2: Introduction to GenAI

What is Generative AI?

Generative AI refers to a class of artificial intelligence models that generate new content, such as text, images, audio, or video. Unlike traditional AI models focused on classification or prediction, generative models create new data based on learned patterns, producing outputs similar to the input data but with variability. The ultimate goal is to produce realistic content that’s indistinguishable from human-created work.

Types of Generative AI Models

  1. Large Language Models (LLMs): Text Generation: Models that generate human-like text using deep learning. Examples: GPT-4, LLaMA, Claude, Mistral, Gemini
  2. Diffusion Models: Image & Video Generation: Generate images/video from noise, refining them over multiple steps. Examples: Stable Diffusion, DALL·E, Midjourney, Sora
  3. Audio & Music Generators: Generate realistic speech, music, or sound effects. Examples: MusicGen, Jukebox, VALL-E, Bark
  4. Multi-modal Models: Can process and generate text, images, video, and audio in a single model. Examples: Gemini, GPT-4 Turbo (Vision), LLaVA

Example Use Cases of GenAI

  • 📝 Text Generation: Article generation, essay writing etc. (ChatGPT, Gemini, LLaMA)
  • 🎨 Image Generation: Creating art, photos, or designs. (Stable Diffusion, DALL·E, Midjourney)
  • 🎶 Audio Generation: Composing music or generating speech. (Jukebox, MusicGen)
  • 🎥 Video Generation: Deepfake technology and AI-assisted filmmaking. (Sora, Pika Labs)
  • 🧑‍🎨 Chatbots: Conversational agents that can interact with users. (ChatGPT, Gemini, LLaMA)

  • GPT (Generative Pretrained Transformer):

    • GPT models are trained to predict the next word in a sentence given the previous words. They use transformer architecture, which allows them to understand and generate human-like text.
    • Use Case: Writing articles, answering questions, generating code, etc.

  • BERT (Bidirectional Encoder Representations from Transformers):

    • Unlike GPT, BERT is trained to understand the context of a word in both directions (left-to-right and right-to-left). It’s mainly used for tasks that require a deep understanding of the context of language.
    • Use Case: Sentiment analysis, text classification, question answering.

  • LLaMA (Large Language Model Meta AI):

    • Developed by Meta (formerly Facebook), LLaMA is an open-source language model similar to GPT. It focuses on providing access to large models while maintaining efficiency and usability.
    • Use Case: Text generation, summarization, and more.

GenAI Applications and Impact

GenAI has various applications across industries:

  • Text Generation: GenAI models like GPT are used in content generation, such as blog writing, coding, and chatbot responses. For example, OpenAI’s GPT-3 is employed for tasks ranging from generating marketing copy to drafting emails.
  • Conversational AI: Models like GPT-3, paired with specialized APIs, are used to build virtual assistants (like Siri and Alexa) or customer service chatbots, which can hold meaningful conversations with humans.
  • Image Generation: DALL·E is an example of an AI that generates images from textual descriptions. This is used in creative industries like marketing and design.
  • Code Generation: AI models like GitHub Copilot (based on GPT) assist developers by suggesting code and helping write functions.

Real-World GenAI Projects and Case Studies

  1. GPT-3 in Action:
    OpenAI’s GPT-3 is used across various sectors, from writing blog posts to generating legal contracts and automating customer service.

  2. DeepMind’s AlphaFold:
    AlphaFold is a deep learning model developed by DeepMind that predicts the 3D structure of proteins. This has significant implications for drug discovery and biology.

  3. Meta’s LLaMA:
    Meta’s LLaMA models are used for efficient natural language processing tasks, offering an open-source alternative to GPT models for research purposes.

Ethical Considerations

  • Bias in AI: AI models can inherit biases from their training data. This can affect the fairness of models in real-world applications.
  • Transparency and Accountability: Models like GPT may produce outputs that are hard to interpret, raising concerns about accountability in AI-generated content.
  • Deepfakes and Misinformation: GenAI models are capable of generating realistic but fake content, such as videos or voices, which can be used maliciously.