Results for 'Gener Subia'

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  1. Problem-Solving Performance and Skills of Prospective Elementary Teachers in Northern Philippines.Jupeth Pentang, Edwin D. Ibañez, Gener Subia, Jaynelle G. Domingo, Analyn M. Gamit & Lorinda E. Pascual - 2021 - Hunan Daxue Xuebao 48 (1):122-132.
    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 (...)
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  2. Cognitive Skills Achievement in Mathematics of the Elementary Pre-Service Teachers Using Piaget’s Seven Logical Operations.Jaynelle G. Domingo, Edwin D. Ibañez, Gener Subia, Jupeth Pentang, Lorinda E. Pascual, Jennilyn C. Mina, Arlene V. Tomas & Minnie M. Liangco - 2021 - Turkish Journal of Computer and Mathematics Education 12 (4):435-440.
    This study determined the cognitive skills achievement in mathematics of elementary pre-service teachers as a basis for improving problem-solving and critical thinking which was analyzed using Piaget's seven logical operations namely: classification, seriation, logical multiplication, compensation, ratio and proportional thinking, probability thinking, and correlational thinking. This study utilized an adopted Test on Logical Operations (TLO) and descriptive research design to describe the cognitive skills achievement and to determine the affecting factors. Overall, elementary pre-service teachers performed with sufficient understanding in dealing (...)
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  3. Generative AI and the Future of Democratic Citizenship.Paul Formosa, Bhanuraj Kashyap & Siavosh Sahebi - 2024 - Digital Government: Research and Practice 2691 (2024/05-ART).
    Generative AI technologies have the potential to be socially and politically transformative. In this paper, we focus on exploring the potential impacts that Generative AI could have on the functioning of our democracies and the nature of citizenship. We do so by drawing on accounts of deliberative democracy and the deliberative virtues associated with it, as well as the reciprocal impacts that social media and Generative AI will have on each other and the broader information landscape. Drawing on this background (...)
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  4.  42
    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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  5. Are Generative Models Structural Representations?Marco Facchin - 2021 - Minds and Machines 31 (2):277-303.
    Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only argument (...)
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  6. Generative memory.Kourken Michaelian - 2011 - Philosophical Psychology 24 (3):323-342.
    This paper explores the implications of the psychology of constructive memory for philosophical theories of the metaphysics of memory and for a central question in the epistemology of memory. I first develop a general interpretation of the psychology of constructive memory. I then argue, on the basis of this interpretation, for an updated version of Martin and Deutscher's influential causal theory of memory. I conclude by sketching the implications of this updated theory for the question of memory 's status as (...)
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  7. Calibrating Generative Models: The Probabilistic Chomsky-Schützenberger Hierarchy.Thomas Icard - 2020 - Journal of Mathematical Psychology 95.
    A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic tools (...)
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  8. Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity.Claudio Novelli, Federico Casolari, Philipp Hacker, Giorgio Spedicato & Luciano Floridi - 2024 - Computer Law and Security Review 55.
    The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and (...)
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  9. The Philosophy of Generative Linguistics.Peter Ludlow - 2011 - Oxford, GB: Oxford University Press.
    Peter Ludlow presents the first book on the philosophy of generative linguistics, including both Chomsky's government and binding theory and his minimalist ...
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  10. 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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  11. Pro-Generative Adversarial Network and V-stack Perceptron, Diamond Holographic Principle, and Pro-Temporal Emergence.Shanna Dobson - manuscript
    We recently presented our Efimov K-theory of Diamonds, proposing a pro-diamond, a large stable (∞,1)-category of diamonds (D^{diamond}), and a localization sequence for diamond spectra. Commensurate with the localization sequence, we now detail four potential applications of the Efimov K-theory of D^{diamond}: emergent time as a pro-emergence (v-stack time) in a diamond holographic principle using Scholze’s six operations in the ’etale cohomology of diamonds; a pro-Generative Adversarial Network and v-stack perceptron; D^{diamond}cryptography; and diamond nonlocality in perfectoid quantum physics.
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  12. 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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  13. Trust and generative AI: embodiment considered.Kefu Zhu - 2024 - AI and Ethics.
    Questions surrounding engagement with generative AI are often framed in terms of trust, yet mere theorizing about trust may not yield actionable insights, given the multifaceted nature of trust. Literature on trust typically overlooks how individuals make meaning in their interactions with other entities, including AI. This paper reexamines trust with insights from Merleau-Ponty’s views on embodiment, positing trust as a style of world engagement characterized by openness—an attitude wherein individuals enact and give themselves to their lived world, prepared to (...)
