To a greater extent than in other technical domains, research and progress in Artificial Intelligence has always been entwined with the fictional. Its language echoes strongly with other forms of cultural narratives, such as fairytales, myth and religion. In this essay we present varied examples that illustrate how these analogies have guided not only readings of the AI enterprise by commentators outside the community but also inspired AI researchers themselves. Owing to their influence, we pay particular attention to the (...) similarities between religious language and the way in which the potential advent of greater than human intelligence is presented contemporarily. We then move on to the role that fiction, science fiction most of all, has historically played and is still playing in the discussion of AI by influencing researchers and the public, shifting the weights of different scenarios in our collectively perceived probability space. We sum up by arguing that the lore surrounding AI research, ancient and modern, points to the ancestral and shared human motivations that drive researchers in their pursuit and fascinate humanity at large. These points of narrative entanglement where AI meets the wider culture should serve to amplify the call to engage ourselves with the discussion of the potential destination of this technology. (shrink)

Emotions play a significant role in human relations, decision-making, and the motivation to act on those decisions. There are ongoing attempts to use artificial intelligence (AI) to read human emotions, and to predict human behavior or actions that may follow those emotions. However, a person’s emotions cannot be easily identified, measured, and evaluated by others, including automated machines and algorithms run by AI. The ethics of emotional AI is under research and this study has examined the emotional variables as (...) well as the perception of emotional AI in two large random groups of college students in an international university in Japan, with a heavy representation of Japanese, Indonesian, Korean, Chinese, Thai, Vietnamese, and other Asian nationalities. Surveys with multiple close-ended questions and an open-ended essay question regarding emotional AI were administered for quantitative and qualitative analysis, respectively. The results demonstrate how ethically questionable results may be obtained through affective computing and by searching for correlations in a variety of factors in collected data to classify individuals into certain categories and thus aggravate bias and discrimination. Nevertheless, the qualitative study of students’ essays shows a rather optimistic view over the use of emotional AI, which helps underscore the need to increase awareness about the ethical pitfalls of AI technologies in the complex field of human emotions. (shrink)

The prelims comprise: Introduction The Breakthrough with Expert Systems in the 1970s The Emergence of Bayesian Networks in the 1980s Philosophical Problems Connected with Probability in AI Appendix.

Use of argumentation methods applied to legal reasoning is a relatively new field of study. The book provides a survey of the leading problems, and outlines how future research using argumentation-based methods show great promise of leading to useful solutions. The problems studied include not only these of argument evaluation and argument invention, but also analysis of specific kinds of evidence commonly used in law, like witness testimony, circumstantial evidence, forensic evidence and character evidence. New tools for analyzing these kinds (...) of evidence are introduced. (shrink)

This paper is divided into two parts. In the first part, I will describe briefly how advances in artificial intelligence in the 1970s led to the crucial problem of handling uncertainty, and how attempts to solve this problem led in turn to the emergence of the new theory of Bayesian networks. I will try to focus in this historical account on the key ideas and will not give a full account of the technical details. Then, in the second part, (...) I will consider the implications of these new results for the long-standing controversy between Bayesians and non-Bayesians. (shrink)

The abundance of literature on ethical concerns regarding artificial intelligence (AI) highlights the need to systematize, integrate, and categorize existing efforts through a systematic literature review. The article aims to investigate prevalent concerns, proposed solutions, and prominent ethical approaches within the field. Considering 309 articles from the beginning of the publications in this field up until December 2021, this systematic literature review clarifies what the ethical concerns regarding AI are, and it charts them into two groups: (i) ethical concerns (...) that arise from the design of AI and (ii) ethical concerns that arise from human–AI interactions. The analysis of the obtained sample highlights the most recurrent ethical concerns. Finally, it exposes the main proposals of the literature to handle the ethical concerns according to the main ethical approaches. It interprets the findings to lay the foundations for future research on the ethics of AI. (shrink)

