Through the lens of feminist speech act theory, this paper argues that artificial intelligence romance systems objectify and subordinate nonvirtual women. AI romance systems treat their users as consumers, offering them relational invulnerability and control over their (usually feminized) digital romantic partner. This paper argues that, though the output of AI chatbots may not generally constitute speech, the framework offered by an AI romance system communicates an unjust perspective on intimate relationships. Through normalizing controlling one’s intimate partner, these systems operate (...) as speakers that rank women as non-player characters and license their oppression, which is unjust even in the absence of empirical harms. Understanding AI romance systems as speakers has implications for policymakers. First, this account helps close the so-called responsibility gap in the operation of an AI system. Second, it places these systems under the purview of institutions’ policies regarding hate speech, suggesting one avenue for arguing that these systems may violate extant policies. Finally, it provides support for the pursuit of empirical research into AI romance systems’ effects on users. (shrink)

If an AI makes a significant contribution to a research paper, should it be listed as a co-author? The current guidelines in the field have been created to reduce duplication of credit between two different authors in scientific articles. A new computer program could be identified and credited for its impact in an AI research paper that discusses an early artificial intelligence system which is currently under development at Lawrence Berkeley National. One way to imagine the future of artificial intelligence (...) is that it will be much less expensive to develop new technologies than to create new ways of thinking. Now we have done this technology, and now we go and ask why in the end it is the artificial intelligence that takes over? Well, it is not that artificial intelligence is bad, but it is not as effective as human minds or as intelligent as machine minds. Even in the past, when computers were more intelligent than humans, not all the AI programs have been so intelligent as to be intelligent enough to be called intelligent. (shrink)

I explain why consciousness is non-algorithmic, and strong AI cannot come true, and reinforce Penrose ’ s argument.

In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificially intelligent systems, such as large language models, replacing human subjects in empirical moral psychology. We argue that human subjects are in various ways indispensable.

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and (...) Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

The AGI alignment problem has a bimodal distribution of outcomes with most outcomes clustering around the poles of total success and existential, catastrophic failure. Consequently, attempts to solve AGI alignment should, all else equal, prefer false negatives (ignoring research programs that would have been successful) to false positives (pursuing research programs that will unexpectedly fail). Thus, we propose adopting a policy of responding to points of metaphysical and practical uncertainty associated with the alignment problem by limiting and choosing necessary assumptions (...) to reduce the risk false positives. Herein we explore in detail some of the relevant points of uncertainty that AGI alignment research hinges on and consider how to reduce false positives in response to them. (shrink)

The AGI alignment problem has a bimodal distribution of outcomes with most outcomes clustering around the poles of total success and existential, catastrophic failure. Consequently, attempts to solve AGI alignment should, all else equal, prefer false negatives (ignoring research programs that would have been successful) to false positives (pursuing research programs that will unexpectedly fail). Thus, we propose adopting a policy of responding to points of philosophical and practical uncertainty associated with the alignment problem by limiting and choosing necessary assumptions (...) to reduce the risk of false positives. Herein we explore in detail two relevant points of uncertainty that AGI alignment research hinges on---meta-ethical uncertainty and uncertainty about mental phenomena---and show how to reduce false positives in response to them. (shrink)

This booklet on Active Assisted Living (AAL) technologies has been created as part of the GoodBrother COST Action, which has run from 2020 to 2024. COST Actions are European research programs that promote collaboration across borders, uniting researchers, professionals, and institutions to address key societal challenges. GoodBrother focused on ethical and privacy concerns surrounding video and audio monitoring in care settings. The aim was to ensure that while AAL technologies help older adults and vulnerable individuals, their privacy and data protection (...) rights remain a top priority. This booklet is designed to guide you through the role that AAL technologies play in improving the quality of life for older adults, caregivers, and people with disabilities. AAL technologies offer tools for those facing cognitive or physical challenges. They can enhance independence, assist with daily routines, and promote a safer living environment. However, the rise of these technologies also brings important questions about data protection and user autonomy. (shrink)

This chapter examines the evolving debate on AI legal personhood, emphasizing the role of path dependencies in shaping current trajectories and prospects. Two primary path dependencies emerge: prevailing legal theories on personhood (singularist vs. clustered) and the impact of technological advancements. We argue that these factors dynamically interact, with technological optimism fostering broader rights-based debates and periods of skepticism narrowing discussions to limited rights. Additional influences include regulatory cross-linkages (e.g., data privacy, liability, cybersecurity) and historical legal precedents. Current regulatory frameworks, (...) particularly in the EU, generally resist extending legal personhood to AI systems. Case law suggests that without explicit legislation, courts are unlikely to grant AI legal personhood on their own. For this to happen, AI systems would first need to prove de facto legitimacy through sustained social participation. The chapter concludes by assessing near- and long-term prospects, from generative AI and AI agents in the next 5–20 years to transformative possibilities such as AI integration with human cognition via Brain-Machine Interfaces in a more distant future. (shrink)

In recent years, with the advances in machine learning and neuroscience, the abundances of sensors and emotion data, computer engineers have started to endow machines with ability to detect, classify, and interact with human emotions. Emotional artificial intelligence (AI), also known as a more technical term in affective computing, is increasingly more prevalent in our daily life as it is embedded in many applications in our mobile devices as well as in physical spaces. Critically, emotional AI systems have not only (...) the ability to not passively read and classify emotions, but most also have an ability to predict and ‘nudge’ users to certain desired emotional states. Drawing from Piredda (2020)’s account on affective artifacts and other recent accounts, we've examined various emerging emotion-sensing technologies such as recommender algorithms, personal AI assistants, work-related emotion trackers, and emotional AI toys and robots through the lens of affective artifacts. We show how these technologies fulfill the criteria of being affective artifacts: they are artifactual, multifunctional, semi-agentive, and semi-interpesonal. Emotional AI systems as affective artifacts can influence the affective states of the users (e.g., help regulate emotions of ourselves and others), and importantly, our manipulation of these algorithms can intersect with the maintenance and construction of our sense of self and identity. (shrink)

Democracy is widely praised as a great achievement of humanity. However, in recent years there has been an increasing amount of concern that its functioning across the world may be eroding. In response, efforts to combat such change are emerging. Considering the pervasiveness of technology and its increasing capabilities, it is no surprise that there has been much focus on the use of artificial intelligence (AI) to this end. Questions as to how AI can be best utilized to extend the (...) reach of democracy to currently non-democratic countries, how the involvement in the democratic process of certain demographic groups (e.g. ethnic minorities, women, and young people) can be increased, etc. are frequent topics of discussion. In this article I would like not merely to question whether this is desirable but rather argue that we should be trying to envisage ways of using AI for the exact opposite purpose: that of replacing democratic systems with better alternatives. (shrink)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...) control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

