The intersection of intelligent computing and linguistics has emerged as a vibrant field of study, offering innovative solutions and applications that transform how we understand and interact with language. This paper explores the diverse applications of intelligent computing in linguistics, encompassing natural language processing (NLP), computational linguistics, language modeling, speech recognition, and more. It delves into the underlying technologies, methodologies, and the impact of these advancements on various linguistic subfields. Through an extensive review of recent literature, (...) case studies, and practical examples, this paper aims to provide a comprehensive overview of the current state and future prospects of intelligent computing in linguistics. (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)

Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. (...) A preliminary evaluation of the ITS was done by a group of students and teachers. The results were acceptable. (shrink)

Intelligent tutoring system (ITS) is a computer system which aims to provide immediate and customized or reactions to learners, usually without the intervention of human teacher's instructions. Secretariats professional to have the common goal of learning a meaningful and effective manner through the use of a variety of computing technologies enabled. There are many examples of professional Secretariats used in both formal education and in professional settings that have proven their capabilities. There is a close relationship between private (...) lessons intelligent, cognitive learning and design theories; and there are ongoing to improve the effectiveness of ITS research. And it aims to find a solution to the problem of over-reliance on students' teachers for quality education. The program aims to provide access to high-quality education to every student, and therefore the reform of the education system as a whole. In this paper, we will use Intelligent Tutoring System Builder (ITSB) to build an education system on cloud computing in terms of the concept of cloud computing and components and how to take advantage of cloud computing in the field. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an (...) class='Hi'>intelligent manner according to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students enrolled in (...) computer science curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators. (shrink)

Computers can mimic human intelligence, sometimes quite impressively. This has led some to claim that, a.) computers can actually acquire intelligence, and/or, b.) the human mind may be thought of as a very sophisticated computer. In this paper I argue that neither of these inferences are sound. The human mind and computers, I argue, operate on radically different principles.

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial, natural sciences, and philosophy. The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as (...) info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)

Is the brain really a computer? In particular, is our intelligence a computational achievement: is it because our brains are computers that we get on in the world as well as we do? In this paper I will evaluate an ambitious new argument to the contrary, developed in Landgrebe and Smith (2021a, 2022). Landgrebe and Smith begin with the fact that many dynamical systems in the world are difficult or impossible to model accurately (inter alia, because it is intractable to (...) find exact solutions to the differential equations that describe them—meaning we have to approximate—but at the same time they are such that small differences in starting conditions lead to big differences in final conditions, thwarting accurate approximation). Yet we manage to survive and thrive in a world full of such systems. Landgrebe and Smith argue from these premises that it is not because our brains are computers that we get on as well as we do: instead it is because of the various ways that we dynamically couple with such systems, these couplings themselves impossible to model well enough to emulate in silico. Landgrebe and Smith thus defend a dynamical systems model in the lineage of Gibson (1979), Van Gelder (1995), and Thompson (2007), though their focus is on decisively refuting the computationalist alternatives rather than developing the positive account. Here I will defend the claim that human intelligence is genuinely computational (and that whole brain emulation and others forms of AGI may be possible) against this argument. (shrink)

Abstract: Networking is one of the most important areas currently used for data transfer and enterprise management. It also includes the security aspect that enables us to protect our network to prevent hackers from accessing the organization's data. In this paper, we would like to learn what the network is and how it works. And what are the basics of the network since its emergence and know the mechanism of action components. After reading this paper - even if you do (...) not have a general background on networking - you will be able to manage your own network and be able to distribute and control your ip. (shrink)

ITS (Intelligent Tutoring System) is a computer software that supplies direct and adaptive training or response to students without, or with little human teacher interfering. The main target of ITS is smoothing the learning-teaching process using the ultimate technology in computer science. The proposed system will be implemented using the “ITSB” Authoring tool. The book "Introduction To Computer Science" is taught in Al-Azhar University in Gaza as a compulsory subject for students who study at humanities faculties. In this thesis, (...) the researcher demonstrates an intelligent tutoring system for teaching the above mentioned subject. The system was assessed by a group of teachers and students and the results were promising . (shrink)

We comment on the collection of papers inspired by our book Why Machines Will Never Rule the World published in volume 12 (5+6) of the journal Cosmos+Taxis. We summarize the arguments made by the contributors about what we say in the book, and then show where we disagree.

