A glossary of terms is always a work in progress, given how technology, methods, and techniques evolve.
This is more true for the fields of Artificial Intelligence, Machine Learning, and Deep Learning than others, which is why our developers have put together a list of more commonly used terms to help you tell your Algorithms from your Active Learning, and Selection Bias from Sentiment Analysis.
A field of computer engineering that focuses on creating systems capable of gathering data and making decisions and/or solving problems.
This is a training approach in which an algorithm chooses some of the data it learns from. An active learning algorithm selectively seeks the particular range of examples it needs for learning, rather than blindly seeking a diverse range of labeled examples. Active learning is particularly valuable when labeled examples are scarce.
Math formulas and/or programming commands that inform non-intelligent computers how to solve problems using AI.
Artificial Neural Network (ANN)
A model used in AI based on the human brain that consists of neural layers.
Bayesian Neural Network
This is a probabilistic neural network that relies on Bayes’ Theorem to calculate uncertainties in weights and predictions. It predicts a distribution of values and can be useful when it is important to quantify uncertainty.
This refers to converting a feature into multiple binary features called buckets or bins, based on value range.
This is a type of Machine Learning model for distinguishing among two or more discrete classes.
This refers to the grouping of related examples, particularly during unsupervised learning. A human can optionally supply meaning to each cluster once all the examples are grouped.
This is a Machine Learning method that consists of a many-layered Artificial Neural Network and uses many layers of nonlinear processing to extract features from the data before transforming it into different levels of abstraction.
This refers to how an understanding of data is obtained by considering samples, measurement, and visualization. It is crucial in understanding experiments and debugging problems with the system.
This is when the range and number of training examples are artificially boosted by transforming existing examples to create additional examples. It can produce many variants of the original example, possibly yielding enough labeled data to enable training.
A collection of examples.
A model represented as a sequence of branching statements.
A model trained online in a continuously updating fashion.
Common-sense rules based on experience.
The first layer that receives the input data in a neural network.
A set of neurons in a neural network that processes a set of input features, or the output of those neurons.
A number that you care about, that may or may not be directly optimized in a Machine Learning system.
A subset of AI that uses algorithms to learn from identified patterns in data, then adjusts actions accordingly, without explicit programming.
A network designed to be similar to the human nervous system and brain that gives AI the ability to solve complex problems.
Natural Language Processing
AI trained to interpret human communication.
A metric that an algorithm is trying to optimize.
A method of teaching a machine that involves running scenarios and reporting results, then using the feedback to achieve better results.
A learning method that provides the machine with the correct answer ahead of time.
This refers to errors in conclusions drawn from sampled data due to a process that generates systematic differences between samples observed in the data and those not observed.
The use of statistical or Machine Learning algorithms to determine a group’s overall attitude, be it positive or negative, toward a service, product or organization.
Feeding the machine data and allowing it to find whatever patterns it is able to.
Developed by Alan Turing in 1950, this tests a machine’s ability to behave in an intelligent manner that is indistinguishable from human behavior.
For more information on any or all of these terms, or how Artificial Intelligence, Machine Learning, and Deep Learning can help your business, talk to us about our award-winning CRM solutions and Intelligent Customer Management platform today.speaker_notes Post Comments