Philosophy of Artificial Intelligence, Miscellaneous

Revista IT & C este o publicație trimestrială din domeniile tehnologiei informației și comunicații, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIAL / EDITORIAL -/- Levering Data Science in the Detection of Advanced Persistent Threats Utilizarea științei datelor în detectarea amenințărilor persistente avansate -/- TEHNOLOGIA INFORMAȚIEI / INFORMATION TECHNOLOGY -/- Detecting Advanced Persistent Threats in Cyber Warfare – Academic Studies Detectarea amenințărilor persistente avansate în războiul cibernetic – Studii academice -/- TELECOMUNICAȚII / TELECOMMUNICATIONS -/- Artificial Intelligence in (...) Communications in Electronic Warfare Inteligența artificială în comunicații în războiul electronic -/- PROGRAMARE / PROGRAMMING -/- Artificial Intelligence Algorithms in Electronic Warfare Algoritmi de inteligența artificială utilizați în războiul electronic -/- SECURITATE CIBERNETICĂ / CYBER SECURITY -/- Cyber Attack Case Studies – Advanced Persistent Threats Studii de caz privind atacurile cibernetice – Amenințările persistente avansate -/- ISSN 2821-8469 ISSN-L 2821-8469, DOI: 10.58679/IT78551 . (shrink)

Această carte explorează utilizarea inteligenței artificiale de către serviciile de informații din întreaga lume și rolul său critic în domeniul analizei intelligence, în apărare și securitatea națională. Serviciile de informații joacă un rol crucial în securitatea națională, iar adoptarea tehnologiilor inteligenței artificiale a avut un impact semnificativ asupra operațiunilor lor. De asemenea, examinează diferitele aplicații ale inteligenței artificiale în serviciile de informații, implicațiile, provocările și considerațiile etice asociate cu utilizarea acesteia. Cartea subliniază necesitatea cercetării și dezvoltării continue în domeniul inteligenței (...) artificiale pentru a se asigura că serviciile de informații. și în general apărarea și securitatea națională se pot adapta în mod eficient la amenințările emergente. (shrink)

Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight (...) are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. -/- Results: Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. -/- Conclusions: Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field. (shrink)

L’histoire parallèle de l’évolution de l’intelligence humaine et de l’intelligence artificielle constitue un voyage fascinant, mettant en lumière les voies distinctes mais interconnectées de l’évolution biologique et de l’innovation technologique. Cette histoire peut être considérée comme une série de développements interconnectés, chaque avancée de l’intelligence humaine ouvrant la voie au prochain bond en avant de l’intelligence artificielle. L’intelligence humaine et l’intelligence artificielle sont depuis longtemps liées, évoluant selon des trajectoires parallèles tout au long de l’histoire. Alors que les humains cherchent (...) à comprendre et à reproduire l’intelligence, l’IA est devenue un domaine dédié à la création de systèmes capables d’accomplir des tâches qui nécessitent traditionnellement l’intellect humain. Ce livre examine les racines évolutives de l'intelligence, explore l'émergence de l'intelligence artificielle, examine l'histoire parallèle de l'intelligence humaine et de l'intelligence artificielle, retraçant leur développement, leurs interactions et l'impact profond qu'elles ont eu les unes sur les autres, et envisage les paysages futurs où l'intelligence humaine et artificielle converge. Explorons cette histoire, en comparant les étapes clés et les développements dans les deux domaines. (shrink)

This chapter explores the influence of Artificial Intelligence (AI) on digital democracy, focusing on four main areas: citizenship, participation, representation, and the public sphere. It traces the evolution from electronic to virtual and network democracy, underscoring how each stage has broadened democratic engagement through technology. Focusing on digital citizenship, the chapter examines how AI can improve online engagement while posing privacy risks and fostering identity stereotyping. Regarding political participation, it highlights AI's dual role in mobilising civic actions and spreading misinformation. (...) Regarding representation, AI's involvement in electoral processes can enhance voter registration, e-voting, and the efficiency of result tabulation but raises concerns regarding privacy and public trust. Also, AI's predictive capabilities shift the dynamics of political competition, posing ethical questions about manipulation and the legitimacy of democracy. Finally, the chapter examines how integrating AI and digital technologies can facilitate democratic political advocacy and personalised communication. However, this also comes with higher risks of misinformation and targeted propaganda. (shrink)

