The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model (...) BERT and the differences between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis. (shrink)

There is an underappreciated respect in which the widespread availability of generative artificial intelligence (AI) models poses a threat to human connection. My central contention is that human creativity is especially capable of helping us connect to others in a valuable way, but the widespread availability of generative AI models reduces our incentives to engage in various sorts of creative work in the arts and sciences. I argue that creative endeavors must be motivated by curiosity, and (...) so they must disclose the creative agent’s inquisitive self. It is through self-disclosure, including the disclosure of the inquisitive self, that we put ourselves in a position to be seen by and connect with others through our creative pursuits. Because relying on AI for certain generative tasks is less self-disclosive than the creative work such technologies supplant, this reliance threatens to weaken our connections to one another. (shrink)

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts (...) from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. (shrink)

This chapter considers the potential of artificial intelligence (AI) to exhibit creativity and imagination, in light of recent advances in generative AI and the use of deep neural networks (DNNs). Reasons for doubting that AI exhibits genuine creativity or imagination are considered, including the claim that the creativity of an algorithm lies in its developer, that generative AI merely reproduces patterns in its training data, and that AI is lacking in a necessary feature for creativity or (...) imagination, such as consciousness, agency, metacognitive awareness, or the use of mental imagery. In each case, a counterargument is provided that AI already has or soon will overcome the apparent barrier. In some instances, this is because the critical features deemed lacking in AI may not be necessary for human creativity and imagination either. In others, it is because important developmental and structural similarities between humans and generative AI have been insufficiently appreciated. (shrink)

The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from (...) synthetic biology, whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied. (shrink)

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well (...) as faculty and staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future. (shrink)

This paper examines the ontological and epistemological implications of artificial intelligence (AI) through posthumanist philosophy, integrating the works of Deleuze, Foucault, and Haraway with contemporary computational methodologies. It introduces concepts such as negative augmentation, praxes of revealing, and desedimentation, while extending ideas like affirmative cartographies, ethics of alterity, and planes of immanence to critique anthropocentric assumptions about identity, cognition, and agency. By redefining AI systems as dynamic assemblages emerging through networks of interaction and co-creation, the paper challenges traditional (...) dichotomies such as human versus machine and subject versus object. Bridging analytic and continental philosophical traditions, the analysis unites formal tools like attribution analysis and causal reasoning with the interpretive and processual methodologies of continental thought. This synthesis deepens the understanding of AI’s epistemic and ethical dimensions, expanding philosophical inquiry while critiquing anthropocentrism in AI design. The paper interrogates the spatial foundations of AI, contrasting Euclidean and non-Euclidean frameworks to examine how optimization processes and adversarial generative models shape computational epistemologies. Critiquing the reliance on Euclidean spatial assumptions, it positions alternative geometries as tools for modeling complex, recursive relationships. Furthermore, the paper addresses the political dimensions of AI, emphasizing its entanglements with ecological, technological, and sociopolitical systems that perpetuate inequality. Through a politics of affirmation and intersectional approaches, it advocates for inclusive frameworks that prioritize marginalized perspectives. The concept of computational qualia is also explored, highlighting how subjective-like dynamics emerge within AI systems and their implications for ethics, transparency, and machine perception. Finally, the paper calls for a posthumanist framework in AI ethics and safety, emphasizing interconnectivity, plurality, and the transformative capacities of machine intelligence. This approach advances epistemic pluralism and reimagines the boundaries of intelligence in the digital age, fostering novel ontological possibilities through the co-creation of dynamic systems. (shrink)

The burden of research ethics review falls not just on researchers, but on those who serve on research ethics committees (RECs). With the advent of automated text analysis and generative artificial intelligence, it has recently become possible to teach models to support human judgment, for example by highlighting relevant parts of a text and suggesting actionable precedents and explanations. It is time to consider how such tools might be used to support ethics review and oversight. This commentary (...) argues that with a suitable strategy of engagement, artificial intelligence can be used in a variety of ways that can genuinely support RECs and manage their workload and improve the quality of review. It would be wiser to take an active role in the development of AI tools for ethics review, rather than to adopt ad hoc tools after the fact. (shrink)

