New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people's life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures.
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http://dx.doi.org/10.1016/j.cogsys.2020.08.010 | DOI Listing |
Handb Clin Neurol
January 2025
Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland; Department of Biomedicine, University of Basel, Basel, Switzerland.
The nonvisual effects of light in humans are mainly conveyed by a subset of retinal ganglion cells that contain the pigment melanopsin which renders them intrinsically photosensitive (= intrinsically photosensitive retinal ganglion cells, ipRGCs). They have direct connections to the main circadian clock in the suprachiasmatic nuclei (SCN) of the hypothalamus and modulate a variety of physiological processes, pineal melatonin secretion, autonomic functions, cognitive processes such as attention, and behavior, including sleep and wakefulness. This is because efferent projections from the SCN reach other hypothalamic nuclei, the pineal gland, thalamus, basal forebrain, and the brainstem.
View Article and Find Full Text PDFHandb Clin Neurol
January 2025
Department of Psychology, Université de Montréal, Montreal, QC, Canada; Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Centre intégré universitaire de santé et de services sociaux du Nord-de-l'Île-de Montréal, Montreal, QC, Canada. Electronic address:
Traumatic brain injury (TBI) is a serious public health concern and is one of the major causes of death and chronic disability in young individuals. Sleep-wake disturbances are among the most persistent and debilitating consequences of TBI and are reported by 50%-70% of TBI patients regardless of TBI severity. Excessive daytime sleepiness, fatigue, hypersomnia, and insomnia are the most common sleep disturbances in TBI patients.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Neurosurgery, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments.
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