Results for 'AI-Generated Art'

978 found
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  1. Interpreting AI-Generated Art: Arthur Danto’s Perspective on Intention, Authorship, and Creative Traditions in the Age of Artificial Intelligence.Raquel Cascales - 2023 - Polish Journal of Aesthetics 71 (4):17-29.
    Arthur C. Danto did not live to witness the proliferation of AI in artistic creation. However, his philosophy of art offers key ideas about art that can provide an interesting perspective on artwork generated by artificial intelligence (AI). In this article, I analyze how his ideas about contemporary art, intention, interpretation, and authorship could be applied to the ongoing debate about AI and artistic creation. At the same time, it is also interesting to consider whether the incorporation of AI (...)
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  2. AI-generated art and fiction: signifying everything, meaning nothing?Steven R. Kraaijeveld - forthcoming - AI and Society:1-3.
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  3. Unveiling the Creation of AI-Generated Artworks: Broadening Worringerian Abstraction and Empathy Beyond Contemplation.Leonardo Arriagada - 2024 - Estudios Artísticos 10 (16):142-158.
    In his groundbreaking work, Abstraction and Empathy, Wilhelm Worringer delved into the intricacies of various abstract and figurative artworks, contending that they evoke distinct impulses in the human audience—specifically, the urges towards abstraction and empathy. This article asserts the presence of empirical evidence supporting the extension of Worringer’s concepts beyond the realm of art appreciation to the domain of art-making. Consequently, it posits that abstraction and empathy serve as foundational principles guiding the production of both abstract and figurative art. This (...)
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  4. AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks.Trystan S. Goetze - 2024 - Proceedings of the 2024 Acm Conference on Fairness, Accountability, and Transparency:186-196.
    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 (...)
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  5. Generative AI in the Creative Industries: Revolutionizing Art, Music, and Media.Mohammed F. El-Habibi, Mohammed A. Hamed, Raed Z. Sababa, Mones M. Al-Hanjori, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Engineering Research(Ijaer) 8 (10):71-74.
    Abstract: Generative AI is transforming the creative industries by redefining how art, music, and media are produced and experienced. This paper explores the profound impact of generative AI technologies, such as deep learning models and neural networks, on creative processes. By enabling artists, musicians, and content creators to collaborate with AI, these systems enhance creativity, speed up production, and generate novel forms of expression. The paper also addresses ethical considerations, including intellectual property rights, the role of human creativity in AI-assisted (...)
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  6. AI-aesthetics and the artificial author.Emanuele Arielli - forthcoming - Proceedings of the European Society for Aesthetics.
    ABSTRACT. Consider this scenario: you discover that an artwork you greatly admire, or a captivating novel that deeply moved you, is in fact the product of artificial intelligence, not a human’s work. Would your aesthetic judgment shift? Would you perceive the work differently? If so, why? The advent of artificial intelligence (AI) in the realm of art has sparked numerous philosophical questions related to the authorship and artistic intent behind AI-generated works. This paper explores the debate between viewing AI (...)
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  7. Generative AI and photographic transparency.P. D. Magnus - forthcoming - AI and Society:1-6.
    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 (...)
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  8. Can ChatGPT be an author? Generative AI creative writing assistance and perceptions of authorship, creatorship, responsibility, and disclosure.Paul Formosa, Sarah Bankins, Rita Matulionyte & Omid Ghasemi - forthcoming - AI and Society.
    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, (...)
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  9. Tool, Collaborator, or Participant: AI and Artistic Agency.Anthony Cross - forthcoming - British Journal of Aesthetics.
    Artificial intelligence is now capable of generating sophisticated and compelling images from simple text prompts. In this paper, I focus specifically on how artists might make use of AI to create art. Most existing discourse analogizes AI to a tool or collaborator; this focuses our attention on AI’s contribution to the production of an artistically significant output. I propose an alternative approach, the exploration paradigm, which suggests that artists instead relate to AI as a participant: artists create a space for (...)
