Kips Class 9 AI Unit 1: Introduction to AI

Unit 1: Introduction to AI

Question 1: Define the term AI.
AI is a technique that facilitates a machine to perform all cognitive functions such as perceiving, learning, and reasoning that are performed by humans.

Question 2: Write the brief history of AI.

1950: Alan Turing “Computing machinery and intelligence”, asks “Can machines think?”
1956: “Artificial Intelligence” coined by John McCarthy at Dartmouth College
1959: Artificial Intelligence Laboratory founded at MIT
1974: First AI winter; funding and interest evaporate
1975: MYCIN, a system that diagnoses bacterial infections and recommends antibiotics, is developed
1987: Second AI winter begins
1989: NASAs AutoClass program used to discover new classes of starts
1994: First web search engines launched
1997: IBM’s Deep Blue beats world champion Garry Kasparov at chess
2002: Amazon replaces human editors with an automated system
2011: Apple releases Siri, a personal voice agent
2016: Google’s AlphaGo defeats Lee Sedol; one of the world’s leading Go players

Question 3: List the behaviours of human intelligence.
The behaviours of human intelligence are Planning, Learning, Reasoning, Perception, Problem Solving, Motion, Manipulation, Creativity, Social intelligence, Knowledge representation

Question 4: Explain the fields closely related to AI.
The fields closely related to AI are
• Machine Learning: It refers to the capability of the artificially created system to learn from experience i.e. by processing data. For example, the process by which a natural language translation program improves itself when used more and more. This is one of the subfields of AI.
• Neural Networks: These are one of the techniques or methods of modelling data within the discipline of AI. This method tries to mimic or stimulates the working of the human brain. The networks created by this method are capable of machine learning i.e. they improve with the usage.
• Data Science: A discipline related to collecting, managing and analysing data where all AI systems are dependent on data. There are number of areas in AI in which data science contributes and these include: Managing Big data, data acquisitions, data modelling, data analysis, data curation.
• Robotics: Robots are computing machines that have both hardware and software components and robotics is the field that deals with construction and programming of robots. 

Question 5: Explain the phases in the development of Artificial Intelligence.
There are 4 phases in the development of AI.
• First Wave
The first serious boost to the development of AI happened with the growth of AI. This development boosted the generation and the use of data at a rate that was never seen in the history of the world previously.
• Second Wave
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) to real-time data.
• Third Wave
The third serious boost to the development of AI happened with the evolution of IoT (Internet of Things). The IoT 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 second wave to 50 billion in third wave. This fivefold increase led to an unprecedented jump in availability of data due to the multiplier effect.
• Fourth Wave
In this wave, true artificial intelligence will emerge. This artificial intelligence will surpass what human beings are capable of doing today.

Question 6: Define ANI and AGI.

• ANI (Artificial Narrow Intelligence): An existing type of AI that is capable of performing simple tasks that require basic intelligence. The intelligence of these systems is narrow in the sense that they are only capable of performing only one task, but in many cases they can perform one task better than human beings.
• AGI (Artificial General Intelligence): It 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 are capable of learning and developing themselves. 

Question 7: Explain with examples what ANI can do.
ANI can do the following tasks:
• Processing videos for understanding them. For example, recognising when an intruder enters the house or when the small children are in danger.
• Performing routine of everyday task. For example, switching on AC when needed, ordering milk.
• Performing simple tasks done by secretaries. For example, scheduling meetings, booking appointments.
• Performing tasks that require processing of data. For example, assisting doctors in diagnosing diseases based on symptoms.

Question 8: List the difference between Weak AI and Strong AI.

• Weak AI: It is the intelligence which mimics intelligence, without processing it or in order words, acts intelligently without having any intelligence. All current AI systems are Weak AI.
• Strong AI: They are theoretical systems that actually process intelligence. These systems are currently in research stage.

Question 9: List and explain some of the real life usage of AI.