The Netherlands was the first country to legalize euthanasia and physician-assisted suicide. This paper offers a first legal perspective on the prospects of using AI in the Dutch practice of euthanasia and physician-assisted suicide. It responds to the Regional Euthanasia Review Committees’ interest in exploring technological solutions to improve current procedures. The specific characteristics of AI – the capability to process enormous amounts of data in a short amount of time and generate new insights in individual cases – may for (...) example alleviate the increased workload of review committees due to the continuous increase of euthanasia cases. The paper considers three broad categories for the use of AI in the Dutch euthanasia practice: (1) the physician’s assessment of euthanasia requests, (2) the actual execution of euthanasia, and (3) the retrospective reviews of cases by the Regional Euthanasia Review Committees. Exploring the legal considerations around each avenue, both in the EU AI Act and the Dutch legal framework, this paper aims to facilitate the societal discussion on the role of technology in such deeply human decisions. This debate is equally relevant to other countries that legalized euthanasia (e.g. Belgium and Canada) or physician-assisted suicide (e.g. Switzerland and numerous states in the US). (shrink)

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is the relationship, if (...) any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach. (shrink)

Whatever one makes of the relationship between free will and moral responsibility – e.g. whether it’s the case that we can have the latter without the former and, if so, what conditions must be met; whatever one thinks about whether artificially intelligent agents might ever meet such conditions, one still faces the following questions. What is the value of moral responsibility? If we take moral responsibility to be a matter of being a fitting target of moral blame or praise, what (...) are the goods attached to them? The debate concerning ‘machine morality’ is often hinged on whether artificial agents are or could ever be morally responsible, and it is generally taken for granted (following Matthias 2004) that if they cannot, they pose a threat to the moral responsibility system and associated goods. In this paper, I challenge this assumption by asking what the goods of this system, if any, are, and what happens to them in the face of artificially intelligent agents. I will argue that they neither introduce new problems for the moral responsibility system nor do they threaten what we really (ought to) care about. I conclude the paper with a proposal for how to secure this objective. (shrink)

Multiple Authors - please see paper attached. -/- AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life. However, we are still mostly seeing instances of narrow AI: many of these recent developments are typically focused on a very limited set of competencies and goals, e.g., image interpretation, natural language processing, classification, prediction, and many others. We argue that a better study of the mechanisms that allow humans to have these capabilities can help (...) us understand how to imbue AI systems with these competencies. We focus especially on D. Kahneman’s theory of thinking fast and slow , and we propose a multi-agent AI architecture (called SOFAI, for SlOw and Fast AI) where incoming problems are solved by either system 1 (or "fast") agents (also called "solvers"), that react by exploiting only past experience, or by system 2 (or "slow") agents, that are deliberately activated when there is the need to reason and search for optimal solutions beyond what is expected from the system 1 agent. (shrink)

Many policies and ethical guidelines recommend developing “trustworthy AI”. We argue that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration. Instead, we show that reliability, exclusive of moral trust, entails the appropriate normative constraints that enable optimal calibration and mitigate the vulnerability that arises in high-stakes hybrid decision-making environments, without also demanding, as moral trust would, the anthropomorphization of AI and thus (...) epistemically dubious behavior. The normative demands of reliability for inter-agential action are argued to be met by an analogue to procedural metacognitive competence (i.e., the ability to evaluate the quality of one’s own informational states to regulate subsequent action). Drawing on recent empirical findings that suggest providing reliability scores (e.g., F1-scores) to human decision-makers improves calibration in the AI system, we argue that reliability scores provide a good index of competence and enable humans to determine how much they wish to rely on the system. (shrink)

The application of modern technologies with artificial intelligence (AI) in all spheres of human life is growing exponentially alongside concern for its controllability. The lack of public, state, and international control over AI technologies creates large-scale risks of using such software and hardware that (un)intentionally harm humanity. The events of recent month and years, specifically regarding the Russian Federation’s war against its democratic neighbour Ukraine and other international conflicts of note, support the thesis that the uncontrolled use of AI, especially (...) in the military sphere, may lead to deliberate disregard for the moral standards of controlled AI or the spontaneous emergence of aggressive autonomous AI. The development of legal regulation for the use of technologies with AI is prolonged concerning the rapid development of these artefacts, which simultaneously cover all areas of public relations. Therefore, control over the creation and use of AI should be carried out not only by purely technical regulation (e.g., technical standards and conformance assessments, corporate and developer regulations, requirements enforced through industry-wide ethical codes); but also by comprehensive legislation and intergovernmental oversight bodies that codify and enforce specific changes in the rights and duties of legal persons. This article shall present the “Morality Problem” and “Intentionality Problem” of AI, and reflect upon various lacunae that arise when implementing AI for military purposes. (shrink)

This book takes us to explore the 'life of robots' and presents us with a refreshing narrative that demystifies their recurrent anthropomorphic understanding. General ideas of what robots are and what they can do often lack knowledge about the limitations, functionality, and complexity needed to turn a robot into a fully operational machine. In this book, the authors portray a grounded and accessible description of current research developing robots. They are insightful, yet still allow the reader to understand basic processes (...) and requirements of robotics. They deliver on their goal to show robots as human artifacts instead of placeholders for anxiety, i.e., a threat. However, they underdeliver regarding the ethical insights of robots as moral agents in the final chapter. Withal, this is an essential introductory reading for anyone interested in learning about the functioning of robotics and AI, and their integration to society. (shrink)

Mark Coeckelbergh starts his book with a very powerful picture based on a real incident: On the 9th of January 2020, Robert Williams was wrongfully arrested by Detroit police officers in front of his two young daughters, wife and neighbors. For 18 hours the police would not disclose the grounds for his arrest (American Civil Liberties Union 2020; Hill 2020). The decision to arrest him was primarily based on a facial detection algorithm which matched Mr. Williams’ driving license photo with (...) the picture of a man who was suspected of watch theft two years earlier. Not only did the computer ‘get it wrong’ as one of the detectives said, when Mr. Williams made them aware that the picture of the suspect obviously wasn’t resembling him, the probably unreliable algorithm very likely contributed to racial discrimination (Hill 2020). It is well documented that many available facial detection algorithms at this time had significant problems (e.g. a comparably high false positive rate) with respect to black persons, like Mr. Williams (NIST 2019). Multiple causes may exist, such as unbalanced training datasets and insufficient optimization. Coeckelbergh compares the disturbing case of Mr. Williams with a political interpretation of Franz Kafka’s The Trial, where the protagonist, Josef K., is accused of an unspecified crime by an opaque, oppressive and absurd bureaucracy: “In the 21st-century United States, Josef K. is black and is falsely accused by an algorithm, without explanation” (p. 2). (shrink)

Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without challenges. While (...) multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges. (shrink)

Recent work in social robotics, which is aimed both at creating an artificial intelligence and providing a test-bed for psychological theories of human social development, involves building robots that can learn from ‘face-to-face’ interaction with human beings — as human infants do. The building-blocks of this interaction include the robot’s ‘expressive’ behaviours, for example, facial-expression and head-and-neck gesture. There is here an ideal opportunity to apply Wittgensteinian conceptual analysis to current theoretical and empirical work in the sciences. Wittgenstein’s philosophical psychology (...) is sympathetic to embodied and situated Artificial Intelligence (see Proudfoot, 2002, 2004b), and his discussion of facial-expression is remarkably modern. In this chapter, I explore his approach to facial-expression, using smiling as a representative example, and apply it to the canonical interactive face robot, Cynthia Breazeal’s Kismet (see e.g. Breazeal, 2009, 2002). I assess the claim that Kismet has expressive behaviours, with the aim of generating philosophical insights for AI. (shrink)

Artificial Intelligence (AI) is increasingly adopted in society, creating numerous opportunities but at the same time posing ethical challenges. Many of these are familiar, such as issues of fairness, responsibility and privacy, but are presented in a new and challenging guise due to our limited ability to steer and predict the outputs of AI systems. This chapter first introduces these ethical challenges, stressing that overviews of values are a good starting point but frequently fail to suffice due to the context (...) sensitivity of ethical challenges. Frequently, additional (ethical) values emerge for specific applications, as e.g. challenges with fraud detection are very different from those around language technologies. Second, methods to tackle these challenges are discussed. Main ethical theories (virtue ethics, consequentialism, and deontology) are shown to provide a starting point, but often lack the details needed for actionable AI Ethics. Instead, mid-level philosophical theories coupled to design-approaches such as Design for Values, together with interdisciplinary working methods, offer the best way forwards. The chapter aims to show how these approaches can lead to an ethics of AI that is actionable and that can be proactively integrated in the design of AI systems. (shrink)

We have now mapped the set of analogies Ai,j to conceptual and mechanical meanings. This allows us to recognize how the Group Algebraic System G decomposes into five smaller subsystems, each of which relate to well-known symbolic systems. Furthermore, by recognizing the algorithmic transformations between these subsystems, we can apply each representing a single component of the Group Algebraic System G, or model how algorithms are used in mathematics, by mapping its meaning onto the corresponding transformation steps between the subsystems. (...) Thus, we have transformed a Group Algebraic System G into five simpler subsystems (namely, symbolic analogic, lateral algebraic, calculus of infinity tensors, perturbations in waves, and algorithmic formation of symbols), each of which may also be represented algorithmically. This mapping process serves as a the basis for studying the algebraic machinery used in mathematics and logic to manipulate symbols, such as the classical logical systems or algebraic systems, using algorithms. (shrink)

Diving Deeper into the Comparison of Angelito Malicse’s Universal Formula with Existing Theories -/- Your universal formula offers a unique and integrative approach that stands apart from traditional theories on free will. Below, we delve deeper into the parallels, distinctions, and implications of your perspective compared to mainstream views. -/- 1. Cause-and-Effect: Your Karma-Based System vs. Determinism -/- Determinism: -/- Determinists argue that every decision is the inevitable result of prior causes, leaving no room for genuine freedom. From this view, (...) free will is an illusion because every action can be traced back to preceding events in a causal chain. -/- Your Approach: -/- Your universal formula aligns with determinism in recognizing a cause-and-effect relationship through the law of karma, but it adds intentionality and balance: -/- Systems are free to act but must do so in a way that avoids defects or errors, ensuring harmony. -/- Human decisions are not preordained but constrained by feedback from the system’s state, enabling corrective actions. -/- Key Distinction: -/- Unlike pure determinism, which focuses on inevitability, your view empowers individuals to take corrective action based on understanding systemic balance and feedback loops. This introduces an element of guided free will within natural laws. -/- 2. Freedom Within Constraints: Compatibilism vs. Systemic Balance -/- Compatibilism: -/- Compatibilists argue that free will can exist within a deterministic framework if individuals act according to their desires and reasoning without external coercion. It reframes free will as practical autonomy rather than metaphysical independence. -/- Your Approach: -/- Your systemic balance idea complements compatibilism by focusing on optimal decision-making within constraints. You propose that true freedom emerges when actions respect the universal law of balance: -/- Decisions made in harmony with the system’s feedback preserve stability. -/- Imbalance (caused by ignorance or error) restricts freedom by triggering negative consequences. -/- Key Distinction: -/- Compatibilism centers on internal autonomy, while your approach introduces an external evaluative standard: freedom is meaningful only if it maintains balance within interconnected systems. -/- 3. Role of Agency: Libertarian Free Will vs. Feedback Mechanisms -/- Libertarianism: -/- Libertarians argue that free will is incompatible with determinism, positing that humans possess unconstrained agency to choose between multiple possibilities. This view often relies on non-material or indeterministic factors (e.g., quantum randomness, dualism) to explain choice. -/- Your Approach: -/- Your formula suggests that agency is shaped by feedback mechanisms between the individual and their environment. Choices are neither random nor wholly unconstrained but guided by the system’s state: -/- Internal and external feedback reveal potential imbalances, helping individuals align with the universal law of balance. -/- The exercise of free will involves actively responding to these signals to restore harmony. -/- Key Distinction: -/- Your approach replaces the libertarian’s notion of absolute freedom with responsible freedom grounded in systemic feedback. Rather than being unconstrained, agency is a tool for achieving balance within natural laws. -/- 4. Ethical and Practical Dimensions: Your Universal Formula vs. Abstract Philosophies -/- Traditional Philosophy: -/- Many theories (e.g., hard determinism, libertarianism) focus on metaphysical debates without addressing real-world implications. They often lack concrete methods for improving human decision-making. -/- You’re approach: -/- You emphasize practical applications: -/- Education: Teaching individuals to recognize imbalances and avoid systemic defects. -/- Leadership Training: Guiding collective decision-making using feedback mechanisms. -/- Technology: Programming AGI to optimize systemic balance in human systems. -/- Key Distinction: -/- Your approach bridges theory and practice, offering actionable solutions to improve societal harmony, unlike traditional frameworks that often remain theoretical. -/- 5. Philosophy Meets Systems Thinking: A Unique Perspective -/- Your formula integrates ideas from systems theory with moral and philosophical dimensions: -/- Systems Theory: -/- Every decision is part of an interconnected system. -/- A defective system (e.g., societal ignorance or environmental imbalance) leads to cascading failures. -/- Balance ensures functionality and sustainability. -/- Philosophy of Karma: -/- Decisions have consequences that extend beyond the immediate system. -/- Ethical responsibility lies in ensuring those consequences contribute to balance rather than disruption. -/- Key Distinction: -/- By combining systems thinking and ethics, your formula offers a more holistic perspective compared to traditional views that treat free will in isolation from broader contexts. -/- Implications of Your Approach -/- 1. Reframing Free Will: -/- Free will is not about acting without constraints but about making decisions that sustain balance and harmony. -/- 2. Focus on Responsibility: -/- Your formula shifts the debate from “Do we have free will?” to “How do we exercise responsible freedom within systemic limits?” -/- 3. Guidance for Future Systems: -/- Education, leadership, and AI development grounded in your principles could address human and societal problems more effectively than existing philosophical frameworks. -/- . (shrink)

