Introduction to Generative-AI

Generative AI is an exciting and innovative branch of artificial intelligence that revolves around the creation of new and original content. Unlike traditional AI approaches that focus on making predictions or decisions based on existing data, Generative AI aims to push the boundaries of creativity by generating novel outputs that resemble the input data it was trained on. At the core of Generative AI lies the idea of training models to understand the underlying patterns and structures of a dataset and then using that understanding to generate content that adheres to those patterns. This process involves leveraging complex algorithms and neural networks to create anything from images, music, and text to more complex outputs like drug compounds or realistic video game environments. One of the prominent techniques in Generative AI is the use of Generative Adversarial Networks (GANs), which involve two neural networks, a generator, and a discriminator, engaged in a ‘game.’ <a href="https://arxiv.org/abs/1406.2661" rel="noopener ugc nofollow" target="_blank">The Generative Adversarial Networks (GANs) — Original Paper by Ian J. Goodfellow</a> quotes, “The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency. Competition in this game drives both teams to improve their methods until the counterfeits are indistinguishable from the genuine articles.” <a href="https://medium.com/@chavan.shardul360/introduction-to-generative-ai-3a8b4ff81d97">Click Here</a>