For several years, scholars have (for good reason) been largely preoccupied with worries about the use of artificial intelligence and machine learning (AI/ML) tools to make decisions about us. Only recently has significant attention turned to a potentially more alarming problem: the use of AI/ML to influence our decision-making. The contexts in which we make decisions—what behavioral economists call our choice architectures—are increasingly technologically-laden. Which is to say: algorithms increasingly determine, in a wide variety of contexts, both the sets (...) of options we choose from and the way those options are framed. Moreover, artificial intelligence and machine learning (AI/ML) makes it possible for those options and their framings—the choice architectures—to be tailored to the individual chooser. They are constructed based on information collected about our individual preferences, interests, aspirations, and vulnerabilities, with the goal of influencing our decisions. At the same time, because we are habituated to these technologies we pay them little notice. They are, as philosophers of technology put it, transparent to us—effectively invisible. I argue that this invisible layer of technological mediation, which structures and influences our decision-making, renders us deeply susceptible to manipulation. Absent a guarantee that these technologies are not being used to manipulate and exploit, individuals will have little reason to trust them. (shrink)

This article examines the potential implications of the implementation of artificial intelligence in health care for both its delivery and the medical profession. To this end, the first section explores the basic features of AI and the yet theoretical concept of autonomous AI followed by an overview of current and developing AI applications. Against this background, the second section discusses the transforming roles of physicians and changes in the patient–physician relationship that could be a consequence of gradual expansion of (...) AI in health care. Subsequently, an examination of the responsibilities physicians should assume in this process is explored. The third section describes conceivable practical and ethical challenges that implementation of a single all-encompassing AI healthcare system would pose. The fourth section presents arguments for regulation of AI in health care to ensure that these applications do not violate basic ethical principles and that human control of AI will be preserved in the future. In the final section, fundamental components of a moral framework from which such regulation may be derived are brought forward, and some possible strategies for building a moral framework are discussed. (shrink)

Organizations are making massive investments in artificial intelligence, and recent demonstrations and achievements highlight the immense potential for AI to improve organizational and human welfare. Yet realizing the potential of AI necessitates a better understanding of the various ethical issues involved with deciding to use AI, training and maintaining it, and allowing it to make decisions that have moral consequences. People want organizations using AI and the AI systems themselves to behave ethically, but ethical behavior means different things to (...) different people, and many ethical dilemmas require trade-offs such that no course of action is universally considered ethical. How should organizations using AI—and the AI itself—process ethical dilemmas where humans disagree on the morally right course of action? Though a variety of ethical AI frameworks have been suggested, these approaches do not adequately address how people make ethical evaluations of AI systems or how to incorporate the fundamental disagreements people have regarding what is and is not ethical behavior. Drawing on moral foundations theory, we theorize that a person will perceive an organization’s use of AI, its data procedures, and the resulting AI decisions as ethical to the extent that those decisions resonate with the person’s moral foundations. Since people hold diverse moral foundations, this highlights the crucial need to consider individual moral differences at multiple levels of AI. We discuss several unresolved issues and suggest potential approaches for thinking about conflicts in moral judgments concerning AI. (shrink)

In this paper we take up the issue of near-term artificial intelligence (AI). "Near-term AI" is used to denote artificial intelligence algorithms that are already in place in a variety of public and private sectors, guiding decision that pertain to advertising, credit ratings, and sentencing in the justice system. Our focus here is to contribute to a critical discussion of the ways that AI is already being widely used in decision-making procedures in these areas. We will argue that (...) developers and deployers of AI systems--in senses to be defined--bear a particular kind of responsibility for the moral consequences of near-term AI. We will present a tool to aid developers and deployers in engaging in the moral reflection we argue is required of them, in order both the help them meet their moral obligations and to help address the material risks posed by what we take to be the status quo of actual near-term AI development. This chapter can be understood as a contribution to the field of technology assessment, but instead of suggesting policy revisions, we will propose a framework for ethical analysis that can be used to facilitate more robust ethical reflection in AI development and implementation. (shrink)

