The vision of integrating artificial intelligence in education is part of an ongoing push for harnessing digital solutions to improve teaching and learning. Drawing from Jasanoff and Hasse, this paper deliberates on how sociotechnical imaginaries are interrelated to the implications of new technologies, such as AI, in education. Complicating Hasses’s call for the development of Socratic ignorance to consider our predispositions about new technologies and open new prospects of thought, this paper revisits postphenomenology and Feenberg’s critical constructivist theories. While (...) embracing the notion of Socratic ignorance, this paper stresses the importance of developing a nuanced understanding of technology that realizes its lack of neutrality and supports the creation of a deeper understanding of how knowledge is produced, deployed, and interpreted in the digital age. Thus, this paper argues that an amalgam of Hasse’s call for advancing Socratic ignorance combined with postphenomenology and critical constructivism can support students in developing a critical understanding of technology and opening new landscapes of imaginaries. (shrink)

AI and people do not compete on a level-playing field. Self-driving vehicles may be safer than human drivers, but laws often penalize such technology. People may provide superior customer service, but businesses are automating to reduce their taxes. AI may innovate more effectively, but an antiquated legal framework constrains inventive AI. In The Reasonable Robot, Ryan Abbott argues that the law should not discriminate between AI and human behavior and proposes a new legal principle that will ultimately improve human well-being. (...) This work should be read by anyone interested in the rapidly evolving relationship between AI and the law. (shrink)

Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future (...) investigations of causal relationships, and to generate possible explanations of target phenomena in cognitive science. In this way, this paper describes how Explainable AI—over and above machine learning itself—contributes to the efficiency and scope of data-driven scientific research. (shrink)

Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine’s understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a (...) potential epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry. (shrink)

Background The integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers’ perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them. Methods A structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical (...) practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives. Results Three hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions. Conclusions This study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Live Like Nobody Is Watching: Relational Autonomy in the Age of Artificial Intelligence Health Monitoring by Anita HoTina Nguyen (bio)Live Like Nobody Is Watching: Relational Autonomy in the Age of Artificial Intelligence Health Monitoring by Anita Ho New York: Oxford University Press, 2023As the reach of artificial intelligence (AI)- and machine learning (ML)-enabled technologies continues to expand in the healthcare field, bioethicists have examined the (...) ethical issues that come as a result of using these technologies. In this ethical assessment, most bioethicists, myself included, have discussed how AI/ML violates the ethical principle of autonomy. The commonly cited definition of autonomy comes from Beauchamp and Childress's Principles of Biomedical Ethics (2019), where autonomy is the right of the individual to make decisions for themselves. However, autonomy regarding AI/ML in healthcare has not been or is rarely discussed from the feminist perspective. In Live Like Nobody Is Watching: Relational Autonomy in the Age of Artificial Intelligence Health Monitoring (further referred to as Live Like Nobody Is Watching), Anita Ho, in addition to other points, argues that AI for health monitoring needs to be examined through a relational autonomy lens. Not only is this perspective innovative but it also shows how a predominantly male field has shaped the narrative surrounding AI/ML. In this review, I provide a brief overview of the chapters, explore Ho's arguments for relational autonomy, and discuss future applications of this work.Live Like Nobody Is Watching begins with a preface where the readers are introduced to a real-life scenario. Ho describes a friend's experience of [End Page 101] debating with themself whether to use surveillance for their mother with early-stage dementia who claims someone has been moving her things. This sets up the rest of the book in which questions of should these technologies be used and in what context are they appropriate are debated using relational autonomy. The introduction serves as a brief primer to AI (ML and deep learning) and its role in healthcare followed by a layout of the subsequent chapters. Chapter 1 connects health monitoring AI with relational autonomy, where Ho suggests that the liberal view of autonomy is not appropriate for health monitoring technologies. This is due to the fact that the patient populations using these technologies are reliant, to various degrees, on social relationships. In chapters 2, 3, and 4, Ho addresses how health-monitoring AI requires relational autonomy through use cases concerning home-health monitoring, direct-to-consumer monitoring, and AI monitoring for medication adherence. Throughout these chapters, Ho discusses the claimed benefits of these AI/ML interventions and cautions about the ethical challenges, such as health inequities and testimonial injustice, that can arise with these technologies. Finally, chapter 5 serves as the conclusion, where Ho ultimately argues that health-monitoring AI should be viewed through relational autonomy instead of the traditional view, and she advocates for dialogic engagement (including bringing patients/patients' families into the conversation). The narrative goes full circle with the events in the preface being addressed in the epilogue, where the friend decided to incorporate a smart frame device that would allow them to have video calls with their mother. However, the friend is still grappling with the idea of surveillance given an incident in which another dementia resident accidentally wandered into their mother's room. To this, Ho ends the book by proposing better lighting in the hallways rather than immediately turning to the next high-tech innovation.Ho presents several arguments throughout the book but emphasizes the main argument that relational autonomy should be practiced over the traditional, liberal view of autonomy. Ho discusses the three elements of socio-relational autonomy (using Catriona Mackenzie's work): self-governance, self-determination, and self-authorization. Components of the traditional bioethical view of autonomy are seen in these three elements, with the process of informed consent being closely tied to the concept of autonomy. Under the traditional view, an autonomous individual should have decision-making capacity and be able to make a decision based on the information provided to them without coercion. In the informed consent process, patients should be provided adequate information along with... (shrink)

