Embedding-space systems usually retrieve, rank, or recommend from a single point representation, even when the task would benefit from uncertainty or controlled diversity. We introduce DESS, a lightweight uncertainty layer that augments an existing embedding model with a predicted mean vector and an independent per-dimension spread vector. DESS is trained with deterministic element-wise losses, requires no sampling during training, and maps inference-time samples back to available outputs by nearest-neighbor selection, leaving the trained predictor decoupled from a fixed output dictionary. The central question is whether such a simple diagonal uncertainty layer is meaningful and safe in high-dimensional embedding spaces. We show that the learned spread captures real local geometry: on 20 Newsgroups sentence-transformer embeddings, it is strongly rank-correlated with empirical within-class variation (Spearman ρ = 0.94 ). However, it is not epistemic uncertainty: the spread norm performs at chance for held-out-class OOD detection (AUROC 0.49). We then show that inference-time sampling is governed by concentration of measure, with sample displacement concentrating near the spread-vector norm. This yields a training-set geometric rule for choosing the spread multiplier β , which predicts the safe sampling regime on AG News. On SQID/ESCI reranking, the strongest DESS variant preserves the frozen SBERT query embedding as the mean and learns only local spread; with β = 0.09 , sampled reranking matches the SBERT-text baseline without surpassing multimodal fusion or cross-encoder systems. DESS therefore provides a modular, geometry-aware uncertainty layer for embedding-space models, provided its spread is calibrated to local embedding geometry.
Over several decades, research on socially interactive agents has transformed human–machine interaction, evolving from early script-based systems to today’s generative, multimodal agents. Tracing this development through the ages, this article reflects on major conceptual and technical shifts that shaped the field, drawing on advances across multiple areas of AI, and discusses how progress in natural language processing and multimodal behavior analysis and generation has redefined AI as a social interface. Rather than presenting this trajectory as a narrative of continuous progress, we also examine persistent limitations, including weak grounding, difficult evaluation, opaque decision processes, safety risks in sensitive contexts, and the gap between convincing social simulation and genuine social understanding.
The Cognitive Robot Abstract Machine (CRAM) was developed to enable robots to perform everyday manipulation tasks in human environments by transforming underspecified task instructions into context-sensitive, executable actions. Rather than proposing a single planning algorithm, CRAM represents a cognitive architecture grounded in symbolic knowledge representation, generalized action plans, and introspective execution. This paper traces the evolution of the artificial intelligence ideas that shaped CRAM, from early work on frames, scripts, program interpretation and action-centered planning to contemporary developments in cognitive robotics. We show how these ideas are operationalized in the CRAM architecture and how successive extensions, including semantic digital twins, episodic memories, and dual-process reasoning, support robust execution, explanation and adaptation. In addition, we discuss the Virtual Research Building as an experimental substrate that enables systematic and reproducible evaluation of CRAM components. Finally, we outline future challenges for CRAM, highlighting the need to complement its top-down task-driven paradigm with more interaction-centered and enactive approaches for long-term deployment in human environments.
The research of the Image Processing group at the Mathematics/Computer Science Department of the University of Bremen was initially driven by requirements from the field of image retrieval, but it progressed very quickly to video and multimedia document analysis including texts, images, videos, and sound. By adopting symbolic image and video feature descriptions, it could also cover different challenging application areas such as Internet image search, search in TV archives and digital media art libraries, support for blind people, RoboCup vision technology, intercultural analysis of TV broadcasts, and next generation visual archives. In a definitely ambitious (and most successful) endeavor in 2005/2006, eleven highly engaged master students of a one-year student course project integrated the available combined image, video, and sound research results (and OpenCV!) with the goal to implement a movie trailer generator for different Hollywood movie genres. Their result was improved by 26 students in a second two-year student course project, especially in the trailer composition phase.
