This paper makes a novel linkage between the multiple-computations theorem in philosophy of mind and Landauer’s principle in physics. The multiple-computations theorem implies that certain physical systems implement simultaneously more than one computation. Landauer’s principle implies that the physical implementation of “logically irreversible” functions is accompanied by minimal entropy increase. We show that the multiple-computations theorem is incompatible with, or at least challenges, the universal validity of Landauer’s principle. To this end we provide accounts of both ideas in terms (...) of low-level fundamental concepts in statistical mechanics, thus providing a deeper understanding of these ideas than their standard formulations given in the high-level terms of thermodynamics and cognitive science. Since Landauer’s principle is pivotal in the attempts to derive the universal validity of the second law of thermodynamics in statistical mechanics, our result entails that the multiple-computations theorem has crucial implications with respect to the second law. Finally, our analysis contributes to the understanding of notions, such as “logical irreversibility,” “entropy increase,” “implementing a computation,” in terms of fundamental physics, and to resolving open questions in the literature of both fields, such as: what could it possibly mean that a certain physical process implements a certain computation. (shrink)

Without a proper restriction on mappings, virtually any system could be seen as implementing any computation. That would not allow characterization of systems in terms of implemented computations and is not compatible with a computationalist philosophy of mind. Information-based criteria for independence of substates within structured states are proposed as a solution. Objections to the use of requirements for transitions in counterfactual states are addressed, in part using the partial-brain argument as a general counterargument to neural replacement arguments.

Contemporary methods of computational cognitive modeling have recently been criticized by Addyman and French (2012) on the grounds that they have not kept up with developments in computer technology and human–computer interaction. They present a manifesto for change according to which, it is argued, modelers should devote more effort to making their models accessible, both to non-modelers (with an appropriate easy-to-use user interface) and modelers alike. We agree that models, like data, should be freely available according to the normal standards (...) of science, but caution against confusing implementations with specifications. Models may embody theories, but they generally also include implementation assumptions. Cognitive modeling methodology needs to be sensitive to this. We argue that specification, replication and experimentation are methodological approaches that can address this issue. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful (...) Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

In this article, after presenting the basic idea of causal accounts of implementation and the problems they are supposed to solve, I sketch the model of computation preferred by Chalmers and argue that it is too limited to do full justice to computational theories in cognitive science. I also argue that it does not suffice to replace Chalmers’ favorite model with a better abstract model of computation; it is necessary to acknowledge the causal structure of physical computers that (...) is not accommodated by the models used in computability theory. Additionally, an alternative mechanistic proposal is outlined. (shrink)

We consider Baars’ "Global Workspace" theory of consciousness and discuss its possible representation within a model of intelligent agents. We first review a particular agent implementation that is given by an abstract machine, and then identify the extensions that are required in order to accommodate the main aspects of consciousness. According to Baars’ theory, this amounts to unconscious process coalitions that result in the creation of contexts. These extensions can be formulated within a reified virtual machine encompassing a representation of (...) the original machine as well as an additional introspective component. This computational framework is illustrating throughout using a simple working example. (shrink)

The rapid advancement of intelligent computing has revolutionized the field of economic forecasting, providing unprecedented capabilities for developing, implementing, and analyzing advanced prediction models. This paper explores the comprehensive process of harnessing intelligent computing for economic forecasting, emphasizing the critical stages of model development, integration, and evaluation. Initially, it discusses data collection and preprocessing techniques essential for building robust models, followed by the selection of suitable statistical, machine learning, and deep learning algorithms. The paper then outlines the practical aspects (...) of model implementation, including real-time data integration, deployment strategies, and scalability considerations. Further, it delves into the continuous monitoring and maintenance required to ensure model accuracy and reliability over time. The analysis section evaluates model performance using various accuracy metrics, scenario analyses, and visualization techniques to provide actionable insights. Through case studies across the financial sector, government policy, and business planning, the paper illustrates the transformative impact of intelligent computing on economic forecasting. The findings underscore the potential of advanced prediction models to enhance decision-making, anticipate economic trends, and respond proactively to dynamic economic environments. (shrink)

The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content (...) is extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing. (shrink)

What is the computational notion of “implementation”? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation.

