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AI Careers Glossary

25 key terms and concepts for navigating a career in artificial intelligence and machine learning.

ACDEFGILMNPRTV

A

AI Agent

An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specified goals.

AI Alignment

The research field focused on ensuring AI systems behave in accordance with human values and intentions.

AI Safety

The multidisciplinary field focused on preventing AI systems from causing unintended harm.

C

Computer Vision

The field of AI that enables machines to interpret and understand visual information from images and videos.

D

Deep Learning

A subset of machine learning that uses multi-layered neural networks to learn complex patterns from large amounts of data.

Diffusion Model

A generative AI model that creates images by learning to reverse a noise-addition process, powering tools like DALL-E and Stable Diffusion.

E

Embeddings

Dense numerical representations of data (text, images, etc.) that capture semantic meaning in a format AI models can process.

F

Few-Shot Learning

A technique where AI models learn to perform tasks from only a small number of examples.

Fine-Tuning

The process of further training a pre-trained AI model on domain-specific data to improve its performance on particular tasks.

Foundation Model

A large-scale AI model trained on broad data that can be adapted to a wide range of downstream tasks.

G

Generative AI

AI systems that create new content — text, images, code, audio, or video — based on patterns learned from training data.

I

Inference

The process of running a trained AI model to generate predictions or outputs from new input data.

L

Large Language Model (LLM)

A neural network trained on massive text datasets that can understand and generate human language.

M

MLOps

The set of practices for deploying, monitoring, and maintaining machine learning models in production environments.

Multimodal AI

AI systems that can process and generate multiple types of data — text, images, audio, and video — within a single model.

N

Natural Language Processing (NLP)

The branch of AI focused on enabling computers to understand, interpret, and generate human language.

Neural Network

A computing system inspired by biological brains, consisting of layers of interconnected nodes that learn patterns from data.

P

Prompt Engineering

The practice of designing and refining inputs to AI models to produce desired outputs.

R

Reinforcement Learning

A machine learning paradigm where agents learn to make decisions by receiving rewards or penalties for their actions.

Retrieval-Augmented Generation (RAG)

A technique that enhances AI model responses by retrieving relevant information from external data sources before generating an answer.

T

Tokenization

The process of breaking text into smaller units (tokens) that language models can process.

Training Data

The curated datasets used to train machine learning models, directly influencing model capabilities and biases.

Transfer Learning

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

Transformer

The neural network architecture behind modern LLMs, using self-attention mechanisms to process sequences in parallel.

V

Vector Database

A specialized database designed to store and efficiently search high-dimensional vector embeddings.