Introduction to AI: Discriminative vs. Generative
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Introduction to AI: Discriminative vs. Generative

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Questions and Answers

Which type of model primarily focuses on generating human-like text?

  • Generative Adversarial Networks
  • Large Language Models (correct)
  • Variational Autoencoders
  • Image Recognition Models

    • What is a primary characteristic of generative AI models for image generation?

  • They use deep learning techniques such as GANs. (correct)
  • They are based on supervised learning.
  • They generate static images without colors.
  • They lack the ability to produce realistic textures.

    • Which of the following is NOT an example of a Large Language Model?

  • GPT-4
  • DALL-E (correct)
  • Llama
  • Gemini

    • Which generative AI tool is specifically designed for video generation?

    <p>Synthesia</p> Signup and view all the answers

    What do Large Language Models (LLMs) learn from their training datasets?

    <p>Patterns and structures in language</p> Signup and view all the answers

    What aspect of image generation do generative AI models aim for?

    <p>Producing realistic textures and details</p> Signup and view all the answers

    Which foundation model is known for its capability to generate multi-format text outputs?

    <p>Gemini</p> Signup and view all the answers

    What kind of outputs can Large Language Models generate?

    <p>Coherent and contextually relevant text</p> Signup and view all the answers

    What type of artwork can DeepArt generate from a sketch?

    <p>Complex and detailed artwork</p> Signup and view all the answers

    Which generative AI model is known for creating original music across different genres?

    <p>OpenAI’s MueNet</p> Signup and view all the answers

    What is a unique feature of DALL-E in the context of image generation?

    <p>Generating images based on textual descriptions</p> Signup and view all the answers

    What type of audio does WaveGAN specifically generate?

    <p>Raw audio waveforms</p> Signup and view all the answers

    Which generative AI model is specifically noted for producing high realistic synthetic speech?

    <p>Mozilla TTs</p> Signup and view all the answers

    What is one capability of GitHub Copilot related to code generation?

    <p>It generates code for various programming languages.</p> Signup and view all the answers

    What distinguishes StyleGAN from other image generation models?

    <p>It produces high-quality high-resolution novel images.</p> Signup and view all the answers

    What technology powers GitHub Copilot's code generation capabilities?

    <p>OpenAI Codex</p> Signup and view all the answers

    What defines Discriminative AI?

    <p>Identifies patterns and classifies data points.</p> Signup and view all the answers

    Which of the following models is capable of generating new content?

    <p>Generative AI</p> Signup and view all the answers

    What limitation is commonly associated with Discriminative AI?

    <p>It cannot generate new content.</p> Signup and view all the answers

    How can Generative AI output different types of media?

    <p>By utilizing training data to capture underlying distributions.</p> Signup and view all the answers

    Which of the following is an example of a task suited for Discriminative AI?

    <p>Identifying whether an email is spam.</p> Signup and view all the answers

    What role does deep learning play in Generative AI?

    <p>It enhances the ability of models to learn from massive data.</p> Signup and view all the answers

    Which statement best reflects Generative AI's capacity?

    <p>It can produce diverse forms of media, including images and videos.</p> Signup and view all the answers

    What is a key difference between Generative AI and Discriminative AI?

    <p>Generative AI can create novel data points.</p> Signup and view all the answers

    Study Notes

    Introduction to AI

    • AI simulates human intelligence through machines
    • AI models learn from existing data through training
    • There are two fundamental approaches to AI:
      • Discriminative AI
      • Generative AI

    Discriminative AI

    • Distinguishes between different data classes
    • Data is labelled with its class
    • Uses advanced algorithms to differentiate, classify, identify patterns, and draw conclusions
    • Example: Email spam filters
    • Limitations:
      • Cannot understand context
      • Cannot generate new content

    Generative AI

    • Creates new content based on training data
    • Captures the underlying distribution of data
    • Generates novel data points
    • Accepts various forms of input: text, image, video, audio, code, etc. and outputs new content in the same format

    Discriminative vs. Generative AI

    • Discriminative AI boosts analytic and decision-making abilities
    • Generative AI heightens creativity

    Deep Learning and Neural Networks

    • Train artificial neural networks on massive data

    Evolution of Generative AI

    Foundation Models

    • AI models with broad capabilities that can be adapted to build specialized and advanced models or tools
    • Large Language Models (LLMs) are a specific category of foundation models that process and generate text

    Foundation Models Examples

    • LLMs:
      • OpenAI's GPT series
      • Google's Gemini
      • Meta's Llama
    • Image generation models:
      • Stable Diffusion
      • DALL-E
      • Midjourney

    Generative AI Tools

    • Text generation:
      • ChatGPT
      • Gemini
    • Image generation:
      • DALL-E
      • Midjourney
    • Video generation:
      • Synthesia
    • Code generation:
      • Copilot
      • AlphaCode
      • Gemini

    Capabilities of Generative AI

    • Generative AI has diverse capabilities: text, image, audio, video, and code generation

    Text Generation Capabilities of Generative AI

    • LLMs are trained in large datasets
    • These models learn patterns and structures from datasets
    • They can generate coherent and contextually relevant text, responses, conversation, summaries, and explanations.

    Image Generation Capabilities of Generative AI

    • Leverage deep learning techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
    • Generate images with realistic textures, natural colors, and fine-grained details.
    • Examples:
      • StyleGAN: high-quality, high-resolution, novel images
      • DeepArt: complex artwork based on sketches
      • DALL-E: generates novel images based on textual descriptions

    Audio Generation Capabilities of Generative AI

    • Examples:
      • WaveGAN: raw audio waveforms with realistic sound (speech, music)
      • OpenAI's MuNet: generates original music in various genres and instrumentations
      • Mozilla TTS and Google's Tacotron 2: high-realistic synthetic speech with tone, pitch, rhythm, and expression

    Video Generation Capabilities of Generative AI

    • Uses deep learning techniques
    • Create realistic and engaging videos

    Code Generation Capabilities of Generative AI

    • AI-based programming assistants that can generate code for various languages
    • Examples:
      • Github Copilot: AI-powered programming assistant that autocompletes code, accelerates tasks, and generates code based on inputs.

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    Related Documents

    Chapter_2 Generative AI Introduction and Applications.pdf

    Description

    Explore the fundamental concepts of Artificial Intelligence, focusing on Discriminative and Generative AI. Understand how these models operate, their strengths, and limitations. This quiz will test your knowledge on AI frameworks and applications.

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