Generative Intelligence: Architecture, Ethics, and Impact
DOI:
https://doi.org/10.15680/IJCTECE.2018.0101003Keywords:
Generative Intelligence, AI Ethics, GANs, Transformers, Deep Learning, Computational Creativity, Bias in AI, Misinformation, Neural Architecture, Societal ImpactAbstract
Generative intelligence, a branch of artificial intelligence focused on the autonomous creation of content, has reshaped our understanding of machine capabilities and human-AI collaboration. With advancements in deep learning, neural networks have evolved from simple classifiers into sophisticated generative systems capable of producing realistic images, coherent text, music, and even simulated environments. This paper examines the foundational architectures underpinning generative intelligence—including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based large language models—while also addressing the critical ethical concerns surrounding their deployment. As generative models become more pervasive in media, design, education, and science, questions about bias, authorship, misinformation, and social responsibility have moved to the forefront of AI discourse.
Through an interdisciplinary approach combining technical analysis, experimental evaluation, and ethical review, this study explores how generative intelligence functions, the structures it relies on, and the broader implications of its widespread adoption. We analyze the performance of leading generative models across various tasks and assess their potential risks and benefits. Methodologically, this research integrates both quantitative metrics and human-centered evaluations to gauge the quality, originality, and societal impact of AI-generated content.
Our findings reveal a dual narrative: on one hand, generative models represent a significant technological achievement in computational creativity and problem-solving; on the other, they introduce profound challenges related to privacy, identity, truth, and artistic ownership. As these systems grow in capability and influence, it is imperative to establish frameworks that guide their ethical development and ensure that their integration into society is responsible, equitable, and transparent. This paper concludes by offering recommendations for developers, researchers, and policymakers, aimed at maximizing the benefits of generative intelligence while mitigating its most pressing risks.
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