Artificial Intelligence (AI) Terms & Definitions

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AI Terms & Definitions

Here is an essential vocabulary list about artificial intelligence that you should know!

Term	Definition
Algorithm	A set of rules that a machine can follow to learn how to do a task.
Artificial intelligence	This refers to the general concept of machines acting in a way that simulates or mimics human intelligence. AI can have a variety of features, such as human-like communication or decision making.
Autonomous	A machine is described as autonomous if it can perform its task or tasks without needing human intervention.
Backward chaining	A method where the model starts with the desired output and works in reverse to find data that might support it.
Bias	Assumptions made by a model that simplify the process of learning to do its assigned task. Most supervised machine learning models perform better with low bias, as these assumptions can negatively affect results.
Big data	Datasets that are too large or complex to be used by traditional data processing applications.
Bounding box	Commonly used in image or video tagging, this is an imaginary box drawn on visual information. The contents of the box are labeled to help a model recognize it as a distinct type of object.
Chatbot	A chatbot is a program that is designed to communicate with people through text or voice commands in a way that mimics human-to-human conversation.
Cognitive computing	This is effectively another way to say artificial intelligence. It’s used by marketing teams at some companies to avoid the science fiction aura that sometimes surrounds AI.
Computational learning theory	A field within artificial intelligence that is primarily concerned with creating and analyzing machine learning algorithms.
Corpus	A large dataset of written or spoken material that can be used to train a machine to perform linguistic tasks.
Data mining	The process of analyzing datasets in order to discover new patterns that might improve the model.
Data science	Drawing from statistics, computer science and information science, this interdisciplinary field aims to use a variety of scientific methods, processes and systems to solve problems involving data.
Dataset	A collection of related data points, usually with a uniform order and tags.
Deep learning	A function of artificial intelligence that imitates the human brain by learning from the way data is structured, rather than from an algorithm that’s programmed to do one specific thing.
Entity annotation	The process of labeling unstructured sentences with information so that a machine can read them. This could involve labeling all people, organizations and locations in a document, for example.
Entity extraction	An umbrella term referring to the process of adding structure to data so that a machine can read it. Entity extraction may be done by humans or by a machine learning model.
Forward chaining	A method in which a machine must work from a problem to find a potential solution. By analyzing a range of hypotheses, the AI must determine those that are relevant to the problem.
General AI	AI that could successfully do any intellectual task that can be done by any human being. This is sometimes referred to as strong AI, although they aren’t entirely equivalent terms.
Hyperparameter	Occasionally used interchangeably with parameter, although the terms have some subtle differences. Hyperparameters are values that affect the way your model learns. They are usually set manually outside the model.
Intent	Commonly used in training data for chatbots and other natural language processing tasks, this is a type of label that defines the purpose or goal of what is said.
Label	A part of training data that identifies the desired output for that particular piece of data.
Linguistic annotation	Tagging a dataset of sentences with the subject of each sentence, ready for some form of analysis or assessment.
Machine intelligence	An umbrella term for various types of learning algorithms, including machine learning and deep learning.
Machine learning	This subset of AI is particularly focused on developing algorithms that will help machines to learn and change in response to new data, without the help of a human being.
Machine translation	The translation of text by an algorithm, independent of any human involvement.
Model	A broad term referring to the product of AI training, created by running a machine learning algorithm on training data.
Neural network	Also called a neural net, a neural network is a computer system designed to function like the human brain.
Natural language generation (NLG)	This refers to the process by which a machine turns structured data into text or speech that humans can understand.
Natural language processing (NLP)	The umbrella term for any machine’s ability to perform conversational tasks, such as recognizing what is said to it, understanding the intended meaning and responding intelligibly.
Natural language understanding (NLU)	As a subset of natural language processing, natural language understanding deals with helping machines to recognize the intended meaning of language.
Overfitting	An important AI term, overfitting is a symptom of machine learning training in which an algorithm is only able to work on or identify specific examples present in the training data.
Parameter	A variable inside the model that helps it to make predictions. A parameter’s value can be estimated using data and they are usually not set by the person running the model.
Pattern recognition	This field is basically concerned with finding trends and patterns in data.
Predictive analytics	By combining data mining and machine learning, this type of analytics is built to forecast what will happen within a given timeframe based on historical data and trends.
Python	A popular programming language used for general programming.
Reinforcement learning	A method of teaching AI that sets a goal without specific metrics, encouraging the model to test different scenarios rather than find a single answer.
Semantic annotation	Tagging different search queries or products with the goal of improving the relevance of a search engine.
Sentiment analysis	The process of identifying and categorizing opinions in a piece of text, often with the goal of determining the writer’s attitude towards something.
Strong AI	This field of research is focused on developing AI that is equal to the human mind when it comes to ability.
Supervised learning	This is a type of machine learning where structured datasets, with inputs and labels, are used to train and develop an algorithm.
Test data	The unlabeled data used to check that a machine learning model is able to perform its assigned task.
Training data	This refers to all of the data used during the process of training a machine learning algorithm.
Transfer learning	This method of learning involves spending time teaching a machine to do a related task, then allowing it to return to its original work with improved accuracy.
Turing test	This tests a machine’s ability to pass for a human, particularly in the fields of language and behavior.
Unsupervised learning	This is a form of training where the algorithm is asked to make inferences from datasets that don’t contain labels.
Validation data	Structured like training data with an input and labels, this data is used to test a recently trained model against new data and to analyze performance.
Variance	The amount that the intended function of a machine learning model changes while it’s being trained.
Variation	Also called queries or utterances, these work in tandem with intents for natural language processing.
Weak AI	Also called narrow AI, this is a model that has a set range of skills and focuses on one particular set of tasks.

Example Usage Of Artificial Intelligence (AI) Terms

Now it’s time to use some of these terms in a sentence!

The algorithm tells the computer the steps it should follow to solve the problem.

Artificial intelligence makes it possible for people to do their jobs much faster.

A robot is called autonomous when it can clean a room without help.

With backward chaining, the system starts from the answer and works backward to find the steps.

Social media creates big data because millions of posts are made every second.

A bounding box is drawn around a car in a photo so the computer can recognize it.

The store’s chatbot answered my question about delivery time.

Some companies use the term cognitive computing instead of artificial intelligence.

The model was trained on a corpus of English sentences.

By using data mining, we found that most customers shop more on weekends

Data science combines math, statistics, and computing to solve problems with data.

A dataset of animal pictures was used to train the program.

Deep learning helps the computer learn patterns in pictures of cats and dogs.

Entity annotation means marking names of people in a text so the machine can see them.

Entity extraction finds important information, like addresses, inside documents.

In forward chaining, the AI looks at the facts and moves step by step toward an answer.

General AI would be able to do any task a human can do.

Machine translation turns an English sentence into French without human help.

The trained model can now predict tomorrow’s weather.

A neural network is designed to work like the human brain.

Frequently Asked Questions About Artificial Intelligence (AI) Terms & Definitions

Now that we’ve learned the common terms and definitions in the world of Artificial Intelligence (AI), let’s answer some frequently asked questions.

What is AI in basic terms?

AI is the ability of machines to mimic human intelligence, such as problem-solving, communication, and decision-making.

What Does Natural Language Generation (NLG) Mean?

NLG is the process by which machines turn structured data into human-friendly text or speech.

What Does Cognitive Computing Mean?

Cognitive computing is another way of saying artificial intelligence, often used to make AI sound less futuristic and more practical.

Who Created AI?

AI was pioneered by John McCarthy (1927–2011), an American computer scientist often called the “father of artificial intelligence.”

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