Causal AI
Causal AI is an artificial intelligence system that can explain cause and effect. The objective of causal AI is to help explain decision making and the causes for a decision..[1] By identifying the underlying web of causality for a behaviour or event, a causal AI system may provide critical insights that solely predictive AI models fail to extract from historical data.[2]
Judea Pearl, the Turing Award winning computer scientist and philosopher, is recognised as a pioneer of research into causal AI. In his 2018 publication, 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.”[3] Pearl has been critical of the limitations of artificial intelligence and machine learning in reasoning with uncertainty, arguing that that the real challenge is reasoning with cause and effect to go beyond merely ‘curve fitting’ of data to predict and diagnose well.[4]
Predictions based only on historical data may prove inadequate in supplementing human decisions in analysis where understanding the actual causes behind an outcome is necessary. For example, quantifying the impact of different interventions on final outcomes and making policy decisions, or performing ‘what if’ analysis and reasoning for scenarios which have not occurred.[5]
Columbia University has established a Causal AI Lab[6] 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].
At an enterprise level, 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] Global semiconductor and telecoms equipment group Qualcomm has explored fundamental research to combine causality with AI, “to solve long-standing open problems in AI, and address limitations of real-world AI systems’’.[10] Developers of causal AI software include causaLens[11] and Geminos[12]
References
- ↑ Blogger, SwissCognitive Guest (2022-01-18). "Causal AI". SwissCognitive, World-Leading AI Network. Retrieved 2022-10-11.
- ↑ Bose, K. S.; Sarma, R. H. (1975-10-27). "Delineation of the intimate details of the backbone conformation of pyridine nucleotide coenzymes in aqueous solution". Biochemical and Biophysical Research Communications. 66 (4): 1173–1179. doi:10.1016/0006-291x(75)90482-9. ISSN 1090-2104. PMID 2.
- ↑ Pearl, Judea (2019). The book of why : the new science of cause and effect. Dana Mackenzie. [London], UK. ISBN 0-14-198241-1. OCLC 1047822662. Search this book on
- ↑ Hartnett, Kevin (15 May 2018). "To Build Truly Intelligent Machines, Teach Them Cause and Effect". Quanta Magazine. Retrieved 11 October 2022. Unknown parameter
|url-status=ignored (help) - ↑ Shekhar, Gaurav (2022-05-26). "Causal AI — Enabling Data Driven Decisions". Medium. Retrieved 2022-10-11.
- ↑ "Causal Artificial Intelligence Lab | Department of Computer Science, Columbia University". www.cs.columbia.edu. Retrieved 2022-10-11.
- ↑ "Elias Bareinboim". causalai.net. Retrieved 2022-10-11.
- ↑ "What is New in the 2022 Gartner Hype Cycle for Emerging Technologies". Gartner. Retrieved 2022-10-11.
- ↑ Sharma, Shubham (2022-08-10). "Gartner picks emerging technologies that can drive differentiation for enterprises". VentureBeat. Retrieved 2022-10-11.
- ↑ "Is causality the missing piece of the AI puzzle?". www.qualcomm.com. Retrieved 2022-10-11.
- ↑ Lunden, Ingrid (2022-01-28). "CausaLens gets $45M for no-code technology that introduces cause and effect into AI decision making". TechCrunch. Retrieved 2022-10-11.
- ↑ "Geminos Software: AI Solutions Driven by Casual Reasoning". CIOReview. Retrieved 2022-10-11.
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