AI Reflection Project Cycle and Ethics Class 9 Questions and Answers

AI Reflection Project Cycle and Ethics Class 9 Questions and Answers inspires AI-readiness in you. In this chapter, you will evaluate yourself to get a deep understanding of AI, and you will be able to create solutions with AI.

AI Reflection Project Cycle and Ethics Class 9 Questions and Answers

Q1. How can AI be used as a tool to transform the world into a better place?

Answer: AI can transform the world with numerous positive changes in society, including enhancing healthcare, environmental sustainability, enhancing productivity, economic development, education development, etc. AI can also be used for solving complex problems and making our daily lives easier.

Q2. Can you list down a few applications in your smartphone that widely make use of computer vision?

Answer: The applications that can be used on smartphones that use computer vision technology are image-based searches, real-time translation, QR code scanning, augmented reality applications, social media, object detection, etc.

Q3. Draw out the difference between the three domains of AI with respect to the types of data they use.

Answer: The three domains of AI are:

• Computer Vision: Computer Vision is an AI domain works with videos and images enabling machines to interpret and understand visual information.
• Natural Language Processing: Natural Language Processing (NLP) is an AI domain focused on textual data enabling machines to comprehend, generate, and manipulate human language.
• Statistical Data: Statistical Data refers to statistical techniques to analyze, interpret and draw insights from numerical/tabular data.

Q4. Identify the features and the domain of AI used in them:

Answer: The features and the domain of AI used in them are:

(a) Online Shopping Recommendations

• Features: Based on user experience personalized product recommendations, past history, browsing history etc.
• Domain of AI: Data Science

(b) Self-driving Car

• Features: Object detection, autonomous navigation.
• Domain of AI: Computer Vision.

(c) Voice-to-Text Conversion

• Features: Converts spoken language into written text using speech recognition.
• Domain of AI: Natural Language Processing (NLP).

Q5. Separate the following areas based on the kinds of domains widely used in them:
a. Crop productivity
b. Traffic regulation
c. Maps and navigation
d. Text editors and autocorrect
e. Identifying and predicting disease

Answer:

a. Crop productivity: Domain: Statistical Data
b. Traffic regulation: Domain: Computer Vision
c. Maps and navigation: Domain: Computer Vision
d. Text editors and autocorrect: Domain: Natural Language Processing
e. Identifying and predicting disease: Domain: Statistical Data

Q6. After the pandemic, it’s been essential for everyone to wear a mask. However, you see many people not wearing masks when in public places. Which domain of AI can be used to build a system to detect people not wearing masks?

Answer: To find the people who are not wearing masks in public, you can do this using the technique of computer vision. Computer vision can have a feature to extract meaningful information from pictures or videos. Computer vision can identify the people using a camera in public places, whether people are wearing masks or not.

Q7. Search for an online game that recognizes the image drawn by you. Write down the observations including the AI domain used by it.

Answer: I found a very interesting online game named “Quick, Draw.” Quick Draw uses artificial intelligence to recognize doodles or drawings made by players. In this particular game, a neural network is used to identify various objects. The game challenges the players to draw the specific object within a time period, and the AI helps to identify the object.

Q8. What are the various stages of Al Project Cycle? Can you explain each with an example?

Answer: The various stages of AI Project Cycle are:

• Problem Scoping: To understand a problem, determine the different aspects that affect the problem, and define the project’s goal are problem scoping.
• Data Acquisition: The method of collecting correct and dependable data to work with is known as data acquisition.
• Data Exploring: Data exploration is a technique used to visualize data in the form of statistical methods or using graphs.
• Data Modeling: Data modeling is a method to create a visual representation that helps to understand how the data is organized or managed in the organization.
• Evaluation: 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.
• Deployment: The last stage where you deploy your solution based on the model you’ve selected is known as deployment.

Q9. How is an Al project different from an IT project?

Answer: AI projects are different from IT projects. AI projects are focused on creating systems where the computer can mimic human intelligence, such as decision-making, learning, reasoning, etc. This module is based on algorithms and models to make predictions. On the other hand, the AI projects are based on developing software, hardware, and network solutions based on business needs.

Q10. Explain the 4Ws problem canvas in problem scoping.

Answer: The 4Ws helps to identify and understand the problem in a better manner. The 4Ws of Problem Scoping are Who, What, Where, and Why.

