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)

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 (...) with the TLO. However, in each logical operation, students were found to have insufficient understanding of probability thinking and correlational thinking. These insufficiencies in other logical operations were attributed to their misconceptions on some mathematical terms, misinterpretation of the problems, poor comprehension skills, poor problem-solving skills, and poor performance in mathematics in general. (shrink)

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 theory, we outline some of the key positive and negative impacts that Generative AI is likely to have on democratic citizenship. The political significance of these impacts suggests the need for further regulation. (shrink)

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 graph theory, generative models to model spatial relations, generate new spatial forms and improve on spatial decision-making processes. The paper surveys such methods, describes typical applications, and outlines further development of the subject. (shrink)

Generative Artificial Intelligence (AI) is reshaping creative industries by automating the creation of art, music, and design. Through algorithms such as Generative Adversarial Networks (GANs) and advanced transformer models, generative AI enables the production of original, high-quality content that mimics human creativity. This paper explores how generative AI is revolutionizing art, music, and design by examining its applications, benefits, and challenges in these fields. It highlights the capabilities of AI in creating visual art, composing music, and (...) generating design concepts. Furthermore, the study delves into the ethical and social implications of AI-driven creativity, including concerns about authorship, originality, and the future of human creativity in these industries. The paper concludes by predicting the future trajectory of AI in creative sectors and offering recommendations for its responsible use. (shrink)

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 (...) a generative epistemic source. (shrink)

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 production, and the potential risks of AI-generated content. As the convergence of AI and creativity deepens, this study highlights the opportunities and challenges posed by generative AI in revolutionizing the landscape of creative industries. (shrink)

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 these challenges, we propose a novel framework that integrates differential privacy with latent representation learning to ensure privacy while providing control over the specificity of generated data. Our approach ensures that the synthetic data does not reveal individual data points, and by learning effective latent codes, it allows for the generation of specific and meaningful data. We evaluate our method using the MNIST dataset, showing that it preserves privacy and demonstrates a privacy-utility trade-off, where increased privacy leads to decreased classification accuracy. Additionally, we highlight the computational challenges, as the training process incurs a tenfold increase in time compared to standard GAN models. Finally, we extend our approach to the CelebA dataset, demonstrating how privacy and specificity can be controlled to generate high-quality, private synthetic data. (shrink)

Generative Artificial Intelligence (AI) has emerged as a powerful force at the intersection of technology and imagination, enabling machines to create novel content across diverse domains such as art, music, design, and literature. This technology is shifting the paradigm of creativity, allowing AI systems to not only automate tasks but also contribute to the creative process by generating original content that mirrors or transcends human imagination. Generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and (...) Transformer-based models, are at the forefront of this revolution. These models are redefining how creativity is perceived, empowering both professional and amateur creators to explore new creative territories. This paper explores the role of generative AI in creative industries, examining its potential, challenges, and ethical implications. Through case studies and real-world applications, we demonstrate how generative AI is influencing various creative fields and shaping the future of human creativity. (shrink)

Peter Ludlow presents the first book on the philosophy of generative linguistics, including both Chomsky's government and binding theory and his minimalist ...

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 offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

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

Generative Artificial Intelligence (AI) has emerged as a powerful force at the intersection of technology and imagination, enabling machines to create novel content across diverse domains such as art, music, design, and literature. This technology is shifting the paradigm of creativity, allowing AI systems to not only automate tasks but also contribute to the creative process by generating original content that mirrors or transcends human imagination. Generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and (...) Transformer-based models, are at the forefront of this revolution. These models are redefining how creativity is perceived, empowering both professional and amateur creators to explore new creative territories. This paper explores the role of generative AI in creative industries, examining its potential, challenges, and ethical implications. Through case studies and real-world applications, we demonstrate how generative AI is influencing various creative fields and shaping the future of human creativity. (shrink)

Generative Adversarial Networks (GANs) represent a breakthrough in the field of deep learning, enabling the generation of high-quality synthetic data. GANs consist of two neural networks, a generator and a discriminator, that compete against each other in a game-theoretic framework. This paper provides an overview of GANs, their architecture, applications, and recent advancements. We discuss various types of GANs, their practical applications in areas such as image synthesis, data augmentation, and machine learning, and present methodologies for improving their training (...) and performance. The paper concludes with challenges in the GAN domain and future directions for research. (shrink)

Generative AI, a subset of artificial intelligence that focuses on creating content such as text, images, audio, and 3D models, is revolutionizing the gaming industry and virtual worlds. This paper explores the role of generative AI in enhancing player experiences, automating content creation, and developing more immersive and responsive virtual environments. It analyzes current research, practical applications, and future directions, with a focus on ethical implications, technical methodologies, and human-AI interaction in game design.