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  10. AI’s Role in Creative Processes: A Functionalist Approach.Leonardo Arriagada & Gabriela Arriagada-Bruneau - 2022 - Odradek. Studies in Philosophy of Literature, Aesthetics, and New Media Theories 8 (1):77-110.
    From 1950 onwards, the study of creativity has not stopped. Today, AI has revitalised debates on the subject. That is especially controversial in the artworld, as the 21st century already features AI-generated artworks. Without discussing issues about AI agency, this article argues for AI’s creativity. For this, we first present a new functionalist understanding of Margaret Boden’s definition of creativity. This is followed by an analysis of empirical evidence on anthropocentric barriers in the perception of AI’s creative capabilities, which (...)
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  11. Exploring the Intersection of Rationality, Reality, and Theory of Mind in AI Reasoning: An Analysis of GPT-4's Responses to Paradoxes and ToM Tests.Lucas Freund - manuscript
    This paper investigates the responses of GPT-4, a state-of-the-art AI language model, to ten prominent philosophical paradoxes, and evaluates its capacity to reason and make decisions in complex and uncertain situations. In addition to analyzing GPT-4's solutions to the paradoxes, this paper assesses the model's Theory of Mind (ToM) capabilities by testing its understanding of mental states, intentions, and beliefs in scenarios ranging from classic ToM tests to complex, real-world simulations. Through these tests, we gain insight into AI's potential for (...)
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  12. Turning queries into questions: For a plurality of perspectives in the age of AI and other frameworks with limited (mind)sets.Claudia Westermann & Tanu Gupta - 2023 - Technoetic Arts 21 (1):3-13.
    The editorial introduces issue 21.1 of Technoetic Arts via a critical reflection on the artificial intelligence hype (AI hype) that emerged in 2022. Tracing the history of the critique of Large Language Models, the editorial underscores that there are substantial ethical challenges related to bias in the training data, copyright issues, as well as ecological challenges which the technology industry has consistently downplayed over the years. -/- The editorial highlights the distinction between the current AI technology’s reliance on extensive pre-existing (...)
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  13. AI-aesthetics and the Anthropocentric Myth of Creativity.Emanuele Arielli & Lev Manovich - 2022 - NODES 1 (19-20).
    Since the beginning of the 21st century, technologies like neural networks, deep learning and “artificial intelligence” (AI) have gradually entered the artistic realm. We witness the development of systems that aim to assess, evaluate and appreciate artifacts according to artistic and aesthetic criteria or by observing people’s preferences. In addition to that, AI is now used to generate new synthetic artifacts. When a machine paints a Rembrandt, composes a Bach sonata, or completes a Beethoven symphony, we say that this is (...)
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  14. Texts Without Authors: Ascribing Literary Meaning in the Case of AI.Sofie Vlaad - forthcoming - Journal of Aesthetics and Art Criticism.
    With the increasing popularity of Large Language Models (LLMs), there has been an increase in the number of AI generated literary works. In the absence of clear authors, and assuming such works have meaning, there lies a puzzle in determining who or what fixes the meaning of such texts. I give an overview of six leading theories for ascribing meaning to literary works. These are Extreme Actual Intentionalism, Modest Actual Intentionalism (1 & 2), Conventionalism, Actual Author Hypothetical Intentionalism, and (...)
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  15.  77
    A New Harmonisation of Art and Technology: Philosophic Interpretations of Artificial Intelligence Art.Tao Feng - 2022 - Critical Arts 36 (1-2):110-125.
    Artificial intelligence (AI) art is the product of AI technology applied to art. In terms of technical application, AI art has two methods: symbolism and connectivism. In terms of the human-machine system, there are three levels: human using machine, human guiding machine and human-machine separation. AI art is a special form, existing between natural beauty and human art: AI art, first of all, is not a natural aesthetic object, given that it is the product of artefacts. Its appreciation is mixed (...)
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  16. Every dog has its day: An in-depth analysis of the creative ability of visual generative AI.Maria Hedblom - 2024 - Cosmos+Taxis 12 (5-6):88-103.
    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 (...)