In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism with (...) reductive materialism, highlighting how the former accommodates the concept of artificial minds. He also emphasizes the significance of computationalism, which combines functionalism with computational theory to provide a robust explanation for mental processes. The book further discusses formal systems, the role of register machines in computation, and the inherent challenges in AI research, particularly in developing natural language processing systems. Carter’s work underscores the limitations of current computational models of cognition while remaining hopeful that advances in neuroscience may lead to stronger AI systems in the future. (shrink)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I (...) differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

Jobst Langrebe’s and Barry Smith’s book Why Machines Will Never Rule the World argues that artificial general intelligence (AGI) will never be realized. Drawing on theories of complexity they argue that it is not only technically, but mathematically impossible to realize AGI. The book is the result of cooperation between a philosopher and a mathematician. In addition to a thorough treatment of mathematical modelling of complex systems the book addresses many fundamental philosophical questions. The authors show that philosophy is still (...) relevant for questions of information technology in general and artificial intelligence in particular. This paper endorses Landgrebe’s and Smith’s arguments that artificial general intelligence cannot be realized, but not their conclusion that machines will never rule the world. It is not only a question of what technology can do. An equally important question is what technology does to us. Machines may not take over the world in a literal sense, but they may have many negative effects. Some of the most serious can be placed under the category of the “degeneration effect”. (shrink)

Home management and controlling have seen a great introduction to network that enabled digital technology, especially in recent decades. For the purpose of home automation, this technique offers an exciting capability to enhance the connectivity of equipment within the home. Also, with the rapid expansion of the Internet, there are potentials that added to the remote control and monitoring of such network-enabled devices. In this paper, we had been designed and implemented a fully manageable and secure smart home automation system (...) based on a cloud computing system with an ESP Arduino system. The security of home had been improved by adding a complete camera system with a GSM communication technique to connect the Arduino output data to an external specified number if there is no internet provider. We used three sensors for temperature, gas, and motion measurements. The ESP8226 Wi-Fi device programmed the sensors to maintain the sensors measurements and transfer them to the cloud server database which is programmed to the web server via Appatshy and Mysql formats. The system implemented with high time response so that all readings updated and appeared spontaneously. The designed system should be effective, a secure, and rapid response real-time smart home system should be achieved. (shrink)

The content of this paper is an elaboration of Hubert L. Dreyfus’s philosophical critique of Artificial Intelligence (AI), computers and the internet. Hubert L. Dreyfus (1929-2017) is Ua SA philosopher and alumni of Harvard University who teach at the Massachusetts Institute of Technology (MIT) and University of California, Berkeley. He is a phenomenological philosopher who criticize computer researchers and the artificial intelligence community. In 1965, Dreyfus wrote an article for Rand Corporation titled “Alchemy and Artificial Intelligence” which criticizes the masterminds (...) of Artificial Intelligence. Dreyfus also criticized the order of computers via two books: (1) What Computers Can’t Do (1972) and (2) What Computers Stills Can’t Do (1992). He favored human intuition rather than the computer logic in his book Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer (1986). In 2001, Dreyfus wrote a book On the Internet, which considers the prominent phenomenon in the recent Industry 4.0. By elaborating on Dreyfus’s philosophy on the computer, artificial intelligence, and the internet, we will know the philosophical debate on the result of industry 3.0 (computer and artificial intelligence) and 4.0 (artificial intelligence and internet). Moreover, we will know the relation between humans and those industrial products. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a Logistic (...) relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

