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)

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)

In recent years, artificial intelligence has been deployed by online platforms to prevent the upload of allegedly illegal content or to remove unwarranted expressions. These systems are trained to spot objectionable content and to remove it, block it, or filter it out before it is even uploaded. Artificial intelligence filters offer a robust approach to content moderation which is shaping the public sphere. This dramatic shift in norm setting and law enforcement is potentially game-changing for democracy. (...) Artificial intelligence filters carry censorial power, which could bypass traditional checks and balances secured by law. Their opaque and dynamic nature creates barriers to oversight, and conceals critical value choices and tradeoffs. Currently, we lack adequate tools to hold them accountable. This paper seeks to address this gap by introducing an adversarial procedure— – Contesting Algorithms. It proposes to deliberately introduce friction into the dominant removal systems governed by artificial intelligence. Algorithmic content moderation often seeks to optimize a single goal, such as removing copyright-infringing materials or blocking hate speech, while other values in the public interest, such as fair use or free speech, are often neglected. Contesting algorithms introduce an adversarial design which reflects conflicting values, and thereby may offer a check on dominant removal systems. Facilitating an adversarial intervention may promote democratic principles by keeping society in the loop. An adversarial public artificial intelligence system could enhance dynamic transparency, facilitate an alternative public articulation of social values using machine learning systems, and restore societal power to deliberate and determine social tradeoffs. (shrink)

This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and applied (...) in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making. (shrink)

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the (...) applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills. (shrink)

Biases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the (...) problem of implicit biases and stereotypes, we propose that a strategy to cope with this phenomenon is to unmask those found in AI systems by understanding their cognitive dimension, rather than simply trying to correct algorithms. To this extent, we present a discussion bridging together findings from cognitive science and insights from machine learning that can be integrated in a state-of-the-art semantic network. Remarkably, this resource can be of assistance to scholars (e.g., cognitive and computer scientists) while at the same time contributing to refine AI regulations affecting social life. We show how only through a thorough understanding of the cognitive processes leading to biases, and through an interdisciplinary effort, we can make the best of AI technology. (shrink)

The article discusses the process of “conceptual borrowing”, according to which, when a new discipline emerges, it develops its technical vocabulary also by appropriating terms from other neighbouring disciplines. The phenomenon is likened to Carl Schmitt’s observation that modern political concepts have theological roots. The authors argue that, through extensive conceptual borrowing, AI has ended up describing computers anthropomorphically, as computational brains with psychological properties, while brain and cognitive sciences have ended up describing brains and minds computationally and informationally, as (...) biological computers. The crosswiring between the technical languages of these disciplines is not merely metaphorical but can lead to confusion, and damaging assumptions and consequences. The article ends on an optimistic note about the self-adjusting nature of technical meanings in language and the ability to leave misleading conceptual baggage behind when confronted with advancement in understanding and factual knowledge. (shrink)

Position papers on artificial intelligence (AI) ethics are often framed as attempts to work out technical and regulatory strategies for attaining what is commonly called trustworthy AI. In such papers, the technical and regulatory strategies are frequently analyzed in detail, but the concept of trustworthy AI is not. As a result, it remains unclear. This paper lays out a variety of possible interpretations of the concept and concludes that none of them is appropriate. The central problem is that, (...) by framing the ethics of AI in terms of trustworthiness, we reinforce unjustified anthropocentric assumptions that stand in the way of clear analysis. Furthermore, even if we insist on a purely epistemic interpretation of the concept, according to which trustworthiness just means measurable reliability, it turns out that the analysis will, nevertheless, suffer from a subtle form of anthropocentrism. The paper goes on to develop the concept of strange error, which serves both to sharpen the initial diagnosis of the inadequacy of trustworthy AI and to articulate the novel epistemological situation created by the use of AI. The paper concludes with a discussion of how strange error puts pressure on standard practices of assessing moral culpability, particularly in the context of medicine. (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)

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)

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.

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)

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.

Should we be allowed to refuse any involvement of artificial intelligence technology in diagnosis and treatment planning? This is the relevant question posed by Ploug and Holm in a recent article in Medicine, Health Care and Philosophy. In this article, I adhere to their conclusions, but not necessarily to the rationale that supports them. First, I argue that the idea that we should recognize this right on the basis of a rational interest defence is not plausible, unless we (...) are willing to judge each patient’s ideology or religion. Instead, I consider that the right must be recognized by virtue of values such as social pluralism or individual autonomy. Second, I point out that the scope of such a right should be limited at least under three circumstances: if it is against a physician’s obligation to not cause unnecessary harm to a patient or to not provide futile treatment, in cases where the costs of implementing this right are too high, or if recognizing the right would deprive other patients of their own rights to adequate health care. (shrink)

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

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.

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)

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)

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)

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I (...) feel fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)