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• Monitoring: Continuously track the system's performance, making sure it correctly identifies items and reduces
sorting errors.
• Maintenance: Regularly update the model with new data to adapt to changes in waste types or introduce new
categories as needed.
Task 21 st Century #Productivity & Accountability
Skills
A school wants to predict student absence due to illness, especially during flu season. By predicting the likelihood
of student absence, the school can prepare by adjusting lesson plans, planning for substitute teachers, or
implementing preventive measures. This project uses AI to help administrators anticipate student's health trends
based on data from previous years.
Prepare a 4Ws Problem Canvas for the given scenario.
Introduction to AI Domains
Artificial Intelligence comprises three key domains: Statistical data, Computer Vision, and Natural Language
Processing. While each of these domains is unique, together they form the foundation of AI.
Domains of AI
Natural Language
Statistical Data Computer Vision
Processing
Statistical Data
Statistical data is a critical domain of Artificial Intelligence (AI) that focuses on the collection, management,
analysis, and interpretation of data systems and processes. In this domain, AI systems are designed to collect
vast amounts of structured, semi-structured, and unstructured data from various sources, organise and maintain
data sets, and apply statistical and machine learning techniques to derive meaningful information from them.
The information extracted through statistical data analysis can be utilised to identify patterns, trends, and
relationships within the data. This information serves as a foundation for data-driven decision-making that
enables organisations and systems to make informed predictions, optimise processes, and solve complex problems.
For instance, if I plan to host an outdoor event in August in Delhi, I would need to collect data on weather
forecasts for that month, as well as historical weather trends from previous years. This data will serve as the basis
for identifying patterns, which can then be used to make predictions about the likely weather conditions on the
day of the event. In the world of AI, data is the most important asset.
Once data is gathered from various sources, AI systems analyse and process it to extract valuable insights. These
insights help AI machines recognise patterns, make informed predictions, and even improve decision-making
processes over time. Predictive models, which are built from historical data patterns, can be used in various
fields such as weather forecasting, healthcare, finance, and more. Therefore, data is not just the starting point;
it’s the very core of the analysis and machine learning process, enabling AI to learn and evolve its capabilities.
Revisiting AI Project Cycle & Ethical Frameworks for AI 143

