Artificial Intelligence and Machine Learning in Africa

 

This three-unit micro course is one of a series in the Carnegie Mellon University Africa online certificate program in Artificial Intelligence and Machine Learning.

Course Description  |  Learning Objectives  |  Outcomes  |  Lecture Notes  |  Recommended Reading  | 

Course Description

This micro course provides a comprehensive overview of the relevance of AI and machine learning to Africa and their potential to solve economic and social problems. It addresses the issues that arise in realizing that potential, distinguishing between the processes of technological invention, innovation, and adoption, and the latter's dependence on socio-cultural factors, including trust. The material is based on fourteen articles on the potential of AI and machine learning in Africa, application case studies, AI business strategy, and the deployment of AI and machine learning in Africa. The micro course requires approximately forty-two hours of study. It comprises fourteen hours of content, i.e., the amount that would normally take fourteen hours to deliver in a classroom setting. Each hour of content is provided in course notes and summarized in a 10-12 minute video presentation. Students will spend the remaining time studying the material covered in the video lecture, encapsulated in the video lecture slides, course notes, and auxiliary reading material, and periodically taking self-check quizzes comprising three to five multiple choice questions. Student progress is assessed by a series of multiple-choice tests.

Learning Objectives

Students will learn about the ways in which AI and machine learning are being used in Africa today to make new products and services available for people and how they might be used in the future to make African businesses and organizations more competitive. They will learn the importance of engendering trust in the technological solutions in order for these products and services to be adopted and for the resultant benefits to be achieved. Students will learn that different applications impose varying constraints that, in turn, require different technical approaches. They will learn the importance of surveying possible solution strategies and identifying their strengths and weaknesses.

Outcomes

After completing this course, students should be able to:

  1. Identify and summarize several applications of AI and machine learning in Africa.

  2. Identify the various sectors that have benefitted from AI and machine learning.

  3. Understand the need to evaluate different solution strategies, techniques, and technologies.

  4. Explain the difference between technological invention and innovation.

  5. Explain the importance of trust and its role in the adoption of new technology solutions.

  6. Identify which AI technologies will have the greatest potential benefit for their organization.

  7. Explain how these technologies enable new business strategies, business models, or business processes.

Lecture Notes

Module 1: The Potential of AI and Machine Learning in Africa
Lecture 1: Realizing the Potential of AI in Africa (Delmus Alupo et al., 2023) and commentary.
Lecture 2: Computational Sustainability and Artificial Intelligence in the Developing World (Quinn et al., 2014) and commentary.

Module 2: Application Case Studies
Lecture 1: Healthcare (Onu et al., 2017; Onu et al., 2019) and commentary.
Lecture 2: Logistics (Ackerman and Koziol, 2019) and commentary.
Lecture 3: Agriculture (Quinn, 2013) and commentary.
Lecture 4: E-Commerce (Uwizera et al., 2020) and commentary.
Lecture 5: Socioeconomics (Yeh et al., 2020) and commentary.
Lecture 6: Conservation (Xu et al., 2020) and commentary.

Module 3: AI Business Strategy
Lecture 1: Artificial Intelligence for the Real World (Davenport and Ronanki, 2019) and commentary.
Lecture 2: How to Choose Your First AI Project (Ng, 2019) and commentary.
Lecture 3: Collaborative Intelligence: Humans and AI Are Joining Forces (Wilson and Daugherty, 2019) and commentary.
Lecture 4: The Future of AI Will Be About Less Data, Not More (Wilson, Daugherty, and Davenport, 2019) and commentary.

Module 4: Deployment of AI and Machine Learning in Africa
Lecture 1: Machine Learning for the Developing World (De-Arteaga et al., 2018) and commentary.
Lecture 2: Artificial Intelligence Deployment in Africa (Gwagwa et al., 2020) and commentary.

Recommended Reading

Ackerman E, Koziol M (2019) The blood is here: Zipline's medical delivery drones are changing the game in Rwanda. IEEE Spectrum 56(5):24-31 (video)

Davenport, T. H., and Ronanki, R. (2019). Artificial Intelligence for the Real World, Harvard Business Review, January - February, pp. 108 - 116.

De-Arteaga, M., Herlands, W., Neill, D. B. and Dubrawski, A. (2018). Machine Learning for the Developing World, Association for Computing Machinery, Vol. 9, No. 2, pp. 1-14.

Delmus Alupo, C., D. Omeiza, D., and Vernon, D. (2023). Realizing the Potential of AI in Africa, in Towards Trustworthy Artificial Intelligence Systems, M. I. Aldinhas Ferreira, O. Tokhi eds. Intelligent Systems, Control and Automation: Science and Engineering. Springer.

Gwagwa, A., Kraemer-Mbula, E., Rizk, N., Rutenberg, I., & De Beer, J. (2020). Artificial intelligence (AI) deployments in Africa: Benefits, challenges and policy dimensions. The African Journal of Information and Communication (AJIC), 26, 1-28.

Ng, A. (2019). How to Choose Your First AI Project, in Insights You Need from Harvard Business Review - Artificial Intelligence, Harvard Business School Publishing Corporation.

Quinn, J. (2013). Computational Techniques for Crop Disease Monitoring in the Developing World. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg.

Quinn, J., Frias-Martinez, V., and Subramanian, L. (2014). Computational sustainability and artificial intelligence in the developing world. AI Magazine, 35(3).

Onu, C. C., Udeogu, I., Ndiomu, E., Kengni, U., Precup, D., Sant'anna, G. M., Alikor, E. A. D., and Opara, P. (2017) Ubenwa: Cry-based Diagnosis of Birth Asphyxia, Machine Learning for Development Workshop, 31st Conference on Neural Information Processing Systems.

Onu, C. C., Lebensold, J., Hamilton, W. L., and Precup, D. (2019). Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia, 20th Annual Conference of the International Speech Communication Association INTERSPEECH, pp. 3053–3057.

Uwizera, D., Bares, W., Voss, C. (2020). Data Centric Face Recognition for African Face Authentication, Smile Identity.

Wilson, H. J. and Daugherty, P. (2019). Collaborative Intelligence: Humans and AI Are Joining Forces, in Insights You Need from Harvard Business Review - Artificial Intelligence, Harvard Business School Publishing Corporation.

Wilson, H. J., Daugherty, P., and Davenport, C. (2019). The Future of AI Will Be About Less Data, Not More, in Insights You Need from Harvard Business Review - Artificial Intelligence, Harvard Business School Publishing Corporation.

Xu L., Gholami, S., Mc Carthy, S., Dilkina, B., Plumptre, A., Tambe, M., Singh, R., Nsubuga, M., Mabonga, J., Driciru, M., Wanyama, F., Rwetsiba, A., Okello, T., Enyel, E. (2020). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (Short Version), IEEE 36th International Conference on Data Engineering (ICDE), pp. 1898-1901, doi: 10.1109/ICDE48307.2020.00198.

Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., and Burke, M. (2020). Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications 11, 2583.

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