CLASS 7 AI NOTES - TERM 1 (AI_Facilitators_Handbook_VII)

1. More About Domains of Al

Main areas in Artificial Intelligence

We want our machines to be able to see things, understand language, and make sense of numbers. This has resulted in 3 domains in AI or 3 broad fields where AI is being used.

1.       Why are there different domains?

Depending on the type of data, we can divide AI into different domains.

 Computer Vision:

The ability of machines to see the world.

Natural Language Processing:

The ability of machines to understand human language.

Statistical Data (Data Science):

The ability of machines to understand numbers.

Q2. Give two applications of NLP in our daily life.

1. Grammar and spelling correction (e.g., Grammarly)
2. Digital assistants like Alexa and Siri

Q3. Mention two applications of Statistical Data in real life.

1. Weather prediction
2. Health monitoring during COVID-19

Q4. Why is Statistical Data important?

• It helps find out hidden and unexpected information from the data.

 • Visual representation of data makes it easier to understand.

 • Analysis of data helps in making decisions

Q5. Give two applications of computer vision in real life.

·         Self-driving cars 

·         Medical image analysis

2. Ethical Considerations in AI

Q1. What is ethics in AI?. 
A: Ethics in AI means creating and using AI in a way that which is right, fair & safe for Society.

Q2. Strategies/Guideline for using computer vision ethically

1. Informed Consent
2. Voluntary Participation
3. Do No Harm
4. Confidentiality
5. Anonymity
6. Only assess relevant components

Q3. What does informed consent mean in computer vision?

It means participants know the project’s purpose, risks, usage of data, and who can access the findings before agreeing.

Q4. What is voluntary participation?
People participate freely, without pressure, and can withdraw anytime without negative consequences.

Q6. Differentiate between confidentiality and anonymity.

·         Confidentiality: Information is protected and not shared with unauthorized persons.

·         Anonymity: Researchers don’t know the participant’s identity.

Q7. What is the main ethical challenge in NLP?
AI in NLP may make unfair decisions if it learns from biased or incomplete data. That means AI assistants (like Alexa, Siri, Google Assistant) may misunderstand accents, use biased data, or spread stereotypes.

Q8. What is historical bias in NLP?
It occurs when stereotypes from society, like linking “nurse” mainly to women, get reflected in AI systems.

Q9. What is representation bias?
It occurs when some groups are underrepresented or overrepresented in data, leading to unfair results. For example,

Q10. Name two other challenges in NLP ethics.

·         Errors in text and speech due to accents or spelling mistakes.

·         Difficulty handling slang and colloquial words.

Q11. Why should AI decisions be fair and unbiased?
To prevent discrimination and ensure reliable outcomes for all users.  

Q12. What does transparency in AI mean?
AI systems should explain their results in a simple way so non-technical people can understand how decisions are made.

Q13. How is privacy maintained in AI using statistical data?
It is maintained by keeping personal information safe, using only the needed data, and hiding names or details so no one can know whose data it is.

Q14. Why should AI be accountable?
AI must allow users to ask questions, give feedback, and ensure quick resolution of issues.

Q15. What is meant by safe, secure, and sustainable AI?
AI should be designed to resist hacking or misuse and work in a way that doesn’t harm people or the environment.

 

Summary

 

1. Artificial Intelligence – Introduction

·         AI Definition: Technology that allows machines to see, understand language, analyze data, and make decisions.

·         Uses: Medicine (diagnosis), transport (self-driving cars), education (personalized learning), voice assistants (Siri, Google Assistant).

·         Machine Learning (ML): AI learns from data to predict/decide (e.g., YouTube suggestions, predictive texting).

·         Deep Learning (DL): Uses neural networks to mimic human thinking (e.g., facial recognition, self-driving cars).

·         Needs of AI: Data + Algorithms + Computers.

·         Chatbots: Programs that talk like humans.

·         Applications: Schools, hospitals, cars, shops, and homes.

·         History: Term "AI" coined in 1956 by John McCarthy (Father of AI).

2. Algorithms & Flowcharts

·         Algorithm: Step-by-step instructions to solve a problem.

·         Flowchart: Visual diagram of an algorithm using shapes and arrows.

·         Benefits: Clear understanding, problem-solving, easy code reference.

3. More About AI

·         Human vs AI: Humans = emotions + creativity; AI = machine-based, data-driven.

·         Automation: Machines performing tasks automatically (washing machines, traffic lights).

·         Domains of AI:

§  Data Science → numbers (weather forecasting).

§  NLP → language (chatbots, Alexa).

§  Computer Vision → images (face recognition).

·         Types of AI:

¨      Narrow AI, General AI, Super AI. (Based on Capabilities)

¨       Reactive Machines, Limited Memory, Theory of Mind, Self-aware AI. (Based on Functionalities)

·         Neural Networks: Programs inspired by the human brain.

 

 4. Ethical Considerations in AI

1. Importance of AI Ethics

• ·         Ethics in AI makes sure that technology helps people and does not harm people
• ·         AI is becoming part of our daily life (phones, healthcare, education, transport).
• ·         Wrong use of AI can cause bias, privacy loss, or even harm.
• ·         So, Students must understand that technology should serve humanity responsibly

2. Guidelines for Ethical Use – Computer Vision

·         Informed Consent – People must know when they are being recorded.

·         Voluntary Participation – No one should be forced into surveillance or studies.

·         Do No Harm – AI should not cause stress, anxiety, or loss of dignity.

·         Confidentiality – Keep information secure, don’t share without permission.

·         Anonymity – Sometimes data must be collected without identifying the person.

·         Only assess relevant components – AI should only analyse the relevant data that are necessary for the task — nothing extra.

3. Main Problems - NLP

• Bias in Language Data
• Historical Bias: Example: AI may associate “nurse = woman” → unfair stereotype.
• Representation Bias: Some groups/languages underrepresented → unfair results.
• Errors in Speech/Text – AI struggles with misspellings, accents, or dialects.
• Slangs & Colloquial Words – AI may not understand informal language.

4. Essential Guidelines - Statistical Data

·         Fair & Unbiased – Data should not favour one group over another.

• Example: AI predicting school performance should not only use city-school data (ignoring rural students).

·         Transparency – People should understand how decisions are made.

• Example: If AI rejects a loan, the reason must be clear.

·         Privacy & Data Protection – Collect only necessary data; encrypt or anonymize it.

·         Accountability – Developers/organizations must take responsibility for AI mistakes.

·         Safe & Secure – AI should not be hackable or misused.

Example for Class: Weather prediction AI uses temperature, humidity data – safe. But if it uses student attendance data carelessly, privacy may be violated.

 

 Domains vs Branches of AI

·         Branches: Techniques to build AI (ML, DL).

·         Domains: Real-world application areas (NLP, Computer Vision, Data Science).

 

Difference between Domains of AI and Branches of AI

 

Aspect	Branches of AI	Domains of AI
Definition	techniques used to build AI	Real-world areas or fields where AI is applied
Focus Area	How AI works internally	Where AI is used and what it does
Purpose	To develop the intelligence of machines	To solve practical problems using AI
Examples	Machine Learning (ML) & Deep Learning (DL)	Natural Language Processing (NLP), Computer Vision & Data Science
Type	Technical / Research-based	Functional / Real-world use-based

 

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