Artificial Intelligence as a buzzword and a technological development is presently cast as the ultimate ‘game changer’ for economy and society; a technology of which we cannot be the master, but which nonetheless will have a pervasive influence on human life. The fast pace with which the multi-billion dollar AI industry advances toward the creation of human-level intelligence is accompanied by an increasingly exaggerated chorus of the ‘incredible miracle’, or the ‘incredible horror’, intelligent machines will constitute for humanity, as (...) the human is gradually replaced by a technologically superior proxy, destined to be configured as a functional component at best, a relic at worst. More than half a century ago, Günther Anders sketched out this path toward technological obsolescence, and his work on ‘Promethean shame’ and ‘Promethean discrepancy’ provides an invaluable means with which to recognise and understand the relationship of the modern human to his/her technological products. In this article, I draw on Anders’s writings to unpack and unsettle contemporary narratives of our relation to AI, with a view toward refocusing attention on the responsibilities we bear in producing such immersive technologies. With Anders, I suggest that we must exercise and develop moral imagination so that the human capacity for moral responsibility does not atrophy in our technologically mediated future. (shrink)

The increasing integration of artificial intelligence (AI) in healthcare presents a host of ethical, legal, social, and political challenges involving various stakeholders. These challenges prompt various studies proposing frameworks and guidelines to tackle these issues, emphasizing distinct phases of AI development, deployment, and oversight. As a result, the notion of responsible AI has become widespread, incorporating ethical principles such as transparency, fairness, responsibility, and privacy. This paper explores the existing literature on AI use in healthcare to examine how it (...) addresses, defines, and discusses the concept of responsibility. We conducted a scoping review of literature related to AI responsibility in healthcare, searching databases and reference lists between January 2017 and January 2022 for terms related to “responsibility” and “AI in healthcare”, and their derivatives. Following screening, 136 articles were included. Data were grouped into four thematic categories: (1) the variety of terminology used to describe and address responsibility; (2) principles and concepts associated with responsibility; (3) stakeholders’ responsibilities in AI clinical development, use, and deployment; and (4) recommendations for addressing responsibility concerns. The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare. Further research is necessary to clarify this notion to contribute to developing frameworks regarding the type of responsibility (ethical/moral/professional, legal, and causal) of various stakeholders involved in the AI lifecycle. (shrink)

