Medical diagnostic reasoning: Epistemological modeling as a strategy for design of computer-based consultation programs

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient's findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient's characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems.     

A qualitative content analysis of ChatGPT's client simulation role-play for practising counselling skills

Role-play is often used as a pedagogical technique by counsellor educators to help counsellors-in-training (CITs) learn and practise counselling skills. This study used qualitative content analysis to assess the effectiveness of Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI) chatbot, in simulating client role-play to facilitate practising counselling skills. The author used 10 case vignettes to conduct a simulated role-play where ChatGPT was asked to simulate the client and author as counsellor. The results indicated that ChatGPT demonstrated authenticity, consistency, appropriate emotional expression, cultural sensitivity, empathy and self-awareness while portraying the client. However, at times, ChatGPT's responses to the counsellor's questions appeared overly idealised, lacking the authenticity typically observed in real clients. Furthermore, ChatGPT, as a simulated client, lacked non-verbal cues, limiting the overall experience of counselling skill practice. Recommendations for CITs and future research are discussed.     

创造性思维计算模型研究综述

本文综述了已有的创造性思维计算模型,根据其采用的计算方法进行分类,分析这些方法的优缺点,对创造性思维的计算模拟方法进行了展望。     

内隐学习与外显学习的相互关系

对内隐与外显学习过程的相互作用进行了探讨。被试为上海高二学生 10 0人 ,实验通过限定状态语法和双条件语法两种材料的会聚操作对 5种实验条件进行了比较。研究结果表明 :(1)内隐学习具有很强的抽象性 ,本研究首次对此给予了高度评价 ;(2 )内隐与外显学习存在相互作用 ,在学习过程中 ,时而互相促进 ,时而互相冲突 ,时而内隐学习贡献大 ,时而外显学习贡献大 ,本研究探明了内隐学习与外显学习的贡献谁大谁小 ,视学习材料的难度而定。实验启示 ,在人类的学习和生活中 ,内隐学习及其与外显学习的相互作用起着不可估量的作用 !     

Sallie McFague and an Ecotheological Response to Artificial Intelligence

Military, commercial, and industrial application of artificial intelligence (AI) presents distinct historical challenges for theological reasoning. To date, interdisciplinary theological responses have been limited. Contextual and ecological theologies provide an understanding of the human that is helpful in theological responses to AI. These theologies emphasize a relational, embodied view of the human that responds to and resolves many of the shortcomings found in AI research and related theological responses. Sallie McFague's emphasis on embodiment and the world as God's body is an especially useful contribution to these efforts.     

How to Be Helpful to Multiple People at Once

When someone hosts a party, when governments choose an aid program, or when assistive robots decide what meal to serve to a family, decision-makers must determine how to help even when their recipients have very different preferences. Which combination of people’s desires should a decision-maker serve? To provide a potential answer, we turned to psychology: What do people think is best when multiple people have different utilities over options? We developed a quantitative model of what people consider desirable behavior, characterizing participants’ preferences by inferring which combination of “metrics” (maximax, maxsum, maximin, or inequality aversion [IA]) best explained participants’ decisions in a drink-choosing task. We found that participants’ behavior was best described by the maximin metric, describing the desire to maximize the happiness of the worst-off person, though participant behavior was also consistent with maximizing group utility (the maxsum metric) and the IA metric to a lesser extent. Participant behavior was consistent across variation in the agents involved and  tended to become more maxsum-oriented when participants were told they were players in the task (Experiment 1). In later experiments, participants maintained maximin behavior across multi-step tasks rather than shortsightedly focusing on the individual steps therein (Experiment 2, Experiment 3). By repeatedly asking participants what choices they would hope for in an optimal, just decision-maker, and carefully disambiguating which quantitative metrics describe these nuanced choices, we help constrain the space of what behavior we desire in leaders, artificial intelligence systems helping decision-makers, and the assistive robots and decision-makers of the future.     

THE NUTS AND BOLTS OF TRANSFORMATION: SCIENCE FICTION'S IMAGINED TECHNOLOGIES AND THE CIVIC IMAGINATION

This is an introduction to the thematic section on Science Fiction's Imagined Technologies, which includes three articles that were presented at the annual meeting of the American Academy of Religion (AAR) in San Diego, CA on November 24, 2019.     

BEMBA MYSTICO-RELATIONALITY AND THE POSSIBILITY OF ARTIFICIAL GENERAL INTELLIGENCE (AGI) PARTICIPATION IN IMAGO DEI

This article interrogates the challenge artificial general intelligence (AGI) poses to religion and human societies, in general. More specifically, it seeks to respond to “Singularity”—when machines reach a level of intelligence that would put into question the privileged position humanity enjoys as imago Dei. Employing the Bemba notion of mystico-relationality in dialogue with the concepts of the “created co-creator” and Christ the Key, it argues for the possibility of AI participating in imago Dei. The findings show that imaging is a fluid, participatory activity that aims at likeness, but also social harmony. It also argues that God is the only original creator, humans are created creators, and that every aspect of visible existence, including AI, is inherently divine imaging. However, strong imaging is only attainable based on the only One and True Image—Christ, whose union of the material and the divine means that all creation can image, excluding nothing, even AI.     

THE POPULAR APPEAL OF APOCALYPTIC AI

The belief that computers will soon become transcendently intelligent and that human beings will “upload” their minds into machines has become ubiquitous in public discussions of robotics and artificial intelligence in Western cultures. Such beliefs are the result of pervasive Judaeo‐Christian apocalyptic beliefs, and they have rapidly spread through modern pop and technological culture, including such varied and influential sources as Rolling Stone, the IEEE Spectrum, and official United States government reports. They have gained sufficient credibility to enable the construction of Singularity University in California. While different approaches are possible (and, indeed, are common in Japan and possibly elsewhere), this particular vision of artificial intelligence and robotics has gained ground in the West through the influence of figures such as Hans Moravec and Ray Kurzweil. Because pop‐science books help frame public discussion of new sciences and technologies for individuals, corporations, and governments alike, the integration of religious and technoscientific claims made by their authors should be clear and open for public and scientific debate. As we move forward into an increasingly robotic future, we should do so aware of the ways in which a group's religious environment can help set the tone for public acceptance and use of robotic technologies.     

人工智能决策的公平感知

不平等问题是全球社会和经济发展需要应对的首要挑战, 也是实现全球可持续发展目标的核心障碍。人工智能(artificial intelligence, AI)为缓解不平等、促进社会公平提供了新的途径。然而, 新近研究发现, 即使客观上AI决策具有公平性和准确性, 个体仍可能对AI决策的公平感知较低。因此, 近年来越来越多的研究开始关注AI决策公平感知的影响因素。然而, 目前研究较为分散, 呈现出研究范式不统一、理论不清晰和机制未厘清等特征。这既不利于跨学科的研究对话, 也不利于研究者和实践者对AI决策公平感知形成系统性理解。基于此, 通过系统的梳理, 现有研究可以划分为两类: (1) AI单一决策的公平感知研究, 主要聚焦于AI特征和个体特征如何影响个体对AI决策的公平感知; (2) AI-人类二元决策的公平感知研究, 主要聚焦于对比个体对AI决策与人类决策公平感知的差异。在上述梳理基础上, 未来研究可以进一步探索AI决策公平感知的情绪影响机制等方向。