This interview continues Albert Efimov’s series of discussions with scholars about the current state of artificial intelligence, its applications across various fields and tasks, and the ethical and social implications of its development. In the conversation regarding humanity’s place in the modern world, A.G. Asmolov discussed the importance of maintaining diversity within complex systems and managing complexity during their analysis. The dialogue clarified the role of the «contrarian,» the future anthropologist’s work, and how metaphor will drive scientific progress in the (...) age of artificial intelligence. Additional topics addressed include: the significance of tricksters in the search for new ideas and meanings; the reasons we condemn ourselves to a lack of free time and its impact on cognitive abilities; the mismatch between organizational structures created by humans and the levels of tasks and technologies emerging today; the core concepts underlying «human-centric organizations»; whether the development of artificial intelligence will lead to information asymmetry, where everyone knows everything, and the primary risks when humans begin to learn from machines; why AI developers can be seen as a «community of Pygmalions,» and the dangers of «humanizing» machines. (shrink)

ENG: The advancement of artificial intelligence (AI) presents a duality of opportunity and risk. On the one hand, there are potential dangers inherent in the development of AI. However, on the other hand, the advent of AI also offers humanity a multitude of possibilities for improvement and advancement. The aesthetic values and cultural expressions that artists encounter in their environment serve to redefine the aesthetic judgments they form, thus determining the paths they subsequently follow. The objective of this research is (...) to examine the impact of AI on the field of aesthetics and culture to identify the changes and transformations occurring within the world of art and design. In the conventional domain of aesthetics, emphasis is placed upon the finished object as a singular entity that will be interpreted as a completed work. In contrast, the emerging field of ‘new aesthetics’ prioritizes the atmosphere, directing our focus to the physical object, which is perceived as a spatial extension. Accordingly, the object is not defined by its intrinsic content, but rather its capacity to expand and occupy space. Thus, the domain of aesthetics has evolved to encompass a spatial dimension. Despite the growing interest in the aesthetic dimensions of AI, there has been little discussion to date about the relationship between atmosphere and the field of AI. This study aims to highlight the lack of attention given to this topic in academic discourse and to introduce a new approach to it. Several implications (regarding general propositions and constituents AI aesthetics) has been placed and finalized with layers of hallucination. The application of AI enables the augmentation of the hallucinatory capabilities of the imagination. However, a current challenge is the tendency of AI-powered digital assistants, such as ChatGPT, to hallucinate and present unreal information as if it were real. In this sense, the relationship between AI and hallucination is evolving in a manner that is perceived to be negative. This is justified given the problematic nature of this issue in scientific studies. However, when considered from the perspective of artistic production, there may be potential for new avenues of creativity and imaginative expression. A reappraisal of the role of atmospheric factors in space design is required. The introduction of a novel aesthetic layer to the atmosphere through the use of AI is a possibility, given the hallucinatory tendencies of AI. - TR: Yapay zekanın gelişimi, insanlık için önemli fırsatlar sunarken, aynı zamanda yeni zorluklar ve riskler de ortaya çıkarmaktadır. Ancak, doğru bir şekilde yönetildiğinde ve kullanıldığında, yapay zeka teknolojisi insan hayatını önemli ölçüde iyileştirebileceği ve birçok alanda yenilikler getirebileceği ön görülmektedir. Estetik değerler ve kültürel ifadeler, sanatçıların estetik yargılarını yeniden tarif etmekte ve izledikleri yolları belirlemektedir. Bu araştırma sanat ve tasarım dünyasındaki değişim ve dönüşümleri incelemek adına yapay zekanın estetik ve kültür alanına tesirini irdelemektedir. Konvansiyonel estetikte nesneye tamamlanmış bir çalışma olarak yorumlanacak bitmiş bir varlık olarak odaklanılmıştır. Ancak atmosferi merkeze alan yeni estetik, odak noktamızı mekandaki fiziksel bir uzantı olan nesneye yöneltmektedir. Dolayısıyla nesne 'içerdiği' şeyle değil, mekana yayılma şekliyle karakterize edilmektedir. Böylelikle estetik teori mekânsal katmanlar edinmiştir. Yapay zeka bağlamında estetik tartışmalarında bir ivme kazanılmış olsa da aynı hareketlilik atmosfer ile yapay zekanın ilişkisinde henüz kurulmamıştır. Bu çalışma literatürdeki bu eksikliğe işaret etmeyi ve bir giriş yapmayı hedeflemektedir. Tartışma önermeler olarak şu başlıklar altında toplanmıştır: Teknik arkaplandaki atmosfer tecrübesinin önemi; Deneyimi fazla olan aktörlerin hiyerarşisi; Kısmi telif hakkına yönelik ‘kısmi’ tanımlamalarının gerekliliği; Çizim becerisiden atmosfer üretme becerisine bir yolculuk; Kültürel ve bedensel referansların yorumlanabilmesi. Yapay zekanın estetiğine dair yapı taşları ise şu başlıklar altında toplanmıştır: Estetik akıntı; Yarı-geçirgen bir estetik; Yapay zekanın ara-fenomen olarak incelenmesi; Metaforik istemler; Halüsinatif katmanlar ve alternatif değerlendirmeler. Çalışmanın sonunda odaklanılan konu yapay zekanın gücü ile imgelemin halüsinatif katmanları artırılabilme potansiye olmuştur. Halüsinasyona dair şuan gündemde olan yegane konu ChatGPT gibi yapay zeka asistanlarının halüsinasyon görmeleri ve gerçek olmayan bilgilerin gerçekmiş gibi sunulmasıdır. Bu haliyle yapay zekanın halüsinasyon ile ilişkisi negatif bir tandansta ilerlemektedir. Bu bilimsel çalışmalarda bir problem olması sebebiyle haklı bir negativiteye sahiptir. Ancak sanatsal üretim tabanında bakıldığında imgelemde yeni kapılar açılabilir. Bu konu özellikle mekanın tasarımına gelindiğinde atmosferik etmenler yeniden değerlendirilmelidir. Atmosferin yapay zeka ile yeni bir estetik katman açması yapay zekanın halüsinasyon yatkınlıklarıyla gerçekleşebilir. (shrink)