Generative Artificial Intelligence (AI) has revolutionized personalized marketing by enabling businesses to create highly targeted, customized, and unique experiences for individual consumers. Unlike traditional methods, generative AI can create content, advertisements, product recommendations, and customer interactions that feel tailored to the preferences and needs of each user. This paper explores the role of generative AI in personalized marketing and its potential to transform the customer journey. By leveraging advanced algorithms such as generative adversarial networks (GANs) and (...) transformer models like GPT-3, businesses can enhance personalization efforts, improving customer engagement, loyalty, and conversion rates. The paper examines key applications of generative AI in content creation, recommendation systems, and dynamic advertising, alongside the challenges and ethical considerations associated with its use. Ultimately, the paper provides a roadmap for businesses looking to integrate generative AI into their marketing strategies to enhance customer experiences and drive growth. (shrink)

Generative Artificial Intelligence (AI) has emerged as a transformative tool in the content creation process, significantly altering the way content is produced, personalized, and consumed. From text and images to videos and music, generative AI is enabling creators and businesses to automate and innovate content generation, facilitating personalized experiences at scale. With technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models, generative AI can create high-quality content autonomously, blurring the lines between human (...) and machine-generated material. This paper explores how generative AI is shaping the future of content creation, examining its applications, benefits, challenges, and ethical considerations. Through case studies and industry examples, we showcase the profound impact AI-driven content generation is having on industries like marketing, entertainment, media, and education, while highlighting future trends and the evolving role of human creativity in the process. (shrink)

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 these challenges, we propose a novel framework that integrates differential privacy with latent representation learning to ensure privacy while providing control over the specificity of generated data. Our approach ensures that the synthetic data does not reveal individual data points, and by learning effective latent codes, it allows for the generation of specific and meaningful data. We evaluate our method using the MNIST dataset, showing that it preserves privacy and demonstrates a privacy-utility trade-off, where increased privacy leads to decreased classification accuracy. Additionally, we highlight the computational challenges, as the training process incurs a tenfold increase in time compared to standard GAN models. Finally, we extend our approach to the CelebA dataset, demonstrating how privacy and specificity can be controlled to generate high-quality, private synthetic data. (shrink)

Generative Artificial Intelligence (AI) is increasingly reshaping the creative landscape, pushing the boundaries of traditional artistic practices and creative industries. Moving beyond the notion of automation, generative AI is now recognized as a collaborator in the creative process. Models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based systems have unlocked new possibilities for art, music, design, literature, and other forms of creative expression. This paper explores how generative AI is transforming the creative process, (...) from enhancing human creativity to producing original content. We examine the implications of AI on industries such as visual arts, music, literature, and design, discussing both the opportunities and challenges it presents. Additionally, ethical considerations surrounding authorship, ownership, and the potential for AI to disrupt traditional creative industries are explored. The paper concludes with a discussion of the future trajectory of AI in creative fields and its potential for collaboration between human creators and machines. (shrink)

Generative Artificial Intelligence (AI) has emerged as a transformative tool in the content creation process, significantly altering the way content is produced, personalized, and consumed. From text and images to videos and music, generative AI is enabling creators and businesses to automate and innovate content generation, facilitating personalized experiences at scale. With technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models, generative AI can create high-quality content autonomously, blurring the lines between human (...) and machine-generated material. This paper explores how generative AI is shaping the future of content creation, examining its applications, benefits, challenges, and ethical considerations. Through case studies and industry examples, we showcase the profound impact AI-driven content generation is having on industries like marketing, entertainment, media, and education, while highlighting future trends and the evolving role of human creativity in the process. (shrink)

