Building deep learning systems at present is part science, part art, and a whole lot of arcana. Rather than focusing on the concepts you want the system to learn and how those can be taught, you often find yourself dealing with low-level details like network topology and hyperparameters.

Databases solved this problem for data by allowing users to program at a higher level of abstraction. Databases eschew low-level implementation details and instead build a model of the information (the schema) using a high-level declarative programming language (such as SQL). The database server actualizes this model and manages its usage with real data. Similarly, for artificial intelligence, one can build a model for conceptual understanding (the mental model) using a high-level declarative programming language (such as Inkling). A runtime server can then be used to actualize this model and manage its usage with real data.

Keen Browne explains how Bonsai’s platform enables every developer to add intelligence to their software or hardware, regardless of AI expertise. Bonsai’s suite of tools—a new programming language, AI engine, and cloud service—abstracts away the lowest-level details of programming AI, allowing developers to focus on concepts they want a system to learn and how those concepts can be taught. Keen explores the underpinnings of this technique, details the Inkling programming language, and demonstrates how to build, debug, and iteratively refine models. To make things concrete and fun, Keen demonstrates how to create a system to play the video game Breakout using deep learning (but requiring codifying only the high-level concepts relevant for intelligent play) and offers a curriculum for how to teach this system.