Human-AI Interaction

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https://africarxiv.ubuntunet.net/handle/1/1628
How humans interact with AI systems, including user interface design, natural language processing, and the psychological and social impacts of AI.

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    A pre-Tranind Model for Driver Drowsiness Detection
    (2023-06-22) Ahmed, Amira
    Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and the driver’s graphic information. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. The technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination. In this paper, we propose a Resnet (50) pre-trained model for driver drowsiness detection that achieves robust results and reaches 98% accuracy.
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    Artificial General Intelligence (AGI) for Medical Education and Training
    (2023-10-20) Lema, Kennedy
    Artificial General Intelligence (AGI) has garnered worldwide attention as a transformative technology, thanks to the emergence of groundbreaking Large AI Models (LAMs), including Large Language Models, Large Vision Models, and Large Multi-Modal Models. AGI represents an ambitious endeavor to replicate human intelligence within computer systems, making it a pivotal technology poised to revolutionize Medical Training. Fueled by recent advancements in large pre-trained models, AGI signifies a remarkable stride in empowering machines to perform tasks demanding human-level intelligence. These tasks encompass reasoning, problem solving, decision-making, and even the comprehension of human emotions and social interactions. This work conducts a comprehensive exploration of AGI, elucidating its fundamental concepts, capabilities, scope, and transformative potential in the realm of Medical Education and Training. It specifically delves into Medical Simulation Environments, Interactive Virtual Labs, Humanoid Robots in Medical Education, Continuing Medical Education (CME), Personalized Learning Pathways, Intelligent Tutoring Systems, Natural Language Processing for Medical Texts, Clinical Decision Support, and Automated Assessment Tools. The examination encompasses a thorough analysis of the prospective advantages, challenges, limitations, risks, and ethical considerations that AGI poses to Medical education and training programs, as well as its implications for Medical educators. The development of AGI necessitates fostering interdisciplinary collaboration between educators and AI engineers to propel research and application endeavors in this transformative field.
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    Individualization of Products and Services with Artificial General Intelligence and General Collective Intelligence
    (2020-12-15) Williams, Andy
    INTRODUCTION: With advances in big data techniques having already led to search results and advertising being customized to the individual user, the concept of an online education designed solely for an individual, or the concept of online news or entertainment media, or any other virtual service being designed uniquely for each individual, no longer seems as far fetched. However, designing services that maximize user outcomes as opposed to services that maximize outcomes for the corporation owning them, requires modeling user processes and the outcomes they target. OBJECTIVES: To explore the use of Human-Centric Functional Modeling (HCFM) to define functional state spaces within which human processes are well-defined paths, and within which products and services solve specific navigation problems, so that by considering all of any given individual’s desired paths through a given state space, it is possible to automate the customization of those products and services for that individual or to groups of individuals. METHODS: An analysis is performed to assess how and whether intelligent agents based on some subset of functionality required for Artificial General Intelligence (AGI) might be used to optimize for the individual user. And an analysis is performed to determine whether and if so how General Collective Intelligence (GCI) might be used to optimize across all users. RESULTS: AGI and GCI create the possibility to individualize products and services, even shared services such as the Internet, or news services so that every individual sees a different version. CONCLUSION: The conceptual example of customizing a news media website for two individual users of opposite political persuasions suggests that while the overhead of customizing such services might potentially result in massively increased storage and processing overhead, within a network of cooperating services in which this customization reliably creates value, this is potentially a significant opportunity.