In advance of the D-Day landings in 1944, the Allies needed on-the-ground pictures of the Normandy beaches in France. In addition to using reconnaissance images, military planners enlisted the BBC and held a vacation photo contest, which netted 10 million photos! Those unsuspecting civilians provided images that helped create a detailed look at the terrain the Allies would encounter.
Imagine the challenges those planners faced in sifting through those millions of photos. How did they accurately sort the content from so many people that arrived in so many formats? Could they verify that a photo labeled Calais wasn’t really Cherbourg?
The planners threw lots of very dedicated people at the mission, using sheer numbers to deal with those challenges. Today, there aren’t enough people in the world to manually tackle the data that’s available.
Thankfully, we now have machine learning, which the government is applying to accelerate mission tasks and do what was previously unachievable. For example, a computer model can be trained to identify people who appear on security cameras and alert personnel to flagged individuals. This dramatically improves security at relatively little cost.
Or, an algorithm can automatically summarize sections of a document, decreasing the time burden of reading through pages of briefs in search of minute details. In each example, machine learning accelerates and augments the work of existing personnel and alleviates the need to hire additional people to achieve time-constrained objectives.
While machine learning provides many benefits, we need to be aware of the limitations of artificial intelligence and other predictive solutions. Machine-learning solutions are largely dependent on high-quality data. Steps must be taken to structure the data as it is created and stored, so that it is readily usable for analysis.
It greatly reduces development costs in analysis. Frequently the most time spent on data science projects involves organizing messy data. Imagine sorting those Normandy photos if none were labeled!
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