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Computing brains: learning algorithms and neurocomputation in the smart city.

Authors :
Williamson, Ben
Source :
Information, Communication & Society; Jan2017, Vol. 20 Issue 1, p81-99, 19p
Publication Year :
2017

Abstract

This article examines IBM’s ‘Smarter Education’ program, part of its wider ‘Smarter Cities’ agenda, focusing specifically on its learning analytics applications (based on machine learning algorithms) and cognitive computing developments for education (which take inspiration from neuroscience for the design of brain-like neural networks algorithms and neurocomputational devices). The article conceptualizes the relationship between learning algorithms, neuroscience, and the new learning spaces of the city by combining the notion of programmable ‘code/space’ with ideas about the ‘social life of the brain’ to suggest that new kinds of ‘brain/code/spaces’ are being developed where the environment itself is imagined to possess brain-like functions of learning and ‘human qualities’ of cognition performed by algorithmic processes. IBM’s ambitions for education constitute a sociotechnical imaginary of a ‘cognitive classroom’ where the practices associated with data analytics and cognitive computing in the smart city are being translated into the neuropedagogic brain/code/spaces of the school, with significant consequences for how learners are to be addressed and acted upon. The IBM imaginary of Smarter Education is one significant instantiation of emerging smart cities that are to be governed by neurocomputational processes modelled on neuroscientific insights into the brain’s plasticity for learning, and part of a ‘neurofuture’ in-the-making where nonconscious algorithmic ‘computing brains’ embedded in urban space are intended to interact with human cognition and brain functioning. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
1369118X
Volume :
20
Issue :
1
Database :
Complementary Index
Journal :
Information, Communication & Society
Publication Type :
Academic Journal
Accession number :
118835141
Full Text :
https://doi.org/10.1080/1369118X.2016.1181194