AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. Statisticians and data scientists have begun experiencing the benefits from the availability of these tools in numerous ways, such as the generation of programming code from text prompts to analyze data or fit statistical models. One area that these tools can make a substantial impact is in research discovery and summarization. Standalone tools and plugins to chatbots are being developed that allow researchers to more quickly find relevant literature than pre-2023 search tools. Furthermore, generative AI tools have improved to the point where they can summarize and extract the key points from research articles in succinct language. Finally, chatbots based on highly parameterized LLMs can be used to simulate abductive reasoning, which provides researchers the ability to make connections among related technical topics, which can also be used for research discovery. We review the developments in AI and generative AI for research discovery and summarization, and propose directions where these types of tools are likely to head in the future that may be of interest to statistician and data scientists.

翻译：人工智能与生成式AI工具（包括依赖大型语言模型（LLM）的ChatGPT等聊天机器人）于今年突然兴起，创造了提升工作效率和改善生活的巨大机遇。统计学家和数据科学家已开始通过多种方式体验到这些工具带来的益处，例如从文本提示生成编程代码以分析数据或拟合统计模型。这些工具能够产生重大影响的领域之一便是科研发现与摘要。当前正在开发的独立工具和聊天机器人插件，使研究人员能够比2023年之前的搜索工具更快地找到相关文献。此外，生成式AI工具已改进至能用简洁语言从研究文章中提取并总结关键要点的程度。最后，基于高度参数化大型语言模型的聊天机器人可模拟溯因推理，使研究人员能够关联相关技术主题，这同样可应用于科研发现。我们综述了人工智能与生成式AI在科研发现与摘要领域的最新进展，并提出这些工具未来可能的发展方向，这些方向或将为统计学家和数据科学家所关注。