Causal AI
Causal AI is an artificial intelligence system that can explain cause and effect. Causal AI technology is used by organisations to help explain decision making and the causes for a decision.[1][2]
Systems based on causal AI, by identifying the underlying web of causality for a behaviour or event, provide insights that solely predictive AI models might fail to extract from historical data.[citation needed] An analysis of causality may be used to supplement human decisions in situations where understanding the causes behind an outcome is necessary, such as quantifying the impact of different interventions, policy decisions or performing scenario planning.[3][4]
The concept of causal AI and the limits of machine learning were raised by Judea Pearl, the Turing Award-winning computer scientist and philosopher, in The Book of Why: The New Science of Cause and Effect. Pearl asserted: “Machines' lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.”[5][6]
Columbia University has established a Causal AI Lab under Director Elias Bareinboim. Professor Bareinboim’s research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning.[7] Technological research and consulting firm Gartner for the first time included causal AI in its 2022 Hype Cycle report, citing it as one of five critical technologies in accelerated AI automation.[8][9]
References
- ↑ Blogger, SwissCognitive Guest (2022-01-18). "Causal AI" (in en-US). https://swisscognitive.ch/2022/01/18/casual-ai/.
- ↑ Sgaier, Sema K; Huang, Vincent; Grace, Charles (2020). "The Case for Causal AI". Stanford Social Innovation Review 18 (3): 50–55. ProQuest 2406979616. ISSN 1542-7099.
- ↑ Shekhar, Gaurav (2022-05-26). "Causal AI — Enabling Data Driven Decisions" (in en). https://towardsdatascience.com/causal-ai-enabling-data-driven-decisions-d162f2a2f15e.
- ↑ "How to Understand the World of Causality | causaLens" (in en-US). 2023-02-28. https://causalens.com/resources/blogs/how-to-understand-the-world-of-causality/.
- ↑ Pearl, Judea (2019). The book of why : the new science of cause and effect. Dana Mackenzie. [London], UK. ISBN 978-0-14-198241-0. OCLC 1047822662. https://www.worldcat.org/oclc/1047822662.
- ↑ Hartnett, Kevin (15 May 2018). "To Build Truly Intelligent Machines, Teach Them Cause and Effect". http://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/.
- ↑ "What AI still can't do" (in en). https://www.technologyreview.com/2020/02/19/868178/what-ai-still-cant-do/.
- ↑ "What is New in the 2022 Gartner Hype Cycle for Emerging Technologies" (in en-GB). https://www.gartner.co.uk/en/articles/what-s-new-in-the-2022-gartner-hype-cycle-for-emerging-technologies.
- ↑ Sharma, Shubham (2022-08-10). "Gartner picks emerging technologies that can drive differentiation for enterprises" (in en-US). https://venturebeat.com/ai/gartner-picks-emerging-technologies-that-can-drive-differentiation-for-enterprises/.
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