The ultimate goal of research into computational intelligence is the construction of a fully embodied and fully autonomous artificial agent. This ultimate artificial agent must not only be able to act, but it must be able to act morally. In order to realize this goal, a number of challenges must be met, and a number of questions must be answered, the upshot being that, in doing so, the form of agency to which we must aim in developing artificial agents comes (...) into focus. This chapter explores these issues, and from its results details a novel approach to meeting the given conditions in a simple architecture of information processing. (shrink)

The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device (...) interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence. (shrink)

If computer ethics is to constitute a real engagement with industry and society that cultivates a genuine sensitivity to ethical concerns in the creation, development, and implementation of technologies, a genuine sensitivity that stands in marked contrast to ethics as “mere compliance,” then computer ethics will have to consist in issuing an open invitation to inquiry, since going beyond mere compliance requires a sensitivity to the importance of what we care about, and inquiry has the potential to leverage what our (...) audiences already care about to bring them to care about computer ethics. This commitment to open inquiry is apparent in the practice of computer ethics at Ethicomp and in our interfaces with various audiences beyond Ethicomp. We mentor professionals, aspiring professionals, managers, end-users, policy makers, and each other by offering an open invitation to inquiry about the broad ethical implications of the practices, professions, and institutions of computing and the design, development, and implementation of computer artifacts. (shrink)

In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically. In this paper, I claim that mechanistic accounts of computation should allow for a broad variation (...) of models of computation. In particular, some non-standard models should not be excluded a priori. The relationship between mathematical models of computation and mechanistically adequate models is studied in more detail. (shrink)

The relationship between abstract formal procedures and the activities of actual physical systems has proved to be surprisingly subtle and controversial, and there are a number of competing accounts of when a physical system can be properly said to implement a mathematical formalism and hence perform a computation. I defend an account wherein computational descriptions of physical systems are high-level normative interpretations motivated by our pragmatic concerns. Furthermore, the criteria of utility and success vary according to our diverse purposes (...) and pragmatic goals. Hence there is no independent or uniform fact to the matter, and I advance the ‘anti-realist’ conclusion that computational descriptions of physical systems are not founded upon deep ontological distinctions, but rather upon interest-relative human conventions. Hence physical computation is a ‘conventional’ rather than a ‘natural’ kind. (shrink)

Implementationalism maintains that conventional, silicon-based artificial systems are not conscious because they fail to satisfy certain substantive constraints on computational implementation. In this article, we argue that several recently proposed substantive constraints are implausible, or at least are not well-supported, insofar as they conflate intuitions about computational implementation generally and consciousness specifically. We argue instead that the mechanistic account of computation can explain several of the intuitions driving implementationalism and noncomputationalism in a manner which is consistent with artificial consciousness. (...) Our argument provides indirect support for computationalism about consciousness and the view that conventional artificial systems can be conscious. (shrink)

Although implementation is ubiquitous in computer science, there is no systematic philosophical analysis of its metaphysical structure. In this article, I argue that the conceptual resources of analytical metaphysics can be very helpful in laying the foundations for a metaphysics of implementation and, by extension, of computer science. More specifically, I hold that implementation is a form of metaphysical grounding, and I show that, by combining the properties of grounding with the specific constraints of computer science, one can clarify what (...) a metaphysics of computer science could look like. In sections 2 and 3 of the article, I discuss various meanings of implementation, while in section 4, I submit my central claim that implementation is a form of metaphysical grounding. I substantiate this claim by showing that implementation and grounding share the same formal properties. If this is correct, then issues of ontological dependence, metaphysical explanation, hyperintensionality, and foundedness, typically associated with grounding, can be re-formulated in computer science using the concept of implementation. This is what I do in sections 5.1-5.4. I conclude with some general remarks about the future development of computer science's metaphysics. (shrink)

