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Journal articles on the topic 'Generative Principle'
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Ahmed, Serwan Samen, and Aram Rasheed Majeed. "Adjective in Linguistic Schools: Traditional, Generative, Cognitive." JOURNAL OF LANGUAGE STUDIES 5, no. 1 (2022): 622–36. http://dx.doi.org/10.25130/jls.5.1.38.
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The research is titled (Adjective in linguistic schools - traditional, generative, Cognitive). The research aims to study the adjective in the Kurdish language as a linguistic category. The types of adjectives were classified according to the linguistic schools, based on many principles. The traditional school concerned with the semantic principle to determine the class of adjectives. In the Generative school, the principle of position and function were two basic principles for defining an adjective. In the Cognitive theory, the principles of the adjective-noun relationship, the position of the adjective from the noun, and how the characteristics of nouns are presented through adjectives, were basic principles for defining the adjective and its types. The research is divided into three parts, the first section includes a review and a historical presentation of the researchers’concerns in classifying adjectives and their forms in the Kurdish language. In the second section of the research, examples of adjectives in the Kurdish language are presented from the perspective of the generative theory. Especially on the principle of syntactic case and the X-bar theory. In the third section, I dealt with the classification of adjectives in the Kurdish language from the perspective of Cognitive grammar.
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Ke, Yan, Min-qing Zhang, Jia Liu, Ting-ting Su, and Xiao-yuan Yang. "Generative steganography with Kerckhoffs’ principle." Multimedia Tools and Applications 78, no. 10 (2018): 13805–18. http://dx.doi.org/10.1007/s11042-018-6640-y.
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Yan, Tingting. "Research on Personal Information Protection in the Context of Generative Artificial Intelligence." Scientific Journal Of Humanities and Social Sciences 6, no. 7 (2024): 116–24. http://dx.doi.org/10.54691/vhyxgj39.
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Personal information is the foundation of generative AI, and ChatGPT-like generative AI needs to process a large amount of personal information at various stages such as model training, model generation, and model optimization, which also has a certain impact on traditional personal information protection rules. During the information collection phase, generative AI may fugitive the informed consent rules and infringe on the privacy rights of information subjects. In the information utilization stage, generative AI may impact basic personal information processing rules such as the principle of purpose limitation and the principle of openness and transparency, increasing the risk of personal information leakage. At the information generation stage, generative AI can generate false and discriminatory information. Therefore, in the context of generative AI, personal information protection is faced with the problems of the notification and consent rules being hollowed out, the principle of minimum necessity being voided, and the frequent leakage of personal information. Based on this, it is necessary to promote the transformation of the "personal control center to the risk control center" of the notification and consent rules, promote the risk-based interpretation of the principle of least necessary, and improve the risk-based personal information protection compliance system to solve the problem of personal information protection in the context of generative AI.
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Cheng, Chung-Ying. "Phenomenology and Onto-Generative Hermeneutics: Convergencies." Journal of Chinese Philosophy 42, no. 1-2 (2015): 221–41. http://dx.doi.org/10.1163/15406253-0420102015.
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In examining phenomenology as a base onto-generative hermeneutics (onto-hermeneutics) I find the gradual movement from pure phenomenology in Husserl to an ontological phenomenology in Merleau-Ponty through Heidegger and Gadamer. I argue thus that there is an implicit connection between the phenomenological and the ontological. In order to bring out the desirable connection between the two we must have hermeneutic interpretation of one in terms of the other. This leads to the idea of onto-hermeneutic circle of phenomenology and ontology based on the integration of the four phenomenologies which represent a wider comprehension and deeper intuition. It is in terms of this wider comprehension and deeper intuition of reality I introduce the Chinese notion “ben-ti 本體” (root-body) as “onto-generative” as well as onto-phenomenological. I suggest five principles as constituting the basic formulation of such a hermeneutic system as both theory and methodology: (i) Principle of comprehensive observation (guan 观) (the Yijing); (ii) Principle of objective reference (wu 物) (the Yijing); (iii) Principle of perception, reflection, and memory (gan 感) (the Yijing and the Confucian); (iv) Principle of intersubjective understanding and interpretation based on (ren 仁) (the Confucian); and (v) Principle of practical end and action (xing 行) (the Confucian).
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Franke, Herbert W. "Mathematics As an Artistic-Generative Principle." Leonardo. Supplemental Issue 2 (1989): 25. http://dx.doi.org/10.2307/1557939.
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Karimi, Mostafa, Arman Hasanzadeh, and Yang Shen. "Network-principled deep generative models for designing drug combinations as graph sets." Bioinformatics 36, Supplement_1 (2020): i445—i454. http://dx.doi.org/10.1093/bioinformatics/btaa317.
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Abstract Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene–gene, gene–disease and disease–disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes’ representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability and implementation https://github.com/Shen-Lab/Drug-Combo-Generator. Supplementary information Supplementary data are available at Bioinformatics online.
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Newmeyer, Frederick J. "Competence vs. performance; theoretical vs. applied." Historiographia Linguistica 17, no. 1-2 (1990): 167–81. http://dx.doi.org/10.1075/hl.17.1-2.13new.
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Summary The past 30 years have seen marked shifts in the generative grammarians’ view of the nature of linguistic competence. The rule-oriented period of early Transformational Grammar, which was ushered in by the publication of Chomsky’s Syntactic Structures in 1957, gave way a decade later to the principle-oriented period of Generative Semantics. By the mid-1970s, the rule-oriented Lexicalist framework had replaced Generative Semantics. Since around 1981, the principle-oriented Principles & Parameters approach is the one to which a majority of generative syntacticians hold allegiance. Each shift in the generativists’ view of the nature of competence has been accompanied by a revised view of how concepts derived from generative syntax might be applied to second language teaching. Since 1957, three different strategies for applying the theory have been propounded: the ‘mechanical’, the ‘terminological’, and the ‘implicational’, each of which has been instantiated during each period in the development of generative syntax. The paper closes with some speculative remarks about the feasibility of applying generativist theory to second language teaching.
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Cengiz Tırpan, Esra. "The ethical issues in generative artificial intelligence: A systematic review." Business & Management Studies: An International Journal 12, no. 4 (2024): 729–47. https://doi.org/10.15295/bmij.v12i4.2431.
