Chaos theory and artificial intelligence (AI) are not directly connected, but they both involve the study of complex systems.

Chaos theory is a branch of mathematics that deals with the behavior of dynamic systems that are highly sensitive to initial conditions, also known as the butterfly effect. This means that small changes in initial conditions can lead to drastically different outcomes over time. Chaos theory is used to study a wide range of systems, including weather patterns, the stock market, and population dynamics.

Artificial intelligence, on the other hand, is the field of computer science that aims to create machines that can simulate human intelligence, such as learning and problem-solving. Machine learning, which is a subset of AI, involves training algorithms to make predictions or decisions based on data.

One way that chaos theory and AI are related is through the study of artificial neural networks, which are modeled after the structure of the human brain. Neural networks can be trained to recognize patterns in data and make decisions, and they have been used to model chaotic systems. However, the connection between chaos theory and AI is largely limited to this application, and the two fields are otherwise quite distinct.

First, connecting chaos to both Brownian motion and stochastic processes :

• Cecconi et al., "Brownian Motion and Diffusion: From Stochastic Processes to Chaos and Beyond", 2005 - https://arxiv.org/pdf/nlin/0407029.pdf
• Hänggi et al., "Introduction: 100 Years of Brownian Motion", 2005 - https://www.physik.uni-augsburg.de/theo1/hanggi/Papers/387.pdf
• Chew et al., "Microscopic Chaos and Gaussian Diffusion Processes", 2002 - https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2874&context=lkcsb_research

Then connecting stochastic processes (in particular Gaussian ones) to machine learning via kernel machines:

• Rasmussen et al., "Gaussian Processes for Machine Learning", 2006 - http://gaussianprocess.org/gpml/chapters/RW.pdf
• Chen et al., "Remarks on Multivariate Gaussian Process", 2021 - https://arxiv.org/pdf/2010.09830.pdf
• Zhu et al., "Markovian Gaussian Process Variational Autoencoders", 2022 - https://arxiv.org/pdf/2207.05543.pdf

Another related issue on RG as an additional source of information:

https://www.researchgate.net/post/Does_Schroedingers_equation_inherently_model_a_complex-valued_stochastic_diffusion_filtering_process_among_other_Gaussian_one

Chaos theory and artificial intelligence are directly connected...

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Good luck to you,

Yurii