Selective Prediction, as a sub-field of reliable Artificial Intelligence, aims to develop methods and techniques to identify uncertain model predictions. Instead of having a model produce an output for every sample, an abstention mechanism is implemented that recognizes instances where predictions are likely inaccurate. Accordingly, the model only makes predictions for samples that are not filtered out by this mechanism and abstains from the others. Colloquially, with Selective Prediction, we are “creating models that know what they don't know.”

Modular AI systems are designed with a modular architecture, where complex tasks are broken down into smaller, more manageable components or modules. Each module handles specific aspects like perception, reasoning, or decision-making. With current end-to-end models growing into monolithic black boxes, there's a clear argument for more modular systems composed of smaller, manageable components.