Ben Williamson

In a new article published in Information, Communication & Society I aim to make some sense of how machine learning algorithms and new forms of ‘brain-inspired’ computing are being imagined for use in education. In particular, the article examines IBM’s ‘Smarter Education’ programme, part of its wider ‘Smarter Cities’ agenda, focusing 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). Together, these developments constitute the emergence of ‘learning algorithms’ that are responsive, adaptive and appear to possess some degree of sentience and cognition.

The article is part of a forthcoming special issue of the journal on ‘the social power of algorithms’ edited by David Beer, and it’s really great to be in the company of other papers by Daniel Neyland & Norma Mollers, Taina Bucher, Bernard Rieder, and Rob Kitchin. It was Rob Kitchin’s work (which he presented at the first Code Acts in Education seminar in 2014) that originally got me interested in ideas about smart ‘programmable cities’–which I’ve taken up to explore ideas about education in smart cities–and in my article I’ve drawn on the concept of ‘code/space‘ he developed with Martin Dodge. My starting place is that recently urban environments have been reimagined as ‘smart cities of the future’ with the computational capacity to monitor, learn about, and adapt to the people that inhabit them. In other words, smart cities are themselves ‘learning environments.’ What does it mean for urban space to learn? For IBM, the answer lies in neuroscience, and particularly in a synthesis of brain science and computer science innovations–both areas in which it has been significantly active, particularly in relation to the field of cognitive computing. IBM’s imaginary of the future smart city is one in which the environment itself is envisaged as being a ‘cognitive environment’–with schools as one such kind of space, as illustrated by its ideas for a ‘classroom that will learn you.’

In the article I explore 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 ‘learning 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 cognition performed by algorithmic processes. I take IBM’s Smarter Education vision as an exemplar of its wider ambitions to make smart cities into highly-coded brainy spaces that are intended to supplement, augment and even optimize human cognition too.

In other words, IBM’s vision for Smarter Education is diagrammatic of its plans for ‘cognitive cities’ that are configured for advanced mental processing–and that rely on neuro-technological renderings of human brain functioning. The learning algorithms of learning analytics and cognitive computing applications imagined by IBM contain particular neuroscientific models of learning processes. Its glossy imaginary of Smarter Education acts as a seemingly desirable model not just for the future of schools in software-enabled urban environments, but as a diagram for future cities that are to be treated as learning environments and enacted by increasingly cognitive forms of computing technology.

The term brain/code/space registers how the learning algorithms of data analytics and cognitive computing are weaving constitutively into the functioning and experience of smart cities, including but not limited to the cognitive classrooms of IBM’s imagined smarter education environments. The brain/code/spaces of IBM’s smart cognitive classrooms are built around models of the brain that are encoded in the functioning of learning algorithms and inserted into the pedagogic space of the classroom. IBM’s imaginary of the brain/code/spaces of such cognitive learning environments is one instantiation of a new kind of urban space in which neuroscientific claims about brain plasticity are built in to the learning algorithms that constitute the functioning and experience of the environment itself. The notion of brain/code/space articulates a novel neurocomputational biopolitics in which brain functions are transcoded into data, and then codified into nonconscious cognitive learning algorithms and applications that are designed to augment human cognition. I suggest that IBM’s imaginary of Smarter Education is a kind of computational neurofuture-in-the-making, one that illustrates how the neuro-technological diagrammatization of the human ‘learning brain’ is being written in to the functioning of smart urban space through the design of learning algorithms.

The full paper, ‘Computing brains: learning algorithms and neurocomputation in the smart city,’ is available open access.