1. Semiosphere

What do bees think? What do fish feel? A flood of scientific studies over the past few decades have revealed a wealth of cognitive and communicative activity perfusing the natural world. A recent paper, for example, suggests that honeybees are capable of advanced numerical cognition.1 Honeybees can “learn to use blue and yellow as symbolic representations for addition or subtraction,” simultaneously employing short-term memory and long-term acquired rules to perform mathematical operations. Zebrafish, meanwhile, have been shown not just to avoid noxious stimuli but to respond to pain differently depending on context; whether, for instance, they were frightened by exposure to the alarm pheromone of another zebrafish.2 In What a Fish Knows: The Inner Lives of Our Underwater Cousins, Jonathan Balcombe explains that evidence of variability of response to pain indicates conscious awareness of it. Zebrafish also seem susceptible to boredom and will try to avoid it unless there are good reasons to endure it. Balcombe describes an experiment conducted by Lynne Sneddon that suggests zebrafish will forego an interesting experience in pursuit of pain relief:

Like most captive animals, fishes like stimulation. For instance, zebrafishes prefer to swim in an enriched chamber with vegetation and objects to explore rather than in a barren chamber in the same tank. When Sneddon injected zebrafishes with acetic acid, this preference didn't change; nor did it change for other zebrafishes injected with saline water (which causes only brief pain). However, if a painkiller was dissolved in the barren, unpreferred chamber of the tank, the fishes injected with the acid chose to swim in the unfavorable, barren chamber. The saline-injected fishes remained in the enriched side of the tank. Thus, zebrafishes will pay a cost in return for gaining some relief from their pain. (Balcombe 81-82)

Studies such as these contribute to a vision of life as intricate relations of affective, cognitive and communicative processes. In his 1993 book Signs of Meaning in the Universe,3 Jesper Hoffmeyer introduces the term semiosphere to denote the vast, complex web of signification within which each earthy organism is enmeshed: “The semiosphere is a sphere just like the atmosphere, the hydrosphere, and the biosphere. It penetrates to every corner of these other spheres, incorporating all forms of communication: sounds, smells, movements, colors, shapes, electrical fields, thermal radiation, waves of all kinds, chemical signals, touching, and so on” (vii). The semiosphere is comprised of innumerable chattering, buzzing relationships, but in the quarter century since Hoffmeyer introduced the term, the chattering and buzzing has gotten alarmingly quiet. Over a million species face imminent extinction, as a recent IPBES report makes clear.4 Complex networks of sign processes which stabilized over vast tracts of evolutionary time are rapidly breaking down. At the same time, digital semiotic phenomena have come to occupy a greater and greater share of the semiosphere. On one hand, denizens of the semiosphere diverge dramatically in the kinds of signs they may be equipped to encounter. On the other hand, sign processes and the relations they enable are not in principle confined to either natural or cultural, organic or synthetic, human or nonhuman realms. This play of openness and opacity means that the effects of future permutations of and interventions into the semiosphere can be difficult to anticipate. How might emerging technologies help maintain, repair or conserve the natural networks that animate ecosystems? What new forms of risk might they create or intensify?

A biosemiotic account of life views organismic relations as interpretive, agentic and meaningful. It also enables visions of digital technologies capable of tuning into and perhaps manipulating these semiotic flows. Hoffmeyer argues in Biosemiotics: An Investigation into the Signs of Life and the Life of Signs5 that it is possible to transform industrial production by replacing the brute force driving the mechanical control of energy flows with a biosemiotic technology capable of leveraging and redirecting, rather than disrupting, nature's communicative, self-organizing intelligence:

The task ahead of us is to embark upon the second half of the industrial revolution. And this will consist in the development of a mastery of the biosemiotic controls that can match (and thus sophisticate) our present mechanical mastery of the gigantic energy flows that, in an overpopulated world, necessarily must destabilize nature's optimal balance points. Another way to say this is that we need to develop a biosemiotic technology base for our production systems – a technology base that can replace natural biosemiotic control mechanisms with biosemiotic control mechanisms artificially set to fulfill human and environmental needs. (347)

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Adopting a biosemiotic framework means rethinking concepts like information and meaning. In Expecting the Earth: Life|Culture|Biosemiotics,6 Wendy Wheeler argues that organisms are active agents engaged in interpreting and reshaping their surroundings, not passive nodes through which disembodied, abstract information is transferred. Information, to have an impact on the world, requires interpretation and contextualization: “In other words, a billion pieces of encoding within a billion channels (whether books, pictures, telephone lines, computers, etc.) is not information until some living being (or part thereof) makes sense of it” (62). Wheeler argues that meaning is fundamentally about relationships. An organism may respond to “a difference that makes a difference” (Bateson, quoted in Wheeler, 41) differently, depending on what else is going on both in their surrounding environment and with the other organisms they are bound up with, as the zebrafish studies alluded to above suggest. Meaning, unlike a notion of information as a straightforward transmission of coded content, requires an ontological setting marked by self-organizing, relational, complex and dynamic living systems: “It seems clear that we are living through a shift, or development, from an Age of Mechanism to an Age of Information. The latter will, I believe, eventually come to be expanded and better understood as a third Age of Systems and Semiosis which is characterized by relational and semiotic ontologies” (66). What would a biosemiotic technological intervention into ecosystemic relations look like?