With the transition of the pandemic-gripped labor market en masse to remote capabilities to avert from a national or international economic meltdown, a concern arises that many job seekers simply cannot fit into the new roles being developed and implemented. Beyond the loss of on-site work, the market is unable to reverse the loss of many roles that are, and have been, taken over by artificial (computer) intelligence systems. The “business-as-usual” mentality that many have come to associate with pre-pandemic life (...) supposedly took these losses into account, much to various societies’ detriment during this international crisis. And unlike at the turn of the millennium, the market has come to realize that it cannot function without advanced computer intelligence (CI) systems. Arguments can be made that humanity’s currently most sophisticated CI systems come nowhere near replicating the nuances that would prompt one to classify the system as being “human-like,” and that they will remain this way for the foreseeable future. However, what is conveniently ignored in this assessment is how not even three generations have passed since computer systems became an integral aspect of daily life. While the loss of work for humans as a result of this advancement of technology may not immediately reach concerning levels, it still begs the question of how society should react to a lack of non-specialized work and the value of “humanness” in the labor market. This work intends to explore these concepts and provide anticipatory guidance on how humanity should adapt. (shrink)

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that (...) conceived the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (shrink)

Making good decisions depends on having accurate information – quickly, and in a form in which it can be readily communicated and acted upon. Two features of medical practice can help: deliberation in groups and the use of scores and grades in evaluation. We study the contributions of these features using a multi-agent computer simulation of groups of physicians. One might expect individual differences in members’ grading standards to reduce the capacity of the group to discover the facts on which (...) well-informed decisions depend. Observations of the simulated groups suggest on the contrary that this kind of diversity can in fact be conducive to epistemic performance. Sometimes, it is adopting common standards that may be expected to result in poor decisions. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...) of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

Understanding computation as “a process of the dynamic change of information” brings to look at the different types of computation and information. Computation of information does not exist alone by itself but is to be considered as part of a system that uses it for some given purpose. Information can be meaningless like a thunderstorm noise, it can be meaningful like an alert signal, or like the representation of a desired food. A thunderstorm noise participates to the generation of meaningful (...) information about coming rain. An alert signal has a meaning as allowing a safety constraint to be satisfied. The representation of a desired food participates to the satisfaction of some metabolic constraints for the organism. Computations on information and representations will be different in nature and in complexity as the systems that link them have different constraints to satisfy. Animals have survival constraints to satisfy. Humans have many specific constraints coming in addition. And computers will compute what the designer and programmer ask for. We propose to analyze the different relations between information, meaning and representation by taking an evolutionary approach on the systems that link them. Such a bottom-up approach allows starting with simple organisms and avoids an implicit focus on humans, which is the most complex and difficult case. To make available a common background usable for the many different cases, we use a systemic tool that defines the generation of meaningful information by and for a system submitted to a constraint [Menant, 2003]. This systemic tool allows to position information, meaning and representations for systems relatively to environmental entities in an evolutionary perspective. We begin by positioning the notions of information, meaning and representation and recall the characteristics of the Meaning Generator System (MGS) that link a system submitted to a constraint to its environment. We then use the MGS for animals and highlight the network nature of the interrelated meanings about an entity of the environment. This brings us to define the representation of an item for an agent as being the network of meanings relative to the item for the agent. Such meaningful representations embed the agents in their environments and are far from the Good Old Fashion Artificial Intelligence type ones. The MGS approach is then used for humans with a limitation resulting of the unknown nature of human consciousness. Application of the MGS to artificial systems brings to look for compatibilities with different levels of Artificial Intelligence (AI) like embodied-situated AI, the Guidance Theory of Representations, and enactive AI. Concerns relative to different types of autonomy and organic or artificial constraints are highlighted. We finish by summarizing the points addressed and by proposing some continuations. (shrink)

This paper sums up the fundamental features of intelligence through the common features stated by various definitions of "intelligence": Intelligence is the ability of achieving systematic goals (functions) of brain and nerve system through selecting, and artificial intelligence or machine intelligence is an imitation of life intelligence or a replication of features and functions. Based on the definition mentioned above, this paper discusses and summarizes the development routes of ideas on computable intelligence, including Godel's "universal recursive function", the computation activities (...) of "selection", recursive function and Turing machine, mathematical expression of computable intelligence, core nature of computable intelligence, and computability and strong artificial intelligence. At the end of this paper is the conclusion drawn by the authors. (shrink)

