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Artificial Intelligence (AI) — Level 3

A concise, non-mathematical introduction to Artificial Intelligence.

What is AI?​

Artificial Intelligence (AI) is the study and engineering of systems that perform tasks which normally require human intelligence. Examples include recognising images, translating languages, making recommendations, and generating text or images.

Core approaches (high-level)​

  • Rule-based systems: explicit rules coded by humans.
  • Machine Learning (ML): systems learn patterns from data. Subtypes include supervised, unsupervised and reinforcement learning.
  • Deep Learning (DL): ML using large neural networks that are effective for images, text and audio.

Uses and examples​

  • Search engines and recommendation systems
  • Voice assistants and chatbots
  • Medical image analysis and diagnostic support
  • Content creation (text, images, music)

Limitations and risks​

  • Data bias: models trained on biased data can produce unfair results.
  • Privacy: models may reveal or misuse personal data.
  • Explainability: many models (especially deep learning) are hard to inspect.
  • Over-reliance: automation can cause skill degradation or misplaced trust.

Ethical considerations​

Discuss who benefits or loses from an AI system, whether decisions can be explained, and how to protect privacy and fairness. These are central questions in contemporary computing education.

Short practical exercise​

  1. Try a simple AI demo (chatbot, image classifier) — many universities and tech providers host safe demo pages. Note what the system does well and one clear limitation.
  2. Pick an AI system you interact with (recommendation, search, etc.) and write 5 bullet points describing: input, output, one likely dataset used, one possible bias, and one mitigation.