When you teach a child how to solve puzzles, you can either let them figure it out through trial and error, or you can guide them with some basic rules and tips. Similarly, incorporating rules and tips into AI training—such as the laws of physics—could make them more efficient and more reflective of the real world. However, helping the AI assess the value of different rules can be a tricky task.
Researchers report March 8 in the journal Nexus that they have developed a framework for assessing the relative value of rules and data in “informed machine learning models” that incorporate both. They showed that by doing so, they could help the AI incorporate basic laws of the real world and better navigate scientific problems like solving complex mathematical problems and optimizing experimental conditions in chemistry experiments.
“Embedding human knowledge into AI models has the potential to improve their efficiency and ability to make inferences, but the question is how to balance the influence of data and knowledge,” says first author Hao Xu of Peking University. “Our framework can be employed to evaluate different knowledge and rules to enhance the predictive capability of deep learning models.”
Generative AI models like ChatGPT and Sora are purely data-driven—the models are given training data, and they teach themselves via trial and error. However, with only data to work from, these systems have no way to learn physical laws, such as gravity or fluid dynamics, and they also struggle to perform in situations that differ from their training data.
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