A little over 2 years ago, Muthoni Wanyoike and I started the Nairobi chapter of Women in Machine Learning and Data Science (WiMLDS). Kathleen Siminyu is the co-organizer of WiMLDS in Nairobi with her friend Muthoni Wanyoike, who leads a team at InstaDeep — an AI startup. Kathleen is the Head of Data Science at Africa’s Talking and part of the Steering Committee of Deep Learning Indaba.
We were searching for our tribe. A community of people, who much like us, were either working in the data space or were curious about it and looking to explore. In the span of two years, this community has grown and evolved tremendously. We have experimented with a variety of activities; monthly meetups, quarterly study groups, hackathons, and round table discussions, and now have a following of over 2,000 people on meetup.com. Some of my personal favourite stories from the community are of the women and men who “earn their stripes” and then give back by taking it upon themselves to organize an AI community activity. Succession and continuity are important for long term impact.
When we started doing this, there was no other such space in Nairobi. Today, there are several other communities that possibly do it much better than we ever did or could. The Nairobi AI ecosystem has grown, and we contributed to it, however, our story is not unique.
Across the continent, we are seeing the creation of a vibrant African AI ecosystem. Data Science Nigeria runs programs strategically focused on capacity building, particularly in secondary schools and universities. Blossoms Academy in Ghana provides university graduates with the skills needed to launch meaningful careers in Data Science. The first edition of the North Africa Machine Learning Summer School will take place in Morocco in June of this year and the third Deep Learning Indaba will take place in August of this year in Nairobi, Kenya, in addition to smaller, independently organized IndabaX events being hosted in 27 countries across the continent; Algeria, Botswana, Burkina Faso, Burundi, Cameroon, Democratic Republic of the Congo, Egypt, Ethiopia, Ghana, Kenya, Lesotho, Malawi, Morocco, Namibia, Nigeria, Rwanda, Senegal, Somalia, South Africa, Sudan, Swaziland, Tanzania, The Gambia, Tunisia, Uganda, Zambia and Zimbabwe.
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My intention with listing all these is to highlight the fact that there are increasingly more opportunities for Africans on the continent to build technical skill in the fields of Data Science and Machine Learning through communities, summer schools and now the African Masters of Machine Intelligence at African Institute for Mathematical Science (AIMS), whereas several years ago there was a dearth of such opportunities.
Clearly, something is a-brewing. Back in January, a friend of mine in the Nairobi WiMLDS community messaged me to wish me a happy new year and we had the following exchange;
M: What’s the plan for Data Science in Kenya in 2019? * I laughed out loud, really I did *
K: The national plan…ama?
M: Si ati national plan. If you are working on something cool I can join in. Side project or stuff…
He was asking me a question I had been asking myself for a while, “What next?”
What next for the individuals who begin engaging with the Nairobi WiMLDS community with little more than an interest. The ones who begin by picking up the basics of programming in R or Python, then collaborate on fun projects, take part in a hackathon, and give back by facilitating sessions for others. What more can they do to grow and further contribute as part of the community? Short of getting them a job, which we cannot possibly do for every individual, what more can we do to further engage and foster this community?
“The future of AI and Machine Learning (ML) will be driven by the community through grassroot movement. This is a bottom-up model where people come together to build solutions for problems they can associate with.”
–Six reasons why community-driven AI is the future by Rudrab Mitra
In the context of our local WiMLDS community, the next step is open source projects. We are crowdsourcing efforts to build natural language processing (NLP) for low-resource languages among Kenya’s 67 living languages, such as Dholuo and Kamba with over 4 million speakers. An ideal problem set to begin with, in my opinion, because anyone with the know-how can begin contributing towards resources for the languages that they care about. Given the great diversity of African languages and the overall lack of tools to easily work with them, this is a problem that we can all relate to when it comes to building machine learning applications based on the processing of our own languages.
Other communities across the continent will soon find that they will be asking themselves the same question, or some variation of it if they are not already. For those focused on capacity building, then comes the question of where and how to make the best use of this capacity?
How can we begin to fix our education systems such that we no longer have to innovate and create spaces outside of it to make up for the gaps?
How can we support individuals who are innovating, get them mentorship and resources, well equip them to increase their chances of succeeding as entrepreneurs, help reimagine Kenya’s industries, and contribute to Africa’s economic and social success? To gain access to funding and credit? Do we have the necessary infrastructure and enabling systems upon which they can build and innovate?
Going forward, the road is long and no one individual holds the answers but we each must play our part if we hope to reap from the collective efforts of the community.
Contribution by Kathleen Siminyu for the AI Research Network of Excellence @AI4Dev #AI4DNetwork. She is the head of Data Science at Africa’s Talking, co-organizer of Nairobi WiMLDS, and part of the steering committee of Deep Learning Indaba.