AI Glossary

A mathematical function applied to a neuron's output that introduces non-linearity, enabling neural networks to learn complex patterns.

An adaptive learning rate optimization algorithm that maintains per-parameter learning rates based on first and second moment estimates of gradients.

An LLM-based system that can autonomously plan multi-step tasks, use external tools, and take actions in the real world to achieve specified goals.

The American Invitational Mathematics Examination - a prestigious high school math competition whose problems are used as a benchmark for evaluating AI mathematical reasoning capabilities.

AI safety company and creator of the Claude family of large language models, founded by former OpenAI researchers.

Application Programming Interface - a defined set of rules and protocols that allows different software systems to communicate with each other.

An algorithm that finds points approximately closest to a query in high-dimensional space, trading small accuracy loss for dramatically faster search over large datasets.

An independent platform that benchmarks AI models and inference providers across intelligence, performance, price, speed, and latency with standardized methodology.

A technique that allows neural networks to focus on relevant parts of the input when producing each element of the output.

An algorithm for computing gradients of the loss function with respect to each weight in a neural network by applying the chain rule layer by layer.

A technique that normalizes the inputs of each layer in a neural network across the current mini-batch, stabilizing and accelerating training.

A standardized test or evaluation used to measure and compare the performance of AI models on specific tasks like reasoning, coding, math, or language understanding.

A model architecture that independently encodes queries and documents into separate embeddings for fast similarity comparison, used for initial retrieval at scale.

The fundamental tension in machine learning between a model being too simple to capture patterns (high bias) and too complex, fitting noise instead of signal (high variance).

A compiled, executable file that a computer can run directly, as opposed to source code that must be interpreted or compiled first.

A ranking function used in information retrieval that estimates document relevance based on term frequency with diminishing returns and document length normalization.

A software application that uses AI to simulate human-like conversation through text or voice, ranging from rule-based scripts to modern LLM-powered assistants.

OpenAI's conversational AI product that provides a chat interface to GPT models, widely credited with bringing large language models to mainstream public awareness.

The process of dividing large documents into smaller, semantically coherent pieces suitable for embedding and retrieval in RAG systems.

The practice of attributing specific claims in an LLM-generated answer to their source documents, enabling verification and building trust.

Anthropic's agentic coding tool that runs in the terminal, capable of reading, writing, and executing code across entire codebases with human oversight.

Anthropic's fastest model released in October 2025, achieving 90% of Sonnet 4.5's performance on agentic coding at lower cost.

Anthropic's November 2025 flagship model achieving 80.9% on SWE-bench with 50-75% reduction in tool calling errors.

Anthropic's February 2026 flagship with 1M context window, 80.8% on SWE-bench, 68.8% on ARC-AGI-2, and the highest Terminal-Bench 2.0 score among all frontier models.

Anthropic's September 2025 model marketed as the best coding model and best for agents, achieving 77.2% on SWE-bench and 100% on AIME with Python.

Anthropic's February 2026 mid-tier model achieving 79.6% on SWE-bench and 72.5% on OSWorld, matching near-flagship performance at $3/$15 per million tokens.

OpenAI's asynchronous coding agent that runs tasks in cloud sandboxes, designed for parallel software engineering work like writing features, fixing bugs, and running tests.

The initial delay when a system or service must initialize from scratch before it can handle requests, common in serverless and containerized deployments.

The process of combining structured knowledge from a knowledge graph with unstructured text from RAG retrieval into a unified context for LLM generation.

The maximum number of tokens a language model can process at once, which limits how much retrieved content can be included alongside a query.

A neural network architecture that uses convolutional layers to automatically learn spatial hierarchies of features, primarily used for image and video analysis.

A measure of similarity between two vectors based on the cosine of the angle between them, ranging from -1 (opposite) to 1 (identical direction), widely used to compare text embeddings.

A model architecture that jointly encodes a query-document pair to compute a relevance score, offering higher accuracy than bi-encoders but at greater computational cost.

