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What is Generative Adversarial Networks (GANs)

The Role of Generative AI in the Communication Classroom
These are AI models where two neural networks (the generator and the discriminator) are trained together. The generator creates data, and the discriminator evaluates it, leading to highly realistic generated content.
Published in Chapter:
Generative AI in Higher Education
Jason S. Wrench (SUNY New Paltz, USA) and Sanae Elmoudden (St. John's University, USA)
Copyright: © 2024 |Pages: 37
DOI: 10.4018/979-8-3693-0831-8.ch001
This chapter provides a comprehensive exploration of generative artificial intelligence (AI), particularly focusing on its implications and applications in higher education. It discusses the evolution and fundamental concepts of AI, including large language models and their development, emphasizing the intricate processes involved in creating and refining these models. The chapter delves into the ethical considerations and potential biases inherent in AI systems, highlighting the importance of responsible AI development. Moreover, the chapter examines the transformative potential of generative AI in enhancing learning, creativity, and information processing in higher education settings.
Full Text Chapter Download: US $37.50 Add to Cart
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Out of Our Minds: Ontology and Embodied Media in a Post-Human Paradigm
An artificial intelligence process that includes a “generator” that produces samples, and a “discriminator” that differentiates between computer-generated samples and samples derived from “real-world” sources.
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Reframing Assessments: Designing Authentic Assessments in the Age of Generative AI
A machine learning framework involving two neural networks, a generator and a discriminator, that work together in a competitive manner to create, refine, and evaluate synthetic data.
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