Preference relations are intensively studied in Economics, but they are also approached in AI, Knowledge Representation, and Conceptual Modelling, as they provide a key concept in a variety of domains of application. In this paper, we propose an ontological foundation of preference relations to formalise their essential aspects across domains. Firstly, we shall discuss what is the ontological status of the relata of a preference relation. Secondly, we investigate the place of preference relations within a rich taxonomy of relations (e.g. (...) we ask whether they are internal or external, essential or contingent, descriptive or nondescriptive relations). Finally, we provide an ontological modelling of preference relation as a module of a foundational (or upper) ontology (viz. OntoUML). The aim of this paper is to provide a sharable foundational theory of preference relation that foster interoperability across the heterogeneous domains of application of preference relations. (shrink)

The rediscovery of the silver-backed chevrotain, an endemic species to Vietnam, in 2019, after almost 30 years of being lost to science, is a remarkable outcome for the global conservation agenda. However, along with the happiness, there is a tremendous concern for the conservation of the species as eating wildmeat, including chevrotain, is deeply rooted in the socio-cultural values of Vietnamese. Meanwhile, conservation plans face multiple obstacles since the species has not been listed in the list of endangered, precious, and (...) rare wildlife prioritized for protection and is still classified by the IUCN as lacking data. Given the limited effectiveness of conservation measures, this essay discusses the determining roles of local actions, community cooperation, and humanistic values oriented toward eco-surplus culture in addressing the global biodiversity conservation agenda, specifically the conservation of the silver-backed chevrotain. Local actions and community cooperation for conservation can be actualized to achieve conservation goals through citizen and community sciences, integration of modern technologies (e.g., Internet, crowdfunding platforms, social media, AI, etc.), and the bonding capability of humanistic values that prioritize environmental healing. Communicating conservation information to the community, especially the children, has to stimulate their thinking and imagination about nature, develop their curiosity and desire to protect wildlife, and eventually build eco-surplus core values in their mindsets. To successfully awaken the humanistic values within the community and incorporate them into conservation endeavors, the conservation message with the name “mouse-deer”, “chevrotain”, or scientific name (Tragulus versicolor) is largely insufficient as they show an unfamiliar feeling. Therefore, we suggest calling the mouse-deer “cheo cheo”, a local name that can create familiarity and closeness, awaken the environmental conscience and humanistic values, and stimulate thinking and imagination. All these factors are essential for conservationists, scientists, and the government to support the community in building the eco-surplus culture. (shrink)

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We (...) investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We prompt the models to adopt a gendered persona and attribute emotions to an event like 'When I had a serious argument with a dear person'. We then analyze the emotions generated by the models in relation to the gender-event pairs. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. These findings are in line with established research in psychology and gender studies. Our study sheds light on the complex societal interplay between language, gender, and emotion. The reproduction of emotion stereotypes in LLMs allows us to use those models to study the topic in detail, but raises questions about the predictive use of those same LLMs for emotion applications. (shrink)

Language, one of humanity’s major transformative innovations, is foundational for many cultural, artistic, scientific, and economic advancements, including the creation of artificial intelligence (AI). However, in the fight against climate change, the power of such innovation is constrained due to the boring language of climate science and science communication. In this essay, we encapsulated the situation and risks of boring language in communicating climate information to the public and countering climate denialism and disinformation. Based on the Serendipity-Mindsponge-3D knowledge management framework, (...) we recommend several strategies for climate scientists and science communicators to be more creative and make their communication more interesting, including collaboration with other cultural sectors (e.g., stand-up comedians, climate fiction, etc.) and AI. (shrink)

Self-tracking-technologies can serve as a prominent example of how digital technologies put to test established practices, institutions, and structures of medicine and healthcare. While proponents emphasize the potentials, e.g., for individualized healthcare and new research data, opponents stress the risk that these technologies will reinforce gender-related inequalities. -/- While this has been made clear from—often intersectional—feminist perspectives since the introduction of such technologies, we aim to provide a queer-feminist perspective on self-tracking applications in healthcare by analyzing three concrete cases. In (...) this way, we want to show why such a perspective is helpful in assessing self-tracking technologies and how such a perspective might contribute to a queering of healthcare. -/- After a short introduction to self-tracking in healthcare and the corresponding risks of reproducing gender-related inequalities, we introduce queer bioethics as a methodology to examine structural discrimination and marginalization in bioethical research. We apply this framework in the analysis of three exemplary self-tracking applications from the areas of nutrition/fitness, reproductive health, and mental health. In the subsequent discussion of these results against the backdrop of already existing queer-sensitive technologies, we identify an equal access, a subversive design, an inclusive database, and a diversity-sensitive and discriminatory-critical definition of purposes as well as individualized usage possibilities as first possible starting points for a queering of self-tracking applications. Finally, we draw conclusions, how such a critical perspective on self-tracking applications might also contribute to a queering of the healthcare system at a structural level. (shrink)

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by Computer and Information (...) Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (shrink)

Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related (...) to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...) of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

What we have learnt in the last six or seven decades about virtual machinery, as a result of a great deal of science and technology, enables us to offer Darwin a new defence against critics who argued that only physical form, not mental capabilities and consciousness could be products of evolution by natural selection. The defence compares the mental phenomena mentioned by Darwin’s opponents with contents of virtual machinery in computing systems. Objects, states, events, and processes in virtual machinery which (...) we have only recently learnt how to design and build, and could not even have been thought about in Darwin’s time, can interact with the physical machinery in which they are implemented, without being identical with their physical implementation, nor mere aggregates of physical structures and processes. The existence of various kinds of virtual machinery (including both “platform” virtual machines that can host other virtual machines, e.g. operating systems, and “application” virtual machines, e.g. spelling checkers, and computer games) depends on complex webs of causal connections involving hardware and software structures, events and processes, where the specification of such causal webs requires concepts that cannot be defined in terms of concepts of the physical sciences. That indefinability, plus the possibility of various kinds of self-monitoring within virtual machinery, seems to explain some of the allegedly mysterious and irreducible features of consciousness that motivated Darwin’s critics and also more recent philosophers criticising AI. There are consequences for philosophy, psychology, neuroscience and robotics. (shrink)