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  14.  40
    Generative AI in Digital Insurance: Redefining Customer Experience, Fraud Detection, and Risk Management.Adavelli Sateesh Reddy - 2024 - International Journal of Computer Science and Information Technology Research 5 (2):41-60.
    This abstract summarizes, in essence, what generative AI means to the insurance industry. The kind of promise generated AI offers to insurance is huge: in risk assessment, customer experience, and operational efficiency. Natural disaster impact, financial market volatility, and cyber threat are augmented with techniques of real time scenario generation and modeling as well as predictive simulation based on synthetic data. One of the challenges that stand in the way of deploying these AI methods, however, is data privacy, model reliability (...)
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  15. Towards a Definition of Generative Artificial Intelligence.Raphael Ronge, Markus Maier & Benjamin Rathgeber - 2025 - Philosophy and Technology 38 (31):1-25.
    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 (...)
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  16.  30
    Improving Generative AI Models for Secure and Private Data Synthesis.Sharma Sidharth - 2015 - International Journal of Engineering Innovations and Management Strategies 1 (1):1-4.
    Generative Adversarial Networks (GANs) have demonstrated significant potential in generating synthetic data for various applications, including those involving sensitive information like healthcare and finance. However, two major issues arise when GANs are applied to sensitive datasets: (i) the model may memorize training samples, compromising the privacy of individuals, especially when the data includes personally identifiable information (PII), and (ii) there is a lack of control over the specificity of the generated samples, which limits their utility for tailored usecases. To address (...)
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  17.  28
    Generative AI for Creative Learning Content Creation: Project-Based Learning and Art Generation.Lakshmi Narasimha Raju Mudunuri Vivekchowdary Attaluri - 2025 - Igi Global Scientific Publishing 1 (1):239-252.
    Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated art is reshaping the (...)
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  18.  18
    Generative AI for Creative Learning Content Creation: Project-Based Learning and Art Generation.Lakshmi Narasimha Raju Mudunuri Vivekchowdary Attaluri - 2025 - Igi Global Scientific Publishing 1 (1):239-252.
    Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated art is reshaping the (...)
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  19. Developments and Uses of Generative Artificial Intelligence and Present Experimental Data on the Impact on Productivity Applying Artificial Intelligence that is Generative.Tambi Varun Kumar - 2024 - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 12 (10):2382-2388.
    In the context of mid-level professional writing jobs, we examine the productivity effects of a generative artificial intelligence technology, namely the assistive chatbot ChatGPT. We used ChatGPT to randomly expose half of the 444 college-educated professionals to occupation-specific, incentive-based writing tasks in a preregistered online experiment. Our results show that ChatGPT considerably increases average productivity: output quality improves by 0.4 standard deviations and task completion time drops by 0.8 standard deviations. By compressing the production distribution, ChatGPT also lessens worker inequality, (...)
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  20. Generative Disgust, Aesthetic Engagement, and Community.Erin Bradfield - 2022 - In Max Ryynänen, Heidi Kosonen & Susanne Ylönen, Cultural Approaches to Disgust and the Visceral. Routledge. pp. 175-187.
    How do individuals and communities respond to negative aesthetic experience? Historically, philosophical aesthetics has devoted much thought to positive aesthetic experience, including the beautiful, agreeable, charming, and tasteful. But this is only a partial picture. Some aesthetic experience displeases: the ugly, disgusting, and horrific are but a few examples with which aestheticians have grappled in recent decades. The aversive and visceral nature of disgust has generated particular interest. But as Carolyn Korsmeyer points out in _Savoring Disgust: The Foul & the (...)
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  21. Generative AI and the value changes and conflicts in its integration in Japanese educational system.Ngoc-Thang B. Le, Phuong-Thao Luu & Manh-Tung Ho - manuscript
    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 (...)
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  22.  67
    Advancements and Applications of Generative Artificial Intelligence and show the Experimental Evidence on the Productivity Effects using Generative Artificial Intelligence.Sharma Sakshi - 2023 - International Journal of Multidisciplinary Research in Science, Engineering and Technology (Ijmrset) 6 (3):657-664.
    We investigate the productivity impacts of a generative artificial intelligence technology—specifically, the assistive chatbot ChatGPT—within the realm of mid-level professional writing tasks. In a preregistered online experiment, we assigned occupation-specific, incentivized writing tasks to 444 college-educated professionals, with half of the participants randomly exposed to ChatGPT. Our findings reveal that ChatGPT significantly enhances average productivity: the time taken to complete tasks decreases by 0.8 standard deviations, and output quality improves by 0.4 standard deviations. Additionally, the use of ChatGPT reduces inequality (...)
