Symbolic computations can indeed be more sophisticated in terms of the precision, exact reasoning, and complexity of the algorithms involved. They are crucial for tasks requiring formal logic and deep reasoning. However, numerical computations are often more practical and efficient for large-scale data-driven tasks, such as those found in machine learning and statistical analysis. Both approaches have their own areas of sophistication and are valuable in different contexts within AI and ML