Over the past couple of years, the field of Artificial Intelligence has been expanding at a frenetic pace. It seems like every industry in the world — gaming, manufacturing, agriculture, health-care, logistics, etc — are hustling to incorporate AI solutions to enhance their customer experience and productivity. Many of the major AI solutions that will have the largest impact on our lives haven’t gone mainstream yet. But regardless of whether AI-driven smartphones or autonomous cars get to the finishing line first, it’s clear that we’re at the advent of the AI era.

Of course, there are plenty of naysayers that might point out that AI isn’t really a breakthrough at all. They might point to the fact that researchers were developing AI computing solutions as far back as in the 1950s and that the developers today aren’t so different. There is truth in that, but that point of view fails to take a few aspects of modern AI development into consideration.

Back in the 1950s, or even a recent as just a few decades ago, scientists would have needed computers the size of an entire state to be able to produce as much raw computing power as we can now produce in penny-sized chips. Our raw computing power and abilities have grown exponentially in recent decades. As such, while it might have been theoretically possible for developers in the past to produce AI solutions, it wasn’t feasible in practice.

AI is a possibility and reality now because we have the necessary hardware and tools for it. However, if you’re a newbie to the world of AI, you’re probably wondering what an Artificial Intelligence is exactly.

It’s difficult to sum up exactly how AI will help you in your day-to-day capacities as its applicability can be diverse and versatile. About two decades ago, it would have been impossible for most people to truly understand what the internet is because words like “router” and “bandwidth” weren’t a part of common discourse.

Similarly, in order to truly understand AI, you need to understand all of the terminologies used in relation to it. Even now, if you try to look for specifications of modern products incorporating AI technology, you might be left confused by some of the words and terms being used casually. To lift the veil of mystery from the realm of AI, we’ve compiled a glossary that includes some of the terms related to Artificial Intelligence.

What is Artificial Intelligence?

Before we start with all the other terms, let’s explore what an “Artificial Intelligence” really is. To put it simply, “Artificial Intelligence” is a specific field of computer engineering that looks into producing systems that can gather data, make decisions, and solve problems automatically, without human intervention. Today’s common modern machines can only function the way they have been programmed to do so. However, “Artificial Intelligence’ resembled human intelligence in that it can grow, think, and modify its behavior.

Let’s say a computer has the ability to analyze the pictures of 1,000s of dog images, determine their similarities, and then automatically find other pictures of dogs on the internet based on those similarities. In this case, the computer is driven by Artificial Intelligence because it automatically learned what those similarities are in order to produce results.

Autonomous AI

Autonomy will be a very commonly used word in relation to Artificial Intelligence. Simply put, it means that a system driven by AI doesn’t need human interference to function. It can work independently, i.e., autonomously. However, the level of autonomy can vary between different AI solutions. Some of them don’t require any human intervention at all, whereas some can just be used to assist humans rather than replacing them altogether.

An example that can illustrate the varying levels of autonomy is the development of autonomous cars. A Level 4 autonomous vehicle is one that requires no human intervention — as such, it needs no steering wheel or even a human sitting within. A Level 2 autonomous vehicle will be one that can provide guidance and assistance in regards to cruise control or providing automatic brakes when an obstacle is detected, but it needs human intervention. Level 5 autonomous vehicles, which are currently purely theoretical, will be able to operate without a driver, GPS, external grid, or any external influence at all.


Mathematics and programming codes are used to instruct a machine on how to use artificial intelligence to solve specific problems. They essentially serve as rules that can teach behaviors to computers and thus are the most important aspect of AI.

Machine Learning

Machine Learning and Artificial Intelligence are often used synonymously. However, there’s a slight difference between them. Machine Learning is the process of an AI using the algorithms to perform functions and learn new skills. As such, Machine Learning is the process and Artificial Intelligence is the outcome.

Black Box

When the algorithms are set in place, an AI has to solve complex mathematic problems. The math problems cannot usually be understood by humans and it wouldn’t be worth the time or effort to try and solve them manually. However, the AI can output useful information based on the math problems and this is called Black Box learning. As such, we aren’t concerned with the methodologies of the computer because we have created the rules it used to produce the results.


A chatbot is basically a form of artificial intelligence that can hold a conversation with humans via text messages, voice messages, or both. They are commonly used by computer programs that employ AI technology. They can perform a number of valuable functions for a company including customer services, order processing, and more. Reasons they are valuable include that they are available 24/7, can handle customer’s questions, save money vs having an army of staff members on-hand to perform repetitive functions, can function in multiple languages, provide a consistent user experience, and more.

Convolutional Neural Network

This is a type of neural network that can identify images and make sense of them. Do you remember the dog example mentioned earlier in which the computer could identify its similarities? That was done using Convolutional Neural Network.

Deep Learning

Artificial Intelligence also has the ability to accurately imitate human patterns of behavior and thoughts through artificial neural networks made of various layers of information and algorithms.


It’s a form of support that works through the process of elimination to remove incorrect solutions and thus arrive at the ideal solution for a problem.

Turing Test

Alan Turing, the father of modern computing, was concerned that AI might reach such a state that it becomes sentient and indistinguishable from humans. He conceived of the Turing Test which was supposed to determine whether an AI is smart enough to fool a human into thinking that they’re holding a conversation (in text form) with an actual human. Since then, Turing Test has been applied to AI that can successfully and believably mimic human behaviors.

Unsupervised Learning

The most fascinating — and terrifying — aspect of Artificial Intelligence is that it’s meant to learn automatically based on layers upon layers of data and processing capability. As such, we don’t program the AI to find particular solutions and patterns, but we give it the ability to learn and determine patterns for itself.

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