AI glossary: Artificial Intelligence terms

The most completed list of Artificial Intelligence terms as a dictionary is here for you. Artificial intelligence is already all around us. As AI becomes increasingly prevalent in the workplace, it’s more important than ever to keep up with the newest words and use types. Leaders in the field of artificial intelligence are well aware that it is revolutionizing business. So, how much do you know about it? You’ll discover concise definitions for automation tools and phrases below.

Here’s a list of all Artificial Intelligence terms you need to know

It’s no surprise that the world is moving ahead quickly thanks to artificial intelligence’s wonders. Technology has introduced new values and creativity to our personal and professional lives.

While frightening at times, the rapid evolution has been complemented by artificial intelligence (AI) technology with new aspects. It has provided us with new phrases to add to our everyday vocab that we have never heard of before. Artificial Intelligence also gave new connotations to old words.

The benefits of AI-assisted customer support for the current generation are self-evident: the technology is here to change people’s lives and will undoubtedly stay.

Artificial Intelligence glossary (2022)

You’ve undoubtedly heard the phrases “data mining” and “machine learning,” but you’ve never been able to find a succinct definition for what you were reading. Now? You don’t have to go very far to find one.

Ai Dictionary: Be A Native Speaker Of Artificial Intelligence
AI glossary: Artificial intelligence terms that you should know (2022)

The following are brief definitions of Artificial intelligence terms. So let’s begin!

Accuracy

This statistic indicates how successful your AI model is at predicting outcomes. The number of correct predictions is divided by the total number of predictions made. We talked about this topic in What Is Data Accuracy? (And How to Improve It).

Adversarial Machine Learning

Adversarial machine learning is a type of machine learning that seeks to toughen models through adversarial input. It seeks to make it easier for machine-learning methods in adversarial settings such as spam filtering, malware detection, and biometric identification.

Application Programming Interface (API)

Software developers may employ a collection of commands, procedures, protocols, and objects to build applications or communicate with an external system.

Algorithm

A pattern of procedures or instructions given to an AI, neural network, or other devices to assist them in learning on their own; classification, clustering, suggestion, and regression are four of the most common sorts.

AI ethics

The ethics of technology are explicitly concerned with artificially intelligent systems. Biases may significantly impact machine learning algorithms that are trained from data, ranging from gender to race to age to economic status and everything in between.

What are AI frameworks?

Artificial intelligence (AI) frameworks make it easier and faster to develop machine learning/deep learning, neural networks, and natural language processing (NLP) applications by providing ready-made solutions. The most popular open-source AI frameworks are TensorFlow, Theano, PyTorch, Sci-KitLearn, Keras, Microsoft Cognitive ToolKit, and Apache Mahout. It is one of the most searched Artificial Intelligence terms.

Artificial General Intelligence (AGI)

AGI is a computational system that may execute any intellectual function that a human can. Also known as “Strong AI.” At the moment, AGI is just a concept. We talked about this more in The search for artificial general intelligence (AGI).

Artificial Intelligence (or Weak AI)

A computer program that simulates aspects of human intelligence but focuses on a single, specific function. Narrow AI is also known as focused artificial intelligence (AI) in distinction to AGI.

Artificial Neural Network

Artificial intelligence and machine learning are based on the human brain’s neural network designs, particularly the brain. It is one of the most searched Artificial Intelligence terms.

Autonomy

Autonomy is a state of being free from external constraints. An autonomous machine or vehicle does not need human input to operate.

Backward chaining

The reverse technique is in which machines work forward from the intended objective or output to see whether there is any evidence to support those aims or outputs.

Backpropagation

Backpropagation is a method of teaching neural networks based on a known, desired output for certain sample circumstances.

Bias

Assumptions are added to the model to make learning easier. Most supervised machine learning algorithms work best with minimal bias since these assumptions might harm results.

Big data

Big data is too large or complicated for general data processing applications to handle. It is one of the most searched Artificial intelligence terms.

Bounding box

The bounding box is drawn on visual information frequently used in image or video tagging. The box contents are labeled to assist the model in recognizing it as a distinct sort of thing.

