The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, !Blackbox Interpretability"#is wrongheaded. But there is a better way. There is an exciting and emerging discipline of !Inner Interpretability"#(and specifically Mechanistic Interpretability) that aims to uncover the internal activations and weights of models in order to understand what they represent and the algorithms they implement. In my view, a crucial mistake in Black-box Interpretability is the failure to appreciate that how processes are carried out matters when it comes to intelligence and understanding. I can#t pretend to have a full story that provides both necessary and sufficient conditions for being intelligent, but I do think that Inner Interpretability dovetails nicely with plausible philosophical views of what intelligence requires. So the conclusion is modest, but the important point in my view is seeing how to get the research on the right track. Towards the end of the paper, I will show how some of the philosophical concepts can be used to further refine how Inner Interpretability is approached, so the paper helps draw out a profitable, future two-way exchange between Philosophers and Computer Scientists.
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis
How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（教育部计算功率网络与信息安全部重点实验室，山东计算机科学中心（济南国家超级计算中心），齐鲁工业大学（山东省科学院））
Brief for the Canada House of Commons Study on the Implications of Artificial Intelligence Technologies for the Canadian Labor Force: Generative Artificial Intelligence Shatters Models of AI and Labor
University of Pittsburgh（匹兹堡大学）；Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）
AI Readiness in Healthcare through Storytelling XAI
Center for Artificial Intelligence in Public Health Research (ZKI-PH) at Robert Koch Institute（罗伯特·科赫研究所公共卫生研究人工智能中心）；Free University of Berlin（柏林自由大学）
针对医疗 AI 落地中的信任与差异化解释需求，本文提出结合多任务蒸馏和可解释性技术的叙事式 XAI，以支持复杂深度模型并提升专家信任。
Explainable Artificial Intelligence: a Systematic Review
School of Computer Science, College of Science and Health, Technological University Dublin（都柏林理工大学科学与健康学院计算机科学学院）；Technological University Dublin（都柏林理工大学）
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
Simula Metropolitan Center for Digital Engineering（西米拉数字工程中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Bonn（波恩大学医院）；Norwegian University of Science and Technology（挪威科技大学）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Birmingham City University（伯明翰城市大学）；ETH Zurich（苏黎世联邦理工学院）；Technical University of Munich（慕尼黑工业大学）
Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence
Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Key Laboratory for Corneal Diseases Research of Zhejiang Province（浙江省角膜病研究重点实验室）；School of Software Technology, Zhejiang University（浙江大学软件学院）
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
ENSTA Paris, Institut Polytechnique Paris（巴黎高等先进技术学院，巴黎综合理工学院）；Inria Flowers（法国国家信息与自动化研究所Flowers团队）；Renault（雷诺公司）；TECNALIA（TECNALIA研究中心）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟）；University of the Basque Country (UPV/EHU)（巴斯克大学）
1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)
Rutgers University（罗格斯大学）；Hong Kong Polytechnic University（香港理工大学）；Massachusetts Institute of Technology（麻省理工学院）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Columbia University（哥伦比亚大学）；Peking University（北京大学）
Tensor Networks for Explainable Machine Learning in Cybersecurity
Multiverse Computing；Department of Basic Sciences, Tecnun - University of Navarra（基础科学系，塔努大学）；Donostia International Physics Center（Donostia国际物理中心）；Ikerbasque Foundation for Science（Ikerbasque科学基金会）
Applications of Explainable artificial intelligence in Earth system science
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））；Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies（广东省气候变化与自然灾害研究重点实验室）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Shanwei Meteorological Service（汕尾市气象局）；College of Computer Science and Technology, Changchun Normal University（长春师范大学计算机科学与技术学院）；ELLIS Unit Jena（ELLIS耶拿单元）
