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Researchers from Stanford and Cornell Introduce APRICOT: A Novel AI Approach that Merges LLM-based Bayesian Active Preference Learning with Constraint-Aware Task Planning

In the rapidly evolving field of household robotics, a significant challenge has emerged in executing personalized organizational tasks, such as arranging groceries in a refrigerator. These tasks require robots to balance user preferences with physical constraints while avoiding collisions and maintaining stability. While Large Language Models (LLMs) enable natural language communication of user preferences, this…

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Open the Artificial Brain: Sparse Autoencoders for LLM Inspection | by Salvatore Raieli | Nov, 2024

|LLM|INTERPRETABILITY|SPARSE AUTOENCODERS|XAI| A deep dive into LLM visualization and interpretation using sparse autoencoders Image created by the author using DALL-EAll things are subject to interpretation whichever interpretation prevails at a given time is a function of power and not truth. — Friedrich Nietzsche As AI systems grow in scale, it is increasingly difficult and pressing…

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The Ultimate Guide to Assessing Table Extraction

Introduction to Table extraction Extracting tables from documents may sound straightforward, but in reality, it is a complex pipeline involving parsing text, recognizing structure, and preserving the precise spatial relationships between cells. Tables carry a wealth of information compacted into a grid of rows and columns, where each cell holds context based on its neighboring…

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