Kips Class 9 AI Unit 1: Introduction to AI

Unit 1: Introduction to AI

Question and Answer
Question 1: Define AI.
Artificial Intelligence can be defined as the ability of computer systems (i.e. hardware and software) to do tasks that normally require human being to use intelligence.

Question 2: List the behaviours of human intelligence.

Behaviours connected to human intelligence:
1. Planning

2. Problem solving

3. Social intelligence

4. Learning

5. Motion

6. Knowledge representation

7. Reasoning

8. Manipulation
9. Perception
10. Creativity

Question 3: Explain the fields closely related to AI.
Fields closely related to AI:
1. Machine learning: Machine learning refers to the capability of the artificially created system to learn from experience i.e. by processing data.
2. Neural network: This method tries to mimic or stimulate the working of the human brain. The networks created by this method are capable of machine learning i.e. they improve with the usage.
3. Data science: Disciplines related to collecting, managing and analyzing data, where all AI systems are dependent on data. Areas where data science contributes:
Managing big data
Data acquisitions
Data modeling
Data analyze
Data curation
4. Robotics: Robots are computing machines that have both hardware and software components and robotics is the field which deals with construction and programming of robots. AI contributes towards the software component of robots which can behave and work like a human being.

Question 4: Explain various phases in development of artificial intelligence.
The phases in the development of artificial intelligence is divided into 4 distinct waves:
First and second wave
1. The first serious boost to the development of the artificial intelligence happened with the growth of the internet. This development boosted the generation and use of data at a rate that was never seen in the history of the world previously. This is known as the first wave of AI.
2. The second substantial boost to the development of AI happened with the advancement of mobile internet. This development shifted the focus from static (and often outdated) data to the real-time data. This brought a fundamental change in the manner in which AI systems were developed and operated. This is known as the second wave of AI.
Third wave
1. The third serious boost to the development of AI happened with the evolution of the ‘Internet of Things’. ‘Internet of Things’ calls for all the appliances and sensors in the world to become smarter, i.e. to be capable of communication. These interconnected and communicating devices increased the number of internet-connected users or devices from 10 billion in the second wave to 50 billion. This fivefold increase led to unprecedented jump in the availability of data due to the multiplier effect. This change in the working and functioning of artificial systems is known as the third wave.
Fourth wave
1. Many scientists and philosophers consider this as the next coming wave. In this wave, true AI will emerge. This artificial intelligence will surpass what human beings are capable of doing today.

Question 5: Explain various types of artificial intelligence.
At a broad level, there are two types of artificial intelligence:
1. Artificial Narrow Intelligence:
An existing type of AI which is capable is performing simple tasks requiring basic intelligence. The intelligence of these systems is narrow in the sense that they are only capable of performing one task, but in many cases, they can perform this one task better than human beings.
2. Artificial General Intelligence:
Artificial General Intelligence refers to the science fiction idea of artificial intelligence i.e. the computer systems, which are at least as intelligent as human beings, if not more. These systems will be capable of learning and developing themselves. They will not be limited by the tasks which they can perform. These systems are at the research stage, and much breakthrough has not been regarding them.

Question 6: What can artificial narrow intelligence do?
Artificial narrow intelligence is capable of undertaking number of actions which includes:
1. Processing videos for understanding them. For example, recognizing when an intruder enters a house or when small children are in danger in the house.
2. Performing routine of everyday tasks which might require some limited decision making. For example, ordering milk, switching off the power in empty rooms, switching on air conditioner when needed.
3. Performing simple tasks done by secretaries. For example, taking dictations, scheduling appointments, taking minutes of meetings, reading emails etc.
4. Performing tasks that require processing data. For example, assisting doctors in diagnosing diseases based on symptoms.

Question 7: Differentiate between weak AI and strong AI.

