Recent times have seen an emergence of intelligent machines that act autonomously on our behalf, such as autonomous vehicles. Despite promises of increased efficiency, it is not clear whether this paradigm shift will change how we decide when our self-interest (e.g., comfort) is pitted against the collective interest (e.g., environment). Here we show that acting through machines changes the way people solve these social dilemmas and we present experimental evidence showing that participants program their autonomous vehicles to act more cooperatively than if they were driving themselves. We show that this happens because programming causes selfish short-term rewards to become less salient, leading to considerations of broader societal goals. We also show that the programmed behavior is influenced by past experience. Finally, we report evidence that the effect generalizes beyond the domain of autonomous vehicles. We discuss implications for designing autonomous machines that contribute to a more cooperative society.
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http://dx.doi.org/10.1073/pnas.1817656116 | DOI Listing |
Adv Sci (Weinh)
January 2025
Helmholtz-Zentrum Hereon, Institute of Membrane Research, Max Planck Str. 1, 21502, Geesthacht, Germany.
This work proposes a fuel cell power supply system for underwater applications (e.g., autonomous underwater vehicles), where artificial gills, based on a polymer membrane, harvest the required oxygen from the ambient water.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Institute of Technology, Anhui Agricultural University, Hefei, China.
Introduction: The rapid urbanization of rural regions, along with an aging population, has resulted in a substantial manpower scarcity for agricultural output, necessitating the urgent development of highly intelligent and accurate agricultural equipment technologies.
Methods: This research introduces YOLOv8-PSS, an enhanced lightweight obstacle detection model, to increase the effectiveness and safety of unmanned agricultural robots in intricate field situations. This YOLOv8-based model incorporates a depth camera to precisely identify and locate impediments in the way of autonomous agricultural equipment.
ACS Appl Mater Interfaces
January 2025
Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States.
Thermal energy harvesting for high-speed moving objects is particularly promising in providing an efficient and sustainable energy source to enhance operational capabilities and endurance. Thermoelectric (TE) technology, by exploiting temperature gradients between a heat source and ambient temperature, can provide a continuous power supply to such systems, reducing the reliance on conventional batteries and extending operation times. However, the integrated thermoelectric generator (TEG) system design research is far behind materials development.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B2K3, Canada.
Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Key Laboratory of System Control and Information Processing, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China.
The suspended sediment plume generated in the deep-sea mining process significantly impacts the marine environment and seabed ecosystem. Accurate boundary estimation can effectively monitor the scope of environmental impact, guiding mining operations to prevent ecological damage. In this paper, we propose a dynamic boundary estimation approach for the suspended sediment plume, leveraging the sensing capability of the Autonomous Underwater Vehicles (AUVs).
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