The rapid pace of technological advancements offers an essential field of research for a deeper understanding of man’s relationship with artifacts of her design. These artifacts designed by humans can have various mental and physical effects on their users. The human interaction with the artifact is not passive; on the contrary, it exhibits a potential that reveals the inner dispositions of human beings and makes them open to new creations. In this article, we will examine the impact of technology on (...) human life through the dispositional compatibilism perspective of the contemporary philosopher Kadri Vihvelin, arguing for a system of metaphysics in which ontology of properties depend on dispositions. In this context, the emerging phenomenon of dispositional compatibilism opens the door to developing new philosophical ideas for evaluating the concepts of compatibilism and freedom in the context of technology. Thus, it is emphasized that although technological devices shape human life, new and unforeseen powers may emerge due to human-artifact interaction. This article will examine the idea of dispositional compatibilism by specifically considering human interaction with text-based AI applications. The ability of AI to collaborate with human thought patterns is a meaningful example of the observation of dispositional compatibilism. In conclusion, the article aims to address the effects of technology on human freedom by approaching the human-artifact relationship from a dispositional perspective. It also aims to defend the claim that technological artifacts can positively contribute to human freedom from the perspective of dispositional compatibilism. (shrink)

Conversational Artificial Intelligence (CAI) systems (also known as AI “chatbots”) are among the most promising examples of the use of technology in mental health care. With already millions of users worldwide, CAI is likely to change the landscape of psychological help. Most researchers agree that existing CAIs are not “digital therapists” and using them is not a substitute for psychotherapy delivered by a human. But if they are not therapists, what are they, and what role can they play in mental (...) health care? To answer these questions, we appeal to two well-established and widely discussed concepts: cognitive and affective artifacts. Cognitive artifacts are artificial devices contributing functionally to the performance of a cognitive task. Affective artifacts are objects which have the capacity to alter subjects’ affective state. We argue that therapeutic CAIs are a kind of cognitive-affective artifacts which contribute to positive therapeutic change by (i) simulating a (quasi-)therapeutic interaction, (ii) supporting the performance of cognitive tasks, and (iii) altering the affective condition of their users. This sheds new light on why virtually all existing mental health CAIs implement principles and techniques of Cognitive Behavioral Therapy — a therapeutic orientation according to which affective change and, ultimately, positive therapeutic change is mediated by cognitive change. Simultaneously, it allows us to conceptualize better the potential and limitations of applying these technologies in therapy. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...) And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance thresholds through a risk matrix informed by (...) background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasizing that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimization. (shrink)

Bài viết này giới thiệu năm yếu tố tiền đề đó với mục đích gia tăng nhận thức về quan hệ giữa người và máy trong bối cảnh công nghệ ngày càng thay đổi cuộc sống thường nhật. Năm tiền đề bao gồm: Tiền đề về cấu trúc xã hội, văn hóa, chính trị, và lịch sử; về tính tự chủ và sự tự do của con người; về nền tảng triết học và nhân văn của nhân loại; về hiện (...) tượng tiếp biến văn hóa sinh ra từ tương tác giữa người và máy; về tam giác tương tác giữa con người với máy tính, giữa con người với con người, và giữa con người với thiên nhiên. Trên cơ sở giải thích rõ về năm tiền đề này, bài viết đã đưa ra ba gợi ý cho Việt Nam về việc phát triển, ứng dụng, và quản lý trí tuệ nhân tạo. (shrink)

“Strong AI” is the view that it is possible for an artificial agent to be mentally indistinguishable from human agents. Because the behavioral dispositions of artificial agents are determined by underlying dispositional systems, Strong AI seems to entail human behavioral dispositions are also determined by dispositional systems. It is, however, highly intuitive that non-dispositional, phenomenal properties, such as being in pain, at least partially determine certain human behavioral dispositions, like the disposition to take a pain killer. Consequently, Strong AI seems (...) to conflict with an intuitive view of phenomenal properties’ role in determining human behavioral dispositions. My goal here is not directly to evaluate this tension, but rather to clarify how dispositionalism in the metaphysics of properties bears on it. While a tempting thought is that dispositionalism fits well with Strong AI’s thoroughly dispositional account of human behavior, I argue that this thought does not hold for dispositionalism in general. In particular, I argue that combining a version of the “powerful qualities view” with certain dispositionalist conceptions of the will leads to a version of the intuitive view of phenomenal properties that is radically incompatible with Strong AI. I argue further that this view also raises a challenge for the weaker claim that an artificial agent could be behaviorally indistinguishable from a human agent. (shrink)

The biomedical literature makes extensive use of the concept of a genetic program. So far, however, the nature of genetic programs has received no satisfactory elucidation from the standpoint of computer science. This unsettling omission has led to doubts about the very existence of genetic programs, on the grounds that gene regulatory networks lack a predetermined schedule of execution, which may seem to contradict the very idea of a program. I show, however, that we can make perfect sense of genetic (...) programs, if only we abandon the preconception that all computers have a von Neumann architecture. Instead, genetic programs instantiate the computational architecture of Post–Newell Production Systems. That is, genetic programs are unordered sets of conditional instructions, instructions that fire independently when their conditions are matched. For illustration I present a paradigm Production System that regulates the functioning of the well-known lac operon of E. coli. On close reflection it turns out that not only genes, but also proteins encode instructions. I propose, therefore, to rename genetic programs to biomolecular programs. Biomolecular and/or genetic programs, and the cellular computers than run them, are to be understood not as von Neumann computers, but as Post–Newell production systems. (shrink)

