It will not be surprising that across our African continent, machine learning is being developed and deployed in ways both fundamental and transformative. In both research and applications, African innovators continue to enhance our knowledge, and address the intractable challenges facing our societies and people: whether these be in the mathematical underpinnings of reasoning and control; in developing solutions for food security, public health, and water and disease management; or in unravelling the mysteries of the universe. We celebrate the success of these African Artificial Intelligence innovators. And to do this, we introduce two new awards, the Kambule and Maathai awards, which recognise excellence in the research and applications of machine learning and artificial intelligence by African researchers and technologists.
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Today we celebrate an anniversary: the Deep Learning Indaba was formed during this week one year ago, with a mission to strengthen African machine learning. Our first Indaba was held in September 2017, and was everything we had hoped for. We saw the creation of a new community, one united by a shared commitment to science and learning, and the potential it has to transform our societies for the better. We executed a technical programme of sharing, teaching and debate around the state-of-the-art in modern machine learning, whose mastery is essential in ensuring that we as Africans become, not just users and receivers, but contributors, shapers and owners of the global advances in machine learning and artificial intelligence. We saw Africans, from across the continent and in all its diversity, represented and included. And a few months later, we saw 20 of our participants at the NIPS conference for their first time.
On the 10th of September this year (2017), we took our first uncertain step through a doorway. For all the months prior, starting in February, we operated in the realm of imagination, of planning, of spreadsheets and budgets, driven by a mission to ‘Strengthen African Machine Learning’. Crossing the threshold that Sunday afternoon, we entered an environment that was everything we had hoped for. We had seen the creation of a new community, one united not by historical injustices, but by a shared commitment to science and learning, and the potential it has to transform our societies for the better. We executed a technical programme of sharing, teaching and debate around the state-of-the-art in modern machine learning, whose mastery is essential to realising the vision of a transformed and prosperous continent. And we saw Africans, from across the continent and in all its diversity, represented and included.
Read in 2 minutes ● Indaba Organisers
In one week, the first Deep Learning Indaba begins: a gathering of our African community to teach, learn and debate the state-of-the-art in machine learning and artificial intelligence. Our aim during the week will be to build an understanding of the principles and practice of modern machine learning. Of equal importance is the creation of an environment that enables continental collaborations, a raised awareness of the breadth of machine learning career-paths, and that fosters new understandings and friendships.
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.
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 . 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 . 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 .