Generative Artificial Intelligence (AI) has emerged as a powerful tool that is transforming various industries, particularly in creative fields like art, music, writing, and design. By leveraging deep learning algorithms, generative AI has revolutionized how creative content is produced, enabling new forms of art, innovation, and collaboration. This paper explores the role of generative AI in creativity, from its foundational deep learning techniques to its profound impact on creative processes. It discusses the advancements in AI models, such (...) as Generative Adversarial Networks (GANs) and transformer models, and how they enable the creation of realistic, original content. The paper also examines the applications of generative AI across creative domains and the challenges faced, including ethical concerns, the role of human creativity, and the potential for AI-driven disruption. Finally, it offers insights into the future of generative AI in the creative industries and its potential to reshape how creativity is defined and experienced. (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)

Generative AI represents a transformative force at the intersection of data, art, and innovation, bringing together advanced computational models and creative processes to push the boundaries of what machines can produce. At its core, generative AI involves machine learning models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer architectures, that enable the generation of new, original content based on data-driven insights. This paper explores the evolution and impact of generative AI, focusing on its (...) applications in art, design, music, and other creative fields. It examines the fundamental technologies enabling generative AI, as well as its implications for industries, innovation, and ethical considerations. The paper concludes by discussing the future of generative AI in creative industries and its role in transforming human-machine collaboration. (shrink)

Generative Artificial Intelligence (AI) is a powerful technological advancement that represents a significant leap forward in the field of machine creativity. Unlike traditional AI systems, which are designed to perform specific tasks or make decisions based on predefined rules, generative AI models are capable of producing novel, original outputs—ranging from art and music to written text and product designs—based on patterns learned from vast datasets. This paper explores the evolution of machine creativity, focusing on the development and application (...) of generative AI in various creative domains. By analyzing key generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, this paper highlights how AI can augment human creativity and push the boundaries of artistic expression and innovation. Additionally, the ethical implications, challenges, and future directions of generative AI are examined, offering insight into how this technology will shape creative industries in the years to come. (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)

Generative AI refers to a class of models capable of creating new data from a given distribution. These models can generate diverse forms of data, including text, images, audio, and videos. The key techniques in generative AI include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models like GPT. This paper explores the underlying principles of these generative techniques, their applications in various fields, and their current challenges. We will review the significant advancements in these (...) areas, compare different generative methods, and discuss future directions for enhancing the quality and applicability of generative models. (shrink)

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 adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning. (shrink)

Generative Artificial Intelligence (AI) is rapidly evolving as a groundbreaking technology that is reshaping the digital landscape. By harnessing the power of machine learning models, generative AI is capable of creating novel data, such as text, images, music, and even code, which has profound implications for various industries. This paper explores the rise of generative AI and its transformative impact on sectors such as healthcare, entertainment, finance, and manufacturing. We examine the key technologies behind generative AI, (...) including Generative Adversarial Networks (GANs), variational autoencoders, and transformer models. Additionally, we explore how generative AI is enhancing creativity, automating tasks, and solving complex problems, while addressing the ethical challenges and potential risks associated with its use. The paper highlights the future of generative AI as it continues to push the boundaries of digital innovation and human creativity. (shrink)

Image synthesis is the process of generating new images from the ground up, frequently utilizing preexisting data or models. By definition, super-resolution methods produce supplementary image content and features that are not present in the original input in order to reconstruct a high-resolution image from a low-resolution source. Surpassing the performance attained with high-resolution images is a challenge when training or analyzing models with low-resolution images. It is not always easy to obtain a higher-resolution image. Recognizing and identifying objects in (...) low-resolution images is a challenging task. As a result, it is imperative to develop a method that simultaneously enhances the resolution of the low-resolution image and enhances its quality. Generative Adversarial Networks (GANs) and other generative models are increasingly acknowledged for their capacity to accurately replicate high-resolution counterparts. This article provides a performance comparison of DCGAN and Wasserstein GAN for image synthesis via image super resolution. The experimental endeavor employs the Set5 image data set. Wasserstein GAN outperforms DCGAN in terms of PSNR, SSIM, and VIF parameters. (shrink)