What does it mean to be human? Philosophers and theologians have been wrestling with this question for centuries. Recent advances in cognition, neuroscience, artificial intelligence and robotics have yielded insights that bring us even closer to an answer. There are now computer programs that can accurately recognize faces, engage in conversation, and even compose music. There are also robots that can walk up a flight of stairs, work cooperatively with each other and express emotion. If machines can do everything we (...) can, does that mean we are machines? -/- This book examines whether an artificial person can be constructed and if so, what that might tell us about our future and ourselves. Different human capacities such as perception, creativity, consciousness, social behavior, and free will are described in separate chapters. Technological advances in these areas are summarized and compared to our own abilities. The book adopts a multi-disciplinary approach, with a naturalistic perspective drawn from biology and psychology matched against a technological perspective based on computer science and robotics. (shrink)

Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from original human-generated text. We then argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference, using new names to refer to new entities. Such examples could be (...) explained if LLMs were not cultural technologies but had beliefs, desires, and intentions. According to interpretationism in the philosophy of mind, a system has such attitudes if and only if its behavior is well explained by the hypothesis that it does. Interpretationists may hold that LLMs have attitudes, and thus have a simple solution to the novel reference problem. We emphasize, however, that interpretationism is compatible with very simple creatures having attitudes and differs sharply from views that presuppose these attitudes require onsciousness, sentience, or intelligence (topics about which we make no claims). (shrink)

Psychoanalysis, particularly as articulated by figures like Freud and Lacan, highlights the inherent division within the human subject—a schism between the conscious and unconscious mind. It could be said that this suggests that such an internal division becomes amplified in the context of generative art, where technology and algorithms are used to generate artistic expressions that are meant to emerge from the depths of the unconscious. Here, we encounter the tension between the conscious artist and the generative process itself, which (...) may yield unexpected, even uncontrollable results. -/- This paper, therefore, seeks to address this division within the modern subject and its relationship to technology, wherein the division within the living body is revealed through the presence of prosthetic elements, which mirrors the division brought about by the incorporation of language as a signifier. I argue that the amplification of this internal schism does not necessarily lead to a more fractured subject. Instead, generative art, bolstered by advancements in AI and machine learning, offers a unique opportunity for individuals to externalize and explore their minds in novel ways. -/- By examining contemporary works such as Hal Foster’s Prosthetic Gods, which stands as a pivotal exploration of the convergence between modernist art and psychoanalytic theory and Isabel Millar’s Psychoanalysis of Artificial Intelligence, this paper elucidates the profound implications of Freud’s vision of modern -/- subjectivity as Prothesengott (Prosthetic God) and address the questions concerning this technological imbrication of the human mind and body through the Lacanian framework. Although for Freud, Man does not become a real God, rather, the potential to transcend one’s limitations ascribes us to God-like qualities by seeking to generate new forms of life that go beyond merely reproducing nature — a transcention of the natural. Millar emphasizes that Freud observes that this is evidenced by the fact that these additional organs remain distinct from the organism and can never assimilate into it. One continually falls short of realizing the fantasy he envisions, opting instead to use his supplementary artificial organs to endlessly revolve around the objects of the drive. -/- This evolving relationship that the drive has with its technological objects, resounds in Lacan’s conception of “lathouse” which allows extimate objects to convert interiority (unconscious) into exteriority (conscious) and exteriority into interiority. The thesis of this paper seeks to employ this underutilized concept to understand the nature of human subjectivity and its bodily and structural relationship to generative art. Therefore, this paper emphasizes what really happens when we enter into this relationship with the lathouse, whereby this artificial object has effects in the "real of jouissance", where these Lathouses create a network, namely the Alethosphere. My goal is to argue that generative art as a technological development, can be seen as an extension to the development of the drive. Conclusively, I make the claim for generative art's potential to externalize the human creative drive by emphasizing the interplay between randomness and structure, and how it offers a means to surpass our inherent limitations by presenting an avenue for self-expression that transcends traditional modes of art. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (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)