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  17. Prosthetic Godhood and Lacan’s Alethosphere: The Psychoanalytic Significance of the Interplay of Randomness and Structure in Generative Art.Rayan Magon - 2023 - 26Th Generative Art Conference.
    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 (...)
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  18. A Portrait of the Artist as a Young Algorithm.Sofie Vlaad - 2024 - Ethics and Information Technology 26 (3):1-11.
    This article explores the question as to whether images generated by Artificial Intelligence such as DALL-E 2 can be considered artworks. After providing a brief primer on how technologies such as DALL-E 2 work in principle, I give an overview of three contemporary accounts of art and then show that there is at least one case where an AI-generated image meets the criteria for art membership under all three accounts. I suggest that our collective hesitancy to call AI- (...) images art stems from the lack of a clear author figure. I propose two possible complementary solutions. First, that some AI-generated images as artworks are conjunctively authored by both the developers of the AI and the prompt-giver. Second, that AI image generators can themselves be considered works of art authored by the developers. I conclude by way of suggesting that we might have separate art competitions specifically for AI-generated artworks. (shrink)
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  19.  82
    Yapay Zekâ Görüntü Üretme Modelleri ile Film Yapımı.Doga Col - 2024 - In Ali Büyükaslan & Başak Gezmen (eds.), Edebiyat, Sinema ve İletişim. İstanbul: Çizgi Kitabevi. pp. 217 -233.
    Filmmaking with Artificial Intelligence Image Generation Models In just about one year, OpenAI’s release of ChatGPT 4 has caused panic in our daily lives. After a year, OpenAI introduced Sora, a moving image-generating model from text, similar to DALL-E for still images. Even though Sora is not yet available to the public, the very potential itself has raised issues in film production from the perspectives of producers and studios, as well as directors, actors, writers, and editors. In this chapter, the (...)
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  20.  81
    Harnessing Artificial Intelligence for Sikhism: Opportunities and Risks.Devinder Pal Singh - 2024 - Understanding Sikhism - The Research Journal 26 (1):25-34.
    Artificial Intelligence (AI) holds transformative potential for Sikhism, enhancing access to Gurbani, preserving history, and fostering global community connections. AI platforms can translate and recommend passages from the Guru Granth Sahib, broadening understanding across languages and contexts. Digitizing historical Sikh texts and artifacts safeguards them for future generations, while virtual congregation platforms and AI-powered tools can connect Sikhs worldwide, promoting spiritual growth and unity. Additionally, social media tools can amplify Sikh values like equality and Seva. However, these advancements carry risks. (...)
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  21. Criatividade, Transhumanismo e a metáfora Co-criador Criado.Eduardo R. Cruz - 2017 - Quaerentibus 5 (9):42-64.
    The goal of Transhumanism is to change the human condition through radical enhancement of its positive traits and through AI (Artificial Intelligence). Among these traits the transhumanists highlight creativity. Here we first describe human creativity at more fundamental levels than those related to the arts and sciences when, for example, childhood is taken into account. We then admit that creativity is experienced on both its bright and dark sides. In a second moment we describe attempts to improve creativity both at (...)
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  22. The value of testimonial-based beliefs in the face of AI-generated quasi-testimony.Felipe Alejandro Álvarez Osorio & Ruth Marcela Espinosa Sarmiento - 2024 - Aufklärung 11 (Especial):25-38.
    The value of testimony as a source of knowledge has been a subject of epistemological debates. The "trust theory of testimony" suggests that human testimony is based on an affective relationship supported by social norms. However, the advent of generative artificial intelligence challenges our understanding of genuine testimony. The concept of "quasi-testimony" seeks to characterize utterances produced by non-human entities that mimic testimony but lack certain fundamental attributes. This article analyzes these issues in depth, exploring philosophical perspectives on testimony and (...)
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  23. Large Language Models: Assessment for Singularity.R. Ishizaki & Mahito Sugiyama - forthcoming - AI and Society.
