When Everything in AI is Unique, How Do You Solve for It?

  1 m, 1 s

We all rely on statistical analysis in many ways, from understanding big-concept issues in the news to understanding specific, localized issues in our businesses. Of course, not many people have the time or skill set to dig into the raw data, so instead we look for averages to tell the story. Yet, as our data analytics needs grow more and more complex, focusing only on averages can actually cause us to miss the bigger picture.

In a recent guest post on Dataversity.net, I talk about the problems of how relying on averages can fail us and how AI can help us past this growing problem. When you find yourself swimming in variables, solving the problem with a single solution becomes near impossible. In some cases, it makes as much sense to craft a solution based on the “average” as it does to craft a strategy around a single ancedotal experience.

I explain things in more detail in the column, using an example about what happened when the U.S. Air Force tried to design a cockpit that works for “average” pilots. It didn’t work. To find out why, and how they fixed it, head on over to Dataversity.net and give the essay a read.

Categories: Artificial Intelligence

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