• Gaming: 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.
• Decision Making: The capability of the AI systems to predict or make forecasts relating to future helps the organisations in making better decisions. For example, diagnosing patients.
• Speech Recognition: In this area, the AI systems are allowing computers and robots to communicate in a natural language without using heavy syntax.
• Intelligent Robots: AI systems are making robots more intelligent and providing them with the ability to learn and adapt.
• Entertaining AI: The entertainment industry is experimenting with AI to produce original music, books etc. Companies like Netflix and Spotify use AI to predict what their users would like to hear or listen.

Question 10: What are the limitations of Weak AI?
Limitations of weak AI are
• Learn a natural language
• Reading and understanding articles and books
• Understand the implications of decisions
• Interpretating visual scenes
• Exhibiting autonomy and intelligences

Question 11: Explain the three domains of AI.
There are three domains of AI.
• Data: Data in AI systems refers to the data that is right for the AI system in question. Data is at core of artificial intelligence. Current AI systems cannot be developed or made functional without adequate data. There are 3 types of data-Sound data, text data, image and video data.
• Computer Vision: Computer Vision is a technology that is used to make machines see and perceive the human world as humans do. This requires image or visual data. The development of computer vision is progressing fast pace. The uses of computer vision are face recognition, image retrieval, gaming and controls, surveillances, and smart cars.
• Natural Language Processing (NLP): The third domain of AI is Natural Language Processing. It works on enabling the computers to understand naturally written and spoken languages like Hindi, English. This will make computer capable of natural communication. There are 2 types of NLP-Natural Language Understanding and Natural Language Generation. For AI systems, NLU is much difficult than NLG.

Question 12: Explain the relevance of AI in daily life.

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

Question 13: Define the following terms
a) Smart Home
b) Smart Buildings
c) Smart Devices
d) Smart Cities

a) Houses that use smart devices.
b) Any building (including homes) that use smart devices.
c) An electronic device connected to and is capable of communicating through the Internet. These devices can also communicate among themselves.
d) Cities that have smart buildings and use smart technology for improving the lives of the citizens.

Question 14: Write the advantages of Smart Building and Smart Cities.
Advantages of Smart Buildings:
• The various functions of the smart building can be regulated from anywhere across the world.
• They provide a safe environment for children and adults by providing various surveillance and security measures.
• They can control the environments of the home for providing of the home for providing inhabitants with the best possible environment.

Advantages of Smart Cities:
• They can better serve the citizens residing in them by providing better services.
• They can use the data collected from smart buildings for improving the services provided by them.
• They can interact with the smart buildings for providing end to end citizen services.
• They can help in reducing resource usage.
• They can help in the sustainable development of the environment.

Question 15: Explain the phases in development of smart cities.

• Phase 1: In this phase, the concept of smart cities was not connected with the use of Al technologies. The concept of smart cities was understood to be limited by providing community services to the citizens using any Information Technology.
• Phase II: In this phase, the use of Al technology started. The smart devices coupled with Artificial Intelligence to allow smart cities to analyse different aspects of the city for improving them.
• Phase III: This is the latest phase in the development of the smart cities. This includes increasing citizen involvement in the decision making process.

Question 16: Define Sustainable Development and Sustainable Development Goals.

• Sustainable development is the development that meets the needs of the present without comprising the ability of future generations to meet their own needs.
• Sustainable Development Goals (SDGs) are the set of 17 goals that were adopted at the United Nations Conference on Sustainable Development in Rio de Janeiro in 2012.

Question 17: List and explain two reasons behind slow adoption of AI system.

• Problems with data availability and accessibility: Data is one of the three primary domains of Al technologies, and Al systems cannot function without adequate data. The problem is that the data in the world is not free or easily accessible.
• Problems with the availability of talented manpower. The Al technology is a relatively new technology. Due to this, there is the shortage of people capable of developing, maintaining, and improving Al capabilities, models, solutions, etc.

Question 18: Explain the phases in AI-research.