This paper examines the ontological and epistemological implications of artificial intelligence (AI) through posthumanist philosophy, integrating the works of Deleuze, Foucault, and Haraway with contemporary computational methodologies. It introduces concepts such as negative augmentation, praxes of revealing, and desedimentation, while extending ideas like affirmative cartographies, ethics of alterity, and planes of immanence to critique anthropocentric assumptions about identity, cognition, and agency. By redefining AI systems as dynamic assemblages emerging through networks of interaction and co-creation, the paper challenges traditional (...) dichotomies such as human versus machine and subject versus object. Bridging analytic and continental philosophical traditions, the analysis unites formal tools like attribution analysis and causal reasoning with the interpretive and processual methodologies of continental thought. This synthesis deepens the understanding of AI’s epistemic and ethical dimensions, expanding philosophical inquiry while critiquing anthropocentrism in AI design. The paper interrogates the spatial foundations of AI, contrasting Euclidean and non-Euclidean frameworks to examine how optimization processes and adversarial generative models shape computational epistemologies. Critiquing the reliance on Euclidean spatial assumptions, it positions alternative geometries as tools for modeling complex, recursive relationships. Furthermore, the paper addresses the political dimensions of AI, emphasizing its entanglements with ecological, technological, and sociopolitical systems that perpetuate inequality. Through a politics of affirmation and intersectional approaches, it advocates for inclusive frameworks that prioritize marginalized perspectives. The concept of computational qualia is also explored, highlighting how subjective-like dynamics emerge within AI systems and their implications for ethics, transparency, and machine perception. Finally, paper calls for a posthumanist framework in AI ethics and safety, emphasizing interconnectivity, plurality, and the transformative capacities of machine intelligence. This approach advances epistemic pluralism and reimagines the boundaries of intelligence in the digital age, fostering novel ontological possibilities through the co-creation of dynamic systems. (shrink)

The world of artificial intelligence appears to be in rapid transition, and claims that artificial general intelligence is impossible are competing with concerns that we may soon be seeing Artificial Godlike Intelligence and that we should be very afraid of this prospect. This article discusses the issues from a psychological and social perspective and suggests that with the advent of Generative Artificial Intelligence, something that looks to humans like Artificial General (...) Intelligence has become a distinct possibility as is the idea of making it semi-autonomous: not just responding to discrete external inputs or performing specific tasks but able to prompt itself to create an ongoing stream of cognition that can possess something akin to a ‘purpose’. Add to that the ability of computers to be connected in vast networks, and we can envisage intelligences with staggering amounts of knowledge and reasoning capability, engaging with humans to anticipate and grant wishes. Such Semi-Autonomous Godlike Artificial Intelligences (SAGAIs—meaning ‘engagements’ in Hindi) could end up like Kali, wreaking death and destruction on our species or like Thor, acting as a saviour and protector of humanity. Which of these our SAGAIs most resemble could depend on whose wishes it is designed to serve: a rich oligarchy seeking ever greater wealth and power whatever the cost to others, or a populace that has compassion for each other and for life on the planet. (shrink)

The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...) that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)

In this article, M. Beatrice Fazi takes up Media Theory’s invitation to engage with Alan Díaz Alva’s analysis of her philosophical work on contingency in computation. The central argument of Fazi’s Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics is that computation can be productive of ontological novelty. This piece revisits that argument in the light of the technological developments that have occurred since 2018, when the book was published. Focusing on generative artificial intelligence (generative (...) AI), the article considers how the concepts of generativity, aesthetics and contingency are tackled in the book and in present-day debates about generative AI systems. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. (...) By enabling rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

