Causal AI
Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to 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.[3] 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.[4] A 2024 paper from Google DeepMind demonstrated mathematically that "Any agent capable of adapting to a sufficiently large set of distributional shifts must have learned a causal model".[5] The paper offers the interpretation that learning to generalise beyond the original training set requires learning a causal model, concluding that causal AI is necessary for artificial general intelligence.
History
[edit]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 2018's 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.”[6][7]
In 2020, Columbia University 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.[8] 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.[9][10]
One significant advance in the field is the concept of Algorithmic Information Dynamics:[11] a model-driven approach for causal discovery using Algorithmic Information Theory and perturbation analysis. It solves inverse causal problems by studying dynamical systems computationally. A key application is causal deconvolution, which separates generative mechanisms in data with algorithmic models rather than traditional statistics. [12] This method identifies causal structures in networks and sequences, moving away from probabilistic and regression-based techniques, marking one of the first practical Causal AI approaches using Algorithmic Complexity and Algorithmic Probability in Machine Learning. [13]
References
[edit]- ^ Blogger, SwissCognitive Guest (18 January 2022). "Causal AI". SwissCognitive, World-Leading AI Network. Retrieved 11 October 2022.
- ^ Sgaier, Sema K; Huang, Vincent; Grace, Charles (2020). "The Case for Causal AI". Stanford Social Innovation Review. 18 (3): 50–55. ISSN 1542-7099. ProQuest 2406979616.
- ^ "Beyond the Limits of Historical Data | causa". causa.tech. 29 June 2024. Retrieved 29 June 2024.
- ^ "How to Understand the World of Causality | causaLens". causalens.com. 28 February 2023. Retrieved 7 October 2023.
- ^ "Robust agents learn causal world models". S2CID 267740124.
{{cite web}}: Missing or empty|url=(help) - ^ Pearl, Judea (2019). The book of why : the new science of cause and effect. Dana Mackenzie. London, UK: Penguin Books. ISBN 978-0-14-198241-0. OCLC 1047822662.
- ^ Hartnett, Kevin (15 May 2018). "To Build Truly Intelligent Machines, Teach Them Cause and Effect". Quanta Magazine. Retrieved 11 October 2022.
- ^ "What AI still can't do". MIT Technology Review. Retrieved 18 October 2022.
- ^ "What is New in the 2022 Gartner Hype Cycle for Emerging Technologies". Gartner. Retrieved 11 October 2022.
- ^ Sharma, Shubham (10 August 2022). "Gartner picks emerging technologies that can drive differentiation for enterprises". VentureBeat. Retrieved 11 October 2022.
- ^ Zenil, Hector (25 July 2020). "Algorithmic Information Dynamics". Scholarpedia. 15 (7). Bibcode:2020SchpJ..1553143Z. doi:10.4249/scholarpedia.53143. hdl:10754/666314. Zenil, Hector; Kiani, Narsis A.; Tegner, Jesper (2023). Algorithmic Information Dynamics: A Computational Approach to Causality with Applications to Living Systems. Cambridge University Press. doi:10.1017/9781108596619. ISBN 978-1-108-59661-9.
- ^ Zenil, Hector; Kiani, Narsis A.; Zea, Allan A.; Tegner, Jesper (2019). "Causal deconvolution by algorithmic generative models". Nature Machine Intelligence. 1 (1): 58–66. doi:10.1038/s42256-018-0005-0. hdl:10754/630919.
- ^ Hernández-Orozco, Santiago; Zenil, Hector; Riedel, Jürgen; Uccello, Adam; Kiani, Narsis A.; Tegnér, Jesper (2021). "Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces". Frontiers in Artificial Intelligence. 3: 567356. doi:10.3389/frai.2020.567356. PMC 7944352. PMID 33733213.