A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging, could support radiologists in classifying different kinds of breast lesions and then improve the diagnosis of breast cancer (BC) with a lower X-ray dose than in Computer Tomography (CT) systems.In previous work, several Convolutional Neural Network (CNN) architectures were evaluated to discriminate four different classes of lesions considering high-resolution images automatically segmented: (a) irregular opacity lesions, (b) regular opacity lesions, (c) stellar opacity lesions and (d) no-lesions. In this paper, instead, we use the same previously extracted relevant Regions of Interest (ROIs) containing the lesions, but we propose and evaluate two different approaches to better discriminate among the four classes.In this work, we evaluate and compare the performance of two different frameworks both considering supervised classifiers topologies. The first framework is feature-based, and consider morphological and textural hand-crafted features, extracted from each ROI, as input to optimised Artificial Neural Network (ANN) classifiers. The second framework, instead, considers non-neural classifiers based on automatically computed features evaluating the classification performance extracting several sets of features using different Convolutional Neural Network models.Final results show that the second framework, based on features computed automatically by CNN architectures performs better than the first approach, in terms of accuracy, specificity, and sensitivity.     

EXISTENTIAL HOPE AND EXISTENTIAL DESPAIR IN AI APOCALYPTICISM AND TRANSHUMANISM

Drawing on observations from on‐ and offline fieldwork among transhumanists and artificial superintelligence/singularity‐focused groups, this article will explore an anthropology of anxiety around the hoped for, or feared, posthuman future. It will lay out some of the varieties of existential hope and existential despair found in these discussions about predicted events such as the “end of the world” and place them within an anthropological theoretical framework. Two examples will be considered. First, the optimism observed at a transhumanist event will be examined to emphasize the positive affective aspects of certain apocalypse scenarios, especially those with an implicit eschatological direction. Second, an online location where examples of existential despair can be noted will be explored further to demonstrate the kinds of negative responses to certain superintelligence/singularity ideas. These examples of existential hope and despair will demonstrate the intrinsic role of anxiety in ideas about a future artificial intelligence apocalypse.     

内隐学习中的知识习得及其无意识性测量*

本研究采用经典的人工语法范式,并引入迁移任务探讨在记忆和规则探索两种指导语条件下,被试所习得的究竟是规则还是组块？同时采用结构知识主观测量的方法,对内隐学习习得知识的无意识性进行测量。结果发现记忆指导语条件下的被试能成功区分合法串和非法串,即出现语法学习效应,但规则探索指导语条件下的被试并没有表现出语法或组块学习效应,结构知识的主观测量发现这种内隐学习的优势效应主要来自无意识结构知识的贡献;在迁移研究中发现附带学习组和有意学习组均在迁移任务上表现出语法学习效应,这表明在人工语法学习中所获得的语法规则是抽象的和可迁移的。     

Open Problems in the Philosophy of Information

Abstract: The philosophy of information (PI) is a new area of research with its own field of investigation and methodology. This article, based on the Herbert A. Simon Lecture of Computing and Philosophy I gave at Carnegie Mellon University in 2001, analyses the eighteen principal open problems in PI. Section 1 introduces the analysis by outlining Herbert Simon's approach to PI. Section 2 discusses some methodological considerations about what counts as a good philosophical problem. The discussion centers on Hilbert's famous analysis of the central problems in mathematics. The rest of the article is devoted to the eighteen problems. These are organized into five sections: problems in the analysis of the concept of information, in semantics, in the study of intelligence, in the relation between information and nature, and in the investigation of values.     

近距离的鞭挞与远距离的哲思

面对当今"人造美女"的现象,仅有批评的激愤与学术睿智是不够的,更需要哲人式的深刻与从容.今后人们需要改变的是既定的认知观念,而不是"医疗美容"所代表的新的心理、社会层面的服务诉求.     

人工语法学习中习得知识的分离:基于信号检测论和结构知识的视角

习得知识的意识觉知问题一直以来是内隐学习领域的研究热点和难点。以加工分离程序来分解击中率和虚报率、以结构知识中意识和无意识测量为视角,采用2（测验方式：SDTT,SKT）×2（学习程度：30试次,60试次）混合设计,试图去分离人工语法学习中习得的意识知识和无意识知识。结果发现：（1）学习程度影响到习得的无意识知识,但还未影响到习得的意识知识;（2）在分离人工语法学习的习得知识上,SDTT的敏感性高于SKT;（3）SKT夸大了元认知中的意识成分。     

The role of cognitive architectures in general artificial intelligence

The term “Cognitive Architectures” indicates both abstract models of cognition, in natural and artificial agents, and the software instantiations of such models which are then employed in the field of Artificial Intelligence (AI). The main role of Cognitive Architectures in AI is that one of enabling the realization of artificial systems able to exhibit intelligent behavior in a general setting through a detailed analogy with the constitutive and developmental functioning and mechanisms underlying human cognition. We provide a brief overview of the status quo and the potential role that Cognitive Architectures may serve in the fields of Computational Cognitive Science and Artificial Intelligence (AI) research.     

大学生形成析取概念的假设检验策略研究

运用布鲁纳等人设计的用以研究合取概念的实验材料,研究了大学生形成合取、维度内析取、维度间析取等人工概念的假设检验策略。结果表明,大学生倾向于优先使用单维检验策略（包括单维变化策略和单维肯定策略）。这种策略有利于很快解决合取问题和维度内析取问题,却不利于维度间析取任务的完成,因此大学生解决维度间析取的成功率显著低于合取和维度内析取。     

The Language of Creativity: Evidence from Humans and Large Language Models

Recent developments in computerized scoring via semantic distance have provided automated assessments of verbal creativity. Here, we extend past work, applying computational linguistic approaches to characterize salient features of creative text. We hypothesize that, in addition to semantic diversity, the degree to which a story includes perceptual details, thus transporting the reader to another time and place, would be predictive of creativity. Additionally, we explore the use of generative language models to supplement human data collection and examine the extent to which machine-generated stories can mimic human creativity. We collect 600 short stories from human participants and GPT-3, subsequently randomized and assessed on their creative quality. Results indicate that the presence of perceptual details, in conjunction with semantic diversity, is highly predictive of creativity. These results were replicated in an independent sample of stories (n = 120) generated by GPT-4. We do not observe a significant difference between human and AI-generated stories in terms of creativity ratings, and we also observe positive correlations between human and AI assessments of creativity. Implications and future directions are discussed.     

人工智能在医学教育领域的应用研究——基于CiteSpace的文献共词分析

人工智能及大数据已经成为包括医学教育在内的各研究领域的热点问题。为了阐明人工智能在医学教育中的发展趋势,利用CiteSpace软件分析2013—2022年Web of Science核心合集文献270篇。从发文量、国家和地区、研究机构、作者、发表杂志、引文情况、关键词及发展趋势等方面进行综合分析得出研究结论：从2018年开始相关论文呈明显增长趋势,Mayo Clinic（梅奥诊所）成为全球发文量最多的研究机构;Friedman等形成了较为完整的研究团体;JAMA成为最高被引杂志;近年医学教育的人工智能应用研究热点问题主要集中在医学影像及虚拟手术等方面。