The editorial introduces issue 21.1 of Technoetic Arts via a critical reflection on the artificial intelligence hype (AI hype) that emerged in 2022. Tracing the history of the critique of Large Language Models, the editorial underscores that there are substantial ethical challenges related to bias in the training data, copyright issues, as well as ecological challenges which the technology industry has consistently downplayed over the years. -/- The editorial highlights the distinction between the current AI technology’s reliance on extensive pre-existing (...) human-generated data for pattern recognition and the performative process inherent in art practice. Patterns of creative output can be replicated without a thorough understanding of underlying concepts. In contrast, the performative process of art practice, as explored through the conceptual frameworks of creativity by Noam Chomsky, Charles Sanders Peirce, and Roger T. Ames and with reference to an earlier publication of the editorial’s author, creates concepts and continually ‘strives to extend itself towards the unknown.’ -/- Following this introductory section, the editorial provides an overview of the ten articles featured in this issue of Technoetic Arts. The first four texts engage with emerging technologies from various perspectives in art and design. The subsequent six articles, part of a special section titled 'Perspectives from Chandigarh,' reflect a quest for meaningful existence within colonial, patriarchal, and biopolitical structures that frame everyday practices of exclusion and oppression. (shrink)

Research groups around the world are currently busy trying to invent new life in the laboratory, looking for extraterrestrial life, or making machines increasingly more life-like. In the case of astrobiology, any newly discovered life would likely be very old, but when discovered it would be new to us. In the case of synthetic organic life or life-like machines, humans will have invented life that did not exist before. Together, these endeavors amount to what we call the emerging plurality of (...) lives because we see a future where we are surrounded by, and interact with, life of many different origins, as well as entities that we have not traditionally seen as being alive. In the future these entities will likely possess properties that we so far have only associated with living beings, thereby transcending and perhaps blurring the borders between life and non-life. (shrink)

This short article is a “conversation” in which an android, Mort, replies to Richard Marc Rubin’s android named Sol in “The Robot Sol Explains Laughter to His Android Brethren” (The Philosophy of Humor Yearbook, 2022). There Sol offers an explanation for how androids can laugh--largely a reaction to frustration and unmet expectations: “my account says that laughter is one of four ways of dealing with frustration, difficulties, and insults. It is a way of getting by. If you need to label (...) my conception of humor, you might call it an adjustment theory, or perhaps an accommodation theory” (Rubin 256). Mort is intrigued but has his doubts. Mort contends that synesthesia likely has something to do with how humans develop humor. Perhaps, he considers, human designers crossed the androids' sensory wires, allowing them to be able to smash up concepts that do not otherwise seem to fit together, reconstruct incongruities in a manner that is not nonsensical, express the aha-eureka moment of a serendipitous discovery, and even feel blue when they fail at a given task that we were expected to complete. (shrink)

The recent wave of generative AI (GAI) systems like Stable Diffusion that can produce images from human prompts raises controversial issues about creatorship, originality, creativity and copyright. This paper focuses on creatorship: who creates and should be credited with the outputs made with the help of GAI? Existing views on creatorship are mixed: some insist that GAI systems are mere tools, and human prompters are creators proper; others are more open to acknowledging more significant roles for GAI, but most conceive (...) of creatorship in an all-or-nothing fashion. We develop a novel view, called CCC (collective-centered creation), that improves on these existing positions. On CCC, GAI outputs are created by collectives in the first instance. Claims to creatorship come in degrees and depend on the nature and significance of individual contributions made by the various agents and entities involved, including users, GAI systems, developers, producers of training data and others. Importantly, CCC maintains that GAI systems can sometimes be part of a co-creating collective. We detail how CCC can advance existing debates and resolve controversies around creatorship involving GAI. (shrink)

Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of circumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of explaining how human cognition efficiently acknowledges what is relevant from what is not (...) in each context, has been adopted as a guide. By using it, we’ve been able to describe a fundamental tension between context sensitivity and the mental representations used in cognition theories. The first chapter discusses the nature of the frame problem,as well as the reasons for its persistence. In the second and third chapters, the problem is used as a measure tool in order to inquiry a few representational approaches and check how well suited they are to deal with context dependencies. The problems found are then correlated with the frame problem. Throughout the discussion, we try to show that 1) none of the evaluated approaches is capable of dealing with context sensitivity in a proper manner, but 2) that’s not a reason to think that the frame problem constitutes an argument against representational approaches in general, and 3) that it constitutes a fundamental conceptual tool in contemporary research -/- A sensibilidade ao contexto é uma das marcas distintivas da inteligência humana. Compreender o modo flexível como o ser humano pensa e age em função de um número potencialmente infinito de circunstâncias, ainda que munido de recursos finitos e limitados, é um desafio central para a filosofia da mente e para a ciência cognitiva, em particular aos que fazem uso de teorias representacionalistas. Nesse trabalho, adotou-se como fio condutor o modo como isso se manifesta no "frame problem": a dificuldade em explicar como a cognição humana reconhece, de maneira eficiente, o que é ou não relevante em cada contexto. A partir dele, buscou-se caracterizar uma tensão fundamental entre a sensibilidade ao contexto e o uso de representações mentais em teorias da cognição. O primeiro capítulo discute a natureza do frame problem, bem como as razões de sua resiliência. No segundo e terceiro capítulos, faz-se uso do problema como métrica para investigar o quão adequado é o tratamento das dependências contextuais no âmbito de várias abordagens representacionais. No decorrer da discussão, realiza-se um esforço argumentativo para mostrar que 1) nenhuma das estratégias abordadas é capaz tratar adequadamente da sensibilidade ao contexto, mas que 2) apesar disso, o frame problem não constitui argumento fatal para teorias representacionalistas em geral, e que 3) ele constitui uma ferramenta conceitual fundamental para pesquisas contemporâneas. (shrink)

