Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges

• URL: http://arxiv.org/abs/2412.11427v2
• Date: Sat, 21 Dec 2024 19:22:53 GMT
• Title: Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges
• Authors: Chandan K Reddy, Parshin Shojaee,
• Abstract summary: This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery.
• Score: 11.232704182001253
• License:
• Abstract: Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific research and discovery. This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery, including the need for science-focused AI agents, improved benchmarks and evaluation metrics, multimodal scientific representations, and unified frameworks combining reasoning, theorem proving, and data-driven modeling. Addressing these challenges could lead to transformative AI tools to accelerate progress across disciplines towards scientific discovery.

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)
• Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science [25.683422870223076]
  We present a large-scale analysis of the AI4Science literature. We quantitatively highlight key disparities between AI methods and scientific problems. We explore the potential and challenges of facilitating collaboration between AI and scientific communities.
  arXiv  Detail & Related papers  (2024-11-27T00:40:51Z)
• Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System [62.832818186789545]
  Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research. VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas. We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
  arXiv  Detail & Related papers  (2024-10-12T07:16:22Z)
• DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [116.09762105379241]
  DeepSpeed4Science aims to build unique capabilities through AI system technology innovations. We showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.
  arXiv  Detail & Related papers  (2023-10-06T22:05:15Z)
• 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)
• AI for Science: An Emerging Agenda [30.260160661295682]
  This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling" The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers.
  arXiv  Detail & Related papers  (2023-03-07T20:21:43Z)
• 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.