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At the 30th conference on Neural Information Processing in 2016, one of the world’s foremost gatherings on machine learning, there was not a single accepted paper from a researcher at an African institution.

The following year, a group of researchers set out to change this, founding a world-class machine learning conference that would strengthen African machine learning – the Deep Learning Indaba.

The indaba (a Zulu word for a gathering or meeting) was a runaway success, with almost 300 participants representing 22 African countries and 33 African research institutes.

It was a week-long event of teaching, sharing and debate around the state of the art in machine learning and artificial intelligence that aimed to be a catalyst for strengthening machine learning in Africa.

When I heard about it, I knew I wanted to contribute to the 2018 Indaba, and I was excited that Microsoft was already signed-up as a headline sponsor, and had our own Danielle Belgrave on the advisory board.

In my eight years working in the UK in the tech community, I have seldom come across African scientists, engineers and researchers sharing their work on the international stage.

We realized that even if we could raise sufficient funds for his participation, the money would have achieved so much more in his home country that he couldn’t justify spending it on a conference.

To quote the organizers, “It is critical for Africans, and women and black people in particular, to be appropriately represented in the advances that are to be made.” The make-up of the Indaba in its first two years is already impressive and leads by example to show how to organize a diverse and inclusive conference.

I have found great support and encouragement from women-in-tech communities and events such as PyLadies/RLadies London and AI Club For Gender Minorities, and saw the Indaba as an opportunity to pay it forward and link up with like-minded women globally.

As there will be a high number of students in attendance, our panel also highlights diverse career paths, from academia to industrial research, to applied machine learning, to start-ups.

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