Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution

Understand

Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance.

• Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI.
• To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks.
• To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.