Beyond the Generative Paradigm: Foundations for Computational Abstraction

This is a conceptual position paper contrasting abstraction and generation as two opposing AI paradigms. The success of current AI systems can be attributed to their data-first paradigm. However, data is mathematically a product of models, such as mathematical structures, variables, and parameters, underlying these systems. In fact, estimating models from data in AI is formalised as an inverse problem in statistics. In this paper, we argue that the mismatch between the data-first paradigm and this model-first approach is a fundamental cause of various long-standing open problems such as unifying logic and probability, unifying learning and reasoning, unifying symbol grounding and inference grounding, and brain-like AI. We overview abstractive AI as opposed to generative AI and discuss promising future research directions.