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Hazardous waste capacity assurance planning: Volumes for one‐time remediation

Incremental Conceptualization for Language Production discusses the simultaneous actions involved in thinking and speaking, as well as the piecemeal way in which individuals construct an internal representation of the external world and use this internal representation for speaking. Author Markus Guhe presents the first computational model that captures these observations in a cognitively adequate fashion. The volume is an innovative look at the mind’s process of producing semantic representations that can be transformed into language. The first section of the book illustrates four stages of conceptualization: construction of a conceptual representation; selection of content to be verbalized; linearization of the selected content; and generation of preverbal messages. Guhe then analyzes incremental processing — processing that takes place in a piecemeal fashion — and offers a blueprint of incremental models while discussing the dimensions along which the processing principles and the blueprint varies. Finally, incremental processing and conceptualization merge to form the incremental conceptualiser model (inC). The effective use of inC is demonstrated through simulations carried out with the implementation of the model. Intended for researchers in cognitive science, particularly cognitive modeling of language, this volume will also interest researchers in artificial intelligence, computational linguistics, psycholinguistics, and linguistics and psychology.

There is increasingly much agreement in the cognitive sciences that human cognition is embodied – to some significant extent. However, there is much less agreement regarding in what sense(s) cognition is embodied. In particular, there is much agreement that sensorimotor interaction with the environment is fundamental to cognition. From a historical perspective, this emphasis on the sensorimotor body is at least partly due to the crucial role that the conceptual shift in artificial intelligence (AI) research – from computational to robotic models – has played in the overall development of embodied cognitive science. Most embodied AI research, however, in particular work on symbol grounding and related approaches, reduces the body to a mere sensorimotor interface for internal processes that are still just as computational as they were 30–40 years ago. In Harnad's terms, this type of AI has only gone from a computational to a robotic functionalism. In theory, this could be limited to AI research as such, but in practice the view of the physical body as the computational mind's sensorimotor interface to the world still pervades much of cognitive science and philosophy of mind. The argument presented here is that there are good reasons to say that at least today's robots are in fact not embodied – in any sense that would allow for anything even close to human embodied cognition and intentionality – and that this has implications for social interactions between humans and robots.

Enactivism has had an important conceptual influence on Artificial Life. Its notorious mind-life continuity thesis is a fundamental idea that once sustained much of the theoretical framework, but has recently lost support, explicitly in cognitive science, and by omission in Artificial Life. Alternative computational accounts, however, do not seem capable of filling the vacuum it leaves behind, given that according to the Church-Turing thesis, the limit of what is computable is bounded by Turing machines (regardless of the specific instantiation, such as cellular automata, deep, spiking or any other form of neural networks, or even quantum computers, to name some well known cases) and that computable functions formally describe the notion of mechanism, every organismic process that determines a consistent response should be both limited to Turing-machine capabilities and amenable to formalization. This impending issue, namely, the relation among life, cognition and computation as the hypothetical elements of the mind, is particularly pertinent and highly relevant for Artificial Life. Moreover, although it is often overlooked or omitted for practical reasons, it will probably define, or so we argue, the direction of future research in cognitive science, artificial intelligence and related fields.

The research aimed to identify the reality of using artificial intelligence in educational research from the point of view of graduate students at the College of Education at Taif University. The study sample consisted of 32.8% of the study population consisting of (128 male and female students) enrolled in the master’s degree- at the College of Education- Taif University In the specializations of (educational leadership, pedagogy, educational policies, special education, curricula, teaching methods and educational techniques) in the second semester of the academic year 1444-1445 AH, and using the questionnaire tool, the research concluded the following: 1- That students use artificial intelligence to a high degree in all stages of research. 2- One of the most important problems in using artificial intelligence in research is related to reliability and security, and the lack of a clear policy for applying artificial intelligence in educational research. 3- One of the most important controls that students believed should be available (formulation The Artificial Intelligence Ethics Document, the establishment of the University Center for Artificial Intelligence Ethics, and the enhancement of cyber security skills for the educational researcher, while there are no statistically significant differences between the sample members in their responses to the questionnaire axes due to specialization and level While the most widely used artificial intelligence program in educational research was Chat GPT, it affected the reality axis without the rest of the axes. Among the most important recommendations reached by this study: establishing a university center for the ethics of artificial intelligence, drafting a document on the ethics of artificial intelligence in educational research, and adding it as a course for students. Undergraduate and postgraduate studies

The advancements of the research on Mobile Robots with high degree of autonomy is possible, on one hand, due to its broad perspective of applications and, on other hand, due to the development and reduction of costs on computer, electronic and mechanic systems. Together with the research in Artificial Intelligence and Cognitive Science, this scenario currently enables the proposition of ambitious and complex robotic projects. Most of the applications were developed outside the structured environment of industry assembly lines and have complex goals, such as planets exploration, transportation of parts in factories, manufacturing, cleaning and monitoring of households, handling of radioactive materials in nuclear power plants, inspection of volcanoes, and many other activities. This chapter presents and discusses the main topics involved on the design or adoption of a mobile robot system and focus on the control and navigation systems for autonomous mobile robots. Thus, this chapter is organized as follows: • The Section 2 introduces the main aspects of the Robot design, such as: the conceptualization of the mobile robot physical structure and its relation to the world; the state of art of navigation methods and systems; and the control architectures which enables high degree of autonomy. • The Section 3 presents the dynamic and control analysis for navigation robots with kinematic and dynamic model of the differential and omnidirectional robots. • And finally, Section 4 presents applications for a robotic platform of Automation, Simulation, Control and Supervision of Navegation Robots, with studies of dynamic and kinematic modeling, control algorithms, mechanisms for mapping and localization, trajectory planning and the platform simulator.

