Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs.
To think inside the box, or to think out of the box? Scientific discovery via the reciprocation of insights and concepts
Peking University（北京大学）；PersLEARN
如果说科学发现是人类进步的主要驱动力之一，那么洞察力就是这台发动机的燃料，长期以来，这台发动机一直吸引着行为层面的研究来理解和模拟其潜在的认知过程。然而，目前对科学发现进行抽象的任务大多集中在顿悟的出现上，而忽视了领域知识所发挥的特殊作用。在这篇概念论文中，我们将科学发现视为积极寻求有见地的解决方案的$thinking \ out \ of \ the \ box$与概括概念领域知识以保持正确的$thinking \ inside \ the \ box$之间的相互作用。因此，我们提出了Mindle，一个语义搜索游戏，触发科学发现一样的思维自发，作为基础设施，探索科学发现的大规模。在此基础上，我们可以进一步研究洞察力的元策略和概念的使用。在试点研究中，几个有趣的观察启发了详细的假设元策略，上下文和个人的多样性进行进一步的调查。
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
University of Minnesota（明尼苏达大学）；University of Cincinnati（辛辛那提大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Northeastern University（东北大学）；North Carolina State University（北卡罗来纳州立大学）
SelfAI: A self-directed framework for long-horizon scientific discovery
University of Electronic Science and Technology of China（电子科技大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Xian Jiaotong University（西安交通大学）；Yale University（耶鲁大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Monash University（莫纳什大学）
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California（加州大学）；Huazhong Agricultural University（华中农业大学）；Huazhong University of Science and Technology（华中科技大学）
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Department of Physics, Massachusetts Institute of Technology（麻省理工学院物理系）；The NSF AI Institute for Artificial Intelligence and Fundamental Interactions（国家科学基金会人工智能与基本相互作用研究所）；Department of Physics, University of Wisconsin, Madison（威斯康星大学麦迪逊分校物理系）
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Wizard Intelligence Learning Lab, Stanford University（智能巫师学习实验室，斯坦福大学）；Peking University（北京大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
Pfizer（辉瑞公司）；Independent Researcher（独立研究员）；University of California San Diego（加州大学圣地亚哥分校）；Institute of Humanities and Social Science Data, Fudan University（复旦大学人文社会科学数据研究院）
Cyber Academia-Chemical Engineering (CA-ChemE): A Living Digital Town for Self-Directed Research Evolution and Emergent Scientific Discovery
State Key Laboratory of Heavy Oil Processing, College of Carbon Neutrality Future Technology, China University of Petroleum (Beijing), Beijing 102249, China（石油加工国家重点实验室，碳中和未来技术学院，中国石油大学（北京），北京102249）
The Use of AI-Robotic Systems for Scientific Discovery
Chalmers University of Technology（查尔姆斯理工大学）；The University of Manchester（曼彻斯特大学）；KTH Royal Institute of Technology（皇家理工学院）；Vanderbilt University（范德比大学）；University of Cambridge（剑桥大学）
Towards a new paradigm of scientific discovery with socialized artificial intelligence
Baize Research（白泽研究院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology（昆明理工大学云南省人工智能重点实验室）；School of Automation, Southeast University（东南大学自动化学院）；School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机学院）；SeetaCloud Technology（思特云科技）；GPUhub Pte. Ltd.（GPUhub私人有限公司）
Decoding complexity: how machine learning is redefining scientific discovery
Alice Author Affiliation（Alice作者机构）；Tom Theory Affiliation（Tom理论机构）；Emma Experiments Affiliation（Emma实验机构）；Affiliation, Department, City, Country（机构、部门、城市、国家）；Simon Simulations Affiliation（Simon模拟机构）
Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery
Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度理工学院德里人工智能学院）；Department of Computer Science and Engineering, Indian Institute of Technology Delhi（印度理工学院德里计算机科学与工程系）；Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi（印度理工学院德里土木与环境工程系）；Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学有机与大分子化学实验室）；Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学能源与环境化学中心）；Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena)（耶拿海德堡聚合物能源应用研究所（HIPOLE耶拿））
Science Edge Evaluation: SEE the Missing Step Toward Real Scientific Discovery
Alibaba Group（阿里巴巴集团）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；University of Alberta（阿尔伯塔大学）；Zhejiang University（浙江大学）