Brute force search

A query that is not limited by clustering/approximations; it explores all inputs. Typically more time-consuming and costly, but more thorough.

Convolutional Neural Network

Convolutional neural networks are deep artificial neural networks used to classify pictures (e.g., identify what they see), group them by similarity (photo search), and recognize objects in scenes.

CPU (Central Processing Unit)

The electronic circuitry in a computer executes the commands of a computer program by executing basic arithmetic, logical, control, and I/O operations specified by the instructions.

Custom Model

Artificial neural networks are a type of machine learning in which small inputs from a user, such as photographs or videos of their products, are converted into predicted concepts.

Chatbot

A chatbot is a software application that imitates human-to-human conversation through text or voice commands.

Cognitive computing

Cognitive computing is often referred to as artificial intelligence, but it’s more accurately described as AI. It’s utilized by marketing teams to minimize AI’s mystique in some businesses. It is one of the most searched Artificial Intelligence terms.

Computational learning theory

The field of machine learning is concerned with creating and analyzing algorithms that learn.

Computer vision

A multidisciplinary scientific discipline that investigates how computers can be programmed to understand digital images or movies at a high level. It focuses on automating activities that the human visual system can perform.

Data architect

A data architect is a person who practices data architecture, a field of study concerned with designing, generating, deploying, and managing an organization’s data architecture. Data architects are frequently involved in AI initiatives.

What is data mining?

Pattern discovery identifies trends in vast data collections to extract useful information from data mining.

Data science

This interdisciplinary subject, data science, which draws from statistics, computer science, and information science, seeks to apply various scientific approaches, methods, and systems to address data issues.

Data lake

Because data is at the heart of every AI application or solution, gathering all of the data necessary to develop machine learning and inference models is critical. A data lake is a term used to describe the process of merging all of the information (both structured and unstructured) into a central repository.

Data manager

A data manager is a person who is responsible for lawfully obtaining the correct data type for training AI systems by working with data scientists. A data manager works with data architects to make sure that obtained information is correctly versioned and stored so that it may be analyzed and audited. A data manager must also ensure that the life cycle of the data is managed under legal and organizational standards while also ensuring that appropriate governance is provided over the collection, usage, and disposal of this information.

Data scientist

A data scientist can be someone, a company, or an application that performs statistical analysis, data mining, and retrieval operations on a large amount of data to discover trends, figures, and other relevant information.

Deep learning

Deep learning is an artificial intelligence technique that mimics the human brain by learning from how data is structured rather than a pre-programmed algorithm. It is one of the most searched Artificial intelligence terms.

Deep Neural Network

The input and output layers of an artificial neural network (ANN) are separated by multiple intermediate layers. It uses sophisticated mathematical modeling to process data in complicated ways.

Ebert Test

It was proposed by film critic Roger Ebert at the 2011 TED conference as a challenge for software developers to have a computerized voice master the inflections, delivery, timing, and intonations of speaking people. The exam is comparable to Turing’s test, which was created in 1950 to determine if a computer could exhibit intelligent behavior by producing a human performance.

Echo State Network (ESN)

A recurrent neural network is used with a sparsely connected hidden layer (with typically 1% connectivity). The hidden neurons’ connectivity and values are set and randomly assigned. Output neuron weights can be learned so that the network may (re)create specific temporal sequences. The focus of this network is that, while its behavior is non-linear, the only weights modified throughout training are those connecting hidden neurons to output neurons. As a result, the error function is quadratic and may be differentiated easily from a linear system. It is one of the most searched Artificial Intelligence terms.

Entity annotation

Labeling unstructured phrases with data so that a computer can comprehend them is known as information extraction. This might include labeling all persons, organizations, and locations in a document.

Entity extraction

An umbrella term that refers to the process of structuring data so that a computer may understand it. Humans or a machine learning algorithm may do entity extraction.

F Score

F Score is the average of recall and precision correct rates.

Facial recognition

Facial recognition is a computer program that can recognize or authenticate a person. One approach to do so is by comparing specific facial features in the picture with a face database. It is one of the most searched Artificial Intelligence terms. We have an interesting article called Hacking Tinder with Facial Recognition & NLP about this topic.

