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Abstract: As Sartre pointed out, philosophical questions are questions that each generation must ask themselves because only this promotes the feeling of being alive, which is especially true for existential questions closely related to time-space, the moment, and our society. Sartre placed his philosophy of existentialism in wartime and the social conditions of the time at the beginning of the 20th century. We can equate these conditions with today's conditions; we are once again facing threats of war, and once again, we are facing chaotic conditions that increasingly lead to absurdity but are also entirely different. Today, at the beginning of the 21st century, the clarity and disambiguation of the 20th century no longer exist, as the relationships between beings and the world have drastically changed. We can observe that (1) the world is not one; there are two worlds, the physical and the cyber world and (2) being is not one; there are two beings (entities), human and AI-based forms of artificial life (ALF), between which there is a permanent tension. We advocate the thesis that in the society of the future, man must still play a master role; he must still be the being who will guide this society. Also, as Sartre claimed, each era must create its philosophy and consider real time–space. Responses to changes in this time–space also relate to existentialism in the 21st century. In this context, it is necessary to redefine the view of the future and the guidelines for the development of future society.
Keywords: existentialism, philosophy of artificial intelligence, philosophy of mind
Published in DKUM: 21.03.2025; Views: 0; Downloads: 0
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Abstract: Purpose: This paper aims to define hybrid reality (HyR) as an ongoing process in which artificial intelligence (AI) technology is gradually introduced as an active stakeholder by using reasoning to execute real-life activities. Also, to examine the implications of social responsibility (SR) concepts as featured in the HyR underlying common framework to progress towards the redefinition of global society. Design/methodology/approach: A combination of systemic tools is used to examine and assess the development of HyR. The research is based on evolutionary and learning concepts, leading to the new meta-system development. It also builds upon the viable system model and AI, invoking SR as a conceptual framework. The research is conducted by using a new approach: using system dynamics based interactions modelling, the following two models have been proposed. The state-of-the-art HyR interactions model, examined using SR concepts; and a SR concept-based HyR model, examined using a smart vehicle case. Findings: In the HyR model, interaction asymmetry between stakeholders is identified, possibly leading to pathological behaviour and AI technology learning corruption. To resolve these asymmetry issues, an interaction model based on SR concepts is proposed and examined on the example of an autonomous vehicle transport service. The examination results display significant changes in the conceptual understanding of transport services, their utilisation and data-sharing concepts. Research limitations/implications: As the research proposal is theoretical in nature, the projection may not display a fully holistic perspective and can/should be complemented with empirical research results. Practical implications: For researchers, HyR provides a new paradigm and can thereby articulate potential research frameworks. HyR designers can recognise projected development paths and the resources required for the implication of SR concepts. Individuals and organisations should be aware of their not necessarily passive role in HyR and can therefore use the necessary social force to activate their status. Originality/value: For the first time, to the best of the author’s knowledge, the term HyR is openly elaborated and systemically examined by invoking concepts of SR. The proposed model provides an overview of the current and potential states of HyR and examines the gap between them.
Keywords: artificial intelligence, social responsibility, systems thinking, cybernetics, hybrid reality, interactions model
Published in DKUM: 04.02.2025; Views: 0; Downloads: 4
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Keywords: convolutional neural networks, generative adversarial networks, classification, synthetic images, explanable artificial intelligence
Published in DKUM: 29.01.2025; Views: 0; Downloads: 7
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Abstract: Because of the prevalence of depression, its often-chronic course, relapse and associated disability, early detection and non-intrusive monitoring is a crucial tool for timely diagnosis and treatment, remission of depression and prevention of relapse. In this way, its impact on quality of life and well-being can be limited. Current attempts to use artificial intelligence for the early classification of depression are mostly data-driven and thus non-transparent and lack effective means to deal with uncertainties. Therefore, in this paper, we propose an end-to-end framework for extracting observable depression cues from diary recordings. Furthermore, we also explore its feasibility for automatic detection of depression symptoms using observable behavioural cues. The proposed end-to-end framework for extracting depression was used to evaluate 28 video recordings from the Symptom Media dataset and 27 recordings from the DAIC-WOZ dataset. We compared the presence of the extracted features between recordings of individuals with and without a depressive disorder. We identified several cues consistent with previous studies in terms of their differentiation between individuals with and without depressive disorder across both datasets among language (i.e., use of negatively valanced words, use of first-person singular pronouns, some features of language complexity, explicit mentions of treatment for depression), speech (i.e., monotonous speech, voiced speech and pauses, speaking rate, low articulation rate), and facial cues (i.e., rotational energy of head movements). The nature/context of the discourse, the impact of other disorders and physical/psychological stress, and the quality and resolution of the recordings all play an important role in matching the digital features to the relevant background. In this way, the work presented in this paper provides a novel approach to extracting a wide range of cues relevant to the classification of depression and opens up new opportunities for further research.
Keywords: digital biomarkers of depression, facial cues, speech cues, language cues, deep learning, end-to-end pipeline, artificial intelligence
Published in DKUM: 17.01.2025; Views: 0; Downloads: 4
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Abstract: Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.
Keywords: artificial intelligence, machine learning, production processes, production planning, production scheduling
Published in DKUM: 04.12.2024; Views: 0; Downloads: 18
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