AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have significantly impacted research and data science. These tools offer new opportunities to enhance productivity and improve research discovery and summarization. Statisticians and data scientists are increasingly using these tools for tasks such as generating code, analyzing data, and summarizing research articles. Generative AI can now efficiently extract key points from research papers and simulate abductive reasoning to connect related technical topics, aiding in research discovery. However, these tools are not without limitations. One major issue is "hallucination," where AI generates incorrect or fabricated information. This can lead to unreliable outputs, especially in academic contexts where accuracy is crucial. Recent improvements have reduced the occurrence of hallucinations, but users must remain cautious and verify information against primary sources. Chatbots can perform abductive reasoning, helping researchers identify existing methods by describing their workings. This capability is particularly useful for discovering related methods or understanding the conventional names of statistical procedures. Tools like Semantic Scholar, Consensus, and Elicit have enhanced literature discovery by providing summaries and relevant citations. These tools use LLMs to interpret research prompts and offer more accurate results than traditional search engines. AI-powered tools like Litmaps and ResearchRabbit help visualize connections among academic publications, highlighting overlooked areas and facilitating literature review. Custom GPTs and plugins, such as ScholarAI and ResearchGPT, further enhance research by integrating with academic databases and providing summaries of research papers. While AI excels at summarizing text-based research, it struggles with highly technical papers, especially those involving complex mathematics and data analysis. ChatGPT and similar tools can provide basic overviews but may not capture detailed technical nuances. Advances in LLMs, such as Google's Gemini 1.5 Pro, are expanding the context length, enabling the processing of larger documents and more comprehensive analysis. The future of AI in research is promising, with potential advancements in multi-source information synthesis, citation improvement, and interdisciplinary translation. However, challenges such as copyright restrictions and access to non-open access journals remain. Collaborations between AI developers and academic publishers could help overcome these barriers, enabling broader access to research and enhancing the capabilities of generative AI in research and knowledge dissemination.AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have significantly impacted research and data science. These tools offer new opportunities to enhance productivity and improve research discovery and summarization. Statisticians and data scientists are increasingly using these tools for tasks such as generating code, analyzing data, and summarizing research articles. Generative AI can now efficiently extract key points from research papers and simulate abductive reasoning to connect related technical topics, aiding in research discovery. However, these tools are not without limitations. One major issue is "hallucination," where AI generates incorrect or fabricated information. This can lead to unreliable outputs, especially in academic contexts where accuracy is crucial. Recent improvements have reduced the occurrence of hallucinations, but users must remain cautious and verify information against primary sources. Chatbots can perform abductive reasoning, helping researchers identify existing methods by describing their workings. This capability is particularly useful for discovering related methods or understanding the conventional names of statistical procedures. Tools like Semantic Scholar, Consensus, and Elicit have enhanced literature discovery by providing summaries and relevant citations. These tools use LLMs to interpret research prompts and offer more accurate results than traditional search engines. AI-powered tools like Litmaps and ResearchRabbit help visualize connections among academic publications, highlighting overlooked areas and facilitating literature review. Custom GPTs and plugins, such as ScholarAI and ResearchGPT, further enhance research by integrating with academic databases and providing summaries of research papers. While AI excels at summarizing text-based research, it struggles with highly technical papers, especially those involving complex mathematics and data analysis. ChatGPT and similar tools can provide basic overviews but may not capture detailed technical nuances. Advances in LLMs, such as Google's Gemini 1.5 Pro, are expanding the context length, enabling the processing of larger documents and more comprehensive analysis. The future of AI in research is promising, with potential advancements in multi-source information synthesis, citation improvement, and interdisciplinary translation. However, challenges such as copyright restrictions and access to non-open access journals remain. Collaborations between AI developers and academic publishers could help overcome these barriers, enabling broader access to research and enhancing the capabilities of generative AI in research and knowledge dissemination.