Towards Language-driven Scientific AI

• URL: http://arxiv.org/abs/2210.15327v2
• Date: Mon, 31 Oct 2022 09:32:32 GMT
• Title: Towards Language-driven Scientific AI
• Authors: Jos\'e Manuel G\'omez-P\'erez
• Abstract summary: We set about designing AI systems that are able to address complex scientific tasks that challenge human capabilities to make new discoveries. Central to our approach is the notion of natural language as core representation, reasoning, and exchange format between scientific AI and human scientists.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inspired by recent and revolutionary developments in AI, particularly in language understanding and generation, we set about designing AI systems that are able to address complex scientific tasks that challenge human capabilities to make new discoveries. Central to our approach is the notion of natural language as core representation, reasoning, and exchange format between scientific AI and human scientists. In this paper, we identify and discuss some of the main research challenges to accomplish such vision.

Related papers

• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• A call for embodied AI [1.7544885995294304]
  We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
  arXiv  Detail & Related papers  (2024-02-06T09:11:20Z)
• AI for social science and social science of AI: A Survey [47.5235291525383]
  Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research.
  arXiv  Detail & Related papers  (2024-01-22T10:57:09Z)
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
  New area of research known as AI for science (AI4Science) Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales. Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
  arXiv  Detail & Related papers  (2023-07-17T12:14:14Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Scientists' Perspectives on the Potential for Generative AI in their Fields [18.753742428223912]
  Generative AI models are on the cusp of transforming many aspects of modern life. There is potential for Generative AI to have a substantive impact on the methods and pace of discovery for a range of scientific disciplines. We interviewed twenty scientists from a range of fields to gain insight into whether or how Generative AI might add value to the practice of their respective disciplines.
  arXiv  Detail & Related papers  (2023-04-04T00:06:28Z)
• The Debate Over Understanding in AI's Large Language Models [0.18275108630751835]
  We survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language. We argue that a new science of intelligence can be developed that will provide insight into distinct modes of understanding.
  arXiv  Detail & Related papers  (2022-10-14T17:04:29Z)
• Learning from learning machines: a new generation of AI technology to meet the needs of science [59.261050918992325]
  We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
  arXiv  Detail & Related papers  (2021-11-27T00:55:21Z)

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.