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Today we release a report that summarises the outcomes of the IndabaX2018 events: summarising its impact, the key recommendations to improve the programme going forward, and celebrating the 13 new IndabaX communities.
The Indaba𝕏 represents some of our most impactful work as an organisation thus far, and its outcomes have made us immensely proud. ‘The central cultural fact of African life’, as Anthony Appiah famously says, ‘is not in its sameness, but in its enormous diversity’. The Indaba𝕏, by creating and supporting local leadership in individual countries, sought to celebrate this diversity. The Indaba𝕏 events were meant as incubators for local ideas and solutions that accommodated the varying levels of experience and motivations, and the different modes of communication and understanding across countries. Yet, we also saw the Indaba𝕏 as a way to create a new type of pan-African unity, one created through a shared commitment to science and technology, using machine learning and artificial intelligence and the opportunities it holds for the advancement of our continent. We hope, like the annual Indaba, that the Indaba𝕏 events will also become a regular fixture within the African AI calendar, and we work towards making this a reality. We are humbled by the leaders who passionately and selflessly push African AI forward, and who made the Indaba𝕏 events such a success; this report is dedicated to them.
Overview
The 2018 Indaba - the annual gathering of the African AI community - will likely be the world’s largest gathering dedicated to teaching and debate at the state of the art in machine learning and artificial intelligence. As the main Indaba grows in size, and as we spread into many smaller IndabaX meetings, it becomes important to make a strong commitment to all the Indaba’s attendees that there will be a welcoming, electrifying, and importantly, safe environment in which everyone can meet and learn. This necessitates a code of conduct, and our's is designed around a philosophy, one that manifests in many ways across our continent, of familyhood and unity. The code of conduct gives everyone a clear understanding of the expected standards of behaviour. Every attendee will have agreed to this code of conduct before registration, and can be assured that we have a clear process for handling any reports or incidents.
The Deep Learning Indaba is the annual gathering of the African machine learning community, a week that in 2018 will be the world’s largest teaching event on the state of the art in machine learning and artificial intelligence. This year’s Indaba meets in Stellenbosch, South Africa from 9-14 September. Attendees will meet their peers from across our continent and beyond, be taught by world-leading experts in the field, learn how to implement their theoretical knowledge as solutions in code, ask advice about careers and research, debate the implications of new technologies on our continent’s future, and strengthen the African machine learning community.
Applications to participate in the 2018 Indaba are now open. We encourage everyone interested in machine learning, data science, and artificial intelligence (and other related areas of statistical sciences and engineering) to apply, and to encourage others they know to apply as well. This year, the Indaba hopes to welcome 600 people.
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.
Read in 2 mins.
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.
Today, we are proud to announce our main week-long events for the next two years!
The first Deep Learning Indaba ended with the formation of a community of Africans who spread across our continent with a commitment to share their knowledge in the principles and practice of modern machine learning and artificial intelligence. Our task was to now further empower our attendees; to become ‘the talking drums of Africa renewed’.
As a first step, we established the Deep Learning IndabaX: an ongoing experiment with the ways in which we can strengthen African machine learning: through locally-organised one day workshops or meetings that spread knowledge and build local expertise and networks—what we think is the best way to include more people in the conversation around artificial intelligence and machine learning. 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. 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.  Left: African nationals attending the Indaba. Right: Flags of the nationalities represented at the Indaba. 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. 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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