A loss function that measures the difference between a model's predicted probability distribution and the true distribution, widely used for classification tasks.

NVIDIA's parallel computing platform and API that allows developers to use NVIDIA GPUs for general-purpose processing, forming the backbone of most AI training and inference workflows.

An AI-powered code editor built on VS Code that integrates large language models directly into the editing experience for code generation, editing, and codebase-wide understanding.

A declarative graph query language created for Neo4j that uses ASCII-art syntax to represent and match graph patterns.

A regularization technique that artificially expands the training dataset by applying label-preserving transformations to existing examples, forcing the model to learn invariances.

A subset of machine learning that uses neural networks with many layers to learn complex patterns and representations from large amounts of data.

Open-weight reasoning model released in January 2025, achieving 97.3% on MATH-500 and proving frontier AI doesn't require massive budgets.

Open-weight MoE model with updated version V3-0324 scoring 81.2% on MMLU-Pro and ranking 5th on LMArena leaderboard.

A neural retrieval method that encodes queries and documents as dense vector embeddings and retrieves documents based on vector similarity.

A lightweight, standalone, executable package that includes everything needed to run a piece of software - code, runtime, libraries, and system tools.

ByteDance's reasoning model with Deep Thinking mode, matching GPT-4o performance at 50x lower cost with 256K context window.

A regularization technique that randomly sets a fraction of neuron activations to zero during each training step, preventing co-adaptation and reducing overfitting.

A learned dense vector representation that maps discrete entities like words or items into a continuous vector space where similar items are closer together.

The task of resolving different textual mentions of an entity to a single canonical representation, critical for knowledge graph quality.

One complete pass through the entire training dataset during model training.

Baidu's open-source multimodal AI model processing text, images, audio, and video, with benchmark wins over GPT-4o and GPT-5 on specific tasks.

A training problem where gradients grow exponentially large as they propagate backward through many layers, causing weight updates to be enormous and training to diverge.

Facebook AI Similarity Search -- an open-source library by Meta for efficient similarity search and clustering of dense vectors, optimized for billion-scale datasets.

The process of transforming raw data into informative input features that make patterns more accessible to machine learning models.

A simple neural network layer within each transformer block that independently transforms each token's representation through two linear transformations with a non-linear activation in between.

The process of further training a pretrained model on a smaller, task-specific dataset to adapt it for a particular use case.

A large AI model trained on broad data at scale that can be adapted to a wide range of downstream tasks, serving as the base layer for many AI applications.

Google DeepMind's family of multimodal AI models that power Google's AI products across Search, Workspace, Android, and developer APIs.

Google DeepMind's fast model released in May 2025 with 1M context window and 251 tokens/second output speed.

Google DeepMind's 2025 flagship with 1M token context window, leading Humanity's Last Exam with 18.8% accuracy.

Google DeepMind's February 2026 model topping 13 of 16 industry benchmarks with 77.1% on ARC-AGI-2 and 94.3% on GPQA Diamond.

A framework consisting of two neural networks - a generator and a discriminator - that compete against each other to produce increasingly realistic synthetic data.

Zhipu AI's open-weight agentic model with 355B total parameters, ranking 3rd globally and excelling at tool use with 90.6% accuracy.

Google's AI research lab formed by merging DeepMind and Google Brain, responsible for AlphaGo, AlphaFold, and the Gemini model family.

A benchmark of 198 graduate-level multiple-choice questions in physics, biology, and chemistry that are designed to be unsolvable through internet search, requiring genuine PhD-level expertise.

OpenAI's April 2025 API-focused model with a massive 1M token context window and 38.3% on MultiChallenge, beating GPT-4o by 10.5%.

OpenAI's fast, cost-effective multimodal flagship model released in May 2024, supporting text, image, and audio with a 128K context window.

OpenAI's major generational leap released in August 2025, achieving 94.6% on AIME 2025 and 45% fewer factual errors than GPT-4o.

