Generative Artificial Intelligence (AI) Model

A Generative Artificial Intelligence (AI) Model is an AI model that can create synthetic data which resembles patterns in the training data.

• Context:
  • ...
• Example(s):
  • a Token-Sequence Generation Model, which generates sequences of tokens representing text, code, or other sequential data.
  • a Latent Diffusion-based Model, which creates high-quality images by applying diffusion processes in latent spaces.
  • Large language models (LLMs), such as Bard, GPT-3, and LaMDA, capable of generating human-like text.
  • Text-to-image models, such as DALL-E 2, Imagen, and Parti, which generate images from text descriptions.
  • Text-to-speech models, such as WaveNet and Tacotron, which produce realistic speech audio from text.
  • Code generation models, like Copilot and AlphaCode, that generate programming code based on natural language descriptions.
  • Music generation models, including Jukebox and MuseNet, capable of composing new music pieces.
  • ...
• Counter-Example(s):
  • Discriminative Artificial Intelligence (AI) Model, which identifies or categorizes input data rather than generating new content.
  • Analytical AI Models, focused on analyzing and interpreting data rather than creating new content.
• See: Text-to-* Model.

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References

2023

• GBard
  • Generative artificial intelligence (AI) models are algorithms that can create new content, including audio, code, images, text, simulations, and videos. They are trained on massive datasets of existing content, and they learn to identify the patterns and relationships that underlie that content. Once trained, generative AI models can be used to generate new content that is similar to the content they were trained on, but often with new twists and variations.

Generative AI models are still in their early stages of development, but they have the potential to revolutionize many industries and aspects of our lives. For example, generative AI models can be used to:

   Generate new creative content, such as music, art, and writing.
   Create new and innovative products and services.
   Improve the efficiency and accuracy of existing processes.
   Develop new scientific and medical breakthroughs.

Here are some examples of generative AI models:

   Large language models (LLMs), such as Bard, GPT-3, and LaMDA, can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
   Text-to-image models, such as DALL-E 2, Imagen, and Parti, can generate realistic images from text descriptions.
   Text-to-speech models, such as WaveNet and Tacotron, can generate realistic speech from text.
   Code generation models, such as Copilot and AlphaCode, can generate code from natural language descriptions.
   Music generation models, such as Jukebox and MuseNet, can generate new music pieces.

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