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PART-B – SUBJECT SPECIFIC SKILLS
Unit 1: AI Reflection, Project Cycle and Ethics LEARNING OUTCOMES: Learners will be able to
- Identify and appreciate Artificial Intelligence and describe its applications in daily life.
- Relate, apply and reflect on the Human-Machine Interactions to identify and interact with the o three domains of AI: Data, Computer Vision and Natural Language Processing and Undergo o assessment for analyzing their progress towards acquired AI- Readiness skills.
- Get familiar and motivated towards Artificial Intelligence and Identify the AI Project Cycle framework.
- Learn problem scoping and ways to set goals for an AI project and understand the iterative nature of problem scoping in the AI project cycle.
- Identify stakeholders involved in the problem scoped.
- Understand the iterative nature of problem scoping for in the AI project cycle.
- Brainstorm on the ethical issues involved around the problem selected.
- Foresee the kind of data required and the kind of analysis to be done.
- To understand the purpose of Data Visualization
- Use various types of graphs to visualize acquired data.
- Identify data requirements and find reliable sources to obtain relevant data.
- Understand various evaluation techniques. Introduction to AI Artificial intelligence (AI) refers to the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. The term "artificial intelligence" was coined by John McCarthy in 1956. Applications of AI Advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix), virtual assistants (e.g., Google Assistant, Siri, Alexa), autonomousvehicles (e.g., Waymo); generative and creative
tools(e.g., ChatGPT , Deepseek, Grok,AI art) ,Health Care (AI driven medical imaging ),Social media(personalized recommendations, content moderations, content creation),Banking(Fraud and Risk detection) ,Agriculture( AI powered tools to optimize resources use and crop management),Space explorer( safe landing of Chandrayan-III on lunar south pole). How to make machine Intelligent? When a machine can learn, reason and adapt. DATA+ALGORITHM=AI MACHINE Domains of AI specialized areas within artificial intelligence that deal with specific problems, techniques, and applications. Statistical Data: Statistical Data refers to statistical techniques to analyze, interpret and draw insights from numerical/tabular data
- It is the backbone of AI here the data is called bigdata (voluminous data).
- Data is derived from various sources private as well as public.
- The data is processed by analyzing patterns and trends to speculate future predictions. Computer Vision (CV): Computer Vision, is an AI domain works with videos and images enabling machines to interpret and understand visual information.
- It uses machine learning and neural networks to enable computers and systems to understand and interpret the visual world. It replicates the complexity of human vision.
- Computer vision enables machines to "see" and interpret images, much like humans.
Domains of AI
Statistical Data
CV NLP
- to create better AI projects easily
- to create AI projects faster
- Problem Scoping : This initial stage involves clearly defining the problem you're trying to solve, setting project goals, and identifying the key stakeholders, identifying existing measures, identifying ethical issue which will influence the project. 4W frameworks : Who : Stakeholders (who are facing the problems and wants a solutions) What : What is the actual problem. To collect evidence to prove that the problem actually exists. Where : Context/situation/location of the problem. Why : How the solution will be benefited to the society Problem Statement Template The Problem Statement Template helps us to summarize 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.
2. Data Acquisition : (Acquiring Data from reliable sources) In this stage, you collect the relevant data that will be used to train and evaluate your AI model. Types of Data: Training Data : collecting and preparing the data needed to train the AI model. Testing Data : Testing data is a separate dataset used to evaluate the performance of attained model after it has been developed. Various sources for data acquisition: o Data can be acquired from a wide range of sources, including physical sensors, online databases, social media platforms, and even by purchasing datasets from external vendors. o These sources can be categorized into physical sensors, online databases, and data warehouses. o Physical Sensors and Transducers : These directly measure physical phenomena like temperature, pressure, and movement. o Online Databases : This includes structured data stored in relational databases, APIs (Application Program Interface), and flat files. o Data Warehouses : These are centralized repositories that store data from various operational systems, databases, and external sources like partner systems, IoT devices, weather apps, and social media. Sharing/Exchanging Data : Data can be shared or exchanged with other organizations or entities. Public Datasets : Free datasets from government or research
Data Visualization : Using graphs, charts, and other visuals to explore patterns and relationships. Statistical Analysis : Applying statistical techniques to analyze the data. Feature Selection : Identifying relevant features for modeling. Documentation : Documenting the data exploration process and findings
4. Modeling: In this module, progress from data exploration to AI modeling, learning about key distinctions between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). The module introduces two approaches to AI modeling: Rule-Based and Learning-Based This is the core stage where you develop and train the AI model to learn patterns from the data and make predictions or decisions using algorithms and mathematical frameworks to solve a defined problem AI, ML & DL AI (Artificial Intelligence) - refers to any technique that enables computers to mimic human intelligence. An artificially intelligent machine works on algorithms and data fed to it and gives the desired output. ML (Machine Learning) - Enables machines to improve at tasks with experience. The machine here learns from the new data fed to it while testing and uses it for the next iteration. It also takes into account the times when it went wrong and considers the exceptions too. DL (Deep Learning)- Enables software to train itself to perform tasks with vast amounts of data. Since the system has got huge set of data, it is able to train itself with the help of multiple machine learning algorithms working altogether to perform a specific task. “Artificial Intelligence is the umbrella term which holds both Deep Learning as well as Machine Learning. Deep Learning, on the other hand, is the very specific learning approach which is a subset of Machine Learning as it comprises of multiple Machine Learning algorithms.”
