Subjective Type Questions - Introduction to AI | Artificial Intelligence for Class 10 PDF Download

Q.1. What do you mean by Intelligence?

Intelligence refers to ability to think, perceive information and apply knowledge within the behavioral environment. Intelligence can be defined as:

• Ability to interact with real world – To perceive, understand and act
• Reasoning and Planning – Modelling the external world, given input
• Learning and Adaption – Continuous learning and adapting graph

Q.2. List out the types of intelligence.

The different types of intelligence are as following:

1. Mathematical Logical Reasoning
2. Linguistic Intelligence
3. Spatial Visual Intelligence
4. Kinaesthetic Intelligence
5. Musical Intelligence
6. Intrapersonal Intelligence
7. Existential Intelligence
8. Naturalist Intelligence
9. Interpersonal intelligence

Q.3. What is artificial intelligence?

When a machine possesses the ability to mimic human traits or copying human behaviors or making decisions, predict the future, learn and improve on its own. In other words, machines are intelligent artificially by collecting data, understand it, analyze it, learn from it and improve it.

Q.4. Write some popular AI apps and tools.

The following are some powerful AI apps and tool.

Google: Google uses AI for responding to our queries through keywords. Gmail spam filter is also using AI technology. Google maps helps through navigation in travelling.

Virtual Assistants: Apple Siri, Amazon Alexa, Google Assistant, MS Cortana are some examples of Virtual Assistants. These assistants are used to accept the command in terms of voice and provides assistance.

Gaming: AI used to enhance the gaming experience for gamers. Along with this AI can help in enhancing graphics, come up with new difficulty levels, encourage gamers etc.

Recommendations: As you have seen some recommendations on amazon, Netflix, Amazon, Spotify, YouTube etc. These all recommendations based on your interests and engagements likes, dislikes and comments. Some recommendations are based on preferences on social media like Facebook and Instagram. Sometimes AI also send some notifications about online shopping details, auto-create playlists according to requests.

Chatbots: Chatbots are generally available on many websites. Chatbots are software applications integrated with AI technology used for online chat support. Sophia was the first humanoid bot.

Health Apps: Health apps are also created integrated with AI which helps users to monitor physical and mental health aspects.

Others: Moreover, there are some other AI applications like face locks in smartphones, language translators, weather forecasts, and many more.

Q.5. What do you mean by machine learning?

Machine learning refers to a branch or a division or a subset of Artificial Intelligence that requires data and a trained model algorithm. It learns by the data provided and improves the performance or processes. It applies to labelled or unlabeled data and makes a prediction.

Q.6. What do you mean by deep learning?

Deep learning is a subset of Artificial Intelligence which works on huge amount of data and train the model according to data received. It is most advanced branch of Artificial Artificial Inyelligence.

Q.7. What do you mean by AI Domains?

AI become intelligent according to the training given through data. The machine requires a dataset to train and learn itself. As the machine receives the data it processes it and then makes a decision. These data models are known as different domains of AI.

Q.8. Enlist the three domains of AI.

• Data Science
• Computer Vision (CV)
• Natural Language Processing (NLP)

Q.9. What is data with respect to the AI domain of Data Science?

Every AI system heavily relies on data. Data is the core of almost every AI systems. AI systems need data for functioning, learning and growing. It is basically input in the system.

Q.10. What do you mean by data science? Illustrate your answer with an example.

Data science is one of the domains of AI. It processes the data for the AI systems. It collects the data input, maintains them into accurate datasets, prepares the output in the proper and appropriate format. The final output or information extracted through data science can be used to make a decision.

• For example, price comparison websites like PriceGrabber, PriceRunner, Junglee, Shopzilla, DealTime are totally driven by data, compares the price of a product from different vendors and then the user can make a suitable decision.

The digital marketing spectrum is also a good example of data science. It shows the targeted ads to the audience according to their data.

Q.11. What do you mean by Computer Vision?

Computer Vision is of the three domains of AI. It gets the visual data and analyses them to make a decision. This process includes acquiring images, screening, analysing, identifying and extracting information. In computer vision, input to the machines are photographs, videos taken from thermal, or infrared sensors, indicators and other sources.

