AI Glossary: The Fundamentals of Artificial Intelligence

_INTRODUCTION

Artificial intelligence rests on a foundation of technical concepts that every decision-maker must understand. Not to write code, but to make the right calls: choosing the right model, evaluating a vendor, balancing performance against cost. These 8 terms are the building blocks of every AI project.

Model

A model is a program trained to recognize patterns in data and produce predictions or content. It is the core of any AI system. When you use ChatGPT, you are interacting with a model (GPT-4, Claude, Gemini). For decision-makers, the choice of model determines result quality, operating cost, and deployment constraints. Not all models are equal, and the most powerful one is not always the most relevant for your use case.

Dataset

A dataset is the collection of data used to train a model. Its quality, size, and diversity directly determine the performance of the final model. A biased dataset produces a biased model. An incomplete dataset produces a limited model. For companies, the dataset question is strategic: your proprietary data is a competitive advantage if well structured, a liability if it is not.

Training

Training is the process through which a model learns from a dataset. The model progressively adjusts its internal parameters to minimize its errors. This phase is costly in time and compute resources: training a large language model can cost several million dollars. For companies, the key distinction is between training a model from scratch (rarely relevant) and fine-tuning an existing model (often sufficient).

Inference

Inference is the moment when a trained model produces a result from new data. It is the practical use of the model in production. Every request you send to an AI generates an inference cost. This cost is recurring, unlike training which is a one-time expense. For companies deploying AI at scale, inference cost often becomes the main budget line.

Parameters

Parameters are the internal numerical values that a model adjusts during training. A model like GPT-4 contains hundreds of billions of them. Parameter count is often used as an indicator of model power, but this shortcut is misleading: a smaller model, well trained on quality data, can outperform a larger one. What matters is the fit between model size and your use case.

Loss Function

The loss function measures the gap between the model's prediction and the expected result. It is the evaluation criterion that guides training. The lower the loss, the better the model performs on its training data. For decision-makers, understanding the loss function means asking the right question: what exact criterion is the model optimized for, and does that criterion truly match your business objective?

Gradient Descent

Gradient descent is the optimization algorithm that enables a model to learn. At each step, it calculates which direction to adjust parameters in order to reduce the loss function. It is the fundamental mechanism behind all deep learning. The key takeaway for decision-makers: learning is not magic, it is an iterative process of mathematical optimization, which explains why it requires time, computing power, and large volumes of data.

Architecture

Architecture refers to the internal structure of a model: how its components are organized and interconnected. The Transformer, for example, is the architecture that revolutionized language processing and made models like GPT and Claude possible. Architecture choice determines what a model can do and its limitations. For companies, understanding a model's architecture helps assess whether it fits a specific use case: a Transformer excels at text, but other architectures are more efficient for image processing or tabular data.