Zambia in AI
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Zambia in AI

Deep Learning Indaba — X Reveals Zambia’s Next Cream

Wednesday 17th of April was no ordinary Day for the world in Tech. As a 100+ plus Tech Enthusiasts, Tech Experts and Students from renowned Learning Institutions gathered in the walls of the University of Zambia Computer Science Laboratory to attend this year’s Deep Learning Indaba-X.

The event was graced by government’s recognition of the event, through Smart Zambia’s National Coordinator. The Deep Learning Indaba X was the second in its series for Zambia with its predecessive event, of 2018, having laid the first layer of the foundation. In a math of progress, this year’s Deep Learning Indaba (DLI)- X enjoyed an overwhelming double increase in overall representation - with Women participation rising from the 1% of 2018 to 20%. Zambia was indeed warming up to change and women were not stopping at nothing in claiming their share by Thinking equal, Building Smart, Innovating for change as their theme for this year’s Women’s Day had suggested.

The Deep Learning Indaba (DLI) X in Zambia was an initiative of Tsogolo Tech, a subsidiary of Family Development Initiatives, and was aimed at preparing Zambia for the main Machine and Deep Learning Indaba to take place in Nairobi, Kenya this year. Many other countries have and will hold their 1-day DLI-X events to gain local footprint and there is no telling what potential they have attracted from their local communities to present at the main Indaba. But attending Zambia’s Indaba — X shows just how much Zambia is unprepared to back down.

photo credits: Tsogolo Tech. Bottom left image speakers Emmanuel Lwele [left] and Francis Chikweto [right] sitting in during Indaba

Opening remarks at the Indaba X, came from partners that made the event possible: Family Development Initiatives, University of Zambia and Government who in their keynotes echoed the need for 'self-relevance' among young professionals in order to adapt to jobs and needs of the 4th Industrial revolution. To emphasize this, Mr Yussuf Ayami of FDI cited how initiatives such as Home Based Care model had passed on from being the in-thing in a fast evolving age, while Director of e-Governance, Mr. Milner, indulged the audience with Government’s new way of thinking called 2018–2030 e-Governance System Master plan, and explained current works on e-Visa and e-Citizen innovations. Government was not ready to remain behind in the race. While Dr. Monde Kabemba, who heads the Computer Science Department of UNZA, played the mother role in hosting the event.

Relevance of the event was seen in the deliberate selection of local speakers to run the sessions planned out for the rest of the day. This showed how much Deep Learning and Machine Learning were no longer a strange tongue, but one that had already made its way in this nation and was only seeking more people to speak and walk it.

To iron out proficiency levels and give a stepping stone to members utterly new to programming, Ms. Cynthia Mulenga led the Audience into the first session of Basic Math and Python programming language, which was followed by a Machine Learning Session by Mr. Emmanuel Lwele before conclusion with a Deep Learning session by Mr. Francis Chikweto.

The Trio were known Zambians in the Tech Community with Ms. Mulenga being the Consult Lead at Bongohive, a Mentor at Asikana Network among other roles. Mr. Lwele is a Lecturer at NIPA and ZCAS with specialty in Science and Computer engineering and is currently a Phd Candidate at Beijing University of Technology, China.

Mr. Chikweto is passionate about his field — Biomedical Engineering — in which he is also currently Lecturing at Evelyn Hone College and took time to display working prototypes of:

1. A Smart House with electrical appliances that could be controlled from a phone.

2. A Blind Aiding System that for assisting Blind people navigate their way in public buildings.

3. A Patient Monitor categorized as IoT device leveraging the limitations of existing similar medical equipment.

He is a Phd Candidate of Tohoku University (Japan) with Research focus on Sensor Fusion and Machine Learning for biomechatronics and control systems in neonatal healthcare technology.

The greatest amazement of the day came from how quick the audience was able to grasp the concepts of Machine Learning as to effectively organize themselves and present mind-blowing ideas of exactly how they felt each aspect of Machine Learning could best solve community problems in a modern Zambia. Smart Zambia has since shown interest in learning more about these innovations presented by group representatives of the audience.

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