How innovation ecosystem is expanding

A new learning phenomenon-self-learning - is emerging in Kenya. FILE PHOTO | NMG

By BITANGE NDEMO

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Summary

• In a nutshell, it is teaching yourself how to code using the new technologies.
• This is boosting the technology innovation ecosystem in the country.
• The Kilimani area is leading with many high tech institutions supporting some of the latest technologies such as Big Data analytics, Machine Learning, Blockchain coding, 3D printing for rapid prototyping, people-centred design and many other emerging technologies.

A new learning phenomenon-self-learning - is emerging in Kenya. In a nutshell, it is teaching yourself how to code using the new technologies.

This is boosting the technology innovation ecosystem in the country. The Kilimani area is leading with many high tech institutions supporting some of the latest technologies such as Big Data analytics, Machine Learning, Blockchain coding, 3D printing for rapid prototyping, people-centred design and many other emerging technologies.

One rainy morning last month, I joined young people attending the Deep Learning IndabaX Kenya. Unlike in the past where majority of the young people would be men, attendance was evenly split between men and women. The lead organiser was Kathleen Siminyu, a recent computer science and math major from Jomo Kenyatta University of Agriculture and Technology. She has brought Indaba to Kenya.

Indaba is a Zulu name that translates to something like an important conference held by the izinDuna (principal men) of the Zulu or Xhosa peoples of South Africa.

It is therefore a borrowed name used for a annual major technology event in South Africa. The organisation at the moment is a grassroots, volunteer-driven organisation with the mission to strengthen African machine learning. Their dual principles are to ensure that Africans are owners and shapers of the coming advances in Artificial Intelligence (AI), and to work towards more diverse representation in these fields of science and technology.

To the founders, these are by design principles that are pan-African; their work is for the entire continent and that differentiates their work as an important agenda for Africa. The Indaba organisation can be seen through the prism of three pillars: teaching and training, leadership and community building and policy and guidance.

The Nairobi event was one of 13 such events that took place across Africa over two weeks. The IndabaX events were created to help build local leadership in deep learning, spread knowledge further, and make those communities more visible. This, however, is a small part of the larger Deep Learning Indaba initiative. The people behind the Kenya IndabaX are the Nairobi Women in Machine Learning and Data Science community, which Siminyu, Muthoni Wanyoike and Deepali Gohil founded.

The team has been running this community for over a year and a half. Powering the IndabaX is just one of the many activities they undertake, but the overall aim is to improve the representation of women, African women in particular, in the fields of Data Science and Machine Learning. The Indaba organisation has already launched the Kambule and Maathai awards to create spaces of recognition to highlight African excellence in AI.

They are committed to supporting and contributing to the policy framework around AI that will affect our continent and that will need to be developed. These young people are visionary and are doing what institutions of higher learning should be doing considering the fact that the older generation has not seen any good in AI and related technologies. Instead, what dominates discourse is how these new technologies will destroy jobs. In football, it is said that the best defence that you can ever mount is offence.

Africa must seek to play in the league of emerging technologies. No one has ever fought technology and won. The best medicine will always be trying to learn and be at the top of any technology. The significance of these new technologies is far-reaching.

Here are some of the research projects that were presented last month. A drowsiness detection system that monitors a driver’s eyes and alerts the driver if they are falling asleep created using OpenCV, a computer vision library and presented by Sharon Waithira; Fraud Detection in Fintech - research conducted to detect and mitigate fraud in online transactions using machine learning presented by Obadiah Obare. This model is in production and is used in the loanbee application.

There was also data driven patient diagnosis with Dr. Elsa - using an ensemble of deep learning algorithms. This is why I look forward to having the main Deep Learning Indaba conference hosted in Kenya in September 2019.

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