acquisition, and why is it important in the AI Project Cycle?
Answer:
Data Acquisition is the process of obtaining relevant data for a project. In the AI Project Cycle, it’s essential for training AI systems with adequate information to make accurate predictions or decisions. This stage ensures that the system learns from real-world data, enabling it to understand patterns and relationships. Without proper data acquisition, AI systems may produce unreliable outcomes or fail to perform effectively.

Question 6.
How does data exploration differ from data acquisition?
Answer:
Data exploration involves analyzing and visualizing the collected data to uncover patterns and insights, while data acquisition is the process of gathering and filtering data from various sources.

Question 7.
Can you name some popular data visualization techniques?
Answer:
Some techniques include pie charts, bar charts, histograms, Gantt charts, heat maps, and various types of diagrams and maps used to represent data visually.

AI Project Cycle Class 9 Long Answer Type Questions

Question 1.
What are the phases involved in the development of an artificial intelligence project?
Answer:
The AI project development involves crucial phases:

1. Problem Scoping Identify and define the issue to solve, setting clear objectives and boundaries.
2. Data Acquisition Gather relevant data from various sources using methods like surveys, web scraping, and sensors.
3. Data Exploration Analyze and understand collected data, visualizing patterns and anomalies to guide modelling techniques.
4. Modelling Develop mathematical models representing data relationships, tailored to the problem’s complexity and available data.
5. Evaluation Assess model performance against predefined metrics using validation datasets to ensure reliability and meet objectives.
6. Deployment Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data.

Question 2.
Write the problem approach in AI Problem Scoping.
Answer:
Problem scoping in AI involves identifying issues through the 4Ws Problem Canvas:

• Who? Identifying stakeholders affected, like financial experts and regulatory bodies in a fraud detection project.
• What? Detailing the problem with evidence from sources like historical data in fraud detection.
• Where? Understanding where the problem is prominent, like specific industries prone to fraud.
• Why? Addressing reasons for solving the problem, such as safeguarding financial assets and societal benefits.

Question 3.
Wildlife India, an autonomous organization dedicated to the rehabilitation of injured wild animals, is keen on monitoring the treated animals by tracking details like their location and thermal information. Which method are most suitable for collecting this specific set of data?
Answer:
Sensors would be the most suitable method for collecting location and thermal information of treated wild animals. Wildlife India can use GPS trackers or radio telemetry tags to monitor the animals’ locations accurately. These devices can provide real-time data on the animals’ movements, allowing for effective tracking and management.

Additionally, thermal sensors can be deployed to monitor the animals’ body temperatures, helping to assess their health and recovery progress. Using sensors ensures continuous and reliable data collection without relying on human observations, which can be limited in accuracy and consistency. Overall, sensors offer a robust and comprehensive approach to monitoring the treated animals, enabling Wildlife India to make informed decisions for their rehabilitation and conservation efforts.

Question 4.
What are some common techniques used in data exploration?
Answer:
Data exploration techniques encompass a range of methods, including statistical analysis, visualization, and data clustering. Analysts utilize tools like histograms, scatter plots, and box plots to visualize data distributions and identify outliers. Statistical measures such as mean, median, and mode help understand data variance and central tendencies.

Moreover, data clustering enables analysts to group similar data points for deeper analysis. By combining these techniques, analysts can gain insights into data structures, relationshipspatterns, facilitating informed decision-making and further analysis.

Question 5.
A wildlife conservation organization has gathered data on the population of endangered species in various national parks. The released statistics include 800 elephants, 600 rhinos, 400 tigers, and 300 pandas. What is the best visualization to represent this type of data in data visualization tools? Competency Based Ques.
Answer:
The best visualization for representing the population of endangered species in various national parks would be a bar chart. A bar chart allows for clear comparison between different categories, making it easy to see the differences in population sizes of elephants, rhinos, tigers, and pandas across the national parks.

Each species can be represented by a different colored bar, and the length of each bar corresponds to the population size. This visualization effectively communicates the relative sizes of the populations and allows for easy interpretation of the data.

AI Project Cycle Class 9 Activities

Activity 1

Designing CottonAce App for Pest Management

Problem
Pest Infestation Damages Crops

Problem Scoping

The cotton industry in India comprises 6 million local farmers. Cotton crops frequently get infected with the Pink Bollworm, which is difficult to see with the naked eye. Small farmers struggle to eliminate these insects due to a lack of advanced tools and techniques.

4 W canvas for Pest Management

Data Acquisition

In interaction with the farmers, different types of worms affecting the cotton crop are identified. The following data is collected:

• Images of the pests
• Farmer names
• Village names
• Farm size
• Pesticide usage

Data Exploration

Upon acquiring the data, it is noted that it is not uniform. Some images are small, others large. Some data is missing, while other data points have multiple copies. The data is cleaned, made uniform, and missing data is filled to enhance its comprehensibility.

