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Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Open accessAbstract
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.
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Cited by
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Richens, J. G., Lee, C. M., & Johri, S. (2020) · Nature Communications · doi:10.1038/s41467-020-17419-7 · Cited by 598
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Oviedo, F., Ferres, J. L., Buonassisi, T., & Butler, K. T. (2022) · Accounts of Materials Research · doi:10.1021/accountsmr.1c00244 · Cited by 435
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Metadata
- Authors
- Pearl, J.
- Venue
- Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2018-02-02
- Pages
- 3-3
- OpenAlex
- W2783740028
- Cited by
- 338
- References
- 0
- FWCI
- 17.69
- Type
- conference paper
- Language
- en
- Open access
- green
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