🤖 Generative AI Terms You Should Know
A
• Agents: Autonomous programs that perform tasks or make decisions on behalf of users.
• Attention: A mechanism in neural networks that allows models to focus on relevant parts of the input sequence.
• Autoencoders: Neural networks used for unsupervised learning, primarily for dimensionality reduction and feature learning.
B
• Back Propagation: An algorithm for training neural networks by propagating the error backward to update weights.
• BigGAN: A type of Generative Adversarial Network (GAN) designed for high-resolution image generation.
• Bias: Systematic errors in AI models due to prejudiced training data or flawed algorithms.
C
• Capsule Network: A neural network architecture that models hierarchical relationships, improving recognition tasks.
• Conditional GAN: A GAN variant where both generator and discriminator receive additional information, enabling controlled generation.
• Chain of Thought: A prompting technique that encourages models to reason step-by-step, enhancing problem-solving capabilities.
D
• DataSpeed: Refers to the rate at which data is processed or transmitted in AI systems.
• Double Descent: A phenomenon where increasing model complexity initially leads to overfitting but eventually improves performance.
• Diffusion Model: A generative model that learns to reverse a diffusion process, used in image and audio generation.
E
• Emergent Behavior: Complex patterns arising from simple rules in AI systems, often unexpected.
• Expert Systems: AI programs that emulate decision-making abilities of human experts using a set of rules.
F
• Few-Shot Learning: Models trained to generalize from a small number of examples.
• Foundation Model: Large-scale models trained on broad data, adaptable to various tasks (e.g., GPT-4).
• Fine-tuning: Adjusting a pre-trained model on a specific task to improve performance.
G
• Generative AI: AI systems that create new content like text, images, or music.
• GPT: Generative Pre-trained Transformer, a type of large language model developed by OpenAI.
• GAN: Generative Adversarial Network, consisting of two networks (generator and discriminator) competing to produce realistic data.
H
• Hyperparameter Tuning: The process of optimizing the parameters that govern the training process of AI models.
• Hallucination: When AI models generate plausible but incorrect or nonsensical outputs.
• Hidden Layer: Layers in a neural network between input and output layers where computations are performed.
I
• Image Generation: Creating images from textual descriptions using models like DALL·E.
• Instruction Tuning: Training models to follow specific instructions, improving task performance.
• Inpainting: Filling in missing parts of images using AI techniques.
K
• Knowledge Graph: A network of entities and their interrelations, used for information retrieval.
• Knowledge Base: A repository of structured information used by AI systems to answer queries.
