The kind of power in the type of GCI we can expect in the future is hard to grasp. Sensors automatically collect petabytes of real-time streaming data that gets send to computers, which harness the computational power of grid computing. Despite the large amounts of data, access and retrieval of information would be easy because computers, being equipped with proper semantics would know what we wanted. We would be able to deal with the most complex issues through multidimensional analysis and achieving excellent interoperability between applications. On one hand, this future is exciting, but on the other, it is also a little daunting to think about how complex systems will become and how well people will be equipped with the ability to question machine outputs.
In part, this is an extension to ClimateNYC’s post. I too am concerned about the opacity of GCIs. Will continuous data feeds really make the world more understandable? The shift in the way science will be done with GCIs will have to be accompanied by an equally educated population, which should include end users as much as developers of the technologies involved. Otherwise, researchers using this technology will not even stand a chance to question the results he obtains. Users must be able to benefit from the amazing potentials of GCI as well as be able to consistently negotiate its terms of developments and the mechanics behind the technologies. The idea of GIC as a black box is a scary one; if we accept it without question, technology (sensors, applications, computational analysis) will be a veil between us and natural phenomena. If we lose the ability to questioning the outcomes we obtain from machines, we will be dominated by technology without even knowing it. Therefore, instead of trying to “relieve scientists and decision makers of the technical details of a GCI”, I believe the opposite must be true. Educating the greater population of the mechanism behind such complex systems is necessary if we do not want to go blind.
– Ally_Nash
Tags: GCI