AI Fundamentals: Foundations for Understanding AI

Responsible AI is the practice of building and running systems whose decisions you could defend to the person on the receiving end of them, covering fairness, transparency, privacy and accountability.

Machine learning is the branch of computing in which a program works out its rule from examples instead of being handed that rule by a programmer, and is judged on how well the rule holds on cases it was never shown.

A neural network is a stack of layers of very simple arithmetic units whose connection strengths are adjusted by training until the whole arrangement turns the inputs you have into the outputs you want.

Hypothesis testing is the statistical procedure for deciding whether an observed difference, between two groups, two periods or a sample and a claimed value, is larger than chance variation would produce if there were no real difference, by computing how probable a result at least that extreme would be under that assumption.

Data analysis is the practice of interrogating data to answer a specific question, and of establishing how much weight the answer can bear.

Data wrangling is the work of repairing and reshaping real records, and of deciding what their gaps and inconsistencies mean, until a table can be trusted with the question being asked of it.