AI for Everyone Class 11 NCERT Solutions

AI for Everyone Class 11 NCERT Solutions – The CBSE has updated the syllabus for St. XI (Code 843). The NCERT Solutions are made based on the updated CBSE textbook. All the important information is taken from the Artificial Intelligence Class XI Textbook Based on the CBSE Board Pattern.

AI for Everyone Class 11 NCERT Solutions

A. Multiple-choice questions (MCQs):

1. Who is often referred to as the “Father of AI”?
a. Alan Turing
b. John McCarthy
c. Marvin Minsky
d. Herbert A. Simon

2. In which year was the term “Artificial Intelligence” first used by John McCarthy?
a. 1930
b. 1955
c. 1970
d. 2000

3. What does the term “Data is the new oil” imply?
a. Data is as valuable as oil.
b. Data is used as fuel for machines.
c. Data is a non-renewable resource.
d. Data and oil are unrelated.

4. Divya was learning neural networks. She understood that there were three layers in a neural network. Help her identify the layer that does processing in the neural network.
a. Output layer
b. Hidden layer
c. Input layer
d. Data layer

5. Which category of machine learning occurs in the presence of a supervisor or teacher?
a. Unsupervised Learning
b. Reinforcement Learning
c. Supervised Learning
d. Deep Learning

6. What does Deep Learning primarily rely on to mimic the human brain?
a. Traditional Programming
b. Artificial Neural Networks
c. Machine Learning Algorithms
d. Random Decision Making

7. What is the role of reinforcement learning in machine learning?
a. Creating rules automatically
b. Recognizing patterns in untagged data
c. Rewarding desired behaviors and/or penalizing undesirable ones
d. Mimicking human conversation through voice or text

8. Which AI application is responsible for automatically separating emails into “Spam” and “Not Spam” categories?
a. Gmail
b. YouTube
c. Flipkart
d. Watson

B. Fill in the Blanks:

1. To determine if a machine or application is AI-based, consider its ability to perform tasks that typically require ___ intelligence.

2. Artificial intelligence (AI) enables a machine to carry out cognitive tasks typically performed by __.

3. Supervised, unsupervised, and reinforcement learning are three categories of __.

4. ____ is a subset of artificial intelligence that is entirely based on artificial neural networks.

5. Machine learning can be used for online fraud detection to make cyberspace a __ place.

C. True or False:

1. Chatbots like Alexa and Siri are examples of virtual assistants.

2. Supervised learning involves training a computer system without labeled input data.

3. Unstructured data can be easily analyzed using traditional relational database techniques.

4. Deep learning typically requires less time to train compared to machine learning.

5. Machine learning is not used in everyday applications like virtual personal assistants and fraud detection.

D. Short Answer Questions:

1. How is machine learning related to AI?

Answer: Artificial Intelligence is a field that combines computer science and robust datasets to enable problem-solving. AI does not replace human decisions; instead, AI adds value to human judgment. Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

2. Define Data. List the types of data.

Answer: Data might be facts, statistics, opinions, or any kind of content that is recorded in some format. This could include voices, photos, names, and even dance moves! It surrounds us and shapes our experiences, decisions, and interactions.

Data is categorized in three types.

• Structured Data: Structured data is like a neatly arranged table, with rows and columns that make it easy to understand and work with. It includes information such as names, dates, addresses, and stock prices.
• Unstructured Data: Unstructured data lacks any specific organization, making it more challenging to analyze compared to structured data. Examples of unstructured data include images, text documents, customer comments, and song lyrics.
• Semi-structured Data: Semi-structured data falls somewhere between structured and unstructured data. While not as organized as structured data, it is easier to handle than unstructured data. Semi-structured data uses metadata to identify certain characteristics and organize data into fields, allowing for some level of organization and analysis. An example of semi structured data is a social media video.

3. Define machine learning.

Answer: Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

4. What is deep learning, and how does it differ from traditional machine learning?

Answer: Deep learning is a type of machine learning that imitates the working of the human brain in processing data and creating patterns for use in decision-making. Deep learning uses a complex neural network with many layers, while traditional machine learning works on one or two layers. Deep learning requires large amounts of data, but traditional machine learning can work on small sets of data. Deep learning can handle complex tasks like natural language processing, image recognition, and speech recognition, but traditional machine learning can handle simple tasks like recognizing patterns and making predictions.

5. What do you mean by Reinforcement Learning? Write any two applications of Reinforcement Learning at School.

Answer: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. The two applications of reinforcement learning are

• Personalized Learning System: The personalized learning system helps the students to improve their understanding of subjects, which will help to enhance overall academic performance.
• Automated Tutoring System: This system provides personalized guidance and support to the students like a human tutor. An automated tutoring system can improve problem-solving skills and can take feedback.

6. How do you understand whether a machine/application is AI based or not? Explain with the help of an example.

Answer: Any machine that has been trained with data and can make decisions/predictions on its own can be termed as AI. For example, the bot or the automation machine is not trained with any data; it is not an AI, while a chatbot that understands, and processes human language is an AI.

E. Case-study/Application Oriented Questions:

1. A hospital implemented an AI system to assist doctors in diagnosing diseases based on medical images such as X-rays and MRI scans. However, some patients expressed concerns about the accuracy and reliability of the AI diagnoses. How can the hospital address these concerns?

Answer: To diagnose disease based on medical images using AI for accuracy and reliability, the hospital has to take care of the following steps:

Transparency and communication

1. Educate patients: Patients have a clear knowledge about the limitation of AI, how the AI will help the doctor regarding diagnostic images such as X-ray and MRI scans.
2. Disclosure: Patients have rights to know when AI is used in their diagnosis, and AI-generated reports should be given to patients.
3. Physician oversight: When AI diagnoses any image or report, then it is a prime responsibility of the doctor to review and validate the report.

AI System Validation

1. Testing and validation: Regularly testing and validation can be done on AI performance using diverse datasets and peer-reviewed studies.
2. Continuous monitoring: Continuous monitoring and updating are required. Track AI performance and update algorithms to maintain accuracy.

Collaboration and Human Oversight

1. Hybrid approach: Combine AI diagnoses with doctor experience for finding the accuracy of AI.
2. Collaboration with doctor: Encourage the doctor to review and correct the report if any error is found.
3. Multi-disciplinary terms: Include clinicians, radiologists, and AI experts in diagnosis validation.

Patient Involvement

1. Patient engagements: Encourage patients to ask questions and provide feedback.
2. Patient Education: Educate patients about the benefits and limitations of AI in healthcare.
3. Counseling and support: Offer counseling and support to patients who have concerns about AI diagnoses.

Addressing Bias and Error

• Bias detection: Implement algorithms to find the potential biases in healthcare.
• Error correction: Establish procedures for correcting AI-generated errors.

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