Page 141 - AI Ver 3.0 class 10_Flipbook
P. 141

These questions serve as a structured approach to ensure that all relevant aspects of the problem are considered,
                 thereby facilitating a more effective and efficient analysis and solution development.

                 Once you’ve completed the 4W's problem canvas, the next step is to consolidate all the information into a single,
                 comprehensive template. The problem statement template allows you to compile all the key details into one
                 format, providing a clear reference point for future use. Problem Statement Template is a structured format to
                 articulate the problem clearly. It ensures that the problem is well-defined and understood by all stakeholders.
                 Below is a template with designated spaces to fill in details aligned with your objectives.

                                                    4W Problem Statement Template

                                  Our                         [stakeholders]                    Who
                                  has a problem that          [issue, problem, need]            What

                                  when/while                  [context, situation]              Where
                                  An ideal solution would     [benefit of solution for them]    Why


                  Stage 2   Data Acquisition

                 The next stage in the AI project cycle is known as data acquisition. This stage involves gathering raw data, which
                 is essential for referencing or performing analysis that will guide the project. The process of data acquisition
                 encompasses the collection of a wide range of data types, including text, numerical values, images, videos, and
                 audio. These various forms of data can be sourced from multiple places such as the internet, academic journals,
                 newspapers, and other relevant publications or databases. The goal of data acquisition is to capture accurate
                 and valuable information that reflects real-world scenarios. This collected data serves as a foundation of the
                 project that provides valuable insights and enabling the improvement of the project’s performance and the
                 development of more precise AI solutions.


                  Stage 3   Data Exploration

                 Data exploration is a crucial step that involves analysing large volumes of data to uncover meaningful patterns,
                 trends, and relationships using various data visualisation and statistical techniques. By transforming raw data
                 into visual formats such as charts, graphs, and plots, data scientists can more easily interpret and draw insights
                 from the information. This analytical approach not only helps to reveal underlying structures within the dataset
                 but also facilitates the identification of anomalies or irregularities, ultimately laying the groundwork for more
                 informed and effective decision-making in subsequent stages of the AI project.

                  Stage 4   Modelling

                 The design phase is an important stage in the AI project cycle, ensuring that AI systems function effectively.
                 Data modeling is a foundational step within the AI development process, happening before model training. It
                 involves identifying and defining the relationships between different data entities, ensuring that the AI model
                 can understand the connections and dependencies within the data. The quality of the data model significantly
                 impacts the performance of the AI model, as well-structured data enables better learning and more accurate
                 predictions.
                   Additionally,  comparative  analysis  is  performed  to  understand  the  advantages  and  disadvantages  of  each
                 model. This helps in identifying the most effective solution that aligns with the project goals and constraints.
                 The design phase often involves iterative refinement, where initial models are fine-tuned based on feedback or
                 preliminary testing.


                                                            Revisiting AI Project Cycle & Ethical Frameworks for AI   139
   136   137   138   139   140   141   142   143   144   145   146