Structural Construct

Structural construct research focuses on understanding and representing the compositional structure of complex systems, whether physical assemblies (like LEGO structures or robotic systems), knowledge graphs, or even abstract concepts like human activities. Current research employs various machine learning approaches, including autoencoders, large language models, and mixture-of-experts architectures, to learn, generate, and reason about these structures from diverse data sources, such as images, text, and sensor readings. This work has implications for improving efficiency in engineering design and manufacturing, enhancing AI's ability to understand and interact with the world, and developing more robust and interpretable AI systems.