False negatives

An incorrect prediction where a model mistakenly assumes an input does not have a required result when one actually exists. (Actual Yes, Predicted No)

False positives

False-positive is an error in a model’s prediction of the presence of the desired result in input when it is not present (Actual No, Predicted Yes).

Forward chaining

A method in which a machine must go from an issue to solving it. The AI must evaluate many options to determine which hypotheses are relevant to the problem.

Game AI

Game AI is a type of AI that uses an algorithm to replace randomness in video games. It’s a computational behavior used by non-player characters to generate humanlike intelligence and reactive behaviors taken by the player in games. It is one of the most searched Artificial intelligence terms.

Ai Dictionary: Be A Native Speaker Of Artificial Intelligence
AI glossary: Artificial intelligence terms that you should know (2022)

Generative Adversarial Network (GAN)

A machine learning approach in which two neural networks compete to create new data with the same statistics as the training set. GANs, for example, are utilized in fashion, art, and marketing, but they are also becoming increasingly popular among malicious attackers to spread false news. It is one of the most searched Artificial Intelligence terms.

General AI

General AI is artificial intelligence (AI) that can successfully complete any intellectual activity that a human may. The term “General AI” is sometimes used interchangeably with “strong AI,” although they aren’t precisely the same thing.

Genetic algorithm

A genetic algorithm, or GA, is a method based on genetics that is used to solve challenging issues in an efficient and timely manner.

Hyperparameter

Though the words “hyperparameter” and “parameter” are sometimes used interchangeably, they have distinct meanings. Hyperparameters are parameters that impact how your model learns. They’re usually set manually outside of the model. It is one of the most searched Artificial Intelligence terms.

Image recognition

Image recognition is the capacity of software to recognize things, places, people, text, and activities in photographs.

Image segmentation

Image segmentation is the process of dividing a digital image into several parts/fragments to make the representation of an image simpler to analyze. Segmentation separates whole images into pixel categories, which may be labeled and classified. 

Segmentation is the act of putting a bounding box around the target object in a picture and drawing a pixel-by-pixel outline of that object, leaving the background intact.

ImageNet

An extensive visual database that is intended to be used in computer vision software development. ImageNet has hand-annotated over 14 million URLs of images to reveal what objects are shown. Bounding boxes are also provided in at least one million of the pictures. It is one of the most searched Artificial intelligence terms.

ImageNet Challenge

The first phase of the competition is a research project. Teams evaluate their algorithms on the supplied data set and compete to achieve higher accuracy on several visual recognition problems.

Junction tree algorithm

In machine learning, a technique for extracting marginalization in general graphs. In other words, it entails performing belief propagation on a modified graph known as a junction tree. Because the graph divides into several parts of data and variable nodes are the branches, it is referred to as a tree.

Knowledge-based System (KBS)

A knowledge-based system is an application that utilizes a knowledge base to tackle complex issues. The phrase is broad and refers to a variety of systems. The one common element that all knowledge-based systems have in common is the desire to express knowledge explicitly and a reasoning mechanism that enables it to generate new information. As a result, a knowledge-based system has two characteristics: a knowledge base and an inference engine. It is one of the most searched Artificial Intelligence terms.

Logic programming

A programming paradigm that is primarily based on formal logic. A set of statements in logical form, expressing facts and rules about a problem domain, is any program written in a logic programming language. Answer set programming (ASP), also known as solution-oriented programming (SOP), and Datalog are examples of significant logic programming languages.

Machine intelligence

Machine intelligence is an umbrella term for machine learning, deep learning, and conventional algorithms.

Machine Learning

Robots are still in the early days of AI, but it is a field that is moving forward quickly. Machine learning refers to the ability of computers to learn without being explicitly programmed. Computers “learn” via patterns they detect and adapt their behavior as a result. It is one of the most searched Artificial Intelligence terms. On the other hand, machine learning engineering is a great career opportunity nowadays.

Machine perception

The capacity for a system to acquire and comprehend information from the environment in the same manner that humans do with their senses. 

Machine Translation

In a nutshell, Machine translation is the application of NLP to language translation (human-to-human) in text and speech-based conversations.

