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We pose a question to you: At the leading machine learning venue, the annual Conference on Neural Information Processing Systems (NIPS), how many accepted papers in 2016 came from research groups on the African continent? Or to be more general, how many accepted papers have at least one of its authors from a research institution in Africa? The answer: zero. And what for the South American continent? Similarly. Zero.  Changes in NIPS accepted papers by country (excl. US) from 2006-2016. The vertical axis is GDP per capita in US$ per year, and the horizontal axis the number of accepted papers.
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Read in 2 minutes ● Indaba Organisers Who are the machine learning scientists? This was the question we asked ourselves during our planning for the Deep Learning Indaba. In our answer, we saw a fresco of geometry and colour that would make even Esther Mahlangu proud: a thriving community of different peoples, backgrounds and viewpoints, and whose support we could use in our mission to strengthen African machine learning. We found a scientific community that was everything we hoped for.  Speakers at the Deep Learning Indaba 2017 Read in 2 minutes ● Indaba Organisers Across the African continent, our communities gather to create spaces where we share our experiences, seek advice, and discuss the pressing issues of the day. In Zulu, this type of gathering is called an Indaba. This September, the first Deep Learning Indaba will take place: a shared space to learn, to share, and to debate the state-of-the-art in machine learning and artificial intelligence, and our African contributions to this scientific endeavour.
African machine learning is strong and varied. To support the food security of our nations, computer vision is used to detect cassava root disease in images captured using low-cost mobile phones [1]. Where health services and advice is limited, especially for HIV and AIDS, machine learning is used to shorten response times in mobile question-answering services, allowing these services to reach more people [2]. And the African contribution to Big Science, in particular in radio astronomy through the square kilometre array telescope, will advance the state of machine learning to provide new insights into the workings of the universe [3]. |
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