Natural Computing is a field of research in Computer Science aimed at reinterpreting biological phenomena as computing mechanisms. This allows unconventional computing architectures to be proposed in which computations are performed by atoms, DNA strands, cells, insects or other biological elements. Membrane Computing is a branch of Natural Computing in which biological phenomena of interest are related with interactions between molecules inside cells. The research in Membrane Computing has lead to very important theoretical results that show how, in principle, cells (...) could be used to solve any (computable) computational problem with performances that cannot be obtained by conventional computers. However, the implementation of a cell-based computational architecture seems not easily achievable. On the other hand, models of Membrane Computing have found an alternative application to the description of biological systems, with the aim of developing simulators and other analysis tools for the study of biological problems. (shrink)

Knowledge representation (KR) is actually more than representation: It involves also inference, namely inference of “new” knowledge, i.e. new facts. Logic programming is a suitable KR medium, but more often than not discussions on this programming paradigm focus on aspects other than KR. In this paper, I elaborate on the general theory of logic programming and give the essentials of two of its main implementations, to wit, Prolog and Datalog, from the viewpoint of deductive computing over knowledge bases, which includes (...) deductive programming. (shrink)

Turner argues that computer programs must have purposes, that implementation is not a kind of semantics, and that computers might need to understand what they do. I respectfully disagree: Computer programs need not have purposes, implementation is a kind of semantic interpretation, and neither human computers nor computing machines need to understand what they do.

The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in out- line why Putnam’s result, and likewise the more restricted result we call the Multiple- Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain why (...) nonreductive approaches to solving the multiple- computations problem, and in particular why computational externalism, are dualistic in the sense that they imply that nonphysical facts in the environment of a computing system single out the computation. We discuss certain attempts to dissolve Putnam’s unrestricted result by appealing to systems with certain kinds of input and output states as a special case of computational externalism, and show why this approach is not workable without collapsing to behaviorism. We conclude with some remarks about the nonphysical nature of mainstream approaches to both statistical mechanics and the quantum theory of measurement with respect to the singling out of partitions and observables. (shrink)

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize (...) intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all. (shrink)

The central motivating idea behind the development of this work is the concept of prespace, a hypothetical structure that is postulated by some physicists to underlie the fabric of space or space-time. I consider how such a structure could relate to space and space-time, and the rest of reality as we know it, and the implications of the existence of this structure for quantum theory. Understanding how this structure could relate to space and to the rest of reality requires, I (...) believe, that we consider how space itself relates to reality, and how other so-called "spaces" used in physics relate to reality. In chapter 2, I compare space and space-time to other spaces used in physics, such as configuration space, phase space and Hilbert space. I support what is known as the "property view" of space, opposing both the traditional views of space and space-time, substantivalism and relationism. I argue that all these spaces are property spaces. After examining the relationships of these spaces to causality, I argue that configuration space has, due to its role in quantum mechanics, a special status in the microscopic world similar to the status of position space in the macroscopic world. In chapter 3, prespace itself is considered. One way of approaching this structure is through the comparison of the prespace structure with a computational system, in particular to a cellular automaton, in which space or space-time and all other physical quantities are broken down into discrete units. I suggest that one way open for a prespace metaphysics can be found if physics is made fully discrete in this way. I suggest as a heuristic principle that the physical laws of our world are such that the computational cost of implementing those laws on an arbitrary computational system is minimized, adapting a heuristic principle of this type proposed by Feynman. In chapter 4, some of the ideas of the previous chapters are applied in an examination of the physics and metaphysics of quantum theory. I first discuss the "measurement problem" of quantum mechanics: this problem and its proposed solution are the primary subjects of chapter 4. It turns out that considering how quantum theory could be made fully discrete leads naturally to a suggestion of how standard linear quantum mechanics could be modified to give rise to a solution to the measurement problem. The computational heuristic principle reinforces the same solution. I call the modified quantum mechanics Critical Complexity Quantum Mechanics (CCQM). I compare CCQM with some of the other proposed solutions to the measurement problem, in particular the spontaneous localization model of Ghirardi, Rimini and Weber. Finally, in chapters 5 and 6, I argue that the measure of complexity of quantum mechanical states I introduce in CCQM also provides a new definition of entropy for quantum mechanics, and suggests a solution to the problem of providing an objective foundation for statistical mechanics, thermodynamics, and the arrow of time. (shrink)

