arXiv:2310.04981v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) transform environment percepts into vision-language semantics interpretable by LLMs. However, completing complex tasks often requires reasoning about information beyond what is currently perceived. We propose latent compositional semantic embeddings z* as a principled learning-based knowledge representation for queryable spatio-semantic memories. We mathematically prove that z* can always be found, and that the optimal z* is the centroid for any set Z. We derive a probabilistic bound for estimating separabi
The rapid advancement of large language models and vision-language models necessitates more sophisticated knowledge representation to handle complex, real-world reasoning beyond immediate perception.
This development proposes a foundational mechanism for AI systems to build and query 'spatio-semantic memories,' enabling more robust autonomous agents capable of complex tasks and nuanced understanding.
The ability to formally represent and query latent compositional semantic embeddings changes how AI systems could interact with and reason about their environment, moving towards more human-like cognitive abilities.
- · AI developers
- · Robotics companies
- · Autonomous systems integrators
- · Research institutions
- · Companies reliant on simple, reactive AI
- · Outdated AI research paradigms
AI models will gain enhanced situational awareness and reasoning capabilities for complex, multi-modal tasks.
This could lead to more capable and reliable AI agents and robotic systems operating in unpredictable environments.
Advanced spatio-semantic memories might eventually enable AIs to construct and query detailed mental models of the world, bridging current gaps in general intelligence.
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Read at arXiv cs.LG