Sentience Book Project

This project looks at the concepts of sentience, smartness, intelligences, etc. across domains.

Index

The 'work-in-progress' list of chapters or logical steps.

• Approach
• Context
• Guidelines
• Implications
• Need
• Plausibility
• Cases

Needs and Challenges

Agents

Mäekivi, Nelly, and Riin Magnus. ‘Hybrid Natures — Ecosemiotic and Zoosemiotic Perspectives’. Biosemiotics 13, no. 1 (2020): 1–7. https://doi.org/10/ggxrdh.

Examples in Various Systems

Plants

Mediano, Pedro A. M., Anthony Trewavas, and Paco Calvo. ‘Information and Integration in Plants: Towards a Quantitative Search for Plant Sentience’. Journal of Consciousness Studies 28, no. 1–2 (2021): 80–105.

On Place

Consider place as a habitat. Consider the whole planet as one place, with specific history, as the object of 'architecture'. It is not reasonable to only focus on buildings and cities when they depend on the surroundings and these days on the global patterns of extraction. Prioritise relationships and dynamics instead. Include a range of environments where technical sentience is intended, such as forest, etc. Explain how they connect to architecture.

Consider the Earth as a large brain/body and ask what death looks like, what waste looks like, how expensive sentience is energetically, what behaviours and motions does it require, etc.

Luers, Amy L. “Planetary Intelligence for Sustainability in the Digital Age: Five Priorities.” One Earth 4, no. 6 (2021): 772–75. https://doi.org/10/gpx3bq.

Artificial Systems

Haikonen, Pentti O. A. ‘On Artificial Intelligence and Consciousness’. Journal of Artificial Intelligence and Consciousness 07, no. 01 (2020): 73–82. https://doi.org/10/gp4hbh.

Measurement

Smil is both a good source but also an example for the use of numerical estimates. My thought was that we could explore the implications/costs/risks of sentience.

Smil, Vaclav. Energy and Civilization: A History. Revised. 1994. Reprint, Cambridge, MA: MIT Press, 2017.

Smil, Vaclav. Growth: From Microorganisms to Megacities. Cambridge, MA: MIT Press, 2019.

Smil, Vaclav. Harvesting the Biosphere: What We Have Taken from Nature. Cambridge, MA: MIT Press, 2013.

On the forthcoming context and ambitions for sensing and processing, see:

Gabrys, Jennifer. Program Earth: Environmental Sensing Technology and the Making of a Computational Planet. Minnepolis: University of Minnesota Press, 2016.

Gabrys, Jennifer. “Smart Forests and Data Practices: From the Internet of Trees to Planetary Governance.” Big Data & Society 7, no. 1 (2020): 1–10. https://doi.org/10/ggmzf2.

Bakker, Karen, and Max Ritts. “Smart Earth: A Meta-Review and Implications for Environmental Governance.” Global Environmental Change 52 (2018): 201–11. https://doi.org/10/gfp858.

Guo, Huadong, Michael F. Goodchild, and Alessandro Annoni. Manual of Digital Earth. Cham: Springer, 2020.

Directions

Can concepts such as senility and dementia apply to large scale sentient systems such as buildings or urban environments?

Build the argument with the foundation coming from process-oriented ontologies with the emphasis on deep history, emergence, and generally the dynamism/motion with integration of concepts such as entropy, errors, stability, metastability, homeostatic/homeorhetic conditions, etc.

Nail, Thomas. Theory of the Earth. Stanford: Stanford University Press, 2021.

Position sentience within the context of evolution, physical, technical, etc. Cf. literature on the evolution of physical laws, cosmic patterns, geology, biosphere, etc.

Risks

Consider implications for the widespread technical sentient systems. For example, consider disruptions from increased lightning, solar flares, flipping of the magnetic orientation, dissipation of the magnetic field, hacking/sabotaging, selfish use, inability to target long-term goals due to the small temporal scale of human-run projects, shortages of necessary minerals or energy, etc

Sarkar, Dipto, and Colin A. Chapman. “The Smart Forest Conundrum: Contextualizing Pitfalls of Sensors and AI in Conservation Science for Tropical Forests.” Tropical Conservation Science 14 (2021): 19400829211014740. https://doi.org/10/gj7cdx.

The introduction of technical systems can have an effect of solidifying the status quo, excluding nonhuman interests.

Prebble, Sarah, Jessica McLean, and Donna Houston. ‘Smart Urban Forests: An Overview of More-Than-Human and More-Than-Real Urban Forest Management in Australian Cities’. Digital Geography and Society 2 (2021): 100013. https://doi.org/10/gj4mrj.

Diversity vs Uniformity

Consider arguments that link flourishing on the Earth to volume and diversity of energy flows. Diversity (biological, cultural, etc.) is good because it maximises density and richness by utilising additional liveable niches and supporting innovation in the face of changing conditions.