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988 ) monograph, “Representation & Reality”, which if correct, has (...) important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set ( x )”. Then, equating Q ( x ) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. (shrink)

Quantum computing is a rapidly developing technology that uses the principles of quantum mechanics to perform information processing tasks that are beyond the capabilities of traditional computers. Quantum computers employ qubits, superposition, and entanglement to rapidly resolve intricate problems, with potential applications in cryptography, artificial intelligence, the development of new medications, and financial modeling. Recent breakthroughs have shown potential, but hurdles like correcting errors, maintaining qubit stability, expanding and the expense of substantial infrastructure continue to hinder widespread usage. Quantum computing (...) also sparks ethical and security concerns, especially in relation to encryption and the development of artificial intelligence. Despite some challenges, continuous research and development are gradually enhancing the capabilities of quantum hardware and algorithms. Advances in quantum technology are anticipated to transform various sectors and scientific inquiry, providing answers to several of the globe's most intricate problems. To maximize the potential of this initiative, additional investment in education, policy refinement, and judicious implementation is essential to guarantee a balanced and positive influence on society. (shrink)

This is the 3rd edition. Although a number of new technological applications require classical deductive computation with non-classical logics, many key technologies still do well—or exclusively, for that matter—with classical logic. In this first volume, we elaborate on classical deductive computing with classical logic. The objective of the main text is to provide the reader with a thorough elaboration on both classical computing – a.k.a. formal languages and automata theory – and classical deduction with the classical first-order predicate calculus (...) with a view to computational implementations, namely in automated theorem proving and logic programming. The present third edition improves on the previous ones by providing an altogether more algorithmic approach: There is now a wholly new section on algorithms and there are in total fourteen clearly isolated algorithms designed in pseudo-code. Other improvements are, for instance, an emphasis on functions in Chapter 1 and more exercises with Turing machines. (shrink)

Most formalisms for representing common-sense knowledge allow incomplete and potentially inconsistent information. When strong negation is also allowed, contradictory conclusions can arise. A criterion for deciding between them is needed. The aim of this paper is to investigate an inherent and autonomous comparison criterion, based on specificity as defined in [POO 85, SIM 92]. In contrast to other approaches, we consider not only defeasible, but also strict knowledge. Our criterion is context-sensitive, i. e., preference among defeasible rules is determined dynamically (...) during the dialectical analysis.We show how specificity can be defined in terms of two different approaches: activation sets and derivation trees. This allows us to get a syntactic criterion that can be implemented in a computationally attractive way. The resulting definitions may be applied in general rulebased formalisms. We present theorems linking both characterizations. Finally we discuss other frameworks for defeasible reasoning in which preference handling is considered explicitly. (shrink)

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant (...) works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested. (shrink)

Abstract – This study aimed primarily to determine the effectiveness of crime prevention strategies implemented by the Salug Valley Philippine National Police (PNP) in terms of Police Integrated Patrol System, Barangay Peacekeeping Operation, Anti-Criminality Operation, Integrated Area Community Public Safety services, Bantay Turista and School Safety Project as evaluated by 120 inhabitants and 138 PNP officers from four Municipalities of Salug Valley Zamboanga del Sur. Stratified random sampling was utilized in determining the respondents. Index crime rate were correlated with the (...) crime prevention strategies of the PNP in town of Salug Valley. A descriptive method of research was applied in this study utilizing self-made questionnaire. The data collected were analyzed using the main statistical tools like frequency count, percentage, mean computation, Kruskal Wallis Analysis of Variance and simple correlation. Findings of the study revealed that the crime prevention strategies in four (4) municipalities were “much effective” to include Integrated Patrol System, Barangay Peace Keeping Operations, Anti-Criminality Operations, Integrated Area Community Public Safety Services, Bantay Turista and School Safety Project in connection to the responses of 158 participants. There is a significant relationship between crime prevention strategies employed and index crime rate. (shrink)

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the (...) muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others. (shrink)

