A retail company wants a system that improves its product recommendation accuracy over time by learning from customer purchase history, without being explicitly programmed with new rules each season. Which AI discipline best describes this approach?
- ARule-based expert systems, which encode domain knowledge as hand-crafted if-then rules.
- BMachine learning, which trains models on historical data so that predictive behaviour improves with experience. Correct
- CBusiness intelligence reporting, which aggregates and visualises historical sales data for human analysts.
- DRobotic process automation, which executes predefined workflow steps to mimic human repetitive tasks.
Why A is wrong: Tempting because rule-based systems do drive recommendations, but they require explicit programming of every new rule. They do not learn autonomously from data, which is the defining requirement in the stem.
Why B is correct: Machine learning is the discipline in which algorithms learn patterns from data and improve performance on a task without being explicitly re-programmed, matching the scenario precisely.
Why C is wrong: Tempting because it uses historical purchase data, but BI reporting surfaces information for human decision-making rather than training a model that autonomously improves its own predictions.
Why D is wrong: Tempting because RPA is often grouped with AI tooling, but it follows a fixed script and does not learn from data. It cannot improve recommendation accuracy through experience.