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)

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)

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)

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 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)

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 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)

The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper analyses the legal and regulatory implications of Generative AI and LLMs in the European Union context, focusing on 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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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 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)

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)

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)

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)

Technology firms are now purchasing access to research papers from academic publishers to train their artificial intelligence (AI) models. Using scientific content to train AI can come with multiple benefits, which help improve AI's capability to generate trustworthy outcomes, understand and process issues across a wide range of topics, as well as analyze information, make logical deductions, and draw conclusions. Journal articles are generally considered reliable because of the rigorous peer review system. However, the evaluation process is constrained (...) by several limitations, so a number of unreliable and invalid studies have been published. Retraction is often viewed as a crucial self-correction mechanism within science, allowing the academic community to identify and flag seriously flawed research that has been published. If retracted articles are used to train AI, it could lead to the spread of misinformation. Given the power of AI, this misinformation could be disseminated on a large scale and in a very short time, which might misinform and significantly increase the entropy (uncertainty) within society. Retracted notices can serve as a signal for AI developers to exclude seriously flawed articles from the training process. However, if the scientific content used to train AI is later discovered to be seriously distorted and retracted, it cannot be removed from the model’s knowledge base after the training is complete. As AI is increasingly applied in critical areas such as education and decision-making, training AI on seriously flawed scientific findings could lead to significant systematic consequences. Therefore, collaboration between the scientific community and AI developers to address these issues is crucial. (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)

This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values in to AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be (...) used to further strengthen design coordination efforts. VSD is shown to be both able to distill these common values as well as provide a framework for stakeholder coordination. (shrink)

This paper explores the theological and philosophical implications of artificial intelligence (AI) and Neuroscience research on the Christian’s notion of the human person. The paschal mystery of Christ is the intuitive foundation of Christian anthropology. In the intellectual history of the Christianity, Platonism and Aristotelianism have been employed to articulate the Christian philosophical anthropology. The Aristotelian systematization has endured to this era. Since the modern period of the Western intellectual history, Aristotelianism has been supplanted by the positive sciences (...) as the standard scientific and philosophical interpretative framework for many. This paper argues that there is need for a new Christian anthropology based on the interpretation of the paschal mystery of Christ that is consistent with contemporary science and philosophy, especially as informed by research in AI and neuroscience. Christian anthropology today ought to be fully aware of the theological and philosophical implications of AI and neuroscience research. This paper denotes this new Christian anthropology as neuro-theological Christian anthropology. (shrink)

Δ.Δακρότσης, «Τεχνητή νοημοσύνη και εκφραστικότητα: η αναγκαιότητα περάσματος της εννοιολογικής γνώσης από το μονοπάτι της αισθητικής εποπτείας» (Artificial Intelligence and expressiveness: The necessity for conceptual knowledge to go through the path of aesthetic perception), Days of Art in Greece, Τεύχος 18, Φθινόπωρο 2024, σς 26-39.

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)

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of (...) a ‘good AI society’; the role and responsibility of the government, the private sector, and the research community in pursuing such a development; and where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. (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)

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated (...) vehicles (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (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)

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence, entitled ‘New Generation Artificial Intelligence Development Plan’. This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this (...) article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents. (shrink)

This paper argues that to negate the ontological difference between the natural and the artificial, is not plausible; nor is the reduction of the natural to the artificial or vice versa possible. Except if one intends to empty the semantic content of the terms and notions: “natural” and “artificial.” Most philosophical discussions on Artificial Intelligence (AI) have always been in relation to the human person, especially as it relates to human intelligence, consciousness and/or mind (...) in general. This paper, intends to broaden the conversation, by discussing AI in relation to the notions of “nature” and the “artificial.” This intention is to more critically understand the artificiality in and of artificial intelligence. To achieve this, the notion of “nature” in Aristotle’s Philosophy of Nature, has been employed as an epistemological tool in interrogating the notion of the artificial and the objectives of the science and technology of Artificial Intelligence. (shrink)

