Yesterday’s Washington Post hosts an article by Abbot Koloff: “It’s increasingly hard to hide from the watchful eyes of cameras” which was posted originally in the Leader Telegram last month. The article is a good reminder that infrastructure-based recordings are becoming ubiquitous, from streets and parking lots to dormitory rooms and hallways; and that such image archives can prove very effective in identifying criminal behavior- particularly when you know when and where to look for it in the data record. As infrastructure sensing of human behavior expands, the interesting part of this massive Big Data problem is that making sense of all the data becomes a superhuman endeavour, and a natural direction for innovation will be to apply automatic machine-based interpretation of human behavior to this dataset that far exceeds even a human army’s ability to review. “Show me all pairs of 30-year old men who photographed that bridge” ; “Show me every person who handed another person a package in Central Park that week.” Of course the boundary between identifying criminal behavior and summarizing high-value information is slippery. How about premonition for new clothes trends in New York, storefront gesture analyses, studies of visitorship to competing restaurants. The infrastructure will build will be all-seeing, and we know people will not change their overall behavior profoundly, even if they are intellectually aware of this level of observation.. Look at email: we repeatedly tell everyone that email is essentially public, and plenty of users still manage to embarrass themselves with incredibly poor email decisions. But that very same sensing infrastructure can move from passive recording to meaning-making as AI proceeds, and this means a treasure trove of semantic information will be available to those with either power, money or subpoenas. Every camera on every street corner will start behaving like a very sharp gremlin is in there taking notes- and these gremlins will be happy to strike a bargain.