Artificial Intelligence terminologies

Richaldo Elias
Machine Learning World
5 min readNov 3, 2017


“Any fool can know. The point is to understand.”
— Albert Einstein

We do not need to know everything in a conversation, but we should at least know the terms used in the conversation.

If we are talking about physics we need to know for example that when we are talking about velocity we mean the speed that an object takes to travel a space in a certain period of time. I think in Artificial Intelligence shouldn’t be different, so, in this post, I’ll let you know the meaning of the most used terminologies (and their acronym) so that the next time you meet with an AI post, you can read it with a deep understanding.

AI (Artificial Intelligence) — The first thing we need to do is understand what an AI actually is. The term “artificial intelligence” refers to a specific field of computer science that focuses on creating systems capable of gathering data and making decisions and/or solving problems.

Some parts of AI

AGI (Artificial General Intelligence) — is an emerging field aiming at the building of “thinking machines”; that is, general-purpose systems with intelligence comparable to that of the human mind, also called “Strong AI”, “Human-level AI”, etc.

ANI (Artificial Narrow Intelligence) — A one trick pony, they can play chess, recognize faces, translate foreign languages.

ASI (Artificial Super Intelligence) — Smarter than the best human brains and has the ability to apply that to absolutely anything (This is the AI that people like Stephen Hawking, Elon Musk, etc. are scared of).

Agent — also called assistants, brokers, bots, intelligent agents is an autonomous entity which observes through sensors and acts upon an environment using actuators.

Chatbot — A computer program that conducts conversations with human users by simulating how humans would behave as a conversational partner.

Data — Any collection of information converted into a digital form.

Data being processed

Data Mining — The process by which patterns are discovered within large sets of data with the goal of extracting useful information from it.

Deep Learning — A subset of AI and Machine learning in which Neural networks are “layered”, combined with plenty of computing power, and given a large measure of training data to create extremely powerful learning models capable of processing data in new and exciting ways in a number of areas, e.g. advancing the field of computer vision.

Neural Network

Genetic Algorithm — A method for solving optimization problems by mimicking the process of natural selection and biological evolution. The algorithm randomly selects pairs of individuals from the population (whereby the best performing individuals are more likely to be chosen) to be used as parents.

Heuristics — It is the knowledge based on Trial-and-error, evaluations, and experimentation.

ML (Machine Learning) — A subsetof AI in which computer programs and algorithms can be designed to “learn” how to complete a specified task, with increasing efficiency and effectiveness as it develops. Such programs can use past performance data to predict and improve future performance.

NLG (Natural Language Generation) — A machine learning task in which an algorithm attempts to generate language that is comprehensible and human-sounding. The end goal is to produce computer-generated language that is indiscernible from language generated by humans

NLP (Natural Language Processing) — The ability of computers to understand, or process natural human languages and derive meaning from them. NLP typically involves machine interpretation of text or speech recognition
RNN (Recurrent Neural Network) — A type of artificial neural network in which recorded data and outcomes are fed back through the network forming a cycle.

OCR (Optical Character Recognition)— A computer system that takes images of typed, handwritten or printed text and converts them into machine-readable text.


Pruning — Overriding unnecessary and irrelevant considerations in AI systems.

RNN (Recurrent Neural Network) — A type of artificial neural network in which recorded data and outcomes are fed back through the network forming a cycle.

Reinforcement Learning — A type of machine learning in which machines are “taught” to achieve their target function through a process of experimentation and reward. In reinforcement learning, the machine receives positive reinforcement when its processes produce the desired result, and negative reinforcement when they do not.

Reinforcement Learning

Rule It is a format of representing knowledge base in Expert System. It is in the form of IF-THEN-ELSE

Supervised learning: A type of machine learning in which human input and supervision are an integral part of the machine learning process on an ongoing basis, like a teacher supervising a student; more common than unsupervised learning.

Strong AI — An area of AI development that is working toward the goal of making AI systems that are as useful and skilled as the human mind.

Turing Test — A test developed by Alan Turing 1950, which is meant as a means to identify true artificial intelligence. The test is based on a process in which a series of judges attempt to discern interactions with a control (human) from interactions with the machine (computer) being tested.

Turing Test

Unsupervised learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

Well, there are several terms in AI, the ones presented above are the ones I find very often. If you have a term in mind, share with us.

Before you go!!!

If you enjoyed the writings leave your comment and claps 👏 to recommend this article so that others can see it.

Thanks Carla Francisco ❤ for recommending this very interesting topic, I enjoyed writing it.

With ❤ by Richaldo L. Elias!