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As generative artificial intelligence (generative AI) technology rapidly develops, new tools are being introduced to the market, and its use in many areas, from education to healthcare, is quickly increasing. Therefore, ethical research must keep pace with these developments and address the new challenges. In this way, AI can benefit society and prevent potential harm. This study was conducted to identify ethical issues in the use of generative AI, highlight prominent issues, and provide an overview through a systematic literature review. A systematic search was conducted in Scopus, Web of Science, and ScienceDirect databases to retrieve articles examining ethical aspects of generative AI with no year restrictions. The search terms were "generative artificial intelligence," "generative AI," "GenAI," or "GAI," with the combination of "ethic," "ethics," or "ethical." Studies were selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Forty-three articles were included in the review after the screening process. According to the research results, the "justice and fairness" principle was emphasized in all the articles examined. The least examined ethical principles were the principle of "solidarity", which expresses unity in society or group, and the principle of "dignity", which means the value an individual feels for himself and his rights. The authors of the 43 articles are mainly from the United States (n = 31), followed by China (n = 15) and the United Kingdom (n = 13). Of the 43 articles reviewed, 41 mentioned ChatGPT, albeit as an example. This study reviews the literature on the ethical use of generative AI and presents challenges and solutions.
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Liu, Qiaochu. "The Application of Generative AI in the Practical Teaching of Environmental Design." Region - Educational Research and Reviews 6, no. 12 (2024): 19. https://doi.org/10.32629/rerr.v6i12.2990.
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With the rapid development of artificial intelligence technology, generative AI, as an emerging technology, is gradually penetrating into various fields including environmental design practice teaching. As a comprehensive subject integrating art, science and technology, environmental design practice teaching is very important for cultivating students' innovative thinking and practical ability. Generative AI has brought unprecedented opportunities for environmental design practice teaching with its powerful content generation ability. However, how to effectively introduce this technology into the classroom and integrate it with traditional teaching methods is an urgent problem that we need to solve. This paper will discuss the application of generative AI in the practical teaching of environmental design from four aspects: technical principle, application scenario, practical effect and challenge. This paper aims to explore the application of generative AI in the practical teaching of environmental design, analyze its technical principles, application scenarios, practical effects and potential challenges, in order to provide new ideas and methods for environmental design education.
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Kuznetsov, Sergey. "Using Bellman Optimality Principle for the Generative Autoencoder Architecture for the Problems of the Attribute Data Typesetting and Semantic Description in Data Management." Differential Equations and Control Processes, no. 2 (2024): 171–82. https://doi.org/10.21638/11701/spbu35.2024.207.
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The publication presents the problems of identifying data types (typesetting) and semantic description of the attributes when managing structured data and master data (Master Data Management). A formal definition of the generalized attribute typesetting problem is given, which allows generation of the additional data types. This problem allows using the discrete Bellman optimality principle under special criteria of the target function. A unified architecture of the deep generative neural network addressing simultaneously the generalized attribute typesetting and semantic description generation problems is proposed. The architecture is based on the generative adversarial autoencoder architecture (AAE) using the mechanisms of soft-attention, and long-term memory (SCRN). The effectiveness of such implementation, in particular, is achieved through the application of the principles of dynamic programming within each epoch of the network training.
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Tanguiane, A. S. "A Principle of Correlativity of Perception and Its Application to Music Recognition." Music Perception 11, no. 4 (1994): 465–502. http://dx.doi.org/10.2307/40285634.
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Correlativity of perception is defined as a capacity to discover similar configurations of stimuli and to form high- level configurations from them. It is equivalent to describing information in terms of generative elements and their transformations. Such a representation saves memory and reveals causality in data generation. This approach is implemented in a model of artificial perception wherein data are selforganized in order to segregate patterns before recognizing them. Input information is described as generative patterns and their transformations. The least complex data representation that leads to a causally related semantic description is chosen, with (Kolmogorov) complexity defined by the amount of memory store required. The model is applied to voice separation and to rhythm/tempo tracking. Chord spectra are described by generative subspectra, which correspond to tonal spectra, and by their translations, which coincide with the intervals of the chord. Time events are also described by generative rhythmic patterns. Tempo and rhythm interdependence is overcome by the optimal sharing of complexity between representations of rhythmic patterns and tempo curve. The model explains the function of interval hearing, certain statements of music theory, and some effects of rhythm perception. Applications to image processing and modeling of abstract thinking are also discussed.
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Li, Wei, and Yongchuan Tang. "Soft Generative Adversarial Network: Combating Mode Collapse in Generative Adversarial Network Training via Dynamic Borderline Softening Mechanism." Applied Sciences 14, no. 2 (2024): 579. http://dx.doi.org/10.3390/app14020579.
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In this paper, we propose the Soft Generative Adversarial Network (SoftGAN), a strategy that utilizes a dynamic borderline softening mechanism to train Generative Adversarial Networks. This mechanism aims to solve the mode collapse problem and enhance the training stability of the generated outputs. Within the SoftGAN, the objective of the discriminator is to learn a fuzzy concept of real data with a soft borderline between real and generated data. This objective is achieved by balancing the principles of maximum concept coverage and maximum expected entropy of fuzzy concepts. During the early training stage of the SoftGAN, the principle of maximum expected entropy of fuzzy concepts guides the learning process due to the significant divergence between the generated and real data. However, in the final stage of training, the principle of maximum concept coverage dominates as the divergence between the two distributions decreases. The dynamic borderline softening mechanism of the SoftGAN can be likened to a student (the generator) striving to create realistic images, with the tutor (the discriminator) dynamically guiding the student towards the right direction and motivating effective learning. The tutor gives appropriate encouragement or requirements according to abilities of the student at different stages, so as to promote the student to improve themselves better. Our approach offers both theoretical and practical benefits for improving GAN training. We empirically demonstrate the superiority of our SoftGAN approach in addressing mode collapse issues and generating high-quality outputs compared to existing approaches.
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Yang, Zhi, and Yuanhong Xu. "Study on the Path of Generative Artificial Intelligence Copyright Protection under the Strategy of Intellectual Property Power." Scientific Journal Of Humanities and Social Sciences 6, no. 7 (2024): 172–79. http://dx.doi.org/10.54691/my07eq41.
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Generative AI copyright protection is in response to the inherent requirements of AI development and intellectual property protection. At present, generative AI copyright protection faces problems such as insufficient legal basis for prevention and control mechanisms, unsmooth preventive systems, and poor operation mechanisms. Therefore, to solve the dilemma of generative AI copyright protection, it is necessary to take the principle of risk prevention as the concept, clarify the legal basis for generative AI copyright prevention, reasonably define the scope of generative AI, establish a multi-principal protection system to protect generative AI copyright, introduce diversified ways to help generative AI copyright maintenance, and set up a composite responsibility system to solidify the generative AI copyright protection bottom line, and establish an administrative protection system to protect the copyright protection bottom line. The bottom line of copyright protection is an administrative protection path to help generative artificial intelligence copyright protection efforts to promote the healthy development of the two in the integration.