This paper undertakes a comparative legal study to analyze the challenges of privacy and personal data protection posed by Artificial Intelligence embedded in Robots, and to offer policy suggestions. After identifying the benefits from various AI usages and the risks posed by AI-related technologies, I then analyze legal frameworks and relevant discussions in the EU, USA, Canada, and Japan, and further consider the efforts of Privacy by Design originating in Ontario, Canada. While various AI usages provide great convenience, many (...) issues, including profiling, discriminatory decisions, lack of transparency, and impeding consent, have emerged. The unpredictability arising from the AI machine learning function poses further difficulties, which have only been partially addressed by legal frameworks in the aforementioned jurisdictions. However, analyzing the relevant discussions yielded several suggestions. The first priority is adopting PbD as the most flexible, soft-legal, and preferable approach toward AI-oriented issues. Implementing PbD will protect individual privacy and personal data without specific efforts, and achieve both the development of AI and the advancement of privacy and personal data protection. Technical measures that can adapt to an individual’s dynamic choices according to the “context” should be further developed. Furthermore, alternative technical measures, including those to solve the “algorithmic black box” or achieve differential privacy, warrant thorough examination. If AI surpasses human intelligence, a terminating function, such as a “kill switch” will be the last resort to preserve individual choice. Despite numerous difficulties, we must prepare for the coming AI-prevalent society by taking a flexible approach. (shrink)

AI is considered to be very abstract to a range of critics. In this regard, algorithms are referred to regularly as black boxes and divorced from human intervention. A particular philosophical maneuver supports this outcome. The aim of this article is to (1) bring the philosophy to the surface that has contributed to this distance between AI and people and (2) offer an alternative philosophical position that can bring this technology closer to individuals and communities. The overall goal of the (...) analysis in this paper is the humanising of AI by addressing the shortcomings of conceptualising algorithms as black boxes. (shrink)

The goal of this paper is to describe the mechanism of the public perception of risk of artificial intelligence. For that we apply the social amplification of risk framework to the public perception of artificial intelligence using data collected from Twitter from 2007 to 2018. We analyzed when and how there appeared a significant representation of the association between risk and artificial intelligence in the public awareness of artificial intelligence. A significant finding is that the image (...) of the risk of AI is mostly associated with existential risks that became popular after the fourth quarter of 2014. The source of that was the public positioning of experts who happen to be the real movers of the risk perception of AI so far instead of actual disasters. We analyze here how this kind of risk was amplified, its secondary effects, what are the varieties of risk unrelated to existential risk, and what is the dynamics of the experts in addressing their concerns to the audience of lay people. (shrink)

Many research-intensive universities have moved into the business of promoting technology development that promises revenue, impact, and legitimacy. While the scholarship on academic capitalism has documented the general dynamics of this institutional shift, we know less about the ground-level challenges of research priority and scientific problem choice. This paper unites the practice tradition in science and technology studies with an organizational analysis of decision-making to compare how two university artificial intelligence labs manage ambiguities at the edge of scientific knowledge. (...) One lab focuses on garnering funding through commercialization schemes, while the other is oriented to federal science agencies. The ethnographic comparison identifies the mechanisms through which an industry-oriented lab can be highly adventurous yet produce a research program that is thin and erratic due to a priority placed on commercialization. However, the comparison does not yield an implicit nostalgia for federalized science; it reveals the mechanisms through which agency-oriented labs can pursue a thick and consistent research portfolio but in a strikingly myopic fashion. (shrink)

To optimize the data clustering effect of public health emergencies, an application research on social governance ability under public health emergencies based on artificial intelligence is proposed. First, the firefly optimization algorithm is used to collect the information data of the social governance ability of public health emergencies, establish a unified format, and save it. Then, artificial intelligence technology is used to mine the correlation of clustering data, and on this basis, a learning model integrating global structure information (...) and local structure information is established. Finally, the social governance model under public health emergencies is established. The experimental results show that the design method has high clustering accuracy, regularization cross index, and adjusted rand index (ARI) index. This shows that the design method can improve the social governance ability of data fusion clustering and improve the social governance ability. (shrink)

