The A-Z of Artificial Intelligence: Key terms you ought to know

A quick guide to the most commonly used terminologies in AI

Artificial Intelligence (AI) is here to stay, so you might as well familiarize yourself with terms that will often come up in discussions.

Here are some of the most commonly used terms in AI and what they stand for:

Abductive reasoning is a form of logical reasoning that seeks the answer to a question, while using the most simple and straight forward way to derive it. In AI, ALP is a knowledge-representation framework that is used to solve problems based on Abductive reasoning principles.

Think of algorithms as a set of hard-coded rules that AI models follow to learn and achieve goals. These goals can be pre-determined like user prompts, or pre-configured, meaning they will react the exact same way given a situation.

A hardware chip or micro-processor, designed for general purpose AI applications. These can be used to train AI models, or in larger neural networks.

A theoretical concept for now, that refers to a class of AI system that can outperform and surpass human cognitive abilities in various tasks.

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Backward Chaining is a method by which AI models start with a desired output and work in reverse to find data to support it.

Large amounts of data collected in the form of massive datasets, that are used for training AI models.

A program that is designed for one-on-one conversations using natural language processing, that helps it mimic human conversations.

An alternate way to say Artificial Intelligence, typically used in marketing.

A dataset of written or spoken material that is used to train linguistic AI models for performing tasks.

The process of deciphering large datasets to find new ways to improve an AI model.

The process by which an AI model imitates a human brain and learns the way we do, through the use of structured data points.

A method by which an AI model works with a given problem to find a solution. This is done by analyzing data sets and finding points that are relevant to the problem.

Manually set values for AI models that will affect how it learns.

A branch of AI that is focused on creating Algorithms that will help AI models to learn and interact with new data, without human involvement.

A blanket term that characterizes an end product of AI training.

A large computer network, designed to mimic a human brain. This is used for both computations and AI model training.

The ability of an AI to understand and decipher human language, and then analyze that data to output text or speech in a language humans can understand.

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This refers to a field within AI, that deals with finding and decoding similar patterns or trends in data.

The ability of an AI model to decipher data points and output detailed analytics and predictions based on it.

A method of teaching AI, by encouraging it to find an answer without any set parameters. The AI is graded on its output by a human handler, which helps it improve the next result, until the desired output is reached.

A test devised by mathematician Alan Turing that tests the AI in various fields to see if it can pass itself off as a human.

A narrowly developed AI model, that is meant for specific tasks. Most AI models that we see today fall under this category.