The field of human-technology interaction is rapidly evolving, with a growing focus on creating more natural and engaging interactions between humans and machines. Recent research has explored the use of large language models (LLMs) to enhance conversational capabilities in social robots, mitigating the uncanny valley effect and improving user experience. Multimodal analysis, incorporating text, images, and audio, is also becoming increasingly important for understanding and analyzing complex phenomena, such as emotional visual scenes, news framing, and euphemism identification. Notable papers in this area have introduced new benchmarks and evaluation suites for empathetic agents, demonstrated the effectiveness of multimodal models in identifying patterns in large datasets, and shown that LLMs can emulate human normative judgments on emotional visual scenes. Overall, the field is moving towards more sophisticated and human-like interactions between humans and machines, with significant implications for areas such as social robotics, content moderation, and human-computer interaction. Noteworthy papers include Mitigating the Uncanny Valley Effect in Hyper-Realistic Robots, which investigated the use of LLMs to mitigate the uncanny valley effect in social robots, and Artificial Intelligence Can Emulate Human Normative Judgments on Emotional Visual Scenes, which demonstrated the ability of multimodal models to emulate human emotional ratings.