Common AI Terms

Most Common AI Terms You Should Know

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From just a part of sci-fi movies directly into our regular lives, Artificial Intelligence has traversed this long path in the last decade exceptionally. Artificial intelligence used to be something that was impossible a few years ago. But now it has gradually seeped into our lives and made us accustomed to it seamlessly.

The scenario about Artificial Intelligence is like children of this era don’t even know that it has come into existence recently. While Artificial Intelligence continues to revolutionize the way businesses function or the ways familiar people do their tasks, it is increasingly becoming a part of almost every industry.

Be it retail, healthcare and pharmaceuticals, logistics, web and mobile applications, transportation, eCommerce, IT (of course), government agencies, and even academics, Artificial Intelligence has disrupted nearly every sector.

With the increasing popularity of Artificial Intelligence, the skill gap has widened and there is a considerable shortage of skilled and trained AI professionals to work in these organizations. Eventually, artificial intelligence has created a plethora of opportunities for professionals who wish to advance their careers in this domain.

Artificial Intelligence Training is becoming popular among the IT pros willing to have lucrative and highly-paying careers as AI experts.

This article will let you go through some of the most common terms, buzzwords, jargon, and lingo used in Artificial Intelligence.

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Artificial Intelligence Glossary

  1. Algorithm

An algorithm is basically a set of rules or formulas that is specifically designed to perform certain tasks or to represent relationships between different variables. Just like a recipe where you have certain ingredients, a series of steps to carry out, and finally come up with a properly prepared dish; the algorithm has a set of variables, a series of steps that use these variables, and come up with the desired output.

  1. Analogical Reasoning

Artificial intelligence involves solving problems by making analogies and making comparisons with past experiences.

  1. Cognitive Science

The term cognitive literally means ‘conscious intellectual activity’. So, the study that combines linguistics, neuroscience, psychology, anthropology, philosophy, and artificial intelligence is referred to as cognitive science. 

These are blended together to understand the way the mind functions and the way machines can simulate the working of the human brain when applied to AI.

  1. Chatbots

Chatbots or simply bots refer to programs that are executed within an application or website meant to interact directly with users and help them in resolving simple issues. A conversation takes place between a bot and a user like a normal conversation between two people. Chatbots are generally employed to provide customer support, but they can also serve more complex roles, such as functioning as an AI coach.

  1. Artificial Intelligence

Artificial intelligence is the branch of computer science that involves the development of systems that are capable of performing tasks that normally require human intervention, such as visual perception, decision-making, speech and image recognition, or translating between different languages.

  1. Artificial Neural Networks

In the brain of animals are present biological neural networks that work as learning models. ANNs are designed on the basis of these neural networks and are meant to perform tasks that are challenging with traditional programming methods.

  1. Autonomous

Autonomy is referred to the ability to act independently separately from a ruling body. In the field of AI, a system is autonomous if it is capable of functioning properly without the need for input from a human operator.

  1. BackPropagation

Backpropagation of errors, or simply backpropagation, is the method of training neural networks on the basis of a studied, desired output meant for a specified case.

  1. Deep Learning

Deep Learning is a subset of machine learning that requires minimal programming and is capable of learning on its own. Deep learning is generally used to identify data and also make predictions on customer behavior, requirements, and feedback.

  1. Machine Learning

Machine Learning is a subset of Artificial Intelligence that focuses on building systems that can act and make decisions without the need for being programmed. It is machine learning that makes the most advanced and exceptional capabilities of AI possible.

  1. Natural Language Generation

It is when AI writes or speaks languages spoken by humans. Natural Language Generation works behind the scenes with virtual assistants, chatbots, and writing tools.

  1. Natural Language Processing

With NLP, an AI-based system is capable of interpreting human language in verbal or written form. The systems with more sophisticated NLP programs can decipher articulation in different languages.

  1. Pattern Recognition

When an AI-powered system recognizes patterns, it helps businesses in optimizing parameters and predicts future trends. It is pattern recognition that gives predictive capabilities to AI systems.

  1. Robotics

A combination of facial recognition, image recognition, and computer vision powers a robot. So, robots are AI-powered machines.

  1. Supervised, Unsupervised, and Reinforcement Learning

The three main types of machine learning are supervised, unsupervised, and unsupervised learning.

Supervised learning is where a machine notices a set of cases and the related outcomes and learns the rules that caused the result. Then it predicts the outcomes of unobserved cases.

In unsupervised learning, the system perceives a set of cases without the related outcomes and learns the patterns to classify the cases into groups that possess similar characteristics.

Reinforcement learning is when a machine performs tasks to attain the objectives, learns the feedback on those tasks, and employs the trial and error method to learn and take better actions to achieve the desired objectives.

  1.  Computer Vision

Computer vision is referred to as advancement in the image and facial recognition. In this, AI is capable of identifying moving objects in a video or aby such content. It is computer vision that enables self-driving cars to drive seamlessly. With this technology, these cars can ‘see’ the world around them with the help of sensors.

Computer vision powers AI-based systems to recognize the objects out in the world or in videos accurately in real-time feeds.

  1. Weak, Strong, and Super Artificial Intelligence

The applications of artificial intelligence that we come across in our daily lives are examples of Weak or Narrow AI. These include speech, image, and facial recognition systems, virtual assistants, mapping systems, recommendation engines, spam filters, and more.

Self-driving cars (which are yet to achieve complete success) are the applications of Artificial General Intelligence. So, it is the concept and is expected to achieve success soon.

Super Artificial Intelligence is supposed to get ahead of human capabilities of thinking and making decisions. But this is just a concept or hypothesis and is yet to come.


Going through the glossary of artificial intelligence may have given you an idea of what AI is all about. This ever-evolving domain has many career opportunities for IT professionals looking for advancement in Artificial Intelligence.

An online training course from a reputed institute like Simplilearn can serve the purpose of helping you get your dream job in AI.

Enroll Yourself Now!!

Also Read: 5 Artificial Intelligence Business Stories



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