• Who – The “Who” element helps us to understand and categorize who is directly and indirectly affected by the problem, and who are known as Stakeholders.
• What – The “What” section aids us in analyzing and recognizing the nature of the problem, and you may also gather evidence to establish that the problem you’ve chosen exists under this block.
• Where – What is the situation, and where does the problem arise.
• Why – Refers to why we need to address the problem and what the advantages will be for the stakeholders once the problem is solved.

Q11. Why is there a need to use a Problem Statement Template during problem scoping?

Answer: The problem statement template is used to find the clarity and understanding of the problem. This template summarizes all of the important points in one place. So, if the same problem comes again, this statement will make it much easier to fix.

Q12. What is Problem Scoping? What are the steps of Problem Scoping?

Answer: To understand a problem, determine the different aspects that affect the problem, and define the project’s goal are problem scoping. To identify the problem, follow the following six methods to be used to address the problem:

• Identify the Topic – First you have to select the topic. For example, Topic: “How to improve quality education in ruler area”
• Brainstorm related problems – Brainstorming is a method for identifying and solving problems. It helps us come up with new ideas. To solve the problem, first we have to identify the problem related to quality education in rural areas.
• 4Ws Problem Canvas – You can use the 4Ws problem canvas to identify the problem. 
• Create a Mind Map – Now you have to create a mind map of the above brainstorm-related problem.
• Evaluate the prioritized problem – After the mind map, now you have to identify what the main problem is; for example, students are having problems in education due to limited internet connectivity and due to a lack of digital learning tools.
• Set the goal for the project – After identifying the problem, now you have a clear goal to address the above problem. Now you can create an offline AI-powered application based on the “Offline Learning Platform” that can provide quality education in rural areas and help the students to overcome the internet connectivity barriers.

Q13. Who are the stakeholders in the problem scoping stage?

Answer: Stakeholders are the people who face this problem and would be benefited with the solution.

Q14. How will you differentiate between Training Data and Testing Data? Elaborate with examples.

Answer: Data can be a piece of information or facts and statistics collected together for reference or analysis. For example, if you want to make an artificially intelligent system that can predict the salary of any employee based on his previous salaries, you would feed the data of his previous salaries into the machine. The previous salary data here is known as training data, while the next salary prediction data set is known as the testing data.

Q15. 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 various methods for collecting data are surveys, web scraping, sensors, cameras, observations, APIs, etc. The data collection is based on the project that we want to develop. For example, if the construction department wants to collect data from the customer satisfaction, then the best way to collect data is through surveys.

Q16. What must you keep in mind while collecting data, so it is useful?

Answer: While collecting data, we should ensure that:

• Data should be related to the project and goals.
• The data should be accurate and trustworthy sources to avoid errors.
• Data should cover all necessary aspects.
• Respect privacy laws while collecting personal or sensitive data.
• Try to collect structured data, which will be easy to analyze.
• Ensure the data is uniform.

Q17. 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: Refer to the notes “identify the problem using Brainstorming, 4Ws Problem canvas and mind map”. https://cbseskilleducation.com/ai-reflection-project-cycle-and-ethics-class-9-notes/

Q18. Name a few government websites from where you can get open-source data.

Answer: Some of the government websites where you can get open-source data are:

• National Data and Analytics Platform (NDAP)
• Open Government Data (OGD) Platform India
• National Government Services Portal

Q19. What is the significance of Data Exploration after you have acquired the data for the problem scoped? Explain with examples.

Answer: While acquiring data is a complex method, after acquiring the data, we need to make some sense out of it, and it is possible with data exploration. For example, if you go to the library and pick up a random book, you first try to go through its content quickly by turning pages and by reading the description before borrowing it for yourself because it helps you in understanding if the book is appropriate to your needs and interests or not. To analyze the data, you need to visualize it in some user-friendly format so that you can:

• Quickly get a sense of the trends, relationships, and patterns contained within the data.
• Define a 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.

Q20. What do you think is the relevance of Data Visualization in Al?

Answer: Data visualisation is important because it enables deeper understanding of complex data. Data visualisation represents the data through common graphs like charts, plots and infographics. Data visualisation helps to explain AI insights and allows humans to understand the complex hidden patterns of data.

