Predicting me: The route to digital immortality?
Predicting me: The route to digital immortality?
An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer (i.e., deep) neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture (centred on predictive processing schemes, hierarchical organization, and generative models) might provide inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the physical structure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or (alternatively) the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection.
deep learning, digital immortality, digital resurrection, generative model, machine learning, mind uploading, predictive processing, virtual reality
185–207
Smart, Paul
cd8a3dbf-d963-4009-80fb-76ecc93579df
29 September 2021
Smart, Paul
cd8a3dbf-d963-4009-80fb-76ecc93579df
Smart, Paul
(2021)
Predicting me: The route to digital immortality?
In,
Clowes, Robert, Gärtner, Klaus and Hipólito, Inês
(eds.)
The Mind-Technology Problem: Investigating Minds, Selves and 21st Century Artefacts.
(Studies in Brain and Mind, 18)
Cham, Switzerland.
Springer Cham, .
(doi:10.1007/978-3-030-72644-7_9).
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Abstract
An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer (i.e., deep) neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture (centred on predictive processing schemes, hierarchical organization, and generative models) might provide inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the physical structure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or (alternatively) the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection.
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Accepted/In Press date: 2020
Published date: 29 September 2021
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Acknowledgements I would like to thank two anonymous referees for their helpful comments on an earlier draft of this material. This work is supported under SOCIAM: The Theory and Practice of Social Machines. The SOCIAM Project is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/J017728/1 and comprises the Universities of Southampton, Oxford, and Edinburgh. Additional support was provided by the UK EPSRC as part of the PETRAS National Centre of Excellence for IoT Systems Cybersecurity under Grant Number EP/S035362/1.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
Keywords:
deep learning, digital immortality, digital resurrection, generative model, machine learning, mind uploading, predictive processing, virtual reality
Identifiers
Local EPrints ID: 441978
URI: http://eprints.soton.ac.uk/id/eprint/441978
ISSN: 1573-4536
PURE UUID: 92774326-f269-4010-8468-3ca4b8cba161
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Date deposited: 03 Jul 2020 16:30
Last modified: 17 Mar 2024 02:56
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Contributors
Author:
Paul Smart
Editor:
Robert Clowes
Editor:
Klaus Gärtner
Editor:
Inês Hipólito
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