CBSE Grade IX AI Curriculum: Key Concepts Flashcards (Video Notes)

Flashcards cover key concepts from the video notes: AI definition, domains, AI project cycle, problem scoping, 4Ws canvas, data acquisition/processing, data features, system maps, data exploration/visualization, modelling, evaluation, deployment, ethics, data literacy, generative AI, and related tools and case studies.

What is Artificial Intelligence (AI) as defined in the notes?

AI is the ability of machines to mimic human traits—make decisions, predict, learn and improve on their own; it is a form of intelligence, a technology, and a field of study.

What are the three AI domains and their typical applications mentioned?

Data for AI (data processing/interpretation), Natural Language Processing (NLP), and Computer Vision (CV).

What is the AI Project Cycle and its six stages?

Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation, Deployment.

What does Problem Scoping involve in AI projects?

Defining the problem, identifying stakeholders, and setting goals using the 4Ws Problem Canvas.

What is the 4Ws Problem Canvas used for?

A framework with Who, What, Where, Why blocks to map stakeholders, the problem, context, and the expected benefits of the solution.

What is Data Acquisition and why are training and testing data important?

The stage of acquiring data from reliable sources; Training data trains the model, Testing data evaluates it; data features determine what data to collect.

What are Data Features and the difference between independent and dependent features?

Data features are properties used to address the problem; Independent features are inputs to the model, Dependent features are the outputs/predictions.

What are System Maps and what do the arrows and signs represent?

Diagrams showing relationships among problem elements; arrows indicate influence; '+' or '-' signs denote positive/negative relationships; time delays are shown by longer arrows; Loopy is a suggested tool.

What is Data Exploration and its purpose?

An activity to explore data using graphs to identify trends and patterns; helps decide data features and modeling approach.

Name common graph types used in data visualization.

Bar charts, Line charts, Pie charts, Scatter plots, and other graphical representations.

What is the Data Pyramid?

A model: Data -> Information -> Knowledge -> Wisdom, illustrating increasing usefulness of data.

Difference between Data Privacy and Data Security in the notes.

Data Privacy concerns handling of personal data and consent; Data Security protects data from unauthorized access, corruption, or theft.

List some Best Practices for Cyber Security.

Strong unique passwords, enable 2FA, download from trusted sources, use HTTPS, keep software updated, manage privacy settings, lock devices, use trusted networks, report online bullying.

What is Data Preprocessing?

Cleaning data by removing duplicates, handling missing values, addressing outliers to improve usability.

What is Modelling in AI, and what are Rule-Based vs Learning-Based approaches?

Modelling is creating AI models; Rule-Based uses defined rules; Learning-Based trains on data and adapts (includes ML and DL).

What is Rule-Based AI and its drawback?

AI that follows predefined rules; learning is static and may not adapt well to new data or exceptions.

What is Learning-Based AI and how does it work?

AI models learn from data, adapt to changes, and improve; encompasses Machine Learning (ML) and Deep Learning (DL).

What is Evaluation in the AI project cycle and why avoid overfitting?

Testing models with a separate testing dataset to assess performance; avoid using training data for evaluation to prevent overfitting.

Define True Positive, False Positive, True Negative, and False Negative.

TP: predicted yes and actual yes; FP: predicted yes but actual no; TN: predicted no and actual no; FN: predicted no but actual yes.

What is ROC in model evaluation?

Receiver Operating Characteristic; a metric/curve to assess classifier performance and compare algorithms.

What is Deployment in the AI project cycle?

The final stage; make the model available in real-world use; steps include testing/validation, integration, and monitoring.

What is Generative AI?

AI systems that can generate new content (text, images, music, etc.); useful for content creation; requires consideration of ethical implications.

Name the AI Ethics Principles highlighted.

Human Rights, Bias, Privacy, Inclusion.

Ethics vs Morals as described in the notes.

Morals are beliefs dictated by society; ethics are guiding principles for what is good or bad; morals can vary across societies.

What is the Moral Machine?

A platform to gather human perspectives on moral decisions made by AI, such as self-driving cars, and compare responses.

What is Data Literacy and why is it important?

The ability to understand, work with, analyze, and present data; enables informed decision-making and critical thinking; differentiates data privacy vs data security.

Name open government data portals mentioned.

data.gov.in and india.gov.in.

What is Loopy used for in System Maps?

An online animated tool to create system maps and illustrate relationships/loops and time delays.

What are Data Discovery, Data Augmentation, and Data Generation?

Data Discovery: finding datasets; Data Augmentation: increasing data via transformations; Data Generation: creating new data via sensors or collection.

Which software is used for Project Interactive Data Dashboard & Presentation?

Tableau (Tableau Public) for data visualization dashboards (alternatives include MS Excel or Datawrapper).