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Peng, Yingying. "A Comparative Analysis Between GAN and Diffusion Models in Image Generation." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 189–95. http://dx.doi.org/10.62051/0f1va465.
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In the field of artificial intelligence, image-generation techniques have been a hotspot for research. Two generative models that have garnered a lot of attention are diffusion models and generative adversarial networks (GANs). This review paper aims to compare and analyze GAN and Diffusion Models in the field of picture generation, as well as to give a thorough discussion of their features, applications, benefits, and drawbacks. Firstly, the work related to the working principle of GAN and diffusion models are introduced, and then their applications and results in image generation are reviewed. By comparing the existing research results, the author find that GAN performs well in generating realistic images but suffers from problems such as pattern collapse and unstable training, while the diffusion model has better stability and controllability. Combining the advantages of the two methods, this paper explores the possible fusion methods and looks forward to the future development direction in the field of image generation. These research results provide important references and insights to further enhance the level and application scope of image generation technology.
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Bayuningrat, Saka Adjie, Moch Zairul Alam, and Diah Pawestri Maharani. "Bentuk Internalisasi Nilai Etik Mengenai Bias Negatif dan Diskriminasi Dalam Platform generative AI." RechtJiva 1, no. 1 (2024): 1–22. http://dx.doi.org/10.21776/rechtjiva.v1n1.1.
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This research discusses a form of classification in the realm of Artificial Intelligence, namely generative AI. Generative AI is an AI technology that can create new content in the form of text, images, audio, and others. However, generative AI has the potential to cause problems such as negative bias and unintentional discrimination in the form of negative stereotyping of a racial group. Current regulations are inadequate to address these challenges and determine the liability of parties in the event of harm. This research aims to analyze the urgency of new regulations related to generative AI for anti-bias and discrimination. The approach is normative juridical using statutory, conceptual, and comparative approaches. The results show that current regulations do not adequately protect the public from bias and discrimination by AI. New regulations are needed that include AI ethical principles, AI audits, classification of parties' responsibilities (developers, data providers, regulators), and application of the principle of liability based on fault. These regulations are important to ensure responsible use of generative AI and respect for human rights. In conclusion, current regulations need to be refined and new ones created to address the ethical and liability challenges in the utilization of generative AI in line with anti-discrimination principles.
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Laine, Joakim, Matti Minkkinen, and Matti Mäntymäki. "Understanding the Ethics of Generative AI: Established and New Ethical Principles." Communications of the Association for Information Systems 56, no. 1 (2025): 1–25. https://doi.org/10.17705/1cais.05601.
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This scoping review develops a conceptual synthesis of the ethics principles of generative artificial intelligence (GenAI) and large language models (LLMs). In regard to the emerging literature on GenAI, we explore 1) how established AI ethics principles are presented and 2) what new ethical principles have surfaced. The results indicate that established ethical principles continue to be relevant for GenAI systems but their salience and interpretation may shift, and that there is a need to recognize new principles in these systems. We identify six GenAI ethics principles: 1) respect for intellectual property, 2) truthfulness, 3) robustness, 4) recognition of malicious uses, 5) sociocultural responsibility, and 6) human-centric design. Addressing the challenge of satisfying multiple principles simultaneously, we suggest three meta-principles: categorizing and ranking principles to distinguish fundamental from supporting ones, mapping contradictions between principle pairs to understand their nature, and implementing continuous monitoring of fundamental principles due to the evolving nature of GenAI systems and their applications. To conclude, we suggest increased research emphasis on complementary ethics approaches to principlism, ethical tensions between different ethical viewpoints, end-user perspectives on the explainability and understanding of GenAI, and the salience of ethics principles to various GenAI stakeholders.
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Liu, Lanting. "Constructing a Multi-Dimensional Governance System for the Application of Generative AI in Schools: Policy Analysis and Implications of the “Australian Framework for Generative AI in Schools”." Journal of Contemporary Educational Research 9, no. 1 (2025): 157–62. https://doi.org/10.26689/jcer.v9i1.9540.
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With the rapid development of generative artificial intelligence (AI) technology in the field of education, global educational systems are facing unprecedented opportunities and challenges, urgently requiring the establishment of comprehensive, flexible, and forward-looking governance solutions. The “Australian Framework for Generative AI in Schools” builds a multi-dimensional governance system covering aspects such as teaching and humanistic care, fairness and transparency, and accountability and security. Based on 22 specific principles and six core elements, it emphasizes a human-centered design concept, adopts a principle-based flexible structure, focuses on fairness and transparency, and stresses accountability and security. The framework provides valuable references for the use of generative AI in China’s education system and holds significant importance for promoting educational modernization and cultivating innovative talents adapted to the era of artificial intelligence.
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Yunqian, Zhang. "AI-DRIVEN BACKGROUND GENERATION FOR MANGA ILLUSTRATIONS: A DEEP GENERATIVE MODEL APPROACH." ORESTA 7, no. 1 (2024): 158–75. https://doi.org/10.5281/zenodo.15080622.
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<em>This paper introduces the generation technique of manga illustration background, discusses the traditional background generation technique and stylized migration technique, and points out that the application of AI technology provides new possibilities for the creation of manga illustration backgrounds. Aiming at the limitations of traditional methods, the design concept and principle of conditional generative model based on deep learning are proposed, the implementation principles of convolutional neural network and generative adversarial network are introduced, and the conditional manga illustration background generation model based on deep learning is proposed by combining the two. The paper uses MindSpore software to train CNN models.CNN models are very good for processing data such as images and are able to reduce the number of parameters in the model and increase the speed of training the model.The model data has a wide range of applications and is capable of removing the influence of character factors from the image while providing high resolution.The model can effectively realize the conditional generation of manga illustration background. The practical effect of this technology is demonstrated through a case study and technical exploration of manga illustration background generation technology, which demonstrates the potential of the new technology and its application in creating richer and more vivid comic backgrounds. In the future, with the continuous improvement and promotion of this technology, more similar cases are expected to be seen, bringing more possibilities and innovations to the creation of comics and illustrations.</em>
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Lye, Che Yee, and Lyndon Lim. "Generative Artificial Intelligence in Tertiary Education: Assessment Redesign Principles and Considerations." Education Sciences 14, no. 6 (2024): 569. http://dx.doi.org/10.3390/educsci14060569.
