|Appears in Collections:||Faculty of Social Sciences Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Computing brains: learning algorithms and neurocomputation in the smart city|
|Citation:||Williamson B (2017) Computing brains: learning algorithms and neurocomputation in the smart city, Information Communication and Society, 20 (1), pp. 81-99.|
|Abstract:||This article examines IBM’s ‘Smarter Education’ programme, 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.|
|Rights:||© 2016 Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Permission is granted subject to the terms of the License under which the work was published. Please check the License conditions for the work which you wish to reuse. Full and appropriate attribution must be given. This permission does not cover any third party copyrighted material which may appear in the work requested.|
|WilliamsonB_ComputingBrains_2016.pdf||1.61 MB||Adobe PDF||View/Open|
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