Tutorial 7: Artificial Intelligence and Generative AI
COMP200 · Unit 4 · Section 7
Objectives
Distinguish rule-based, machine-learning, and generative systems.
Explain training, inference, uncertainty, and evaluation.
Identify bias, privacy, provenance, and misuse risks.
AI systems perform tasks associated with perception, prediction, planning, or language. Machine learning infers patterns from data; generative models produce new outputs from learned distributions. An output can be fluent and still be wrong, biased, private, or unsupported.
data -> training -> model -> prompt or input -> inference -> evaluation
Exercises
Compare classification with text generation.
Design a verification workflow for generated content.
Identify data and bias risks.
Self-check
What is inference?
Why evaluate training data?
Why verify generated output?
Self-Check Quiz
1. Does fluent output guarantee accuracy?
AnswerNo. Generative systems can produce plausible but false or unsupported content.
2. What is training data used for?
AnswerIt provides examples from which a model learns patterns or parameters.
Homework
Evaluate an AI-assisted workflow.
Specify human verification, privacy, and attribution controls.
Discuss one appropriate and inappropriate use.
Sample answerUse AI for drafts or alternatives, verify factual claims against authoritative sources, avoid confidential inputs, disclose material assistance, check bias and accessibility, and keep a human accountable for the final decision.