Robjects Real Prediction Machines

Murray, Alan and Auger, James and Loizeau, Jimmy and Ramamoorthy, Subramanian (2014) Robjects Real Prediction Machines. The University of Edinburgh, Edinburgh. ISBN 978-1-904443-60-5

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Abstract / Summary

Real Prediction Machines (RPMs)

Modern day fortune-telling is far-removed from the mystical readings of natural and celestial phenomena of the past.

Today it is all about data.

Contemporary use of digital networked technology has effectively created a live global human behaviour laboratory with data scientists experimenting on an (often) unknowing pool of billions. The futures that might emerge from this research are as yet mostly unknown, but there are hints – as this data accumulates it can be analysed, mined and used in algorithms; patterns or trends invisible to the human observer can be identified and seemingly random events become predictable.

Prediction algorithms are predominantly being exploited by big industries such as banking, insurance and commerce or examined in massive research projects such as the EU funded FuturICT project.

The aim of Real Prediction Machines is take the promise of Big Data and prediction algorithms into the domestic domain, effectively translating or domesticating these complex emerging technologies into a plausible and desirable functional artefact.

Individuals can select a specific event to be predicted such as a domestic argument; the likelihood of ones own death or the chances of a meteor strike. A service provider then determines the necessary data/ sensory inputs required for an algorithm to predict the event. The output from the algorithm controls a visual display on the prediction machine, informing the owner if the chosen event is approaching, receding or impending.

Item Type: Book
ISBN: 978-1-904443-60-5
Subjects: Research > AIR > Sustainable Design
Courses by Department: Academy of Innovation and Research > Centre for Sustainable Design
Depositing User: Alan Murray
Date Deposited: 08 May 2015 12:44
Last Modified: 20 Oct 2015 15:32
URI: http://repository.falmouth.ac.uk/id/eprint/1594

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