Generative AI: An In-depth Exploration of Methods, Uses, and Challenges
DOI:
https://doi.org/10.54097/czptrz11Keywords:
Generative AI, Generative Adversarial Network, Applications.Abstract
Recently, artificial intelligence has surged to the forefront of computer science, with generative AI emerging as the most sought-after research area. The success of generative AI hinges on advancements in algorithms, training frameworks, and data. Among these, training algorithms are of paramount importance. This article aims to shed light on several leading training algorithms in the domain. Generative AI has left a profound imprint on a myriad of industries. Intelligent publishing, advertising content creation, and finance are just a few sectors that have been revolutionized by this technology. As with all significant technological shifts, generative AI is not without its challenges. The implications it holds for employment, intellectual property rights, as well as security and privacy concerns, are profound. It's vital for stakeholders in the AI domain and beyond to consider and address these challenges. As generative AI continues to integrate more deeply into industries and our daily lives, proactive steps need to be taken to ensure ethical, secure, and equitable use. This not only guarantees the continued growth and trust in the technology but also safeguards society's values and norms in the face of rapid innovation.
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