Symbolic Computation

Symbolic computation focuses on manipulating mathematical expressions and algorithms symbolically, rather than numerically, aiming to automate mathematical reasoning and problem-solving. Current research emphasizes integrating symbolic methods with machine learning, particularly using transformer models and neural networks, to improve accuracy, efficiency, and the ability to handle complex problems like symbolic integration and program synthesis. This interdisciplinary field is crucial for advancing AI safety, enhancing computer algebra systems, and enabling more robust and explainable AI in various scientific and engineering applications, including robotics and high-energy physics.