In this era where automation is ruling the world, artificial intelligence (AI) has gained immense popularity due to its wide range of applications. With the advent of new technologies every day, the field of AI is growing enormously. Keeping in mind the growing demand for AI professionals, individuals undergo AI training to enhance their skill set. However, before taking any course or training, it’s imperative to gain an insight into the key terminologies used in AI. This will furthermore help you in understanding the AI concepts better and faster.

Learn more about the most common AI terms you must know!

Algorithms: Algorithms are a set of automated instructions that a machine can follow to perform a specific task.
Artificial Intelligence: It’s the capacity of a machine to make decisions and act in a way mimicking human intelligence.
Artificial Neural Networks: It’s a learning model designed to perform like a human brain to solve complex problems that a conventional computer can’t resolve.
Autonomous: A machine is called autonomous if it can accomplish all the tasks without any human intervention.
Backward Chaining: As the name implies, backward chaining refers to a process when a model starts from the goal, moves backward with the help of interference rules, and finds out data that may help meet the goal.
Bias: It refers to the assumptions made by a model to ease out the learning process of performing a specific task. In most cases, models are made to work with low bias since these assumptions can negatively impact the outcomes.
Big Data: It is a big pool of data sets that are highly complex to be processed by conventional data processing tools.
Bounding Box: It’s an imaginary rectangular box used in digital image processing for recognizing a target object with the help of coordinates of the border enclosing the image.
Chatbot: Chatbots or chat robots are developed to promote communication via text, voice messages, or mimicking human communication.
Cognitive Computing: It is a computerized model simulating the human thought process. It encompasses self-learning via data mining, natural language processing, and pattern recognition.
Computational Learning Theory: It’s a sub-field of AI dealing with the creation and analysis of machine learning algorithms.
Corpus: It refers to a written or verbal dataset utilized to train a machine to accomplish linguistic tasks.
Data Mining: It’s the process of analyzing an extensive set of data to find out new patterns that may aid in finding valuable insights to promote the enhancement of the model.
Data Science: It’s a multidisciplinary field collaborating with Statistics, IT, and Computer Science for finding solutions to complex problems by using data.
Deep Learning: It’s an AI function that mimics how the human brain functions to process data via artificial neural networks made up of layers of information.
Decision Tree: It’s a supervised machine learning algorithm based on a tree and branch model. It’s primarily used for mapping decisions and their probable outcomes.
Entity Annotation: It refers to the process of locating and labeling unstructured sentences with information that a machine can easily interpret.
Entity Extraction: It’s a process that allows machines to automatically identify or extract entities from an unstructured text and categorize them as per the predefined groups.
Logic Programming: A programming method wherein logic is used to represent knowledge and interference is used to manipulate it. This is used for teaching the machines the process of reasoning.
Linguistic Annotation: It refers to tagging every sentence of a language dataset present in the text or verbal form.
Machine Intelligence: It’s a branch of AI that allows machines to self-learn from available datasets and improve automatically without any human interference.
Machine Learning: It’s a subfield of AI focusing on developing algorithms that will guide the machines to interpret data and respond to the data changes without any human intervention.
Machine Perception: It’s the ability of machines to sense and interpret data in a fashion similar to humans.
Natural Language Generation: It’s a type of AI leading to the generation of natural language output from a structured dataset.
Natural Language Processing: It’s a subfield of AI that examines how machines comprehend and interpret human language and also allows humans to interact with machines using natural language.
Recurrent Neural Network: It’s a kind of neural network aiding in interpreting sequential information, recognizing patterns, and generating outputs based on past calculations.
Supervised Learning: It’s a kind of machine learning wherein the output data is mapped with the input data for training the machine to develop new algorithms as per the requirement.
Transfer Learning: It’s a learning method involving developing deep learning models and training neural networks while solving a problem and using the acquired knowledge to solve similar problems in the future.
Turing Test: It’s an inquiry method used in AI for determining if a machine can simulate human intelligence, precisely to find out whether its thinking process is undistinguished from humans.
Unsupervised Learning: It refers to the ability of machines to interpret unlabelled datasets and draw conclusions using algorithms. And with the help of the conclusions drawn, machines enhance their learning further.
Validation of Data: It’s one of the essential processes of AI used for assuring the quality of input data before it’s processed further for model development and drawing inferences.
Variance: It primarily refers to the error arising due to minute fluctuations in the training set.
Weak AI: Also termed narrow AI, weak AI refers to algorithms focussing only on one task. It has a more comprehensive application and is one of the most commonly used AI wherein an algorithm cannot perform functions outside its unique skill set.

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