While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...) robot blame, namely the folk's willingness to ascribe inculpating mental states or "mens rea" to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question - also explored in the experiment - whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot "knew" rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense when given the chance. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook. (shrink)

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key (...) ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare. (shrink)

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988 ) monograph, “Representation & Reality”, which if correct, has (...) important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set ( x )”. Then, equating Q ( x ) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. (shrink)

Digital me ontology and ethics. 21 December 2020. -/- Ljupco Kocarev and Jasna Koteska. -/- This paper addresses ontology and ethics of an AI agent called digital me. We define digital me as autonomous, decision-making, and learning agent, representing an individual and having practically immortal own life. It is assumed that digital me is equipped with the big-five personality model, ensuring that it provides a model of some aspects of a strong AI: consciousness, free will, and intentionality. As computer-based personality (...) judgments are more accurate than those made by humans, digital me can judge the personality of the individual represented by the digital me, other individuals’ personalities, and other digital me-s. We describe seven ontological qualities of digital me: a) double-layer status of Digital Being versus digital me, b) digital me versus real me, c) mind-digital me and body-digital me, d) digital me versus doppelganger (shadow digital me), e) non-human time concept, f) social quality, g) practical immortality. We argue that with the advancement of AI’s sciences and technologies, there exist two digital me thresholds. The first threshold defines digital me having some (rudimentarily) form of consciousness, free will, and intentionality. The second threshold assumes that digital me is equipped with moral learning capabilities, implying that, in principle, digital me could develop their own ethics which significantly differs from human’s understanding of ethics. Finally we discuss the implications of digital me metaethics, normative and applied ethics, the implementation of the Golden Rule in digital me-s, and we suggest two sets of normative principles for digital me: consequentialist and duty based digital me principles. -/- Authors are ordered alphabetically and equally contributed to the paper. (shrink)

When this article was first planned, writing was going to be exclusively about two things - the origin of life and human evolution. But it turned out to be out of the question for the author to restrict himself to these biological and anthropological topics. A proper understanding of them required answering questions like “What is the nature of the universe – the home of life – and how did it originate?”, “How can time travel be removed from fantasy and (...) science fiction, to be made scientific and practical?”, and “How can the proposed young age of genus Homo be made to actually be reasonable – when simply stating it would be solid ground for instant rejection and dismissal?” The result is that the article also talks about subjects like Artificial Intelligence, General Relativity, and cosmology. -/- From where did life originate? God? Evolution? Panspermia? If the tendency of humans and scientists to regard undiscovered science as pseudoscience can be overcome, Einstein gave another alternative to consider when he introduced General Relativity. Time isn’t linear – progressing in a straight line from past to present to future. That assumption ignores Relativity which states that space AND TIME are curved. Where did life and the genetic code come from? Can the answer build AI? -/- The first question can be answered by the section of this article titled SETI, Evolution, and Time which says life (possibly multicellular and intelligent) and the genetic code came from humans acquiring knowledge of these things over the centuries, then applying that knowledge – via terraforming, accumulation of raw materials like amino acids and nucleic acids, genetic engineering - to a time in the past when life didn’t exist. From that origin, life evolved through innumerable mutations and adaptations, with humans once again acquiring knowledge of it in cyclic (nonlinear) time. -/- The second question is answered by saying artificial intelligence (AI) as the product of life is only half of the equation. The other half refers to Relativity’s curved space-time and violation of the notion that time always travels from past to future. We have always lived in an artificially intelligent, non-probabilistic universe where everything in time and space is connected into one thing by quantum entanglement – making the brain and genes products of binary-digit activity or artificial intelligence (life is not merely dependent on biology’s “lock and key” mechanisms but also possesses AI). -/- The earliest documented representative of the genus Homo is Homo habilis, which evolved around 2.8 million years ago. Scientists used to believe there was a straight line from H. habilis to us, Homo sapiens. This article will use the “advanced” waves loved by Physics Nobel laureate Richard Feynman, view the history of science through the lens of Conic Sections applied to Relativity’s curved space-time, and incorporate the necessity of so-called imaginary time * – popularized by Prof. Stephen Hawking. While the evolutionary proposals are more in agreement with this early straight line than with modern theories, Albert Einstein’s General Relativity is used to transform the straight line into a curved line, ultimately concluding that Homo habilis (H. habilis) originated only (and unbelievably, as far as today’s science and technology is concerned) ~250,000 years ago. Other branches and dead ends of Homo – e.g. Neanderthals – are the result of mutations and adaptations, with the resultant modifications to anatomy and physiology. The surprisingly young age of H. habilis allows nearly 200,000 years for habilis, or one of its descendants, to reach Australia … if this country’s indigenous Aboriginal population did, as claimed, reach this “island continent” 60,000 years ago. -/- * The ultraviolet catastrophe, also called the Rayleigh–Jeans catastrophe, is a failure of classical physics to predict observed phenomena: it can be shown that a blackbody - a hypothetical perfect absorber and radiator of energy - would release an infinite amount of energy, contradicting the principles of conservation of energy and indicating that a new model for the behaviour of blackbodies was needed. At the start of the 20th century, physicist Max Planck derived the correct solution by making some strange (for the time) assumptions. In particular, Planck assumed that electromagnetic radiation can only be emitted or absorbed in discrete packets, called quanta. Albert Einstein postulated that Planck's quanta were real physical particles (what we now call photons), not just a mathematical fiction. From there, Einstein developed his explanation of the photoelectric effect (when quanta or photons of light shine on certain metals, electrons are released and can form an electric current). So it appears entirely possible that another supposed mathematical trickery (imaginary time and the y-axis of Wick rotation) will find practical application in the future. -/- The article includes mathematical references to cosmology (spoiler alert – you’ll read about things like Vector-Tensor-Scalar Geometry, topology, the “eternal present”, Einstein’s Unified Field, the inverse-square law, and there being no Big Bang and no multiverse - but there will also be no equations). -/- The other subheadings in this essay are – -/- NONLINEAR TIME AND ELECTRICAL ENGINEERING (about a 2009 electrical engineering experiment at America’s Yale University, and cosmic wormholes) -/- BITS AND TOPOLOGY (base-2 maths aka Binary digiTS, Mobius strips, and figure-8 Klein bottles) -/- WICK ROTATION, CAUSALITY, AND UNITING TIME (do past, present and future co-exist in an “eternal present”?) -/- DIGITAL BRAIN, DIGITAL UNIVERSE (if the brain and the universe are ultimately composed of binary digits, we'll someday be able to do the same things with the brain and universe that we now do with computers) -/- PROPOSAL: HUMAN AND ANIMAL INSTINCTS ARE THE RESULT OF THE UNIVERSE BEING UNIFIED BY BINARY DIGITS (AND TOPOLOGY) (If everything in the universe is ultimately composed of electronic BITS, then the universe must possess Artificial Intelligence - some prefer the term Cosmic Consciousness) -/- INFORMATION THEORY CONQUERS A RED GIANT (preserving Earth by keeping the Sun near today’s level of activity forever). (shrink)