Research in artificial intelli-gence, artificial life and cogni-tive science has not yet provided answers to any of the most perplexing questions about the mind, such as the nature of consciousness or of the self; in this article the authors make a suggestion for a new approach. They begin by setting their project in the broader cognitive science context and argue that little recent research adequately addresses the question of what are the necessary requirements for conscious experience to be possible. Kant addresses this question in his transcendental psychology, and although Kant's work is now over 200 years old the authors believe his approach is worthy of re-examination in the current debate about the mind.

In recent years, the topic of emotion reading has increasingly received attention from researchers in Cognitive Science and Artificial Intelligence. To study this phenomenon, in this paper an adaptive agent model is presented with capabilities to interpret another agent’s emotions. The presented agent model is based on recent advances in neurological context. First a non-adaptive agent model for emotion reading is described involving (preparatory) mirroring body states of the other agent. Here emotion reading is modelled taking into account the Simulation Theory perspective as known from the literature, involving the own body states and emotions in reading somebody else’s emotions. This models an agent that first develops the same feeling, and after feeling the emotion imputes it to the other agent. Next the agent model is extended to an adaptive model based on a Hebbian learning principle to develop a direct connection between a sensed stimulus concerning another agent’s body state (e.g., face expression) and the emotion recognition state. In this adaptive agent model the emotion is imputed to the other agent before it is actually felt. The agent model has been designed based on principles of neural modelling, and as such has a close relation to a neurological realisation.Keywordsagent modelemotion readingcognitivetheory of mindadaptive

How people plan is an active area of research in cognitive science, neuroscience, and artificial intelligence. However, tasks traditionally used to study planning in the laboratory tend to be constrained to artificial environments, such as Chess and bandit problems. To date there is still no agreed-on model of how people plan in realistic contexts, such as navigation and search, where values intuitively derive from interactions between perception and cognition. To address this gap and move towards a more naturalistic study of planning, we present a novel spatial Maze Search Task (MST) where the costs and rewards are physically situated as distances and locations. We used this task in two behavioral experiments to evaluate and contrast multiple distinct computational models of planning, including optimal expected utility planning, several one-step heuristics inspired by studies of information search, and a family of planners that deviate from optimal planning, in which action values are estimated by the interactions between perception and cognition. We found that people's deviations from optimal expected utility are best explained by planners with a limited horizon, however our results do not exclude the possibility that in human planning action values may be also affected by cognitive mechanisms of numerosity and probability perception. This result makes a novel theoretical contribution in showing that limited planning horizon generalizes to spatial planning, and demonstrates the value of our multi-model approach for understanding cognition.

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 languag 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. This book provides critical insights for researchers in AI and cognitive science, motivating further inquiry into the philosophical foundations of artificial intelligence and its practical implications.

This letter highlights the necessity of nursing engagement in artificial intelligence (AI) research and its potential impact on healthcare. As AI transforms patient care, diagnostics, and operational efficiency, it is crucial for nurses, the largest healthcare workforce, to engage in AI development actively. Nurses’ experiential knowledge and ethical perspectives can enhance AI tools, ensuring they are patient-centered and practically applicable. This letter underscores the importance of interdisciplinary collaboration and advocates for increased nursing involvement in AI research to achieve better healthcare outcomes.

Countries, companies, and universities are increasingly competing over top-tier artificial intelligence (AI) researchers. Where are these researchers likely to immigrate and what affects their immigration decisions? We conducted a survey $(n = 524)$ of the immigration preferences and motivations of researchers that had papers accepted at one of two prestigious AI conferences: the Conference on Neural Information Processing Systems (NeurIPS) and the International Conference on Machine Learning (ICML). We find that the U.S. is the most popular destination for AI researchers, followed by the U.K., Canada, Switzerland, and France. A country's professional opportunities stood out as the most common factor that influences immigration decisions of AI researchers, followed by lifestyle and culture, the political climate, and personal relations. The destination country's immigration policies were important to just under half of the researchers surveyed, while around a quarter noted current immigration difficulties to be a deciding factor. Visa and immigration difficulties were perceived to be a particular impediment to conducting AI research in the U.S., the U.K., and Canada. Implications of the findings for the future of AI talent policies and governance are discussed.

Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic contexts. It is at once a theoretical model to express meaning, a practical methodology to construct semantic representations, a computational framework for acquiring meaning from language data, and a cognitive hypothesis about the role of language usage in shaping meaning. This book aims to build a common understanding of the theoretical and methodological foundations of distributional semantics. Beginning with its historical origins, the text exemplifies how the distributional approach is implemented in distributional semantic models. The main types of computational models, including modern deep learning ones, are described and evaluated, demonstrating how various types of semantic issues are addressed by those models. Open problems and challenges are also analyzed. Students and researchers in natural language processing, artificial intelligence, and cognitive science will appreciate this book.

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.

Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine