Unit 1 AI Reflection Project Cycle and Ethics Class 9 Question Answers Important for Exams

Ans. AI can be used as a tool to transform the world into a better place in many ways like:

1. Healthcare: AI is helping doctors to diagnose the diseases more accurately.
2. Education: AI is helping students to provide personalised study material according to their speed and need.
3. Environment: AI can predict natural disaster.
4. Smart Assistant: Smart assistant like Alexa or Siri helping people to do complicated task in less time.

Ans. Few applications in smartphone that widely make use of computer vision are:

1. Google Lens
2. Face Unlock
3. Instagram Filters
4. Face App like B612
5. Snapchat Filters

Ans. Computer Vision, is an AI domain works with videos and images enabling machines to interpret and understand visual information

Natural Language Processing (NLP) is an AI domain focused on textual data enabling machines to comprehend, generate, and manipulate human language.

Statistical Data analyse, interpret and draw insights from numerical/tabular data

Ans.

a. Data Analysis

b. Computer Vision

c. NLP

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Ans. Computer Vision

Ans. An online game that recognizes the image drawn is Quick, Draw developed by Google. It uses a neural network artificial intelligence to guess what the drawings represent.

The AI domain used by this game is Computer Vision

Ans. There are six stages of AI Project Cycle

1. Problem Scoping: It is the first stage of AI Project cycle. It means to identify a specific problem. for example Cotton fields are damaged by the pink bollworm.

2. Data Acquisition: In this stage, we collect the data for our AI Project from various sources. for example collecting the images of fields, names of farmers, villages etc.

3. Data Exploration: This stage helps to analyse and understand the data by exploring different types of graphs and identify the pattern and trend out of it.

4. Modelling: This stage involves selecting appropriate AI models which match our requirement. After choosing the model, we implement it. This is known as the modelling stage.

5. Evaluation: In this stage, we evaluate each and every model tried and choose the model which gives the most efficient and reliable result. This stage of testing the model is called Evaluation.

6. Deployment: The last stage where we deploy our solution based on the model we have selected is called Deployment.

Ans.

Ans. 4Ws problem canvas help in identifying four important parameters we need to know for solving a problem. It refers to Who, What, Where and Why

1. Who: In this block we find out who the ‘Stakeholders’ to this problem and what we know about them..

2. What: At this block, we need to determine the nature of the problem. What is the problem and how do you know that it is a problem?

3. Where: This blocks helps to focus on the context/situation/location of the problem.

4. Why: This block helps to think about the benefits which the stakeholders would get from the solution and how would it benefit them as well as the society.

Ans. The Problem Statement Template helps us to summarise all the key points into one single Template so that in future, whenever there is a need to look back at the basis of the problem, we can take a look at the Problem Statement Template and understand the key elements of it.

Ans. Problem Scoping is the first step of AI Project cycle. It means to identify a specific problem.

Steps of Problem Scoping are:

1. Identify the problem

2. Understand the problem by using 4Ws Problem Canvas.

3. Summarise all the key points into one single Template called Problem Statement Template

Ans. Stakeholders are the people who are facing a problem and would be benefited from the solution which we develop.

Ans.

Ans. Various methods for collecting data are:

Ans. We should keep in mind while collecting data that the data which we collect is open-sourced and not someone’s property. Extracting private data can be an offense.

One of the most reliable and authentic sources of information are the open-sourced websites hosted by the government.

Ans. Government websites from where we can get open-source data are:

1. data.gov.in

2. india.gov.in

Ans. Data Exploration means to have a closer look of data which we collected in the previous step ie Data Acquisition to understand the data in a better way.

Significance of Data Exploration are:

1. It helps to understand the data in a better way.

For example: To create an AI solution to predict the next salary of employee then the data collected would be service years, salary amount, increment percentage, increment period, bonus, etc.

2. It is very useful to find Patterns and Trends in data.

For example: By exploring data, we might notice that increment also depends on total service years. Identifying such pattern is helpful in designing AI Solutions

3. Identifying missing terms.

For example: After collecting data we may find that salary of few employees are missing.

Ans. We use data visualization in AI to:

1. Quickly get a sense of the trends, relationships and patterns contained within the data.

2. Define strategy for which model to use at a later stage.

3. Communicate the same to others effectively.

Ans. Five graphs used for data visualization are:

Column Chart

Bar Chart

Line Chart

Scatter Plot

Gantt Chart

Ans. Data Acquisition: It is the second stage of AI Project cycle. In this stage, we collect the data for our AI Project from various sources like surveys, web scraping, sensors etc

Data Exploration: It is the third step of AI Project Cycle. This stage mainly focus on understanding the data by exploring different types of graphs and identify the pattern and trend out of it.

Ans. Bar chart is very commonly used Data visualization technique. for example we can use the Bar Chart to display the following data graphically

Ans. A machine is said to be intelligent when it

1. Mimics human intelligence
2. Can solve real world problems
3. Improve on its own from past experiences.
4. Can predict and make decision on it’s own.

Ans. A machine becomes artificially intelligent when it start learning from data using algorithm.

