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The Mind-Technology Problem pp 185–207Cite as

Predicting Me: The Route to Digital Immortality?

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Part of the Studies in Brain and Mind book series (SIBM,volume 18)

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.

To die,—to sleep,

To sleep! perchance to dream…

For in that sleep of death what dreams may come…

—Hamlet, William Shakespeare

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Fig. 9.1
Fig. 9.2
Fig. 9.3
Fig. 9.4

Notes

  1. 1.

    See http://eterni.me/ (accessed: 7th March 2018).

  2. 2.

    In essence, the brain is viewed as a multi-layered prediction machine, with ‘higher’ layers attempting to predict the activity of ‘lower’ layers. It is in this sense that the brain can be seen to predict its own activity, i.e., to predict the activity of its constituent neural elements.

  3. 3.

    It is doubtful whether the current state-of-the-art in deep learning is sufficient to achieve the sort of digital immortality vision being proposed here. Nevertheless, it is worth bearing in mind that deep learning research is likely to be a prominent focus of global research attention over the next 10–20 years. Given the amount of time, effort, and money that is likely to be devoted to deep learning systems in the coming years, it is likely that we will see significant changes in their capabilities during the course of the twenty-first century.

  4. 4.

    This highlights one of the differences between DBNs and PP accounts of cognition. In PP, it is typically assumed that elements within the same layer of the processing hierarchy engage in some form of lateral processing.

  5. 5.

    The Galaxy Zoo data set consists of 900,000 galaxy images, which were collected as part of the Sloan Digital Sky Survey. The data set was originally used as part of a citizen science project investigating the distribution of galaxies with particular morphologies (see Lintott et al. 2008). It has since been used as the basis for a number of studies exploring the capacities of both conventional and deep neural networks (Dieleman et al. 2015; Banerji et al. 2014).

  6. 6.

    The general idea, here, is that different kinds of data environment provide the basis for different kinds of minds, with phenomenological differences linked to the causal mechanisms that operate in each environment. A generative model of the human social environment, for example, might yield a ‘mind of society’ that tracks the hidden causal structure of social mechanisms. Inasmuch as subjective experiences are tied to the properties of generative models, then such a mind may yield a subjective reality that is profoundly different from the sort of ‘reality’ we know (or could, perhaps, even imagine).

  7. 7.

    A variety of other terms are sometimes used to refer to the same phenomenon. These include “lifelogging,” “measured me,” “self-tracking,” and “self-surveillance.”

  8. 8.

    These scenarios do not, of course, exhaust the possibilities for digital resurrection. In addition to virtual reality technologies, the twenty-first century is likely to see significant advances in the development of biomimetic materials, 3D printing technology, and robotic systems. These may open the door to a more concrete form of digital afterlife, one in which the biological body is substituted with a synthetic, but no less substantial, corporeal presence.

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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.

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  1. Electronics and Computer Science, University of Southampton, Southampton, UK

    Paul Smart

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Correspondence to Paul Smart .

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  1. Instituto de Filosofia da Nova (IFILNOVA) Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa, Lisboa, Portugal

    Robert W. Clowes

  2. Departamento de História e Filosofia das Ciências & Centro de Filosofia das Ciências da Universidade de Lisboa (CFCUL), Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

    Klaus Gärtner

  3. Department of Philosophy & Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Berlin, Germany

    Dr. Inês Hipólito

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Smart, P. (2021). Predicting Me: The Route to Digital Immortality?. In: Clowes, R.W., Gärtner, K., Hipólito, I. (eds) The Mind-Technology Problem . Studies in Brain and Mind, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-030-72644-7_9

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