Components and Stages (Alias Al Project Cycle)

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An AI Project Cycle is the sequence of phases a project goes through from its initiation to its closure. In Artificial Intelligence, it has five stages: Problem Scoping, Data Acquisition, Data Explora…

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The five stages are: 1. Problem Scoping 2. Data Acquisition 3. Data Exploration 4. Modelling 5. Evaluation. These stages move from identifying the problem to checking how well the AI model performs.

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Problem Scoping means identifying a problem and having a vision to solve it. It also means narrowing a broad theme into a clear problem statement. A common tool used here is the 4Ws Problem Canvas.

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The 4Ws Problem Canvas helps identify the key elements of a problem using four blocks: Who, What, Where, and Why. It makes the problem clearer by showing who is affected, what the problem is, where it…

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The Who block identifies the stakeholders, meaning the people who face the problem and would be benefitted by the solution. It helps in understanding who is directly or indirectly affected.

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The What block is used to define the nature of the problem. It asks what the problem is and how evidence shows that it really exists. Evidence can include newspaper articles, media, or announcements.

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The Where block focuses on the context, situation, and location where the problem happens. It helps identify where the problem is prominent and in what situation stakeholders experience it.

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The Why block explains why the problem is worth solving. It shows the value of the solution for the stakeholders and how it can improve their situation and benefit society.