AI for Everyone Introduction Class 11 Questions and Answers

These Class 11 AI Important Questions Chapter 1 AI for Everyone Introduction Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

AI for Everyone Introduction Class 11 Important Questions

Class 11 AI for Everyone Introduction Important Questions

Important Questions of AI for Everyone Introduction Class 11 – Class 11 AI for Everyone Introduction Important Questions

AI for Everyone Introduction Class 11 Very Short Answer Type Questions

Question 1.
What is an artificial intelligence?
Answer:
Artificial intelligence is the branch of computer science that emphasizes on making machine’s (computer system’s) intelligent, in order to make them think and act like human beings.

Question 2.
Who is the father of artificial intelligence.
Answer:
John McCarthy is known as the father of artificial intelligence as he first coined the term ‘Artificial Intelligence’.

Question 3.
When McCarthy coined the term artificial intelligence?
Answer:
1956

Question 4.
What are the types of AI?
Answer:
There are three types of AI-

• Artificial Narrow Intelligence
• Artificial General Intelligence
• Artificial Super Intelligence

Question 5.
What are the domains of AI?
Answer:
Artificial Intelligence has the following sub-domains.

• Machine Lęarning
• Neural Network
• Computer Vision
• Robotics
• Expert Systems
• Natural Language Processing
• Fuzzy logic Systems

Question 6.
What is Data?
Answer:
Data is the collection of raw facts and figures.

Question 7.
Why Data is important in AI?
Answer:
The whole concept of Artificial Intelligence is roaming around Data. Using data only machines became Intelligent.

Question 8.
How Machine Learning works?
Answer:
Machine learning works on the basis of previous database experience available with it.

Question 9.
What are the categories of ML?
Answer:
ML is divided into three categories-Supervised, Unsupervised and Reinforcement learning.

Question 10.
What is neural network?
Answer:
Neural network is a technique to build computer program that learns from the data. It is the layers of software units called neurons. (or nodes ) Connected with different nodes (neurons) in a layered manner.

Question 11.
Write the structure of neural network?
Answer:
The entire neural network is divided into three separate nodes or layers known as input, hidden and output.

Question 12.
Is deep learning is the imitation of the Human brain?
Answer:
Yes

Question 13.
How is machine learning related to AI ?
Answer:
Machine learning enables machines to learn, forecast, and improve on their own, contributing to the broader field of AI.

Question 14.
How do you understand whether a machine/application is AI based or not? Explain with the help of an example. [CBSE TextBook]
Answer:
To understand whether a machine/application is AI based or not, we need to check if it learns with data and whether it’s able to decide/predict.

AI for Everyone Introduction Class 11 Short Answer Type Questions

Question 1.
Why artificial intelligence is the need of the hour?
Answer:
These days AI is the inevitable part of everyone’s life as:

• It can solve many real life problems i.e. health related issues, transport issues.
• Various virtual assistances made with the help of AI such as Cortona, Google assistant, Siri can help challenged people also.
• AI can work in an environment that is very difficult or dangerous for human beings.
• Through AI India is able to land on the Moon.

Question 2.
What type of data is used in making machines Artificially Intelligent?
Answer:
Artificial intelligence (AI) systems use various types of data to make machines artificially intelligent. The choice of data depends on the specific AI task and the domain it operates in. Here are some common types of data used in AI are:

• Information
• Graphs
• Facts
• Numbers
• Figures
• Quantities etc.

Question 3.
What is Machine Learning?
Answer:
A branch of artificial intelligence called machine learning (ML) focuses on creating statistical models and algorithms that let computers learn from data and experience to perform better on a given task without needing to be explicitly programmed.

Question 4.
Is there any difference between machine learning and artificial intelligence?
Answer:
Machine learning is considered as a tool for achieving artificial intelligence. If AI is a technology to create intelligence machine then machine learning provides machines the ability to learn forecast and progress on their own without any specific program.

Question 5.
Write some examples of Machine learning algorithm in our daily-life.
Answer:
In practice, machine learning algorithms are used to predict stock prices, identify spam emails, and project class attendance. These outcomes can be attained by utilising previously gathered spam messages, historical price records, or five or more years’ worth of class attendance data.

Question 6.
What are the jobs you can get with the help of AI?
Answer:
With the knowledge of AI you can get:

• Creative job
• Management job
• Tech job like data detective
• Man-machine teaming manager
• AI business development manager
• AI assisted medical professional (this I am sure, will appear before 2030)
• AI tutor

Question 7.
What ML cannot do?
Answer:
Although ML has made many advancement in the recent years still it has some issues:

• It takes time to solve social causes related problems
• According to text analytics, Ambiguity and Variability problem is associated with ML,there are many challenges that the same word can have many meanings. Second there are many ways to say the same word also.
• Also ML cannot solve ethical issues for instances a person’s death is caused by a self driving car on the road then it is difficult to find out whose fault is it?