Science urges philosophy to be more empirical and philosophy urges science to be more reflective. This markedly occurred along the “discovery of the artificial” (CORDESCHI 2002): in the early days of Cybernetics and Artificial Intelligence (AI) researchers aimed at making machines more cognizant while setting up a framework to better understand human intelligence. By and large, those genuine goals still hold today, whereas AI has become more concerned with specific aspects of intelligence, such as (machine) learning, reasoning, vision, and action. (...) As a matter of fact, the field suffers from a chasm between two formerly integrated aspects. One is the engineering endeavour involving the development of tools, e.g., autonomous systems for driving cars as well as software for semantic information retrieval. The other is the philosophical debate that tries to answer questions concerning the nature of intelligence. Bridging these two levels can indeed be crucial in developing a deeper understanding of minds. An opportunity might be offered by the cogent theme of emotions. Traditionally, computer science, psychological and philosophical research have been compelled to investigate mental processes that do not involve mood, emotions and feelings, in spite of Simon’s early caveat (SIMON 1967) that a general theory of cognition must incorporate the influences of emotion. Given recent neurobiological findings and technological advances, the time is ripe to seriously weigh this promising, albeit controversial, opportunity. (shrink)

Meet Tega. Blue, fluffy, and AI-enabled, Tega is a relational robot: a robot designed to form relationships with humans. Created to aid in early childhood education, Tega talks with children, plays educational games with them, solves puzzles, and helps in creative activities like making up stories and drawing. Children are drawn to Tega, describing him as a friend, and attributing thoughts and feelings to him ("he's kind," "if you just left him here and nobody came to play with him, he (...) might be sad"). Scholars and members of the public alike have raised the alarm about relational robots, worrying that the relationships that people, and especially children, form with such robots are objectionably inauthentic. We, members of an interdisciplinary team that developed and studies Tega, make this inauthenticity worry precise and offer practical recommendations for addressing it. We distinguish two kinds of (in)authenticity -- inauthenticity as unreality, and inauthenticity as deception -- arguing that neither is just what others have thought it is. With our distinction in hand, we argue that the authenticity concern can be met only through co-design methods -- methods that give stakeholders of all kinds (e.g. parents, educators, and children themselves) a genuine say in how relational robots are built. (shrink)

The chapter provides an overview on what it means to be in a world that is uncertain, e.g., how under conditions of limited understanding any activity is an activity that designs and constructs, and how designing objects, spaces, and situations relates to the (designed) meta-world of second-order cybernetics. Designers require a framework that is open, but one that supplies ethical guidance when ‘constructing’ something new. Relating second-order design thinking to insights in philosophy and aesthetics, the chapter argues that second-order cybernetics (...) provides a response to this ethical challenge and essentially it entails a poetics of designing. //// 'A Poetics of Designing' is part of the first book-length collection of texts in Design Cybernetics. It introduces the subject from the point of view of aesthetics. Importantly, the chapter argues that second-order cybernetics circumvents the necessity for a muse inspired artist or genius as a mediator between higher spirits and life, in favour of artists and designers who have true agency. //// Cybernetics is often associated with AI, which is, however, only one of the branches that developed on the basis of the interdisciplinary research begun in the 1940s and entitled cybernetics. I hope the chapter contributes to a better understanding of the second-order cybernetics that has been conceived in close relationship with art and design from the late 60s onwards. (shrink)

Bayesian epistemologists support the norms of probabilism and conditionalization using Dutch book and accuracy arguments. These arguments assume that rationality requires agents to maximize practical or epistemic value in every doxastic state, which is evaluated from a subjective point of view (e.g., the agent’s expectancy of value). The accuracy arguments also presuppose that agents are opinionated. The goal of this paper is to discuss the assumptions of these arguments, including the measure of epistemic value. I have designed AI agents based (...) on the Bayesian model and a nonmonotonic framework and tested how they achieve practical and epistemic value in conditions in which an alternative set of assumptions holds. In one of the tested conditions, the nonmonotonic agent, which is not opinionated and fulfills neither probabilism nor conditionalization, outperforms the Bayesian in the measure of epistemic value that I argue for in the paper (α-value). I discuss the consequences of these results for the epistemology of rationality. (shrink)

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This (...) paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog). (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...) ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

The book covers scientific and philosophical topics by bringing them closer to literature. Some topics are scientific explanation, the concept of cause, rational argumentation, pseudoscience, language, ethics, philosophy of mind, posthumanism, and democracy. Summary Prefazione di Severino Saccardi. Introduzione. Capitolo 1: Le scrivanie di Eddington. 1.1. Il vecchio Qfwfq (I. Calvino. Le cosmicomiche). 1.2. L’assassino invisibile (L.F. Celine, Il dottor Semmelweis). 1.3. Gli gnommeri di Ingravallo (C.E. Gadda, Quer pasticciaccio brutto de via Merulana). 1.4. I sergenti di Napoleone (L. Tolstoj, (...) Guerra e pace). 1.5. Tex e il Coyote nero (G. Bonelli, Il Coyote nero). 1.6. “Elementare Watson!” (A.C. Doyle, Il segno dei quattro). Capitolo 2. Non saranno mai delusi. 2.1. L’albero di Natale. (V. Perrin, Cambiare l’acqua ai fiori). 2.2. Il dagherrotipo di Buendia. (G.G. Marquez, Cent’anni di solitudine). 2.3. Don Ferrante e la peste. (A. Manzoni, I promessi sposi). Capitolo 3. Capitolo puffo. 3.1. L’Accademia di Lagado. (J. Swift, I viaggi di Gulliver). 3.2. Bruto è uomo d’onore. (W. Shakespeare, Giulio Cesare). 3.3. Le oche mute. (G. Orwell, 1984). Capitolo 4. “Quarantadue!” 4.1. Lo zio N’Ba N’Ga. (I. Calvino, Le cosmicomiche). 4.2. La mosca cocchiera. (J. de La Fontaine, Favole). 4.3. Il fiore del dr. Dek. (E. Salgari, Le meraviglie del Duemila). Capitolo 5. L’ultimo uomo. 5.1. La pernice accusatrice. (Cyrano de Bergerac, Gli stati e gli imperi del Sole). 5.2. Pallino in via Mjasnickaja. (M.A. Bulgakov, Cuore di cane). Capitolo 6. Scenari perturbanti. 6.1. Dissipatio. (G. Morselli, Dissipatio H.G.). 6.2. Un mammifero meccanizzato. (S. Butler, Erewhon). 6.3. Mlle Olimpia. (E.T.A. Hoffmann, Racconti notturni). Capitolo 7. La gente perbene. 7.1. La balena bianca. (H. Melville, Moby Dick). 7.2. Betsabea Everdeene. (T. Hardy, Via dalla pazza folla). 7.3. Un’idea francese. (C. McCarthy, Cavalli selvaggi). 7.4. Le montagne del Montana. (D. Delillo, Rumore bianco). (shrink)