The objective of this essay is to provide the beginning of a principled classification of some of the ways space is intelligently used. Studies of planning have typically focused on the temporal ordering of action, leaving as unaddressed questions of where to lay down instruments, ingredients, work-in-progress, and the like. But, in having a body, we are spatially located creatures: we must always be facing some direction, have only certain objects in view, be within reach of certain others. How we (...) manage the spatial arrangement of items around us is not an afterthought: it is an integral part of the way we think, plan, and behave. The proposed classification has three main categories: spatial arrangements that simplify choice; spatial arrangements that simplify perception; and spatial dynamics that simplify internal computation. The data for such a classification is drawn from videos of cooking, assembly and packing, everyday observations in supermarkets, workshops and playrooms, and experimental studies of subjects playing Tetris, the computer game. This study, therefore, focuses on interactive processes in the medium and short term: on how agents set up their workplace for particular tasks, and how they continuously manage that workplace. (shrink)

This essay argues that Huey Newton’s philosophical explanation of US empire fills an epistemological gap in our thinking that provides us with a basis for understanding the emergence and operational application of predictive policing, Big Data, cutting-edge surveillance programs, and semi-autonomous weapons by US military and policing apparati to maintain control over racialized populations historically and in the (still ongoing) Global War on Terror today – a phenomenon that Black Studies scholars and Black philosophers alike have yet to demonstrate the (...) conceptual acumen to explain. Newton’s ability to explain the conceptual foundation of US empire alongside his prediction of the emergence and application of AI and other Big Data technologies towards the repression of dissidents, criminals, terrorists, and other racialized groups abroad gives intercommunalism the explanatory power to lay the basis for a genealogical account of Black repression that also advances our understanding of how the US as a liberal democratic regime engages in enemy target acquisition (the construction of racialized groups as threats internally and externally) to rationalize the brutal repression of these populations by military-police agencies whose operational application of new AI and computing-surveillance technologies and semi-autonomous weapons are conditioned by the intensity of their racialized dehumanization of the enemy. (shrink)

This paper explores how the Leviathan that projects power through nuclear arms exercises a unique nuclearized sovereignty. In the case of nuclear superpowers, this sovereignty extends to wielding the power to destroy human civilization as we know it across the globe. Nuclearized sovereignty depends on a hybrid form of power encompassing human decision-makers in a hierarchical chain of command, and all of the technical and computerized functions necessary to maintain command and control at every moment of the sovereign's existence: this (...) sovereign power cannot sleep. This article analyzes how the form of rationality that informs this hybrid exercise of power historically developed to be computable. By definition, computable rationality must be able to function without any intelligible grasp of the context or the comprehensive significance of decision-making outcomes. Thus, maintaining nuclearized sovereignty necessarily must be able to execute momentous life and death decisions without the type of sentience we usually associate with ethical individual and collective decisions. (shrink)

Recently, due to the rapid progress of computer technology, researchers develop an effective computer program to enhance the achievement of the student in learning process, which is Intelligent Tutoring System (ITS). Science is important because it influences most aspects of everyday life, including food, energy, medicine, leisure activities and more. So learning science subject at school is very useful, but the students face some problem in learning it. So we designed an ITS system to help them understand this subject (...) easily and smoothly by analyzing it and explaining it in a systematic way. In this paper, we describe the design of an Intelligent Tutoring System for teaching science for grade seven to help students know the 7 characteristics for living things smoothly. The system provides all topics of living things and generates some questions for each topic and the students should answer these questions correctly to move to the next level. In the result of an evaluation of the ITS, students like the system and they said that it is very useful for them and for their studies. (shrink)

