ARDIAS: AI-Enhanced Research Management, Discovery, and Advisory System

• URL: http://arxiv.org/abs/2301.10577v1
• Date: Wed, 25 Jan 2023 13:30:10 GMT
• Title: ARDIAS: AI-Enhanced Research Management, Discovery, and Advisory System
• Authors: Debayan Banerjee, Seid Muhie Yimam, Sushil Awale and Chris Biemann
• Abstract summary: ARDIAS is a web-based application that aims to provide researchers with a full suite of discovery and collaboration tools. ARDIAS currently allows searching for authors and articles by name and gaining insights into the research topics of a particular researcher. With the aid of AI-based tools, ARDIAS aims to recommend potential collaborators and topics to researchers.
• Score: 24.42822218256954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present ARDIAS, a web-based application that aims to provide researchers with a full suite of discovery and collaboration tools. ARDIAS currently allows searching for authors and articles by name and gaining insights into the research topics of a particular researcher. With the aid of AI-based tools, ARDIAS aims to recommend potential collaborators and topics to researchers. In the near future, we aim to add tools that allow researchers to communicate with each other and start new projects.

Related papers

• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• OpenResearcher: Unleashing AI for Accelerated Scientific Research [35.31092912532057]
  We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. We develop various tools for OpenResearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers.
  arXiv  Detail & Related papers  (2024-08-13T14:59:44Z)
• A FAIR and Free Prompt-based Research Assistant [0.0]
  Research Assistant (RA) tool developed to assist with six main types of research tasks. RA's reliance on generative AI tools like ChatGPT or Gemini means the same research task assistance can be offered in any scientific discipline.
  arXiv  Detail & Related papers  (2024-05-23T14:16:46Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work. ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
  This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
  arXiv  Detail & Related papers  (2024-04-09T15:01:51Z)
• Acceleron: A Tool to Accelerate Research Ideation [15.578814192003437]
  Acceleron is a research accelerator for different phases of the research life cycle. It guides researchers through the formulation of a comprehensive research proposal, encompassing a novel research problem. We leverage the reasoning and domain-specific skills of Large Language Models (LLMs) to create an agent-based architecture.
  arXiv  Detail & Related papers  (2024-03-07T10:20:06Z)
• Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face [104.2943594704532]
  Spacerini is a tool that integrates the Pyserini toolkit for reproducible information retrieval research with Hugging Face. Spacerini makes state-of-the-art sparse and dense retrieval models more accessible to non-IR practitioners.
  arXiv  Detail & Related papers  (2023-02-28T12:44:10Z)
• Human-Centered Responsible Artificial Intelligence: Current & Future Trends [76.94037394832931]
  In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. All of this work is aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends.
  arXiv  Detail & Related papers  (2023-02-16T08:59:42Z)
• Researching Alignment Research: Unsupervised Analysis [14.699455652461726]
  AI alignment research is dedicated to ensuring that artificial intelligence (AI) benefits humans. In this project, we collected and analyzed existing AI alignment research. We found that the field is growing quickly, with several subfields emerging in parallel.
  arXiv  Detail & Related papers  (2022-06-06T18:24:17Z)
• A New Neural Search and Insights Platform for Navigating and Organizing AI Research [56.65232007953311]
  We introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
  arXiv  Detail & Related papers  (2020-10-30T19:12:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.