Hackles have been raised in biosemiotic circles by T. L. Short's assertion that semiosis, as defined by Peirce, entails "acting for purposes" and therefore is not found below the level of the organism (2007a:174-177). This paper examines Short's teleology and theory of purposeful behavior and offers a remedy to the disagreement. Remediation becomes possible when the issue is reframed in the terms of the complexity sciences, which allows intentionality to be understood as the interplay between local and global aspects of a system within a system. What is called "acting for purposes" is not itself a type of behavior so much as a relationship between a dynamic system that "exists for a purpose" and its microprocesses that "serve purposes." The "intentional object" of philosophy is recast here as the holistic self-organized dynamics of a system, which exists for the purpose of selfmaintenance, and that constrains the parts' behaviors, which serve the purpose of forming the system. (A "system" can be any emergent, e.g. an abiotic form, an adapted species, a self, a conditioned response, thought, or a set of ideas.) The selforganized whole, which is represented to the parts in their own constrained behaviors, assumes the guiding function so long attributed to the mysterious "intentional object." If emergent self-causation is not disallowed, creative originality, as well as directionality, becomes part of the definition of purposeful behavior. Thus, key tools used here, required for understanding emergence, come from poetics rather than semoitics. In the microprocesses of self-organization, I find what I call "accidental" indices and icons -which are poetic in the sense that they involve mere metonymic contiguity and metaphoric similarity -and which are preferentially selected under constrained conditions allowing radically new connections to habituate into an "intentional" self-organized system that, not coincidentally, has some of the emergent characteristics of a conventional symbolic system.
Artificial Intelligence (AI) designers try to mimic human brain capabilities with “self-learning” neural networks trained by selection processes. Yet decades on, AI still fails the Turing Test. While computers use codes and develop algorithms apart from contexts, living cells use signs and develop semiotic habits within contexts. This difference, I argue, is partly due to the collective activities of biological neurons that produce traveling waves, which, in turn, further constrain neural activity. It appears wave patterns function as contexts shaping the content of the local connections. At the time of his death, Alan Turing was investigating the organizing role of emergent wave patterns on biological development. Had he lived to continue this work, he might have reoriented AI research, which instead has become merely a tool for stereotyping and regularizing, not thinking.
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