Generative AI is a subset of deep learning focused on generating new content such as text, images, audio, video, and even code. Unlike traditional AI, which is used for classification or prediction, generative AI creates new data based on patterns learned from large datasets.
Key Points:
- Generative AI Models: Trained on vast datasets, these models generate outputs (like text or images) based on the patterns they have learned. They use large neural networks called foundation models, which have billions of parameters.
- Transformers: The core of generative AI is the transformer network, which is used in models like ChatGPT. These networks help process large datasets, and they are based on the 2017 paper “Attention Is All You Need“.
- Fine-Tuning: Foundation models can be fine-tuned for specific tasks with minimal data, making them highly versatile for different use cases.
- Generative AI Tasks: Generative AI is used for various content creation tasks. It works by taking a prompt (input) and generating an output based on it. Large Language Models (LLMs) perform tasks by following human-written instructions.
- Prompt Engineering: When the model doesn’t produce the expected output, prompt engineering can help. In-context learning, which includes examples in the prompt, can help guide the model to generate more accurate outputs.
- Statistical Methods: Generative AI relies on statistics and linear algebra (such as probability modeling, loss functions, and matrix multiplication) for computations. These methods enable the model to handle data like text or images.
- Key Concepts: Understand terms like prompt, inference, completion, context window, tokens, tokenizer, prompt engineering, and more. These concepts are essential for working with generative AI models.
You should understand the fundamentals of how generative AI works and the importance of using effective prompts to guide the model toward generating the desired results.
Terms like prompt, inference, completion, context window, tokens, tokenizer, prompt engineering, and more are some concepts that are essential for working with generative AI models.
- Prompt: The input or question you give to an AI model to generate a response. It guides the model on what kind of response to generate.
- Inference: The process of using a trained model to make predictions or generate outputs based on a given prompt. Inference happens when the model processes input data and provides an output.
- Completion: The output or response generated by the AI model after processing a prompt.
- Context Window: The amount of input text (or data) that the model can “remember” or consider at one time when generating a response.
- Tokens: The units of text (words, sub-words, punctuation) the model processes.
- Tokenizer: Converts human text into vectors (lists of numbers). These vectors help the model understand the meaning and relationships between words.
- Vector Basics: A vector is a list of numbers representing features or attributes of something, like words. In generative AI, these vectors represent text and allow the model to understand meaning and context.
- Embeddings: Vectors that represent tokens are called embeddings. Embeddings capture the meaning of tokens, allowing the model to understand and process language in a meaningful way.
- Self-Attention in Transformers: A core part of generative AI is the transformer model, which uses self-attention. This mechanism helps the model focus on important parts of the input data and captures long-term relationships between tokens. It is more effective than older models like recurrent neural networks (RNNs).
- Position Embeddings: Transformers also use position embeddings to capture the order of tokens in a sentence. This is crucial for understanding sentence structure and word order.
- Model Inference: During inference, the transformer model uses self-attention to generate a response to an input prompt, capturing the meaning and context of the input.
Key Terms to Remember:
Position Embedding: Information about the position of tokens in a sequence to preserve word order.
Tokenizer: Converts text into vectors.
Vector: A list of numbers representing tokens or phrases.
Embedding: A vectorized representation of tokens.
Self-Attention: Mechanism that helps the model focus on important parts of the input.
