1. Generative AI
Definition: Generative AI refers to AI systems designed to create new content such as text, images, music, or code.
Example: ChatGPT creates human-like text based on user input.
Learn More: Wikipedia - Generative AI
Definition: A neural network architecture that uses self-attention mechanisms for tasks like language modeling and translation.
Example: GPT (Generative Pre-trained Transformer) is based on this architecture.
Learn More: Attention Is All You Need (Original Paper)
3. Large Language Model (LLM)
Definition: AI models trained on massive datasets to understand and generate human-like text.
Example: OpenAI's GPT-4 and Google's PaLM.
Learn More: Wikipedia - Language Model
4. Fine-Tuning
Definition: Adapting a pre-trained model to a specific task by training it on a smaller, task-specific dataset.
Example: Fine-tuning GPT-3 for customer support.
Learn More: OpenAI Fine-Tuning Guide
5. Prompt Engineering
Definition: Crafting input prompts to achieve desired outputs from generative AI models.
Example: Asking GPT, “Summarize this text in one sentence.”
Learn More: Prompt Engineering Blog
6. Zero-Shot Learning
Definition: An AI model's ability to perform a task without prior examples or training for that task.
Example: GPT-4 answering questions on a topic it hasn't explicitly been trained on.
Learn More: Wikipedia - Zero-Shot Learning
7. Few-Shot Learning
Definition: Training AI models with a small number of examples to perform a task.
Example: Providing a few labeled examples of sentiment analysis to guide a model.
Learn More: Wikipedia - Few-Shot Learning
8. Self-Attention
Definition: A mechanism in which every part of an input sequence is compared to others to compute a weighted representation.
Example: Used in transformers to relate words in a sentence for context.
Learn More: Self-Attention Explainer
9. Diffusion Models
Definition: AI models that iteratively refine noise to generate new data, commonly used for images.
Example: DALL·E generates images from text using diffusion techniques.
Learn More: Diffusion Models Overview
10. Autoencoders
Definition: Neural networks that compress data into a latent space and reconstruct it back to the original format.
Example: Denoising images.
Learn More: Wikipedia - Autoencoder
11. GAN (Generative Adversarial Network)
Definition: A system with two neural networks (generator and discriminator) working against each other to create realistic data.
Example: Generating photorealistic images.
Learn More: GAN Introduction
12. Latent Space
Definition: A compressed representation of data in lower dimensions, often used in generative models.
Example: Manipulating attributes in generated faces.
Learn More: Understanding Latent Space
13. Tokenization
Definition: The process of breaking down text into smaller units, like words or subwords, for model processing.
Example: Splitting "unbelievable" into "un," "believ," and "able."
Learn More: Wikipedia - Tokenization
14. Beam Search
Definition: A decoding algorithm that finds the most probable output sequence in text generation tasks.
Example: Selecting coherent sentences during translation.
Learn More: Beam Search in NLP
15. Attention Mask
Definition: A mechanism used in transformers to ignore padding tokens during training.
Example: Preventing models from attending to padding in input sequences.
Learn More: Hugging Face Guide
16. Pre-training
Definition: Training a model on large datasets to learn general features before fine-tuning on specific tasks.
Example: Training GPT on vast internet data.
Learn More: Wikipedia - Pretraining
17. Positional Encoding
Definition: A technique in transformers to inject sequence order into input embeddings.
Example: Encoding word positions in a sentence.
Learn More: Positional Encoding Explained
18. Overfitting
Definition: When a model performs well on training data but poorly on unseen data.
Example: Memorizing training examples instead of generalizing.
Learn More: Wikipedia - Overfitting
19. Perplexity
Definition: A metric to evaluate language models based on how well they predict a sample.
Example: Lower perplexity indicates better predictions.
Learn More: Wikipedia - Perplexity
20. Encoder-Decoder Architecture
Definition: A structure where an encoder processes input, and a decoder generates output.
Example: Used in machine translation.
Learn More: Encoder-Decoder Guide
Conclusion
Understanding these terminologies is vital for anyone diving into the world of generative AI. Each concept provides a building block for mastering this technology and contributing to innovative applications. For further study, explore the provided links and keep experimenting with the tools to deepen your understanding.
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