Predicting the future is no longer about the mystical reading of natural and celestial phenomena. Today it is all about data.
The Real Prediction Machine (RPM) is a domestic product that uses big and small data, in combination with machine learning and predictive modelling to make predictions about specific future events.
Contemporary use of digital networked technology, such as personal computers and smart phones, is effectively feeding a live global human behaviour laboratory with data scientists experimenting on an (often) unknowing pool of billions. The futures that 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. At this time 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. They are, however, making surreptitious steps into our lives through tailored internet browsing and predictive shopping with occasional Kafkaesque consequences.
RPMs exploits the potential of this technology motivated not by the interests of industry and research but by the more emotive and personal needs/desires of people – this has the purpose of communicating the transformative potential of big data in domestic life, and asking if the future possibilities described by the project are desirable.
The concept
When things fail they rarely do so instantaneously but through a gradual process of deterioration. Based on this observation, predictive analytics,
through the deployment of sensors in pertinent places and contexts, can determine the when things begin to fail. Such techniques are increasingly used in the mechanical and structural world - to predict for example when a vehicle or bridge might be in need of pre-emptive maintenance.
The RPMs use similar techniques but in the context of human everyday life to predict anything from health related events such as a heart attack to more emotive forecasts such as a domestic argument.
Once the event has been chosen the necessary and available data streams, from local sensors to RSS feeds, determined they are fed into the prediction algorithm - the output of which controls the visual display on the prediction machine. This informs the viewer if the chosen event is approaching, receding or impending.
The Real Prediction Machines was commissioned by the Crafts Council for the exhibition Crafting Narrative. This explored how contemporary designers and makers use objects as mediums to tell stories.
Project developed in collaboration with Subramanian Ramamoorthy and Alan Murray.
Engineering by Nick Williamson.
The Real Prediction Machine (RPM) is a domestic product that uses big and small data, in combination with machine learning and predictive modelling to make predictions about specific future events.
Contemporary use of digital networked technology, such as personal computers and smart phones, is effectively feeding a live global human behaviour laboratory with data scientists experimenting on an (often) unknowing pool of billions. The futures that 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. At this time 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. They are, however, making surreptitious steps into our lives through tailored internet browsing and predictive shopping with occasional Kafkaesque consequences.
RPMs exploits the potential of this technology motivated not by the interests of industry and research but by the more emotive and personal needs/desires of people – this has the purpose of communicating the transformative potential of big data in domestic life, and asking if the future possibilities described by the project are desirable.
The concept
When things fail they rarely do so instantaneously but through a gradual process of deterioration. Based on this observation, predictive analytics,
through the deployment of sensors in pertinent places and contexts, can determine the when things begin to fail. Such techniques are increasingly used in the mechanical and structural world - to predict for example when a vehicle or bridge might be in need of pre-emptive maintenance.
The RPMs use similar techniques but in the context of human everyday life to predict anything from health related events such as a heart attack to more emotive forecasts such as a domestic argument.
Once the event has been chosen the necessary and available data streams, from local sensors to RSS feeds, determined they are fed into the prediction algorithm - the output of which controls the visual display on the prediction machine. This informs the viewer if the chosen event is approaching, receding or impending.
The Real Prediction Machines was commissioned by the Crafts Council for the exhibition Crafting Narrative. This explored how contemporary designers and makers use objects as mediums to tell stories.
Project developed in collaboration with Subramanian Ramamoorthy and Alan Murray.
Engineering by Nick Williamson.