A shared vocabulary for developing AI

Original article was published on Artificial Intelligence on Medium

Active Learning (see Supervised Learning)
– a machine learning system that chooses examples to be labeled

“AI is the science of making machines capable of performing tasks that would require intelligence if done by humans” Marvin Minsky

“AI is the science and engineering of making machines do tasks they have never seen and have not been prepared for beforehand” Hernandez-Orallo

Artificial Intelligence (see Intelligence)
– intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans
– examples: an elevator, AlphaGo, language translation

Big Data
– what data science was called in the 2000’s
– handling data that is too large for Excel

Business Case (see Use Case)
– financially sustainable application of technology
– balance of value versus cost
– making hitting nails valuable enough to justify buying a hammer
– making a hammer cheap enough to justify hitting nails

– tabular — rows & columns, 2 dimensional (rows & columns)
– images — spatial structure (height, width, channels)
– text — grammatical & syntactic structure
– time-series — temporal structure

Data Science
– using data to make predictions that drive business decisions

Deep Learning
– using multi-layered neural networks for prediction or generation

– the ability to handle situations (or tasks) that differ from previously encountered situations
– acceptable performance of a prediction on a holdout test set

“Intelligence measures an agent’s ability to achieve goals in a wide range of environments.” Shane Legg & Marcus Hutter

“The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty.” François Chollet

“In order to be a perfect and beautiful computing machine, it is not requisite to know what arithmetic is.” Alan Turing

Intelligence (see Artificial Intelligence)
– learning
– problem-solving
– prediction, generation & explainability

Machine Learning (see Supervised, Unsupervised & Reinforcement Learning)
– programming without being explicitly programmed
– trial and error learning
– pattern recognition
– dimensionality reduction
– the part of A.I. that is working

Reinforcement Learning
– a type of machine learning
– learning to maximize expected reward
– learning to take actions
– learning to make decisions

Semi-Supervised Learning (see Supervised Learning)
– a mix between supervised and unsupervised machine learning
– learning from a small amount of labeled data with a large amount of unlabeled data

Supervised Learning
– a type of machine learning
– learning from an observed pattern
– prediction

Unsupervised Learning
– a type of machine learning
– learning from an observed pattern
– generation

Use Case (see Business Case)
– application of technology
– using a hammer to hit a nail

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