Beginner's Guide to Commonly Used Terms in AI & Intelligent Automation
- BY Lucy Hu
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You often hear terms thrown around when discussing intelligent automation, but what do they really mean? Here, I got you.
Citizen Developer
Business users with varied coding experience who build business-critical applications using no-code or low-code technology
Computer Vision
The capability of software to interpret the world visually through cameras, video, and images
Deep Learning
A subset of machine-learning that teaches computers to imitate the way humans think and learn
Exceptions
A value not confidently captured by the model
F1-Score
A mean of the precision and recall
Field
A value defined by the customer that they want to extract
GPU (Graphic Processing Unit)
A computer chip with high computational power designed to process large amounts of data. Recommended for AI software.
Human-in-the-Loop
Having a human involved in the model training process (i.e. enabling them to make changes/corrections to the training data to enhance model accuracy)
Machine Learning
How we “teach” a computer model to make predictions and draw conclusions from data
Natural Language Processing (NLP)
The capability for a computer to interpret written or spoken language
Optical Character Recognition (OCR)
Converting handwritten, typed, or scanned text into machine-readable text
Precision
The percentage of predicted values that match the actual values
Recall
The percentage of correct fields identified by the model
Semi-Structured Data
Data that is partially defined and searchable
Semi-supervised learning
A training method where the training set is composed of both “known” (labelled) and “unknown” (unlabelled) data
Straight-through processing
End-to-end automation of a process; ideally, requires no human intervention.
Structured Data
Data that is clearly defined, searchable, and can be organized into a clear, tabular format
Supervised learning
A training method using “known” (labelled) data to train a model; the values we train the model to predict are pre-defined, or based off known inputs in the data
Transfer Learning
A machine-learning technique that focuses on storing knowledge gained while solving one problem and applying it to a different, but related, problem
Unsupervised learning
A training method using “unknown” (unlabelled) data to train a model; the model is analyzing and organizing the data into meaningful groups based on self-similar characteristics
Unstructured Data
Data that comes in all forms and cannot be easily processed by machines
Business users with varied coding experience who build business-critical applications using no-code or low-code technology
Computer Vision
The capability of software to interpret the world visually through cameras, video, and images
Deep Learning
A subset of machine-learning that teaches computers to imitate the way humans think and learn
Exceptions
A value not confidently captured by the model
F1-Score
A mean of the precision and recall
Field
A value defined by the customer that they want to extract
GPU (Graphic Processing Unit)
A computer chip with high computational power designed to process large amounts of data. Recommended for AI software.
Human-in-the-Loop
Having a human involved in the model training process (i.e. enabling them to make changes/corrections to the training data to enhance model accuracy)
Machine Learning
How we “teach” a computer model to make predictions and draw conclusions from data
Natural Language Processing (NLP)
The capability for a computer to interpret written or spoken language
Optical Character Recognition (OCR)
Converting handwritten, typed, or scanned text into machine-readable text
Precision
The percentage of predicted values that match the actual values
Recall
The percentage of correct fields identified by the model
Semi-Structured Data
Data that is partially defined and searchable
Semi-supervised learning
A training method where the training set is composed of both “known” (labelled) and “unknown” (unlabelled) data
Straight-through processing
End-to-end automation of a process; ideally, requires no human intervention.
Structured Data
Data that is clearly defined, searchable, and can be organized into a clear, tabular format
Supervised learning
A training method using “known” (labelled) data to train a model; the values we train the model to predict are pre-defined, or based off known inputs in the data
Transfer Learning
A machine-learning technique that focuses on storing knowledge gained while solving one problem and applying it to a different, but related, problem
Unsupervised learning
A training method using “unknown” (unlabelled) data to train a model; the model is analyzing and organizing the data into meaningful groups based on self-similar characteristics
Unstructured Data
Data that comes in all forms and cannot be easily processed by machines