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The emergence of generative artificial intelligence (AI) such as ChatGPT has sparked significant assessment concerns within tertiary education. Assessment concerns have largely revolved around academic integrity issues among students, such as plagiarism and cheating. Nonetheless, it is also critical to consider that generative AI models trained on information retrieved from the Internet could produce biased and discriminatory outputs, and hallucination issues in large language models upon which generative AI acts provide made-up and untruthful outputs. This article considers the affordances and challenges of generative AI specific to assessments within tertiary education. It illustrates considerations for assessment redesign with the existence of generative AI and proposes the Against, Avoid and Adopt (AAA) principle to rethink and redesign assessments. It argues that more generative AI tools will emerge exponentially, and hence, engaging in an arms race against generative AI and policing the use of these technologies may not address the fundamental issues in assessments.
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Dhawan, Ravi, Kendall Douglas Brooks, Orr Shauly, Denys Shay, and Albert Losken. "Ethical Considerations for Generative Artificial Intelligence in Plastic Surgery." Plastic and Reconstructive Surgery - Global Open 13, no. 6 (2025): e6825. https://doi.org/10.1097/gox.0000000000006825.
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Summary: The integration of artificial intelligence (AI) into surgical care is rapidly transforming healthcare by enhancing efficiency, clinical decision-making, and patient outcomes. Generative AI (genAI), a subfield using large language models such as ChatGPT, Bard, and Midjourney, holds significant promise in automating tasks such as surgical planning and discharge summaries. However, it raises concerns about misinformation, data breaches, biases, and misuse. No genAI technology has yet received Food and Drug Administration approval for surgical use, emphasizing the need for thorough regulatory evaluation. This article proposed 5 ethical principles, adapted from World Health Organization recommendations, to guide the adoption and governance of genAI in plastic surgery. These principles include ensuring data transparency, maintaining patient autonomy, prioritizing safety and accountability, promoting equity, and investing in sustainability. Each principle is illustrated with a hypothetical case to highlight potential ethical breaches and the importance of rigorous testing, clear communication, and continuous monitoring. By adhering to these guidelines, stakeholders can ensure that genAI serves to enhance patient care and uphold the highest standards of ethical practice in surgical settings.
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Keum, Sunyoung, Zhenyan Li, and Minji Kim. "Metaverse Design Principle for Academic Counseling Room Using Generative AI." Journal of Educational Technology 40, no. 3 (2024): 613–41. http://dx.doi.org/10.17232/kset.40.3.613.
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Wang, Xinghao. "Analysis and Application Research of InfoGAN." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 21–26. http://dx.doi.org/10.54097/hset.v57i.9891.
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Generative Adversarial Networks (GAN) have been a revolutionary development in the field of Artificial Intelligence, particularly in the domain of generative models. The information maximizing generative adversarial nets, or infoGAN, is one of the most recent and promising developments in the world of GAN. InfoGAN focuses on maximizing the mutual information between the generator's output and some input variables. This means that the generated images or data can be controlled more easily while maintaining high-quality results.Apart from these applications, infoGAN has also been used in other areas such as natural language processing, anomaly detection, and even in music generation. With its versatility and robust performance, infoGAN looks set to become an increasingly important tool for researchers and practitioners in the field of machine learning.This paper focuses on the principle of infoGAN (information maximizing generative adversarial nets) and tries to put forward several ways to apply infoGAN to solve different kinds of problems in daily life.
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Dumitru, Alexandra-Mihaela, Sorin Anagnoste, and Marco Savastano. "Unleashing the potential: harnessing generative artificial intelligence for empowering model training." Proceedings of the International Conference on Business Excellence 18, no. 1 (2024): 3618–35. http://dx.doi.org/10.2478/picbe-2024-0294.
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Abstract Recent strides in generative artificial intelligence, particularly large language models, have been propelled by foundation models – learning algorithms trained on extensive and diverse datasets encompassing various subjects. This technology, inspired by the complexity of the human brain, unveils a new frontier in generative Artificial Intelligence (AI), showing its potential in creativity by generating innovative content based on absorbed data and user prompts. It is forecasted that the conversational AI and virtual assistant segment is experiencing the highest growth rate within the contact center industry, projected to fuel a 24% increase in the market during 2024. In spite of all remarkable performances, the incipient stage of generative AI calls for a careful consideration, as technological and ethical challenges demand attention and awareness. This research delves into the base principle which empowers users to build personalized chatbots trained on your data. This stand-alone footprint can further exemplify the transformative potential of generative artificial intelligence, extending its reach beyond professionals to individuals and tremendously remodeling the landscape of chatbots. Text generation lies at the intersection of computational linguistics and artificial intelligence, forming a specialized area within natural language processing. It implies a thorough procedure where a model is trained to be able to recognize and interpret the context of specific input data, subsequently generating text that pertains to the input’s subject matter. We have identified gap areas that require in-depth research. For instance, a broader number of papers relies solely on architecture optimization, performance comparison or application-specific studies. Therefore, this paper gives a bird’s eye view of the effective algorithm flow of a traditional generative model, using Long Short-Term Memory networks – part of the recurrent neural networks part family. The purpose of the current study focuses to enrich the existing body of knowledge on how a response generation-based model operates, therefore paving the way for chatbots development and deployment.
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Tomar, Snehal Singh, Maitreya Suin, and A. N. Rajagopalan. "Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16350–51. http://dx.doi.org/10.1609/aaai.v37i13.27035.
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The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the latent space of a Deep Generative Model as a solution to these bottlenecks. To this end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative Autoencoder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images. We present qualitative and quantitative results for the same and their analysis. This work shall serve as a guiding primer for future work.
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Seo, Ji-Young. "College Students’ Preferences on Principles for the Effective Instructional Video Design for Online General English Classes in Korea." Electronic Journal of e-Learning 20, no. 3 (2022): pp313–324. http://dx.doi.org/10.34190/ejel.20.3.2336.
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The present study investigates the preferences of students regarding the principles for the effective design of instructional videos to identify factors that influence engagement. A questionnaire was distributed to 232 students enrolled in online liberal arts classes at a private university in South Korea. Frequency analysis was conducted to determine preferences, whereas an independent sample t-test and one-way analysis of variance were administered to verify any differences in preferences according to gender and grade. The findings are as follows. First, out of the 12 principles that should be considered in the design of instructional videos, the students most preferred the review quiz principle. Moreover, this factor was found to exert the greatest influence on engagement. Second, incorporating real-life situation principles into instructional videos also had a significant impact on engagement. Third, female students expressed higher levels of preference than did male students in terms of the preview, course content on screen, and review quiz principles. Fourth, sophomores preferred the review quiz principle more than the freshmen did. The results of the present study are in line with those of previous research in that the effective instructional design of multimedia lessons requires reducing extraneous processing, managing essential processing, and fostering generative processing. Particularly, the study found that Korean students value video lectures with generative activities for meaningful learning. Based on the findings of the study, pedagogical considerations of the design of recorded lectures and its structure for active engagement, and suggestions for future studies are provided.
