What’s missing from machine learning research: an East African perspective
Reinforcement Learning - No interviewee reported using any reinforcement learning methods. However, interest was expressed in it, particularly regard ing machine teaching and using RL in simulations, e.g. using RL in epidemiological simulations to find worst case scenarios in outbreak planning. Machine Teaching - There is a shortage of good educational resources and teachers in East Africa. Several initiatives exist that use mobile phones as an education platform. Practitioners were interested in using ideas from machine teaching in their work to personalize content delivered. However, the author did not encounter anyone who had employed any results from the machine teaching literature at this point. Uncertainty Quantification - An important factor that keeps the wealth of rich regions from moving into poorer regions like East Africa, despite the fact that it should earn greater returns there, is risk [1]. Not all risk can be machine–learned away by any means. But (accurate) predictive models are risk-reduction tools. Machine learning models are most useful for risk–reduction when they can (accurately) quantify their uncertainty. This is particularly true when data are scarce, as they usually are in East Africa. UQ is not a new problem by any means, but it is listed here to reiterate its importance to the organizations interviewed. Importantly, when used in East Africa, UQ is typically much more concerned with conservatively quanti fying overall downside risk (with respect to some quantity of interest) than characterizing overall model uncertainty around point predictions.
Some Requests for Machine Learning Research from the East African Tech Scene [Milan Cvitkovic/Arxiv] (via Four Short Links) (Image: Cryteria, CC-BY)