Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Advanced technologies and mathematical metacognition: The present and future orientation

The intersection of mathematical cognition, metacognition, and advanced technologies presents a frontier with profound implications for human learning and artificial intelligence. This paper traces the historical roots of these concepts from the Pythagoreans and Aristotle to modern cognitive science and explores their relevance to contemporary technological applications. We examine how the Pythagoreans' view of mathematics as fundamental to understanding the universe and Aristotle's contributions to logic and categorization have shaped our current understanding of mathematical cognition and metacognition. The paper investigates the role of Boolean logic in computational processes and its relationship to human logical reasoning, as well as the significance of Bayesian inference and fuzzy logic in modelling uncertainty in human cognition and decision-making. We also explore the emerging field of Chemical Artificial Intelligence and its potential applications. We argue for unifying mathematical metacognition with advanced technologies, including artificial intelligence and robotics, while identifying the multifaceted benefits and challenges of such unification. The present paper examines essential research directions for integrating cognitive sciences and advanced technologies, discussing applications in education, healthcare, and business management. We provide suggestions for developing cognitive robots using specific cognitive tasks and explore the ethical implications of these advancements. Our analysis underscores the need for interdisciplinary collaboration to realize the full potential of this integration while mitigating potential risks.

Memristive Architectures Exploiting Self-Compliance Multilevel Implementation on 1 kb Crossbar Arrays for Online and Offline Learning Neuromorphic Applications

This paper suggests the practical implications of utilizing a high-density crossbar array with self-compliance (SC) at the conductive filament (CF) formation stage. By limiting the excessive growth of CF, SC functions enable the operation of a crossbar array without access transistors. An AlOx/TiOy, internal overshoot limitation structure, allows the SC to have resistive random-access memory. In addition, an overshoot-limited memristor crossbar array makes it possible to implement vector-matrix multiplication (VMM) capability in neuromorphic systems. Furthermore, AlOx/TiOy structure optimization was conducted to reduce overshoot and operation current, verifying uniform bipolar resistive switching behavior and analog switching properties. Additionally, extensive electric pulse stimuli are confirmed, evaluating long-term potentiation (LTP), long-term depression (LTD), and other forms of synaptic plasticity. We found that LTP and LTD characteristics for training an online learning neural network enable MNIST classification accuracies of 92.36%. The SC mode quantized multilevel in offline learning neural networks achieved 95.87%. Finally, the 32 × 32 crossbar array demonstrated spiking neural network-based VMM operations to classify the MNIST image. Consequently, weight programming errors make only a 1.2% point of accuracy drop to software-based neural networks.

Determining Chemical Microheterogeneity from the Analysis of Absorption and Luminescence Transient Signals

The characterization of chemical microheterogeneity is compelling due to its relevant role in soft materials, high-entropy materials, and systems chemistry, to cite just a few instances. This work investigates the microheterogeneity of photochromic samples and metal oxide solid solutions by fitting time-resolved absorption and luminesce signals recorded after photoexcitation. The transient spectroscopic signals have been analyzed using polyexponential functions determined through the Maximum Entropy Method (MEM) and discrete exponential, Kohlrausch, and Becquerel functions through the Levenberg-Marquardt algorithm. The outputs of the different fitting functions and algorithms are compared and exploited to characterize chemical microheterogeneity quantitatively. The practical relevance of chemical microheterogeneity is supported by the demonstration that photochromic samples are transformed from binary to multistate systems, capable of encoding much more information, and that microheterogeneous photocatalysts are provided with several structural defects that guarantee the coexistence of many active sites and higher catalytic activity.

Living cells and biological mechanisms as prototypes for developing chemical artificial intelligence

Artificial Intelligence (AI) is having a revolutionary impact on our societies. It is helping humans in facing the global challenges of this century. Traditionally, AI is developed in software or through neuromorphic engineering in hardware. More recently, a brand-new strategy has been proposed. It is the so-called Chemical AI (CAI), which exploits molecular, supramolecular, and systems chemistry in wetware to mimic human intelligence. In this work, two promising approaches for boosting CAI are described. One regards designing and implementing neural surrogates that can communicate through optical or chemical signals and give rise to networks for computational purposes and to develop micro/nanorobotics. The other approach concerns "bottom-up synthetic cells" that can be exploited for applications in various scenarios, including future nano-medicine. Both topics are presented at a basic level, mainly to inform the broader audience of non-specialists, and so favour the rise of interest in these frontier subjects.