Question 8.
What is deep learning, and how does it differ from traditional machine learning?
Ans.
Deep learning, a branch of machine learning that relies heavily on high-end computing equipment and can tackle end-to-end issues, is built solely on artificial neural networks.

Question 9.
What do you mean by Reinforcement Learning? Write any two applications of Reinforcement Learning at School.
Answer:
Through interaction with the environment and feedback in the form of rewards or penalties, an agent can learn to make decisions through a sort of machine learning called reinforcement learning. Adaptive learning systems, which customise content, and educational games and simulations, which include students in interactive learning, are two examples of applications used in schools.

AI for Everyone Introduction Class 11 Long Answer Type Questions

Question 1.
Briefly explain all categories of Machine learning algorithms.
Answer:
Supervised Learning:

• In supervised learning, the algorithm is trained on a labeled dataset, means each input data point is paired with the corresponding correct output or target.
• The goal is to learn a mapping from inputs to outputs, allowing the algorithm to make predictions or classifications on new, unseen data.

Unsupervised Learning:

• Unsupervised learning deals with unlabeled data, means the algorithm tries to find hidden patterns, structures, or clusters within the data.
• Mostly used for tasks like clustering (grouping similar data points together) and dimensionality reduction (reducing the number of features while preserving important information).

Reinforcement Learning:

• Reinforcement learning is concerned with training agents to make a sequence of decisions in an environment to maximize a cumulative reward.
• The agent interacts with the environment and learns by receiving feedback in the form of rewards or penalties for its actions.
• Applications of reinforcement learning include game playing (e.g., AlphaGo).

Question 2.
What’s the difference between traditional programming approach and Machine learning approach?
Answer:

Question 3.
How Machine learning approach is different from traditional approach, explain with an example.
Answer:
Traditional approach steps to convert Celcius scale to Fahrenheit scale
Step -1: Take input deg_celcius
Step-2: Apply the conversion formula: deg_Fahrenheit = deg_Celcius * 1.8+32
Step -3: Print the Output deg_fahrenheit
Note: we are telling the computer what to do on the input data i.e. multiply deg_Celcius with 1.8
and then add 32 to obtain the value in deg_Fahrenheit.
Machine Learning approach to convert deg celcius to deg_fahrenheit
Step 1: Feed or input many values in deg_Celcius (i.e. -40,-10,0,8,15,22,38 )
Step -2: Feed or input corresponding deg_Fahrenheit values (i.e. -40,14,32,46,59,72,10 Q )
Step -3: Pass these 2 sets of values to Machine Learning (ML) algorithm
Step- 4: Now you ask the ML program to predict (convert) any other celcius value to Fahrenheit, and program will tell you the answer.
For example, ask the computer to predict (convert) 200 Celcius to Fahrenheit, and you will get the answer as 392.

Note: in the ML approach, nowhere this conversion or calculation step (F=C^* 1.8+32) has been mentioned. Only the code was provided with the input data deg_celcius and corresponding output data (deg fahrenheit) and the model (i.e. ML code) will automatically generates the relationship between Celsius and Fahrenheit.

Question 4.
What is Deep Learning? How it is related to Neural networks?
Answer:
A branch of machine learning known as “deep learning” is centred on multi-layered artificial neural networks, or “deep neural networks.” It is a branch of machine learning that has seen great success and attention in a number of areas, such as speech recognition, computer vision, and natural language processing. Deep learning is therefore also a form of brain mimicking. We don’t have to explicitly programme anything in deep learning.

Competency-Based Questions

Question 1.
Read the following Information and answer the questions based on the information.
For instance, suppose the machine is given an image having both dogs and cats which it has not seen before. Logically the machine has no idea about the physical characteristics of dogs and cats and therefore it cannot categorize the animals. But it can surely categorize them according to their similarities, patterns, and differences i.e. we can easily categorize this above picture into two parts. First category may contain all pictures having dogs in it and second category may contain all pictures having cats in it. Here you didn’t learn anything before, means no training data or examples were provided for prior training.