Q21. List any five graphs used for data visualization.

Answer: The five graphs used in data visualisation are bar chart, line chart, scatter plot, tree diagram, pie chart, histogram, etc.

Q21. How is Data Exploration different from Data Acquisition?

Answer: Data acquisition involves collecting raw data from different sources, while data exploration is a method of analyzing, summarizing, and visualizing data to know the insights of the data, which helps to understand the characteristics of the data.

Q22. Use an example to explain at least one Data Visualization technique.

Answer: Data visualization is the graphical representation of information and data using graphs, charts, and maps. Suppose you have a monthly sales report for a retail store that you want to visualize.

The best way to represent this data is with a bar chart, where each bar represents a month, and in the bar chart, the height of the bar chart represents the sales amount. Bar charts provide a clear comparison between the categories, which helps to easily identify trends.

Q23. What are the various stages of the Al Project Cycle? Explain each with examples.

Answer: The various stages of the AI Project Cycle are:

• Stage 1: Problem Scoping: In problem scoping, we try to find the problem; we look at various parameters that affect the problem we wish to solve so that the picture becomes clearer.
• Stage 2: Data Acquisition: You need to acquire data, which will become the base of your project; data can be collected from various reliable and authentic sources.
• Stage 3: Data Exploration: The data you collect would be in large quantities; you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the patterns that your acquired data follows.
• Stage 4: Modeling: After exploration, you have to decide which type of model you would build to achieve the goal. For this, you can research online and select various models that give a suitable output.
• Stage 5: Evaluation: Once the modeling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and improving it.
• Stage 6: Deployment: Finally, after evaluation, the deployment stage is crucial for ensuring the successful integration and operation of AI solutions in real-world environments, enabling them to deliver value and impact to users and stakeholders.

Q24. What is Artificial Intelligence? Give an example where Al is used in day-to-day life.

Answer: Artificial intelligence refers to any technique that enables computers to mimic human intelligence. The AI-enabled machines think algorithmically and execute what they have been asked for intelligently. An example of AI in day-to-day life is virtual assistants like Alexa, Siri and Google Assistant. These tools understand voice commands.

Q25. How is Machine Learning related to Artificial Intelligence?

Answer: Artificial intelligence is just like an umbrella which covers machine learning and deep learning. Machine learning is a subset of artificial intelligence, which requires data and algorithms to perform any tasks. Machine learning focuses on enabling machines to learn and improve from experience without being explicitly programmed.

Q26. Compare and contrast Rule-based and Learning-based approach in Al modeling indicating clearly when each of these may be used.

Answer: The comparison between rule-based and learning-based is:

Q27. 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.

Q28. What are various Model evaluation techniques?

Answer: There can be different Evaluation techniques, depending of the type and purpose of the model.

• Accuracy
• Precision
• Recall
• F1 Score

Q29. Why is model evaluation important in AI projects?

Answer: The evaluation model is important in AI projects because:

• We test our modelsto check their performance and improve our models for best performance.
• The model is tested with collected data.
• We also check if the model is solving the identified AI problem properly

Q30. What do you understand by the terms True Positive and False Positive?

Answer: True Positive (TP) and False Positive (FP) are used in classification tasks to evaluate the statistics of machine learning. A true positive occurs when the model correctly identifies a positive instance. The false positive is used when the model incorrectly identifies a negative instance.

Q31. Differentiate between Ethics and Moral with suitable examples.

Answer: Difference between Ethics and Moral with suitable examples:

Q32. Define principles of AI.

Answer: The principle of AI Ethics.

• Human Rights: When building AI solutions, we need to ensure that they follow human rights.
• Bias: Bias (partiality or preference for one over the other) often comes from the collected data. The bias in training data also appears in the results.
• Privacy: We need to have rules which keep our individual and private data safe.
• Inclusion: AI MUST NOT discriminate against a particular group of population, causing them any kind of disadvantage.

Q33. Explain Data privacy.

Answer: Data privacy rules which keep individual and private data safe.

• Here are a few things that you should take care of
• Does your AI collect personal data from people?
• What does it do with the data?
• Does your AI let people know about the data that it is collecting for its use?
• Will your AI ensure a person’s safety? Or will it compromise it?
• What are some other ways in which AI can breach someone’s privacy?

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