The application of artificial intelligence technology has evolved into an influential endeavor to improve firm performance, but little research considers the relationship among artificial intelligence capability, management, driven decision making, and firm performance. Based on the resource-based view and existing findings, this paper constructs a higher-order model of AIC and suggests a research model of e-commerce firm AIC and firm performance. We collected 394 valid questionnaires and conducted data analysis using partial least squares structural equation modeling. As a (...) second-order variable, AIC was formed by three first-order variables: basic, proclivity, and skills. AIC indirectly affects firm performance through creativity, AIM, and AI-driven decision making. Firm creativity, AIM, and AIDDM are essential variables between AIC and firm performance. Innovation culture positive moderates the relationship between firm creativity and AIDDM as well as the relationship between AIDDM and firm performance. Environmental dynamism positive mediates the connection between AIM and AIDDM. Among the control variables, firm age negatively affects firm performance, and employee size does not. This study helps enterprises leverage AI to improve firm performance, achieve a competitive advantage, and contribute to theory and management practice. (shrink)

Multimorbidity, the presence of multiple health conditions that must be addressed, is a particularly difficult situation in patient management raising issues such as the use of multiple drugs and drug-disease interactions. Clinical Guidelines are evidence-based statements which provide recommendations for specific health conditions but are unfit for the management of multiple co-occurring health situations. To leverage these evidence-based documents, it becomes necessary to combine them. In this paper, using a case example, we explore the use of argumentation schemes to reason (...) and combine evidence-based recommendations from clinical guidelines, expected effects, conflicts stemming from said recommendations, and preferences regarding treatment goals. We compare the results of reasoning using the schemes for practical reasoning and argument from negative consequences in the Carneades Argumentation System with those of ASPIC-G, an extension of the artificial intelligence system ASPIC+. (shrink)

In an increasingly complex military operating environment, next generation wargaming platforms can reduce risk, decrease operating costs, and improve overall outcomes. Novel Artificial Intelligence enabled wargaming approaches, based on software platforms with multimodal interaction and visualization capacity, are essential to provide the decision-making flexibility and adaptability required to meet current and emerging realities of warfighting. We highlight three areas of development for future warfighter-machine interfaces: AI-directed decisional guidance, computationally informed decision-making, and realistic representations of decision spaces. Progress in these (...) areas will enable development of effective human-AI collaborative decision-making, to meet the increasing scale and complexity of today’s battlespace. (shrink)

