Overview of the Glasswing AI Palette
AI, in its entirety, encompasses a vast array of learning methods and models, including ML and Deep Learning, which are two key areas covered by the Glasswing AI Palette, but also extends to search and optimization, decision-making mechanisms, such as Markov Decision Processes, knowledge representation and reasoning, and even game theory. The Glasswing AI Palette, however, focuses on a very important subset of this broad spectrum: ML and Deep Learning. This is particularly relevant as Deep Learning has shown rapid advancements and increasing ability to encroach upon other AI domains.
If we consider AI—the ability of a system to mimic humans to complete tasks—as the what, then ML is the how. The Glasswing AI Palette explicitly focuses on ML as a proxy for delivering AI-native algorithms and software. Accordingly, it is divided into two halves, one for each of the major forms of ML: Classical ML and Deep Learning.
Whereas Classical ML models are better suited to analyze simple datasets and require significant human interaction to produce results, Deep Learning models leverage multi-layered neural networks to autonomously identify patterns and features within data, which makes them significantly more adept at handling large, intricate data sets with minimal human input and allow for much more model flexibility.
In this walkthrough, we will take a closer look at both Classical ML and Deep Learning, as well as the Ensemble Methods that combine their capabilities. Then, we will describe the various Degrees of Supervision employed for training based on use cases and available data sets. Finally, we will describe the distinct types of data required to train Classical ML and Deep Learning models and highlight common use cases for each.