When It Comes to Building Trusted AI Outcomes, Knowledge Must Precede Speed

B. Shimmin

Summary Bullets:

• Looking to build good artificiaI intelligence (AI)? Don’t let the speed and availability of open source frameworks, modules, libraries, and languages lull you into a false sense of confidence.

• Good AI needs to start with good data and good data needs to be ingested, registered, described, validated, and processed well before it reaches the ready hands of AI practitioners.

These are heady times. Enterprises have at their disposal both the raw materials and the necessary tools to achieve great things with AI, be that something grandiose as self-driving cars or unassuming as a fraud detection algorithm. The trouble with an abundance of materials (e.g., data) and tools (e.g., open source machine learning models), however, is speed. Speed kills, as they say.

For AI practitioners, this means learning to run before learning to walk by hastily automating decisions via AI models that are built on unsound data. With a few simple open source frameworks, modules, libraries, and languages, seemingly useful but ultimately erroneous predictions and conclusions can be readily drawn from any old data set in very short order. What’s the answer? More or better tools? No. As with most human problems, good old human knowhow and understanding are necessary. And that begins with data.