Open optimism as an "embodied-health" ethic for the information era

This article forms part of a series on "openness," "non-linearity," and "embodied-health" in the post-physical, informational (virtual) era of society. This is vital given that the threats posed by advances in artificial intelligence call for a holistic, embodied approach. Typically, health is separated into different categories, for example, (psycho)mental health, biological/bodily health, genetic health, environmental health, or reproductive health. However, this separation only serves to undermine health; there can be no separation of health into subgroups (psychosomatics, for example). Embodied health contains no false divisions and relies on "optimism" as the key framing value. Optimism is only achieved through the mechanism/enabling condition of openness. Openness is vital to secure the embodied health for individuals and societies. Optimism demands that persons become active participants within their own lives and are not mere blank slates, painted in the colors of physical determinism (thus a move away from nihilism-which is the annihilation of freedom/autonomy/quality). To build an account of embodied health, the following themes/aims are analyzed, built, and validated: (1) a modern re-interpretation and validation of German idealism (the crux of many legal-ethical systems) and Freud; (2) ascertaining the bounded rationality and conceptual semantics of openness (which underlies thermodynamics, psychosocial relations, individual autonomy, ethics, and as being a central constitutional governmental value for many regulatory systems); (3) the link between openness and societal/individual embodied health, freedom, and autonomy; (4) securing the role of individualism/subjectivity in constituting openness; (5) the vital role of nonlinear dynamics in securing optimism and embodied health; (6) validation of arguments using the methodological scientific value of invariance (generalization value) by drawing evidence from (i) information and computer sciences, (ii) quantum theory, and (iii) bio-genetic evolutionary evidence; and (7) a validation and promotion of the inalienable role of theoretic philosophy in constituting embodied health, and how modern society denigrates embodied health, by misconstruing and undermining theoretics. Thus, this paper provides and defends an up-to-date non-physical account of embodied health by creating a psycho-physical-biological-computational-philosophical construction. Thus, this paper also brings invaluable coherence to legal and ethical debates on points of technicality from the empirical sciences, demonstrating that each field is saying the same thing.

The Conformational Contribution to Molecular Complexity and Its Implications for Information Processing in Living Beings and Chemical Artificial Intelligence

This work highlights the relevant contribution of conformational stereoisomers to the complexity and functions of any molecular compound. Conformers have the same molecular and structural formulas but different orientations of the atoms in the three-dimensional space. Moving from one conformer to another is possible without breaking covalent bonds. The interconversion is usually feasible through the thermal energy available in ordinary conditions. The behavior of most biopolymers, such as enzymes, antibodies, RNA, and DNA, is understandable if we consider that each exists as an ensemble of conformers. Each conformational collection confers multi-functionality and adaptability to the single biopolymers. The conformational distribution of any biopolymer has the features of a fuzzy set. Hence, every compound that exists as an ensemble of conformers allows the molecular implementation of a fuzzy set. Since proteins, DNA, and RNA work as fuzzy sets, it is fair to say that life's logic is fuzzy. The power of processing fuzzy logic makes living beings capable of swift decisions in environments dominated by uncertainty and vagueness. These performances can be implemented in chemical robots, which are confined molecular assemblies mimicking unicellular organisms: they are supposed to help humans "colonise" the molecular world to defeat diseases in living beings and fight pollution in the environment.

Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part II: systems chemistry

Inspired by some traits of human intelligence, it is proposed that wetware approaches based on molecular, supramolecular, and systems chemistry can provide valuable models and tools for novel forms of robotics and AI, being constituted by soft matter and fluid states as the human nervous system and, more generally, life, is. Bottom-up mimicries of intelligence range from the molecular world to the multicellular level, i.e., from the Ångström (10 - 10 meters) to the micrometer scales (10 - 6 meters), and allows the development of unconventional chemical robotics. Whereas conventional robotics lets humans explore and colonise otherwise inaccessible environments, such as the deep oceanic abysses and other solar system planets, chemical robots will permit us to inspect and control the microscopic molecular and cellular worlds. This article suggests that systems made of properly chosen molecular compounds can implement all those modules that are the fundamental ingredients of every living being: sensory, processing, actuating, and metabolic networks. Autonomous chemical robotics will be within reach when such modules are compartmentalised and assembled. The design of a strongly intertwined web of chemical robots, with or without the involvement of living matter, will give rise to collective forms of intelligence that will probably reproduce, on a minimal scale, some sophisticated performances of the human intellect and will implement forms of "general AI." These remarkable achievements will require a productive interdisciplinary collaboration among chemists, biotechnologists, computer scientists, engineers, physicists, neuroscientists, cognitive scientists, and philosophers to be achieved. The principal purpose of this paper is to spark this revolutionary collaborative scientific endeavour.

