The University of Southampton
University of Southampton Institutional Repository

Predicting me: The route to digital immortality?

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 connectomic microstructure 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.
machine learning, deep learning, digital immortality, mind uploading, predictive processing, digital resurrection, virtual reality, generative model
1573-4536
Springer International Publishing
Smart, Paul
cd8a3dbf-d963-4009-80fb-76ecc93579df
Clowes, Robert
Gärtner, Klaus
Hipólito, Inês
Smart, Paul
cd8a3dbf-d963-4009-80fb-76ecc93579df
Clowes, Robert
Gärtner, Klaus
Hipólito, Inês

Smart, Paul (2020) 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 Artifacts. (Studies in Brain and Mind) Springer International Publishing. (In Press)

Record type: Book Section

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 connectomic microstructure 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.

Text
Predicting Me - Proof
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 2020
Keywords: machine learning, deep learning, digital immortality, mind uploading, predictive processing, digital resurrection, virtual reality, generative model

Identifiers

Local EPrints ID: 441978
URI: http://eprints.soton.ac.uk/id/eprint/441978
ISSN: 1573-4536
PURE UUID: 92774326-f269-4010-8468-3ca4b8cba161
ORCID for Paul Smart: ORCID iD orcid.org/0000-0001-9989-5307

Catalogue record

Date deposited: 03 Jul 2020 16:30
Last modified: 29 Jul 2020 01:35

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×