Artifact Design

Artifact design research focuses on creating optimized artifacts across diverse domains, from materials science and computer hardware to graphic design and even traffic signs, using computational methods to improve efficiency and performance. Current research emphasizes leveraging machine learning models, including large language models, neural networks (e.g., for image editing and binary neural networks), and gradient-based optimization techniques, to generate novel designs and improve existing ones. This work is significant because it promises to accelerate innovation across various fields by automating design processes, enhancing the capabilities of existing systems, and enabling the creation of more robust and efficient artifacts.