Chemical Systems for Wetware Artificial Life: Selected Perspectives in Synthetic Cell Research

The recent and important advances in bottom-up synthetic biology (SB), in particular in the field of the so-called "synthetic cells" (SCs) (or "artificial cells", or "protocells"), lead us to consider the role of wetware technologies in the "Sciences of Artificial", where they constitute the third pillar, alongside the more well-known pillars hardware (robotics) and software (Artificial Intelligence, AI). In this article, it will be highlighted how wetware approaches can help to model life and cognition from a unique perspective, complementary to robotics and AI. It is suggested that, through SB, it is possible to explore novel forms of bio-inspired technologies and systems, in particular chemical AI. Furthermore, attention is paid to the concept of semantic information and its quantification, following the strategy recently introduced by Kolchinsky and Wolpert. Semantic information, in turn, is linked to the processes of generation of "meaning", interpreted here through the lens of autonomy and cognition in artificial systems, emphasizing its role in chemical ones.

Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part I: molecular and supramolecular chemistry

Chemical Artificial Intelligence (CAI) is a brand-new research line that exploits molecular, supramolecular, and systems chemistry in wetware (i.e., in fluid solutions) to imitate some performances of human intelligence and promote unconventional robotics based on molecular assemblies, which act in the microscopic world, otherwise tough to be accessed by humans. It is undoubtedly worth spreading the news that AI researchers can rely on the help of chemists and biotechnologists to reach the ambitious goals of building intelligent systems from scratch. This article reports the first attempt at building a Chemical Artificial Intelligence knowledge map and describes the basic intelligent functions that can be implemented through molecular and supramolecular chemistry. Chemical Artificial Intelligence provides new tools and concepts to mimic human intelligence because it shares, with biological intelligence, the same principles and materials. It enables peculiar dynamics, possibly not accessible in software and hardware domains. Moreover, the development of Chemical Artificial Intelligence will contribute to a deeper understanding of the strict link between intelligence and life, which are two of the most remarkable emergent properties shown by the Complex Systems we call biological organisms.

Artificial Synapse Emulated by Indium Tin Oxide/SiN/TaN Resistive Switching Device for Neuromorphic System

In this paper, we fabricate an ITO/SiN/TaN memristor device and analyze its electrical characteristics for a neuromorphic system. The device structure and chemical properties are investigated using transmission electron microscopy and X-ray photoelectron spectroscopy. Uniform bipolar switching is achieved through DC sweep under a compliance current of 5 mA. Also, the analog reset phenomenon is observed by modulating the reset voltage for long-term memory. Additionally, short-term memory characteristics are obtained by controlling the strength of the pulse response. Finally, bio-inspired synaptic characteristics are emulated using Hebbian learning rules such as spike-rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP). As a result, we believe that the coexistence of short-term and long-term memories in the ITO/SiN/TaN device can provide flexibility in device design in future neuromorphic applications.

Intelligence across humans and machines: a joint perspective

This paper aims to address the divergences and contradictions in the definition of intelligence across different areas of knowledge, particularly in computational intelligence and psychology, where the concept is of significant interest. Despite the differences in motivation and approach, both fields have contributed to the rise of cognitive science. However, the lack of a standardized definition, empirical evidence, or measurement strategy for intelligence is a hindrance to cross-fertilization between these areas, particularly for semantic-based applications. This paper seeks to equalize the definitions of intelligence from the perspectives of computational intelligence and psychology, and offer an overview of the methods used to measure intelligence. We argue that there is no consensus for intelligence, and the term is interchangeably used with similar, opposed, or even contradictory definitions in many fields. This paper concludes with a summary of its central considerations and contributions, where we state intelligence is an agent's ability to process external and internal information to find an optimum adaptation (decision-making) to the environment according to its ontology and then decode this information as an output action.

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT.

Bioethics and artificial intelligence: between deliberation on values and rational choice theory

The present work revisits how artificial intelligence, as technology and ideology, is based on the rational choice theory and the techno-liberal discourse, supported by large corporations and investment funds. Those that promote using different algorithmic processes (such as filter bubbles or echo chambers) create homogeneous and polarized spaces that reinforces people's ethical, ideological, and political narratives. These mechanisms validate bubbles of choices as statements of fact and contravene the prerequisites for exercising deliberation in pluralistic societies, such as the distinction between data and values, the affirmation of reasonable dissent, and the relevance of diversity as a condition indispensable for democratic deliberation.

