Definitions of uncommon AI terms
Artificial intelligence has reshaped not only technology but also the language we use every day. Words like “neural network,” “backpropagation,” and “inference” have moved from research papers into posts and articles, often faster than they can be clearly defined. In this edition, Lomar unpacks these terms with care—explaining how they connect, where they differ, and what they truly mean in practice. The aim is not to simplify the science, but to make its language transparent, so readers can think about AI with accuracy, not abstraction.
backpropagation
1. noun (artificial intelligence, machine learning) A method used in training artificial neural networks, where the computer slowly learns by adjusting its settings (called weights) after checking where it made mistakes, so it can perform better next time.
Example 1: Backpropagation is used to improve the accuracy of image recognition in computers.
Example 2: With backpropagation, the AI system learns from its errors and becomes smarter over time.
Etymology
The word “backpropagation” comes from “back,” meaning reverse or going backward, and “propagation,” which means spreading or moving forward. It refers to how the correction of mistakes moves backward through the neural network to help the system adjust and learn.
inference
1. noun Artificial Intelligence (AI): The process in which a computer system or an AI model uses what it has learned or has been trained on to make decisions, predictions, or understand new data.
Example 1: During inference, the AI recognized a cat in the photo.
Example 2: Inference helps voice assistants understand what you are saying and respond correctly.
Etymology
The word "inference" comes from the Latin word "inferre," which means "to bring in" or "to conclude." Over time, it started to be used in English for describing the act of coming to a conclusion from evidence or reasoning, and now it is also used in computer science and AI for the same basic idea.
overfitting
1. noun (Artificial Intelligence, Machine Learning) When a computer or AI model learns too much from specific details in training data, including random noise or mistakes, so that it works very well on the examples it has seen before, but not well on new or different data.
Example 1: The AI showed high accuracy during testing, but due to overfitting, it performed poorly with new images.
Example 2: Adding more diverse data helped reduce overfitting in the system.
Etymology
The word "overfitting" comes from "over-" meaning "too much" and "fit," which means to match or suit. In this case, it means "fitting too closely" to the training data. It started being used in artificial intelligence and statistics in the late 20th century.
tokenization
1. AI: The process of breaking down text into smaller parts, like words or sentences, so that a computer or AI can understand and work with the text more easily.
Example 1: In AI, tokenization turns a sentence into words like "The", "cat", and "ran".
Example 2: Tokenization helps AI models figure out the meaning of each word in a message.
2. computer science: The process of changing important information, like credit card numbers, into special codes (tokens) that are safer for computers to use and store.
Example 1: Tokenization protects your credit card number by turning it into a random code when you shop online.
Example 2: Many apps use tokenization to keep personal data safe from hackers.
Etymology
The word "tokenization" comes from "token," which means a small piece or symbol that represents something else. The "-ization" ending means "the process of making." So, tokenization means the process of turning something into tokens.
attention mechanism
1. *Artificial Intelligence* A system in AI and machine learning, especially in neural networks, that helps the computer focus on the most important parts of the data it is working with. It works a bit like human attention, deciding which information is most relevant for the task.
Example 1: The attention mechanism in the model helped it understand which words in the sentence were most important for translation.
Example 2: New AI systems use the attention mechanism to look at key parts of an image while ignoring the background.
Etymology
The term comes from the word "attention," meaning to focus, and "mechanism," meaning a process or system. In the 2010s, computer scientists borrowed the idea from how humans pay attention and applied it to AI models to improve how computers process information.
neural network
1. A system designed to work like the human brain, made by connecting many small units called “neurons” together in a computer. Neural networks can learn from data and make decisions, predictions, or recognize patterns.
Example 1: The neural network learned to recognize cats in pictures by looking at thousands of images.
Example 2: Scientists use neural networks to help doctors find signs of cancer in X-ray images.
Etymology
“Neural” comes from “neuron,” which is a nerve cell in the brain. “Network” means a group of things connected together. The term “neural network” was first used to describe systems that try to copy the way real animal and human brains work, especially in thinking and learning.
few-shot learning
1. AI: Few-shot learning is a way for an artificial intelligence system (a computer program that tries to act like a human brain) to learn to do something new with only a small number of examples or lessons, instead of needing thousands or millions.
Example 1: Few-shot learning helps AI recognize new animals after seeing just three or four pictures of each.
Example 2: The chatbot improved its responses using few-shot learning, needing only a few sample conversations.
Etymology
The term "few-shot learning" combines "few" (meaning a small number) and "shot" (here meaning an attempt or example), with "learning" from the field of artificial intelligence. It describes the idea that the AI needs only a few tries or examples to learn a new task.