The outputs of generative artificial intelligence (generative AI) are often called “synthetic” to imply that they are not natural but artificial. Against that use of the term, this article focuses on a different denotation of synthesis, stressing the unifying and compositional aspects of anything synthetic. The case of large language models (LLMs) is used as an example to address synthesis philosophically alongside notions of representation in contemporary computational systems. It is argued that synthesis in (...) class='Hi'>generative AI should be understood as a search for unity that is fundamental to the making of a representational reality. This representational reality, internal to computation, is a stable (if imperfect) whole, a togetherness of distributed representations. The article thus demonstrates that developing the philosophical concept of synthesis to investigate today’s generative AI involves examining how structuring occurs in LLMs and studying the kinds of forms that structuring results in. (shrink)

Concerns around Generative Artificial Intelligence (GenAI) in higher education have so far largely centred on assessment integrity, resulting in fundamental questions about students’ broader engagement with these tools remaining underexplored. This paper reports on the findings of a survey that forms part of a wider study, comprising the first empirical investigation of GenAI use by university students as a method of engaging with their academic readings. Our survey of 101 students shows that over half of all students (...) surveyed used GenAI to some degree as a method of engaging with their unit readings. Our findings suggest that students turn to these tools in response to time constraints and conceptual difficulties, while maintaining complex attitudes toward their use: most welcome their availability, yet few report trusting or relying on GenAI-generated interpretations or summaries of texts. Importantly, our data reveals substantial demographic variations in usage patterns, with international students and those taking subjects as electives showing significantly higher rates of AI use to assist with reading. This suggests GenAI tools may be serving as important mediators for attempting to overcome epistemic barriers to learning, particularly for students who face additional linguistic or disciplinary challenges. (shrink)

This paper examines the convergence of large language models, multimodal AI, and generative spatial technologies to enable sophisticated simulated worlds for studying agentic behavior. Recent developments in generative architectures, particularly GenEx and topology-aware mesh generation, facilitate the creation of coherent, explorable environments with artificial agents capable of complex interactions. The proposed framework for "virtual anthropology" presents novel opportunities for studying emergent behaviors and cognitive processes in controlled, generative environments, with implications for both theoretical research and practical (...) applications in artificial intelligence. (shrink)

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, (...) which argues that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper (...) identifies potential gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence (...) can comprehend prompts in natural language, on virtually any topic, and generate relevant, original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public as of the time of this writing. This paper presents select examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. We use these examples to raise specific, fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. Our goal is to instigate not just a sense of urgency, but of a present tardiness on the part of the academic community in addressing these questions. (shrink)

There is a history of thinking that photographs provide a special kind of access to the objects depicted in them, beyond the access that would be provided by a painting or drawing. What is included in the photograph does not depend on the photographer’s beliefs about what is in front of the camera. This feature leads Kendall Walton to argue that photographs literally allow us to see the objects which appear in them. Current generative algorithms produce images in response (...) to users’ text prompts. Depending on the parameters, the output can resemble specific people or things which are named in the prompt. This resemblance does not depend on the user’s beliefs, so generated images are in this sense like photographs. Given this parallel, how should we think about AI-generated images? (shrink)

ChatGPT – a large language model – recently passed the U.S. bar exam. The startling rise and power of generative artificial intelligence (AI) systems such as ChatGPT lead us to consider whether and how more specialized systems could be used to overcome existing barriers to the legal system. Such systems could be employed in either of the two major stages of the pursuit of justice: preliminary information gathering and formal engagement with the state’s legal institutions and professionals. (...) We focus on the former and argue that developing and deploying publicly funded AI legal advisors can reduce economic and shame-based cultural barriers to the information-gathering stage of pursuing justice. (shrink)

