Next, there are much narrower applications of machine learning, tailored to solving specific challenges, much like the “solution organizations” in Movement as Network. It might be using machine vision to identify animals and protect biodiversity. Or it could be using it to create real-time ice maps to keep shipping routes safer in the frigid waters of Greenland, or for building flood water warning systems in Ohio. It could also entail using machine learning to analyze satellite imagery and assist small-scale farmers in developing nations, more reliably flag public health risks posed by restaurants in Boston, or detect signs of diabetes-related eye disease in rural India.
The players are too numerous to comprehensively list here and there is considerable overlap with big-data analytics work. The field includes big players like Google, IBM, and Microsoft working both on their own and in partnership with mission-driven organizations. It also includes a number of smaller, more focused organizations like Delta Analytics, Alethiom, and DrivenData. One of the more interesting new applications is One Concern, which combines machine learning and hazard modeling to protect communities before, during and after natural disasters. If you know of other interesting projects or organizations like these, please drop me a pointer in the comments below.
The final category for mission-driven applications of machine learning is a bit more ‘out there’ and doesn’t yet exist as far as I know. It maps to the “people organizations” in Movement as Network — organizations defined by audiences rather than issues. Here, the opportunity is to use machine learning as a way to engage very large networks of constituents, much the way Facebook, Google, and Amazon do with their end-users. The opportunity here is to use machine learning to determine how best to engage and have impact with very large numbers of citizens. One intriguing example is pol.is, which uses machine learning to facilitate Internet-scale conversations that converge on a kind of smarter, collaborative democracy.
Given the huge quantity of data necessary for today’s machine learning algorithms, it seems somewhat unlikely that individual non-profit organizations could tackle an opportunity like this on their own. Perhaps organizations like Avaaz with its 46-million members or Change.org with its 100-million members could prove me wrong. Alternatively, there may be an opening here for formal coalitions or loose collaborative networks of organizations and people to figure out a way to pull something like this off.
People-Engaging Machine Learning:
Solution-Specific Machine Learning:
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