Ans. Yes Artificial Intelligence can be a threat to Human Intelligence because of the following reason:

1. AI may replace jobs as it can do work faster than humans.

2. Students are using AI for their project and homework which in turn reduces their thinking skills.

3. AI can create fake videos which can spread rumors.

Ans. There are six stages of AI Project Cycle

1. Problem Scoping: It is the first step of AI Project cycle. It means to identify a specific problem. for example Cotton fields are damaged by the pink bollworm.

2. Data Acquisition: In this stage, we collect the data for our AI Project. for example collecting the images of fields, names of farmers, villages etc.

3. Data Exploration: This stage helps to analyse and understand the data by exploring different types of graphs and identify the pattern and trend out of it.

4. Modelling: This stage involves selecting appropriate AI models which match our requirement. After choosing the model, we implement it. This is known as the modelling stage.

5. Evaluation: In this stage, we evaluate each and every model tried and choose the model which gives the most efficient and reliable result. This stage of testing the model is called Evaluation.

6. Deployment: The last stage where we deploy our solution based on the model we have selected is called Deployment

Ans. Artificial intelligence is a technology that refers to the development of such machines which can perform such task that required Human Intelligence.

Some AI Applications

1. Face Lock in Smartphones

The front camera detects and captures the face and saves its features during initiation. Next time onwards, whenever the features match, the phone is unlocked.

2. Smart assistants

Smart assistants like Apple’s Siri and Amazon’s Alexa recognize and understand and then provide a useful response.

3. Fraud and Risk Detection

Finance companies were fed with bad debts and losses every year. They decided to bring in data scientists to rescue them from losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures, and other essential variables to analyse the probabilities of risk and default.

4. Medical Imaging

The application is used to read and convert 2D scan images into interactive 3D models that enable medical professionals to gain a detailed understanding of a patient’s health condition.

Ans. Artificial Intelligence is the umbrella terminology which covers machine learning under it. In other words we can say that Machine learning is the subset of Artificial Intelligence.

Ans. A Rule based approach is generally based on the data and rules fed to the machine, where the machine reacts accordingly to deliver the desired output.

Under learning approach, the machine is fed with data and the desired output to which the machine designs its own algorithm (or set of rules) to match the data to the desired output fed into the machine

Ans.

Ans. Evaluation is the process of understanding the reliability of any AI model, based on outputs by feeding test dataset into the model and comparing with actual answers.

Ans. Various Model evaluation techniques are:

1. Accuracy
2. Precision
3. Recall
4. F1 Score

Ans. Model evaluation important in AI projects because it helps to

1. to check the performance of AI models

2. to improve our AI models for best performance.

3. to find errors

Ans. True Positive: When the Prediction of AI model matches with the Reality, this condition is termed as True Positive.

False Positive: When the Prediction of AI model does not matches with the Reality, this condition is termed as False Positive.

Ans. Deployment is the final stage in the AI project cycle where the AI model or solution is implemented in a real-world scenario.

Ans. Some common challenges in deploying AI models are:

1. Over time, real-world data may change which may affect the efficiency and accuracy of AI models.

2. Continuous monitoring is required.

3. AI model may be biased if the data on which it is trained is biased.

4. Models should give response fast as in some cases we can’t afford delay like Fraud detection.

Ans. The steps in sequence are:

a. Problem Scoping

b. Data Acquisition

c. Data Exploration

d. Modelling

e. Evaluation

f. Deployment

Ans. A – Artificial Intelligence

B – Machine Learning

C – Deep Learning

Ans. The guiding principles to decide what is good or bad is known as Ethics

Ans. When building AI solutions, we need to ensure that they follow human rights

Ans. No, it will not be considered as theft. It is an ethical concern.

Ans. Privacy

Ans. “Preference for one over the other” is known as Bias.

Ans. True

Ans. Search for images of personal secretary on Google, displaying predominantly the images of Women is an example of Bias

Ans. True

Ans. Difference between Ethics and Morals are:

Ans. The following principles in AI Ethics affect the quality of AI solutions

Human Rights: AI systems should respect human rights and ensure that AI should not be used to take away their freedom.

Bias: Bias (partiality or preference for one over the other) often comes from the collected data. The bias in training data also appears in the results.

Privacy: AI system should keep our personal data safe and protected. We need to have rules which keep our individual and private data safe.

Inclusion: It means that AI must not discriminate against a particular group of population, causing them any kind of disadvantage.

Ans. Data Privacy: AI system should keep our personal data safe and protected. We need to have rules which keep our individual and private data safe. Here are a few things that we should take care of

▪ Does our AI collect personal data from people?

▪ What does it do with the data?

▪ Does our AI let people know about the data that it is collecting for its use?

▪ Will our AI ensure a person’s safety? Or will it compromise it?

Ans. Inclusivity in AI development is addressed by ensuring that different dataset is used in designing AI models, people from different background are involved in designing process so that AI system will work well without biasedness.

Ans. Major Issues around AI Ethics are:

1. AI System can be biased, if they are trained on faulty data.

2. AI often uses personal data which can be misused.

3. AI may cause unemployment in society.

4. AI system can be hacked and modified by hackers.

Ans. a. Bias

b. The possible reason would be that the training data must be biased.

Ans. According to me AI is to support human and not to replace human. Instead of fearing AI we have to focus on increasing our skills so that we can use AI to do our task smartly and quickly.