Artificial intelligence is now capable of generating sophisticated and compelling images from simple text prompts. In this paper, I focus specifically on how artists might make use of AI to create art. Most existing discourse analogizes AI to a tool or collaborator; this focuses our attention on AI’s contribution to the production of an artistically significant output. I propose an alternative approach, the exploration paradigm, which suggests that artists instead relate to AI as a participant: artists create a (...) space for interaction with the AI algorithm by way of their prompts, thereby allowing them to explore the way that the algorithm “sees” and “represents.” AI art practiced in this fashion bears a striking resemblance to contemporary conceptual and participatory art. Viewing AI art in this way has implications for the appreciation of style in AI art; concerns about novelty and originality; and the assignment of artistic credit and copyright. (shrink)

This paper critically examines Japan's approach toward the adoption of Generative AI such as ChatGPT in education via studying media discourse and guidelines at both the national as well as local levels. It highlights the lack of consideration for socio-cultural characteristics inherent in the Japanese educational systems, such as the notion of self, teachers’ work ethics, community-centric activities for the successful adoption of the technology. We reveal ChatGPT’s infusion is likely to further accelerate the shift away from traditional notion (...) of self, which has been ongoing during past educational reforms, towards active and self-directed learning. The current discourse on Generative AI’s infusion in education is driven by neoliberal agendas aimed at increasing employability and individualism. Moreover, the introduction of ChatGPT in the context of Japan's educator culture offers potential solutions to address issues of overwork but also necessitates considerations regarding increased responsibilities for teachers and the preservation of their central role in student education. Finally, the irreplicable social growth fostered by community-centric activities in the Japanese educational system highlights the limitations of ChatGPT in replicating dynamic social interactions, emphasizing the importance of holistic approaches to education beyond technological integration. (shrink)

Generative artificial intelligence has given us synthetic media that are increasingly easy to create and increasingly hard to distinguish from photographs and videos. Whereas an existing literature has been concerned with how these new media might make a difference for would-be knowers—the viewers of photographs and videos—I advance a thesis about how they will make a difference for would-be communicators—those who embed photos and videos in their speech acts. I claim that the presence of these media in (...) our information environment reduces our ability to show one another things, even as it may increase our resources for telling. And I argue that this has consequences beyond the disruption of knowledge acquisition; showing is a way that we preserve relational equality through superficial asymmetries in political communication, and thereby express respect for our audiences. If synthetic media reduce our options for showing, they then interfere in the way that we manage our relationships in the context of collective political action. (shrink)

Human civilization is very clearly reaching a point of critical mass when it comes to technology and how it transforms culture and the economics that is therefore driven forward. The conversation around the practical aspects of generative artificial intelligence (Chat GPT, Q Star, Bard, Claude, Genesis, Firefly, and others) and their ethical implications is massively trending. The political conversations around it are slow to catch up but will soon take over once the general public feels their impact, (...) which is likely to happen in the next two to three years. -/- What is not being spoken of is the need for new economic models that can integrate the enormous and continuous shifts in trade that will occur, as we continue to implement more efficient manufacturing and logistics. Things like 3D printing in hub regions, the automation of transportation, augmented and virtual reality, and new creativity-based industries will all disrupt capital markets as well as social systems due to what we could call "meta-scarcity", which is to say, "the scarcity of scarcity"; In effect, we will lack the scarcity to maintain predictive power over the future. This is because when technology is capable of optimizing for creativity, people come up with new demands, and when new demands are supplied as quickly as they arise, no one is capable of knowing what kind of trends the market will take, yet it is necessary to try as public discussion could beneficially shape the economic landscape. (shrink)

The value of testimony as a source of knowledge has been a subject of epistemological debates. The "trust theory of testimony" suggests that human testimony is based on an affective relationship supported by social norms. However, the advent of generative artificial intelligence challenges our understanding of genuine testimony. The concept of "quasi-testimony" seeks to characterize utterances produced by non-human entities that mimic testimony but lack certain fundamental attributes. This article analyzes these issues in depth, exploring philosophical perspectives (...) on testimony and the implications of conversational AI technologies on our epistemic practices. (shrink)