The paper aims to analyze three discussions pertaining to the artificial intelligence project that took place on both sides of the “Iron Curtain” since the 1960s: 1) E.V. Ilyenkov – D.I. Dubrovsky (USSR), 2) H. Dreyfus – computer scientists (USA), 3) H. Dreyfus – H. Collins (USA – UK). The authors observe the originality of the arguments of Soviet philosophers in comparison with the discussions in the US and UK. The basis for a comparative analysis of these discussions is (...) the typology of social intercourse paradigms, which assumes the existence of three fundamentally different views of humans as social beings: the information-instrumental, the existential-phenomenological, and the social-institutional paradigms. These views correlate with the ideas about humans within different theoretical structures examining artificial intelligence. The information-instrumental paradigm poses the problem of social intercourse regarding communication, material, and informational exchange (D.I. Dubrovsky, computer scientists). The social-institutional paradigm considers social intercourse as personification, actualization, and implementation of the structural characteristics of social relations (E.V. Ilyenkov). The existential-phenomenological paradigm characterizes social intercourse as the realization of the internal potency of a self-conscious individual (H. Dreyfus). The paper exposes that the theoretical and methodological foundations of the discussions under consideration are similar to the paradigmatic foundations of the analysis of social intercourse in social philosophy and social sciences. There are “two and a half floors” in the research of problems associated with the entry of artificial intelligence technologies into the everyday life of society: 1) philosophy, computer and cognitive sciences; 2) social sciences; 3) interdisciplinary research. A particular paradigmatic structure and a unique research problem characterize each of them. In conclusion, “new old questions” are highlighted that structure the field of contemporary social analytics for studying artificial intelligence and artificial sociality. (shrink)

The purpose of the article is to research the unseparable connection between objective aspects of cognition linked with metodology and logic and subjective ones which are covered to the subject’s mind and world outlook. According to psychology such a connection directly influences on understanding of truth and can be considered in the problem of artificial intelligence.

Intellectual humility (IH) is often conceived as the recognition of, and appropriate response to, your own intellectual limitations. As far as we are aware, only a handful of studies look at interventions to increase IH – e.g. through journalling – and no study so far explores the extent to which having high or low IH can be predicted. This paper uses machine learning and natural language processing techniques to develop a predictive model for IH and identify top terms and features (...) that indicate degrees of IH. We trained our classifier on the dataset from an existing psychological study on IH, where participants were asked to journal their experiences with handling social conflicts over 30 days. We used Logistic Regression (LR) to train a classifier and the Linguistic Inquiry and Word Count (LIWC) dictionaries for feature selection, picking out a range of word categories relevant to interpersonal relationships. Our results show that people who differ on IH do in fact systematically express themselves in different ways, including through expression of emotions (i.e., positive, negative, and specifically anger, anxiety, sadness, as well as the use of swear words), use of pronouns (i.e., first person, second person, and third person) and time orientation (i.e., past, present, and future tenses). We discuss the importance of these findings for IH and the value of using such techniques for similar psychological studies, as well as some ethical concerns and limitations with the use of such semi-automated classifications. (shrink)

Computer Science and Artificial Intelligence will be the twenty-first century logic development of the main power source, and in the twenty-first century will largely determine the face of logic. At least in the early twenty-first century, logic will focus on the following topics: how to deal with common sense reasoning in the logic of Eph coordination, non-monotonic and fault tolerance factors? how to make robots with human intelligence and creativity, as established from the empirical evidence used to guide future (...) action in the induction of judgments can be wrong? how the knowledge representation and knowledge reasoning, in particular, is based on the existing knowledge base and between the cognitive subject knowledge and the reasoning? how contextual factors, combined with a variety of natural language understanding and reasoning, so that intelligent robots to employing natural language for successful communication with people? . . . . And so on. (shrink)

