Day 1 – AI Foundations

8. Activities and Reflection

Day 1 · Apply and Reflect

Day 1 Activities and Reflection

Consolidate your understanding of AI foundations, complete the Day 1 interactive activities and connect the concepts to your institutional context.

Day 1 summary

Artificial intelligence is a broad field that includes systems capable of prediction, pattern recognition and decision support. Machine learning is one branch of AI in which models learn relationships from data, while deep learning uses multilayer neural networks for complex tasks such as analysing satellite images and gridded weather fields.

AI can support forecast post-processing, bias correction, downscaling, nowcasting, data quality control, extreme-event monitoring and impact-based early warning.

However, operational value depends on suitable data, independent evaluation, local relevance, transparency, institutional capacity, sustainable infrastructure and continued human oversight.

Five key takeaways

Start with the problem Define the operational need, users and decision before choosing a model.

Data determine possibilities Model performance depends on data quality, coverage and representativeness.

Evaluation must be independent Models should be tested on unseen data and compared with appropriate baselines.

AI should complement experts Forecasters remain essential for interpretation, communication and accountability.

Responsibility is essential Transparency, fairness, inclusion, sustainability and data stewardship must be considered.

Complete the Day 1 activities

Use the activities below to review the concepts, test your understanding, exchange experiences with other participants and identify a possible AI application for your institution.

Review

Day 1 presentation slides Review the main definitions, diagrams, examples and responsible AI principles introduced during the sessions.

Interact

AI concepts and applications Match AI concepts, weather and climate challenges, datasets and possible applications.

Assess

Day 1 knowledge check Test your understanding of AI, machine learning, model workflows, limitations and responsible use.

Discuss

Responsible AI in the African context Share one possible AI application and one important limitation or risk relevant to your institution.

Apply

Institutional AI reflection Describe an operational problem, possible AI application, required data, expected benefit and one limitation.

Group case study: should AI be used?

A national meteorological service wants to develop a system that predicts high-impact rainfall at district level using satellite products, numerical-weather-prediction outputs and available station observations.

Discuss the following:

What operational problem would the system address?
Who would use the prediction and what decision would it support?
Which input data would be required?
How could missing station observations affect performance?
How should the model be evaluated before operational use?
How should uncertainty be communicated?
What role should forecasters continue to play?
Who would operate and maintain the system?

Personal reflection

Before completing Day 1, write brief responses to the following questions:

What new concept was most useful to you today?
What AI application is most relevant to your institution?
Which dataset would be essential for that application?
What is the greatest limitation or risk?
Which responsible AI principle deserves the most attention?
What question would you like to explore during the remaining days?

Final self-check

Can you explain these ideas?

What is the difference between AI and machine learning?

AI is the broader field of computer systems that perform tasks such as prediction, recognition or decision support. Machine learning is one approach within AI in which models learn relationships from data.

Why should a simple baseline be evaluated?

A baseline shows whether the machine-learning model provides a real improvement over a simpler or existing operational method.

Why might a model perform poorly in data-sparse areas?

Those locations may be under-represented during training and evaluation, and available gridded products may not reproduce local conditions accurately.

Why is human oversight necessary?

Human experts provide physical understanding, local knowledge, interpretation of uncertainty and accountability for operational decisions and warnings.

Day 1 completion checklist

Before finishing Day 1, confirm that you have:

Reviewed all eight chapters of the Day 1 Book.
Watched or reviewed the keynote material.
Completed the interactive AI activity.
Taken the Day 1 knowledge-check quiz.
Contributed to the responsible AI discussion.
Submitted your institutional reflection.
Identified one question to carry forward into Day 2.