During our two day workshop, we heard talks on a range of topics illustrating the enormous potential of large language models in transforming the landscape of scientific research and education. We heard from Kavita Bala, the Dean of Cornell Bowers CIS and Professor of Computer Science, who outlined the actions the university has taken in response to the rapid rise of generative AI, including a report that includes a series of recommendations put out by the university regarding how to adapt the classroom environment in response to the rapid rise of large language models and other forms of generative AI. This report illustrates that this transformation is indeed a serious one, and that more universities should take action. On the research front, some of the most impactful talks highlighted the enormous potential of large language models in parsing large literature databases, such as the arxiv. We saw talks illustrating specific models engineered to parse the entire arxiv, and with a high level of completeness seek out all literature relevant to a topic, and even rank order this literature in a reliable way according to level of relevance. Such capabilities have great promise in revolutionizing the way graduate students and more senior researchers approach their day-to-day workflows. Finally, we saw an impactful talk on the use of generative AI tools by graduate students in MIT astrophysics, which highlighted that the use of these tools is widespread, and encompasses a large range of tasks, ranging from grammatical editing, to coding, and planning of daily tasks and workflows. It was clear from this talk that widespread adoption of LLMs as a tool in daily workflows is already happening, but little instruction or guidance has been given on how to effectively use these powerful tools, leaving universities with an important role to play if they take the initiative on this.