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  23.  58
    Optimization of Generative AI Costs in Multi-Agent and Multi-Cloud Systems.Sankara Reddy Thamma Sankara Reddy Thamma - 2024 - International Journal of Scientific Research in Science and Technology 11 (6):953-965.
    The generative AI system is being adopted across the several fields to provide novel solutions for text generation, image synthesis, and decision-making. But when they are used in multi-agent and multi-cloud systems, they are expensive in terms of computation and finance. Regarding the aforementioned factors, this paper aims to examine methods of reducing such costs while achieving system efficiency. Such measures as dynamic workload distribution, resource scaling, as well as cost-conscious model selection is described. Through the examples of case studies (...)
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  24. Sociocommunicative functions of a generative text: the case of GPT-3.Auli Viidalepp - 2022 - Lexia. Rivista di Semiotica 39:177-192.
    Recently, there have been significant advances in the development of language-transformer models that enable statistical analysis of co-occurring words (word prediction) and text generation. One example is the Generative Pre-trained Transformer 3 (GPT-3) by OpenAI, which was used to generate an opinion article (op-ed) published in “The Guardian” in Septem- ber 2020. The publication and reception of the op-ed highlights the difficulty for human readers to differentiate a machine-produced text; it also calls attention to the challenge of perceiving such a (...)
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  25. Simulation as formal and generative social science: the very idea.Nuno David, Jaime Sichman & Helder Coelho - 2007 - In Carlos Gershenson, Diederik Aerts & Bruce Edmonds, Worldviews, Science and Us: Philosophy and Complexity. World Scientific. pp. 266--275.
    The formal and empirical-generative perspectives of computation are demonstrated to be inadequate to secure the goals of simulation in the social sciences. Simulation does not resemble formal demonstrations or generative mechanisms that deductively explain how certain models are sufficient to generate emergent macrostructures of interest. The description of scientific practice implies additional epistemic conceptions of scientific knowledge. Three kinds of knowledge that account for a comprehensive description of the discipline were identified: formal, empirical and intentional knowledge. The use of formal (...)
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  26.  27
    Improving Generative AI Models for Secure and Private Data Synthesis.Sharma Sidharth - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):210-215.
    Generative Adversarial Networks (GANs) have demonstrated significant potential in generating synthetic data for various applications, including those involving sensitive information like healthcare and finance. However, two major issues arise when GANs are applied to sensitive datasets: (i) the model may memorize training samples, compromising the privacy of individuals, especially when the data includes personally identifiable information (PII), and (ii) there is a lack of control over the specificity of the generated samples, which limits their utility for tailored use-cases. To address (...)
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  27.  9
    Generative Models with Privacy Guarantees: Enhancing Data Utility while Minimizing Risk of Sensitive Data Exposure.Kommineni Mohanarajesh - 2024 - International Journal of Intelligent Systems and Applications in Engineering 12 (23):1036-1044.
    The rapid advancement in generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, has significantly enhanced our ability to create high-quality synthetic data. These models have been instrumental in various applications, ranging from data augmentation and simulation to the development of privacy-preserving solutions. However, the generation of synthetic data also raises critical privacy concerns, as there is potential for these models to inadvertently reveal sensitive information about individuals in the original datasets. This paper delves into the (...)
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  28. Essence, Necessity, and Non-Generative Metaphysical Explanation.Michael Wallner - 2022 - Argumenta 7 (2):439-462.
    Finean essentialists take metaphysical necessity to be metaphysically explained by essence. But whence the explanatory power of essence? A recent wave of criticism against the Finean account has put pressure on essentialists to answer this question. Wallner and Vaidya (2020) have responded by offering an axiomatic account of the explanatory power of essence. This paper discusses their account in light of some recent criticism by Bovey (2022). Building on work by Glazier (2017), Bovey succeeds in showing that Wallner and Vaidya’s (...)
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  29. Transforming Industries: The Role of Generative AI in Revolutionizing Banking and Healthcare.M. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):580-600.
    The research evaluates generative AI’s capabilities through a multi-phase framework, addressing how data synthesis, language models, and predictive algorithms contribute to sector-specific applications. In banking, the model assesses the impact of AI-driven chatbot interactions, credit risk assessment, and personalized financial services on customer experience and bank performance. Healthcare applications are explored through image synthesis for diagnostics, predictive modeling in patient care, and drug discovery simulations. The experimental setup is rigorously tested across metrics such as response accuracy, cost-effectiveness, and data privacy (...)
