Artificial Intelligence: Introduction and Basics for CS Professional

What this chapter covers

Artificial Intelligence - Introduction and Basics: topics in the order to study them

1. 1Introduction to Artificial IntelligenceStart here for the definition, scope and background of AI, which every later topic uses.
2. 2Types and Categories of AIOnce you know what AI is, learn how it is classified, for example by capability and by function.
3. 3Machine Learning and Deep LearningThese are the main methods behind modern AI, so understand them before looking at applications.
4. 4Key AI Technologies and ApplicationsWith the methods clear, you can connect them to uses such as language processing, vision and robotics.
5. 5AI Lifecycle, Data and AlgorithmsThis shows how an AI system is built and run, which helps you see where risks and compliance duties arise.
6. 6Benefits, Risks and Challenges of AIStudy this last, because it draws on everything before it and leads directly into the legal and governance chapters.

How to prepare Artificial Intelligence - Introduction and Basics

1. Read the six topics once in the given order without making notes, to get the full picture.
2. Make a one-page glossary of key terms such as model, algorithm, training data, supervised learning, neural network and bias, each in your own words.
3. For types of AI, build a simple classification chart and attach one example to each category.
4. Compare machine learning and deep learning side by side: how each learns, data needs and typical uses.
5. Draw the AI lifecycle as a flow from data collection to monitoring, and note one risk at each stage.
6. Write answers to two or three case-style questions, in the form of issue, analysis and conclusion, using risks and benefits as your analysis.
7. Revise the glossary and chart a few days before the exam, and keep them ready as quick reference for the open-book paper.

Common mistakes in Artificial Intelligence - Introduction and Basics

• Using AI, machine learning and deep learning as if they mean the same thing

  Fix: Remember the nesting: deep learning sits inside machine learning, which sits inside AI. State this relationship in your answer.

• Writing only definitions with no example

  Fix: Add one short practical example, such as a bank using a model to screen loan applications, to every concept.

• Mixing up classification by capability and by functionality

  Fix: Keep two separate columns in your chart and revise them as two distinct sets.

• Listing risks without explaining their cause or impact

  Fix: For each risk, say what causes it, who is harmed and what control reduces it.

• Skipping the lifecycle and data topic as too technical

  Fix: Learn it as a simple flow of stages. Case questions often hinge on a failure at one stage, such as biased training data.

• Relying on the open book instead of preparing

  Fix: Prepare as if it were closed book, and use the open book only to confirm details and mark quick references.

Last-day revision: Artificial Intelligence - Introduction and Basics

• ✓Artificial Intelligence means machines performing tasks that normally need human intelligence, such as learning, reasoning and decision-making.
• ✓AI can be classified by capability (narrow, general, super) and by functionality (such as reactive and limited memory).
• ✓Most AI in use today is narrow AI, built for a specific task.
• ✓Machine learning lets systems learn patterns from data instead of following only fixed rules.
• ✓Main machine learning styles are supervised, unsupervised and reinforcement learning.
• ✓Deep learning is a subset of machine learning that uses multi-layer neural networks and usually needs large data and computing power.
• ✓Key technologies include natural language processing, computer vision, speech recognition and robotics.
• ✓The AI lifecycle runs from data collection and preparation to model training, testing, deployment and monitoring.
• ✓The quality of an AI output depends heavily on the quality of the data and the design of the algorithm.
• ✓Benefits include efficiency, accuracy and better decisions; risks include bias, lack of transparency, privacy harm and misuse.
• ✓In answers, link each risk to a governance or compliance response.

Artificial Intelligence - Introduction and Basics practice questions

• A Chennai hospital group builds a diagnostic AI using patient records collected for treatment. A data scientist proposes using the full iden…
• A Mumbai lender trains a model on historical loan records where each record already carries a label showing whether the borrower defaulted. …
• A Bengaluru retailer's AI team splits its customer data into a training set, a validation set and a test set. What is the main purpose of th…
• A private bank in Mumbai deploys a system that reads scanned cheque images and converts the handwritten and printed text into machine-readab…
• An NBFC in Pune trains a model on past loan records labelled 'defaulted' or 'repaid' and then uses it to predict whether a new applicant wil…
• An e-commerce company's fraud-detection model was trained on 2021 transaction patterns and performed well. By 2025 its accuracy has fallen s…
• A bank's credit-scoring model was accurate at launch, but two years later its accuracy falls because customer income patterns and spending b…
• A retailer feeds unlabelled purchase histories of two million customers into an algorithm that, without any predefined categories, groups th…

Artificial Intelligence - Introduction and Basics in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

• CSEETAI and Business Environment
• FRM Part IMachine Learning and Prediction
• CA FinalAccounting and Technology
• CFA Level IIMachine Learning