New research reveals that intentionally fostering a diversity of values among artificial intelligence agents can lead to more effective collaboration and the emergence of complex, self-governing communities.
New research sheds light on the dynamics of collaborative coding between humans and large language models, revealing both the promise and the pitfalls of this emerging partnership.
A new approach to causal discovery reframes the problem around the principle of exchangeability, offering a potentially more robust alternative to traditional methods.
A new approach leverages clustered data to significantly improve the accuracy and efficiency of discovering causal relationships.
A new agent framework is simplifying the process of controlling robots with natural language, opening the door to more intuitive and adaptable human-robot collaboration.
Researchers have created a highly realistic urban simulator designed to accelerate progress in robot navigation, collaboration, and understanding of the world around them.
New research suggests that the behavior of complex AI agents can be understood through the principles of equilibrium systems and potential energy landscapes.
A new approach combines the power of large language models with retrieved data to achieve accurate and flexible human activity recognition, even for actions the system hasn’t seen before.
A new platform combines the power of human insight and artificial intelligence to rapidly develop standardized metadata vocabularies for the field.
As machine learning increasingly powers everyday applications like activity recognition, understanding when a model is unsure is just as crucial as knowing what it predicts.