Approximate and Situated Causality in Deep Learning

Abstract

Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality.

Cross-Embodied Cognitive Morphologies

“…Does it mean that the classic GOFAI will again dominate the research of the next decades? Although all the current challenges indicate that the next step will require the combination of symbolic and statistical AI, always having in mind the situated nature of cognitive systems [2][3][4], quick successes of Deep Learning are blocking such complex attempts.…”