Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as (...) argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence. (shrink)

Under the assumption that anticipatory models are required for anticipatory behavior, an important question arises about the different manners in which organisms acquire anticipatory models. This article aims to articulate four different non-exhaustive ways that anticipatory models might possibly be acquired over both phylogenetic and ontogenetic timescales and explore the relationships among them. To articulate these different model-acquisition mechanisms, four schematics will be introduced, each of which represents a particular acquisition structure that can be used for the purposes of comparison, (...) analysis, and hypothesis formulation. By bringing to the fore the differences and similarities between each of the four ways that anticipatory models are acquired, a more complete picture of both anticipatory behavior and its pervasive role in biological self-maintenance can be offered. In doing so, this article helps not only to shed light on how anticipatory behavior might arise in the wide range of organisms that it has been observed in but also to throw into relief the subtle and often still overlooked causal interplay between ontogenetic and phylogenetic plasticity. (shrink)

Biogenic approaches investigate cognition from the standpoint of evolutionary function, asking what cognition does for a living system and then looking for common principles and exhibitions of cognitive strategies in a vast array of living systems—non-neural to neural. One worry which arises for the biogenic approach is that it is overly permissive in terms of what it construes as cognition. In this paper I critically engage with a recent instance of this way of criticising biogenic approaches in order to clarify (...) their theoretical commitments and prospects. In his critique of the biogenic approach, Fred Adams uses the presence of intentional states with conceptual content as a criterion to demarcate cognition-driven behaviour from mere sensory response. In this paper I agree with Adams that intentionality is the mark of the cognitive, but simultaneously reject his overly restrictive conception of intentionality. I argue that understanding intentionality simpliciter as the mark of the mental is compatible with endorsing the biogenic approach. I argue that because cognitive science is not exclusively interested in behaviour driven by intentional states with the kind of content Adams demands, the biogenic approach’s status as an approach to cognition is not called into question. I then go on to propose a novel view of intentionality whereby it is seen to exist along a continuum which increases in the degree of representational complexity: how far into the future representational content can be directed and drive anticipatory behaviour. Understanding intentionality as existing along a continuum allows biogenic approaches and anthropogenic approaches to investigate the same overarching capacity of cognition as expressed in its different forms positioned along the continuum of intentionality. Even if all organisms engage in some behaviour that is driven by weak intentional dynamics, this does not suggest that every behaviour of all organisms is so driven. As such, the worry that the biogenic approach is overly permissive can be avoided. (shrink)

Harold Saxton Burr was a biologist working throughout the 1930s–1950s on an important set of problems related to biological organization and the origin of complex living forms. He was a profound thinker, suggesting a complementary focus on field concepts in addition to the emphasis on particle models and integrating concepts from physics and philosophy in his work. He developed innovations in electrophysiological technique and used them to perform a wide experimental survey of bioelectricity in normal and pathological growth. Here, I (...) briefly review his classic paper with philosopher F. S. C. Northrop, “The Electro-Dynamic Theory of Life,” in the context of advances in this field over the last few decades. Based on recent progress, it is now clear that Burr was a prescient and visionary thinker. His main hypothesis, that bioelectric gradients serve as prepatterns guiding morphogenesis, has been confirmed using modern molecular physiology, as have his ideas about the place of cancer and the nervous system in the question of biological organization. With limited technology but deep insight, he derived insights that anticipated many modern discoveries. Even more importantly, Burr’s view of bioelectricity as a convenient entry point for rigorous investigation of the broader question of self-organizing properties of life highlights a frontier of inquiry that awaits today’s researchers. Burr and Northrop’s “The Electro-Dynamic Theory of Life,” originally published in the Quarterly Review of Biology :322–333, 1935), is available as supplementary material in the online version of this essay. (shrink)

Meaning has traditionally been regarded as a problem for philosophers and psychologists. Advances in cognitive science since the early 1960s, however, broadened discussions of meaning, or more technically, the semantics of perceptions, representations, and/or actions, into biology and computer science. Here, we review the notion of “meaning” as it applies to living systems, and argue that the question of how living systems create meaning unifies the biological and cognitive sciences across both organizational and temporal scales.

Birch, Ginsburg, and Jablonka lay out a very convincing case for an important transition marker: unlimited associative learning. Especially welcome are the empirical predictions. I focus here not on the question of how to infer phenomenal consciousness from this behavioral metric, but on possible novel applications of this useful and fundamental framework. Specifically, I highlight two aspects of biology that are often not considered in philosophy of mind approaches that focus on natural species and evolutionary time scales. These are the (...) ability of minds and bodies to change drastically on the time scale of an individual experiencing subject, and bioengineering of novel living forms with no evolutionary history at the organism level. Both of these aspects provide interesting new contexts within which to explore UAL and its implications. (shrink)

When the history of life on earth is viewed as a history of cell division, all of life becomes a single cell lineage. The growth and differentiation of this lineage in reciprocal interaction with its environment can be viewed as a developmental process; hence the evolution of life on earth can also be seen as the development of life on earth. Here, in reviewing this field, some potentially fruitful research directions suggested by this change in perspective are highlighted. Variation and (...) selection become, for example, bidirectional information flows between scales, while the notions of “cooperation” and “competition” become scale relative. The language of communication, inference, and information processing becomes more useful than the language of causation to describe the interactions of both homogeneous and heterogeneous living systems at any scale. Emerging scale‐free theoretical frameworks such as predictive coding and active inference provide conceptual tools for reconceptualizing biology as the study of a unified, multiscale dynamical system. (shrink)