Track 10.2 - Emerging technologies and new innovation practices

Jeremy KLEIN,  Dr, Technologia Ltd and RADMA
Jonathan LINTON,  Professor, University of Sheffield

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Track's Contacts : 

jeremy.klein[AT]technologia.co.uk
j.linton[AT]sheffield.ac.uk

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R&D and innovation are often based on generic concepts and frameworks that are agnostic to the particular technologies involved. By contrast, this track explores how R&D and associated innovation processes are closely coupled to particular technologies. With their own characteristics and internal logics, individual technologies set the context for innovation and influence many dimensions of the innovation process, for example: timescales, research methods, skills requirements, financing requirements, risks, international topologies and information flows.

A particular focus will be with emerging ‘deep tech’ or ‘frontier technologies’ such as: machine learning, AI, big data, nanotechnology, additive manufacturing and the Internet of Things. These new technologies – with their specific characteristics – are expected to impact both how innovation is organised and how it is executed: ‘innovation in innovation’.

Papers are encouraged that concentrate on one or more specific technologies, identified at a sufficient level of granularity that their distinct characteristics can be appreciated. In this respect, it may be necessary to go beyond some of the commonly used categories such as ‘nanotechnology’ to more finely grained categories such as ‘nanomachines’.

For example, the branch of AI known as ‘deep learning’ is based on the use of multi-layer neural networks, which in turn need to be trained on a large data set. Such data sets have to be either generated from scratch or obtained from existing sources. The technology developers may not have in-house access to such data. The need for training data therefore fundamentally drives the innovation relationships or collaborations necessary for the technology to be developed. However, not all AI approaches are so dependent on training data. Looking at the technology only at the aggregated level of AI could miss insights that are important to theory, policy and practice.

As well as currently emerging new technologies, the track is open to papers that explore past examples of new technologies that have shaped innovation practices.

Finally, the track will also welcome papers that conceptualise or take an overview of the relationship between new technologies, R&D and innovation.

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