Work

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

Year: 2018

Type: conference-paper

Abstract: Current machine learning systems operate, almost exclusively, in a statistical, or model-blind 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. 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 inference.

Source: ACM International Conference on Web Search and Data Mining (WSDM)

DOI: 10.1145/3159652.3176182

Author: Judea Pearl

Institution: University of California, Los Angeles

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Advanced Research Projects Agency, Office of Naval Research