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Although large language models (LLMs) are reshaping various aspects of human life, our current understanding of their impacts remains somewhat constrained. Here we investigate the impact of LLMs on human communication, using data on consumer complaints in the financial industry. By employing an AI detection tool on more than 820K complaints gathered by the Consumer Financial Protection Bureau (CFPB), we find a sharp increase in the likely use of LLMs shortly after the release of ChatGPT. Moreover, the likely LLM usage was positively correlated with message persuasiveness (i.e., increased likelihood of obtaining relief from financial firms). Computational linguistic analyses suggest that the positive correlation may be explained by LLMs' enhancement of various linguistic features. Based on the results of these observational studies, we hypothesize that LLM usage may enhance a comprehensive set of linguistic features, increasing message persuasiveness to receivers with heterogeneous linguistic preferences (i.e., linguistic feature alignment). We test this hypothesis in preregistered experiments and find support for it. As an instance of early empirical demonstrations of LLM usage for enhancing persuasion, our research highlights the transformative potential of LLMs in human communication.

How will superhuman artificial intelligence (AI) affect human decision making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 years (1950-2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making., Comment: This paper is published in PNAS: https://www.pnas.org/doi/10.1073/pnas.2214840120 Minor edits to v1 include the addition of watermark and link to the published paper in the footer

Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision making abilities. But how can we quantify such human adaptation to AI? We develop a simple measure of human adaptation to AI and test its usefulness in two case studies. In Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe the reasoning processes of AI, rather than mere actions of AI. These findings based on our measure highlight the importance of explainability for human learning from AI. In Study 2, we test whether our measure is sufficiently sensitive to capture a negative form of adaptation to AI (cheating aided by AI), which occurred in a match between professional Go players. We discuss our measure's applications in domains other than Go, especially in domains in which AI's decision making ability will likely surpass that of human experts.

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.