Exploring the data helps researchers identify patterns and trends related to Pink Bollworm infestations, pesticide usage, crop yields, and other relevant factors.

Modelling

After exploring the data, an AI-enabled app is developed. Farmers use the app to click pictures of pests using their phone cameras. The AI app validates the images, recognizes the pests, and provides recommendations based on the number of pests identified and entomologists’ guidelines.

Evaluation

The pest management system is tested by first emptying pest traps onto a blank sheet of paper and using the app to click pictures of pests. Initial results show a 70% accuracy rate. Further refinement of the model and exploration of other AI algorithms are undertaken to impròve performance.

Deployment

• After thorough testing, the pest management app, named CottonAce, is deployed and installed on farmers’ mobile phones.
• Note CottonAce is a mobile application that helps farmers protect their crops from pests.

Impact

• Small farms that used the app saw profit margins increase by up to 26.5 %.
• Pesticide costs dropped by up to 38 %.

Activity 2

A balloon debate is a fun and engaging activity where participants imagine they are in a hot air balloon that is losing altitude. The goal is to convince others why they should stay on the balloon, and each participant represents a different person, character, or concept. The debate continues until the participants have made their cases, and the group votes on who should stay on the balloon.

The impact of AI in our daily lives, you can organize the debate into two teams Team Pro (in favor of AI) and Team Con (against AI). Each team should have multiple students representing various aspects of the topic.

Team Pro (Pros of AI)

1. AI boosts efficiency and productivity by automating tasks, allowing humans to focus on more creative endeavors.
2. AI aids in medical advancements, leading to faster and more accurate diagnoses and personalized medicine.
3. AI enhances safety through autonomous vehicles and surveillance systems, reducing human errors and identifying security threats.
4. AI assists people with disabilities, providing them with independence and opportunities.
5. AI accelerates innovation and research, leading to breakthroughs in various fields.

Team Con (Cons of AI)

• AI can lead to job displacement, particularly in sectors where routine tasks are easily automated.
• Privacy concerns arise due to AI’s reliance on data and the potential misuse of personal information.
• AI systems may perpetuate societal biases present in training data, leading to discriminatory outcomes.
• Security risks increase as AI systems become more sophisticated, making them vulnerable to cyber-attacks and misuse.
• Overreliance on AI may result in a loss of human interaction and empathy, particularly in fields like healthcare and customer service.

Finally compile all the points of the team after a proper discussion on each point.

Activity 3

This activity will help you brainstorm a theme, identify a specific problem within that theme, and set a goal for your AI project.
Theme (Choose a theme relevant to your interests or current events)

Step 1 Brainstorming & Topic Selection

1. Mind Map Draw a mind map centered on your chosen theme. Write down any ideas, questions, or problems that come to mind related to the theme. This will help you explore different aspects of the theme.

2. Discussion Discuss the various topics you identified in your mind map. Consider these questions:

• What problems or challenges exist within this theme?
• How could AI potentially address these problems?
• What are some potential benefits and drawbacks of using AI in this context?

3. Topic Selection Based on your discussion, select one specific topic within the theme that you find most interesting and has potential for an AI project.

Step 2 Problem Definition

Now, let’s delve deeper into the chosen topic and define the specific problem you want your AI project to address.
1. 4Ws Problem Canvas Fill out the following table to understand the problem better

• Who Who is affected by this problem? (Individuals, organizations, society as a whole)
• What What exactly is the problem? (Describe the specific issue)
• When When and where does this problem occur? (Frequency, location)
• Why Why is this a problem? (What are the negative consequences?)

2. Problem Statement Write a clear and concise problem statement using the following template People who [who are affected] struggle with [specific problem] because [reasons for the problem]. This has negative consequences such as [negative impacts].

Step 3 Goal Setting

Based on your problem definition, set a clear and achievable goal for your AI project.

Example

• Theme Education
• Selected Topic Personalized Learning for Students

4Ws Problem Canvas

• Who Students
• What Difficulty keeping up or being challenged in traditional one-size-fits-all learning environments
• When Throughout their educational journey
• Why Students may become disengaged or lose motivation if the learning pace is not tailored to their individual needs.

Problem Statement Students struggle to find a personalized learning experience that caters to their individual pace and needs because traditional classroom settings often follow a one-size-fits-all approach.
This can lead to disengagement, a lack of motivation, and hinder their academic potential.

Goal Develop an AI-powered educational assistant that personalizes learning materials and adapts to a student’s individual needs and progress.

This activity provides a framework to brainstorm and define a problem you can address with your AI project. Remember, the goal should be specific, measurable, achievable, relevant, and time-bound (SMART).

Additional Tips

• Research existing solutions to your chosen problem. Can AI be used to improve upon them?
• Consider the feasibility of your project given your resources and timeline.
• Stay focused on a well-defined problem to ensure your AI project has a clear purpose.

This activity should give you a solid foundation for developing your AI project.

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