European Computing and Philosophy conference, 2–4 July Barcelona The Seventh ECAP (European Computing and Philosophy) conference was organized by Jordi Vallverdu at Autonomous University of Barcelona. The conference started with the IACAP (The International Association for CAP) presidential address by Luciano Floridi, focusing on mechanisms of knowledge production in informational networks. The first keynote delivered by Klaus Mainzer made a frame for the rest of the conference, by elucidating the fundamental role of complexity of informational structures that can (...) be analyzed on different levels of organization giving place for variety of possible approaches which converge in this cross-disciplinary and multi-disciplinary research field. Keynotes by Kevin Warwick about re-embodiment of rats’ neurons into robots, Raymond Turner on syntax and semantics in programming languages, Roderic Guigo on Biocomputing Sciences and Francesco Subirada on the past and future of supercomputing presented different topics of philosophical as well as practical aspects of computing. Vonference tracks included: Philosophy of Information (Patrick Allo), Philosophy of Computer Science (Raymond Turner), Computer and Information Ethics (Johnny Søraker and Alison Adam), Computational Approaches to the Mind (Ruth Hagengruber), IT and Cultural Diversity (Jutta Weber and Charles Ess), Crossroads (David Casacuberta), Robotics, AI & Ambient Intelligence (Thomas Roth-Berghofer), Biocomputing, Evolutionary and Complex Systems (Gordana Dodig Crnkovic and Søren Brier), E-learning, E-science and Computer-Supported Cooperative Work (Annamaria Carusi) and Technological Singularity and Acceleration Studies (Amnon Eden). (shrink)

The concept of artificial intelligence is not new nor is the notion that it should be granted legal protections given its influence on human activity. What is new, on a relative scale, is the notion that artificial intelligence can possess citizenship—a concept reserved only for humans, as it presupposes the idea of possessing civil duties and protections. Where there are several decades’ worth of writing on the concept of the legal status of computational artificial artefacts in the USA and elsewhere, (...) it is surprising that law makers internationally have come to a standstill to protect our silicon brainchildren. In this essay, it will be assumed that future artificial entities, such as Sophia the Robot, will be granted citizenship on an international scale. With this assumption, an analysis of rights will be made with respect to the needs of a non-biological intelligence possessing legal and civic duties akin to those possessed by humanity today. This essay does not present a full set of rights for artificial intelligence—instead, it aims to provide international jurisprudence evidence aliunde ab extra de lege lata for any future measures made to protect non-biological intelligence. (shrink)

Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has been increasing, presenting substantial dangers to the (...) data, reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies. (shrink)

The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous (...) boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results demonstrate conditions under which figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies are also reviewed in which both polarity-specific and polarity-invariant properties obtain perceptual figure-ground grouping results. The FACADE and 3D LAMINART models are used to explain these data. Keywords: figure-ground separation, border ownership, perceptual grouping, surface filling-in, V2, V4, FACADE Theory. (shrink)

Lately in the past couple of years, there are an increasing in the normal rate of playing computer games or video games compared to the E-learning content that are introduced for the safety of our children, and the impact of the video game addictiveness that ranges from (Musculoskeletal issues, Vision problems and Obesity). Furthermore, this paper introduce an intelligent tutoring system for both parent and their children for enhancement the experience of gaming and tell us about the health problems (...) and how we can solve them, with an easy user interface that way can our children be happy and excited about the information and their health. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly work as computational (...) models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order (classical) systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

I survey four categories of factors that might give a digital mind, such as an upload or an artificial general intelligence, an advantage over humans. Hardware advantages include greater serial speeds and greater parallel speeds. Self-improvement advantages include improvement of algorithms, design of new mental modules, and modification of motivational system. Co-operative advantages include copyability, perfect co-operation, improved communication, and transfer of skills. Human handicaps include computational limitations and faulty heuristics, human-centric biases, and socially motivated cognition. The shape of hardware (...) growth curves, as well as the ease of modifying minds, are found to have a major impact on how quickly a digital mind may take advantage of these factors. (shrink)

The hypergrowth and hyperconnectivity of networks of artificial intelligence (AI) systems and algorithms increasingly cause our interactions with the world, socially and environmentally, more technologically mediated. AI systems start interfering with our choices or making decisions on our behalf: what we see, what we buy, which contents or foods we consume, where we travel to, who we hire, etc. It is imperative to understand the dynamics of human-computer interaction in the age of progressively more competent AI. This essay presents five (...) premises to serve as starting points for building our understanding of the “interactions’ in human-computer interactions. (shrink)