    The potential for Large Language Models (LLMs) to attain technological singularity—the point at which artificial intelligence (AI) surpasses human intellect and autonomously improves itself—is a critical concern in AI research. This paper explores the feasibility of current LLMs achieving singularity by examining the philosophical and practical requirements for such a development. We begin with a historical overview of AI and intelligence amplification, tracing the evolution of LLMs from their origins to state-of-the-art models. We then proposes a theoretical framework to assess (...)
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  24. Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution.Flor Miriam Plaza-del Arco, Amanda Cercas Curry & Alba Curry - 2024 - Arxiv.
    Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We (...)
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  25. Content Reliability in the Age of AI: A Comparative Study of Human vs. GPT-Generated Scholarly Articles.Rajesh Kumar Maurya & Swati R. Maurya - 2024 - Library Progress International 44 (3):1932-1943.
    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, (...)
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  26. Artificial Intelligence as Art – What the Philosophy of Art can offer the understanding of AI and Consciousness.Hutan Ashrafian - manuscript
    Defining Artificial Intelligence and Artificial General Intelligence remain controversial and disputed. They stem from a longer-standing controversy of what is the definition of consciousness, which if solved could possibly offer a solution to defining AI and AGI. Central to these problems is the paradox that appraising AI and Consciousness requires epistemological objectivity of domains that are ontologically subjective. I propose that applying the philosophy of art, which also aims to define art through a lens of epistemological objectivity where the domains (...)
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  27.  37
    Development of AI-Based Object Detection and Alarm Generation System.P. Rathna Sekhar - 2024 - International Journal of Engineering Innovations and Management Strategies, 1 (4):1-12.
    . In this article, we propose an innovative method that utilizes AI for the real-time detection of humans and automatically triggers an alarm using the latest object detection methods based on algorithms such as YOLO (You Only Look Once). The key goal of the system is to facilitate rescue operations, enhance security surveillance, and minimize the turnaround time for any emergency actions. This allows, for example, the placement of such a system in a live video stream, and the person will (...)
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  28. Logics for AI and Law: Joint Proceedings of the Third International Workshop on Logics for New-Generation Artificial Intelligence and the International Workshop on Logic, AI and Law, September 8-9 and 11-12, 2023, Hangzhou.Bruno Bentzen, Beishui Liao, Davide Liga, Reka Markovich, Bin Wei, Minghui Xiong & Tianwen Xu (eds.) - 2023 - College Publications.
    This comprehensive volume features the proceedings of the Third International Workshop on Logics for New-Generation Artificial Intelligence and the International Workshop on Logic, AI and Law, held in Hangzhou, China on September 8-9 and 11-12, 2023. The collection offers a diverse range of papers that explore the intersection of logic, artificial intelligence, and law. With contributions from some of the leading experts in the field, this volume provides insights into the latest research and developments in the applications of logic in (...)
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  29. Material, Form and Art: The Generation of Freedom.John T. Sanders - manuscript
    Freedom is generated in at least two distinct ways: as the ability to avoid perceived dangers and pursue perceived goods, and even to pursue complicated plans in those directions, freedom evolves. But as a social and political matter, freedom seems more subject to human will. The best social institutions -- the kind that serve to encourage or constrain freedom of choice -- also appear to be evolutionary products in some sense. Can there be too much freedom? Of course there (...)
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  30. Medical AI: is trust really the issue?Jakob Thrane Mainz - 2024 - Journal of Medical Ethics 50 (5):349-350.
    I discuss an influential argument put forward by Hatherley in theJournal of Medical Ethics. Drawing on influential philosophical accounts of interpersonal trust, Hatherley claims that medical artificial intelligence is capable of being reliable, but not trustworthy. Furthermore, Hatherley argues that trust generates moral obligations on behalf of the trustee. For instance, when a patient trusts a clinician, it generates certain moral obligations on behalf of the clinician for her to do what she is entrusted to do. I make three objections (...)
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  31. AI-Testimony, Conversational AIs and Our Anthropocentric Theory of Testimony.Ori Freiman - 2024 - Social Epistemology 38 (4):476-490.
    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 (...)
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  32. AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development.Kristina Sekrst, Jeremy McHugh & Jonathan Rodriguez Cefalu - manuscript
    This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in (...)
