This article discusses the challenges of assembly planning in manufacturing, highlighting its complexity and the need for AI-powered solutions. It explains the gap between 'as-designed' and 'as-manufactured' views of a product and how AutoAssembler aims to bridge this gap with a 'virtual build' approach. It details why classic approaches to assembly planning have stalled and how recent advancements in compute power, AI, and data models are making industrial-scale assembly planning tractable.
The article discusses the limitations of Large Language Models (LLMs) in planning and self-verification tasks, and proposes an LLM-Modulo framework to leverage their strengths in a more effective manner. The framework combines LLMs with external model-based verifiers to generate, evaluate, and improve plans, ensuring their correctness and efficiency.
"Simply put, we take the stance that LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for human or machine agents, if rightly used."
"Simply put, we take the stance that LLMs are amazing giant external non-veridical memories that can serve as powerful cognitive orthotics for human or machine agents, if rightly used."
A new model developed by researchers at MIT and the University of Washington predicts human goals or actions more accurately than previous models. The latent inference budget model identifies patterns in human or machine decision-making and uses this information to forecast behavior.
