List the five stages in the AI project cycle. Write brief description of each.

The AI project cycle consists of five stages. It is a structured framework that outlines the step-by-step process of developing and implementing an AI system. The stages are as follows:

1. Problem Scoping: In this stage, we clearly identify the problem, set clear goals, and define what we want the AI system to achieve. It involves understanding the needs, objectives, constraints, and planning how to solve the problem.
2. Data Acquisition: Here we collect relevant data needed for the AI system from reliable sources (e.g., surveys, sensors, cameras, observations, APIs, web scraping). The data must be accurate and suitable for the problem.
3. Data Exploration: In this stage, we explore and study the data to find patterns, trends, relationships, and possible errors. Data is often represented using charts/graphs to understand it better and decide what information (features) is important.
4. Modelling: After understanding the data, we choose and build an AI model (algorithm/program) and train it using data so that it can make predictions or decisions.
5. Evaluation: Finally, we test the model’s performance by comparing its results with expected outcomes. If the accuracy is good, the model can be used/deployed otherwise, it is improved and tested again.