In this paper we provide evidence that a virtual model of U.S. congresspersons based on a collection of language models satisfies the definition of a digital twin. In particular, we introduce and provide high-level descriptions of a daily-updated dataset that contains every Tweet from every U.S. congressperson during their respective terms. We demonstrate that a modern language model equipped with congressperson-specific subsets of this data are capable of producing Tweets that are largely indistinguishable from actual Tweets posted by their physical counterparts. We illustrate how generated Tweets can be used to predict roll-call vote behaviors and to quantify the likelihood of congresspersons crossing party lines, thereby assisting stakeholders in allocating resources and potentially impacting real-world legislative dynamics. We conclude with a discussion of the limitations and important extensions of our analysis.

Five months after ChatGPT set off an investment frenzy over artificial intelligence, Beijing is moving to rein in China's chatbots, a show of the government's resolve to keep tight regulatory control over technology that could define an era. The Cyberspace Administration of China this month unveiled draft rules for so-called generative AI -- the software systems, like the one behind ChatGPT, that can formulate text and pictures in response to a user's questions and prompts. According to the regulations, companies must heed the Chinese Communist Party's strict censorship rules, just as websites and apps have to avoid publishing material that besmirches China's leaders or rehashes forbidden history. The content of AI systems will need to reflect "socialist core values" and avoid information that undermines "state power" or national unity. This could be due to a conflict with your ad-blocking or security software.