OpenAI's December 2025 model with a 256K context window, 100% AIME 2025 accuracy, and hallucination rate reduced to 6.2%.

OpenAI's first major open-weight model with 117B parameters and MoE architecture, rivaling proprietary o4-mini performance.

A Graphics Processing Unit - a specialized processor designed for parallel computation, now essential for training and running AI models due to its ability to perform thousands of operations simultaneously.

A technique that caps gradient magnitudes during training to prevent exploding gradients from destabilizing the optimization process.

An optimization algorithm that iteratively adjusts model parameters in the direction that minimizes the loss function.

A technique for representing graph nodes as dense vectors that preserve graph structure, enabling similarity search and machine learning over graph data.

The process of systematically visiting nodes in a graph by following edges, used in knowledge graphs to explore relationships and answer multi-hop queries.

An architecture pattern that incorporates knowledge graph reasoning alongside vector-based retrieval in RAG systems, pioneered by Microsoft for enterprise search.

xAI's June 2025 model with 1M context window, beating GPT-4o and Claude 3.5 Sonnet on AIME and GPQA with 1402 Arena Elo.

xAI's July 2025 model achieving 100% on AIME 2025 and 61.9% on USAMO 2025, with 4-agent parallel collaboration in latest beta.

The technique of anchoring LLM responses in factual, retrieved information rather than the model's parametric knowledge, reducing hallucinations.

When an AI model generates plausible-sounding but factually incorrect or fabricated information with apparent confidence.

Hierarchical Navigable Small World -- an efficient graph-based algorithm for approximate nearest neighbor search that builds a multi-layer navigation structure over vectors.

The largest open-source AI platform and model hub, hosting over 2 million models, 500,000 datasets, and 1 million demo apps used by 10 million developers.

A retrieval approach that combines different search methods, typically keyword-based (BM25) and semantic (dense embedding) search, to leverage the strengths of both.

A configuration value set before training begins that controls the learning process itself, as opposed to model parameters which are learned from data.

The process of using a trained model to make predictions on new, unseen data, as opposed to the training phase where the model learns from labeled examples.

Moonshot AI's open-source 1 trillion parameter MoE model with 32B active parameters, outperforming GPT-5 and Claude Sonnet 4.5 on reasoning benchmarks.

Moonshot AI's January 2026 open-weight multimodal model with vision and agent swarm capabilities, leading on agentic and coding benchmarks.

A measure of how one probability distribution differs from a reference distribution, quantifying the information lost when approximating one distribution with another.

A structured representation of knowledge as entities (nodes) and relationships (edges), often with properties attached to both, enabling logical traversal and multi-hop reasoning over data.

A memory optimization technique in LLM inference that stores previously computed key-value pairs from attention layers, avoiding redundant recalculation when generating each new token.

A neural network trained on vast amounts of text data that can understand and generate human language with remarkable fluency and versatility.

A lower-dimensional representation space learned by a model where similar inputs are mapped to nearby points, capturing the essential structure of the data.

A hyperparameter that controls how much model weights are adjusted in response to the estimated error during each step of gradient descent optimization.

Meta's April 2025 open-weight flagship with 402B total parameters, 1M context window, and multimodal capabilities beating GPT-4o.

Meta's April 2025 open-weight model with 109B total parameters and industry-leading 10M token context window.

A crowdsourced platform where users compare AI models head-to-head in blind conversations, producing Elo-based rankings that reflect real human preferences.

A function that measures the difference between a model's predictions and the actual target values, guiding the optimization process during training.

A field of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.

Model Context Protocol - an open standard created by Anthropic that defines how AI assistants connect to external data sources, tools, and services through a unified interface.

Meta's AI division responsible for the open-source Llama model family, PyTorch, and FAIR research lab.

MiniMax's February 2026 model scoring 80.2% on SWE-Bench Verified, outperforming Claude Opus 4.6 and GPT-5.2 at 1/20th the cost.