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Hu, Ling, Shu-Hao Wu, Weizhou Cai, et al. "Quantum generative adversarial learning in a superconducting quantum circuit." Science Advances 5, no. 1 (2019): eaav2761. http://dx.doi.org/10.1126/sciadv.aav2761.
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Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum-state generator can be trained to replicate the statistics of the quantum data output from a quantum channel simulator, with a high fidelity (98.8% on average) so that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing long-sought-after quantum advantages in machine learning tasks with noisy intermediate–scale quantum devices.
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Wang, Chuang, Jianwen Song, and Lijun Wang. "P‐2.9: A review of image generation methods based on deep learning." SID Symposium Digest of Technical Papers 54, S1 (2023): 507–12. http://dx.doi.org/10.1002/sdtp.16343.
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Image as a medium of visual information transmission have the advantages of vividness, intuition and easy understanding. They play an important role in information transmission and utilization. In recent years, due to the rapid development of deep learning technology in the field of image processing, image generative model based on neural network has become one of the current research hotspots. In the field of deep learning, unsupervised learning model has received more and more attentions, especially in the field of deep generative models, which has made breakthrough progress [1] . Among them, Variational Auto-Encoder (VAE), Generative Adversarial Network (GAN), and Diffusion Model are the three most representative research methods in the field of unsupervised learning. They have been applied more and more in the field of deep generative models. Especially, the high-quality image generative models based on the generative adversarial network continue to be hot. The diffusion model is a rising star, which is favored by more and more researchers. This paper first summarizes the main research work, improvement mechanism and features of image generation methods based on VAE and GAN, then introduces the principle of the rising diffusion model and its representative models. Finally, the advantages and limitations of the above methods are compared and analyzed, and prospects for future research are put forward.
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Sosna, Nina. "From Analytical Аnthropology to Generative Anthropology". Chelovek 32, № 5 (2021): 103. http://dx.doi.org/10.31857/s023620070017441-2.
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This article discusses the role of the technical in V.A. Podoroga&apos;s project of studying the literature worlds as archives of human experience. On the level of content, among many other components of these worlds he distinguished working and non-working machines, “gizmos” and various optical devices, including the mechanic eye, camera, mounting, etc. Formally, the action of these machines can be assessed as alienation, though in the context of modern media studies and exploration of the perspectives of anthropology, they can also be described as a problematic contact zone between the human and non-human, with a bias towards breaking the already historically disrupted bodily-affective interaction with the “outside”. Studies of Andrei Platonov&apos;s show the most radical interaction with the technical, in this case causing dissimilation of the human. However, even for this “inhuman” material, Podoroga chooses a form of human-measured approximation and distancing, which allows defending the position of an active researcher, observer, anthropologist. His efforts produce a kind of reconstruction, which at a certain time distance reveals the “seams”, “folds”, “cuts”, “plexuses”, “gaps” that formed at once the experience of the body and experience of writing. This is how the components of literature worlds are extracted, and from them “a picture of the universe can be deduced”. Although an external position to technics is considered to be the only guarantee of the human, even if it is strange, symptomatic, seizure-like, a different understanding of the technical is possible. Without claiming that a “machine” can assemble components in the absence of the observer&apos;s (reader&apos;s) comprehension ability, it seems nevertheless possible to relate the technical to more general principles: the methods of dispersion and gathering that form the form, the principles of intervalization and binding the heterogeneous, and most importantly, the principle of generating conditions for detecting an event. Then it will be necessary to clarify the relationship between techne and literature in the broader sense of poiesis, in the process of which elements can be formed from the indivisible “compressed”.
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Salvucci, Peter. "Multistability and Complex Isotopy in Makam Music Analysis: The Link Between Cognition and Semiotics in Aesthetics of Turkish Music." Roczniki Humanistyczne 72, no. 12 (2024): 207–23. https://doi.org/10.18290/rh247212.13.
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Multistability in perceptual psychology involves ambiguity or rivalry between two or more percepts of sensory stimuli. This phenomenon can be compared to the Greimassian concept of complex isotopy, where multiple terms of semantic content are simultaneously signified in the same reading of a text, providing multiple interpretational meanings. This study proposes that a key aesthetic principle of classical Turkish makam music involves the multistable perception of musical events, which in the semantic sense manifest as interoceptive, complex isotopies. The aesthetic of multiple subjective interpretations is examined in related traditional arts such as miniature art, poetry and theater. Generative theory is employed for compositional analysis in this study, as it reflects the cognitive apprehension of music. The principles of multistable perception as complex isotopy therefore provide a cognitive and aesthetic context to the author’s thesis proposal for a generative theory of makam music.
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ТІТОВ, Іван Геннадійович. "МЕТОДОЛОГІЧНІ ПРИНЦИПИ ПОСТНЕКЛАСИЧНОЇ ПСИХОЛОГІЇ". Психологія і особистість 1, № 15 (2019): 3–34. https://doi.org/10.5281/zenodo.2559611.
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<em>The transformation of psychological science at postnonclassical stage of its development demands theoretical and methodological reflection of the conceptual grounds of psychology. In this article, we have attempted to outline themost significant principles regarded to postnonclassicalpsychology such as 1) structural-genetic principle; 2) principle of subject (personal agency); 3) principle of orientation on cultural context; 4) principle of ontological constructivism; 5) principle of integration.</em> <em>Structural-genetic principlegrounds the view on psychological phenomenon as the unique and integral formation,which is able to self-organization and self-development. This principle emphasizes the necessity to consider a) structural peculiarities of such phenomenon (level of inner differentiation and integration of its components, dynamics of functional coordination between them); b) ordering influences of system-creating factor on it; c) diachronic aspects of its functioning (determination, mechanisms, stages, synergy effects etc.).</em> <em>The principle of subject (personal agency) focuses on the content and dynamic (motivational and value, cognitive, regulative and reflexive etc.) aspects of person’s interaction with the world.</em> <em>The principle of orientation on cultural contextprinciple proposes to interpret mental phenomena as the modus of culture, taking into consideration the interconnection between generative worlds of culture and subjective worlds of the human.</em> <em>The fifth principle points out the constructive (mediated by constructs, narrative structures etc.) nature ofthe cognition. It indicates that personal mental constructions are to be ideal models (hypertext representations of the world which provide interpretative and adaptive functions) based on conventional assumptions about the reality.</em> <em>The principle of integration reveals the possibility ofa) inter-paradigmdialogue and inter-disciplinary synthesis, content interaction between different psychological theories; b) complex applying of different research strategies and methods; c) combination of the fundamental theoretical achievements with the practical technologies.</em>
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Machueva, D. A., D. R. Baraev, and T. M.-A. Bechurkaev. "VARIOUS ASPECTS OF THE WORK OF GENERATIVE INTELLIGENCE IN THE FINE ARTS USING THE DALL-E NEURAL NETWORK AS AN EXAMPE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 243 (September 2024): 20–25. https://doi.org/10.14489/vkit.2024.09.pp.020-025.
