本文提出人工专家智能（AEI）框架，以 PAC 推理实现复杂问题的可靠分解与精度控制，并引入 System 3 精确推理，为误差有界的推理时学习奠定基础。
摘要：
人工专家智能（Artificial Expert Intelligence，AEI）旨在通过将特定领域的专业知识与类似于顶尖人类专家所具备的批判性、精确推理能力相结合，超越人工通用智能（AGI）和狭义人工智能的局限。现有 AI 系统通常擅长预定义任务，但在解决新问题时难以兼顾适应性与精确性。为克服这一点，AEI 提出了一个“可能近似正确（Probably Approximately Correct，PAC）推理”框架。该范式为可靠地分解复杂问题提供了坚实的理论保证，并提供了一种用于控制推理精度的实用机制。参照人类思维被划分为负责直觉思维的 System 1 和负责反思性推理的 System 2，我们将这种新型推理称为用于精确推理的 System 3，其灵感来自科学方法的严谨性。因此，AEI 为误差有界的推理时学习奠定了基础。
英文摘要：
Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with critical, precise reasoning capabilities akin to those of top human experts. Existing AI systems often excel at predefined tasks but struggle with adaptability and precision in novel problem-solving. To overcome this, AEI introduces a framework for ``Probably Approximately Correct (PAC) Reasoning". This paradigm provides robust theoretical guarantees for reliably decomposing complex problems, with a practical mechanism for controlling reasoning precision. In reference to the division of human thought into System 1 for intuitive thinking and System 2 for reflective reasoning~\citep{tversky1974judgment}, we refer to this new type of reasoning as System 3 for precise reasoning, inspired by the rigor of the scientific method. AEI thus establishes a foundation for error-bounded, inference-time learning.
Emergence is Overrated: AGI as an Archipelago of Experts
Research School of Social Sciences（社会科学研究学校）；Machine Intelligence and Normative Theory (MINT) Lab（机器智能与规范理论实验室）；Australian National University（澳大利亚国立大学）
Influential Papers in Artificial Intelligence and Paediatrics: Assessing RPYS by Experts Review
Faculty of Electrical Engineering and Computer Science, University of Maribor（马里博尔大学电气工程与计算机科学学院）；Community Healthcare Centre Dr. Adolf Drolc Maribor（马里博尔阿道夫·德罗尔克博士社区医疗中心）；Alma Mater Europaea—ECM（欧洲阿尔玛材料大学—ECM）；Science and Research Center Koper（科佩尔科学研究中心）；Faculty of Natural Sciences and Mathematics, University of Maribor（马里博尔大学自然科学与数学学院）；Faculty of Health and Social Sciences Slovenj Gradec（斯洛文尼格拉代茨健康与社会学院）
Clinical Semantic Intelligence (CSI): Emulating the Cognitive Framework of the Expert Clinician for Comprehensive Oral Disease Diagnosis
Gilan University of Medical Sciences（加兹万大学医学科学学院）；University of Göttingen（哥廷根大学）；University of Tehran（德黑兰大学）；Hamadan University of Technology（哈马丹技术大学）；Islamic Azad University（伊斯兰 Azad 大学）
From prediction markets to interpretable collective intelligence
St. Petersburg State University（圣彼得堡国立大学）；Syncretis LLC（Syncretis有限责任公司）；St. Petersburg Department of V. A. Steklov Institute of Mathematics of the Russian Academy of Sciences（俄罗斯科学院V. A. 斯捷克洛夫数学研究所圣彼得堡分部）；HSE University（高等经济大学）
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI
Collective intelligence in science: direct elicitation of diverse information from experts with unknown information structure
Swiss Re Institute（瑞士再保险学院）；St. Petersburg State University（圣彼得堡国立大学）；St. Petersburg Department of V. A. Steklov Institute of Mathematics of the Russian Academy of Sciences（俄罗斯科学院圣彼得堡维克托·阿列克谢耶维奇·斯捷克洛夫数学研究所圣彼得堡分所）；HSE University（俄罗斯高等经济大学）
Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology
Charnock Hospital（查诺克医院）；Jagannath Gupta Institute of Medical Sciences and Hospital（贾格纳特·古普塔医学科学与医院）
MIST: Missing Person Intelligence Synthesis Toolkit
Arizona State University（亚利桑那州立大学）；Find Me Group（寻我小组）
在美国，每天大约有500起失踪人口案件没有得到解决。由前执法专业人员领导的非营利组织Find Me Group（FMG）致力于解决这些案件。本文介绍了失踪人员情报综合工具包（MIST），它利用了数据驱动的地理空间溯因推理的变体。该系统采用由一组专家提供的搜索位置，并根据分配给区域的概率对它们进行排序，该区域基于作为一组的专家的先前表现。我们将我们的方法与Find Me Group目前采用的做法进行了比较，发现它大大减少了搜索区域-导致我们在实验中检查的24个案例减少了31平方英里。目前，我们正在使用MIST来帮助寻找我组在一个活跃的失踪人员案件。
Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison
Stanford University Palo Alto CA USA（斯坦福大学）；Department of Neurology Mayo Clinic Rochester MN USA（梅奥诊所神经科）；Department of Neurology Dalhousie University Halifax Canada（达尔豪斯大学神经科）；Jefferson Headache Center Department of Neurology Thomas Jefferson University PA USA（泰勒大学神经科）；Beth Israel Deaconess Medical Center Boston MA USA（贝斯以色列医疗中心）；Department of Neurology University of Florida Gainesville FL USA（佛罗里达大学神经科）；University of Colorado School of Medicine Department of Pediatrics Division of Child Neurology Aurora CO USA（科罗拉多医学院儿科部儿童神经科）；Department of Medicine Mount Sinai Hospital Icahn School of Medicine at Mount Sinai New York NY USA（西奈医院医学部）；Department of Neurology Mayo Clinic Scottsdale AZ USA（梅奥诊所Scottsdale分部）；Harvard Medical School Boston MA USA（哈佛医学院）；Department of Neurology Mount Sinai Hospital Icahn School of Medicine at Mount Sinai New York NY USA（西奈医院神经科）
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）