The interpretation of many texts in the everyday world is concerned with their truth value in relation to the reality around us. The recent publication experiments with computer-generated texts have shown that the distinction between true and false, or reality and fiction, is not always clear from the text itself. Essentially, in today’s media space, one may encounter texts, videos or images that deceive the reader by displaying nonsensical content or nonexistent events, while nevertheless appearing as genuine human-produced messages. This (...) article outlines certain problems with artificial intelligence (AI)-generated content, and frames the issue as a problem of recognising its proper referential reality. Examples include generative texts, deepfakes and their functioning in contemporary culture. The article makes use of the concepts of mimicry and nonsense to reveal the elements and counterparts in the communicative processes involving generated media. (shrink)

The increasing use of Generative AI raises many ethical, philosophical, and legal issues. A key issue here is uncertainties about how different degrees of Generative AI assistance in the production of text impacts assessments of the human authorship of that text. To explore this issue, we developed an experimental mixed methods survey study (N = 602) asking participants to reflect on a scenario of a human author receiving assistance to write a short novel as part of a 3 (...) (high, medium, or low degrees of assistance) X 2 (human or AI assistant) factorial design. We found that, for a human author, the degree of assistance they receive matters for our assessments of their level of authorship, creatorship, and responsibility, but not what or who rendered that assistance, although it was more important to disclose human rather than AI assistance. However, in our assessments of the assisting agent, human assistants were viewed as warranting higher rates of authorship, creatorship, and responsibility, compared to AI assistants rendering the same level of support. These results help us to better understand emerging norms around collaborative human-AI generated text, with implications for other types of collaborative content creation. (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)

We are pleased to feature a video interview with Jonathan Sim, where he shares his ongoing journey of integrating artificial intelligence (AI) in his teaching, the challenges encountered along the way, and what educators can do to get their students to meaningfully engage with AI tools like ChatGPT to enhance their learning.

This thesis argues against the claim that AI is intelligent due to instrumental rationality, refuting both the reduction and emergence thesis. It contends that intelligence cannot be reduced to instrumental rationality and highlights issues in AI development and application. Instead, it proposes the motivation adaptation approach, where intelligence arises from network of generative motivations and the ability to adapt. This alternative is conceptually intuitive, avoids counterexamples, and provides clear development goals and foundations for ethical development. Thus, the (...) thesis concludes that AI's intelligence is not due to instrumental rationality but rather its motivation adaptability. (shrink)

L’Intelligenza Artificiale Generativa (IAG) pone sfide inedite all’educazione universitaria. Dalla definizione dei contenuti alla valutazione delle prove e alla determinazione delle modalità di esame, questo nuovo e potente strumento inaugura una stagione di cambiamenti su cui è urgente riflettere. La tecnologia contribuisce da sempre a dare forma al lavoro didattico, il quale evolve anche alla luce delle opportunità e dei rischi che essa introduce. Come far sì che l’evoluzione stimolata dall’IAG sia felice, ovvero serva i valori e gli obiettivi della (...) didattica e delle persone che la vivono? Ne ho discusso con alcuni colleghi dell’Unità di Studi META del Politecnico di Milano – docenti impegnati a vario titolo in corsi di carattere filosofico e sociale rivolti a studenti di formazione tecnica. (shrink)

Andy Clark and David Chalmers’s theory of extended mind can be reevaluated in today’s world to include computational and Artificial Intelligence (AI) technology. This paper argues that AI can be an extension of human mind, and that if we agree that AI can have mind, it too can be extended. It goes on to explore the example of Ganbreeder, an image-making AI which utilizes human input to direct behavior. Ganbreeder represents one way in which AI extended mind could (...) be achieved. The argument of this paper is that AI can utilize human input as a social extension of mind, allowing AI to access the external world that it would not otherwise be able to access. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, (...) users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