The postmodern era can be defined as a period in which absolute truths are questioned, realities are differentiated and these differences make them-selves felt strongly in social, cultural, economic, religious, etc. areas, and tech-nology is at the centre of individual and social life. In this period, con-ventio-nal lifestyles, social and cultural values, and traditional understan-dings of religiosity are being reshaped as a reflection of the search for a new reality. However, artificial intelligence, which is rapidly developing as a result (...) of the change and transformation in technological possibilities, is tho-ught to feed concerns in the context of both the opportunities it offers and the threats it poses to human values. Therefore, it is of great importance to examine the relationship between religion, which plays an important role in shaping social life, and religiosity, which is its form of practice, and the level of anxiety arou-sed by artificial intelligence, which is becoming increasingly visible in all areas of our lives. In this framework, the aim of the research is to examine religio-sity and artificial intelligence anxiety in the postmodern period in terms of various variables and to reveal the relationship between these two concepts. The study, which used the relational survey model, one of the quantitative research methods, was carried out on 450 individuals between the ages of 17-49 residing in Polatlı district of Ankara province. The data obtained as a result of the research were analysed through SPSS 22.0 statistical software. As a result of the analyses, it was determined that the participants' levels of religio-sity and artificial intelligence anxiety in the postmodern period were above the medium level. At the same time, it has been revealed that the levels of religiosity in the postmodern period are sta-tistically significantly higher for women than men, for individuals between the ages of 17-29 than those in other age groups, for single people than mar-ried people, for students, salaried employees in the public sector and sala-ried employees in the private sector than housewives, for bachelor's, mas-ter's and doctorate graduates than pri-mary, secondary and high school graduates, and for those who are interested in technological products than those who are uninterested and moderately interested. According to the monthly income level, the level of knowledge about artificial intelligence and the frequency of following the developments related to artificial intelli-gence, it was determined that the religiosity levels of individuals in the postmodern period did not differ statistically significantly. In addition, it was determined that artificial intelligence anxiety levels of the participants differed significantly according to their gender and women had higher anxiety levels than men. Again, it was determined that those with low level of knowledge about artificial intelligence had statistically significantly hig-her levels of artificial intelligence anxiety than those with medium level of knowledge and those who never follow the developments related to artifi-cial intelligence and those who rarely follow the developments related to artificial intelligence were statistically significantly higher than those who frequently and always follow the developments related to artificial intelli-gence. Similarly, it was concluded that artificial intelligence anxiety did not differ statistically significantly according to age groups, marital status, oc-cupational type, educational level, monthly income and frequency of fol-lowing technological developments. (shrink)

Artificial intelligence (AI) is being tightly integrated into healthcare today. Even though AI is being utilized in healthcare, its application in clinical settings and health professions education is still controversial. The study described the perceptions of AI and its integration into health professions education and healthcare among health professions students. This descriptive phenomenological study analyzed the data from a purposive sample of 33 health professions students at a university in Kazakhstan using the thematic approach. Data collection was conducted from (...) March 30 to May 5, 2023, using an online questionnaire with four open-ended questions. Four significant themes describing the perceptions of the health professions students on AI and its integration into health profession education and healthcare was derived: enhanced interactive learning experiences, integrating artificial intelligence-powered diagnostic and decision-support tools into health professions education, AI technologies assist students in preparing for future roles in healthcare and integrating AI technologies presents several challenges. Health professions students perceived that integrating AI into health professions education and healthcare enhances their interactive learning experiences, increases knowledge and application of complicated medical topics, and prepares them for future healthcare roles. (shrink)

Artificial intelligence (AI) art is the product of AI technology applied to art. In terms of technical application, AI art has two methods: symbolism and connectivism. In terms of the human-machine system, there are three levels: human using machine, human guiding machine and human-machine separation. AI art is a special form, existing between natural beauty and human art: AI art, first of all, is not a natural aesthetic object, given that it is the product of artefacts. Its appreciation is (...) mixed with interests, and its artistic generation is a kind of “purposeful non-purposefulness.” Secondly, AI art is not equal to traditional human art, because AI has no intentionality or consciousness. Thirdly, AI participates in the construction of artistic logic and breaks the boundary of traditional art. AI can be regarded as an “agent” in art generation, and together with people constitute the “actor network” of art. The processes of generation, identification and appreciation of AI art include the judgment of true and false. AI art reflects the forced integration of intelligent technology into art, an attempt to rid of contingency, dialectics, and negativity of art. But what we need is the real harmonisation of art and technology. People should use aesthetic reason to guide AI technology. (shrink)

We discuss here one of our projects, aimed at developing an automatic facial expression interpreter, mainly in terms of signaled emotions. We present some of the relevant findings on facial expressions from cognitive science and psychology that can be understood by and be useful to researchers in Human-Computer Interaction and Artificial Intelligence. We then give an overview of HCI applications involving automated facial expression recognition, we survey some of the latest progresses in this area reached by various approaches in (...) computer vision, and we describe the design of our facial expression recognizer. We also give some background knowledge about our motivation for understanding facial expressions and we propose an architecture for a multimodal intelligent interface capable of recognizing and adapting to computer users¿ affective states. Finally, we discuss current interdisciplinary issues and research questions which will need to be addressed for further progress to be made in the promising area of computational facial expression recognition. (shrink)

