【我的阅读】【nature |ai4science】Scientific discovery in the age of artificial intelligence【人工智能时代的科学发现】

相关资料:https://www.nature.com/articles/s41586-023-06221-2#Sec15

文章目录

    • Abstract 摘要
    • Conclusion 结论

Abstract 摘要

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
人工智能(AI)正越来越多地融入科学发现中,以增强和加速研究,帮助科学家生成假设,设计实验,收集和解释大型数据集,并获得仅使用传统科学方法可能无法获得的见解。在这里,我们研究了过去十年的突破,包括自我监督学习,它允许模型在大量未标记的数据上进行训练,以及几何深度学习,它利用有关科学数据结构的知识来提高模型的准确性和效率。生成式人工智能方法可以通过分析包括图像和序列在内的各种数据模式来创建设计,例如小分子药物和蛋白质。我们将讨论这些方法如何在整个科学过程中帮助科学家,以及尽管取得了这些进展,但仍然存在的核心问题。 人工智能工具的开发人员和用户都需要更好地了解这些方法何时需要改进,以及数据质量差和管理不善带来的挑战。这些问题跨越了科学学科,需要开发基础算法方法,这些方法可以促进科学理解或自主获取科学理解,使其成为人工智能创新的关键领域。

Conclusion 结论

AI systems can contribute to scientific understanding, enable the investigation of processes and objects that cannot be visualized or probed in any other way, and systematically inspire ideas by building models from data and combining them with simulation and scalable computing. To realize this potential, safety and security concerns that come with the use of AI must be addressed through responsible and thoughtful deployment of the technology. To use AI responsibly in scientific research, we need to measure the levels of uncertainty, error, and utility of AI systems. This understanding is essential for accurately interpreting AI outputs and ensuring that we do not rely too heavily on potentially flawed results. As AI systems continue to evolve, prioritizing reliable implementation with proper safeguards in place is key to minimizing risks and maximizing benefits. AI has the potential to unlock scientific discoveries that were previously out of reach.
人工智能系统可以促进科学理解,能够调查无法以任何其他方式可视化或探索的过程和对象,并通过从数据构建模型并将其与模拟和可扩展计算相结合来系统地激发想法。为了实现这一潜力,必须通过负责任和深思熟虑的技术部署来解决使用人工智能带来的安全和安全问题。为了在科学研究中负责任地使用人工智能,我们需要衡量人工智能系统的不确定性、错误和实用性。这种理解对于准确解释人工智能输出并确保我们不会过于依赖可能有缺陷的结果至关重要。随着人工智能系统的不断发展,优先考虑可靠的实施,并采取适当的保障措施,是最大限度地降低风险和实现利益最大化的关键。人工智能有可能解开以前无法实现的科学发现。

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