MLOps

Taking a cutting-edge machine learning model into production web infrastructure is known as MLops or machine learning operations. Machine learning models are developed and tested in experimental environments. When an algorithm is deemed fit for widespread usage, data scientists, DevOps, and machine learning experts collaborate to move it to production systems through MLOps.

Misclassification rate

A model’s accuracy is measured by how frequently its predictions are incorrect.

Neural network

A neural network is a computer system that functions as the brain of a human. Although researchers are still attempting to construct a computer model of the human brain, current neural networks can already accomplish many things regarding speech, vision, and board game strategy. It is one of the most searched Artificial Intelligence terms. If you’d like to learn more about this topic, check out article: The History of Neural Networks.

Natural Language Generation (NLG)

Natural language generation (NLG) converts structured data into text or voice that humans can comprehend. NLG is concerned with what a machine writes or says as the conclusion of the communication process.

Natural Language Processing (NLP)

Natural language processing (NLP) is the capacity of computers to comprehend or extract meaning from natural human languages. NLP generally entails computer interpretation of text or speech recognition.

Natural Language Understanding (NLU)

Natural language understanding, as a part of natural language processing, aims to assist machines in understanding the intended meaning of language — taking into account its subtleties and grammatical mistakes.

Optical Character Recognition (OCR)

The process of converting text images (typed, handwritten, or printed) either electronically or manually into machine-encoded text is called OCR.

Overfitting

Overfitting is a term frequently used in AI. Overfitting is a sign of machine learning training in which the algorithm can only work on or identify specific examples from the training data. A functioning model should be able to generalize patterns seen in the data to tackle new instances. It is one of the most searched Artificial intelligence terms.

Pattern recognition

The difference between pattern recognition and machine learning is sometimes blurred, but the goal of this discipline is to discover patterns and trends in data.

Prescriptive analytics

Prescriptive analytics is a form of data analysis that uses technology to assist organizations in making better judgments by analyzing unprocessed data. Prescriptive analytics involves the analysis of factors such as possible situations or scenarios, accessible resources, past performance, and present performance. It offers recommendations for action or strategy on a time frame ranging from immediate to long-term. Businesses may use it to make decisions on any time scale, from immediate to long-term.

Predictive analytics

This type of analytics is designed to forecast what will happen in a specified time frame based on previous data and patterns. We prepared article about preventing bias in Predictive Analytics before. You might want to check that out.

Positive Predictive Value (PPV)

The PPV is similar to precision, but it considers prevalence. The positive predictive value is equivalent to precision when the classes are perfectly balanced (the prevalence is 50%).

Pruning

Pruning is the practice of limiting undesirable answers to a problem in an AI system using a search algorithm. The number of alternatives available to the AI system is reduced.

Quantum computing

Quantum computing is applied to compute using quantum-mechanical phenomena such as superposition and entanglement. This calculation may be done theoretically or in reality, and a quantum computer is used to perform it. It is one of the most searched Artificial intelligence terms. You can also learn about quantum machine learning in this article.

Recurrent Neural Network (RNN)

A recurrent neural network (RNN) is a sort of neural network that understands sequential data and patterns, generates outputs resulting from those computations, and learns.

Recommendation engines

A recommendation system (sometimes also known as a recommendation engine) is a form of information filtering that aims to determine the “rating” or “preference” that a user would give an item.

Reinforcement learning

Reinforcement learning is a type of machine learning that involves an algorithm that learns by interacting with its surroundings and is then penalized or rewarded depending on how it acts.

Robotic Process Automation (RPA)

AI-powered automation software such as RPA helps automate dull, time-consuming activities that people might have previously done. You can read our article about how RPA is getting advanced in The age of hyperautomation: Automate everything possible.

Ai Dictionary: Be A Native Speaker Of Artificial Intelligence
AI glossary: Artificial intelligence terms that you should know (2022)

ROC (Receiver Operating Characteristic) Curve

ROC Curve is a typical graph that summarizes the performance of a classifier over all possible levels. It’s made by plotting the True Positive Rate against the False Positive Rate as you change the threshold for putting observations into different categories on the y-axis.