Deep learning and artificial intelligence are jointly applied to concrete piano teaching for children to comprehensively promote modern piano teaching and improve the overall teaching quality. First, the teaching environment and the functions of the intelligent piano are expounded. Then, a piano note onset detection method is proposed based on the convolution neural network. The network can analyze the time-frequency of the input piano music signal by transforming the original time-domain waveform of the piano music signal into the frequency (...) distribution varying with time. Besides, it can detect the note onset at a stable state after 8 × 104 iterations. Moreover, an intelligent piano teaching method is designed to teach Jingle Bells for 40 preschool children aged 4–6 years. Finally, a questionnaire survey is performed to investigate the teaching situation, including the learning interest and learning effect of children and learning feedback from parents. The results show that 80% of children like smart music scores, 82% of children like intelligent piano lessons with games, and 84% of children can learn actively in the intelligent piano class. Besides, 85% of parents believe that their children are more interested in learning piano. In general, the intelligent piano teaching method effectively combines DL with AI to realize the overall optimization of piano performance. It is widely favored by preschool children and their parents and plays an important role in improving the interest of preschool children in piano learning. (shrink)

Mahoney, Carl Artificial intelligence is so widespread now, and so well embedded into our latest technology, that nearly all of us know it as AI. It is virtually impossible to fully catalogue its uses and applications because by now it has reached every corner of human activities. This is the premise on which I based a lecture to the Humanist Society of Victoria on October 22, 2015 entitled 'The Artificial Intelligence Debate'. The talk was followed by a spirited (...) discussion at a HSV meeting on November 8 chaired by Dr Jennie Mills, who gave an excellent account of the original talk in the Dec. 2015 Victorian Humanist. This article consists of a series of notes following on from the talk and discussion. (shrink)

This study aimed to assess the integration, ethical considerations, and governance of artificial intelligence (AI) within the PhD programs at Midocean University. It specifically sought to understand PhD researchers' perceptions and attitudes towards AI and identify areas for enhancement in AI-related policies and educational initiatives. A descriptive analytical approach was adopted, utilizing an electronic questionnaire distributed to 105 PhD researchers, with 54 completing the survey. The questionnaire was designed to measure various aspects of AI usage, ethical concerns, and governance (...) practices. Statistical analysis was conducted to evaluate the relationships between AI awareness, ethical application, and governance perceptions. The findings revealed diverse perceptions of AI among researchers, indicating both opportunities and challenges in AI integration. The statistical analysis confirmed significant correlations between AI awareness and positive perceptions of ethical AI usage. However, the response rate and sample size posed limitations on the generalizability of the results. The study highlights the need for Midocean University to strengthen its AI governance frameworks and expand ethical guidelines to keep pace with technological advancements. Recommendations include enhancing AI-related educational programs, updating AI policies regularly, and promoting interdisciplinary research to foster an informed and ethically aware research community. (shrink)

In this paper I discuss the nature of a broad class of biomedical theories which I have termed "middle-range theories." I define them and relate the nature of biomedical theorizing to other investigations, such as a recent inquiry by the National Academy of Sciences. I also suggest that some of the knowledge representation tools from artificial intelligence may give us a purchase on this type of biological theorizing, and try to show in a rather preliminary and exploratory manner by (...) using the lac operon model of genetic control, what some of those AI applications, such as frames and semantic nets, might look like in this context. Finally, I suggest some difficulties, such as the non-monotonicity of reasoning, which such tools may generate. (shrink)

