“Big Tech is not the problem”

Ciira Maina, a professor of electrical  at Dedan Kimathi University of Technology in Kenya, is the Chair of Data Science Africa (DSA). DSA is a founding member of the International Computation and AI Network (ICAIN) which was launched at the World Economic Forum (WEF) Annual Meeting in Davos in January 2024. Its mission is to develop AI technologies that benefit society as a whole and which are also sustainable and accessible to all, thereby helping to reduce global inequality.

Ciira wa Maina, what motivated your organisation to become a part of ICAIN?

Access to supercomputers is a problem that AI researchers around the world are struggling with. ICAIN gives its members access to the expertise and computing power of organisations such as the Swiss National Supercomputing Centre (CSCS) in Lugano. This allows us to expand our research. On a personal level, relationships  already in place between DSA members and European scientists, for example from the European Laboratory for Learning and Intelligent Systems (ELLIS), which is also a founding member. With ICAIN, we can now consolidate and formalise these relationships.

ICAIN’s motto is “Rebalancing the Global AI Landscape”. Does the organisation see itself as a rival to the Big Tech companies?

It is important that someone takes a stand when AI resources are monopolised. Many people have acknowledged this problem. However, someone has to stand up and take the lead in order for action to take place: they must bring the right people together and look for solutions. The Swiss government and ETH Zurich have done just that, laying the foundation for this important international initiative. I hope that we will soon be able to expand the partnership to other continents.
This is not to say that Big Tech is bad. We all depend on their products and use them daily. However, for AI to be fair and inclusive, it needs a healthy ecosystem, not a single, colossal organism.

Data Science Africa is one part of this ecosystem. What are the organisation’s objectives?

Data Science Africa is a pan-African organisation based in Kenya that was founded in 2015. Our aim is to connect data scientists and industry partners across the continent, provide training opportunities and support non-profit research projects in the field of machine learning and data science. Our goal is to harness artificial intelligence and other new technologies to solve African problems.

What are the problems specific to Africa?

There are challenges that affect the African continent specifically, whether for geographical, economic or cultural reasons. If we have a problem that is unique to the African context, we must solve it ourselves. Otherwise, no one will do it.

DSA is supporting the development of ICAIN with two pilot projects. One is about improving local weather forecasting and the other is about enabling African farmers to detect plant diseases at an early stage using a smartphone and a simple spectrometer. Is it a coincidence that both projects aim to make agriculture more efficient?

No. When prioritising problems, one starts with basic needs, and that includes nutrition. Here we also have two good examples of specifically African problems. African farms rely heavily on rainfed agriculture and are therefore highly dependent on the weather. Furthermore, Africa is by far the world’s largest producer and consumer of cassava. In bad years, up to 70 percent of production can fall victim to disease. We want to counteract this by providing farmers with an affordable tool that allows them to take action at an early stage. Of course, the members of DSA also work on various projects in parallel, for example in the areas of health, language and the environment.

You are a Kenyan and a data scientist. Who can you communicate with more easily: a Kenyan farmer or a Swiss data scientist?

That’s a difficult question. I think I’m good at both. In Kenya, we are proud of the fact that almost all of us are farmers and have some kind of connection to the land, even if it’s just growing vegetables in our own gardens. We are not disconnected from this tradition, as is perhaps the case elsewhere.

Science likes to see itself as a global endeavour. In reality, however, ETH Zurich has significantly more collaborations with partners in Europe and the United States. Cultural and/or geographical proximity certainly plays a role in this. How do you see it?

Everyone tends to work with people they already know. The more functional working relationships there are, the less likely people are to engage with new ones. In data science, there is this trade-off between exploration and exploitation: do we want to focus on exploring new spaces or deepening what we already know? Those who only do the latter are missing a lot.

Can you give an example?

When entering new spaces, we suddenly discover parallels to what we already know. For example, I didn’t know before ICAIN that different languages and local dialects are spoken in Switzerland. I thought it was a very Africa-specific challenge when using AI. Now it turns out that both sides can benefit from each other’s experiences.

You worked with researchers from ETH Zurich for the first time on these two projects. What was the experience like for you?

We have been able to build a very good relationship. We work in close collaboration with CSCS. They help us to develop expertise in the field of high-performance computing. This is an important aspect of ICAIN. Since access to supercomputers is limited on our continent, only a few students can be trained on them. ICAIN is working to change this.

When do you expect to see the first concrete results from the two pilot projects?

It depends on what you consider to be a result. It will take some time before we have fully functioning solutions. However, on the way there, many students will carry out subprojects, publish papers and attend conferences, thereby building up valuable expertise. These are also results.

Is there a danger that these people will leave Africa once they have this expertise?

That is a matter of supply and demand. Everyone should have the right to live wherever they want. There will always be some who leave to discover the world and others who stay at home to create something new here. This phenomenon is not limited to Africa. We need to train so many skilled workers that the system can cope with the personal decisions of individuals.