The AI Spy Who Photographed Me
March 29, 2018
Artificial intelligence is one of the of the tools that law enforcement is using to thwart potential terrorist attacks and other illegal activities. Applications use AI to run data analysis, scan the Dark Web, and monitor identity theft. One major use for AI is image analysis and facial recognition. IEEE Spectrum takes a look at how there is a huge demand for more accurate image AI, “Wanted: AI That Can Spy.” While fear over spy satellites is not much a plot point anymore, the US has hundreds of satellites orbiting the planet capturing photographic data. Humans are only capable of observing so many photographic data and the US government has FOMO “fear of missing out” on something important.
US intelligence officials sponsored an AI challenge to identify objects of interest in satellite images. The entire goal is to improve AI standards and capabilities:
Since July, competitors have trained machine-learning algorithms on one of the world’s largest publicly available data sets of satellite imagery—containing 1 million labeled objects, such as buildings and facilities. The data is provided by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The 10 finalists will see their AI algorithms scored against a hidden data set of satellite imagery when the challenge closes at the end of December.
The agency’s goal in sponsoring the Functional Map of the World Challenge aligns with statements made by Robert Cardillo, director of the U.S. National Geospatial-Intelligence Agency, who has pushed for AI solutions that can automate 75 percent of the workload currently performed by humans analyzing satellite images.
Lockheed research scientist Mark Pritt guessed that the US government wants to automatically generate maps, instead of relying on manual labor. Pritt’s Lockheed team is one of the many teams competing for the $100,000 prize to develop the best deep-learning algorithm that can recognize specific patterns and identify objects of interest in satellite images. Satellite images are more complex than other images because they are shot from multiple angles, cloud coverage is a problem, and a variety of resolutions.
Even if a deep-learning algorithm was developed it would not be enough, because the algorithm lacks the ability for refinement. Think sentimental analysis, except with images. The perfect solution for the moment is a combination of AI and human interaction. The AI does the bulk of the work, while humans examine flagged photos for further investigation.
Whitney Grace, March 29, 2018
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