Durt, Christoph and Fuchs, Thomas and Froese, Tom

Large language models such as ChatGPT are deep learning architectures trained on immense quantities of text. Their capabilities of producing human-like text are often attributed either to mental capacities or the modeling of such capacities. This paper argues, to the contrary, that because much of meaning is embedded in common patterns of language use, LLMs can model the statistical contours of these usage patterns. We agree with distributional semantics that the statistical relations of a text corpus reflect meaning, but only part of it. Written words are only one part of language use, although an important one as it scaffolds our interactions and mental life. In human language production, preconscious anticipatory processes interact with conscious experience. Human language use constitutes and makes use of given patterns and at the same time constantly rearranges them in a way we compare to the creation of a collage. LLMs do not model sentience or other mental capacities of humans but the common patterns in public language use, clichés and biases included. They thereby highlight the surprising extent to which human language use gives rise to and is guided by patterns.

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Sai Krishna Katla, Chenyu Lin, Juan Pérez-Mercader

Cell Reports Physical Science

Essential for Darwin’s “struggle for existence,” the competitive exclusion principle (CEP) states that any “two species occupying the same niche will compete with each other to the detriment of one of the species, which will thus be excluded.” Here, we report on competition experiments between two populations of autonomous, artificial, self-booting, self-reproducing polymer-based protocells emerging from a homogeneous blend of small synthetic chemicals in a one-pot reactor using polymerization-induced self-assembly (PISA). These protocells are carbon chemistry based but biochemistry free. The populations share their environment, differing only in that one contains a photocatalyst that confers advantages in reproduction. Competition in the shared environment follows the CEP. Thus, biochemistry is sufficient, not necessary, to drive the CEP. This has implications for protocell research, the origin and early evolution of life, and the laboratory synthesis of life and also relaxes constraints for the potential presence and evolution of generalized life in exoplanets.

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Luigi Zanovello, Richard J. G. Löffler, Michele Caraglio, Thomas Franosch, Martin M. Hanczyc & Pietro Faccioli 

Scientific Reports volume 13, Article number: 5616 (2023)

Artificial cells can be engineered to display dynamics sharing remarkable features in common with the survival behavior of living organisms. In particular, such active systems can respond to stimuli provided by the environment and undertake specific displacements to remain out of equilibrium, e.g. by moving towards regions with higher fuel concentration. In spite of the intense experimental activity aiming at investigating this fascinating behavior, a rigorous definition and characterization of such “survival strategies” from a statistical physics perspective is still missing. In this work, we take a first step in this direction by adapting and applying to active systems the theoretical framework of Transition Path Theory, which was originally introduced to investigate rare thermally activated transitions in passive systems. We perform experiments on camphor disks navigating Petri dishes and perform simulations in the paradigmatic active Brownian particle model to show how the notions of transition probability density and committor function provide the pivotal concepts to identify survival strategies, improve modeling, and obtain and validate experimentally testable predictions. The definition of survival in these artificial systems paves the way to move beyond simple observation and to formally characterize, design and predict complex life-like behaviors.

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Klaus Jaffe

Classical thermodynamics focused on reversible processes in closed systems. Most processes however are irreversible, in both closed and open systems. A non classical thermodynamics is being developed to tackle complex open systems suffering irreversible processes. That is the case for Synergy that emerges from synchronized reciprocal positive feedback loops between a network of diverse actors. For this process to proceed, compatible information from different sources synchronically coordinates the actions of the actors resulting in a nonlinear increase in the useful work or potential energy the system can manage. In contrast noise is produced when incompatible information is mixed. This synergy produced from the coordination of different agents achieves non-linear gains in free energy and in information (negentropy). Free energy can be estimated by proxies such as individual autonomy of an organism, emancipation from the environment, productivity, efficiency, capacity for flexibility, self-regulation, and self-control of behavior; whereas entropy, or the lack of it, is revealed by the degree of synchronized division of ever more specialized labor, structural complexity, information, and dissipation of energy. Empirical examples that provide quantitative data for these phenomena are presented. Results show that increases in free energy density are concomitant with decreases in entropy density. This may be a rule for synergistic processes in non-equilibrium thermodynamics, which is consilient with the first and second laws of classical thermodynamics. Under this light, biological evolution is the task of self reproducing irreversible synergistic systems to discover empirically (through natural, inclusive, and sexual selection) types of order that increase their free energy.

Read the full article at: www.qeios.com