Semantic annotation

A form of semantically based indexation is the labeling of distinct search queries or items to enhance a search engine’s relevance.

Search Query

A request is submitted to a search engine by a user to fulfill their information needs. A “visual search query” is defined as the result of a query comprised entirely of visual content.

Sentiment analysis

The study of the viewpoints and opinions within a piece of text is generally to determine the writer’s attitude toward something. It is one of the most searched Artificial intelligence terms.

Standard classification

A “Standard Classification” concept is to categorize an input into one of a predetermined number of categories. This is sometimes accomplished in machine learning by learning a function that maps an input to a score for each potential category.

Structured data

The term “structured data” refers to any information that is generated, collected, and analyzed in a linear, tabular, organized way. Business data produced within companies using standard applications would be an example of structured data.

Strong AI

The main objective of AI technology is to produce systems that are as intelligent and skilled as the human mind. It is one of the most searched Artificial intelligence terms.

Supervised learning

Supervised Machine Learning is a type of machine learning in which output data train the machine to produce the correct algorithms, such as a teacher guiding a student. It’s more prevalent than unsupervised learning.

Swarm behavior

From the perspective of the mathematical modeler, it is an emergent behavior based on basic rules that people follow without any central coordination.

Test data

The uncategorized data demonstrates that a machine learning model can complete its designated function.

TensorFlow

TensorFlow is an open-source machine learning library that’s used for various purposes, including neural networks. It was released under the Apache 2.0 open source license in 2015 and is utilized for research and production at Google. It is one of the most searched Artificial intelligence terms.

Training data

The term “training data” refers to all of the data used throughout training a machine learning algorithm and the particular dataset utilized to train rather than test.

Transfer learning

Transfer learning is a technique that combines existing knowledge with novel data and uses it to tackle new activities.

Turing test

The Turing test assesses the capacity of a machine to mimic human behavior. The evaluation consists of a real-world conversation between a person and another individual and a computer, in which the participants are assessed on their understanding. It is one of the most searched Artificial intelligence terms. Do you want to know which AI has come closest to passing the Turing test?

Unstructured data

The term “unstructured data” refers to data that may have multiple sources, such as online digital files, text documents, SMS messages, video clips, photos, voices, sensors, pings, etc. The majority of the data created today is unstructured data, which is one of the keys to AI’s growth.

Unsupervised learning

Unsupervised learning is a form of machine learning technique that concludes datasets with unannotated data. Cluster analysis is the most frequent type of unsupervised learning. You can also read about what’s The Difference Between Supervised and Unsupervised Learning.

Validation data

This data is structured similarly to training data, with input and labels, and it’s used to evaluate a recently trained model against new data and assess performance, with a particular emphasis on detecting overfitting.

Variance

The degree to which a machine learning model’s intended purpose varies as it is being educated. Despite their flexibility, models with considerable variance are vulnerable to overfitting and low predictive success since they are dependent on their training data.

Variation

Queries, also known as sentences or utterances, are artificial language processing that works in tandem with intents. The distinction is what someone might say to achieve a specific aim or goal. For example, if the aim is “pay by credit card,” the variation maybe “I’d want to pay by credit card, please.”

Validation Data Set

Validation Data Set is a sample of data used to evaluate the accuracy of a model fitted on the training set while tuning model hyperparameters. As skill on the validation dataset is incorporated into the model configuration, the evaluation becomes more biased.

Vision Processing Unit (VPU)

It’s a new type of microprocessor and an AI accelerator created to speed up machine vision activities.

Visual Recognition

Visual Recognition is the capacity of computer software to recognize items, locations, people, text, and activities in photographs and videos.

Weak AI

The term “weak AI” refers to a non-sentient computer system that operates under set parameters and focuses on a single activity or a small number of activities. Weak AI is the most prevalent sort of AI currently in use. It is one of the most searched Artificial intelligence terms.

Web Crawler (Spider)

An automated Web surfer goes from one site to another, usually seeking to index the Internet for a search engine.

Web Scraper

A bot or web crawler is used to automate operations. It’s a type of replication in which data from the Internet is collected and copied into a central local database or spreadsheet for future retrieval or analysis.


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