Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa. The goal of this work is to give machine learning researchers a fuller picture of where and how their efforts as scientists can be useful. The goal is thus not to highlight research problems that are unique to East Africa -- indeed many of the problems listed below are of general interest in machine learning. The problems on the list are united solely by the fact that technology practitioners and organizations in East Africa reported a pressing need for their solution. The author is aware that listing machine learning problems without also providing data for them is not a recipe for getting those problems solved. If the reader is interested in any of the problems below, please get in touch.

These words of wisdom found their way to that part of my brain responsible with long-term memory after watching RIO 2. There was something about that toucan that made this phrase stick. Couple this philosophy with the idea that interviewing and recruiting talent is just like dating and the fact that full time employees spend more time at work than with their spouses at home, and I think you'll agree that working relationships between employees and employers are a lot like marriage. It's not uncommon for companies to take employees for granted and think that just because they are on payroll they will always give 110% or that they will be loyal forever and ever. When all companies want is to take, take, take and think that employees should only give, give, give, their relationship will eventually suffer and their employee turnover rate will get big, big, big. But when both parties treat each other right, and play with all cards on the table, things change.

Thanks to António Alegria, Head of AI at OutSystems for taking me through how OutSystems is using AI to improve the quality and speed of software development. António also heads up OutSystems' AI Center of Excellence -- Project Turing. António began his presentation explaining that tools were key to humanity's progress and that software became the ultimate tool to drive great achievements. The tools to create software have evolved; however, there is still room to grow -- program complexity is growing, we still have to manually update our games and TVs. The best way to address complexity is to take a complex problem and to break into smaller components.

Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the previous knowledge is a challenge issue in machine learning. We propose a Kalman Filter based modifier to maintain the performance of Neural Network models under non-stationary environments. The result shows that our proposed model can preserve the key information and adapts better to the changes. The accuracy of proposed model decreases by 0.4% in our experiments, while the accuracy of conventional model decreases by 90% in the drifts environment.

Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of adaptive moment techniques by proposing a family of double adaptive stochastic gradient methods~\textsc{DASGrad}. They leverage the complementary ideas of the adaptive moment algorithms widely used by deep learning community, and recent advances in adaptive probabilistic algorithms.We analyze the theoretical convergence improvements of our approach in a stochastic convex optimization setting, and provide empirical validation of our findings with convex and non convex objectives. We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning.

Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning. In this work, we propose a novel unsupervised representation learning framework called neighbor-encoder, in which domain knowledge can be easily incorporated into the learning process without modifying the general encoder-decoder architecture of the classic autoencoder.In contrast to autoencoder, which reconstructs the input data itself, neighbor-encoder reconstructs the input data's neighbors. As the proposed representation learning problem is essentially a neighbor reconstruction problem, domain knowledge can be easily incorporated in the form of an appropriate definition of similarity between objects. Based on that observation, our framework can leverage any off-the-shelf similarity search algorithms or side information to find the neighbor of an input object. Applications of other algorithms (e.g., association rule mining) in our framework are also possible, given that the appropriate definition of neighbor can vary in different contexts. We have demonstrated the effectiveness of our framework in many diverse domains, including images, text, and time series, and for various data mining tasks including classification, clustering, and visualization. Experimental results show that neighbor-encoder not only outperforms autoencoder in most of the scenarios we consider, but also achieves the state-of-the-art performance on text document clustering.

Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning. One popular approach for addressing this challenge is the options framework (Sutton et al., 1999). However, only recently in (Bacon et al., 2017) was a policy gradient theorem derived for online learning of general purpose options in an end to end fashion. In this work, we extend previous work on this topic that only focuses on learning a two-level hierarchy including options and primitive actions to enable learning simultaneously at multiple resolutions in time. We achieve this by considering an arbitrarily deep hierarchy of options where high level temporally extended options are composed of lower level options with finer resolutions in time. We extend results from (Bacon et al., 2017) and derive policy gradient theorems for a deep hierarchy of options. Our proposed hierarchical option-critic architecture is capable of learning internal policies, termination conditions, and hierarchical compositions over options without the need for any intrinsic rewards or subgoals. Our empirical results in both discrete and continuous environments demonstrate the efficiency of our framework.

A newly launched think tank researching policies for accepting more foreign workers said Monday that as a condition for new visa statuses currently being discussed in the Diet, the government should require prospective applicants to have a college degree. The Research Institute for Embracement of Global Human Resources said Japan is still an attractive destination for college graduates in emerging countries, even for blue-collar jobs. People with lower educational and economic backgrounds in such nations tend to be slower to learn Japanese, and their overall level of Japanese language skills tends to be poorer than that of college graduates, said Yohei Shibasaki, who heads the think tank that was established last week. "This could isolate them from the community and create areas" in which they seek out only people of the same nationality, causing trouble with other communities, Shibasaki said during a news conference in Tokyo. Last Friday Prime Minister Shinzo Abe's Cabinet approved a bill that will allow foreign individuals to work in blue-collar industries for an indefinite amount of time if they meet certain conditions.

When the Boston public school system announced new start times last December, some parents found the schedules unacceptable and pushed back. The algorithm used to set these times had been designed by MIT researchers, and about a week later, Kade Crockford, director of the Technology for Liberty Program at the ACLU of Massachusetts, emailed asking me to cosign an op-ed that would call on policymakers to be more thoughtful and democratic when they consider using algorithms to change policies that affect the lives of residents. Kade, who is also a Director's Fellow at the Media Lab and a colleague of mine, is always paying attention to the key issues in digital liberties and is great at flagging things that I should pay attention to. I made a few edits to her draft, and we shipped it off to the Boston Globe, which ran it on December 22, 2017, under the headline "Don't blame the algorithm for doing what Boston school officials asked." In the op-ed, we piled on in criticizing the changes but argued that people shouldn't criticize the algorithm, but rather the city's political process that prescribed the way in which the various concerns and interests would be optimized.

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