Artificial Intelligence, Data Analytics and Cyber Security - Laws and Practice · Artificial Intelligence - Introduction and Basics

Introduction to Artificial Intelligence: Meaning, History and Evolution

Updated 11 October 2026 · Fact-checked

Artificial intelligence (AI) is the ability of a machine or software system to perform tasks that normally need human intelligence, such as learning, reasoning, understanding language and making decisions. Unlike traditional software, which follows fixed rules written by a programmer, AI learns patterns from data. To answer exam questions, define, trace the history, compare and conclude.

Understand Introduction to Artificial Intelligence

Artificial intelligence (AI) is a branch of computer science that builds machines and software able to do tasks we link with human thinking. These tasks include learning from experience, reasoning, solving problems, recognising speech and images, understanding language and making decisions.

The term "artificial intelligence" was coined by John McCarthy for the 1956 Dartmouth workshop, which is widely treated as the birth of AI as a field. Earlier, Alan Turing's 1950 paper asked whether machines can think and proposed the Turing Test: if a human judge cannot tell a machine's replies from a person's, the machine can be said to show intelligent behaviour.

The usual story of AI's evolution runs in phases. Early years (1950s-60s) brought symbolic, rule-based programs and optimism. Expert systems (1970s-80s) captured specialist knowledge as rules. Funding and interest fell in periods often called AI winters, because results fell short of promises. From the 2000s, more data, faster hardware and better algorithms made machine learning practical. Since the 2010s, deep learning and, more recently, generative AI have driven rapid growth. Check the exact dates and milestones against your ICSI study material.

The key difference from traditional software is how behaviour is created. Traditional software follows explicit instructions: input plus rules gives output. AI, especially machine learning, takes input data and expected outputs and works out the rules itself. So traditional software is predictable and its logic can be read, while AI can handle new situations but may be less explainable, depends on data quality and can be wrong or biased.

AI also differs from human intelligence. AI is fast, consistent and good at narrow, data-heavy tasks. Humans have common sense, emotions, creativity and can transfer learning across fields. Today's AI is narrow: it does specific tasks well but lacks general understanding. For a Company Secretary, this matters because AI use raises questions of accountability, data protection, security and governance.

Key rules to remember

Working definition of AI: AI = machine performing tasks that normally need human intelligence (learning + reasoning + perception + decision-making); Write this in your own words in the first line of any definition answer.
Traditional software logic: Input + Rules (written by programmer) → Output; Behaviour is fixed and explicit; same input gives same output.
AI / machine learning logic: Input data + Expected outputs → Learned rules (model) → Predictions on new data; Behaviour comes from training data and may change when retrained.
Turing Test: Machine passes if a human judge cannot reliably distinguish its responses from a human's; Proposed by Alan Turing in 1950; a test of behaviour, not of true understanding.

How to solve Introduction to Artificial Intelligence questions

Most questions on this topic ask you to define, trace history or compare. Use one structure for all of them.

1Read the verb: define, explain, trace, distinguish or discuss. It decides the shape of your answer.
2Open with a one-sentence definition of AI in plain words, including learning, reasoning and decision-making.
3If history is asked, give a short timeline in order: Turing 1950, Dartmouth 1956, expert systems, AI winters, machine learning, deep learning and generative AI.
4If a comparison is asked, make a point-wise table-style list with the same parameters on both sides: logic, learning, adaptability, explainability, data need, errors.
5Add one Indian business or governance example, such as chatbots, fraud detection or document review in compliance.
6Close with a one-line conclusion linking to risks or governance, since this paper is about laws and practice.

Quickest way: D-T-C-E: Define, Timeline, Compare, Example

When to use it: When you have about 8-10 minutes for a descriptive answer on AI basics.

Define AI in two lines.
List four to five timeline milestones with years.
Give four to six comparison points against traditional software or human intelligence.
Add one practical example and a one-line conclusion.