2. Biohybrid Systems

In the March 20th issue of Science Robotics, Frank Bonnet et al. report using the internet and robots to enable communication between zebrafish and honeybees, two species unlikely to engage in very much cross-species conversation without a highly motivated mediator.7 They describe their experiment as a case of digitally mediated interspecies communication. The researchers constructed a “biohybrid system” in which decisions about which direction to move made by one species affected the behavior of the robot mimicking them, which led the other species to move in sync:

These results demonstrate the feasibility of generating and controlling behavioral patterns in biohybrid groups of multiple species. Such interspecies connections between diverse robotic systems and animal species may open the door for new forms of artificial collective intelligence, where the unrivaled perceptual capabilities of the animals and their brains can be used to enhance autonomous decision-making, which could find applications in selective “rewiring” of ecosystems. (Bonnet et al., Abstract)

Robots have been used in a variety of ways in recent studies of animal behavior. In some cases, biohybrid systems have been created that are made up of groups of robots and animals.8 The robots are designed to socially integrate into groups of animals by mimicking some of the signs used in the species' social interactions. These sign exchanges are described as closed interaction loops which can be used to test hypotheses about self-organized collective behavior. The researchers describe them as new biohybrid information and communication technologies systems “because the animals can enrich the capabilities of the machines, and vice versa” (Bonnet et al., Introduction). While earlier studies have focused only on interactions involving robots and single species, this study is interested in coupling two distinct biohybrid systems. Expanding biohybridity into multispecies networks enables researchers to study how “collective decision-making can arise at a larger scale, among multiple individuals of different species, with their own sensing and acting properties...” (Bonnet et al., Introduction).

Why honeybees and zebrafish? Honeybees are well known for their self-organizing collective behaviors, while zebrafish are model organisms in fields like genetics and neurophysiology. Both are social species and serve as common models for understanding the link between individual variability and collective action. Honeybees and zebrafish differ in habitat as well as in the dynamics of their group and individual interactions, but they both exhibit decision-making at the collective level. The researchers argue that this collective behavior opens the possibility of indirect information exchange.

The robots have to be designed with enough semiotic sophistication to be socially acceptable to and capable of reaching consensus with the animal group: “For each species, we created the simplest and smallest set of robotic agents that could either autonomously reproduce some of the signals used by animals during their social interactions or emit physical cues that are present in the animals’ natural environment and to which the animals will react in a predictable way” (Bonnet et al., Results). The honeybees used in the experiment were 1-24 hours old. The honeybee robots were designed specifically to interact with juvenile honeybees: the robots produced heat, which the researchers describe as an “attractive cue” for young honeybees who cannot yet produce heat themselves. For the zebrafish, they used a lure that has the same shape and size of a zebrafish and which moves in patterns similar to the fishes. The animals respond to the robots in ways similar to how they respond to their conspecifics. Both groups were given a binary collective choice, which is a common framework for studying self-organization. The honeybees could congregate around one robot or the other, while the zebrafish could swim either clockwise or counterclockwise. The robots allowed the two groups to share their collective decision-making processes, from which a consensus emerged: “We observed how the collective decision from one species was transferred to another via information exchanged between the robots” (Bonnet et al., Introduction).

3. Mediation

The researchers claim to be able to mediate interactions between animal species: “We developed an autonomous robotic system capable of coordinating the collective behavior of two animal species using socially integrated robots” (Bonnet et al., Discussion). They imagine inserting robots into interspecies relations in the wild, working with the self-organizing communicative activity that already exists within a group and then redirecting that activity toward some end. For this experiment, the researchers tried to use the smallest number of robots and animals necessary to demonstrate the interspecies interactions, but they argue that the system they designed could accommodate a large number of robots and species:

This approach may also be generalized to other living species, such as plants, fungi or even microorganisms, to allow systems to interact at different scales. It would then be possible, on the one hand, to exploit the unrivaled sensory properties of the living systems, their behaviors and their ease to move in the wild, and, on the other hand, to influence their choices and to add physical properties like telecommunication and other capacities. (Bonnet et al., Discussion)