정확하고 빠르고 강건하게 이동하는 모빌리티 인공지능(MAI) 구현을 위해서 체화된 인공지능(EAI)은 고전적 인공지능의 한계를 극복할 것을 주장하지만, 그 자신의 고유한 난점들에 직면해 있다. 그러한 난점의 주요 원인 중 하나는 EAI를 규정하는 “상황 속에서 환경과의 역동적인 실시간 상호작용”이 지니는 의미가 불명료한 데에도 있다. 이 문제를 고찰하면서 본 논문은 세 가지를 제안하고자 한다. 첫째, ‘상황’을 “과제유관 맥락”으로, ‘환경’을 “과제무관 맥락”으로 구분하여 정의할 것을 제안한다. 둘째, 이러한 구별에 기초하여 EAI가 해결해야할 두 가지 목표(1. ‘상황’에서 정확·신속·강건하게 이동하는 AI 구현 및 2. ‘환경’에 적응하여 학습·발달·진화하는 AI (...) 구현)를 분명히 구분하고, 상황과 환경에 대한 현상학적 분석에 기초하여 목표 2의 달성이 목표 1의 달성의 토대임을 확인할 것을 제안한다. 셋째, 두 목표의 해결 모두 키네스테시스에 기초해야 함을 제안한다. (shrink)

이 논문은 인공지능의 존재론적 지위를 평가하기 위한 두 개의 물음을 던진다. 하나는 인공지능이 개념을 가질 수 있는가 하는 물음이고, 다른 하나는 인공지능이 행위의 능력을 가졌는가 하는 물음이다. 개념 능력은 인공지능이 통상적인 언어의 형태로 주어지는 명령이나 정보를 능동적으로 이해하고 그것의 작동에 반영할 수 있기 위한 필요조건이다. 최근 인공지능 기술은 영상자료에서 비지도 학습의 방식으로 특정 범주의 사물을 식별해내는 성과를 거뒀지만, 이런 식별의 능력은 개념 능력의 필요조건도 충분조건도 아니다. 인공지능이 우리와 같은 개념을 가지도록 하는 것은 인간 사회의 언어 관행을 고스란히 학습시키는 방식으로 가능할 (...) 것인데, 이것은 불가능하지는 않지만 사실상 완결되기 어려운 목표다. 한편, 행위 능력에 대한 평가는 인공지능 프로그램이나 지능형 로봇 같은 인공물을 모종의 책임과 권리를 지닌 행위 주체로 간주할 수 있는가 하는 물음을 다루기 위한 열쇠다. 행위는 행위 주체와 환경 간의 상호작용으로, 행위의 실현에는 환경 속 대상에 대한 지각과 그것에 관한 동작의 협응, 달리 말하자면 감수자 체계와 행위자 체계의 통합이 필수적이다. 그리고 이러한 협응이나 통합의 배후에는 행위를 낳는 의도를 가지고 협응을 조율하는 존재, 즉 자아가 있다. 행위는 감수자 체계와 행위자 체계의 협응을 통해 자아의 의도를 실현하는 과정이다. 인공물의 작동이 이런 조건을 충족한다고 볼 이유는 아직 없다. 결국, 두 물음에 대한 이 논문의 대답은 부정적이거나 기껏해야 유보적이다. (shrink)

Public sector institutions are increasingly investing resources in data collection and data analytics to provide better public services at lower cost, to anticipate demand for services, to identify high-risk groups, and to develop targeted interventions. Prior research has shown that the media shape understanding of the possibilities of technology and creates related expectations. In this article we explore how artificial intelligence and emerging data-driven technologies are made familiar and by whose voices they are talked about in the media. Empirically, (...) we explore communication materials and media coverage from the joint AI experiment between the City of Espoo and Tieto Corporation in 2017–18. In this experiment, Tieto Corporation combined customer data from the City of Espoo’s social and health care and early childhood education from 2002–2016 and analysed this data using artificial intelligence methods. Theoretically, we draw on the tradition of the social construction of technology and especially its branch that focuses on the domestication of technologies. The results show that the means of narration used by the media – repetition and highlighting the voice of the representatives of the institutional actors involved in the AI experiment – strengthen the agency and interests of the parties involved as well as their visibility in the media, while reducing the space for possible critical discussion. We argue that this way, AI is made familiar and acceptable to citizens and in public services in Finland. (shrink)

1. Artificial Intelligence is an ambiguous umbrella term. Until today there is no uniform definition of AI and the term carries several meanings. As exemplary, I will give two definitions of AI tha...