1. Weak AI: It is the intelligence that mimics intelligence but doesn’t process it or in other words, acts intelligently without having any intelligence. All of the AI systems in existence today fall under this category. For example, a self-driving car might recognize that it is raining, but it will not understand what rain is. A smart home security system can recognize intruders without understanding what intruder is.
2. Strong AI: Strong AI systems are theoretical systems that actually process intelligence. These systems currently are in the research stage.

Question 8: Brief history of AI.

Question 9: Define AI ethics.

1. We can define ‘ethics’ as a system of moral principles that govern individual’s behaviour or actions. Ethics are concerned with what is suitable for individuals and societies. Similarly, ethical concerns are the issues, situations, or concerns that cause individuals, societies and/or organisations to evaluate different choices in terms of what is right (ethical) and what is wrong (unethical).
2. The term ‘AI ethics’ is used to deal with all the ethical concerns and issues related to AI systems. AI ethics are normally divided into two categories:
Concerns related to data (BIAS and inclusions) used in the AI systems
The concerns related to implications of the AI technology itself
3. For example, AI-control nuclear missiles capable of making independent decisions or AI equipped armed military drones. On the other end, we have considerations regarding human cost of adoption for AI technology. And then, there is a third side of the concerns that is related to the data used in the AI systems, and the manner in which AI makes decisions. For example, can AI system make unbiased decisions when human data on which the system is trained always have biases?

Question 10: Explain real life applications of AI.

1. Games: AI systems help make the games more interesting by not limiting it to pre-programmed interactions. AI systems learn from their experience and in turn, provide better gaming experience to the players.
2. Entertaining AI: The entertainment industry is experimenting with AI to produce original music, books, recipes etc. Companies like Netflix and Spotify use AI to predict what the user would like to watch or listen.
3. Natural language processing: The development of AI technology helps in advancing the natural language processing (NLP) capabilities of computer systems i.e. their ability to understand the natural languages spoken by human beings.
4. Decision making: The capability of AI systems to predict or make forecasts relating to the future is helping the organisations in making better decision making, for example, diagnosing patients.
5. Vision systems: AI systems are helping in many real situations by interpreting and understanding visual data. For example, identifying intrusions in restricted areas, identifying fugitives, smart nannies for protecting children etc.
6. Speech recognition: In this area, the AI systems are allowing computers and robots to communicate in a natural language without using heavy syntax.
7. Handwriting recognition: The AI helps in converting the handwritten text into computer editable text. This has dramatically helped in preserving and restoration of ancient texts and documents. This has also allowed users to interact with their systems by using handwritten commands/ interactions with the help of a stylus.
8. Intelligent robots: AI systems are making robots more intelligent and providing them with the ability to learn and adapt.
9. Commercial establishments: Commercial establishments and big business along with the government, are the driver for artificial intelligence development. Adopting AI-based solution allows businesses to cut both cost, time, and provide better services and products to the customers. Leading companies in every field are using AI solution to stay ahead of the competition.
10. Life saving AI: The healthcare industry is using AI to improve the quality of life and save it. The developments in this field include personalized drug treatment based on 24 x 7 monitoring using various sensors. Robot-assisted surgeries improve diagnosis. In the field of disaster management, these techniques are helping in both reducing the impact of disasters and managing the post-disaster rescue.

Question 11: What AI systems can do? What AI systems can’t do (limitations of AI)?

What AI systems can do:
1. Recognizing faces in images and videos
2. Increasing automation of vehicles
3. Making simple machine translations
4. Help in decision making
5. Converting thousands of spoken words into text.
6. Categorizing text, images and videos.
7. Beating best of the human players in strategy games.