In this process, the visual data can be translated into descriptions and turned into computer-readable language to make a decision.

The examples of computer visions are as following:

• Face recognition systems: Apps such as Google Photos, Snapchat, Facebook, Interpol
• Content-Based Image Retrieval (CBIR): Search engines such as Google, Bing, CT scans and MRI scans in hospitals, Earth science etc.
• Smart Interactions: Gaming

Q.12. How does the face lock system work in a smartphone?

Face lock system in a smartphone detects and capture the image.

It saves the features of the face at the beginning or first time when the lock is applied.

After than whenever the features matched it will unlock the smartphone.

Q.13. What is Natural Language Processing ?

Natural language processing is capable to communicate between machine and human being. It uses natural language for both oral and spoken languages.

Natural Language Processing tries to capture the information from spoken or written words in the system.

It has two main components:

• Natural Language Understanding: It interprets the words and establish a link with natural input and analyse the different aspects of language.
• Natural Langauge Generation: It generates meaningful phrases and sentences in Natural Language. It includes the processes of text planning, sentence planning, and text realization.

NLU is easier than NLG.

Examples are Email filters, smart assistants, translators etc.

Q.14. What do you understand by Ethics?

The concerns related to good and ideal behaviour or manners, moral principles are known as ethics.

In other words, ethics refers to what is right and what is wrong then teaches for moral conduct.

Q.15. What are the moral issues related to self-driving cars?

• Self-driving cars are totally under the control of AI.
• For example, an animal is crossing your way suddenly hit by a self-driving car. The animal got hurt.
• Who is responsible for this accident?
• The following are some ethical concerns:
  • The developer of the car goes through all dilemmas while developing the car’s algorithm.
  • The morality of the developer gets transferred into the machine as what according to him/her is the right priority and hence would be the selection made by the machine.
  • Every person has their own perspective and he/she takes the decision according to their moralities.

Q.16. What are the concerns related to the use of AI to control defense equipment?

AI controlled nuclear missiles capable of making independent decisions or AI-equipped armed military drones.

Another concern is human cost of adoption of AI technology.

Q.17. What do you understand by data privacy?

• Data plays a vital role in AI systems. The existence of AI system is impossible without data.
• The data management like storage, usage, collection and maintaining data is one of the important concern.
• When user works on the system, user is sharing data. This data needs permission to access the data. This consent is known as data privacy.

Q.18. What are the permission asked by the app before using it?

The following permissions are required to be asked by the app before using it:

• Contacts
• Location
• Camera
• Storage
• Photos
• Notes

Q.19. What are the components of good AI Policy?

• Transparent Policy – A clear guidelines and purposes for users
• Right of data collection – The data whatever collected by AI system must be right to the data
• Freedom of leaving the system – The freedom for to leave system anytime should be given to the user
• Design – The design should be done in such a way that data collection and purpose should be limited
• Data deletion – Data stored by user should be deleted when they leave the system.

Q.20. What do you understand by problem of inclusion?

• The problem of inclusion refers to the problem that some people are left out of the AI decision making system.
• Here we have a great example of AI problem inclusion.
• Amazon has started recruitment process through AI, but the AI system left our many eligible females into the consideration.

Q.21. What do you understand by the fact interpretation?

• The AI system can collect the facts or scans data and learn from it but not able to understand the reason behind particular conclusion or learning.
• Sometimes the AI system learn negatively also and produce the result in same manner.
• This is called fact interpretation.

Q.22. What is “Tay”? What controversies created by Tay?

• The “Tay” is a chatbot developed by Microsoft to run on the Twitter social media platform.
• It was designed to experiment with conversational understanding.
• As per the Microsoft claim, more users Tay will be more smarter and learn engage people through casual and playful conversations.
• The controversy started after the launch of Tay.
• In first 24 hours of launch of Tay it learned from thousands of tweets and developed unethical personality.
• It just started mimicking the personality talking to it and learned from the data fed into it without interpreting it.

Q.23. What are the major AI ethical concerns related to AI adoptions?

• Unemployment
• Inequalities
• Negative Adoptions
• Black Box problem