• First Phase:
The first phase of Al research started with the Dartmouth Conference and involved techniques related to General Problem Solving (GPS). During this phase, researchers assumed that any problem can be solved by using a program code and mathematical models. To solve these problems, the researchers used computer data that was searched until a solution emerged.
• Second Phase:
The GPS approach was not fit for solving real world problems because the number of search combinations increased exponentially with an increase in the problem complexity. The second phase of Al research focused on reducing the search space so that the searching can become better. During this period:
→ Attempts were made to find better ways of representing data.
→ The research in Al initially slowed down due to negative publicity regarding them.
→ Japan announced the Fifth Generation Computer Systems Project (FGCS) in 1982, which once again increased the interest in development of Al systems.
• Third Phase:
The third phase of Al research focused on developing Intelligent Knowledge Based Systems or IKBS. Such systems were also known as Expert Systems. These systems relied on specific domain-based data for solving Al problems. The main highlights of this phase were:
→ IKBS worked on data in the form of rules.
→ IKBS was restricted in the sense that it was incapable of learning and its focus was extremely narrow.
→ IKBS needed to be regularly updated. This was a time-consuming and resource-intensive work.
→ IKBS lacked common sense that made humans more suited for certain tasks and work.
• Further Phases
The problem with Als is that all the research in the Al field is leading to the development of weak Als. The further phases of Al research involves developing systems that can learn and as such would not need to be regularly updated The ongoing developments in this area are:
→ Cyc project of Cycorp Inc. was the first project towards developing Al systems capable of common sense reasoning.
→ The recent attempts in this direction use a concept known as big data. Sometimes, this approach involves capturing data from the web.
→ The development of techniques like neural networks and genetic algorithms for developing machine learning.

Question 19: List the two types of AI Careers.

• Core Research Development and Deployment: Software Analysts, Software developers, Computer scientists, Computer engineers, Algorithm specialists, Data scientists, Research scientists, Machine Learning engineers, Business Intelligence developer
• Field and Area specialists: Engineers (mechanical, electrical, construction etc.), Medical professionals, Defence forces specialists, Graphic designers, Architects, Musicians, Entertainment specialists

Question 20: Explain ethical concerns of AI related to data (Bias, Inclusion Concerns).

• Bias in real world data: The problem here is that Al system learns from the real-world data fed into it. This means that Al systems can reinforce the biases found in Al systems. Example: A computer system trained on the data for last 200 years might find that more females were involved in certain jobs or that more percentage of successful businesses were established by men and conclude that specific genders are better equipped for handling certain jobs (gender bias). Understanding or even detecting such biases is not easy because many Al systems act as black boxes.
• Problem of Inclusion: Al systems trained on biased real world data create problem of inclusion, i.e. the problem that some people are left out of Al decision-making system. Example: Al system used by Amazon for recruitment. This created a situation in which many eligible females were left out of consideration. This is known as the problem of inclusion.

Question 21: Explain ethical concerns related to adoption of AI systems.

• Job Loss: For a long time now people have been arguing about the number of the jobs that will be lost due to adoption of Al systems and advanced robots. There have been different researches published by different organisations that measure this impact differently.
• Increasing Inequalities: The current economic system provides economic rewards based on contributions to the economy. The Al era will reduce the number of people required for doing jobs. This will also reduce economic remuneration given to them. In this case, most of the economic benefits will go to companies who own systems. This will further widen the already wide income gaps.
• Negative Adoptions: AI, in the hands of terrorists, can be used to spread terror. They can use Al based technologies to make attacks that can do most damage in terms of life and infrastructure losses. Cyber criminals can use Al technology to hack into more systems and for doing more damage. Countries can use this technology to increase war damages inflicted by them.
• Black Box Problem: Black Box problem refers to inherent problems of the Al systems. Sometime what happens in the Al system is normally not easily understood by common individuals and not even by the programmers. This problems stems out of the fact that Al systems do not provide reasoning for decisions made by them.


  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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