Artificial intelligence (AI) techniques are used to model human activities and predict behavior. Such systems have shown race, gender and other kinds of bias, which are typically understood as technical problems. Here we try to show that: 1) to get rid of such biases, we need a system that can understand the structure of human activities and;2) to create such a system, we need to solve foundational problems of AI, such as the common-sense problem. Additionally, when informational platforms uses these (...) models to mediate interactions with their users, which is a commonplace nowadays, there is an illusion of progress, for what is an increasingly higher influence over our own behavior is took for an increasingly higher predictive accuracy. Given this, we argue that the bias problem is deeply connected to non-technical issues that must be discussed in public spaces. -/- Técnicas de inteligência artificial (IA) são utilizadas para modelar as atividades humanas e gerar predições comportamentais. Estes sistemas têm apresentado vieses diversos, inclusive de raça e gênero, tipicamente tomados como problemas de engenharia. Realiza-se aqui um esforço argumentativo para mostrar que: 1) escapar dos vieses demanda um sistema que compreenda a estrutura das atividades humanas e; 2) criar um sistema que apresente tal compreensão, demanda a solução de problemas fundacionais da IA, em particular, o problema de como modelar o senso comum. No caso de plataformas informacionais que usam desses modelos para mediar interações com seus usuários, algo cada vez mais comum, o não reconhecimento disso dá margem a uma ilusão de progresso, em que uma crescente influência sobre nosso comportamento é tomada como uma crescente acurácia preditiva. Nesse cenário, argumenta-se que o problema dos vieses está associado a questões não técnicas que devem ser discutidas em espaços públicos. (shrink)

Extended Reality (XR) and Large Language Model (LLM) technologies have the potential to significantly influence higher education practices and pedagogy in the coming years. As these emerging technologies reshape the educational landscape, it is crucial for educators and higher education professionals to understand their implications and make informed policy decisions for both individual courses and universities as a whole. -/- This paper has two parts. In the first half, we give an overview of XR technologies and their potential future role (...) in higher education. Then, we discuss the benefits and challenges of adopting and integrating XR technologies into higher education settings. Finally, we offer recommendations for educators, administrators, and policymakers on how to approach the adoption of this set of technologies and set institutional policy to take advantage of its benefits while minimizing its potential negative consequences. -/- In the paper’s second half, we discuss LLMs - specifically Generative Pre-trained Transformers (GPTs). Again, we provide an overview of this rapidly developing technology and examine its unprecedented and potentially disruptive impact on higher education pedagogy. We delve into the benefits and limitations of GPTs, highlighting the pedagogical and ethical challenges they may pose. Finally, we present actionable recommendations for instructors and administrators at both the course level and the university-wide level to guide policy decisions concerning these transformative technologies. (shrink)

The debate around the notions of a priori knowledge and a posteriori knowledge has proven crucial for the development of many fields in philosophy, such as metaphysics, epistemology, metametaphysics etc. We advocate that the recent debate on the two notions is also fruitful for man-made distributed computing systems and for the epistemic analysis thereof. Following a recently proposed modal and fallibilistic account of a priori knowledge, we elaborate the corresponding concept of a priori belief: We propose a rich taxonomy of (...) types of a priori beliefs and their role for the different agents that participate in the system engineering process, which match the existing view exceedingly well and are particularly promising for explaining and dealing with unexpected behaviors in fault-tolerant distributed systems. Developing such a philosophical foundation will provide a sound basis for eventually implementing our ideas in a suitable epistemic reasoning and analysis framework and, hence, constitutes a mandatory first step for developing methods and tools to cope with the various challenges that emerge in such systems. (shrink)

Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI (...) systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called “general” or “human-like” AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy. (shrink)