By contrast, human technical systems, and especially AI systems, tend to be universal and reusable across the planet. There are only so many approaches, so many large organisations able to maintain them, so many ways to pay for them, etc. This leads to losses in diversity and opportunity.

The loss of diversity leads to social segregation, injustice and colonialism via other means.

Couldry, Nick, and Ulises Ali Mejias. The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism. Stanford: Stanford University Press, 2019.

The Link to Place

Ethics

Issues to consider:

• intersectionality, consider all forms of oppression and injustice
• Longtermism, consider current and future moral agents
• long term risks
• sentience risks
• sentient rights Universal Declaration of Sentient Rights
• negative human rights
• metahuman systems 1

Harris, Jamie, and Jacy Reese Anthis. ‘The Moral Consideration of Artificial Entities: A Literature Review’. Science and Engineering Ethics 27, no. 4 (2021): 53. https://doi.org/10/gp4m93

Selter, Jan-Lukas, Katja Wagner, and Hanna Schramm-Klein. ‘Ethics and Morality in AI - A Systematic Literature Review and Future Research’. In ECIS 2022 Research Papers, edited by Roman Beck, Dana Petcu, and Marin Fotache, 1278. Timișoara: ECIS, 2022.

Relevant Topics

• Accountability
• Decision Making
• Artificial Intelligence
• Sentience
• Subjectivity
• Energy
• Robots
• Smart City
• Smart Systems
• Consciousness
• Smart Novel Ecology
• Scale
• Longtermism
• Risk
• Politics

Mariska Jung

Books in This Space

Salter, Chris. Sensing Machines: How Sensors Shape Our Everyday Life. Cambridge, MA: MIT Press, 2022.

Seniors are important, some history, some art examples. Does not cover nonhuman sensing or place. Chris Salter, "Sensing Machines: How Sensors Shape Our Everyday Life" (MIT Press, 2022) - New Books in Science, Technology, and Society

Nowotny, Helga. In Ai We Trust: Power, Illusion and Control of Predictive Algorithms. Medford: Polity Press, 2021.

Lee, Edward A. The Coevolution: The Entwined Futures of Humans and Machines. Cambridge, MA: MIT Press, 2019.

There are utopian and dystopian camps in the conversation about AI. This books aims to be more reflective.

Predictive algorithms are only extrapolating from the past. However humans experience a powerful illusion that tells them that algorithms can predict the future, similar to oracles. As a result, humans start behaving as algorithm's predict. No engagement with nonhuman sensing or ethics.

Politics, Law, and Governance

Relevant in the sense that life, its interpetations, its control, its self-reproduction are political. Cf. Politics, Self

Davies, Margaret. Ecolaw: Legality, Life, and the Normativity of Nature. Abingdon: Routledge, 2022.

Nussbaum, Martha C. Justice for Animals: Our Collective Responsibility. Simon & Schuster, 2022.

Ruckenstein, Minna, and Natasha Dow Schüll. ‘The Datafication of Health’. Annual Review of Anthropology 46, no. 1 (2017): 261–78. https://doi.org/10/gd6bzg.

Mouton, Morgan, and Ryan Burns. ‘(Digital) Neo-Colonialism in the Smart City’. Regional Studies 55, no. 12 (2021): 1890–1901. https://doi.org/10/gj3jkj.

Methods

Approaches and methods of the book.

• commit to the notion of place
• consider issues through the relationships, knowledges and experiences of concrete human and nonhuman agents
• construct future imaginaries to illustrate and think through implications
• study algorithms and computational objects as agents? need to find the way to study them while coping with their closeness, sometimes practical and sometimes conceptual 2
• use Scale as a unifying framework that requires the alignment of intrinsic (evolved), observational, experimental and policy scales. Show and address misalignments. 3

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Subnotes

1. Approach
2. Cases
3. Context
4. Guidelines
5. Implications
6. Need
7. Plausibility
8. Proposal

Footnotes

1. Lyytinen, Kalle, Jeffrey V. Nickerson, and John L. King. ‘Metahuman Systems = Humans + Machines That Learn’. Journal of Information Technology 36, no. 4 (2021): 427–45. https://doi.org/10/ghj3x7.˄

2. Lange, Ann-Christina, Marc Lenglet, and Robert Seyfert. ‘On Studying Algorithms Ethnographically: Making Sense of Objects of Ignorance’. Organization 26, no. 4 (2019): 598–617. https://doi.org/10/gf3767.˄

3. Spake, Rebecca, Martha Paola Barajas-Barbosa, Shane A. Blowes, Diana E. Bowler, Corey T. Callaghan, Magda Garbowski, Stephanie D. Jurburg, et al. ‘Detecting Thresholds of Ecological Change in the Anthropocene’. Annual Review of Environment and Resources 47, no. 1 (2022): 797–821. https://doi.org/10/gq36zp.˄

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Backlinks

• Grief