This article critically explores Japan’s policy of introducing Generative AI into schools by analyzing the national and local policies to implement it. The article reveals pessimistic contradictions between AI introduction strategies and inherent features of Japanese educational culture. Three cultural dimensions are seen in the research: the relational and interdependent sense of self (jiko) vs. individualized learning pathways offered by AI; the selfless teacher culture (kenshin-teki kyōshi-zō) endangered by contradictory impacts through AI implementation; and community-centric school activities (Tokubetsu katsudō) (...) because they cannot be optimally replaced by AI. While the vision of Japan’s Society 5.0 promotes human-centered technology rooted in dignity, diversity, and sustainability values, day-to-day practice in daily life adheres to neoliberal ideology likely to speed up tendencies toward Western-style individualism. Current AI training procedures are most commonly found to prioritize technical aspects in a way that neglects socio-cultural contexts. This gap represents a missed chance to develop typically Japanese educational technology policies that are able to harness the potential of AI without compromising the values of traditional education. The article concludes that successful AI integration in Japanese education requires systems that actively promote interdependent learning, preserve the irreplaceable roles of teachers, and enhance rather than replace community-forming aspects of education. (shrink)

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 intersection of generative models and data privacy, focusing on the development of techniques that safeguard privacy while ensuring the synthetic data produced remains meaningful and useful. We provide a comprehensive review of privacy-preserving strategies employed in the context of generative models. Key approaches discussed include differential privacy, which guarantees that the inclusion or exclusion of any individual data point does not significantly alter the output of a function; federated learning, which enables collaborative model training across decentralized data sources without sharing raw data; and secure multi-party computation (MPC), which allows for computations on encrypted data while preserving privacy. The paper evaluates these techniques in terms of their effectiveness, trade-offs, and integration challenges. (shrink)

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 art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

Generative AI models, such as OpenAI’s Codex and Google’s BERT, have revolutionized software development by automating code generation, bug detection, code completion, and refactoring. These models use natural language processing (NLP) techniques, deep learning, and neural networks to assist developers in enhancing productivity and improving code quality. This paper explores the applications of generative AI in software development, analyzes current advancements, and evaluates their implications for the future of coding. The study highlights how generative AI can reduce (...) human error, increase efficiency, and enable the next generation of intelligent development tools. (shrink)

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 and interpretability. The insurance industry must address these issues if it is to comply with regulations like GDPR and CCPA or mitigate reputational risks, as anybody can make complaints about any company based on any perceived data breach. Indeed, generative AI models, often convoluted and biased, can propagate unfair outcomes, especially in pricing and policy access. Navigating these challenges requires a whole new frayed society of AI specialists in collaboration with the industry’s insurers to offer bespoke solutions that address the firm’s needs. Based on this work, future developments will center on designing and implementing ethical and regulatory frameworks around AI-driven decisions that are transparent, fair and sustainable. Generative AI has historically been the most feared phenomenon in the history of western civilization, but with predictive analytics and risk simulation capabilities taking shape, generative AI is set to disrupt the insurance industry and add real-time decision support, automating claims processing and personalized interaction. To successfully integrate generative AI, technical, ethical and regulatory challenges will need to be addressed, and a trust-driven ecosystem will emerge to serve evolving customer needs. (shrink)

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.

Generative Artificial Intelligence (AI) has emerged as a transformative technology, offering new possibilities in automating business processes, enhancing creativity, and driving innovation. From product design and marketing to customer service and content creation, generative AI is reshaping industries by enabling businesses to produce high-quality outputs quickly and efficiently. This paper explores the potential applications of generative AI in business, focusing on its impact across various sectors, including manufacturing, marketing, customer service, and finance. It also discusses the key (...) opportunities and challenges of adopting generative AI, such as the need for advanced skills, data privacy concerns, and the ethical implications of AI-generated content. By examining case studies and expert perspectives, the paper outlines how generative AI can be harnessed to drive business growth and innovation in the future. Finally, the paper presents recommendations for businesses to integrate generative AI effectively while addressing the associated risks. (shrink)