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Neural creativity, or Artificial Intelligence Art, is becoming increasingly widespread, competing with traditional works of art created by hand. The study is aimed at analyzing the operating principles and capabilities of neural networks that generate images, as well as assessing their advantages and limitations in various fields of application using the DALL-E neural network as an example. The analysis of domestic and foreign literature in the field of artificial intelligence (AI) technologies and law, structuring and systematization of the received information, is included. The principles of operation of the DALL-E neural network, its training process and the architecture of GANs (Generative Adversarial Networks) are considered. The generation stages from processing the query text to image generation are shown, and the concept of a diffuse model is described. Generative neural networks can serve as an independent tool for obtaining the final result. However, most often the technical capabilities of AI are used in combination with human creative abilities and ideas, complementing, expanding and enriching them, offering new creative ideas, significantly speeding up work and increasing its efficiency. It is important to ensure responsible use of technology and develop appropriate regulatory standards to minimize possible risks. The current issues of authorship of generated works are considered. Existing copyright legislation, both Russian and foreign, allows for some legal uncertainty in the area of establishing and protecting rights to works of neural networks. Based on the analysis of the principle of interaction with neural networks, possible solutions to problems are given.
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Wang, Liwei, Xiong Li, Zhuowen Tu, and Jiaya Jia. "Discriminative Clustering via Generative Feature Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1162–68. http://dx.doi.org/10.1609/aaai.v26i1.8305.
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Existing clustering methods can be roughly classified into two categories: generative and discriminative approaches. Generative clustering aims to explain the data and thus is adaptive to the underlying data distribution; discriminative clustering, on the other hand, emphasizes on finding partition boundaries. In this paper, we take the advantages of both models by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers. Such the feature mapping strategy maps nonlinear boundaries of generative clustering to linear ones in the feature space where we explicitly impose the maximum entropy principle. We also propose the unified probabilistic framework, enabling solvers using standard techniques. Experiments on a variety of datasets bear out the notable benefit of our method in terms of adaptiveness and robustness.
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Gao, Jingjing. "Generative adversarial network based image inpainting." Applied and Computational Engineering 5, no. 1 (2023): 93–98. http://dx.doi.org/10.54254/2755-2721/5/20230540.
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Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are listed. After that, the evaluation metrics, and common datasets of deep learning-based image inpainting are listed. Finally, the existing image inpainting methods are summarized and summarized, and the ideas for future key research directions are presented and prospected.
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Lei, Feifei, Jieren Cheng, Yue Yang, Xiangyan Tang, Victor S. Sheng, and Chunzao Huang. "Improving Heterogeneous Network Knowledge Transfer Based on the Principle of Generative Adversarial." Electronics 10, no. 13 (2021): 1525. http://dx.doi.org/10.3390/electronics10131525.
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Deep learning requires a large amount of datasets to train deep neural network models for specific tasks, and thus training of a new model is a very costly task. Research on transfer networks used to reduce training costs will be the next turning point in deep learning research. The use of source task models to help reduce the training costs of the target task models, especially heterogeneous systems, is a problem we are studying. In order to quickly obtain an excellent target task model driven by the source task model, we propose a novel transfer learning approach. The model linearly transforms the feature mapping of the target domain and increases the weight value for feature matching to realize the knowledge transfer between heterogeneous networks and add a domain discriminator based on the principle of generative adversarial to speed up feature mapping and learning. Most importantly, this paper proposes a new objective function optimization scheme to complete the model training. It successfully combines the generative adversarial network with the weight feature matching method to ensure that the target model learns the most beneficial features from the source domain for its task. Compared with the previous transfer algorithm, our training results are excellent under the same benchmark for image recognition tasks.
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Srebre, Matej, Pascal Schmolz, Hosein Hashemi, Martin Ritter, and Thomas Kuhr. "Generation of Belle II Pixel Detector Background Data with a GAN." EPJ Web of Conferences 245 (2020): 02010. http://dx.doi.org/10.1051/epjconf/202024502010.
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To match the statistical precision due to the large dataset that Belle II is expected to collect, simulations that accurately describe real data are required. The effect of beam background must be considered and can be taken into account with overlaying random trigger data, but its large size is a technical challenge. This problem can be mitigated by generating beam background data with generative adversarial networks. A proof of principle is shown for the background data recorded by the pixel vertex detector.
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I. Titov. "METHODOLOGICAL PRINCIPLES OF POSTNONCLASSICAL PSYCHOLOGY." Psychology and Personality, no. 1 (May 20, 2019): 9–40. http://dx.doi.org/10.33989/2226-4078.2019.1.163976.
Full textAbstract:
The transformation of psychological science at postnonclassical stage of its development demands theoretical and methodological reflection of the conceptual grounds of psychology. In this article, we have attempted to outline themost significant principles regarded to postnonclassicalpsychology such as 1) structural- genetic principle; 2) principle of subject (personal agency); 3) principle of orientation on cultural context; 4) principle of ontological constructivism; 5) principle of integration.Structural-genetic principlegrounds the view on psychological phenomenon as the unique and integral formation,which is able to self-organization and self- development. This principle emphasizes the necessity to consider a) structural peculiarities of such phenomenon (level of inner differentiation and integration of its components, dynamics of functional coordination between them); b) ordering influences of system-creating factor on it; c) diachronic aspects of its functioning (determination, mechanisms, stages, synergy effects etc.).The principle of subject (personal agency) focuses on the content and dynamic (motivational and value, cognitive, regulative and reflexive etc.) aspects of person’s interaction with the world.The principle of orientation on cultural contextprinciple proposes to interpret mental phenomena as the modus of culture, taking into consideration the interconnection between generative worlds of culture and subjective worlds of the human.The principle of ontological constructivism points out the constructive (mediated by constructs, narrative structures etc.) nature ofthe cognition. It indicates that personal mental constructions are to be ideal models (hypertext representations of the world which provide interpretative and adaptive functions) based on conventional assumptions about the reality.The principle of integration reveals the possibility ofa) inter-paradigmdialogue and inter-disciplinary synthesis, content interaction between different psychological theories; b) complex applying of different research strategies and methods; c) combination of the fundamental theoretical achievements with the practical technologies.