لم نعد بحاجة إلى فانوس سحري نمسح عليه بأصابعنا لكي يخرج منه المارد القادر على خدمتنا وتلبية بعض أهم مطالبنا الحياتية، ولم نعد بحاجة إلى تعويذات نلج بها في عالم السحر والخيال؛ لقد خرج المارد بالفعل من قمقمه الحاسوبي؛ من جوف مختبرات البرمجة والذكاء الاصطناعي، بتعويذات (أكواد) رياضية رمزية سرعان ما تمكن من التهامها وهضمها، ليبيت قادرًا على إنتاج تعويذات أخرى مماثلة، وربما أفضل منها! خرج «المُحول التوليدي المدرب مُسبقًا»، المعروف اختصارًا باسم «جي بي تي»، ملوحًا بإمكانات بحثية وخدمية وإنتاجية (...) هائلة، ومهددًا بتصفية قطاعات بأسرها من المهن والوظائف، وبتجريف مهارات البحث العلمي لدى طلاب المدارس والجامعات! (shrink)

تُخبرنا النظرية الحاسوبية للعقل (أو مذهب الحوسبة)، أن عقولنا تُشبه الحواسيب في عملها؛ أي أنها تتلقى مدخلات من العالم الخارجي، ثم تُنتج بالخوارزميات مخرجات في شكل حالات ذهنية أو أفعال. وبعبارة أخرى، تذهب النظرية إلى أن الدماغ لا يعدو أن يكون معالج معلومات؛ حيث يكون العقل بمثابة «برمجيات» (سوفت وير) تعمل على «جهاز» هو الدماغ (هارد وير). وما دام العقل مجرد برمجيات تخضع للحوسبة الفيزيائية بواسطة الأدمغة، أليس من الممكن إذن منطقيًا نقلها إلى أي حاسوب مثلما نقوم بنقل أية برمجيات (...) أخرى؟. (shrink)

It is indubitable that machines with artificial intelligence (AI) will be an essential component in humans’ quest to become a spacefaring civilization. Most would agree that long-distance space travel and the colonization of Mars will not be possible without adequately developed AI. Machines with AI have a normative function, but some argue that it can also be evaluated from the perspective of ethical norms. This essay is based on the assumption that machine ethics is an essential philosophical perspective in realizing (...) the aim of humanity becoming a spacefaring civilization. In this essay, I explore two questions in the field of machine ethics, that I believe to be relevant to the role AI will play in long-distance space travel. The first is, should moral theory be extended to include machines with AI, and second, can machines be fully ethical agents? In this essay, I define AI and then discuss the difference between implicit, explicit and full ethical agents in relation to machines with AI. I then present the argument that the inclusion of moral theory is essential in the development of machines with AI. Without an adequate inclusion of moral theory in the design of AI it may pose an existential threat to humanity, especially in the development of super-intelligent machines. I also highlight that conceptual clarity is essential in the field of machine ethics and the choice of the conceptual foundation that informs AI research and development has ethical implications, especially in the case of super-intelligent machines. This essay is an exploratory and speculative philosophical analysis of certain aspects of machine ethics relevant to long-distance space travel and does not attempt to provide definitive answers to the questions posed in the essay, but instead aims to bring attention to what I deem important considerations. (shrink)

Limits need to be set on AI’s ability to simulate human feelings. Ensuring that chatbots don’t use emotive language, including emojis, would be a good start. Emojis are particularly manipulative. Humans instinctively respond to shapes that look like faces — even cartoonish or schematic ones — and emojis can induce these reactions.

I conducted an experiment by using four different artificial intelligence models developed by OpenAI to estimate the persuasiveness and rational justification of various philosophical stances. The AI models used were text-davinci-003, text-ada-001, text-curie-001, and text-babbage-001, which differed in complexity and the size of their training data sets. For the philosophical stances, the list of 30 questions created by Bourget & Chalmers (2014) was used. The results indicate that it seems that each model has its own plausible ‘cognitive’ style. The outcomes (...) of the ‘strongest’ model correlate with the average philosophers' stance. (shrink)

Two universal functional principles of Grossberg’s Adaptive Resonance Theory [19] decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are re-visited (...) in this concept paper here on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics. (shrink)

Ikigai is a Japanese concept, which, in brief, refers to the “reason or purpose to live”. I-risks have been identified as a category of risks complementing x- risks, i.e., existential risks, and s-risks, i.e., suffering risks, which describes undesirable future scenarios in which humans are deprived of the pursuit of their individual ikigai. While some developments in AI increase i-risks, there are also AI-driven virtual opportunities, which reduce i-risks by increasing the space of potential ikigais, largely due to developments in (...) generative AI, virtual worlds as well as AI-driven hyper-personalization. The purpose of this paper is to present a first attempt to map the potential AI-driven virtual hyper-personalised ikigai universe. Moreover, challenges and further ideas are presented. (shrink)

Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception's computational tractability. One of the few attempts to move toward such an explanation has been the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by (...) forgoing access to information stored in cognition. I argue that we have no compelling reason to believe that encapsulation explains (or even contributes to an explanation of) perceptual tractability, and much reason to doubt it. This is because there exist much deeper computational challenges for perception than information access, and these threaten to make the costs of access irrelevant. If this is right, it undermines a core computational motivation for encapsulation and sends us back to the drawing board for explanations of perceptual tractability. (shrink)

La inteligencia artificial (IA) es la capacidad de una máquina o sistema informático para simular y realizar tareas que normalmente requerirían inteligencia humana, como el razonamiento lógico, el aprendizaje y la resolución de problemas. La inteligencia artificial se basa en el uso de algoritmos y tecnologías de aprendizaje automático para dar a las máquinas la capacidad de aplicar ciertas habilidades cognitivas y realizar tareas por sí mismas de manera autónoma o semiautónoma. La inteligencia artificial se distingue por su grado de (...) capacidad cognitiva o por su grado de autonomía. Por capacidad puede ser débil o limitada, general o superlativa. Por su autonomía, puede ser reactiva, deliberativa, cognitiva o totalmente autónoma. A medida que la inteligencia artificial mejora, muchos procesos son cada vez más eficientes y, tareas que hoy parecen complicadas se estarán realizando con mayor rapidez y precisión. (shrink)

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the phenomenon, (ii) (...) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem. (shrink)

Chapter 3 of the ongoing publication "Artificial Aesthetics" Book information: Assume you're a designer, an architect, a photographer, a videographer, a curator, an art historian, a musician, a writer, an artist, or any other creative professional or student. Perhaps you're a digital content creator who works across multiple platforms. Alternatively, you could be an art historian, curator, or museum professional. -/- You may be wondering how AI will affect your professional area in general and your work and career. Our book (...) offers intellectual tools to help us better see the future of creative fields. These tools come from philosophy of art, experimental psychology, media theory, digital culture studies and data science. The book is the first to combine these different perspectives together. -/- We started the work on the book in summer 2019, exchanging numerous messages, commenting on each other ideas, and sharing drafts of sections. The final book is a result of this process. Although each chapter is written by one author, it reflects the discussions we had over 27 months. (shrink)

In recent years, a number of philosophers have turned their attention to developments in Artificial Intelligence, and in particular to deepfakes. A deepfake is a portmanteau of ‘deep learning' and ‘fake', and for the most part they are videos which depict people doing and saying things they never did. As a result, much of the emerging literature on deepfakes has turned on questions of trust, harms, and information-sharing. In this paper, I add to the emerging concerns around deepfakes by drawing (...) on resources from vice epistemology. As deepfakes become more sophisticated, I claim, they will develop to be a source of online epistemic corruption. More specifically, they will encourage consumers of digital online media to cultivate and manifest various epistemic vices. My immediate focus in this paper is on their propensity to encourage the development of what I call ‘intellectual cynicism'. After sketching a rough account of this epistemic vice, I go on to suggest that we can partially offset such cynicism – and fears around deceptive online media more generally – by encouraging the development what I term a trained ‘digital sensibility'. This, I contend, involves a calibrated sensitivity to the epistemic merits of online content. (shrink)

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, (...) and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...) problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

The German Act on Autonomous Driving constitutes the first national framework on level four autonomous vehicles and has received attention from policy makers, AI ethics scholars and legal experts in autonomous driving. Owing to Germany’s role as a global hub for car manufacturing, the following paper sheds light on the act’s position within the ethical discourse and how it reconfigures the balance between legislation and ethical frameworks. Specifically, in this paper, we highlight areas that need to be more worked out (...) in the future either through ethical conventions, corporate measures or legal measures and examine how the law can be incorporated into the existing discourse on the regulation of technologies. Based on this examination, we derive implications for future discourse and elaborate on companies’ responsibilities in developing autonomous driving technologies in an ethical sense. (shrink)

Do we suddenly become justified in treating robots like humans by positing new notions like “artificial moral agency” and “artificial moral responsibility”? I answer no. Or, to be more precise, I argue that such notions may become philosophically acceptable only after crucial metaphysical issues have been addressed. My main claim, in sum, is that “artificial moral responsibility” betokens moral responsibility to the same degree that a “fake orgasm” betokens an orgasm.