Here in the above paragraph, we are discussing about:
a) Supervised Learning
b) Unsupervised Learning
c) Reinforcement Learning
d) Deep Learning
Answer:
(b) Unsupervised Learning

Question 2.
Read the following example and answer the question below it.
Suppose there is an Al agent presert within a maze environment, and his goal is to find the diamond. The agent interacts with the environment by performing some actions, and based on those actions, the state of the agent gets changed, and it also receives a reward or penalty as feedback.
The agent continues doing these three things (take action, change state/remain in the same state, and get feedback) and by doing these actions, he learns and explores the environment.

a) Neural Network
c)Deep Learning
b) Reinforcement Learning
d) Unsupervised Learning
Answer:
b) Reinforcement Learning

Question 3.
A hospital implemented an AI system to assist doctors in diagnosing diseases based on medical images such as X-rays and MRI scAnswer: However, some patients expressed concerns about the accuracy and reliability of the AI diagnoses. How can the hospital address these concerns?
Answer:
By carrying out extensive validation studies to evaluate the precision and dependability of the AI system in comparison to human diagnosis, the hospital can allay these worries. Additionally, they can guarantee transparency by giving thorough explanations of the AI system’s operation and how it enhances the knowledge of medical professionals.

Question 4
Rahul is an architect. He has designed and built a beautiful home for his client in Pune. He has installed these systems/appliances/gadgets at the newly constructed home. Identify which of these are AI systems and which of these are not AI systems. Solar water heater, Smart TV, Security cameras, rainwater harvesting system, cleaning robots, smart lighting, automatic door, Siri, automatic washing machine. After separating the AI systems, mention some parameters on which you choose these appliances/systems as AI systems.
Answer:
Siri, smart lights, cleaning robots, and smart TVs are examples of AI systems. The following systems do not use artificial intelligence: automatic doors, solar water heaters, security cameras, rainwater harvesting systems, and automatic washing machines. The criteria are: decision-making, problem-solving, suggestion-making, situational adaptation, and experiencebased learning. You can add any additional AI feature.

Question 5
If you were designing a robot to sort recyclable items like glass, plastic, and paper, which type of learning would be used to help the robot.
Answer:
Supervised learning

Question 6.
Can you think of a scenario where you have a bunch of different fruits mixed together and you want the computer to organize them into groups based on similarities?
Answer:
Unsupervised learning

Question 7.
Mr. Shankar owns a company that deals with services to customers related to financial investments. Lately, he has been using AI technology in his company due to which his employees are facing less job responsibility, customers are feeling insecure about their data. What is this scenario known as?
Answer:
Potential impact of AI on society

Question 8.
Jatin is a student who has just enrolled in a course in AI. He attended a few introductory classes and learned that systems can learn from the data using algorithms to perform a task without explicitly programming it. In some situations, the system mimics the human brain’s learning process. Identify the concepts in this scenario. [CBSE TextBook]
Answer:
Machine learning and Deep learning

Scenario Based Question Answer

Scenario 1: Understanding Neural Networks

Scenario: Alok is taking a course on artificial intelligence and is learning about neural networks. During a class discussion, the professor asks Alex to describe how neural networks function.

Question:
How should Alok describe a neural network in terms of its structure and function?
a) A linear sequence of commands
b) A single computational unit
c) A network of interconnected neurons
d) A random collection of algorithms
Answer:
c) A network of interconnected neurons

Scenario 2: Identifying AI Systems

Scenario: Priya is a software developer tasked with evaluating new software to determine if it incorporates artificial intelligence. She is looking for specific characteristics that indicate AI capabilities.

Question:
What characteristic should Priya look for to determine if the software is AI-based?
a) Ability to perform repetitive tasks quickly
b) Ability to perform tasks that typically require human-like intelligence
c) Ability to store large amounts of data d) Ability to connect to the internet
Answer:
b) Ability to perform tasks that typically require human-like intelligence

Scenario 3: Cognitive Tasks and AI

Scenario: During a technology seminar, a speaker explains how AI can transform industries by performing tasks that usually require human intelligence.

Question:
According to the speaker, what types of tasks does AI enable machines to carry out?
a) Physical tasks typically performed by robots
b) Cognitive tasks typically performed by humans
c) Mechanical tasks typically performed by machines
d) Emotional tasks typically performed by therapists
Answer:
b) Cognitive tasks typically performed by humans

Scenario 4: Categories of Machine Learning

Scenario: Maria is studying machine learning and comes across a section that categorizes different learning techniques used in AI applications.

