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At its core, generative AI relies on machine learning models, particularly deep learning architectures like *Generative Adversarial Networks (GANs)* and *Variational Autoencoders (VAEs)*. GANs, for example, involve two neural networks: a generator that creates new data instances and a discriminator that evaluates their authenticity. The generator tries to fool the discriminator, while the discriminator tries to identify the fake data. Through this adversarial process, both networks improve, leading to the generation of increasingly realistic and novel content. Understanding this mechanism helps in appreciating the complexities and potential of **generative AI startups** that are harnessing these technologies.