The contralateral organization of the human nervous system as a quantum unfolded, holographic-like, artifactual representation of the underlying dynamics of a fundamentally two-dimensional universe

A working hypothesis is put forward in this article that the contralateral organization of the human nervous system appears to function like a quantum unfolded holographic apparatus by appearing to invert and reverse quantum unfolded visual and non-visual spatial information. As such, the three-dimensional contralateral organization would be an artifactual representation of the underlying dynamics of a fundamentally two-dimensional universe. According to the holographic principle, nothing that is experienced as three-dimensional could have been processed in a three-dimensional brain. Everything we would experience at a two-dimensional level would appear as a three-dimensional holographic representation, including the architecture of our brains. Various research observations reported elsewhere are reviewed and interpreted here as they may be related in a process that is fundamental to the underlying two-dimensional dynamics of the contralateral organization. The classic holographic method and characteristics of image formation contained by a holograph are described as they relate to the working hypothesis. The double-slit experiment is described and its relevance to the working hypothesis.

The Versatile Photo-Thermal Behaviour of a 2-Hydroxyazobenzene

Photochromic compounds are employed in implementing neuron surrogates. They will boost the development of neuromorphic engineering in wetware. In this work, the photochromic behaviours of (E)-3,4,6-trichloro-2-(p-diazenil)-phenol (t-DZH) and its conjugated phenoxide base (t-DZ) have been investigated experimentally in three different media: (1) pure acetonitrile, (2) in water and acetonitrile mixed in a 1/1 volume ratio, and (3) in an aqueous micellar solution of 3-(N,N-Dimethylmyristylammonio)propanesulfonate (SB3-14). The analysis of the spectral and kinetic features of t-DZH and t-DZ has been supported by quantum-mechanical DFT calculations, the maximum entropy method, and the determination of their colourability (C). The versatility of t-DZH and t-DZ makes them promising molecular probes of micro-environments and potential ingredients of photochemical oscillators required for implementing pacemaker neurons capable of communicating through optical signals in wetware.

Thin-Plate Superstructures of the Immunogenic 33-mer Gliadin Peptide

Gluten related-disorders have a prevalence of 1-5 % worldwide triggered by the ingestion of gluten proteins in wheat, rye, barley, and some oats. In wheat gluten, the most studied protein is gliadin, whose immunodominant 33-mer amino acid fragment remains after digestive proteolysis and accumulates in the gut mucosa. Here, we report the formation of 33-mer thin-plate superstructures using intrinsic tyrosine (Tyr) steady-state fluorescence anisotropy and cryo-TEM in combination with water tension measurements. Furthermore, we showed that fluorescence decay measurements of 33-mer intrinsic fluorophore Tyr provided information on the early stages of the formation of the thin-plate structures. Finally, conformational analysis of Tyr residues using minimalist models by molecular dynamic simulations (MD) demonstrated that changes in Tyr rotamer states depend on the oligomerization stage. Our findings further advance the understanding of the formation of the 33-mer gliadin peptide superstructures and their relation to health and disease.

Quantitative estimation of chemical microheterogeneity through the determination of fuzzy entropy

Chemical micro-heterogeneity is an attribute of all living systems and most of the soft and crystalline materials. Its characterization requires a plethora of techniques. This work proposes a strategy for quantifying the degree of chemical micro-heterogeneity. First of all, our approach needs the collection of time-evolving signals that can be fitted through poly-exponential functions. The best fit is determined through the Maximum Entropy Method. The pre-exponential terms of the poly-exponential fitting function are used to estimate Fuzzy Entropy. Related to the possibility of implementing Fuzzy sets through the micro-heterogeneity of chemical systems. Fuzzy Entropy becomes a quantitative estimation of the Fuzzy Information that can be processed through micro-heterogeneous chemical systems. We conclude that our definition of Fuzzy Entropy can be extended to other kinds of data, such as morphological and structural distributions, spectroscopic bands and chromatographic peaks. The chemical implementation of Fuzzy sets and Fuzzy logic will promote the development of Chemical Artificial Intelligence.

Language of fungi derived from their electrical spiking activity

Fungi exhibit oscillations of extracellular electrical potential recorded via differential electrodes inserted into a substrate colonized by mycelium or directly into sporocarps. We analysed electrical activity of ghost fungi (Omphalotus nidiformis), Enoki fungi (Flammulina velutipes), split gill fungi (Schizophyllum commune) and caterpillar fungi (Cordyceps militaris). The spiking characteristics are species specific: a spike duration varies from 1 to 21 h and an amplitude from 0.03 to 2.1 mV. We found that spikes are often clustered into trains. Assuming that spikes of electrical activity are used by fungi to communicate and process information in mycelium networks, we group spikes into words and provide a linguistic and information complexity analysis of the fungal spiking activity. We demonstrate that distributions of fungal word lengths match that of human languages. We also construct algorithmic and Liz-Zempel complexity hierarchies of fungal sentences and show that species S. commune generate the most complex sentences.