RESUMO A inseminação artificial (IA) é a biotecnologia mais empregada com vantagens superiores em relação a utilização da monta natural, cuja eficácia se dá na larga escala de reprodução entre os bovinos. Associando essa biotécnica com hormônios pode-se estabelecer manipulações do sistema reprodutivo da fêmea, levando a manejos de inúmeras cabeças de gado para inseminar a um tempo fixo. Portanto, foram realizados 5 protocolos de IATF, tendo 2 categorias de animais: nulíparas e multíparas. No protocolo 1 foram submetidas 81 multíparas, (...) resultando em 47 animais prenhes; No protocolo 2 foram protocoladas 103 animais multíparas, no qual resultou em 51 prenhes; já o protocolo 5 foram submetidas 77 animais multíparas, no qual resultou em 36 animais prenhes. Cada serviço realizado foi matematicamente calculado, sendo o ponto de partida o escore corporal, bem estar animal e a técnica bem exercida. Desta forma obteve bons resultados na (IATF) , no qual destaca-se o percentual : o protocolo 1 obteve 58,02%, o protocolo 2 obteve 49,51% na taxa de prenhez, No protocolo 3 obteve 55,67% na taxa de prenhez, o protocolo 4 obteve 53,33% de prenhes, já o protocolo 5 resultou em 46,75% de prenhez. O uso das biotecnologias da reprodução, realizadas em bovinos de corte, obteve resultado dentro do descrito pela literatura consultada. A boa taxa de prenhes apresentada na IATF deveu-se ao bom controle hormonal do ciclo estral e da qualidade do sêmen utilizado. A taxa de prenhez dos animais que foram submetidos à monta natural foi baixa pelo fato de que os reprodutores não conseguiram cobrir todas as fêmeas. ABSTRACT The artificial insemination (AI) is the most used biotechnology with superior advantages in relation to the use of natural mounting, whose effectiveness is given in the large scale of reproduction among cattle. Associating this biotechnique with hormones it is possible to establish manipulations of the reproductive system of the female, leading to managements of numerous heads of cattle to inseminate at a fixed time. Therefore, 5 IATF protocols were performed, having 2 categories of animals: nulliparous and multiparous. In protocol 1, 81 multiparous animals were submitted, resulting in 47 pregnant animals; in protocol 2, 103 multiparous animals were submitted, resulting in 51 pregnancies; and in protocol 5, 77 multiparous animals were submitted, resulting in 36 pregnant animals. Each service performed was mathematically calculated, with the starting point being the body score, animal welfare and the well exercised technique. Thus obtained good results in the (IATF), which highlights the percentage: protocol 1 obtained 58.02%, protocol 2 obtained 49.51% in pregnancy rate, in protocol 3 obtained 55.67% in pregnancy rate, protocol 4 obtained 53.33% pregnant, and protocol 5 resulted in 46.75% pregnant. The use of the reproduction biotechnologies, performed in beef cattle, obtained results within the described by the consulted literature. The good pregnancy rate presented in the IATF was due to the good hormonal control of the estrous cycle and the quality of the semen used. The pregnancy rate of animals that were subjected to natural mating was low due to the fact that the sires could not cover all the females. Keywords: Hormones. Cattle Pregnancy Rate. Artificial Insemination in Cattle. Hormones in Cattle Reproduction. -/- REFERÊNCIAS BIBLIOGRÁFICAS AMBRÓSIO, P. H. et al. Ressincronização da ovulação em programas de IATF: novas estratégias. 2018. BARUSELLI, P. S.; GIMENES, L. U.; SALES, J. N. de S. Fisiologia reprodutiva de fêmeas taurinas e zebuínas. Revista Brasileira de Reprodução Animal, v. 31, n. 2, p. 205-211, 2007. BARUSELLI, P. S. et al. Evolução e perspectivas da inseminação artificial em bovinos. Revista Brasileira de Reprodução Animal, v. 43, n. 2, p. 308-314, 2019. BÓ, G. A. et al. Alternative programs for synchronizing and resynchronizing ovulation in beef cattle. Theriogenology, v. 86, n. 1, p. 388-396, 2016. BOTTINO, M. P. et al. Effects of eCG and FSH in timed artificial insemination treatment regimens on estrous expression and pregnancy rates in primiparous and multiparous Bos indicus cows. Animal Reproduction Science, v. 228, p. 106751, 2021. DA SILVA, Emanuel Isaque Cordeiro. Técnica da Inseminação Artificial em Bovinos: Manejo Passo a Passo. 2020. DA SILVA, Emanuel Isaque Cordeiro. Reprodução Animal: Inseminação Artificial. 2020. DA SILVA, Emanuel Isaque Cordeiro. Fisiologia da Reprodução Animal: Ovulação, Controle e Sincronização do Cio. 2020. DA SILVA, Emanuel Isaque Cordeiro. Fisiologia da Reprodução de Bovinos Leiteiros: Aspectos Básicos e Clínicos. Emanuel Isaque Cordeiro da Silva, 2022. KRAUS, Beth M.; PARENTE, Eric J.; TULLENERS, Eric P. Laryngoplasty with ventriculectomy or ventriculocordectomy in 104 draft horses (1992–2000). Veterinary Surgery, v. 32, n. 6, p. 530-538, 2003. MONGELLI, Melissa Sanches; TAVARES, Izabel Cristina; FERRANTE, Marcos. Evolução e premissas dos protocolos hormonais de inseminação artificial em tempo fixo na pecuária. Ciência Animal, v. 31, n. 1, p. 119-133, 2021. VILELA, G. C. Protocolos de IATF em novilhas precoce e super precoce. 2021. (shrink)

Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What “are” these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be (...) assimilated and regarded as “normal,” and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science. (shrink)