Sora from Open AI has shown exceptional performance, yet it faces scrutiny over whether its technological prowess equates to an authentic comprehension of reality. Critics contend that it lacks a foundational grasp of the world, a deficiency V-JEPA from Meta aims to amend with its joint embedding approach. This debate is vital for steering the future direction of Artificial General Intelligence(AGI). We enrich this debate by developing a theory of productive imagination that generates a coherent world model based (...) on Kantian philosophy. We identify three indispensable components in the coherent world model that enable genuine world understanding: latent representations of isolated objects, an a priori law of change across space and time, and Kantian categories. Our analysis reveals that Sora is limited because of its oversight of the a priori law of change and Kantian categories, flaws that are not rectifiable through scaling up the training. V-JEPA learns the context-dependent aspect of the a priori law of change. Yet it fails to fully comprehend Kantian categories and incorporate experience, leading us to conclude that neither system currently achieves a comprehensive world understanding. Nevertheless, each system has developed components essential to advancing an integrated AI productive imagination-understanding engine. Finally, we propose an innovative training framework for an AI productive imagination-understanding engine, centered around a joint embedding system designed to transform disordered perceptual input into a structured, coherent world model. Our philosophical analysis pinpoints critical challenges within contemporary video AI technologies and a pathway toward achieving an AI system capable of genuine world understanding, such that it can be applied for reasoning and planning in the future. (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)

The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green (...) honeymoon between health and sustainability caused by AI, it should aim for a lasting marriage. To this end, we conclude by proposing that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility. (shrink)

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to partial data collection, rare updates, and significant resource demands. To address these issues, the article suggests that specific data management and Machine Learning techniques, such as natural language processing and sentiment analysis, can (...) improve the measurement and practice of IPD. (shrink)

Artificial Intelligence (AI) is a technology that makes machines smart and capable of doing things that usually require human intelligence. It is a rapidly evolving field with ongoing research and development to advance its capabilities and address its limitations. AI has permeated various aspects of our daily lives, and its applications can be found in numerous products and services. The integration of AI continues to expand across multiple sectors, providing convenience, personalization, and efficiency in our daily lives. (...) While Punjabi culture is rich and diverse, characterized by several distinctive traits that define its identity, AI has started to make its presence felt in Punjabi culture through various avenues. Though AI is gradually enriching Punjabi culture, it is still in its early stages, and there is room for further exploration and integration. However, it is essential to ensure that AI's influence aligns with the values and aspirations of Punjabi culture while preserving its authenticity and uniqueness. (shrink)

Artificial intelligence has arrived. In the online world it is already a part of everyday life, sitting invisibly behind a wide range of search engines and online commerce sites. It offers huge potential to enable more efficient and effective business and government but the use of artificial intelligence brings with it important questions about governance, accountability and ethics. Realising the full potential of artificial intelligence and avoiding possible adverse consequences requires societies to find satisfactory (...) answers to these questions. This report sets out some possible approaches, and describes some of the ways government is already engaging with these issues. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young (...) and inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is transforming organizational dynamics and This decision-making processes. paper explores how AI can enhance leadership effectiveness by providing data-driven insights, optimizing decision-making, and automating routine tasks. It also examines the challenges leaders face in adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to provide a comprehensive overview (...) of the opportunities AI presents for effective leadership and the strategies required to address its associated challenges. (shrink)

Recent progress in artificial intelligence (AI) has drawn attention to the technology’s transformative potential, including what some see as its prospects for causing large-scale harm. We review two influential arguments purporting to show how AI could pose catastrophic risks. The first argument — the Problem of Power-Seeking — claims that, under certain assumptions, advanced AI systems are likely to engage in dangerous power-seeking behavior in pursuit of their goals. We review reasons for thinking that AI systems might seek (...) power, that they might obtain it, that this could lead to catastrophe, and that we might build and deploy such systems anyway. The second argument claims that the development of human-level AI will unlock rapid further progress, culminating in AI systems far more capable than any human — this is the Singularity Hypothesis. Power-seeking behavior on the part of such systems might be particularly dangerous. We discuss a variety of objections to both arguments and conclude by assessing the state of the debate. (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, (...) linguistic competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

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)

There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. (...) We thus designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity. (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)