This is the first installment of a two-part essay. Limitations of space prevented the publication of the full essay in present issue of the Journal. The second installment will appear in the next issue, 2021 (1). My overall goal is to outline a strategy for integrating generative linguistics with a broadly pragmatist approach to meaning and communication. Two immensely useful guides in this venture are Robert Brandom and Paul Pietroski. Squarely in the Chomskyan tradition, Pietroski’s recent book, Conjoining Meanings, (...) offers an approach to natural-language semantics that rejects foundational assumptions widely held amongst philosophers and linguists. In particular, he argues against extensionalism—the view that meanings are (or determine) truth and satisfaction conditions. Having arrived at the same conclusion by way of Brandom’s defl ationist account of truth and reference, I’ll argue that both theorists have important contributions to make to a broader anti-extensionalist approach to language. What appears here as Part 1 of the essay is largely exegetical, laying out what I see as the core aspects of Brandom’s normative inferentialism (§1) and Pietroski’s naturalistic semantics (§2). In Part 2 (next issue), I argue that there are many convergences between these two theoretical frameworks and, contrary to first appearances, very few points of substantive disagreement between them. If the integration strategy that I propose is correct, then what appear to be sharply contrasting commitments are better seen as interrelated verbal differences that come down to different—but complementary—explanatory goals. The residual disputes are, however, stubborn. I end by discussing how to square Pietroski’s commitment to predicativism with Brandom’s argument that a predicativist language is in principle incapable of expressing ordinary conditionals. (shrink)

Generative Artificial Intelligence (AI) is revolutionizing the world of art by offering unprecedented ways of creating, experiencing, and interpreting art. With the advent of powerful AI models such as Generative Adversarial Networks (GANs), neural networks, and deep learning techniques, machines can now generate stunning and original works of visual art, music, and even literature. This paper explores the transformative role of generative AI in the creative industries, examining how these technologies are not only enabling artists to push (...) creative boundaries but also challenging traditional notions of authorship and creativity. Through case studies and analysis, we look at the impact of AI-generated art on the art world, its potential for expanding creative expression, and the ethical implications of machine-generated creativity. Ultimately, generative AI represents a paradigm shift in the creation of art, blending human ingenuity with machine-driven innovation. (shrink)

Large language models (LLMs) lead a new era of computational innovation brought forth by generative artificial intelligence (AI). Designed around transformer architectures and trained on large-scale data, these models shine in producing both creative and functional code. This work examines the emergence of LLMs with an emphasis on their two uses in content generation and software development. Key results show great mastery in daily activities, balanced by restrictions in logic, security, and uniqueness. We forecast future developments, therefore concluding with (...) ramifications for industry, education, and society. Particularly with the progress of Large Language Models (LLMs), Generative Artificial Intelligence (AI) has seen explosive expansion recently. From sophisticated software code to plain language writing, these models have shown amazing capacity in content creation. Focusing on the performance, problems, and consequences of LLMs in code and content creation, this work investigates the advent of generative artificial intelligence. We assess these models' accuracy, efficiency, and inventiveness while also attending to ethical issues and social effects. We also go over the direction LLMs will take and their possible uses in several sectors. (shrink)

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 art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

This paper explores the integration of generative artificial intelligence (AI) into modern technology stacks. With rapid advances in machine learning, especially large language models (LLMs) and generative adversarial networks (GANs), businesses are rethinking how they build, deploy, and scale applications. We review current academic and industry research, outline practical methodologies for integration, and present a framework to evaluate generative AI tools. The paper concludes with key considerations, potential risks, and future directions in enterpriselevel AI integration.

Generative AI, powered by advanced machine learning models such as Generative Adversarial Networks (GANs) and Transformer-based architectures, is poised to reshape a wide array of industries. From automating content creation to transforming healthcare diagnostics and manufacturing processes, the potential applications of generative AI are vast and varied. This paper examines the future trajectory of generative AI and its impact across multiple sectors, including creative industries, healthcare, finance, and manufacturing. The research explores the current state of (...) class='Hi'>generative AI technologies, identifies emerging trends, and evaluates the challenges and opportunities that these innovations present for businesses and society. With a focus on both the technological advancements and the broader societal implications, this paper aims to provide a comprehensive understanding of the transformative role of generative AI. (shrink)

Generative Artificial Intelligence (AI) has revolutionized numerous industries by enabling the creation of realistic and novel content across various mediums, including art, music, writing, and video. However, with this power comes a significant set of ethical challenges. These challenges revolve around issues such as authorship, deception, bias, and the potential for misuse. This paper explores the ethical concerns surrounding generative AI, examining the delicate balance between creativity and deception. By analyzing the implications of AI-generated content, including deepfakes and (...) synthetic media, the study investigates the risks associated with AI in terms of misinformation, intellectual property rights, and the authenticity of creative work. Additionally, the paper evaluates current ethical frameworks and proposes guidelines for the responsible use of generative AI. Ultimately, it aims to provide a comprehensive overview of the ethical landscape in the realm of AI-generated content. (shrink)