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  30. Growing the image: Generative AI and the medium of gardening.Nick Young & Enrico Terrone - forthcoming - Philosophical Quarterly.
    In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the limited (...)
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  31. Using text-to-image generative AI to create storyboards: Insights from a college psychology classroom.Shantanu Tilak, Blake Bagley, Jadalynn Cantu, Mya Cosby, Grace Engelbert, Ja'Kaysiah Hammonds, Gabrielle Hickman, Aaron Jackson, Bryce Jones, Kadie Kennedy, Stephanie Kennedy, Austin King, Ryan Kozlej, Allyssa Mortenson, Muller Sebastien, Julia Najjar, Sydney Queen, Milo Schuehle, Nolan Schulte, Emily Schwarz, Joshua Shearn, Kalyse Williams & Malik Williams - 2024 - Journal of Sociocybernetics 19 (1):1-42.
    This participatory study, conducted in an introductory psychology class, recounts self-reflections of 22 undergraduate students and their instructor engaging in an GenAI-mediated storyboard generation process. It relies on Gordon Pask’s conversation theory, structuring out the nature of interactions between students, instructor, and GenAI, and then uses a qualitative narrative to describe these conversational feedback loops constituting the creation of draft and final storyboards. Results suggest students engaged in cyclical feedback driven processes to master their creations, used elements of photography related (...)
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  32. Embracing ChatGPT and other generative AI tools in higher education: The importance of fostering trust and responsible use in teaching and learning.Jonathan Y. H. Sim - 2023 - Higher Education in Southeast Asia and Beyond.
    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.
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  33. Slopaganda: The interaction between propaganda and generative AI.Michal Klincewicz, Mark Alfano & Amir Fard - forthcoming - Filosofiska Notiser.
    At least since Francis Bacon, the slogan “knowledge is power” has been used to capture the relationship between decision-making at a group level and information. We know that being able to shape the informational environment for a group is a way to shape their decisions; it is essentially a way to make decisions for them. This paper focuses on strategies that are intentionally, by design, impactful on the decision-making capacities of groups, effectively shaping their ability to take advantage of information (...)
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  34. Diffusing the Creator: Attributing Credit for Generative AI Outputs.Donal Khosrowi, Finola Finn & Elinor Clark - 2023 - Aies '23: Proceedings of the 2023 Aaai/Acm Conference on Ai, Ethics, and Society.
    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 (...)
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  35.  52
    Revolutionizing Healthcare: Spatial Computing Meets Generative AI.Sankara Reddy Thamma Sankara Reddy Thamma - 2024 - International Journal of Scientific Research in Science, Engineering and Technology 11 (5):324-336.
    The health industry is experiencing change, the newest forerunner of which is being propelled by spatial computing and generative AI. Spatial computing simply refers to the ability to interface with physical space through computation and digital devices; on the other hand, generative AI means using advanced machine learning to generate new output. This paper examines the roles and the combined possibilities of these two technologies with the view of transforming health care and diagnostics in the field of patient care. Precision (...)
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  36. Generativities: Western Philosophy, Chinese Painting, and the Yijing.Eric S. Nelson - 2013 - Orbis Idearum 1 (1):97–104.
    Western philosophy has been defined through the exclusion of non-Western forms of thought as non-philo-sophical. In this paper, I place the notion of what is “properly” philosophy into question by contrasting the essence/appearance paradigm governing Western metaphysics and its deconstructive critics with the more fluid, dynamic, and participatory forms of encountering and performatively enacting the world that are articulated in Chinese thinking and made apparent in Chinese painting. In this hermeneutical contrast, Western and Chinese thinking themselves are interpeted as co-relational (...)
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  37. An Experimental Analysis of Revolutionizing Banking and Healthcare with Generative AI.Sankara Reddy Thamma - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):580-590.
    Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.
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  38. Meaning and Formal Semantics in Generative Grammar.Stephen Schiffer - 2015 - Erkenntnis 80 (1):61-87.
    A generative grammar for a language L generates one or more syntactic structures for each sentence of L and interprets those structures both phonologically and semantically. A widely accepted assumption in generative linguistics dating from the mid-60s, the Generative Grammar Hypothesis , is that the ability of a speaker to understand sentences of her language requires her to have tacit knowledge of a generative grammar of it, and the task of linguistic semantics in those early days was taken to be (...)