We increasingly rely on AI-related applications (smart technologies) to perform tasks that would be simply impossible by un-aided or un-augmented human intelligence. This is possible because the world is becoming an infosphere increasingly well adapted to AI’s limited capacities. Being able to imagine what adaptive demands this process will place on humanity may help to devise technological solutions that can lower their anthropological costs.

Artificial intelligence (AI) as a general-purpose technology has great potential for advancing the United Nations Sustainable Development Goals (SDGs). However, the AI×SDGs phenomenon is still in its infancy in terms of diffusion, analysis, and empirical evidence. Moreover, a scalable adoption of AI solutions to advance the achievement of the SDGs requires private and public actors to engage in coordinated actions that have been analysed only partially so far. This volume provides the first overview of the AI×SDGs phenomenon and its (...) related challenges and opportunities. The first part of the book adopts a programmatic approach, discussing AI×SDGs at a theoretical level and from the perspectives of different stakeholders. The second part illustrates existing projects and potential new applications. (shrink)

In Steven Spielberg's A.I. Artificial Intelligence a company called Cybertronics is responsible for creating, building, and disseminating a large number of ‘mechas’ – androids built specifically to address a multitude of human needs, including the desire to have children. David, an android mecha-child, has the capacity to genuinely love on whomever he ‘imprints.’ The first of this kind of mecha, he is ultimately abandoned by his ‘mother’ Monica, and David spends the rest of the film searching for Pinocchio's Blue (...) Fairy so that he can be made into a ‘real boy’ and gain Monica's love. Their reunion finally occurs at the end of the film, after hundreds of years. Typically, the ending in A.I. is panned by critics, and written off as an egregrious example of Spielberg's sentimentalism. However, we argue that the ending is essential in order to portray a certain conception of the nature of human personhood. While many science fiction films about artificial intelligence are centered on the issue of what constitutes personhood, A.I. is one of the very few films that does not regard personhood as something purely intrinsic to the biological construction of the organism. We contend that one of the many the messages of this film is that the journey to complete personhood requires social recognition. (shrink)

Artificial Intelligence is supposed to perform tasks autonomously, make competent decisions, and interact socially with people. From a psychological perspective, AI can thus be expected to impact users’ three Basic Psychological Needs, namely autonomy, competence, and relatedness to others. While research highlights the fulfillment of these needs as central to human motivation and well-being, their role in the acceptance of AI applications has hitherto received little consideration. Addressing this research gap, our study examined the influence of BPN Satisfaction on (...) Intention to Use an AI assistant for personal banking. In a 2×2 factorial online experiment, 282 participants watched a video of an AI finance coach with a female or male synthetic voice that exhibited either high or low agency. In combination, these factors resulted either in AI assistants conforming to traditional gender stereotypes or in non-conforming conditions. Although the experimental manipulations had no significant influence on participants’ relatedness and competence satisfaction, a strong effect on autonomy satisfaction was found. As further analyses revealed, this effect was attributable only to male participants, who felt their autonomy need significantly more satisfied by the low-agency female assistant, consistent with stereotypical images of women, than by the high-agency female assistant. A significant indirect effects model showed that the greater autonomy satisfaction that men, unlike women, experienced from the low-agency female assistant led to higher ITU. The findings are discussed in terms of their practical relevance and the risk of reproducing traditional gender stereotypes through technology design. (shrink)

챗봇 테이(Tay)는 딥러닝을 통해 스스로 학습하면서 사람들과 대화가 가능한 인공지능이다. 이러한 인공지능은 일상 언어를 잘 사용하는 듯 하지만 편향된 방식으로 학습할 경우 개념을 결여한 듯 보이는 발언을 하기도 한다. 이에 대해 필자는 약한 의미의 개념 소유와 강한 의미의 개념 소유를 구분한 다음, 테이가 약한 의미에서는 개념을 소유하지만 강한 의미에서는 개념을 소유하지 않음을 논증한다. 이는 생각하며 대화하는 인공지능이 가능한가에 대한 비판적 접근의 일환이다.

