Several technologies have emerged recently to support decision-making in organizations including: LLMS that allow defining opportunities, obtaining data, and building user interfaces; machine learning methods for building predictors with a variety of data types; population-based search methods that discover good decision strategies; methods for estimating uncertainty in the predictions and in LLM output; and systems for coordinating multiple agents to integrate knowledge into comprehensive answers. This paper reviews a system called NeuroSAN+NeuroAI that brings them together into a synergetic approach, with applications in business, engineering, healthcare, education, and society in general.

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Technical Report 2025-01, Cognizant AI Lab, San Francisco, CA, 2025.

@techreport{miikkulainen:cailtr25, title={NeuroSAN+NeuroAI: AI-assisted Decision-making through a Synergy of Technologies}, author={Risto Miikkulainen and Dan Fink and Olivier Francon and Babak Hodjat and Noravee Kanchanavatee and Elliot Meyerson and Xin Qiu and Darren Sargent and Hormoz Shahrzad and Deepak Singh and Jean Celestin Yamegni Noubeyo and Daniel Young}, number={2025-01}, month={ }, address={San Francisco, CA}, institution={Cognizant AI Lab}, type={Technical Report}, url="http://nn.cs.utexas.edu/?miikkulainen:cailtr25", year={2025} }