Artificial intelligence, as a nonhuman entity, is increasingly used to inform, direct, or supplant nursing care and clinical decision‐making. The boundaries between human‐ and nonhuman‐driven nursing care are blurred with the advent of sensors, wearables, camera devices, and humanoid robots at such an accelerated pace that the critical evaluation of its influence on patient safety has not been fully assessed. Since the pivotal release of To Err is Human, patient safety is being challenged by the dynamic healthcare environment like (...) never before, with nursing at a critical juncture to steer the course of artificial intelligence integration in clinical decision‐making. This paper presents an overview of artificial intelligence and its application in healthcare and highlights the implications which affect nursing as a profession, including perspectives on nursing education and training recommendations. The legal and policy challenges which emerge when artificial intelligence influences the risk of clinical errors and safety issues are discussed. (shrink)

Open peer commentary on the article “A Computational Constructivist Model as an Anticipatory Learning Mechanism for Coupled Agent–Environment Systems” by Filipo Studzinski Perotto. Upshot: In making a contribution to artificial intelligence research, Perotto has taken note of work on human cognition. However, there are certain aspects of human cognition that are not taken into account by the author’s model and that, generally, are overlooked or ignored by the artificial intelligence research community at large.

Logic; Predicate calculus; Axiomatic systems; Knowledge representation and reasoning; Logic and revisable reasoning; Formal grammars and logic programming; Prolog and logic programming.

Clark and Fischer argue that humans treat social artifacts as depictions. In contrast, theories of distributed cognition suggest that there is no clear line separating artifacts from agents, and artifacts can possess agency. The difference is likely a result of cultural framing. As technology and artificial intelligence grow more sophisticated, the distinction between depiction and agency will blur.

The complex relationship between the current advancement of technology, including the wide scope of settings at which machinery plays substantial roles, and the cultural, historical, and political realities that have long existed across the history of mankind, is one that deserves absolute attention and exploration. This interconnection has been investigated in light of bread, and the meaning it signifies to people from all over the world. Drawing on the commonly unnoticed value of bread, and the everlasting impregnable imprint it has (...) always had on revolutions throughout history, “Collective Bread Diaries” came to being as an interactive art project, employing the artificial intelligence of the MTurk platform in its investigation of “bread” as one’s peculiar voice and political statement. The author amongst other participants was granted the opportunity to draw and share personal visual representations of bread, eventually forming an array of visual diaries, each peculiar to its creator and each reflecting the cultural significance of the place from which it originated. The results are exceptionally reproduced drawings by a machine, with no apparent threads to culture, tradition, or history, emphasizes the conscious perception of one’s distinct identity. A number of 100 distinct drawings were machine-generated featuring various bread types. The process eventually investigates one’s perception of cultural identity, by exploring the cultural, socio-political, and religious threads that have long been weaved into the definition of “bread” across history. (shrink)

Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine, adaptive neurofuzzy inference system, feed-forward neural network, and one conventional multilinear regression modes, (...) were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble, ANFIS ensemble, weighted average ensemble, and simple average ensemble, were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year discharge and SSL data for training and verification of the applied models. Determination coefficient and root mean square error were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI-based ensemble in particular provide excellent performance in SSL estimation. (shrink)

Although activity aimed at the construction of artificial intelligence started about 60 years ago however, contemporary intelligent systems are effective in very narrow domains only. One of the reasons for this situation appears to be serious problems in the theory of intelligence. Intelligence is a characteristic of goal-directed systems and two classes of goal-directed systems can be derived from observations on animals and humans, one class is systems with innately and jointly determined goals and means. The other class contains (...) systems that are able to construct arbitrary goals and means. It is suggested that the classes (that implicitly underlie most models of artificial intelligence) are insufficient to explain human goal-directed activity. A broader approach to goal-directed systems is considered. This approach suggests that humans are goal-directed systems that jointly synthesize arbitrary goals and means. Neural and psychological data favoring this hypothesis and its experimental validation are considered. A simple computer model based on the idea of joint synthesis to simulate goal-directed activity is presented. The usage of the idea of joint synthesis for the construction of artificial intelligence is discussed. (shrink)