Interview by Marco Emanuele of James Brusseau. October 27, 2021, The Science of Where. Topics include: AI ethics, Accelerationism, Decentralization.

La logométrie et l’Intelligence artificielle (deep learning) appliquées aux textes politiques permettent de repérer dans le discours d’Emmanuel Macron les emprunts linguistiques qu’il contracte auprès de ses prédécesseurs à l’Elysée (de Gaulle, Pompidou, Giscard, Mitterrand, Chirac, Sarkozy et Hollande). Les emprunts les plus importants, lexicaux autour de la valeur travail et énonciatifs autour de l’exhibition du « je » et du « je veux », concernent statistiquement Nicolas Sarkozy.

Our synthetic review of the relevant and related literatures on the ethics and effects of using AI in education reveals five qualitatively distinct and interrelated divides associated with access, representation, algorithms, interpretations, and citizenship. We open our analysis by probing the ethical effects of algorithms and how teams of humans can plan for and mitigate bias when using AI tools and techniques to model and inform instructional decisions and predict learning outcomes. We then analyze the upstream divides that feed into (...) and fuel the algorithmic divide, first investigating access and then representation. After that, we analyze the divides that are downstream of the algorithmic divide associated with interpretation and citizenship. At present, lacking ongoing reflection and action by learners, educators, educational leaders, designers, scholars, and policymakers, the five divides collectively create a vicious cycle and perpetuate structural biases in teaching and learning. However, increasing human responsibility and control over these divides can create a virtuous cycle that improves diversity, equity, and inclusion in education. We conclude the article by looking forward and discussing ways to increase educational opportunity and effectiveness for all by mitigating bias through a cycle of progressive improvement. (shrink)

First published in 1998. Routledge is an imprint of Taylor & Francis, an informa company.

This question might seem odd, but it is, nevertheless, directly relevant to our life today. My intention is to bring ancient Daoist philosophy into a conversation about the challenges that technology poses. Today, cutting-edge technologies do not exist just in research labs but have already easily penetrated all aspects of our lives. It is difficult to argue that we do not yet inhabit a world with Artificial Intelligence, for it has become a pervasive and effective technology woven into the (...) fabric of everyday living. (shrink)

Visual programming language is a crucial part of learning programming. On this basis, it is essential to use visual programming to lower the learning threshold for students to learn about artificial intelligence to meet current demands in higher education. Therefore, a 3-h AI course with an RGB-to-HSL learning task was implemented; the results of which were used to analyze university students from two different disciplines. Valid data were collected for 65 students in the Science -student group and 39 students (...) in the Humanities -student group. Independent sample t-tests were conducted to analyze the difference between cognitive styles and computational thinking. No significant differences in either cognitive style or computational thinking ability were found after the AI course, indicating that taking visual AI courses lowers the learning threshold for students and makes it possible for them to take more difficult AI courses, which in turn effectively helping them acquire AI knowledge, which is crucial for cultivating talent in the field of AI. (shrink)

A groundbreaking endeavor that explores human spirituality using the evolving technology of artificial intelligence.

Cryptocurrencies like Bitcoin are offering new avenues for economic empowerment to individuals around the world. However, they also provide a powerful tool that facilitates criminal activities such as human trafficking and illegal weapons sales that cause great harm to individuals and communities. Cryptocurrency advocates have argued that the ethical dimensions of cryptocurrency are not qualitatively new, insofar as money has always been understood as a passive instrument that lacks ethical values and can be used for good or ill purposes. In (...) this paper, we challenge such a presumption that money must be ‘value-neutral.’ Building on advances in artificial intelligence, cryptography, and machine ethics, we argue that it is possible to design artificially intelligent cryptocurrencies that are not ethically neutral but which autonomously regulate their own use in a way that reflects the ethical values of particular human beings – or even entire human societies. We propose a technological framework for such cryptocurrencies and then analyse the legal, ethical, and economic implications of their use. Finally, we suggest that the development of cryptocurrencies possessing ethical as well as monetary value can provide human beings with a new economic means of positively influencing the ethos and values of their societies. (shrink)