Computer Vision

Use of AI to examine and interpret images to define or recognize them like the way humans see.

Confirmation Bias

Confirmation bias is a human tendency to find answers that match preconceived beliefs. It may manifest through selective gathering of evidence that supports desired conclusions and/or by interpreting results in ways that reinforce beliefs.

Confirmation bias can enter data analysis through unbalanced selection of the data to be analyzed and/or by filtering the resulting analyses in ways that support preconceived notions.

Confusion Matrix

A Confusion Matrix, if calculated, is a table depicting performance of prediction models on false positives, false negatives, true positives, and true negatives. It is so named because it shows how often the model confuses the two labels. The matrix is generated by cross-validation – comparing predictions against a benchmark hold-out of data.

Data Science

An interdisciplinary field encompassing scientific processes and systems that extract knowledge or insights from data in various forms, either structured or unstructured. It is an extension of data analysis fields such as statistics, machine learning, data mining, and predictive analytics.

Date Factoring

Date factoring is a feature engineering technique that splits date-time data into its component parts. For instance, a date-time field with a format of MM-DD-YYY HH:SS can be separated into variables of Month, Date, Year, Time, Day of Month, Day of Week, and Day of Year. Pre-processing data sets to add columns for these individual variables may add predictive value when building models.

Where the sequence in which events occur is important, regression models that forecast values based solely on discrete date/time factors may not provide useful predictions. Sales forecasting or market projections are classic examples. See Time-series Forecasting.

Decision Tree

A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart.

Deep Learning

Deep Learning is a machine learning technique where the system leans by example, similar to human learning. Deep Learning is often used where the size and complexity of data sets overwhelm more structured techniques. Ability for deep learners to extract features from the data automatically from unstructured data enables use for applications such as image and voice processing.

The “deep” refers to the algorithms’ passing data from one layer of analysis to another – up to hundreds of layers. Each layer adds progressive refinement to classifications.

Embodied AI

Robots that are equipped with AI functionality.

Ethical AI

Ethical AI uses artificial intelligence to enhance the human condition by performing tasks that are menial or impractically slow to accomplish manually. Key factors in maintaining ethical AI include:

  • Explainability – the algorithms used can be understood by humans, with calculations that can be explained in plain language. This is essential to verifying that AI is serving its intended purpose.
  • Boundedness – Ethical AI is set to operate within pre-determined boundaries, and does not have the ability to create its own pathways to exploring or learning unintended information.
  • Purpose – Ethical AI is modeled to produce only specific answers to well-defined problems.