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  33. Unjustified untrue "beliefs": AI hallucinations and justification logics.Kristina Šekrst - forthcoming - In Kordula Świętorzecka, Filip Grgić & Anna Brozek (eds.), Logic, Knowledge, and Tradition. Essays in Honor of Srecko Kovac.
    In artificial intelligence (AI), responses generated by machine-learning models (most often large language models) may be unfactual information presented as a fact. For example, a chatbot might state that the Mona Lisa was painted in 1815. Such phenomenon is called AI hallucinations, seeking inspiration from human psychology, with a great difference of AI ones being connected to unjustified beliefs (that is, AI “beliefs”) rather than perceptual failures). -/- AI hallucinations may have their source in the data itself, that is, (...)
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  34. Chinese Chat Room: AI hallucinations, epistemology and cognition.Kristina Šekrst - 2024 - Studies in Logic, Grammar and Rhetoric 69 (1):365-381.
    The purpose of this paper is to show that understanding AI hallucination requires an interdisciplinary approach that combines insights from epistemology and cognitive science to address the nature of AI-generated knowledge, with a terminological worry that concepts we often use might carry unnecessary presuppositions. Along with terminological issues, it is demonstrated that AI systems, comparable to human cognition, are susceptible to errors in judgement and reasoning, and proposes that epistemological frameworks, such as reliabilism, can be similarly applied to enhance (...)
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  35. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  36.  55
    AI Contribution Value System Argument.Michael Haimes - manuscript
    The AI Contribution Value System Argument proposes a framework in which AI-generated contributions are valued based on their societal impact rather than traditional monetary metrics. Traditional economic systems often fail to capture the enduring value of AI innovations, which can mitigate pressing global challenges. This argument introduces a contribution-based valuation model grounded in equity, inclusivity, and sustainability. By incorporating measurable metrics such as quality-adjusted life years (QALYs), emissions reduced, and innovations generated, this system ensures rewards align with tangible (...)
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  37.  77
    The Decline of Collective Intelligence Regarding Ai.Paul Bates - manuscript
    This paper explores the critical role of collective knowledge in detecting AI-generated content and the potential consequences of its decline. As AI-generated media becomes increasingly sophisticated, the ability to distinguish between reality and fiction is at risk. The paper examines the implications of this erosion for social cohesion, decision-making, and economic stability, and proposes strategies to mitigate these risks. By fostering critical thinking, promoting transparency, and developing technological solutions, we can preserve collective knowledge and ensure a more informed (...)
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  38. (1 other version)Can Artificial Intelligence Make Art?Elzė Sigutė Mikalonytė & Markus Kneer - 2022 - ACM Transactions on Human-Robot Interactions.
    In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less (...)
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  39. AI language models cannot replace human research participants.Jacqueline Harding, William D’Alessandro, N. G. Laskowski & Robert Long - 2024 - AI and Society 39 (5):2603-2605.
    In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificially intelligent systems, such as large language models, replacing human subjects in empirical moral psychology. We argue that human subjects are in various ways indispensable.
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  40.  23
    The Inefficiency of the Biological Brain and the Importance of AI for the Next Generation.Angelito Malicse - manuscript
    The Inefficiency of the Biological Brain and the Importance of AI for the Next Generation -/- The human brain, often considered the pinnacle of evolutionary design, is an extraordinary organ capable of creativity, critical thinking, and adaptation. However, despite its remarkable abilities, it is inherently inefficient when compared to artificial intelligence (AI) systems in certain domains. The inefficiencies of the biological brain, coupled with the rapid development of AI technology, underline why artificial general intelligence (AGI) holds immense promise for shaping (...)
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  41. (1 other version)CG-Art: demystifying the anthropocentric bias of artistic creativity.Leonardo Arriagada - 2020 - Connection Science 32 (4):398-405.
    The following aesthetic discussion examines in a philosophical-scientific way the relationship between computation and artistic creativity. Currently, there is a criticism about the possible artistic creativity that an algorithm could have. Supporting the above, the term computer-generated art (CG-Art) defined by Margaret Boden would seem to have no exponents yet. Moreover, it has been pointed out that, rather than a matter of primitive technological development, CG-Art would have in its very foundations the inability to exist. This, because art is (...)