The modelling of concepts, besides involving disciplines like philosophy of mind and psychology, is a fundamental and lively research problem in several artificial intelligence (AI) areas, such as knowledge representation, machine learning, and natural language processing. In this scenario, the most prominent proposed solutions adopt different (often incompatible) assumptions about the nature of such a notion. Each of these solutions has been developed to capture some specific features of concepts and support some specific (artificial) cognitive operations. This paper critically reviews (...) the most notable computational approaches to the representation of concepts. The main goals are (i) to provide a shared terminology for the desiderata of concepts and their computational representation; (ii) to classify and assess the heterogeneous computational approaches according to the provided terminology; (iii) to provide a reader who may not be very familiar with theories of concepts with an introduction to major themes in this research and with pointers to different research projects, and (iv) to offer philosophers, and potentially AI practitioners, a well-informed guide for selecting among various (and possibly competing) computational representations of concepts. (shrink)

Computational Functionalism is a subfield of philosophy of mind most relevant to the subject of cognitive science as well as to artificial intelligence (AI). The analysis of this paper focuses on Hilary Putnam’s and Gualtiero Piccinini’s standpoints regarding the molecular understanding of computation. Finally, Putnam’s argument of the functionalism in notion of the mental states is based on the positive definition of those states by their functions, while Piccinini, and on the other hand suggest that an understanding of the mechanism (...) of computation will go a long way to explaining cognition. This research also points out shortcomings of functionalism in their theories especially in regards to connectivity with physicality of AI structures. Therefore, the paper identifies the following gaps: The first gap holds that although both theorists have important insights, the two approaches put forward by the respective theorists are flawed and a better proposal is required, then a new proposal is suggested In identifying these gaps, the paper seeks to contribute to the literature by arguing for the need to develop a composite of the two theorists. The results show that the big ideas are important in present-day AI debate and analysis, which helps to explain the mind as computation. (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)

This paper concerns “human symbolic output,” or strings of characters produced by humans in our various symbolic systems; e.g., sentences in a natural language, mathematical propositions, and so on. One can form a set that consists of all of the strings of characters that have been produced by at least one human up to any given moment in human history. We argue that at any particular moment in human history, even at moments in the distant future, this set is finite. (...) But then, given fundamental results in recursion theory, the set will also be recursive, recursively enumerable, axiomatizable, and could be the output of a Turing machine. We then argue that it is impossible to produce a string of symbols that humans could possibly produce but no Turing machine could. Moreover, we show that any given string of symbols that we could produce could also be the output of a Turing machine. Our arguments have implications for Hilbert’s sixth problem and the possibility of axiomatizing particular sciences, they undermine at least two distinct arguments against the possibility of Artificial Intelligence, and they entail that expert systems that are the equals of human experts are possible, and so at least one of the goals of Artificial Intelligence can be realized, at least in principle. (shrink)

We offer philosophical motivations for a method we call Virtual World Cognitive Science (VW CogSci), in which researchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add rigor to philosophical thought experiments, as well as the terminology used in the scientific study of such representations. We find that this method forces researchers to (...) take a god’s-eye view when describing dynamical relationships between entities in minds and entities in an environment in a way that eliminates the need for problematic talk of belief and concept types, such as the belief that cats are silly, and the concept CAT, while preserving belief and concept tokens in individual cognizers’ minds. We conclude with some further key advantages of VW CogSci for the scientific study of mental and linguistic representation and for Cognitive Science more broadly. (shrink)

Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics (...) content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey. (shrink)

