AI Glossary

Artificial general intelligence, or AGI, refers to AI that's more capable than the average human at many tasks. OpenAI CEO Sam Altman describes AGI as the equivalent of a median human that you could hire as a co-worker. Meanwhile, OpenAI's charter defines AGI as highly autonomous systems that outperform humans at most economically valuable work.

AI Agent

An AI agent is a tool that uses AI technologies to perform a series of tasks on your behalf, such as filing expenses, booking tickets, or writing and maintaining code. Infrastructure is still being built out to deliver on its envisaged capabilities, but the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks.

API Endpoints

API endpoints are like buttons on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations, allowing one application to pull data from another or enabling an AI agent to control third-party services directly without human manual operation.

Chain of Thought

Chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. This process usually takes longer but yields more accurate answers, especially in logic or coding contexts. Reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking through reinforcement learning.

Coding Agent

A coding agent is a specialized AI agent applied to software development. It can write, test, and debug code autonomously, handling iterative work that typically consumes a developer's day. These agents can operate across entire codebases, spotting bugs, running tests, and pushing fixes with minimal human oversight.

Compute

Compute refers to the vital computational power that allows AI models to operate. This type of processing fuels the AI industry, enabling it to train and deploy powerful models. The term is often shorthand for the hardware that provides computational power, such as GPUs, CPUs, TPUs, and other forms of infrastructure.

Deep Learning

Deep learning is a subset of self-improving machine learning that uses artificial neural networks to make complex correlations. These algorithms draw inspiration from the human brain's interconnected pathways of neurons, allowing them to identify important characteristics in data without human engineers defining these features.

Diffusion

Diffusion is the technology behind many art-, music-, and text-generating AI models. It slowly destroys the structure of data by adding noise until there's nothing left, then learns to restore the data, gaining the ability to recover it from noise.

Distillation

Distillation is a technique used to extract knowledge from a large AI model with a teacher-student model. Developers send requests to a teacher model, record the outputs, and use them to train a student model, which is trained to approximate the teacher's behavior.

Fine-Tuning

Fine-tuning refers to the further training of an AI model to optimize performance for a specific task or area. Many AI startups take large language models as a starting point and supplement earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise.

GAN

A GAN, or Generative Adversarial Network, is a type of machine learning framework that underpins developments in generative AI. GANs involve a pair of neural networks, one generating output and the other evaluating it, trying to outdo each other to optimize AI outputs and make them more realistic.

Hallucination

Hallucination refers to AI models making up information, generating incorrect data. This problem arises from gaps in training data and contributes to the push toward specialized and/or vertical AI models to reduce knowledge gaps and disinformation risks.

Inference

Inference is the process of running an AI model to make predictions or draw conclusions from previously seen data. Many types of hardware can perform inference, but not all can run models equally well, with very large models taking longer to make predictions on less powerful hardware.

Large Language Model (LLM)

Large language models are the AI models used by popular AI assistants. They are deep neural networks made of billions of numerical parameters that learn the relationships between words and phrases, creating a representation of language.

Memory Cache

Memory cache is an optimization technique designed to make inference more efficient. It saves particular calculations for future user queries and operations, reducing the number of calculations a model needs to run and cutting down on power usage.

Model Context Protocol (MCP)

Model Context Protocol is an open standard that lets AI models connect to outside tools and data without a custom connector. It's like a USB-C port for AI, introduced by Anthropic and adopted by OpenAI, Google, and Microsoft.

Mixture of Experts (MoE)

Mixture of Experts is a model architecture that splits a neural network into smaller specialized sub-networks. It only activates a handful of them for any given task, making it possible to build enormous models that stay relatively fast and cheap to run.