Preserving and combining knowledge in robotic lifelong reinforcement learning
Humans can continually accumulate knowledge and develop increasingly complex behaviours and skills throughout their lives, which is a capability kn…
Category: Computer science
Discovering fully semantic representations via centroid- and orientation-aware feature learning
Learning meaningful representations of images in scientific domains that are robust to variations in centroids and orientations remains an importan…
Rethinking machine unlearning for large language models
We explore machine unlearning in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undes…
Large language models that replace human participants can harmfully misportray and flatten identity groups
Large language models (LLMs) are increasing in capability and popularity, propelling their application in new domains—including as replacements f…
Goals as reward-producing programs
People are remarkably capable of generating their own goals, beginning with child’s play and continuing into adulthood. Despite considerable empi…
Teaching robots to build simulations of themselves
The emergence of vision catalysed a pivotal evolutionary advancement, enabling organisms not only to perceive but also to interact intelligently wi…
Generative language models exhibit social identity biases
Social identity biases, particularly the tendency to favor one’s own group (ingroup solidarity) and derogate other groups (outgroup hostility), a…
Deep Bayesian active learning using in-memory computing hardware
Labeling data is a time-consuming, labor-intensive and costly procedure for many artificial intelligence tasks. Deep Bayesian active learning (DBAL…