The researchers anticipate applications in which future robots can learn on their own how to evolve as part of biohybrid systems: “We envision robotic systems that can discover by themselves new properties of biohybrid artificial intelligence toward synthetic transitions and organic computing devices, where robots could passively evolve among animals” (Bonnet et al., Discussion). Evolving robots could repair or “rewire” damaged ecosystemic relations (Bonnet et al., Discussion). They could relocate species, encourage them to avoid some areas, and use whatever data the robots obtain to build more resilient ecosystems. Evolving robots embedded in ecosystems would construct a technological infrastructure around living beings, incorporating and partially redirecting their semiotic capabilities into recursive systems entwining artificial and organic semiotic processes.

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What does it mean for a robot to passively evolve among animals within a biohybrid system? Could such a system be constructed that would not impede or constrain the semiotic relations among the organisms imbricated in it? As biosemiotic theory and the empirical studies cited above suggest, semiosis in a real environment is highly complex, relational, opaque and sensitive to continually shifting contexts. We do not possess comprehensive knowledge of the semiotic abilities of any species. What the evolving robots would have to learn for themselves goes beyond mapping flows of information and movement. Wheeler points out that information requires a living organism (or at least an interpreter with the “unrivalled sensory properties” of a living organism) to turn it into meaning. The difference between information and meaning is minimized as much as possible in the experiment by making both the environments and the organisms themselves very simple (in the case of the honeybees, even isolating a specific stage in their semiotic development). The animals are placed in tightly semiotically constrained environments and presented with a binary choice, leaving little room for interpretation. This kind of semiotic control is entirely appropriate for a scientific experiment, but to introduce social robotics into ecosystemic relations would be to alter the contexts within which those relations are made in ways not necessarily discernible in advance to human meaning-making processes.

Integrating digital semiotic phenomena more deeply into the semiosphere offers the alluring promise of a future in which environments could be monitored and protected in real time, with minimal disruption to these complex systems and their chattering, buzzing relationships. The difficult part is knowing whether biohybrid systems could be constructed that would keep open, rather than reify and close down, the dynamic, creative and exploratory semiotic activity that animates life. In a semiosphere full of meaning-making beings that perform math, feel bored, and do who knows what else, attempts at constructing biohybrid systems bring with them a number of difficult but interesting questions. What is the difference between information and meaning? How do we negotiate between what we can identify as semiosis and what our considerable, technologically mediated human semiotic processing powers might miss? When do digital technologies risk imposing tightly coordinated, functioning but boring semiotic circuits, and how might they be deployed to foster interesting webs of relationships that are given space to grow and develop on their own terms? Is there any possibility of approaching nature as an intelligent and useful technology and also as a realm of agency and autonomy beyond the human which must be accorded respect and value? Is it possible to manage and control urgent, catastrophic ecosystemic risks through digital technologies without potentially creating new risks in the process?

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Notes

1. Howard, Scarlett R. et al., “Numerical Cognition in Honeybees enables Addition and Subtraction.” Science Advances Vol.5 no. 2 (06 Feb. 2019). Internet. Accessed June 11, 2019. DOI: 10.1126/sciadv.aav0961

2. Study by Caio Maximino referenced in Balcombe, Jonathan. What a Fish Knows: The Inner lives of Our Underwater Cousins. Scientific American, 2017.

3. Hoffmeyer, Jesper. Signs of Meaning in the Universe. 1993. Translated by Barabara J. Haveland, Indiana University Press, 1996.

4. Díaz, Sandra, Josef Settele, Eduardo Brondízio et al. “IPBES Global Assessment Summary for Policymakers” Bonn, Germany, Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Internet. Accessed June 8, 2019. https://www.ipbes.net/sites/default/files/downloads/spm_unedited_advance_for_posting_htn.pdf

5. Hoffmeyer, Jesper. Biosemiotics: An Examination into the Signs of Life and the Life of Signs. Translated by Jesper Hoffmeyer and Donald Favareau, University of Scranton Press, 2009.

6. Wheeler, Wendy. Expecting the Earth: Life|Culture|Biosemiotics. Lawrence & Wishart, 2016.

7. Bonnet, Frank et al. “Robots Mediating Interactions between Animals for Interspecies Collective Behaviors.” Science Robotics vol. 4, issue 28 (March 20, 2019). Internet. Accessed May 27, 2019. DOI: 10.1126/scirobotics.aau7897

8. See for example Kernbach, Serge, editor. Handbook of Collective Robotics: Fundamentals and Challenges. Jenny Stanford Publishing, 2013.