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Articles 61 - 63 of 63
Full-Text Articles in Physical Sciences and Mathematics
The Threat Of Artificial Superintelligence, Joseph D. Ebhardt
The Threat Of Artificial Superintelligence, Joseph D. Ebhardt
Exigence
This paper discusses the development of AI and the threat posed by the theoretical achievement of artificial superintelligence. AI is becoming an increasingly significant fixture in our lives and this will only continue in the future. The development of artificial general intelligence (AGI) would quickly lead to artificial superintelligence (ASI). AI researcher Steve Omohundro’s universal drives of rational systems demonstrate why ASI could behave in ways unanticipated by its designers. A technological singularity may occur if AI is allowed to undergo uncontrolled rapid self-improvement, which could pose an extinction-level risk to the human race. Two possible safety measures, AI “boxing” …
Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang
Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang
Journal of the Arkansas Academy of Science
The purpose of this paper is to introduce deep learning-based framework LeNet-5 architecture and implement the experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.
Quantitative Forecasting Of Risk For Ptsd Using Ecological Factors: A Deep Learning Application, Nuriel S. Mor, Kathryn L. Dardeck
Quantitative Forecasting Of Risk For Ptsd Using Ecological Factors: A Deep Learning Application, Nuriel S. Mor, Kathryn L. Dardeck
Journal of Social, Behavioral, and Health Sciences
Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning …