Since the release of ChatGPT in November 2022, there has been an explosion of interest in generative artificial intelligence. Among social scientists, there is increasing recognition that these technologies open up new methodological opportunities. Large language models not only make advanced computational text analysis more accessible and flexible, enabling more scholars to use techniques like supervised text classification, but they also enable new forms of prompt-based text analysis. The latest generation of generative AI models are also multimodal, making them useful for analyzing images, audio, and other media. Moreover, these models can reproduce patterns and associations present in the vast troves of text and images they are trained on. By experimenting with generative AI, we can potentially gain insights into culture, cognition, and other domains. Yet, at the same time, these emerging technologies pose challenges for social science research because the training data and models are often black boxes controlled by corporations, and the outputs can be unreliable, misleading, and biased. These issues not only raise methodological questions but may have widespread societal ramifications as large language models and other generative AI are integrated into the home, workplace, and other institutions. This workshop convenes nearly two dozen social scientists to explore the use of generative AI in social science research. It is generously supported by the Institution for Social and Public Policy at Yale University and Yale's Data-Intensive Social Science Center.