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)

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)

Trust is the foundation for learning, and we must not allow ignorance of this new technologies, like Generative AI, to disrupt the relationship between students and educators. As a first step, we need to actively engage with AI tools to better understand how they can help us in our work.

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

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research (...) on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia. (shrink)

“Throw away anything that sounds too complicated. Only keep what is simple to grasp...If the information appears fuzzy and causes the brain to implode after two sentences, toss it away and stop listening. Doing so will make the news as orderly and simple to understand as the truth.” - In “GHG emissions,” The Kingfisher Story Collection, (Vuong 2022a).

The recent remarkable success of generative AI models to create text and images has already started altering our perspective of intelligence and the “uniqueness” of humanity in this world. Simultaneously, arguments on why AI will never exceed human intelligence are ever-present as seen in Landgrebe and Smith (2022). To address whether machines may rule the world after all, this paper zooms in on one of the aspects of intelligence Landgrebe and Smith (2022) neglected to consider: creativity. Using Rhodes four (...) Ps of creativity as a starting point, this paper evaluates the creative ability in visual generative AI models with respect to the state of the art in creativity theory. The most part of the reflective evaluation is performed through a case study in generating illustrations of dogs using the generative AI tool Midjourney. (shrink)

In January 2024, non-consensual deepfakes came to public attention with the spread of AI generated sexually abusive images of Taylor Swift. Although this brought new found energy to the debate on what some call non-consensual synthetic intimate images (i.e. images that use technology such as AI or photoshop to make sexual images of a person without their consent), female celebrities like Swift have had deepfakes like these made of them for years. In 2017, a Reddit user named “deepfakes” posted several (...) videos in which he had used opensource machine learning tools to swap the faces of female celebrities on to the faces of female porn actors, displaying what appeared to be live video footage of the celebrity engaging in sex acts she never engaged in. Since that time, deepfake technology has advanced astronomically. What once were choppy sexualized videos are now nearly flawless videos that can be difficult to distinguish from a real video. According to recent research on deepfakes by Sensity AI, this technology has been used primarily on women to create sexual videos. These women’s sexual autonomy has been co-opted for the purposed of gratifying men’s sexual pleasure, but have also been used in campaigns to delegitimize and humiliate female journalists and politicians. In Canada, civil and criminal legislation has addressed the non-consensual distribution of intimate images, but in only a few provinces – British Columbia, New Brunswick, Prince Edward Island and Saskatchewan – does this offence include altered images that could include deepfakes. This paper explores the evolution of synthetic technology such as AI image generators and Canada’s legal responses to the non-consensual sharing of intimate 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)

Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other things, Habermas's inquiries (...) into the analogy between human minds and computers, and into the status of atypical participants in the linguistic community such as genetically modified subjects and animals. Major conclusions are the LLMs seem to qualify as authors that originally participate in discursive practices but do display only a structurally derivative form of communicative competence and fail to meet the status of communicative agents. In this sense, while the contribution of AI writing systems in public discourse and deliberation can support the process of mutual understanding within the community of speakers, the human actors involved in the development, use, and diffusion of these systems share a collective responsibility for the disclosure of AI authorship and verification and adjudication of validity claims. (shrink)

Since the release of OpenAI's ChatGPT, the world has entered a race to develop more capable and powerful AI, including artificial general intelligence (AGI). The development is constrained by the dependency of AI on the model, quality, and quantity of training data, making the AI training process highly costly in terms of resources and environmental consequences. Thus, improving the effectiveness and efficiency of the AI training process is essential, especially when the Earth is approaching the climate tipping points and planetary (...) boundaries. It is evident that AI can function better when trained to mimic certain human mental processes. Based on insights from quantum mechanics and the informational entropy-based notion of value, we suggest AI developers can enhance the AI training processes' effectiveness and efficiency by creating and implementing parameter systems that are capable of effectively assigning probabilities to informational quanta. Such systems can help reduce entropy within the system, which lowers the energy required for data storage and processing while decreasing the likelihood of information loss during training. Successfully applying the concept of information entropy-based value in AI development will be crucial for advancing generative AI and achieving AGI in a sustainable manner. (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)

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact (...) can give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

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)

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

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...) effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

According to virtue epistemology, the main aim of education is the development of the cognitive character of students (Pritchard, 2014, 2016). Given the proliferation of technological tools such as ChatGPT and other LLMs for solving cognitive tasks, how should educational practices incorporate the use of such tools without undermining the cognitive character of students? Pritchard (2014, 2016) argues that it is possible to properly solve this ‘technology-education tension’ (TET) by combining the virtue epistemology framework with the theory of extended cognition (...) (EXT) (Clark and Chalmers, 1998). He argues that EXT enables us to consider tools as constitutive parts of the students’ cognitive system, thus preserving their cognitive character from technologically induced cognitive diminishment. The first aim of this paper is to show that this solution is not sufficient to solve the TET. Second, I aim to offer a complementary and more encompassing framework of tool-use to address the TET. Then, I apply it to the educational uses of ChatGPT as the most notable example of LLM, although my arguments can be extended to other generative AI systems. To do so, in Sect. 1.1, I present Pritchard’s framework of cognitive character and virtue epistemology applied in education, to which I am committed in this treatment. In Sects. 2 and 3, I respectively illustrate Pritchard’s (2014) solution to the TET, and I highlight the general limitations of his proposal. Thus, in Sect. 4.1 I characterize ChatGPT as a computational cognitive artifact using Fasoli’s (2017, 2018) taxonomy of cognitive artifacts. In Sect. 4.2, I introduce my proposal, which combines Pritchard’s account of virtue epistemology with Fasoli’s (2017, 2018) taxonomy of cognitive artifacts to address the TET. Finally, in Sect. 5.1, I present some epistemically virtuous uses of ChatGPT in educational contexts. To conclude, I argue in favor of a multidisciplinary approach for analyzing educational activities involving AI technologies such as ChatGPT. (shrink)

The generative AI boom is unleashing its minions. Enterprise software vendors have rolled out legions of automated assistants that use large language model (LLM) technology, such as ChatGPT, to offer users helpful suggestions or to execute simple tasks. These so-called copilots and chatbots can increase productivity and automate tedious manual work. In this article, we explain how that leads to the risk that users' ethical competence may degrade over time — and what to do about it.