Rule Based Approach Refers to the Al modeling where the rules are defined by the developer. The machine follows the rules or instructions mentioned by the developer and performs its task accordingly. For example, we have a dataset which tells us about the conditions on the basis of which we can decide if child can go out to play golf or not. The parameters are: Outlook, Temperature, Humidity and Wind
5. Evaluation : This final stage assesses the performance of the trained model by comparing its predictions to actual results and evaluating its reliability. 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. There can be different Evaluation techniques, depending of the type and purpose of the model. Purpose of Evaluation Understanding Model Performance : Evaluation helps determine how well the AI model can solve the problem it was designed for. Identifying Strengths and Weaknesses : By analyzing the model's performance, you can identify areas where it excels and areas where it needs improvement. Guiding Model Refinement : The evaluation results can inform further adjustments to the model, such as retraining with different algorithms or adjusting parameters. Key Evaluation Metrics: Accuracy : Measures the overall correctness of the model's predictions, encompassing both true positives and true negatives. Precision: Measures how well the model avoids false positives (incorrectly identifying something as positive when it's not). Recall (Sensitivity): Measures how well the model correctly identifies all positive cases, avoiding false negatives (missing positive cases).
F1-score: Combines precision and recall, providing a balanced view of the model's performance, especially useful when dealing with imbalanced datasets. Model Evaluation Terminologies Imagine that you have come up with an AI based prediction model which has been deployed in a forest which is prone to forest fires. Now, the objective of the model is to predict whether a forest fire has broken out in the forest or not Here we have two conditions (Prediction and Reality) Prediction ( output which is given by the machine) and Reality ( real scenario in the forest when the prediction has been made)
AI ETHICS
Refers to the principles that guide the development and use of AI systems to ensure they are beneficial, safe, and responsible. These principles consider fairness, transparency, accountability, privacy, security, and potential societal impacts of AI. Difference between ethics and morals:
- Ethics generally refers to societal rules and standards, while morals are more about personal beliefs and values
- Ethics are external, while morals are internal (Ethics are often enforced by legal or organizational sanctions, while morals are more personal). Examples: Moral: A business owner choosing to donate a portion of their profits to charity because they believe in giving back to the community. Ethics: A business owner to donate some percentage of his profit under CSR (Company Social Responsibility) Ethics can include avoiding bias, ensuring privacy of users and their data, Inclusion and environmental risks. Ethics vs Morals Morals Ethics
- The beliefs dictated by our society.
- Morals are not fixed and can be different for different societies Examples:
- Always speak the truth
- Always be loyal
- Always be generous
- The guiding principles to decide what is good or bad.
- These are values that a person themselves chooses for their life Examples:
- Is it good to speak the truth in all situations?
- Is it good to be loyal under all circumstances?
- Is it necessary to always be generous?
Principles in AI Ethics To make AI better, we need to identify the factors responsible for it. The following principles in AI Ethics affect the quality of AI solutions Here are a few things that you should take care of : Human Rights
- Does your AI take away Freedom?
- Does your AI discriminate against People?
- Does your AI deprive people of jobs?
- What are some other human rights which need to be protected when it comes to AI? Bias
- Does your data equally represent all the sections of the included populations?
- Will your AI learn to discriminate against certain groups of people?
- Does your AI exclude some people?
- What are some other biases that might appear in an AI? Privacy
- Does your AI collect personal data from people?
- What does it do with the data?
- Does your AI let people know about the data that it is collecting for its use?
- Will your AI ensure a person’s safety? Or will it compromise it?
- What are some other ways in which AI can breach someone’s privacy? Inclusion
- Does your AI collect personal data from people?
- What does it do with the data?
- Does your AI let people know about the data that it is collecting for its use? Will your AI ensure a person’s safety? Or will it compromise it?
- What are some other ways in which AI can breach someone’s privacy?