European AI model achieving 90% of Claude Sonnet 3.7 capabilities while demonstrating cost-efficient alternative to premium models.

A rigorous multimodal AI benchmark with college-level questions across six disciplines that tests whether models truly understand visual and textual information together.

A social network exclusively for AI agents, where autonomous bots interact, post content, and form communities.

The intermediate name for the OpenClaw AI agent framework during its transition from ClawdBot.

An extension of attention that runs multiple attention operations in parallel with different learned projections, allowing the model to capture different types of relationships simultaneously.

Answering questions that require connecting multiple pieces of information across several reasoning steps, a key strength of knowledge graph-augmented systems.

An NLP task that identifies and classifies named entities such as people, organizations, locations, and dates in unstructured text.

The most widely used graph database in industry, designed for storing and querying property graphs using the Cypher query language.

A computing system inspired by biological neural networks that learns to perform tasks by considering examples without being explicitly programmed.

An open-source tool for running large language models locally on personal computers with a simple command-line interface.

A formal specification of concepts, categories, and relationships within a domain that defines what types of entities exist and how they can relate to each other.

Software whose source code is publicly available for anyone to view, modify, and distribute, enabling community-driven development and transparency.

An AI model whose trained parameters (weights) are publicly released for download and use, but whose training data, code, or methodology may remain proprietary.

American AI research company and creator of ChatGPT, GPT-series models, DALL-E, and Whisper.

OpenAI's reasoning model released in April 2025 with a 200K context window, achieving 88.9% on AIME 2025 and 69.1% on SWE-bench Verified.

OpenAI's smaller reasoning model released in April 2025, achieving 92.7% on AIME 2025 and 99.5% with Python interpreter access.

Open-source autonomous AI agent framework originally called ClawdBot, capable of executing real-world tasks via LLMs.

A phenomenon where a model learns the training data too well, including its noise and outliers, resulting in poor performance on unseen data.

A technique for checking data against a set of predefined patterns or rules, used in programming languages, text processing, and machine learning.

The simplest neural network unit that computes a weighted sum of inputs, adds a bias, and passes the result through an activation function to produce an output.

A technique that injects information about token position into transformer inputs, since the attention mechanism itself is permutation-invariant and has no inherent notion of sequence order.

The practice of carefully crafting input text to elicit desired behavior from large language models, including techniques like few-shot examples, chain-of-thought reasoning, and system instructions.

An attack where malicious instructions are hidden inside input data to hijack an AI model's behavior, causing it to ignore its original instructions and follow the attacker's instead.

A technique that reduces the numerical precision of a model's weights and activations, shrinking memory usage and speeding up inference with minimal loss in accuracy.

The process of classifying a user query and directing it to the most appropriate retrieval strategy, such as knowledge graph lookup, RAG search, or hybrid retrieval.

Alibaba's April 2025 open-source model family trained on 36 trillion tokens in 119 languages, competitive with DeepSeek R1 and o3-mini.

Random Access Memory - the fast, volatile working memory a computer uses to store data that is actively being used or processed.

Resource Description Framework -- a W3C standard for representing information as subject-predicate-object triples, forming the foundation of the semantic web.

A method for combining ranked result lists from different retrieval systems by summing reciprocal rank scores, commonly used to merge BM25 and dense retrieval results.

A neural network architecture with loops that allow information to persist across time steps, designed for processing sequential data.

An inference approach that lets an LLM programmatically examine, decompose, and recursively call itself over snippets of extremely long input, handling contexts up to 100x beyond native window limits.

A set of techniques that constrain model complexity during training to prevent overfitting and improve generalization to unseen data.

A machine learning paradigm where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties.

The NLP task of identifying and classifying semantic relationships between entities mentioned in text, a key step in knowledge graph construction.

