AI Exam 0 - General

AI Practitioner Exam Prep

General & Math Terms

General Abbreviations

APIs = Application Programming Interfaces

BI = Business Intelligence

dbs = databases

docs = documents

ex = example

GPU = Graphics Processing Unit

IDE = Integrated Development Environment

ISV = Independent Software Vendor

NIST = National Institute of Standards and Technology

OWASP = Open Web Application Security Project

PII = Personally Identifiable Info

SME = Subject matter expert.

General Terms

Bias = data missing important feature due to not sampling enough or correct data. Types are selection/sampling bias (unrepresentative data), interaction/participant bias (user do not want to tell so prejudiced), reporting/measurement bias (over-represents one group), and confirmation/automation bias (reinforcing existing stereotypes, when output is over trusted), and historical/societal bias (from data given in a historical setting tracking data that we feel important).

Decision trees = CASE statement algorithm. Good for transparency.

Explainability = explains WHY after we get the results. Black box. Good with debugging and troubleshooting. 

Fairness - ensures equitable outcomes across all demographic groups.

Governance - ensures max society benefit with min risk

Interpretability\Transparency = explains HOW it might make decisions, before it does anything. White box. Good with accountability and builds trust.

Latency = response time (after sent request).  Real-time applications require low latency.

Nondeterminism = you can run it with the same input data, and it produces different outputs.

Overfitting = when works on the training data but not on the actual evaluation data. Because the model is memorizing the data it has seen and fails to generalize to unseen examples.

Transparency - provides clear explanations of how decisions are made.

Underfitting = when works on the training data but not on the actual evaluation data. Because the model is not capturing all the features of the data and fails to generalize to unseen examples.

Variance = sensitivity to noise or overfitting.

Veracity\Robustness = operates well despite uncertainty.