. Computational chemistry grew in a new era of “desktop modeling,” which coincided with a growing demand for modeling software, especially from the pharmaceutical industry. Parameterization of models in computational chemistry is an arduous enterprise, and we argue that this activity leads, in this specific context, to tensions among scientists regarding the epistemic opacity transparency of parameterized methods and the software implementing them. We relate one flame war from the Computational Chemistry mailing List in order to assess in detail (...) the relationships between modeling methods, parameterization, software and the various forms of their enclosure or disclosure. Our claim is that parameterization issues are an important and often neglected source of epistemic opacity and that this opacity is entangled in methods and software alike. Models and software must be addressed together to understand the epistemological tensions at stake. (shrink)

The transition to a Circular Economy (CE) is a critical strategy in addressing global challenges related to resource depletion, waste generation, and environmental sustainability. Cloud computing, as a scalable and data-driven technology, has a significant role to play in enabling the circular economy by enhancing resource efficiency, waste reduction, and the optimization of product lifecycles. This paper explores how cloud computing facilitates circular economy practices by leveraging advanced technologies such as data analytics, Internet of Things (IoT), and Artificial Intelligence (AI). (...) We discuss how cloud platforms support businesses in tracking and managing resource flows, implementing closed-loop systems, and optimizing product design, repair, and reuse. By analyzing real-world case studies from industries such as manufacturing, retail, and energy, we highlight the potential of cloud-based systems in driving sustainable business models that minimize waste and maximize resource recovery. The paper also addresses challenges in the integration of cloud computing with circular economy practices and provides insights into the future potential of cloud technologies in advancing sustainability. (shrink)

The electric activities of cortical pyramidal neurons are supported by structurally stable, morphologically complex axo-dendritic trees. Anatomical differences between axons and dendrites in regard to their length or caliber reflect the underlying functional specializations, for input or output of neural information, respectively. For a proper assessment of the computational capacity of pyramidal neurons, we have analyzed an extensive dataset of three-dimensional digital reconstructions from the NeuroMorphoOrg database, and quantified basic dendritic or axonal morphometric measures in different regions and layers of (...) the mouse, rat or human cerebral cortex. Physical estimates of the total number and type of ions involved in neuronal electric spiking based on the obtained morphometric data, combined with energetics of neurotransmitter release and signaling fueled by glucose consumed by the active brain, support highly efficient cerebral computation performed at the thermodynamically allowed Landauer limit for implementation of irreversible logical operations. Individual proton tunneling events in voltage-sensing S4 protein alpha-helices of Na+, K+ or Ca2+ ion channels are ideally suited to serve as single Landauer elementary logical operations that are then amplified by selective ionic currents traversing the open channel pores. This miniaturization of computational gating allows the execution of over 1.2 zetta logical operations per second in the human cerebral cortex without combusting the brain by the released heat. (shrink)

In this paper, we deal with the computation of Lie derivatives, which are required, for example, in some numerical methods for the solution of differential equations. One common way for computing them is to use symbolic computation. Computer algebra software, however, might fail if the function is complicated, and cannot be even performed if an explicit formulation of the function is not available, but we have only an algorithm for its computation. An alternative way to address the (...) problem is to use automatic differentiation. In this case, we only need the implementation of the algorithm that evaluates the function in terms of its analytic expression in a programming language, but we cannot use this if we have only a compiled version of the function. In this paper, we present a novel approach for calculating the Lie derivative of a function, even in the case where its analytical expression is not available, that is based on the In finity Computer arithmetic. A comparison with symbolic and automatic differentiation shows the potentiality of the proposed technique. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...) ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

This technical article presents a comprehensive analysis and implementation framework for intelligent customer service automation using Pega's advanced capabilities. The solution integrates natural language processing, machine learning, and automated escalation mechanisms to achieve significant operational improvements, including a 35% reduction in response times and a 45% increase in first-contact resolution rates. Through detailed case studies and performance metrics, we demonstrate how organizations can achieve up to 82% first-contact resolution rates and reduce average handling time by 30%. The implementation framework encompasses (...) security protocols, performance optimization strategies, and change management guidelines supported by quantitative data from industry research. The article provides detailed technical specifications, including system architecture, implementation workflows, and best practices, offering organizations a blueprint for successful AI-powered customer service transformation. (shrink)

