KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model
- URL: http://arxiv.org/abs/2409.18695v2
- Date: Mon, 07 Apr 2025 10:25:31 GMT
- Title: KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model
- Authors: Weichen Dai, Yezeng Chen, Zijie Dai, Yubo Liu, Zhijie Huang, Yixuan Pan, Baiyang Song, Chengli Zhong, Xinhe Li, Zeyu Wang, Zhuoying Feng, Yi Zhou,
- Abstract summary: We present our perspectives on how AI can better assist scientific inquiry and explore corresponding technical approach.<n>We have proposed and open-sourced two large models of our KALE-LM model series, KALE-LM-Chem(-1.5), which have achieved outstanding performance in tasks related to the field of chemistry.
- Score: 8.538062912643424
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence is gradually demonstrating its immense potential, and increasing attention is being given to how AI can be harnessed to advance scientific research. In this vision paper, we present our perspectives on how AI can better assist scientific inquiry and explore corresponding technical approach. We have proposed and open-sourced two large models of our KALE-LM model series, KALE-LM-Chem(-1.5), which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development.
Related papers
- The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS) [61.845407777089726]
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS)<n>We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025.<n>We propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students.
arXiv Detail & Related papers (2025-09-02T18:00:00Z) - From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery [108.1082357960201]
Agentic AI shows capabilities in hypothesis generation, experimental design, execution, analysis, and iterative refinement.<n>This survey provides a domain-oriented review of autonomous scientific discovery across life sciences, chemistry, materials science, and physics.
arXiv Detail & Related papers (2025-08-18T05:25:54Z) - Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges [11.232704182001253]
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.
arXiv Detail & Related papers (2024-12-16T03:52:20Z) - AI in the Cosmos [0.0]
I highlight examples of AI applications in astrophysics, including source classification, spectral energy distribution modeling, and discuss the achievable advancements through generative AI.
The use of AI introduces challenges, including biases, errors, and the "black box" nature of AI models, which must be resolved before their application.
These issues can be addressed through the concept of Human-Guided AI (HG-AI), which integrates human expertise and domain-specific knowledge into AI applications.
arXiv Detail & Related papers (2024-12-13T12:30:11Z) - Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
Next several decades may well be a turning point for humanity, comparable to the industrial revolution.
Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts.
We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
arXiv Detail & Related papers (2024-04-06T22:18:31Z) - 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) - AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
Mathematics is one of the most powerful conceptual systems developed and used by the human species.
Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
arXiv Detail & Related papers (2023-10-19T02:00:31Z) - Advancing Perception in Artificial Intelligence through Principles of
Cognitive Science [6.637438611344584]
We focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment.
We present a collection of methods in AI for researchers to build AI systems inspired by cognitive science.
arXiv Detail & Related papers (2023-10-13T01:21:55Z) - 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) - Quantifying the Benefit of Artificial Intelligence for Scientific Research [2.4700789675440524]
We estimate both the direct use of AI and the potential benefit of AI in scientific research.
We find that the use of AI in research is widespread throughout the sciences, growing especially rapidly since 2015.
Our analysis reveals considerable potential for AI to benefit numerous scientific fields, yet a notable disconnect exists between AI education and its research applications.
arXiv Detail & Related papers (2023-04-17T08:08:50Z) - When Brain-inspired AI Meets AGI [40.96159978312796]
We provide a comprehensive overview of brain-inspired AI from the perspective of Artificial General Intelligence.
We begin with the current progress in brain-inspired AI and its extensive connection with AGI.
We then cover the important characteristics for both human intelligence and AGI.
arXiv Detail & Related papers (2023-03-28T12:46:38Z) - Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
This chapter focuses on differentiable intelligence and on-board machine learning.
We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
arXiv Detail & Related papers (2022-12-10T07:49:50Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
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.