Generative AI

YouTube search... ...Google search

• AI Solver
• Generative Query Network (GQN)
• Discriminative vs. Generative
• Natural Language Generation (NLG)
• Emergence from Analogies
• Generative Model | Wikipedia
• Synthetic Labeling
• Video Synthesis
• Generated Image
  • Stable Diffusion | Stable Diffusion ... a latent text-to-image diffusion model capable of generating photo-realistic images given any text input
• Demos, generating...
  • Music: Generating Piano Music with Transformer | I. Simon, A. Huang, J. Engel, C. "Fjord" Hawthorne - Google on Colab play with pretrained Transformer models for piano music generation, based on the Music Transformer model
  • Faces: TF-Hub generative image model | The TensorFlow Hub Authors - Google use of a TF-Hub module based on a generative adversarial network (GAN). The module maps from N-dimensional vectors, called latent space, to RGB images.
  • 3D Objects: 3D Style Transfer | Google uses Lucid to implement style transfer from a textured 3D model and a style image onto a new texture for the 3D model by using a Differentiable Image Parameterization.
• Assistants ... Hybrid Assistants ... Agents ... Negotiation
• Conversation Generative AI tools:
  • ChatGPT | OpenAI
  • Bard | Google
  • Perplexity | Perplexity.ai ... current information, including footnotes with links to the sources of the data
  • You | You.com ... the AI search engine you control; YouChat, YouCode, YouWrite, YouImagine, YouStudy, & YouSocial
  • Neeva
• Three main types:
  • Autoencoder (AE) / Encoder-Decoder
  • Sequence Models
    • Transformer
    • Recurrent Neural Network (RNN)
    • Sequence to Sequence (Seq2Seq)
  • Adversarial Networks
    • Conditional Adversarial Architecture (CAA)
    • Generative Adversarial Network (GAN)
    • Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
    • Context-Conditional Generative Adversarial Network (CC-GAN)

Background: What is a Generative Model? | Google

More formally, given a set of data instances X and a set of labels Y:

• Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels.
• Discriminative models capture the conditional probability p(Y | X).

A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.

Informally:

• Generative models can generate new data instances.
• Discriminative models discriminate between different kinds of data instances.