Artificial Intelligence

Chapter 5. The Simplest Way to Build an Artificial Intelligence

I will avoid the details of how to build a proof-of-concept from a particular algorithm. That kind of information is already abundant. If you do a web search on the kind of algorithm you want to tinker with and select a tool, you’ll need only to follow the tutorial. Instead, we cover the more difficult-to-find best practices for building a process for running AI that has the best chance for a long life and widespread use.

Treat Your AI as an Experiment

The simplest way to build an AI application is to follow the scientific method: form a hypothesis; model the problem domain; build, train, and test the algorithm; predict the business impact; and monitor the results. Decide on the minimum functionality you need to deliver business value. Gather just enough data to model the business domain and run an experiment.

The simplest way to decide what to build is to choose the minimum set of technologies needed to tell the target AI data story. In every story, the AI application will need to assess input, make an inference, and emit a response. We can organize the universe of AI technologies based on the assess-infer-respond taxonomy and choose an appropriate technology from each category. To design an AI that helps customer service agents serve live wireless customers, you may choose speech recognition to monitor the call, predictive inference to determine the customer’s intent, and automation controls to adjust services ...