![]() Audio: Music, audio, and speech are also emerging fields within generative AI.Large language models are being leveraged for a wide variety of tasks, including essay generation, code development, translation, and even understanding genetic sequences. One of the most popular examples of language-based generative models are called large language models (LLMs). ![]() Language: Text is at the root of many generative AI models and is considered to be the most advanced domain.Both of these technologies help represent time and allow for the algorithm to focus on how words relate to each other over long distances ![]() Two mechanisms make transformers particularly adept for text-based generative AI applications: self-attention and positional encodings. Transformer networks: Similar to recurrent neural networks, transformers are designed to process sequential input data non-sequentially. It is important to understand how it works in the context of generative AI. One of the most popular is the transformer network. While GANs can provide high-quality samples and generate outputs quickly, the sample diversity is weak, therefore making GANs better suited for domain-specific data generation.Īnother factor in the development of generative models is the architecture underneath. This procedure repeats, pushing both to continually improve after every iteration until the generated content is indistinguishable from the existing content. The two models are trained together and get smarter as the generator produces better content and the discriminator gets better at spotting the generated content. GANs pit two neural networks against each other: a generator that generates new examples and a discriminator that learns to distinguish the generated content as either real (from the domain) or fake (generated).
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