Use of AI to examine and interpret images to define or recognize them like the way humans see.
- Area Under the Curve or AUC
- Artificial Intelligence (AI)
- Artificial Neural Networks (ANNs)
- Automated Machine Learning (AutoML)
- Big Data
- Black Box Algorithms
- Computer Vision
- Confirmation Bias
- Confusion Matrix
- Data Science
- Date Factoring
- Decision Tree
- Deep Learning
- Embodied AI
- Ethical AI
- Explainable AI (XAI)
- Feature Engineering
- Few-shot Learning
- Generative Adversarial Networks (GANs)
- Linear Algebra
- Machine Learning
- Max F1
- Mean Absolute Error or MAE
- Mean Per Class Error
- Mean Square Error or MSE
- Natural Language Processing
- Pragmatic AI
- Predictive Analytics
- Reinforcement Learning
- Residual Deviance
- Root Mean Square Error or RMSE
- Root Mean Square Logarithmic Error or RMSLE
- Supervised Learning
- Time Series Forecasting
- Transfer Learning
- Unsupervised Learning
- Variable Importance
- Weak AI
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.
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.
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 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.
A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart.
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.
Robots that are equipped with AI functionality.
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.
AI that reveals to human users how it arrived at its conclusions.