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)

Ask most any cognitive scientist working today if a digital computational system could develop aesthetic sensibility and you will likely receive the optimistic reply that this remains an open empirical question. However, I attempt to show, while drawing upon the later Wittgenstein, that the correct answer is in fact available. And it is a negative a priori. It would seem, for example, that recent computational successes in generative AI and textual attribution, most notably those of Donald Foster (famed finder (...) of Ted Kazinski a.k.a. “the Unibomber”) speak favorably of the connectionist model's capacity to overcome the “aspect blindness” handicap in this domain. I argue however that such results are only achievable when rigid input‐to‐output parameters are given, and that this element is precisely what is absent in standard examples of aesthetic judgment. I thus conclude that while the connectionist model anticipated by Turing may provide the best approach for the AI project, its capacity for meeting its own sufficiency requirements is necessarily crippled by its inability to share in what can be generally referred to as the collective engagements of human solidarity. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered by conflations (...) of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (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)

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)

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead (...) to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology. (shrink)

This study aims to understand the perceptions and opinions of academicians towards ChatGPT-3 by collecting and analyzing social media comments, and a survey was conducted with library and information science professionals. The research uses a content analysis method and finds that while ChatGPT-3 can be a valuable tool for research and writing, it is not 100% accurate and should be cross-checked. The study also finds that while some academicians may not accept ChatGPT-3, most are starting to accept it. The study (...) is beneficial for academicians, content developers, and librarians. (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)

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)

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

As educators, we often ask if our students are ready for the future, but how often do we ask ourselves if we educators are ready for the future?

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research (...) on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia. (shrink)

The potential of using personalized Large Language Models (LLMs) or “generative AI” (GenAI) to enhance productivity in academic research, as highlighted by Porsdam Mann and colleagues (Porsdam Mann...

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)

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)

With the recent renewed interest in AI, the field has made substantial advancements, particularly in generative systems. Increased computational power and the availability of very large datasets has enabled systems such as ChatGPT to effectively replicate aspects of human social interactions, such as verbal communication, thus bringing about profound changes in society. In this paper, we explain that the arrival of generative AI systems marks a shift from ‘interacting through’ to ‘interacting with’ technologies and calls for a reconceptualization (...) of socio-technical systems as we currently understand them. We dub this new generation of socio-technical systems synthetic to signal the increased interactions between human and artificial agents, and, in the footsteps of philosophers of information, we cash out agency in terms of ‘poiêsis’. We close the paper with a discussion of the potential policy implications of synthetic socio-technical system. -/- . (shrink)

Jonathan takes us through his experiences of being a mentor for UTOP (University Teaching Opportunities Programme), particularly how it enabled him to collaborate with his UTOP student mentees to design a learning activity in which students could think critically about AI-generated output.

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.

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 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)

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 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)

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)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in saliva (...) diagnostics made possible by the Saliva Ontology and SDxMart. (shrink)

We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how the (...) body fluids ontology initiative may provide support to basic and translational science. (shrink)

Hiện nay, chế độ sở hữu ở Việt Nam đã có những đổi mới cơ bản, nhưng vẫn còn những khác biệt rất lớn so với chế độ sở hữu ở các nền kinh tế thị trường hiện đại. Trong cơ cấu của chế độ sở hữu ở Việt Nam, tỷ trọng của sở hữu nhà nước còn quá lớn; kinh tế nhà nước giữ vai trò chủ đạo… Chính những khác biệt này đã làm cho nền kinh tế thị (...) trường Việt Nam bị méo mó và giảm lợi thế cạnh tranh quốc gia. Do vậy, công cuộc đổi mới và hoàn thiện chế độ sở hữu ở Việt Nam phải hướng tới việc khắc phục những hạn chế trên và đạt tới sức cạnh tranh ngang hàng với các nền kinh tế thị trường hiện đại khác. (shrink)

The study determined the problem-solving performance and skills of prospective elementary teachers (PETs) in the Northern Philippines. Specifically, it defined the PETs’ level of problem-solving performance in number sense, measurement, geometry, algebra, and probability; significant predictors of their problem-solving performance in terms of sex, socio-economic status, parents’ educational attainment, high school graduated from and subject preference; and their problem-solving skills. The PETs’ problem-solving performance was determined by a problem set consisting of word problems with number sense, measurement, geometry, algebra, and (...) probability. A mixed-method research design was employed. Senior PETs purposively served as a sample where they mostly preferred to teach other subjects than mathematics. PETs who preferred math performed satisfactorily, while prospective teachers who opted for other subjects performed unsatisfactorily. The PETs’ unsatisfactory output indicates the need for remediation to advance the mathematical material skills and enrich the problem-solving abilities of these primary schools' potential teachers. Besides, results showed that subject preference strongly affected and predicted the problem-solving success of the PETs. PETs who preferred to teach mathematics performed significantly better than their counterparts; hence, mathematics as a field of specialization in the Bachelor of Elementary Education program may be considered by teacher education institutions. Further, most PETs displayed lack of problem-solving skills; thus, a Problem-Solving course is recommended for them. (shrink)