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Sheppard, Daniel. "The International Criminal Court and "Internationally Recognized Human Rights": Understanding Article 21(3) of the Rome Statute." International Criminal Law Review 10, no. 1 (2010): 43–71. http://dx.doi.org/10.1163/157181209x12584562670811.
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AbstractArticle 21(3) of the Rome Statute requires that the law applied by the ICC be interpreted and applied in accordance with "internationally recognized human rights." Notwithstanding its paramountcy over other sources of law, Article 21(3) has yet to receive satisfactory consideration and analysis by the Court. In constructing a principled framework for how international human rights should operate within the applicable law of the Court, certain principles serve as important guideposts: rules of statutory interpretation, the complementarity principle, the structure of international human rights law, and principles of international legal personality. Relying on these principles, the Court's jurisprudence and the Statute's travaux préparatoires, it is possible to map out some of the features of Article 21(3). The Article is not merely a rule of interpretation, but is generative of powers and remedies that would otherwise not be available. However, in order to be applied rationally, the scope of "internationally recognized human rights" should be contingent on which state would ordinarily exercise jurisdiction over a prosecution. The institutional relationships between The Court, state parties, and other bodies that interpret and apply human rights norms should also influence how the Court applies these principles, with decisions of international human rights courts being prima face binding in certain circumstances.
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Carl, Michael. "Models of the Translation Process and the Free Energy Principle." Entropy 25, no. 6 (2023): 928. http://dx.doi.org/10.3390/e25060928.
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Translation process research (TPR) has generated a large number of models that aim at explaining human translation processes. In this paper, I suggest an extension of the monitor model to incorporate aspects of relevance theory (RT) and to adopt the free energy principle (FEP) as a generative model to elucidate translational behaviour. The FEP—and its corollary, active inference—provide a general, mathematical framework to explain how organisms resist entropic erosion so as to remain within their phenotypic bounds. It posits that organisms reduce the gap between their expectations and observations by minimising a quantity called free energy. I map these concepts on the translation process and exemplify them with behavioural data. The analysis is based on the notion of translation units (TUs) which exhibit observable traces of the translator’s epistemic and pragmatic engagement with their translation environment, (i.e., the text) that can be measured in terms of translation effort and effects. Sequences of TUs cluster into translation states (steady state, orientation, and hesitation). Drawing on active inference, sequences of translation states combine into translation policies that reduce expected free energy. I show how the notion of free energy is compatible with the concept of relevance, as developed in RT, and how essential concepts of the monitor model and RT can be formalised as deep temporal generative models that can be interpreted under a representationalist view, but also support a non-representationalist account.
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Zhang, Zhao, He Yan, Ping Wang, Richard Millham, and Renjie He. "Image Inpainting Method Based on Generative Adversary Networks." Journal of Internet Technology 25, no. 6 (2024): 945–53. http://dx.doi.org/10.70003/160792642024112506014.
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Aiming at the problems of missing and damaged image details, this paper proposes an image inpainting method that is preprocessed and then inpainted. First, through preprocessing that enhances and purifies the image, the clarity, saturation and contrast of the image are improved. Secondly, the image features are dynamically divided through the convolutional neural network, and the image is generated according to the principle of a generative adversarial network, finally, the adversarial strategy is used to promote the expression of the model, to make the repair result more realistic. Compared with other models, the method proposed in this paper has a better quality of repairing effect.
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Jiang, Jiacheng. "Study on the Attribution of Rights in Generative Artificial Intelligence Works." Journal of Intelligence and Knowledge Engineering 1, no. 2 (2023): 18–22. http://dx.doi.org/10.62517/jike.202304203.
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In modern life with ever-changing science and technology, artificial intelligence has become more and more popular, existing in all aspects of people's lives, playing an important and difficult to replace role. However, the lag of the law leads to the fact that it can not solve all disputes, especially in the copyright ownership of artificial intelligence works, whether it is infringement, contract disputes or copyright protection. It is an urgent problem to be solved. Because most of the disputes in the present stage of artificial intelligence are in the aspect of economic property, this paper discusses the ownership of the rights of works from the perspective of law and economics and economic principles. Based on the incentive principle in economics, the feasible path in academic research and the subject of legal responsibility, this paper should ascribe its rights to investors. However, in order to avoid conflicts between the right to use and the right to publish, the node where the user "pays fees" should be regarded as the investor's transfer of the ownership of the work in a "silent" way. If the investment company refuses, it shall do so in an express manner.
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Besserve, Michel, Remy Sun, Dominik Janzing, and Bernhard Schölkopf. "A Theory of Independent Mechanisms for Extrapolation in Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6741–49. http://dx.doi.org/10.1609/aaai.v35i8.16833.
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Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating process, such that one can intervene on each node independently of the others. However, the nodes of this graph are usually unobserved, leading to overparameterization and lack of identifiability of the causal structure. We develop a theoretical framework to address this challenging situation by defining a weaker form of identifiability, based on the principle of independence of mechanisms. We demonstrate on toy examples that classical stochastic gradient descent can hinder the model's extrapolation capabilities, suggesting independence of mechanisms should be enforced explicitly during training. Experiments on deep generative models trained on real world data support these insights and illustrate how the extrapolation capabilities of such models can be leveraged.
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Zhou, Guoqiang, Yi Fan, Jiachen Shi, Yuyuan Lu, and Jun Shen. "Conditional Generative Adversarial Networks for Domain Transfer: A Survey." Applied Sciences 12, no. 16 (2022): 8350. http://dx.doi.org/10.3390/app12168350.
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Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle diverse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how to improve it to achieve better performance and how to evaluate such performance across the variants. Afterward, the main applications of CGAN in domain transfer are presented. Finally, as another major contribution, we also list the current problems and challenges of CGAN.
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Larson, Theodore, Fangyu Du, and Goutam Mylavarapu. "Proposal for organizational rules for new and prospective employees on the use of Generative tools." American Journal of Advanced research 8, no. 1 (2024): 7–10. https://doi.org/10.5281/zenodo.12211112.
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This paper proposes organizational guidelines for new and prospective employees on using generative tools like ChatGPT. It highlights the importance of setting clear expectations for these tools' utility, given their complex nature and the potential for misuse. The paper outlines a principle rule aiming for a targeted and fixed increase in worker productivity, alongside justification for setting minimum and maximum productivity targets. It discusses the necessity for a balanced approach to utilizing generative AI, emphasizing politeness in interactions, safeguarding private data, and the critical review of AI-generated content to avoid the pitfalls of uncritical acceptance. This approach seeks to harness the benefits of generative AI while mitigating risks, ensuring these tools serve as effective aids in the workplace.
