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.
0 Comments
An aim of the Deep Learning Indaba was to make the excellent research in all areas of machine learning and data science more visible, offering a showcase of the continental research base. We believe we took the first positive steps in this direction. It is our privilege to be able to recognise the excellence in research shown by all the students at the Indaba: they are clear evidence of the capacity in deep learning and machine learning that exists in our continent. They have inspired us all to do our best work.
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 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.
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]. |
|