Google AI researchers present a new method to train models, 'DeepCTRL'

Deep neural networks (DNNs) provide increasingly accurate outputs as the volume and variety of their training data increases. While investing in high-quality, large-scale labelled datasets are one strategy to improve models, and another is to apply "rules" – reasoning heuristics, equations, associative logic, or limitations. Consider the classic physics problem of forecasting the future state of a double pendulum system using a model. While the model may learn to predict the system's total energy at a given point in time only from empirical data, it will often overestimate the energy unless given an equation that incorporates known physical constraints, such as energy conservation. The model can't represent such well-established physical principles on its own.