Question:
What are the three categories of learning techniques Maria learns about in her studies?
a) Supervised, unsupervised, and collaborative learning
b) Supervised, unsupervised, and reinforcement learning
c) Supervised, unsupervised, and competitive learning
d) Supervised, unsupervised, and associative learning
Answer:
b) Supervised, unsupervised, and reinforcement learning

Scenario 5: Deep Learning and AI
Scenario: A tech startup is focusing on implementing cutting-edge AI technologies. The CTO mentions a specific subset of AI that relies heavily on artificial neural networks to achieve its advanced capabilities.

Question:
What subset of AI is the CTO referring to?
a) Supervised Learning
b) Deep Learning
c) Data Mining
d) Natural Language Processing
Answer:
b) Deep Learning

Scenario 6: Machine Learning for Cybersecurity

Scenario: An online retail company is enhancing its cybersecurity measures by using advanced technologies to detect and prevent fraud.

Question:
Which technology is the company using to make cyberspace more secure and detect online fraud?
a) Blockchain
b) Machine Learning
c) Cloud Computing
d) Internet of Things (IoT)
Answer:
b) Machine Learning

AI for Everyone Introduction Class 11 Notes

Artificial Intelligence (AI) Definition:

AI refers to the ability of a machine to learn patterns and make predictions. It combines computer science and robust datasets to enable problem-solving without replacing human decisions but adding value to human judgment.

Examples of AI Applications:

• Understand Language: Virtual assistants like Siri or Alexa.
• Recognize Images: Identifying animals in photos.
• Make Predictions: Predicting the weather or movie recommendations.
• Play Games: AI playing chess or video games.
• Drive Cars: Autonomous vehicles sensing the road and making decisions.

Non-AI Examples:

• Traditional Rule-Based Systems: Follow set rules without learning from data.
• Simple Automation Tools: Timers or calculators that do not think or learn.
• Mechanical Devices: Pulleys or gears that do not learn or think.
• Fixed-Function Hardware: Microwave ovens that perform tasks without learning.
• Non-Interactive Systems: Basic electric fans that do not change based on new information.
• Basic Sensors: Collect data but do not analyze or understand it.

Evolution of AI:

• 1950: Alan Turing’s paper “Computing Machinery and Intelligence” proposed the “imitation game” (Turing test).
• 1956: Dartmouth Conference organized by John McCarthy, marking the birth of AI as a field.
• 1960-1970: Development of expert systems, early neural networks, and symbolic reasoning.
• 1980-1990: Breakthroughs in machine learning and neural networks, leading to “AI winter.”
• 21st Century: Advancements in computing power, data availability, and algorithmic innovation leading to transformative AI applications.

Types of AI:

• Narrow AI: Focuses on single tasks (e.g., virtual assistants) and lacks broader understanding.
• Broad AI: Handles a wider range of related tasks, often used in businesses.
• General AI: Performs any intellectual task a human can, though current AI lacks abstract thinking, strategizing, and creativity.

Domains of AI:

• Data Science: Deals with numerical, alphabetical, and alphanumeric data for extracting insights using statistical methods, machine learning, and data visualization.
• Natural Language Processing (NLP): Processes text and speech inputs for tasks like language translation, sentiment analysis, and speech recognition.
• Computer Vision: Interprets visual data inputs (images and videos) for tasks like object detection, image classification, and facial recognition.

Data Types:

• Structured Data: Neatly arranged in rows and columns, easy to analyze (e.g., names, dates).
• Unstructured Data: Lacks specific organization, challenging to analyze (e.g., images, text documents).
• Semi-Structured Data: Uses metadata for some level of organization (e.g., social media videos with hashtags).

Cognitive Computing:

Mimics human brain functions for processing information and making decisions using Machine Learning, Reasoning, NLP, and Computer Vision.

AI Terminologies:

• Machine Learning: Developing algorithms that enable computers to learn from data and make predictions without explicit programming.
• Deep Learning: AI function imitating the human brain’s data processing for creating decision-making patterns. Uses neural networks with multiple layers for feature extraction and recognition.
• Neural Networks (Artificial Neural Networks – ANNs): Core of Machine Learning with node layers (input, hidden, output) for data processing.

Types of Machine Learning:

• Supervised Learning: Learning from labeled data to map input to output.
• Unsupervised Learning: Finding hidden patterns in unlabeled data.
• Reinforcement Learning: Learning to make decisions by interacting with an environment to maximize rewards.

Benefits of AI:

• Increased efficiency and productivity.
• Improved decision-making.
• Enhanced innovation and creativity.
• Progress in science and healthcare.

Limitations of AI:

• Job displacement concerns.
• Ethical considerations like bias and misuse.
• Lack of explainability in complex models.
• Data privacy and security vulnerabilities.

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