Data: Individual facts, statistics, or items of information
Collection of data is a collection of facts
Quantitative Variables: describes measurable characteristics, such as height, weight, temperature
These are numbers
Qualitative Variables: describes qualities or characteristics, such as country of origin, gender, name, or hair color
These are NOT numeric
Two subtypes of Qualitative variables
Nominal Variables: Characteristics that CANNOT be ranked
E.g., types of fruit
Ordinal Variables: Can be ranked
E.g., Case Priority of high, medium, or low
Machine Learning: How computers can learn new things without being explicitly programmed how to do so
Not specific algorithms
Structured Data: Organized and formatted in a specific way, like in database tables or spreadsheets
Unstructured Data: Not formatted in any specific way
Supervised Learning: Model learns from explicit examples, usually prepared and reviewed by a human
Unsupervised Learning: Process of letting AI find hidden patterns in your data without any guidance
Neural Networks: computer system modeled on the human brain and nervous system.
Utilizes interconnected nodes with multiple layers
Natural Language Understanding (NLU) : Systems that handle communication between people and machines
Processes data from unstructured to structured
Natural Language Processing (NLP): Describes a machine’s ability to understand what humans mean when they speak as they naturally would to another human
Is distinct from NLU
Natural Language Generation (NLG): Data processed from structured to unstructured
Named Entity Recognition (NER): labels sequences of words and picks out the important things like names, dates, and times
Involves breaking apart a sentence into segments that a computer can understand and respond to quickly
Deep Learning: Artificial neural networks being developed between data points in a large database
Principles of Inclusive AI Design
Recognize Exclusion: When we solve problems using our own biases, exclusion happens
We need to recognize exclusion before we can address it
Learn from Diversity: Humans are the real experts in adapting to diversity
Solve for One, Extend to Many: Focus on what’s universally important to all humans