April 2011 archive
April 6, 2011
Can we deploy machine learning software to help out in a crisis? Are our software tools flexible enough such that we can quickly put together a prediction system within a few hours or days? I'd like to briefly examine what types of prediction tasks could be useful.
The most obvious questions relate to mapping:
Given sensor readings for several locations, how do we generalize to the others? This is a classical regression task, and there is a lot of work on this in geospatial analysis. There is crowd sourced data available on radiation levels in Japan, and http://rdtn.org/ tries to make it easier for people to submit readings. As far as I know, there has been no interpolation of results, probably due to the fear of making wrong predictions.
Where else do we need readings? This active learning type question has gained popularity in recent years in the machine learning community.
Where is help most needed? Image overlays was used after the hurricane in New Orleans and the floods in Pakistan to create before and after photos, which then were used for manually identifying priorities, planning logistics and working out access routes for relief operations. There are all sorts of machine learning questions here, such as ranking, path planning, etc.
Which is the closest team? Related to the previous point, when there is already a call for help, how do we decide to allocate our resources? Just allocating the nearest neighbour may not be optimal, as there may be other resources further away that are free. In addition, there is travel time to be taken into account. These are the types of questions that the sensor networks community have been investigating.
Assuming we know what problem we need to solve, it may be still a long way to getting a working implementation. One bottleneck commonly faced by applications of machine learning is the data issue, but for mapping problems, the two popular sources (google maps and open street map) have good APIs. This allows users to get data, and also to include predictions. And there seems to be lots of open source software solving the formal tasks. How much work is the remaining "glue"?
It would be wonderful if machine learning could make an impact in times of crisis.
This post was motivated by a very nice survey by Peter Suber on how open access can change things during a humanitarian crisis. "Beyond those survival basics, several forms humanitarian assistance take the form of free online access to research". The sources mentioned above comes from this newsletter.