Both in formal and computational natural language semantics, the classical correspondence view of meaning – and, more specifically, the view that the meaning of a declarative sentence coincides with its truth conditions – is widely held. Truth (in the world or a situation) plays the role of the given, and meaning is analysed in terms of it. Both language and the world feature in this perspective on meaning, but language users are conspicuously absent. In contrast, the inferentialist semantics that Robert (...) Brandom proposes in his magisterial book ‘Making It Explicit’ puts the language user centre stage. According to his theory of meaning, the utterance of a sentence is meaningful in as far as it is a move by a language user in a game of giving and asking for reasons (with reasons underwritten by a notion of good inferences). In this paper, I propose a proof-theoretic formalisation of the game of giving and asking for reasons that lends itself to computer implementation. In the current proposal, I flesh out an account of defeasible inferences, a variety of inferences which play a pivotal role in ordinary (and scientific) language use. (shrink)

The essential difficulty about Computer Ethics' (CE) philosophical status is a methodological problem: standard ethical theories cannot easily be adapted to deal with CE-problems, which appear to strain their conceptual resources, and CE requires a conceptual foundation as an ethical theory. Information Ethics (IE), the philosophical foundational counterpart of CE, can be seen as a particular case of environmental ethics or ethics of the infosphere. What is good for an information entity and the infosphere in general? This is the ethical (...) question asked by IE. The answer is provided by a minimalist theory of deseerts: IE argues that there is something more elementary and fundamental than life and pain, namely being, understood as information, and entropy, and that any information entity is to be recognised as the centre of a minimal moral claim, which deserves recognition and should help to regulate the implementation of any information process involving it. IE can provide a valuable perspective from which to approach, with insight and adequate discernment, not only moral problems in CE, but also the whole range of conceptual and moral phenomena that form the ethical discourse. (shrink)

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers (...) are semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. (shrink)

Emotions and feelings (i.e. affects) are a central feature of human behavior. Due to complexity and interdisciplinarity of affective phenomena, attempts to define them have often been unsatisfactory. This article provides a simple logical structure, in which affective concepts can be defined. The set of affects defined is similar to the set of emotions covered in the OCC model, but the model presented in this article is fully computationally defined, whereas the OCC model depends on undefined concepts. Following Matthis, affects (...) are seen as unconscious, emotions as preconscious and feelings as conscious. Affects are thus a superclass of emotions and feelings with regards to consciousness. A set of affective states and related affect-specific behaviors and strategies can be defined with unconscious affects only. -/- In addition, affects are defined as processes of change in the body state, that have specific triggers. For example, an affect of hope is defined as a specific body state that is triggered when the agent is becomes informed about a future event, that is positive with regards to the agent’s needs. -/- Affects are differentiated from each other by types of causing events. Affects caused by unexpected positive, neutral and negative events are delight, surprise and fright, respectively. Affects caused by expected positive and negative future events are hope and fear. -/- Affects caused by expected past events are as follows: satisfaction results from a positive expectation being fulfilled, disappointment results from a positive expectation not being fulfilled, fears-confirmed results from a negative expectation being fulfilled, and relief results from a negative expectation not being fulfilled. Pride is targeted towards a self-originated positive event, and shame towards a self-originated negative event. Remorse is targeted towards a self-originated action causing a negative event. Pity is targeted towards a liked agent experiencing a negative event, and happy-for towards a liked agent experiencing a positive event. Resentment is targeted towards a disliked agent experiencing a positive event, and gloating towards a disliked agent experiencing a negative event. An agent is liked/loved if it has produced a net utility greater than zero, and disliked/hated if the net utility is lower than zero. An agent is desired if it is expected to produce a positive net utility in the future, and disliked if the expected net utility is negative. -/- The above model for unconscious affects is easily computationally implementable, and may be used as a starting point in building believable simulation models of human behavior. The models can be used as a starting point in the development of computational psychological, psychiatric, sociological and criminological theories, or in e.g. computer games. -/- [please cite using the arXiv url below.]. (shrink)

