Real Human
Research on "Real Human" focuses on understanding and replicating human capabilities, particularly in perception, cognition, and social interaction, using artificial intelligence models. Current efforts concentrate on developing and evaluating large language models (LLMs) and large vision-language models (LVLMs), often incorporating architectures like transformers and diffusion models, to benchmark AI performance against human benchmarks in tasks ranging from visual perception and emotion recognition to complex decision-making and social interaction. These studies aim to improve AI systems' alignment with human behavior and understanding, ultimately impacting fields like human-computer interaction, robotics, and social sciences.
Papers
Natural Selection Favors AIs over Humans
Dan Hendrycks
In Sync: Exploring Synchronization to Increase Trust Between Humans and Non-humanoid Robots
Wieslaw Bartkowski, Andrzej Nowak, Filip Ignacy Czajkowski, Albrecht Schmidt, Florian Müller
Novel View Synthesis of Humans using Differentiable Rendering
Guillaume Rochette, Chris Russell, Richard Bowden