Sitting in a hotel lobby in Tangier, Morocco, Charity Wayua laughs as she recounts her journey to the city for a conference on technology and innovation. After starting her trip in Nairobi, Kenya, where she leads one of IBM’s two research centers in Africa, she had to fly past her destination for a layover in Dubai, double back to Casablanca, and then take a three-and-a-half-hour drive to Tangier. What would have been a seven- to eight-hour direct flight was instead a nearly 24-hour odyssey. This is not unusual, says the IBM Research team lead in Nairobi, Kenya.
The hassle of traveling within the region isn’t the only thing making things difficult for Africa’s research community: the difficulty of traveling out of the region has often left its researchers out of the international conversation. While these issues have affected every scientific field, they are amplified in AI research. The pace of innovation means, for example, that repeatedly missing conferences over visa problems—which have made it hard for African scientists to attend some of the world’s largest AI events in the US and Canada—can easily cause a researcher to fall behind.
Despite the odds, the African machine-learning community has blossomed over the last few years. In 2013, a local group of industry practitioners and researchers began Data Science Africa, an annual workshop for sharing resources and ideas. In 2017, another group formed the organization Deep Learning Indaba, which now has chapters in 27 of the continent’s 54 countries. University courses and other educational programs dedicated to teaching machine learning have burgeoned in response to increasing demand.
The international community has also taken note. In late 2013, IBM Research opened its first African office in Nairobi; it added another in Johannesburg, South Africa, in 2016. Earlier this year Google opened a new AI lab in Accra, Ghana, and next year ICLR, a major AI research conference, will host its event in Addis Ababa, Ethiopia.
The shift is a positive one for the field, which has suffered from a lack of diversity and, in many ways, a detachment from the real world. Many of the academic and corporate research labs that dominate AI research are concentrated in wealthy bubbles of innovation like Silicon Valley and China’s Zhongguancun, outside Beijing. That limited purview shows in the scope of the products these hubs create. Africa, on the other hand, might offer a context with which AI can return to its original promise: creating technology that tackles pressing global challenges like hunger, poverty, and disease.
“I think for anyone who’s looking for tough challenges,” says Wayua, “this is the place to be.”
The African model of innovation
Both IBM Research’s offices in Kenya and South Africa and Google’s AI lab in Ghana share the same mission as their parent organizations: to pursue fundamental and cutting-edge research. They focus on issues like increasing access to affordable health care, making financial services more inclusive, strengthening long-term food security, and streamlining government operations. The list is not unlike that for a lab located anywhere else in the world, but the context adds nuance to the objectives.
“Research cannot be detached from the environment in which it is performed,” says Moustapha Cisse, the director of Google AI Ghana. “Being in an environment where the challenges are unique in many ways gives us an opportunity to explore problems that maybe other researchers in other places would not be able to explore.”
Researchers discuss a project on personalized education in Nairobi.
Before founding its AI lab in Ghana, for example, Google began working with farmers in rural Tanzania to understand some of the struggles they faced in maintaining consistent food production. The researchers learned that crop disease can significantly reduce yield, so they created a machine-learning model that could diagnose early stages of disease in the cassava plant, an important staple crop in the region. The model, which works directly on farmers’ phones without needing access to the internet, helps them intervene earlier to save their plants.
Wayua gives another example. In 2016, the Johannesburg team at IBM Research discovered that the process of reporting cancer data to the government, which used it to inform national health policies, took four years after diagnosis in hospitals. In the US, the equivalent data collection and analysis takes only two years. The additional lag turned out to be due in part to the unstructured nature of the hospitals’ pathology reports. Human experts were reading each case and classifying it into one of 42 different cancer types, but the free-form text on the reports made this very time-consuming. So the researchers went to work on a machine-learning model that could label the reports automatically. Within two years, they had developed a successful prototype system, and they are now striving to make it scalable so it can be useful in practice.
“Technology is only half of the equation,” Wayua says. “The other half is being able to understand the problems that we see and being able to define them objectively in a way that science and engineering can address.”
IBM Research’s office in Nairobi, Kenya.
Once a research project is ready for the real world, then comes another hard bit: getting buy-in from the intended users. “Relationships really matter in driving change,” says Wayua. It’s easy to collect data and design a perfect system in a vacuum, but that’s pointless if no one wants to use it. “It’s the relationships that you continuously build over time that help you can understand why what you are trying to implement is not really working,” she adds.
Responding to the needs of users also helps drive fundamental advances in the technology’s capabilities. Google AI Ghana is now working on improving natural-language understanding, for example, to accommodate the roughly 2,000 languages spoken in Africa. “It is by far the most linguistically diverse place on Earth,” says Cisse. “There’s a lot to learn and to research from that.”
The next generation
Cisse and Wayua share similar career trajectories. Each left Africa for higher education before coming back, hoping to apply their skills in ways that would maximize their impact. Cisse worked at Facebook in Europe while he waited for the right opportunity to return.
An African scientist teaching students.
Now, both are deeply invested in developing more local educational opportunities for youth interested in AI. Cisse founded and directs the African Master’s in Machine Intelligence, a one-year intensive program that operates learning programs around the region and brings in some of the best AI researchers around the world. Wayua’s lab hires high-performing undergraduates to work alongside full-time staff and pays for them to take the online master’s program in computer science offered by Georgia Tech University.
“The main resource for doing research is talented people, and you will find more talent in Africa than anywhere else,” says Cisse, pointing to the disproportionately young population. “The energy for tech here is just amazing. The question is how do you equip those talented people with the skills so that they own the transformation of the continent and build their own future?”
When Cisse teaches his students in the master’s program, he tells them that in five years’ time, they will be the ones leading the field and returning to teach the classes. Of this, he has no doubt.
“The future of machine-learning research is in Africa,” he says, “whether people know it or not.”
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