There’s a lot of marketing buzz and technical spin on artificial intelligence, machine learning, and deep learning. Most of what’s out there is either too fluffy or too mathy, either too general or too focused on specific applications, too disconnected from business outcomes and metrics, and too undirected.
This article provides an overview of these related technologies by:
Defining AI, machine learning, and deep learning, explaining the differences from traditional approaches, describing when to use them, and noting their advantages and disadvantages.
Explaining how they complement business frameworks and enable business outcomes and metrics.
Describing common types of machine learning and deep learning model training, algorithms, architectures, performance assessments, and obstacles to good performance.
Providing examples of machine learning models and algorithms at work.
Presenting a potential framework for AI implementation for business outcomes.