Conversational AI

YouTube ... Quora ...Google search ...Google News ...Bing News

• Generative AI ... Conversational AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's Bing ... You ...Google's Bard ... Baidu's Ernie
  • ChatGPT Integration
• Assistants ... Hybrid Assistants ... Agents ... Negotiation ... HuggingGPT ... LangChain
• Development ...AI Pair Programming Tools ... Analytics ... Visualization ... Diagrams for Business Analysis
• Case Studies
  • Neeva
  • Alpaca | Stanford
  • Generative AI 100 use case | Shunsuke Nakaji - Medium
  • Generative AI Statistics By Industry, Sector, Revenue and Facts | Barry Elad - Enterprise Apps Today
• Capabilities
  • Video/Image ... Vision ... Colorize ... Image/Video Transfer Learning
  • End-to-End Speech ... Synthesize Speech ... Speech Recognition
• AI Solver
• Discriminative vs. Generative
  • Generative Model | Wikipedia
  • Generative Pre-trained Transformer (GPT)
  • Generative Query Network (GQN)
  • Data Augmentation, Data Labeling, and Auto-Tagging
  • Python ... Generative AI with Python ... Javascript ... Generative AI with Javascript ... Game Development with Generative AI
  • Generative AI for Business Analysis
  • Natural Language Generation (NLG)
• Emergence from Analogies
• 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
• Demo, summarizing, priority listing, & analysis...
  • ChatPDF
• 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)
• Natural Language Processing (NLP) ...Generation ...LLM ...Tools & Services

Generative AI could improve the speed and accuracy of product research and development. Generative AI technology can also help with product engineering by allowing teams to simulate products in virtual environments. This allows for complex problems to be solved more quickly and efficiently, leading to improved design accuracy. - The Generative AI Revolution Is Creating The Next Phase Of Autonomous Enterprise | Mark Minevich - Forbes

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Generative AI are predictive models, filtering available information to draw inferences on what should logically follow

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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.

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SynthAI

To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information. That is, they will show us less content by synthesizing the information available. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change. -For B2B Generative AI Apps, Is Less More? | Zeya Yang and Kristina Shen - Andreessen Horowitz

As we think through what Wave 2 might look like, we believe the use cases that will benefit most from synthesis AI will be when there is both:

A high volume of information, such that it’s not pragmatic for a human to manually sift through all the information. A high signal-to-noise ratio, such that the themes or insights are obvious and consistent. In the name of accuracy, you don’t want to task an AI model with deciphering nuance. In the diagram below, we categorize examples of common analysis and synthesis by these dimensions to help bring this to life.