Artificial intelligence is man's attempt to use software to emulate the intelligence of human beings. But the complexity of the human neurological system formed in the course of evolution is impossible to replicate: "Human languages and societies are complex systems, indeed complex systems of many complex systems," so much so that their mathematical modeling is impossible. Barry Smith, philosopher and professor at the University at Buffalo. shows no uncertainty about this. His latest book written with Jobst Landgrebe, (...) a mathematician and entrepreneur in the AI field, leaves no doubt – starting with the title: Why machines will never rule the world (Routledge 2023). The book was presented in Turin during an evening organized by the Fondazione CRT and the Bruno Leoni Institute, in which Smith was interviewed based on questions developed by generative Ai. We have accepted the challenge to conduct an interview parallel to the one done with GPT4. (shrink)

Have you been surprised by the recent development and diffusion of generative artificial intelligence (AI)? Many institutions of civil society have been caught off guard, which provides them with motivation to think ahead. And as many new plausible pathways of socio-technical development are opening up, a growing interest in technology ethics that addresses our corresponding moral uncertainties is warranted. In Sven Nyholm’s words, “[t]he field of technology ethics is absolutely exploding at the moment” (p. 262), and so (...) the publication of his introduction to the topic is well timed. (shrink)

In philosophy of language, a distinction has been proposed by Diego Marconi between two aspects of lexical competence, i.e. referential and inferential competence. The former accounts for the relation-ship of words to the world, the latter for the relationship of words among themselves. The aim of the pa-per is to offer a critical discussion of the kind of formalisms and computational techniques that can be used in Artificial Intelligence to model the two aspects of lexical competence, and of (...) the main difficulties related to the use of these computational techniques. The first conclusion of our discussion is that the dis-tinction between inferential and referential semantics is instantiated in the literature of Artificial Intelli-gence by the distinction between symbolic and connectionist approaches. The second conclusion of our discussion is that the modelling of lexical competence needs the advent of hybrid models integrating symbolic and connectionist frameworks. Our hypothesis is that Conceptual Spaces, a framework devel-oped by Gärdenfors more than fifteen years ago, can offer a lingua franca that allows to unify and gener-alize many aspects of the representational approaches mentioned above and to integrate “inferential” (=symbolic) and “referential” (=connectionist) computational approaches on common ground. (shrink)

In an important new Article, titled A Matter of Facts: The Evolution of the Copyright Fact-Exclusion and Its Implications for Disinformation and Democracy, Professor Jessica Silbey argues provocatively that we “‘only” know that facts are excluded from copyright protection because Feist Publications v. Rural Telephone Service “says so.” She argues that both the nature and importance of facts has been underdefined and is in flux, nonetheless tracing it to the foundational cases of United States (U.S.) copyright law, and argues for (...) a stronger exclusion of facts, which are publici juris, and belong in the public domain. This central thesis is most agreeable, as is her analysis of doctrine and legislative history. The Article’s rich study of American pragmatism, legal realism, the Cambridge school of analytic philosophy, and the “new social scientists,” which Professor Silbey uses to support a strong fact-exclusion, is a tour de force. The Article ends with a list of proposals for a strong Feist application to evaluations, catalogs, manuals, and legal documents. It also includes a call not to allow for industrial dilution of copyright’s framework, including the facts and ideas exclusion. Both are laudable and timely in the era of generative artificial intelligence (AI), where proposals to protect non-authorial quasi-expression or to prevent copying of non-expressive subject matter abound. -/- While I agree with a call for a robust public domain and strong fact-exclusion, and devote Part II of this Response to explore further doctrinal grounds for them, in Part III I explore Silbey’s argument that “pragmatist philosophy’s challenge to universal truths combined with legal realist challenges to formalist jurisprudence eventually shape what is (or should be) copyright law’s broad public domain in ‘facts.’” It is not immediately clear if pragmatism and realism are a solid ground for justifying strong legal principles and axioms. Engaging with this provocative argument allows us to uncover what realism means to Professor Silbey and the much more radical consequences of her arguments. Part IV concludes. (shrink)