Common mistakes in Introduction to Artificial Intelligence

Defining AI only as robots or chatbots
Popular media shows AI as humanoid machines.
Fix: Define AI as a field of systems that perform tasks needing human intelligence; robots and chatbots are only applications.
Saying AI and traditional software are the same because both are programs
Both run on computers, so the learning difference is missed.
Fix: State that traditional software follows fixed programmed rules while AI learns patterns from data and improves with training.
Mixing up dates and people in history
Students memorise names without a timeline.
Fix: Anchor on two points: Turing's 1950 paper and the 1956 Dartmouth workshop where McCarthy's term was used.
Claiming AI is always more accurate than humans
Overstating AI's capability.
Fix: Say AI is strong at narrow, data-heavy tasks but can err, inherit bias from data and lacks common sense.
Writing a comparison without parameters
Students write two paragraphs rather than matched points.
Fix: Use matching points such as logic, adaptability, data dependence, explainability and error handling.

Worked examples

Example 1

Define artificial intelligence and briefly trace its evolution. (Short answer)

Show the solution

Define: AI is the capability of machines or software to perform tasks that normally require human intelligence, such as learning, reasoning, perception and decision-making.
1950: Alan Turing asks whether machines can think and proposes the Turing Test.
1956: The term artificial intelligence is coined by John McCarthy for the Dartmouth workshop, marking the start of AI as a field.
1970s-80s: Expert systems encode specialist knowledge as rules; periods of reduced funding, called AI winters, follow unmet expectations.
2000s onwards: Big data, powerful hardware and machine learning make AI practical; deep learning and generative AI follow.
Conclude: AI has moved from rule-based systems to data-driven learning, which raises governance and security issues.

Answer: AI is the ability of machines to perform tasks needing human intelligence. It evolved from Turing's 1950 idea and the 1956 Dartmouth workshop, through expert systems and AI winters, to today's machine learning, deep learning and generative AI.

Example 2

A bank uses (a) a program that rejects any loan application where income is below ₹3,00,000, and (b) a model trained on past repayment data that scores each applicant. Identify which is traditional software and which is AI, and explain the difference. (Case-based)

Show the solution

Provision: Traditional software follows explicit rules written by a programmer; AI learns patterns from data.
Analysis of (a): The cut-off of ₹3,00,000 is a fixed rule coded by a person. The same input always gives the same output. This is traditional software.
Analysis of (b): The model derives its own decision patterns from historical repayment data and can be retrained as data changes. This is AI (machine learning).
Difference in practice: (a) is transparent and easy to audit; (b) may be more flexible but is harder to explain and can carry bias from past data.
Compliance point: The bank should test (b) for bias, keep records of training data and be able to explain adverse decisions.

Answer: (a) is traditional rule-based software; (b) is AI. The first applies fixed programmed rules, the second learns from data, so it needs stronger checks on bias, explainability and data quality.

Exam tips

Always begin with a clear definition; examiners award marks for it even in long answers.
Learn a short timeline with years: 1950, 1956, expert systems, AI winters, machine learning, deep learning, generative AI.
For distinguish questions, give at least five matched points and one example.
Link AI basics to governance, data protection and cyber security in your conclusion, as this is a laws and practice paper.
Write only the dates and milestones you are sure of; follow the ICSI study material for finer details.

Practice questions from Artificial Intelligence - Introduction and Basics

Introduction to Artificial Intelligence in other exams

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

Introduction to Artificial Intelligence: frequently asked questions

What is artificial intelligence in simple words?

It is technology that lets machines do tasks that normally need human intelligence, such as learning, understanding language and making decisions. It works by finding patterns in data rather than only following fixed instructions.

How is AI different from traditional software?

Traditional software follows rules written by a programmer and gives predictable output. AI learns rules from data, can adapt to new inputs, but is harder to explain and depends heavily on data quality.

Who coined the term artificial intelligence?

John McCarthy coined the term for the 1956 Dartmouth workshop. Alan Turing had earlier posed the question of machine thinking in 1950.

Is the history of AI important for the CS Professional exam?

It is usually asked as a short note or as a lead-in to a longer answer. Know the main milestones in order and be ready to explain the term and basic definition.