Generative AI models perform better when they are larger, but training these large models is difficult and expensive. As models grow in size, they become more capable without needing additional training. Researchers have developed transformer architectures and used large amounts of data and computing power to build these larger models.
Key Concepts:
- Pre-training Large Models: Large Language Models (LLMs) learn language patterns from vast amounts of unstructured data, like text from the internet. This requires significant computing resources, such as GPUs.
- Data Processing: Before training, data must be processed to remove bias and harmful content. Only 1% to 3% of tokens from the data are used in the pre-training process after this curation.
- Unimodal vs. Multimodal Models:
- Unimodal models work with one type of data (e.g., LLMs for text).
- Multimodal models handle multiple data types (e.g., text, images, audio) and can perform tasks like:
- Image Captioning: Generating text descriptions of images.
- Text-to-Image Synthesis: Creating images from text prompts.
- Diffusion Models: These generative models reverse a gradual noising process to create high-quality content. They are more stable, easier to train, and produce better results than other models like GANs.
- Stable Diffusion: A diffusion model that generates images from text descriptions. It works by removing noise to create clear, detailed images.
Multimodal Tasks:
- Image Captioning: Generating text descriptions of images.
- Text-to-Image Synthesis: Creating images from text descriptions (e.g., models like DALL-E).
- Diffusion Models: Generate and improve images or audio, such as with Stable Diffusion and Whisper.
Generative AI Use Cases
Generative AI, especially Large Language Models (LLMs), can perform a variety of tasks across different domains without the need for fine-tuning. Some key use cases include:
Key Use Cases:
Text Generation: LLMs can adapt and generate text for different audiences. For example, simplifying technical documents for beginners.
Text Summarization: Generative AI can create short summaries of long texts, maintaining the core message. This is useful for summarizing reports, news articles, and legal documents.
Information Extraction: AI can extract specific details from large data sets or text, helping organize and understand content.
Question Answering: LLMs can answer questions based on the provided text or data.
Translation: Generative AI can translate text between different languages, facilitating communication.
Code Generation: AI can generate code snippets or even full programs from natural language descriptions or examples. It can automate programming tasks, suggest code completions, and translate code between languages.
AWS Tools for Generative AI:
- Amazon Bedrock & Amazon Titan: Pre-trained models for text, image, and audio generation.
- Amazon SageMaker & Amazon Q Developer: Tools for code generation and completion.
- Amazon Nimble Studio & Amazon Sumerian: Support for virtual production and 3D content creation.
- AWS SageMaker supports deep learning frameworks like TensorFlow and PyTorch for working with multimodal data.
- AWS provides pre-trained models like Stable Diffusion, which are text-to-image generative models.These models create images from textual descriptions, enabling users to quickly generate visual content for various applications.Using Amazon Bedrock and SageMaker, AWS makes it easy to deploy and integrate these pre-trained models, saving time, resources, and costs.
Exam Focus:
Ensure you understand various AI architectures such as:
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Transformers
Each architecture has specific advantages and limitations, so it’s important to choose the right one based on the task and dataset.