The History of Ideas is presently enjoying a certain renaissance after a long period of disrepute. Increasing quantities of digitally available historical texts and the availability of computational tools for the exploration of such masses of sources, it is suggested, can be of invaluable help to historians of ideas. The question is: how exactly? In this paper, we argue that a computational history of ideas is possible if the following two conditions are satisfied: (i) Sound Method . A computational history (...) of ideas must be built upon a sound theoretical foundation for its methodology, and the only such foundation is given by the use of models , i.e., fully explicit and revisable interpretive frameworks or networks of concepts developed by the historians of ideas themselves. (ii) Data Organisation. Interpretive models in our sense must be seen as topic-specific knowledge organisation systems (KOS) implementable (i.e. formalisable) as e.g. computer science ontologies. We thus require historians of ideas to provide explicitly structured semantic framing of domain knowledge before investigating texts computationally, and to constantly re-input findings from the interpretive point of view. In this way, a computational history of ideas maximally profits from computer methods while also keeping humanities experts in the loop. We elucidate our proposal with reference to a model of the notion of axiomatic science in 18th -19th century Europe. (shrink)

Quantum-enhanced machine learning (QML) is transforming artificial intelligence through the application of quantum computing concepts to solving computationally challenging problems more effectively than conventional methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the capability to speed up deep learning model training, solve combinatorial optimization problems, and improve feature selection in high-dimensional space. It covers basic quantum computer concepts employed within AI, for example, quantum circuits, quantum variational algorithms, and kernel quantum methods, and their impacts on neural networks, generative (...) models, and reinforcement learning. It further refers to the hybrid quantum-classical architectures in AI where a combination of quantum subroutines and classical deep learning models are employed together with the purpose to gain computational speedup in optimization and handling massive data. Despite the transformative promise of quantum AI, technical issues of qubit noise, error correction, and scaling hardware continue to hold back full implementation. This contribution offers a qualitative overview of quantum-enhanced AI, surveying current applications, research endeavors, and upcoming innovation in quantum deep learning, autonomous systems, and scientific computing. The results open the door for large-scale quantum machine learning architectures, which provide new solutions to future uses of AI in finance, medicine, cyber security, and robotics. (shrink)

Fundamental to Computer Science is the distinction between abstractions and implementations. When that distinction is applied to various philosophical questions it yields the following conclusions. -/- • EMERGENCE. It isn’t as mysterious as it’s made out to be; the possibility of strong emergence is not a threat to science. -/- • INTERACTIONS BETWEEN HIGHER-LEVEL ENTITIES. Physical interaction among higher-level entities is illusory. Abstract interactions are the source of emergence, new domains of knowledge, and complex systems. -/- • PHYSICS and the (...) SPECIAL SCIENCES. The new domains of knowledge derived from abstract interactions are the basis of the autonomy of the special sciences. -/- • DOWNWARD CAUSATION. It’s a zombie idea that should have a stake put through its heart and be replaced by downward entailment. (shrink)

In this paper we focus our attention on tableau methods for propositional interval temporal logics. These logics provide a natural framework for representing and reasoning about temporal properties in several areas of computer science. However, while various tableau methods have been developed for linear and branching time point-based temporal logics, not much work has been done on tableau methods for interval-based ones. We develop a general tableau method for Venema's \cdt\ logic interpreted over partial orders (\nsbcdt\ for short). It combines (...) features of the classical tableau method for first-order logic with those of explicit tableau methods for modal logics with constraint label management, and it can be easily tailored to most propositional interval temporal logics proposed in the literature. We prove its soundness and completeness, and we show how it has been implemented. (shrink)

