AI & AGI Glossary

A hypothetical type of AI that can understand, learn, and apply knowledge across any intellectual task that a human can perform. Also known as Strong AI or Full AI.

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed, using data-driven algorithms.

A subset of machine learning that uses multi-layer neural networks to model complex patterns in data. Powers modern AI applications like image recognition and language models.

A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons) that process information using mathematical operations.

A field of AI focused on enabling computers to understand, interpret, and generate human language. Powers chatbots, translation, and text analysis.

A field of AI that trains computers to interpret and understand visual information from images and videos, enabling applications like facial recognition and autonomous driving.

A type of AI model trained on vast amounts of text data to understand and generate human-like text. Examples include GPT-4, Claude, and Gemini.

A neural network architecture that uses self-attention mechanisms to process sequential data. The foundation of modern LLMs and many state-of-the-art AI models.

A type of ML where models learn from labeled training data to make predictions on new, unseen data. Used in classification and regression tasks.

A type of ML where models find patterns and structures in data without labeled examples. Used for clustering and dimensionality reduction.

A type of ML where agents learn optimal behavior through trial and error, receiving rewards or penalties for actions in an environment.

A technique where a model trained on one task is reused as the starting point for a model on a different task, reducing data and compute requirements.

AI systems that can create new content including text, images, audio, and video. Examples include ChatGPT, DALL-E, and Midjourney.

The challenge of ensuring AI systems act in accordance with human values and intentions. Critical for safe AGI development.

A hypothetical AI that surpasses human intelligence across all domains. The potential end result of recursive self-improvement in AGI systems.

AI systems designed to perform specific tasks, as opposed to general intelligence. All current AI systems are narrow AI, including GPT-4 and other LLMs.

A deep learning architecture designed for processing structured grid data like images, using convolutional layers to detect patterns and features.

A neural network architecture designed for sequential data, where connections between nodes form directed cycles to maintain memory of previous inputs.

A technique in neural networks that allows models to focus on relevant parts of input data, crucial for transformers and modern NLP models.

The process of taking a pre-trained model and further training it on a specific dataset or task to improve performance for that particular application.

Long Short-Term Memory - A type of recurrent neural network architecture designed to learn long-term dependencies, solving the vanishing gradient problem of standard RNNs.

A class of machine learning frameworks where two neural networks compete: a generator creates fake data while a discriminator tries to distinguish real from fake. Used for image generation and data augmentation.

The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.