Results 161 to 170 of about 2,986,386 (301)
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano +3 more
wiley +1 more source
Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies. [PDF]
Holbrook C +3 more
europepmc +1 more source
AI-generated recommendations often appear to be straightforward answers to consumer questions. This study examines how recommendation responses adapt as consumer information changes, using responses generated by four major AI platforms across home services recommendation scenarios.
openaire +1 more source
Artificial intelligence (AI) robots can learn from their experiences, make decisions in real time, understand natural language and human gestures, and utilize computer vision to perceive and comprehend their environments.
Khanna, Vinod Kumar
core +1 more source
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Autonomous field robots equipped with ISFET‐based sensors enable rapid, spatially resolved soil nutrient analysis directly in the field. An automated system based on the BoniRob platform is presented for standardized on‐site extraction and measurement of key macronutrients and soil pH.
Vadim Riedel +3 more
wiley +1 more source
The Impact of Expectation Management and Model Transparency on Radiologists' Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study. [PDF]
Ewals LJS +10 more
europepmc +1 more source
Why AI Visibility Does Not Guarantee AI Recommendation
Over the past two years, consumer brands have invested heavily in improving their visibility inside conversational AI systems. The prevailing assumption has been straightforward: if a brand appears clearly and positively in AI-generated answers, it benefits. That assumption is incomplete.
openaire +2 more sources
AI Recommendations and Non-instrumental Image Concerns
There is growing enthusiasm about the potential for humans and AI to collaborate by leveraging their respective strengths. Yet in practice, this promise often falls short. This paper uses an online experiment to identify non-instrumental image concerns as a key reason individuals underutilize AI recommendations.
openaire +3 more sources
ChatGPT as a linguistic informant. A comparison of human and AI-generated translations
In the past sixty years various research methods have been proposed to help the researcher to collect reliable data to which theoretical analyses can be applied.
Sleeman, Petra
core +1 more source