What AI systems can’t do:
1. Learn a natural language
2. Reading and understanding articles and books
3. Understand the implications of decisions
4. Interpreting visual scenes
5. Exhibiting autonomy and intelligence

Question 12: Explain the domains of AI.
The three domains of AI are:
1. Data
Every AI system - irrespective of its function, nature or capability - is heavily dependent on data for its functioning or in other words data is at the core of every AI system. Both of the other AI domains also need data for their functioning. Data is also at the core of the General AI systems, as these systems will have the capability of processing data for learning and growing.
Data for Artificial Intelligent System mean data that is relevant for the AI system in question. For example, an AI-based automatic student attendance system for school will not be helped by the data of images of world leaders. Different type of data serves different purposes in Artificial Intelligent Systems.

The relation between data type and its usage in AI systems

Audio data

Video or image data

Textual data

Big data

1. Voice recognition
2. Natural Language Processing

1. Computer Vision

1.Classification systems
2. Natural Language Learning
3. Decision making

1. Predictions
2. Insights
3. Forecasts
4. Decision making

2. Computer Vision
This domain of Artificial Intelligence is working towards the development of AI systems, which will be able to perceive the human worlds as human beings do. There has been sustainable development in this domain, and this technology is currently being used in number of AI-based systems.

Face recognition

Identifying faces in images and videos

1. Applications like Google Photos, Snapchat etc.
2. Social media networks like Facebook, Instagram etc.
3. Law enforcement agents like Interpol, FBI etc.


Content based Image Retrieval: Identifying images based on their composition, color, texture etc.

1. Search engines like Google and Bing
2. Medical Inage Databases of CT, MRI etc.
3. Scientific Databases like Earth Sciences

Smart Interactions

One more way of providing inputs to the computer systems

1. Gaming systems like Microsoft Kinect
2. Games like Emoji Scavenger Hunt
3. Systems for differently abled individuals

Environment Perception

Analyzing videos, images or video feeds for identifying patterns and perceiving environment

1. Law enforcement agencies for identifying illegal/suspicious behaviour
2. Home Security Systems
3. Office Security Systems
4. Drone Based Surveillance Systems
5. Smart Vehicles

3. Natural Language Processing (NLP)

This domain of AI is working towards the creation of artificial intelligence systems which will be capable of communicating with human beings using natural language rather than by syntax or by identification of keywords. This domain is working for the development of both oral and spoken languages.
Components of NLP:
1. Natural Language Understanding: For understanding spoken or written language which includes:
→ Establishing linkage with natural language inputs and what they represent
→ Analyzing different aspects of the language

2. Natural Language Generation: For producing meaningful phrases and sentences in the form of natural language which involves:
→ Text planning: Retrieving relevant text from the data stores
→ Sentence Planning: Deciding on the correct words, linking them into meaningful phrases, etc.
→ Text realization: Combining phases and words for forming sentences.

Question 13: List contribution of AI in various fields.

1. Computer Science
2. Computer Engineering
3. Mathematics
4. Linguistics
6. Psychology
7. Biology
8. Philosophy

Question 14: Explain six dimensions of AI.
There are six dimensions of AI:
1. Automation
In pre-AI era, automation was limited to hardware-based robotic automation. The advent of AI changed this and guided the automation that uses data for learning and discoveries. This created solutions and products which improved over a period of time.

2. Augmentation
The development of new AI-based products is rare. Organizations across the globe prefer to use AI technology for augmenting their existing services, solutions and products.

3. Adaptation
AI systems don’t execute programmed instructions. In fact, these systems adapt or program themselves by learning from the data fed to them. This led to the creation of products and services which keep on adapting themselves to serve users better.

4. Analysis
Some AI data models like neural networks use multiple layers for processing data. These insights gained from the data becomes more in-depth with the addition of more layers. This in turn increases the understanding and leads to better predictions.

5. Accuracy
The accuracy of AI systems is more for tasks that require insights, decision making or analyse a large amount of data.

6. Acquisition
Organizations across the world have collected massive amounts of data. This data already has solution for many problems and situations. The AI systems can look solutions in this data.


  1. Helped me alot thanks 🙏

  2. keep posting these notes even when the offline school starts. I will be kinda help full


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