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Jiang, Wei, Tianyi Yang, Ang Li, Yiyu Lin, and Xinzhu Bai. "The Application of Generative Artificial Intelligence in Virtual Financial Advisor and Capital Market Analysis." Academic Journal of Sociology and Management 2, no. 3 (2024): 40–46. https://doi.org/10.5281/zenodo.11112424.
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This paper explores the application of Generating Artificial Intelligence (GAI) to virtual financial advisory and capital market analysis. This paper first introduces the basic principle of GAI and its importance in financial decision-making, and then analyzes the problems and limitations of traditional financial advisory model through literature review and actual data. Then, it discusses the development trend and advantages of intelligent financial advisor, and the difference compared with traditional financial advisor model. Then, it describes the application of generative AI in the financial field, including intelligent investment advisory, risk assessment, investment decision-making and so on. Finally, the realization path of building intelligent financial decision based on generative AI is proposed, including data collection and collation, building large model of enterprise real data, building interactive data analysis and decision system based on natural language, personalized financial analysis and decision support, etc. Through these measures, we can better achieve intelligent, personalized and efficient financial decision support, and provide more intelligent and efficient support for financial decision making.
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MAVROUDIS, Ioannis, Ioana –. Miruna BALMUS, and Alin CIOBICA. "Brain Function: Free Energy, Predictive Processing and Active Inference." Annals of the Academy of Romanian Scientists Series on Biological Sciences 12, no. 1 (2023): 108–10. http://dx.doi.org/10.56082/annalsarscibio.2023.1.108.
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"A potential new theory of brain function based on Bayesian inference could be that the brain is a predictive processing system that generates internal models of the world to make predictions about future sensory inputs. According to this theory, the brain generates internal models based on prior beliefs and past experiences, which are used to make predictions about future sensory inputs. In summary, the free energy principle focuses on minimizing the difference between the predicted and actual sensory inputs using a hierarchical generative model, while the predictive processing theory focuses on generating and updating internal models to make predictions about future sensory inputs."
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Et. al., Dr Priyank Jain,. "Differentially Private Data Release: Bias Weight Perturbation Method - A Novel Approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 7165–73. http://dx.doi.org/10.17762/turcomat.v12i10.5607.
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Differential privacy plays the important role to preserve the individual data. In this research work, discussing a novel approach of releasing private data to the public, which is differentially private, called Bias Weight Perturbation Method. The approach follow here align with principle of differential privacy, it also used concept of statistical distance and statistical sample similarity to quantify the synthetic data generation loss, which is then used to validate our results. Our proposed approach make use of the deep generative models for providing privacy and it further produce synthetic dataset which can be released to public for further use.
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Harker, Michael. "The Ethics of Argument: Rereading Kairos and Making Sense in a Timely Fashion." College Composition & Communication 59, no. 1 (2007): 77–97. http://dx.doi.org/10.58680/ccc20076381.
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This study challenges the prevailing interpretations of the Greek rhetorical principle of kairos “saying the right thing at the right time” and attempts to draw on a more nuanced understanding of the term in order to provide generative re-readings of three Braddock Award–winning essays.
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Scheiter, Matthias, Andrew Valentine, and Malcolm Sambridge. "Upscaling and downscaling Monte Carlo ensembles with generative models." Geophysical Journal International 230, no. 2 (2022): 916–31. http://dx.doi.org/10.1093/gji/ggac100.
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SUMMARY Monte Carlo methods are widespread in geophysics and have proved to be powerful in non-linear inverse problems. However, they are associated with significant practical challenges, including long calculation times, large output ensembles of Earth models, and difficulties in the appraisal of the results. This paper addresses some of these challenges using generative models, a family of tools that have recently attracted much attention in the machine learning literature. Generative models can, in principle, learn a probability distribution from a set of given samples and also provide a means for rapid generation of new samples which follow that approximated distribution. These two features make them well suited for application to the outputs of Monte Carlo algorithms. In particular, training a generative model on the posterior distribution of a Bayesian inference problem provides two main possibilities. First, the number of parameters in the generative model is much smaller than the number of values stored in the ensemble, leading to large compression rates. Secondly, once trained, the generative model can be used to draw any number of samples, thereby eliminating the dependence on an often large and unwieldy ensemble. These advantages pave new pathways for the use of Monte Carlo ensembles, including improved storage and communication of the results, enhanced calculation of numerical integrals, and the potential for convergence assessment of the Monte Carlo procedure. Here, these concepts are initially demonstrated using a simple synthetic example that scales into higher dimensions. They are then applied to a large ensemble of shear wave velocity models of the core–mantle boundary, recently produced in a Monte Carlo study. These examples demonstrate the effectiveness of using generative models to approximate posterior ensembles, and indicate directions to address various challenges in Monte Carlo inversion.
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Yang, Zhe. "A review of motion generation technology." Applied and Computational Engineering 30, no. 1 (2024): 68–73. http://dx.doi.org/10.54254/2755-2721/30/20230073.
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Nowadays, deep learning and neural network-related research play a very important role in the widely use of artificial intelligence -related technologies, Among them, the hot development in the direction of generative adversarial networks (GAN) has given birth to many generation-related techniques. For example, MoCoGAN is based on the implementation principle of GAN, which enables video generation of different actions of the same character or the same action of different characters, through the method that decompose video into actions and content. This paper introduces the history and principles of MoCoGAN, starting from the prospect of using MoCoGAN in artificial intelligence (AI) industry and the technical challenges that need to be overcome in the future application of action generation. Besides, this paper also discusses the two main issues of how to improve the quality of video generation using MoCoGAN and the input conditions that are the most central problem to GAN networks. By summarizing the optimization solutions of other researchers in these two areas in recent years, this paper searches the core problems need to be solved and propose a broad prospect for future video generation techniques that can be implemented by using MoCoGAN in human-computer interaction (HCI) area.
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Liu, Haotian. "Scene Modeling in Game Development Based on Generative Adversarial Networks." Applied and Computational Engineering 110, no. 1 (2024): 36–41. http://dx.doi.org/10.54254/2755-2721/110/2024melb0114.
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Due to booming advance on generative models, people have great interest on designing model structure to produce wonderful pictures, or even 3d shapes. The motivation of this work is that the 3d modeling manufacturing in game development is still challenging and time-consuming, and 3d shape produced from generative models may be powerful tools for the problem. The work tried to build game scenes, such as a city and a cave, the typical scene that requires many random but similar objects. This paper aims to explore a complete workflow for applying the GANs to the game development. The structure of the paper is introducing the background of the game development and the progress of the generative model, giving interpretation about the principle of Generative Adversarial Networks, and propose the process on how to utilize it to improve the productivity in developing game scene. Finally, this work found that it could considerably reduce the repetitive work on making massive and similar objects.