Problem statement. Modern marketing requires effective tools to attract and retain customers, as well as improve communication with the audience. In this context, the use of artificial intelligence, in particular, ChatGPT (Generative Pre-trained Transformer), can be a promising innovative solution. However, the conclusions about the potential benefits and limitations of using ChatGPT in marketing are ambiguous, due to the little experience gained in this area. The purpose of the study is to assess the potential of using ChatGPT (...) in marketing strategies, to study the opportunities and challenges it presents for improving the efficiency of communication with customers and brand development. The methodological basis of the study is based on modern research on marketing and artificial intelligence and analysis of the results of using ChatGPT in real marketing campaigns. The main hypothesis of the study is the assumption that the use of ChatGPT in marketing can improve the quality of customer service, increase brand engagement, and increase customer loyalty. Summary of the main research material. The study examines the results of using ChatGPT in marketing strategies, in particular in online communications, especially in areas where personal interaction with customers is important. The working hypotheses were confirmed based on the analysis of data and information collected during the study. Possible limitations and challenges related to the implementation of ChatGPT in marketing practices were identified. The originality and practical significance of the study lies in the fact that the analysis helped to reveal the potential of using ChatGPT in marketing strategies and made it possible to formulate recommendations on the optimal use of this technology to attract and retain customers. Conclusions and prospects for further research. The conducted research allowed us to state that the use of ChatGPT in marketing contributes to increasing the efficiency of marketing activities of the enterprise in general and improving communications with customers in particular. Further research may include the development of new algorithms and strategies for the optimal use of artificial intelligence in marketing. (shrink)

The rapid advancement of Artificial Intelligence (AI) and the developments of Large Language Models (LLMs) like Generative Pretrained Transformers (GPTs) have significantly influenced content creation in scholarly communication and across various fields. This paper presents a comparative analysis of the content reliability between human-generated and GPT-generated scholarly articles. Recent developments in AI suggest that GPTs have become capable in generating content that can mimic human language to a greater extent. This highlights and raises questions about the quality, (...) accuracy, and reliability of such content, especially in academic contexts. Statistical evaluations and quantitative assessments conducted in this study uncover key differences in content accuracy, coherence, citation usage, and overall reliability between human and GPT-generated articles. The paper also examines the potential biases, the role of context, and the implications of AI-generated content for the future of scholarly communication. The study concludes with framework for predictive model indicating the potential future impact of GPT-driven content creation and recommendations for ensuring content quality and ethical considerations in AI usage. (shrink)

In this paper, I explore the philosophical apprehension of the face through art history in order to signal a moment of rupture in the contemporary times of the face and its signifying relationship to the subject. Drawing from Francis Galton’s nineteenth century photographic experiments on analytical portraiture, one sees how the face when conceived as an atlas, functions very differently for the subject and its recognition than when understood as a mere image. With the advent of futuristic technology like AI (...) and machine learning feeding in much of our technocratic imaginations, where does the face and its constituency lie? Modernist artists found in the face, various motivations of their own – it was a field to explore their own subjectivities and those inclined towards the transcendental managed to liberate the subject from its image i.e. the face. This manner of freeing the subject of its image and thinking of the face as a structure of the subject, has potential to help us imagine new kinds of polyvalent subjectivities, as can be seen in works of contemporary artist- philosophers like Hito Steyerl. This paper articulates this journey of the face which began its story as the constitutive technology of the subject in the 19th century but today find its best reckoning as an insufficient one, in our quest for understanding new post-humanist subjectivities. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly evolved, offering transformative capabilities for business decision-making. This paper explores how AI can be leveraged to enhance strategic decision-making in business contexts. It examines the integration of AI-driven analytics, predictive modeling, and automation to improve decision accuracy and operational efficiency. By analyzing current applications and case studies, the paper highlights the opportunities AI presents, including enhanced data insights, risk management, and personalized customer experiences. Additionally, it addresses the challenges businesses face in adopting (...) AI, such as data privacy concerns, integration issues, and the need for skilled personnel. The paper concludes with recommendations for effectively implementing AI solutions to maximize their benefits while navigating potential pitfalls. (shrink)