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  39. Digital Homunculi: Reimagining Democracy Research with Generative Agents.Petr Špecián - manuscript
    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, (...)
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  40. The Computational Search for Unity: Synthesis in Generative AI.M. Beatrice Fazi - 2024 - Journal of Continental Philosophy 5 (1):31-56.
    The outputs of generative artificial intelligence (generative AI) are often called “synthetic” to imply that they are not natural but artificial. Against that use of the term, this article focuses on a different denotation of synthesis, stressing the unifying and compositional aspects of anything synthetic. The case of large language models (LLMs) is used as an example to address synthesis philosophically alongside notions of representation in contemporary computational systems. It is argued that synthesis in generative AI should be understood as (...)
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  41. A generative approach to the understanding of cognitive skills.Brian Josephson & Nils A. Baas - 1996 - In Andrée Ehresmann, George Farre & Paul Vanbremeersch, Actes du Symposium ECHO. Université de Picardie Jules Verne. pp. S1–S8.
    We describe a new approach to understanding the functioning of the nervous system, unifying previous ideas of Josephson and Hauser, Baas, and Brooks. Its basis consists in analysing the total developmental process into basic components of development, whose corresponding mechanisms (skill constructors) are organised together into a coherent total system.
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  42.  17
    Privacy-Enhanced Generative AI for Healthcare Synthetic Data Creation.Sharma Sidharth - 2015 - International Journal o Fengineering Innovations and Managementstrategies, 1 (1):1-5.
    The exponential growth of healthcare data, along with its sensitive nature, has necessitated the development of innovative solutions for protecting patient privacy. Generative AI techniques, such as Generative Adversarial Networks (GANs), have shown promise in creating synthetic healthcare data that mirrors real-world patterns while preserving confidentiality. This paper proposes a privacy-enhanced generative AI framework for the creation of synthetic healthcare data. By incorporating differential privacy and federated learning, the system aims to enhance privacy while maintaining data utility for healthcare research (...)
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  43.  95
    Virtual Anthropology: When and What Could We Learn From Multimodal Agentic Behavior in Generative Worlds?Fabian Kerj - manuscript
    This paper examines the convergence of large language models, multimodal AI, and generative spatial technologies to enable sophisticated simulated worlds for studying agentic behavior. Recent developments in generative architectures, particularly GenEx and topology-aware mesh generation, facilitate the creation of coherent, explorable environments with artificial agents capable of complex interactions. The proposed framework for "virtual anthropology" presents novel opportunities for studying emergent behaviors and cognitive processes in controlled, generative environments, with implications for both theoretical research and practical applications in artificial intelligence.
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  44. 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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  45. (1 other version)Generative AI and human labor: who is replaceable?AbuMusab Syed - 2023 - AI and Society:1-3.
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  46. Machines That Create: Contingent Computation and Generative AI.M. Beatrice Fazi - 2024 - Media Theory 8 (2):1-12.
    In this article, M. Beatrice Fazi takes up Media Theory’s invitation to engage with Alan Díaz Alva’s analysis of her philosophical work on contingency in computation. The central argument of Fazi’s Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics is that computation can be productive of ontological novelty. This piece revisits that argument in the light of the technological developments that have occurred since 2018, when the book was published. Focusing on generative artificial intelligence (generative AI), the article considers (...)
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  47. Peirce and Generative AI.Catherine Legg - forthcoming - In Robert Lane, Pragmatism Revisited. Cambridge University Press.
    Early artificial intelligence research was dominated by intellectualist assumptions, producing explicit representation of facts and rules in “good old-fashioned AI”. After this approach foundered, emphasis shifted to deep learning in neural networks, leading to the creation of Large Language Models which have shown remarkable capacity to automatically generate intelligible texts. This new phase of AI is already producing profound social consequences which invite philosophical reflection. This paper argues that Charles Peirce’s philosophy throws valuable light on genAI’s capabilities first with regard (...)
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  48. Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3).Nassim Dehouche - 2021 - Ethics in Science and Environmental Politics 21:17-23.
    As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence can comprehend prompts (...)
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  49. Escape climate apathy by harnessing the power of generative AI.Quan-Hoang Vuong & Manh-Tung Ho - 2024 - AI and Society 39 (6):1-2.
    “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).
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  50. On the Putative Epistemic Generativity of Memory and Imagination.Kengo Miyazono & Uku Tooming - 2022 - In Anja Berninger & Íngrid Vendrell Ferran, Philosophical Perspectives on Memory and Imagination. New York, NY: Routledge. pp. 127-145.
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