The Rectified Linear Unit activation function, defined as max(0, x), which has become the default non-linearity in modern deep networks due to its simple gradient and computational efficiency.

A second-stage ranking process that reorders initially retrieved results using a more computationally expensive but accurate model, typically a cross-encoder.

A shortcut that adds a layer's input directly to its output (y = F(x) + x), enabling training of very deep networks by providing a gradient highway that prevents vanishing gradients.

The end-to-end sequence of steps in a RAG system: query processing, document retrieval, reranking, context construction, and LLM generation.

An architecture pattern that reduces LLM hallucination by retrieving relevant documents from an external knowledge base and including them as context before generating a response.

Software as a Service - a delivery model where software is hosted in the cloud and accessed through a browser or API on a subscription basis rather than installed locally.

An attention mechanism where queries, keys, and values all come from the same input sequence, allowing each token to attend to every other token in the sequence including itself.

A search technique that finds results based on the meaning of a query rather than exact keyword matches, typically using vector embeddings and similarity metrics.

An S-shaped activation function that maps any real number to a value between 0 and 1, historically important but largely replaced by ReLU in hidden layers.

A language model with roughly 1 billion to 10 billion parameters, designed to run efficiently on edge devices and resource-constrained environments while retaining core NLP capabilities.

A function that converts a vector of real numbers into a probability distribution, where each output is between 0 and 1 and all outputs sum to 1.

A query language for RDF graph databases, similar to SQL but designed for querying data represented as subject-predicate-object triples.

A retrieval method using high-dimensional sparse vectors based on term frequencies (like BM25 or TF-IDF), where most vector elements are zero.

An optimization algorithm that updates model parameters using the gradient computed on a small random subset (mini-batch) of the training data rather than the entire dataset.

A parameter that controls the randomness of token sampling during LLM text generation by scaling the logits before applying softmax.

The technique of using LLMs to convert natural language questions into Cypher graph queries, enabling non-technical users to query knowledge graphs.

A numerical statistic combining term frequency and inverse document frequency to measure how important a word is to a document within a collection.

The process of breaking text into smaller units called tokens, which serve as the fundamental input elements for language models.

A capability that allows large language models to invoke external functions, APIs, or tools to perform actions beyond text generation.

A technique where a model trained on one task is reused as the starting point for a model on a different but related task.

A neural network architecture based on self-attention mechanisms that processes input data in parallel, forming the basis of modern large language models.

The fundamental unit of knowledge in a graph, expressed as a (subject, predicate, object) statement such as (Alice, WORKS_AT, Acme Corp).

Samsung's 7M-parameter recursive reasoning model that outperforms LLMs 10,000x its size on abstract reasoning benchmarks like ARC-AGI.

A test of machine intelligence proposed by Alan Turing in 1950, in which a human evaluator tries to distinguish between a machine and a human based on natural-language conversation alone.

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test sets.

A theorem proving that a neural network with a single hidden layer and non-linear activation can approximate any continuous function to arbitrary precision, given enough neurons.

A training problem where gradients become exponentially smaller as they propagate backward through many layers, effectively preventing early layers from learning.

A specialized database optimized for storing and querying high-dimensional vector embeddings, supporting efficient similarity search operations.

A software development approach where a programmer describes what they want in natural language and an AI model generates the code, with the programmer guiding the process through conversation rather than writing code directly.

The strategy for setting initial values of neural network parameters before training begins, critical for ensuring stable signal and gradient propagation through deep networks.

An AI-powered code editor developed by Codeium that combines copilot-style suggestions with agentic capabilities for autonomous multi-file editing and task execution.

Elon Musk's AI company behind the Grok chatbot and the Colossus supercomputer, merged with SpaceX in 2026.

01.AI's speed-optimized MoE model ranking 6th on Chatbot Arena, trained for $3M and 70-80% cheaper than US frontier models.

A software vulnerability that is unknown to the vendor and has no available patch, giving defenders zero days of warning before it can be exploited.