AI Glossary

Autonomous AI systems that can perceive their environment, make decisions, and take actions to achieve specific goals, often using LLMs as their reasoning engine.

The challenge of ensuring AI systems behave in accordance with human intentions, values, and goals, particularly as they become more capable.

Systematic errors or unfair outcomes in AI systems, often resulting from skewed training data, flawed model design, or societal inequalities reflected in the data.

The coordination and management of multiple AI tools, models, agents, and workflows to accomplish complex tasks that no single model can handle alone.

The field focused on ensuring artificial intelligence systems are developed and deployed in ways that are beneficial, aligned with human values, and do not cause unintended harm.

A hypothetical form of AI that possesses human-level cognitive abilities across any intellectual task, capable of learning and adapting to any problem domain without specialized training.

The simulation of human intelligence in machines that are programmed to think, learn, and make decisions. AI encompasses a broad range of techniques from rule-based systems to machine learning and deep learning.

A technique that lets neural networks focus on the most relevant parts of input data, forming the core innovation behind the Transformer architecture.

A prompting technique where models are instructed or trained to break down complex reasoning into intermediate steps, dramatically improving accuracy on multi-step problems.

A field of AI that trains computers to interpret and understand the visual world, enabling machines to identify objects, scenes, and activities in images and videos.

The maximum amount of text (measured in tokens) that an LLM can process at once, including both input and generated output.

A subset of machine learning that uses multi-layered neural networks (deep neural networks) to model complex patterns in large amounts of data.

A class of generative models that create data by gradually removing noise, reversing a process of adding random noise to training data. The technology behind modern AI image generators.

The deployment of AI algorithms directly on local devices (smartphones, IoT devices, cameras) rather than in the cloud, enabling real-time processing with lower latency and improved privacy.

Numerical vector representations of data (text, images, audio) that capture semantic meaning, enabling machines to understand relationships and similarity between concepts.

Techniques where models perform tasks with few or no examples. Few-shot provides a handful of examples; zero-shot requires none, relying purely on instructions.

The process of taking a pre-trained model and further training it on a specific, typically smaller, dataset to adapt it for a particular task or domain.

A large AI model trained on broad, diverse data that can be adapted to a wide range of downstream tasks through fine-tuning or prompting. The backbone of modern generative AI.

A category of AI that creates new content — text, images, audio, video, or code — by learning patterns from existing data and generating novel outputs.

A family of large language models developed by OpenAI based on the transformer architecture, known for their ability to generate coherent and contextually relevant text.

A phenomenon where an AI model generates plausible-sounding but factually incorrect or nonsensical information, often with high confidence.

The process of running a trained AI model on new input data to produce predictions or outputs, as opposed to the training phase where the model learns from data.

A deep learning model trained on massive text datasets to understand, generate, and manipulate human language. LLMs power modern chatbots, code generators, and text-based AI tools.

A parameter-efficient fine-tuning technique that adds small, trainable matrices to existing model weights, enabling fine-tuning of large models with minimal computational cost.

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed, using statistical techniques to find patterns in data.

A neural network architecture that uses multiple specialized sub-networks (experts) with a gating mechanism that activates only relevant experts for each input, improving efficiency and capability.

The numerical weights and biases that define a neural network's learned knowledge. Larger parameter counts generally indicate greater model capacity and capability.

AI systems capable of processing and generating multiple types of data simultaneously, such as text, images, audio, and video, often within a single model.

A subfield of AI focused on enabling computers to understand, interpret, and generate human language in a meaningful way.

A computing system inspired by biological neural networks in the human brain, consisting of interconnected nodes (neurons) organized in layers that process information.

AI models, tools, and frameworks that are publicly released with permissive licenses, allowing anyone to use, modify, study, and distribute them.

The practice of designing and refining input prompts to guide LLMs toward producing desired outputs more reliably and accurately.

A technique that reduces the precision of a model's numerical parameters (e.g., from 32-bit to 8-bit or 4-bit), decreasing memory usage and computational requirements with minimal accuracy loss.

A machine learning paradigm where agents learn optimal behavior through trial and error, receiving rewards for good actions and penalties for bad ones.

A training technique that uses human preferences to fine-tune language models, aligning them with human values and making them more helpful, harmless, and honest.

A technique that combines information retrieval with text generation, allowing LLMs to access external knowledge sources to produce more accurate and up-to-date responses.

An open-source deep learning model for generating images from text descriptions, released by Stability AI in 2022.

Artificially generated data that mimics real-world data, used to train AI models when real data is scarce, expensive, or privacy-sensitive.

The basic unit of text that an LLM processes. Tokens can be words, parts of words, or individual characters. LLMs have a maximum context window measured in tokens.

A neural network architecture introduced by Google in 2017 that uses self-attention mechanisms to process sequential data. It is the foundation of virtually all modern LLMs.

A specialized database designed to store, index, and search high-dimensional vector embeddings efficiently using similarity search algorithms.