This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

In developing countries especially Liberia, when elections are held, voters' decisions are never based on or guided by any solid intelligence or knowledge about the candidate or his/her policies. In fact, it is a common phenomenon to see citizens base their voting decisions on factors such as: the candidate's popularity, political affiliation, ethnic or religious values, emotions, tribalism, political rhetoric, candidate's educational background, or the amount of money (aka CASH VIOLENCE), that the candidate spends on them (voters). Arguably, this could be attributed to poverty and illiteracy. And so, candidates especially politicians, take advantage of these social and economic challenges, to campaign using lies, empty promises and commitments which they cannot fulfill after winning elections. When this happens, voters would have to wait for those elected officials to complete their tenure, before getting another opportunity to vote for a new group of leaders in hopes that "GOD" might send them better leaders, if not a messiah. Because of this, many nations remain underdeveloped, poor, and heavily dependent on international aid.

Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning. We developed a method for mapping communities using a deep learning approach that excels at detecting objects within images. We trained an algorithm to detect individual buildings, then examined building clusters to identify groupings suggestive of communities. The approach was validated in southeastern Liberia, by comparing algorithmically generated results with community location data collected manually by enumerators and community health workers. The deep learning approach achieved 86.47% positive predictive value and 79.49% sensitivity with respect to individual building detection. The approach identified 75.67% (n 451) of communities registered through the community enumeration process, and identified an additional 167 potential communities not previously registered. Several instances of false positives and false negatives were identified.

The United Nations Development Programme (UNDP) helps countries implement the United Nations (UN) Sustainable Development Goals (SDGs), an agenda for tackling major societal issues such as poverty, hunger, and environmental degradation by the year 2030. A key service provided by UNDP to countries that seek it is a review of national development plans and sector strategies by policy experts to assess alignment of national targets with one or more of the 169 targets of the 17 SDGs. Known as the Rapid Integrated Assessment (RIA), this process involves manual review of hundreds, if not thousands, of pages of documents and takes weeks to complete. In this work, we develop a natural language processing-based methodology to accelerate the workflow of policy experts. Specifically we use paragraph embedding techniques to find paragraphs in the documents that match the semantic concepts of each of the SDG targets. One novel technical contribution of our work is in our use of historical RIAs from other countries as a form of neighborhood-based supervision for matches in the country under study. We have successfully piloted the algorithm to perform the RIA for Papua New Guinea’s national plan, with the UNDP estimating it will help reduce their completion time from an estimated 3-4 weeks to 3 days.

Chatbots are great for customer service, ordering tickets, or just giving you weather updates, but others have nobler goals for their bots. Here are 6 bots, developed using a variety of technologies and APIs, and delivered via different interfaces, that are helping improve the world for everyone. DoNotPay started out as a cheeky service to help drivers get out of parking tickets. Stanford student Joshua Browder became more interested in bots after the online tool automatically challenged over 160,000 of them. People began contacting him asking for help with other legal issues relating to evictions, bankruptcies, and repossessions, so he decided to expand the capabilities of the bot to help homeless people.