Buehler used this method to analyze a collection of 1,000 scientific papers about biological materials, turning them into a knowledge map in the form of a graph. The graph revealed how different pieces of information are connected and was able to find groups of related ideas and key points that link many concepts together. The AI model found unexpected similarities between biological materials and “Symphony No. 9,” suggesting that both follow patterns of complexity. In another experiment, the AI model recommended creating a new biological material inspired by the abstract patterns found in Wassily Kandinsky’s painting, “Composition VII.” The AI suggested a new mycelium-based composite material, which could lead to the development of innovative sustainable building materials, biodegradable alternatives to plastics, wearable technology, and even biomedical devices.
Key takeaways:
- A novel AI method developed by Markus J. Buehler at MIT uses generative AI and graph-based computational tools to uncover shared patterns of complexity and order in seemingly unrelated creations, such as biological tissue and Beethoven’s “Symphony No. 9.”
- The AI method integrates generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning, using graphs developed using methods inspired by category theory to understand symbolic relationships in science.
- The AI model was used to analyze a collection of 1,000 scientific papers about biological materials, turning them into a knowledge map in the form of a graph, revealing how different pieces of information are connected and finding groups of related ideas and key points.
- The AI model found unexpected similarities between biological materials and “Symphony No. 9,” and also suggested creating a new biological material inspired by the abstract patterns found in Wassily Kandinsky’s painting, “Composition VII.”