An AI agent is a software system that can autonomously plan, decide, and execute multi-step tasks to achieve a defined g...

AI Alignment is the field of ensuring that artificial intelligence systems behave in ways that are consistent with human...

An AI benchmark is a standardized test or evaluation framework used to measure the performance of AI models on specific ...

AI Bias refers to systematic and repeatable errors in AI systems that result in unfair, skewed, or discriminatory outcom...

An AI copilot is an AI system designed to assist users in performing tasks by augmenting human decision-making rather th...

AI hallucination occurs when an AI model generates information that appears plausible but is factually incorrect, fabric...

AI Infrastructure refers to the compute, storage, networking, and deployment systems required to train, serve, and scale...

An AI model is a computational system that has been trained on data to recognize patterns and generate predictions or ou...

AI Safety is the field focused on preventing unintended harm from AI systems by ensuring they operate reliably, predicta...

An AI system is a complete setup that combines models, data, infrastructure, and interfaces to deliver a functional AI-d...

An algorithm is a finite set of well-defined instructions or rules used to solve a problem or perform a computation....

An API (Application Programming Interface) is a way for software systems to interact with AI models by sending inputs an...

Artificial General Intelligence (AGI) refers to AI systems that can perform any intellectual task that a human can, acro...

Artificial Intelligence (AI) is the field of designing computational systems that can perform tasks requiring human-like...

Artificial Superintelligence (ASI) refers to a hypothetical form of AI that surpasses human intelligence across all doma...

An autonomous agent is an AI system capable of making decisions and taking actions independently with minimal human inte...

Backpropagation is an algorithm used to train neural networks by propagating errors backward through the network to upda...

Benchmark leakage occurs when AI models are trained on data that overlaps with evaluation benchmarks, leading to inflate...

The bias-variance tradeoff describes the balance between a model's ability to fit training data (low bias) and its abili...

Big Data refers to datasets that are too large, complex, or fast-changing to be processed using traditional data managem...

A black box model is an AI system whose internal decision-making process is not transparent or interpretable to humans....

A context window is the maximum amount of text an AI language model can process in a single interaction, measured in tok...

Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn complex...

Emergence refers to unexpected abilities or behaviors that arise in AI models as they scale in size, data, or complexity...

Generalization is the ability of an AI model to perform well on new, unseen data after being trained on a specific datas...

Inference is the process of using a trained AI model to generate predictions or outputs from new input data....

Interpretability is the ability to understand how and why an AI model makes a particular decision or prediction....

A large language model (LLM) is a neural network trained on massive text datasets that can understand, generate, and rea...

Latency is the time it takes for an AI system to process an input and return a response....

Machine learning is a subset of artificial intelligence where systems learn patterns from data rather than being explici...

Model serving is the process of deploying a trained AI model so it can receive inputs and return outputs in real-time or...

A multi-agent system is a system where multiple autonomous AI agents interact, collaborate, or compete to complete tasks...

Overfitting occurs when an AI model learns the training data too closely, including its noise and anomalies, resulting i...

A prompt is the text input given to an AI language model to generate a response....

Prompt engineering is the practice of designing and refining text inputs to get optimal outputs from AI language models....

Reasoning in AI refers to the ability of a model to process information step-by-step to arrive at a logical conclusion o...

Reinforcement Learning from Human Feedback (RLHF) is a training technique where human evaluators rate AI outputs and tho...

Retrieval-Augmented Generation (RAG) is an AI architecture that combines information retrieval from a knowledge base wit...

Scaling laws are research findings showing that AI model performance improves predictably as training data, model size, ...

A token is the basic unit of text that AI language models process, roughly equivalent to three-quarters of a word or abo...

Training is the process of teaching an AI model to recognize patterns, make predictions, or generate outputs by exposing...

A transformer is the neural network architecture that powers virtually all modern large language models, introduced in t...

Underfitting occurs when an AI model is too simple to capture the underlying patterns in the data, resulting in poor per...

A vector database is a specialized database designed to store, index, and retrieve high-dimensional vector representatio...