Summary of Ai and Generative Ai For Research Discovery and Summarization, by Mark Glickman and Yi Zhang
AI and Generative AI for Research Discovery and Summarization
by Mark Glickman, Yi Zhang
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential of artificial intelligence (AI) and generative AI tools, particularly chatbots like ChatGPT, to revolutionize research discovery and summarization. Large language models (LLMs) enable the generation of programming code from text prompts, improving data analysis and statistical model fitting. Plugins to chatbots can speed up literature searches, while generative AI can summarize research articles in concise language. Furthermore, highly parameterized LLMs can simulate abductive reasoning, facilitating connections between related technical topics. The paper reviews these developments and proposes future directions that may interest statisticians and data scientists. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) has changed the way researchers work! AI tools like chatbots are helping us find important papers faster and understand what they say in a few words. These chatbots can even generate code to help us analyze data or fit statistical models. We’re also using them to make connections between ideas and find new research areas. This paper looks at how these AI tools are changing the way we do research and what might happen next. |
Keywords
* Artificial intelligence * Statistical model * Summarization
