Tag
This paper introduces a method called 'Learn to Cluster' to quantify and interpret social interactions among pedestrians for better trajectory prediction. It uses probabilistic latent variable generative learning to cluster social interactions without labels, improving robustness for autonomous driving and social robots.
Introduces KARMA, a framework that trains a reward model on Reddit conversations to improve LLMs' context-sensitive conversational behavior via reinforcement learning, finding that the best reward model for predicting karma does not yield the best downstream alignment.
The article notes that the cost of replacing human employees with AI may be comparable to or higher than that of real employees, and humans have an inherent preference for human interaction. Thus, large-scale AI substitution faces two significant hurdles: cost and user experience.
This paper analyzes spontaneous dyadic Zoom conversations using multimodal features (acoustic, facial, turn-taking) to identify markers of perceived conversational success, finding that entrainment in speech and facial movements correlates with higher interaction quality.