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April Fools. Who is fooling who?  A year ago, CloudResearch posted an April Fools’ Day spoof about creating AI-generated responses via AI-generated simulated humans. A year later, innovative researchers are already using LLMs to enhance and reduce costs of Social and Economics Research. This paper (https://lnkd.in/dfr9TCeN) is a 2023 review of the methods where LLMs work and don't work. Some examples below: - Gilardi et al. (2023) present evidence that ChatGPT "exceeds that of human annotators in four out of five tasks". - Törnberg (2023) examined the accuracy, reliability, and bias of ChatGPT when classifying political affiliations, suggesting that LLMs have "substantial potential for use in the social sciences." - Hämäläinen et al. (2023) explored using LLMs for designing and assessing experiments.  - Kim and Lee (2023) analyzed how LLMs could augment surveys and enable missing data imputation, retrodiction, and zero-shot prediction. Their conclusion is the same as our approach at Subconscious AI: that "LLMs have the potential to address some of the challenges associated with survey research" and "should be used in conjunction with other methods and approaches" to ensure the accuracy and validity of the survey results. By using both Human and our Digital Twin of Earth, Subconscious AI dramatically decreases the cost of research while simultaneously increasing the information (reducing the entropy) of any study. www.subconscious.ai

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