Generative Artificial Intelligence (AI) is transforming business landscapes by enabling companies to innovate, optimize, and enhance productivity in ways that were previously unimaginable. By harnessing the power of advanced algorithms such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models, generative AI is unlocking new avenues for automation, creativity, and decision-making in industries ranging from marketing and finance to manufacturing and customer service. This paper explores the diverse applications of generative AI in business, focusing (...) on its ability to generate content, streamline processes, and enhance product development. We also examine the challenges and ethical considerations associated with integrating AI into business models and discuss the future implications of AI-driven transformation across various sectors. As businesses continue to evolve in the digital age, generative AI is set to play a pivotal role in shaping the future of work and innovation. (shrink)

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 conceptions of computation for describing simulation is refuted; the roles of programming languages according to intentional accounts of computation are identified; and the roles of iconographic programming languages and aesthetic machines in simulation are characterized. The roles that simulation and intentional decision making may be able to play in a participative information society are also discussed. (shrink)

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, mainly helping individuals with lesser starting abilities. ChatGPT shifts the focus of activities from rough-drafting to idea generation and editing, thereby replacing worker effort rather than enhancing worker skills. Furthermore, exposure to ChatGPT increases job satisfaction and self-efficacy while also heightening both concerns and excitement about automation technologies. (shrink)

Generative Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various fields, particularly in creativity and innovation. By leveraging advanced machine learning models such as Generative Adversarial Networks (GANs) and Transformer models, generative AI can produce original content ranging from art and music to written text and even product designs. This paper explores the impact of generative AI on creative industries, examining its role in augmenting human creativity, enhancing productivity, and enabling (...) new forms of innovation. Through a detailed review of existing literature, case studies, and industry applications, this paper highlights both the opportunities and challenges posed by generative AI in the creative landscape. (shrink)

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 (...) Fair in Aesthetics_, there is also a paradoxical attraction to that which arouses disgust. Following Kant and Korsmeyer from the Western philosophical tradition, I claim that the aversive-attractive response is integral to disgust’s power to motivate aesthetic engagement. On the one hand, people might feel its force and refuse to engage with that which disgusts. On the other hand, unshakeable interest may spur active responses including the exchange of judgments of taste; protests of a given artist, work, or exhibition; or even violent actions intended to damage or destroy a particular work. While the negative dimension of disgust response is often regarded as a liability from an aesthetic standpoint, disgust also has a corresponding productive dimension that has important implications for communities. In this chapter, I coin the term “generative disgust” in order to explain the productive capacity of disgust to inspire communal, often subcultural, activity. On my view, generative disgust has two orientations – destructive and constructive. Both forms activate the community in question based upon the valence of the group’s comportment towards a particular work of art. As such, destructive generative disgust galvanizes the community and spurs increased activity based upon negative response to art, whereas constructive generative disgust galvanizes the community and spurs increased activity based upon positive response to art. I explore two examples that reveal the dual character of generative disgust in communities: Andres Serrano’s _Immersion (Piss Christ)_ (destructive deployment) and Bryan Fuller’s _Hannibal_ (constructive deployment). In the case of _Piss Christ_, the intensity of generative disgust spurred some Christians to police moral-religious boundaries and restrict aesthetic expression in order to protect a community. Their activity culminated in vandalism and destruction of Serrano’s photograph. In the case of Hannibal, the intensity of generative disgust, combined with hedonic ambivalence inspired the “Fannibals” (the Hannibal fandom) to forge community, make art, and to try to find the show a new home when it was not renewed by NBC. Both instances manifest how generative disgust can incite aesthetic engagement and increase organized activity on the part of subcultural groups. (shrink)

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 and simulations, we show that incorporating these strategies can drastically decrease expenses and ensure immediate and accurate scalability across clouds of different ecosystems. (shrink)

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 among workers by compressing the productivity distribution, primarily benefiting those with lower initial ability. ChatGPT primarily substitutes for worker effort rather than complementing worker skills, shifting the focus of tasks from rough-drafting to idea generation and editing. Furthermore, exposure to ChatGPT increases job satisfaction and self-efficacy while also heightening both concerns and excitement about automation technologies. (shrink)