🤖 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.