What makes a model: data and algorithm

From the dictionary

Terms used in this post

Quick reference for the 14 terms you met above. Each one comes from the AI dictionary.

Artificial IntelligenceAI

Umbrella term for software that performs tasks usually associated with human reasoning — language, perception, decision-making. Coined at the 1956 Dartmouth Summer Research Project. In everyday 2026 use, "AI" almost always means a large language model like ChatGPT, Claude, or Gemini, even though the textbook definition is much broader.

e.g. When a product page says "AI-powered", it could mean a 70-billion-parameter LLM or a hand-written if-statement. The label moves with the times.

AlgorithmML

In ML, the recipe used to turn data into a model — the architecture plus the training procedure. Different algorithms (decision trees, gradient-boosted trees, neural networks, transformers) produce different model types.

ChatGPTAI

OpenAIs consumer chat product, launched November 30, 2022. The first LLM to reach mass adoption — 100 million users in two months. The product most people mean when they say AI today.

DatasetData

The collection of examples a model learns from during training. The shape, size, quality, and bias of the dataset determines almost everything about the resulting model.

Deep LearningDL

A subset of machine learning that uses neural networks with many layers ("deep" stacks). Powers image recognition, speech, and the LLMs behind ChatGPT/Claude/Gemini. Needs much more data and compute than classical ML, but scales further.

e.g. Every modern LLM is a deep-learning model — a transformer with billions of parameters trained on internet-scale text.

GPTAI

OpenAIs family of large language models — Generative Pre-trained Transformer. GPT-4 (2023) and successors are the most widely used closed-source LLMs in production.

Large Language ModelAI

A deep-learning model trained on huge volumes of text to predict the next token given the previous ones. Scaling next-token prediction to billions of parameters yields the chat-like behaviour of ChatGPT, Claude, and Gemini. Capabilities are bounded by training data and the context window.

e.g. Claude is an LLM — it reads your message as tokens and generates a response one token at a time.

Machine LearningML

A subset of AI where the system learns patterns from data instead of following hand-written rules. The output is a model — a set of learned numbers that maps inputs to outputs. Spam filters, recommendation systems, and credit-risk scorers are classical ML.

e.g. Gmail's spam filter learns which emails you mark as junk and updates its model — that's machine learning, not a rule someone wrote.

ModelML

In ML, a model is a file of learned numbers (parameters or weights) plus an architecture that tells the program how to use them. Loading a model means reading those numbers; running it means doing arithmetic with them.

Neural NetworkDL

A model architecture loosely inspired by neurons in the brain — in practice, a stack of matrix multiplications with non-linear functions between them. Deep learning is what you get when you stack many layers of these and train them on a lot of data.

ParametersML

The individual learned numbers inside a model. "7B parameters" means 7 billion of them. More parameters generally means more capacity, more memory needed, and slower inference.

TrainingML

The expensive one-time process of running a learning algorithm over data until the models parameters settle into useful values. Frontier-model training costs $100M+ and tens of thousands of GPUs.

TransformerDL

The neural-network architecture every modern LLM is built on. Introduced by Google in the 2017 paper "Attention Is All You Need". GPT, Claude, Gemini, Llama, Mistral — all transformers.

WeightsML

The numbers inside a trained model. They start out random and get adjusted during training until they encode the patterns in the data. "Open weights" means the trained numbers are downloadable; it does not mean the training data or code is open.