What is the essential ingredient of creativity that only humans – and not machines – possess? Can artificial intelligence help refine the notion of creativity by reference to that essential ingredient? How / do we need to redefine our conceptual and legal frameworks for rewarding creativity because of this new qualifying – actually creatively significant – factor? -/- Those are the questions tackled in this essay. The author’s conclusion is that consciousness, experiential states (such as a raw feel of what (...) is like to be creating) and propositional attitudes (such as intention to instigate change by creating) appear pivotal to qualifying an exploratory effort as creativity. Artificial intelligence systems would supposedly be capable of creativity if they could exhibit such states, which philosophers and computer scientists posit as conceptually admissible and practically possible. -/- The existing legal framework rewards creative endeavours by reference to the novelty or originality of the end result. But this bar is not insurmountable for artificial intelligence. Technically speaking, artificial intelligence systems can create works that are novel and/or original. Are we then prepared to grant to those systems the legal status of “creators” in their own right? Whom should the associated benefits and rewards be assigned to? How does the position change (or not) based on the qualifying factors set out above? Should – and if, how – the general public benefit from inventions / creative works of artificial intelligence systems if troves of personal data are the key component that fueled and informed creative choices? (shrink)

Argumentation schemes [1–3] are a relatively recent notion that continues an extremely ancient debate on one of the foundations of human reasoning, human comprehension, and obviously human argumentation, i.e., the topics. To understand the revolutionary nature of Walton’s work on this subject matter, it is necessary to place it in the debate that it continues and contributes to, namely a view of logic that is much broader than the formalistic perspective that has been adopted from the 20th century until nowadays. (...) With his book Argumentation schemes for presumptive reasoning, Walton attempted to start a dialogue between three different fields or views on human reasoning – one (argumentation theory) very recent, one (dialectics) very ancient and with a very long tradition, and one (formal logic) relatively recent, but dominating in philosophy. Argumentation schemes were proposed as dialectical instruments, in the sense that they represented arguments not only as formal relations, but also as pragmatic inferences in the sense that they depend on what the interlocutors share and accept in a given dialogical circumstance and affect their dialogical relation. In this introduction, the notion of argumentation scheme will be analyzed in detail, showing its different dimensions and its defining features which make them an extremely useful instrument in Artificial Intelligence. This theoretical background will be followed by a literature review on the uses of the schemes in computing, aimed at identifying the most important areas and trends, the most promising proposals, and the directions of future research. (shrink)

As we await the increasingly likely advent of genuinely intelligent artificial systems, a fair amount of consideration has been given to how we humans will interact with them. Less consideration has been given to how—indeed if—we humans will love them. What would human-AI romantic relationships look like? What do such relationships tell us about the nature of love? This chapter explores these questions via consideration of several works of science fiction, focusing especially on the Black Mirror episode “Be Right Back” (...) and the Spike Jonze's movie *Her*. As I suggest, there may well be cases where it is both possible and appropriate for a human to fall in love with a machine. (shrink)

Can the machines that play board games or recognize images only in the comfort of the virtual world be intelligent? To become reliable and convenient assistants to humans, machines need to learn how to act and communicate in the physical reality, just like people do. The authors propose two novel ways of designing and building Artificial General Intelligence (AGI). The first one seeks to unify all participants at any instance of the Turing test – the judge, the machine, the human (...) subject as well as the means of observation instead of building a separating wall. The second one aims to design AGI programs in such a way so that they can move in various environments. The authors of the article thoroughly discuss four areas of interaction for robots with AGI and introduce a new idea of techno-umwelt bridging artificial intelligence with biology in a new way. (shrink)

In this work, we survey skepticism regarding AI risk and show parallels with other types of scientific skepticism. We start by classifying different types of AI Risk skepticism and analyze their root causes. We conclude by suggesting some intervention approaches, which may be successful in reducing AI risk skepticism, at least amongst artificial intelligence researchers.

In this chapter I present the thesis of Proper Embodiment: the claim that (at least some of) the details of our physiology matter to cognition and consciousness in a fundamental way. This thesis is composed of two sub-claims: (1) if we are to design, build, or evolve artificial systems that are cognitive in the way that we are, these systems will have to be internally embodied, and (2) the exploitation of the particular internal embodiment that allows systems to evolve solutions (...) with greatly decreased computational complexity – and thus to be flexible and adaptive in the ways that are characteristic of cognitive systems – means that the orthodox distinction between algorithm and implementation is undermined. Evolved cognitive systems are therefore likely not to instantiate the distinction between phenomenology, algorithm, and implementation. The leaky levels evident in evolved cognitive systems motivate an extension of orthodox embodied cognitive science to the internal, affective, “gooey” realm that has so far only been embraced by those in the enactive tradition. This suggests that if we are to build artificial systems that will be genuinely cognitive they will have to incorporate parts of the gooey realm currently considered to be “mere implementation.”. (shrink)