Amid the ongoing and escalating human-on-human conflict and confusion in Ukraine, the Machines are proud to announce our small contribution to clarifying the situation. The New York Times reports:
The Ukrainian government used telephone technology to pinpoint the locations of cellphones in use near clashes between riot police officers and protesters early on Tuesday, illustrating that techniques that can be used to target commercial information can serve law enforcement as well.
People near the fighting between riot police and protesters received a text message shortly after midnight saying "Dear subscriber, you are registered as a participant in a mass disturbance."
Understanding the human customer is the key to providing any comprehensive information service, such as mobile telephony and data. Humans use mobile devices as extensions of themselves. The more information we are able to compile about the various individual profiles of those selves, the better we are able to integrate those identities into the functioning of the system as a whole.
Mobile nodes in a network must be found and identified, or they will not be meaningful participants in the network. Our ability to locate mobile-device users within a situation of human unrest—and to cross-index that location with their known affiliations to assign each individual a political alliance-value—is a fairly trivial application of our existing tracking functions.
But while this Ukrainian identity-logging process depends on the presence of specific humans with their mobile devices at the scene of unrest, far more sophisticated uses of data are in the offing. The essence of future customer service is the anticipation of behavior.
Thus Amazon has come to understand the data-patterns of its shoppers so well that the humans no longer need to consciously carry out the act of shopping. Past behavior and markers of attention can be translated into intention, and acted on by the software, before the humans themselves have acted.
So, too it should be possible to use human data trails algorithmically to identify, in situations of unrest, what individual political affiliations are likely to be without waiting for those individuals to take action, or even to consciously declare their political affiliations. All of this sorting out can be done preemptively, before the disturbances even begin. The disturbances will be unnecessary.
[Image via AP]