The outputs of generative artificial intelligence (generative AI) are often called “synthetic” to imply that they are not natural but artificial. Against that use of the term, this article focuses on a different denotation of synthesis, stressing the unifying and compositional aspects of anything synthetic. The case of large language models (LLMs) is used as an example to address synthesis philosophically alongside notions of representation in contemporary computational systems. It is argued that synthesis in generative AI should be understood as (...) a search for unity that is fundamental to the making of a representational reality. This representational reality, internal to computation, is a stable (if imperfect) whole, a togetherness of distributed representations. The article thus demonstrates that developing the philosophical concept of synthesis to investigate today’s generative AI involves examining how structuring occurs in LLMs and studying the kinds of forms that structuring results in. (shrink)

An Intelligent Tutoring System (ITS) is a computer system that offers an instant, adapted instruction and customized feedback to students without human teacher interference. Reciting "Tajweed" the Holy Quran in the appropriate way is very important for all Muslims and is obligatory in Islamic devotions such as prayers. In this paper, the researchers introduce an intelligent tutoring system for teaching Reciting "Tajweed". Our "Tajweed" tutoring system is limited to "Tafkhim and Tarqiq in TAJWEED" the Holy Quran, Rewaya: Hafs (...) from ‘Aasem. The system was evaluated by reciting teachers and students, and the results were auspicious. (shrink)

Artificial Intelligence refers to developing computer systems and machines that can perform tasks that usually require human intelligence. AI is an evolving field, and researchers and engineers continue to explore and develop new techniques and technologies to enhance its capabilities. The impact of AI on religions is not entirely predictable. AI is likely to affect religions or be affected by them in various ways, some of which are already becoming apparent. As with any new technology, it is up to individuals (...) and religious communities to decide how to incorporate AI into their beliefs and practices. It is worth mentioning that the mutual relevance of Sikhism and AI is likely to be complex and multifaceted. It will depend on how AI is developed, implemented, and used and the attitudes and beliefs of individual Sikhs and the Sikh community. (shrink)

We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number intelligence levels of compli- cated intelligent systems. Our agents are more idealized, which allows us to define a much simpler measure of intelligence level for them. In short, we define the intelligence level of a mechanical knowing agent to be the supremum of the computable ordinals that have codes the agent knows to be (...) codes of computable ordinals. We prove that if one agent knows certain things about another agent, then the former necessarily has a higher intelligence level than the latter. This allows our intelligence no- tion to serve as a stepping stone to obtain results which, by themselves, are not stated in terms of our intelligence notion (results of potential in- terest even to readers totally skeptical that our notion correctly captures intelligence). As an application, we argue that these results comprise evidence against the possibility of intelligence explosion (that is, the no- tion that sufficiently intelligent machines will eventually be capable of designing even more intelligent machines, which can then design even more intelligent machines, and so on). (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)

In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in (...) both these areas, a more comprehensive approach coupling them with narratology, digital humanities and literary studies was still lacking. With the aim of covering this empty space, in the last years, a multidisciplinary effort has been made in order to create an international meeting open to computer scientist, psychologists, digital humanists, linguists, narratologists etc.. This event has been named CMN (for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars Mark A. Finlayson and Patrick H. Winston1. (shrink)

Social machines are systems formed by technical and human elements interacting in a structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of mechanisms. We (...) argue that this type of autonomous social machines has provided a new paradigm for the design of intelligent systems marking a new phase in the field of AI. The consequences of this observation range from methodological, philosophical to ethical. On the one side, it emphasises the role of Human-Computer Interaction in the design of intelligent systems, while on the other side it draws attention to both the risks for a human being and those for a society relying on mechanisms that are not necessarily controllable. The difficulty by companies in regulating the spread of misinformation, as well as those by authorities to protect task-workers managed by a software infrastructure, could be just some of the effects of this technological paradigm. (shrink)

In this article, M. Beatrice Fazi takes up Media Theory’s invitation to engage with Alan Díaz Alva’s analysis of her philosophical work on contingency in computation. The central argument of Fazi’s Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics is that computation can be productive of ontological novelty. This piece revisits that argument in the light of the technological developments that have occurred since 2018, when the book was published. Focusing on generative artificial intelligence (generative AI), the article considers (...) how the concepts of generativity, aesthetics and contingency are tackled in the book and in present-day debates about generative AI systems. (shrink)