The exponential growth of high-frequency real-time financial transactions necessitates scalable machine learning infrastructures capable of processing and forecasting data in real time. This paper proposes a comprehensive design and implementation strategy for such infrastructures using distributed computing frameworks like Apache Spark and cloud services such as Amazon Web Services (AWS). Emphasizing technical specifics, the paper delves into architectural designs, implementation strategies, and optimization techniques that address critical challenges in data ingestion, real-time processing, model training, and deployment. A proof-of-concept implementation demonstrates (...) the feasibility of the proposed architecture on a small scale, highlighting its potential benefits. The findings suggest that implementing a scalable distributed machine learning infrastructure can enhance computational efficiency and significantly improve the accuracy and timeliness of financial forecasts. Future work will involve deploying the proposed architecture in large-scale industry settings to validate its effectiveness in real-world scenarios. (shrink)

Quantum-enhanced machine learning (QML) is transforming artificial intelligence through the application of quantum computing concepts to solving computationally challenging problems more effectively than conventional methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the capability to speed up deep learning model training, solve combinatorial optimization problems, and improve feature selection in high-dimensional space. It covers basic quantum computer concepts employed within AI, for example, quantum circuits, quantum variational algorithms, and kernel quantum methods, and their impacts on neural networks, generative (...) models, and reinforcement learning. It further refers to the hybrid quantum-classical architectures in AI where a combination of quantum subroutines and classical deep learning models are employed together with the purpose to gain computational speedup in optimization and handling massive data. Despite the transformative promise of quantum AI, technical issues of qubit noise, error correction, and scaling hardware continue to hold back full implementation. This contribution offers a qualitative overview of quantum-enhanced AI, surveying current applications, research endeavors, and upcoming innovation in quantum deep learning, autonomous systems, and scientific computing. The results open the door for large-scale quantum machine learning architectures, which provide new solutions to future uses of AI in finance, medicine, cyber security, and robotics. (shrink)

Self models contribute to key functional domains of human intelligence that are not yet presented in today’s artificial intelligence. One important aspect of human problem-solving involves the use of conceptual self-knowledge to detect self-relevant information presented in the environment, which guides the subsequent retrieval of autobiographical memories that are relevant to the task at hand. This process enables each human to behave self-consistently in our own way across complex situations, manifested as self-interest and trait-like characteristics. In this paper, we outline (...) a computational framework that implements the conceptual aspect of human self models through a modified version of the joint-embedding predictive architecture. We propose that through the incorporation of human-like autobiographical memory retrieval and self-importance evaluation, the modified architecture could support machine agents with significantly enhanced self-consistency, which could be applied to deliver more believable simulations of human behaviors. (shrink)

Cloud computing is considered as a new type of technology, in fact, it is an extension of the information technology's developments which are based on the pooling of resources and infrastructure to provide services depend on using the cloud, in the sense that instead of these services and resources exist on local servers or personal devices, they are gathered in the cloud and be shared on the Internet. This technology has achieved an economic success no one can deny it and (...) resorting to use it has many advantages, today it becomes known and recognized and begins to invade the libraries' area and imposes itself day by day because of the benefits afforded by using applications that have the ability to change the nature of library's services through the web hosting services which can be accessed via any internet connected device. And actually, libraries started using services provided by this technology especially in the field of digitization, indexing, supply, storage and the service of sharing and exchanging information resources in the virtual environment. For this, there is a need to conduct scientific research to find out librarians' trends and motivations about the use of cloud computing in their field of work. Where this research examines the librarians' trends towards the use of cloud computing according to the technology acceptance model (TAM). The study depended on descriptive analytical method using a questionnaire tool, and it has included all the libraries of Mentouri-Constantine1, libraries of university Abdelhamid Mehri-Constantine2, libraries of university Constantine 3, and libraries of Emir Abdelkader university of the Islamic science. And it reached a set of results including: that cloud computing is not used in a very large proportion by librarians, and they haven't a good cognitive level of its services, but in return, an interest was found by them to the cloud as a technology helps the office work implementation and the performance efficiency lifting and appreciates library services. (shrink)

The computational properties of a system are generally thought to be independent in some sense from its physical properties, in virtue of the fact that computation is a formally characterized concept. Several philosophers have recently challenged the idea that such “medium-independence” is an essential feature of computation by arguing that some kinds of computation lack medium-independence. This paper explores and rejects three such arguments in an attempt to defend the essential medium-independence of computation.