Abstract: Artificial Intelligence (AI) is revolutionizing the agricultural sector by enhancing productivity and sustainability. This paper explores the transformative impact of AI technologies on agriculture, focusing on their applications in precision farming, predictive analytics, and automation. AI-driven tools enable more efficient management of crops and resources, leading to improved yields and reduced environmental impact. The paper examines key AI technologies, including machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource (...) use. Additionally, it addresses challenges such as data privacy, technology adoption barriers, and the ethical implications of AI in farming. By integrating AI into agricultural practices, the industry can achieve greater efficiency and sustainability, paving the way for future advancements. (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)

AI has strengths that humans cannot replicate, such as scalability, speed, and automation, but this must not mean that we depend entirely on AI for intellectual advancement. For a future where humans coexist with advanced AI, we must acknowledge the existence of intrinsic natural stupidity and absurdity of humans and take them into consideration. Otherwise, increasing the information and processing capabilities of AI may amplify the magnitude of humans’ poor decisions and their consequences, but not the other way around.

Artificial intelligence (AI) is increasingly expected to disrupt the ordinary functioning of society. From how we fight wars or govern society, to how we work and play, and from how we create to how we teach and learn, there is almost no field of human activity which is believed to be entirely immune from the impact of this emerging technology. This poses a multifaceted problem when it comes to designing and understanding regulatory responses to AI. This article aims (...) to: (i) defend the need for a novel conceptual model for understanding the systemic legal disruption caused by new technologies such as AI; (ii) to situate this model in relation to preceding debates about the interaction of regulation with new technologies (particularly the ‘cyberlaw’ and ‘robolaw’ debates); and (iii) to set out a detailed model for understanding the legal disruption precipitated by AI, examining both pathways stemming from new affordances that can give rise to a regulatory ‘disruptive moment’, as well as the Legal Development, Displacement or Destruction that can ensue. The article proposes that this model of legal disruption can be broadly generalisable to understanding the legal effects and challenges of other emerging technologies. (shrink)

Over the last two years, the third wave of artificial intelligence (AI) has emerged powerful tools for both artistic expression and scientific research. In design, image generators display an equivalent disruption to text generators, while the medium of text creates the new scope of writing prompts. This contribution discusses the ambivalences between text and image generators via two main theses: first about the potential of prompting and generated images as a medium of discourse; second, it examines the reasoning (...) behind their tendency to be mystified. Prompts can be automated through text generators and used to generate images on a wider scale, making it a praxis in which precise wording becomes crucial for generating anything. Generated images practice a form of mimesis. With Generative Adversial Networks (GANs) producing non-referential images, the discourse of illustration is concerning the obsolescence of authorship. AI is observed to manifest a dual character, serving as both tool and author, even though AI does not own the classification of a person and is not an author in legal terms. The utilization of text and image generators is intertwined with a mythicized notion due to their non-verifiable mode of operation. The myth of AI is a product of its indeterminacy. (shrink)

Artificial Intelligence (AI) is a technology that makes machines smart and capable of doing things that usually require human intelligence. AI works by training machines to learn from data and experiences. Such devices can recognize patterns, understand spoken language, see and understand images, and even make predictions based on their learning. Voice assistants like Siri or Alexa can understand our voice commands, answer questions, and perform tasks for us. AI-based self-driving cars can sense their surroundings, make decisions, (...) and drive safely without a human driver. AI also helps doctors diagnose diseases, detect fraud in financial transactions, and recommend movies or songs based on one’s preferences. Furthermore, AI has started to make its presence felt in Punjabi culture through various avenues. (shrink)

There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives that structure (...) their development. This perspective elaborates on some of these structural challenges and provides recommendations to address potential shortcomings. (shrink)