Editorial: Toward and beyond human-level AI, volume II

Dunin-Barkowski, Witali L.; Gorban, Alexander N.

doi:10.3389/fnbot.2022.1120167

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…AI can be instructed to work relentlessly and utilize any suitable strategy to ensure those functions, analogous to worker ants dedicating themselves to the colony. In the process, AI can effectively disrupt current human systems [ 38 ], such as swarming social media [ 80 ] or producing controversial artworks [ 81 ]. As another example in the digital market, think about AI agents designed to try to prolong their functioning for advertisement purposes through psychological manipulation of human consumers.…”

Section: Discussionmentioning

confidence: 99%

“…Another study discusses common misconceptions that AI, through machine learning, can create and learn in ways that resemble human abilities [ 37 ]. Recently, it has been suggested that AI systems can now perform many tasks once thought to be the exclusive domain of humans [ 38 ]. However, the knowledge of how people’s perceptions of AI’s self-prolongation affect the perceptions of AI having its own mind remains limited.…”

Section: Introductionmentioning

confidence: 99%

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How AI’s Self-Prolongation Influences People’s Perceptions of Its Autonomous Mind: The Case of U.S. Residents

Vuong

Nguyen

et al. 2023

Behavioral Sciences

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The expanding integration of artificial intelligence (AI) in various aspects of society makes the infosphere around us increasingly complex. Humanity already faces many obstacles trying to have a better understanding of our own minds, but now we have to continue finding ways to make sense of the minds of AI. The issue of AI’s capability to have independent thinking is of special attention. When dealing with such an unfamiliar concept, people may rely on existing human properties, such as survival desire, to make assessments. Employing information-processing-based Bayesian Mindsponge Framework (BMF) analytics on a dataset of 266 residents in the United States, we found that the more people believe that an AI agent seeks continued functioning, the more they believe in that AI agent’s capability of having a mind of its own. Moreover, we also found that the above association becomes stronger if a person is more familiar with personally interacting with AI. This suggests a directional pattern of value reinforcement in perceptions of AI. As the information processing of AI becomes even more sophisticated in the future, it will be much harder to set clear boundaries about what it means to have an autonomous mind.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How AI’s Self-Prolongation Influences People’s Perceptions of Its Autonomous Mind: The Case of U.S. Residents

Vuong

Nguyen

et al. 2023

Behavioral Sciences

View full text Add to dashboard Cite

show abstract

“…Additionally, humans can process and integrate a large amount of sensory information simultaneously. Thus, it is also expected that robots can perceive the attributes of objects as accurately as humans (Dunin-Barkowski and Gorban, 2023). The object recognition algorithm based on visual images has been developed in many fields (De Vries et al, 2019;Qi et al, 2021;Grimaldi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Adaptive visual–tactile fusion recognition for robotic operation of multi-material system

et al. 2023

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The use of robots in various industries is evolving from mechanization to intelligence and precision. These systems often comprise parts made of different materials and thus require accurate and comprehensive target identification. While humans perceive the world through a highly diverse perceptual system and can rapidly identify deformable objects through vision and touch to prevent slipping or excessive deformation during grasping, robot recognition technology mainly relies on visual sensors, which lack critical information such as object material, leading to incomplete cognition. Therefore, multimodal information fusion is believed to be key to the development of robot recognition. Firstly, a method of converting tactile sequences to images is proposed to deal with the obstacles of information exchange between different modalities for vision and touch, which overcomes the problems of the noise and instability of tactile data. Subsequently, a visual-tactile fusion network framework based on an adaptive dropout algorithm is constructed, together with an optimal joint mechanism between visual information and tactile information established, to solve the problem of mutual exclusion or unbalanced fusion in traditional fusion methods. Finally, experiments show that the proposed method effectively improves robot recognition ability, and the classification accuracy is as high as 99.3%.

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Editorial: Toward and beyond human-level AI, volume II

Abstract: January CITATION Dunin-Barkowski W and Gorban A () Editorial: Toward and beyond human-level AI, volume II.

Cited by 2 publications

References 32 publications

How AI’s Self-Prolongation Influences People’s Perceptions of Its Autonomous Mind: The Case of U.S. Residents

How AI’s Self-Prolongation Influences People’s Perceptions of Its Autonomous Mind: The Case of U.S. Residents

Adaptive visual–tactile fusion recognition for robotic operation of multi-material system

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