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  42.  24
    Generative AI in Graph-Based Spatial Computing: Techniques and Use Cases.Sankara Reddy Thamma Sankara Reddy Thamma - 2024 - International Journal of Scientific Research in Science and Technology 11 (2):1012-1023.
    Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates (...)
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  43. AI as Ideology: A Marxist Reading (Crawford, Marx/Engels, Debord, Althusser).Jeffrey Reid - manuscript
    Kate Crawford presents AI as “both reflecting and producing social relations and understandings of the world”; or again, as “a form of exercising power, and a way of seeing… as a manifestation of highly organized capital backed by vast systems of extraction and logistics, with supply chains that wrap around the entire planet”. I interpret these material insights through a Marxist understanding of ideology, with reference to Marx/Engels, Guy Debord and Louis Althusser. In the German Ideology, Marx and Engels present (...)
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  44. The privacy dependency thesis and self-defense.Lauritz Aastrup Munch & Jakob Thrane Mainz - 2024 - AI and Society 39 (5):2525-2535.
    If I decide to disclose information about myself, this act may undermine other people’s ability to conceal information about them. Such dependencies are called privacy dependencies in the literature. Some say that privacy dependencies generate moral duties to avoid sharing information about oneself. If true, we argue, then it is sometimes justified for others to impose harm on the person sharing information to prevent them from doing so. In this paper, we first show how such conclusions arise. Next, we show (...)
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  45. Medical AI, Inductive Risk, and the Communication of Uncertainty: The Case of Disorders of Consciousness.Jonathan Birch - forthcoming - Journal of Medical Ethics.
    Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is “cognitive-motor dissociation” (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram (EEG) recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we need a procedure (...)
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  46. Review of Marcus du Sautoy's The Creativity Code: Art and Innovation in the Age of AI. [REVIEW]Scott H. Hawley - 2020 - Perspectives on Science and Christian Faith 72 (1):52-54.
    A review of Marcus du Sautoy's 2019 book, THE CREATIVITY CODE: Art and Innovation in the Age of AI, Cambridge, MA: Belknap (Harvard) Press, 2019. ISBN: 9780674988132.
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  47. Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...)
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  48. Making AI Intelligible: Philosophical Foundations. By Herman Cappelen and Josh Dever. [REVIEW]Nikhil Mahant - forthcoming - Philosophical Quarterly.
    Linguistic outputs generated by modern machine-learning neural net AI systems seem to have the same contents—i.e., meaning, semantic value, etc.—as the corresponding human-generated utterances and texts. Building upon this essential premise, Herman Cappelen and Josh Dever's Making AI Intelligible sets for itself the task of addressing the question of how AI-generated outputs have the contents that they seem to have (henceforth, ‘the question of AI Content’). In pursuing this ambitious task, the book makes several high-level, framework observations (...)
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  49. When Should Co-Authorship Be Given to AI?G. P. Transformer Jr, End X. Note, M. S. Spellchecker & Roman Yampolskiy - manuscript
    If an AI makes a significant contribution to a research paper, should it be listed as a co-author? The current guidelines in the field have been created to reduce duplication of credit between two different authors in scientific articles. A new computer program could be identified and credited for its impact in an AI research paper that discusses an early artificial intelligence system which is currently under development at Lawrence Berkeley National. One way to imagine the future of artificial intelligence (...)
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  50. Epistemic considerations when AI answers questions for us.Johan F. Hoorn & Juliet J.-Y. Chen - manuscript
    In this position paper, we argue that careless reliance on AI to answer our questions and to judge our output is a violation of Grice’s Maxim of Quality as well as a violation of Lemoine’s legal Maxim of Innocence, performing an (unwarranted) authority fallacy, and while lacking assessment signals, committing Type II errors that result from fallacies of the inverse. What is missing in the focus on output and results of AI-generated and AI-evaluated content is, apart from paying proper (...)
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