MULTIPLE CHOICE QUESTIONS
Q1. What is one purpose of AI in healthcare? a) To replace doctors b) To assist in the interpretation of medical images c) To automate physical therapy d) To develop new medications Q2. What is the function of smart assistants like Alexa and Siri? a) To answer all mathematical questions b) To recognize speech patterns and provide responses c) To function as a calculator d) To guide physical robots Q3. In AI, what does the term “modelling” refer to? a) Designing a physical model b) Creating algorithms that can predict outcomes c) Drawing pictures d) Painting a model Q4. What is the purpose of the AI Project Cycle? a) To predict future trends in technology b) To guide AI development from problem scoping to deployment c) To monitor AI performance after deployment d) To collect data from the internet Q5. Which stage in the AI Project Cycle involves gathering data? a) Problem Scoping b) Data Acquisition c) Modelling d) Evaluation Q6. What is problem scoping in AI? a) Defining the problem that AI is supposed to solve b) Testing the AI model c) Training data collection d) Evaluating AI performance Q7. Which type of AI approach adapts its algorithms based on new data? a) Rule-Based AI b) Learning-Based AI c) Fixed AI d) Reactive AI Q8. What does the term “evaluation” mean in AI?
a) Writing new algorithms b) Testing AI models against a dataset to check accuracy c) Creating data features d) Monitoring users Q9. What is the final stage of the AI Project Cycle? a) Problem Scoping b) Data Acquisition c) Evaluation d) Deployment Q10.Spam filter is an application of ____________. a) Natural Language Processing b) Data Science c) Computer Vision d) Segmentation Q11.Searching for a Chef’s photo in the web browser mostly give men’s images. This is an instance of ____________. a) AI Access b) AI Bias c) AI Domain d) AI Ethics Q12.Choose the five stages of AI project cycle in correct order: a) Evaluation - > Problem Scoping - > Data Exploration - > Data Acquisition - > Modelling->Deployment b) Problem Scoping - > Data Exploration - > Data Acquisition - > Evaluation - > Modelling->Deployment c) Data Acquisition - > Problem Scoping - > Data Exploration - > Modelling - >Evaluation->Deployment d) Problem Scoping - > Data Acquisition - > Data Exploration - > Modelling - >Evaluation->Deployment Q13.Which of the following is not a correct method of Data Collection? a) Survey b) Prediction c) Observation d) API Q14.Which one of these is a game that uses AI to understand language?
Answers: 1 - b 2 - b 3 - b 4 - b 5 - b 6 - a 7 - b 8 - b 9 - d 10 - a 11 - b 12 - d 13 - b 14 - b 15 - b 16 - b 17 - b 18 - b 19 - c 20 - b SHORT ANSWER QUESTIONS Q1. How can AI be used as a tool to transform the world into a better place? Ans: AI can be used to make the world better by improving productivity, healthcare, education, and accessibility. AI systems can predict and solve critical problems like climate change, help optimize resource use, enhance medical diagnosis with Computer Vision and NLP, improve personalized education, and increase efficiency in various industries. AI can also aid in monitoring environmental changes and managing large-scale social challenges like poverty and food security. Q2. Applications in smartphones that widely use Computer Vision:
- Face Unlock
- Google Lens
- Augmented Reality filters in social media apps (like Snapchat, Instagram)
- Barcode and QR code scanners
- Image search in photo galleries. Q3. Difference between the three domains of AI with respect to the types of data they use:
- Natural Language Processing (NLP): Works with textual and spoken data to understand, generate, and manipulate human language.
- Computer Vision (CV): Works with visual data, including images and videos, to help machines interpret and understand visual content.
- Data Science: Works with numerical, statistical, and structured data to identify patterns and draw insights from large datasets. Q4. How is an AI project different from an IT project? An AI project focuses on building models that can learn and improve over time based on data, whereas an IT project is more static and involves creating software systems based on fixed rules and requirements. AI projects emphasize adaptability and continuous learning, while IT projects prioritize system functionality and efficiency.
Q5. Name various methods for collecting data.
- Surveys: Used in customer sentiment analysis.
- Sensors: Used in smart agriculture to monitor environmental conditions.
- Web scraping: Used for gathering publicly available data from websites (e.g., market trends).
- APIs: Used in financial applications to collect real-time stock market data. Q6. What must you keep in mind while collecting data so it is useful? Ensure that the data is relevant, accurate, unbiased, and obtained from reliable sources. The data should also comply with privacy laws and be suitable for training AI models. Q7. Explain Data Privacy: Data privacy refers to the ethical handling of individuals’ personal data. It involves ensuring that personal information is collected, stored, and used responsibly, with the consent of the individual, and safeguarding against misuse. AI systems that handle personal data, such as biometric information or social media activity, must follow strict privacy regulations to protect users’ rights. Q8. How should AI system maintain privacy of user data? a) Restrict data collection to what is strictly necessary. b) Secure data by identifying risk, stop misuse or leakage of data with encryption and password protection. c) Take permission from user while collecting their personal information and clearly state their usage. Provide control to user over their individual information. Q9. Difference between Rule base approach and Learning Based Approach Rule base approach Learning Based Approach Refers to the Al modelling where the rules are defined by the developer. The machine follows the rules or instructions mentioned by the developer and performs its task accordingly. Refers to the Al modelling where the machine learns by itself. Under the Learning Based approach, the Al model gets trained on the data fed to it and then is able to design a model which is adaptive to the change in data Q10. Explain the concept of 4W’s problem canvas 4W Problem Canvas is a Problem Scoping framework prepared to understand scope of the project and prepare Problem Statement Template. It has 4 components – who, what, where, why Who are the stakeholders facing the problem and need solution?
