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The Necessary (and often Missing) “U” in the DIKUW Pyramid [AI Today Podcast]


One of the most vexing problems in even today’s highly capable intelligence systems is for systems to actually understand what they are generating as output. Repeating a pattern, even a sophisticated pattern, while showing good knowledge of the pattern, doesn’t really help if the system doesn’t really understand what it is generating.  In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer discuss this DIKUW pyramid and why the “U” understanding level is a critical, but often left out, layer of the pyramid.

What does the DIK(U)W pyramid represent?

Data is the heart of AI. So it makes sense that data is at the base of the pyramid. In order to get value from data, which is what the DIKUW pyramid is meant to explain, you can’t just jump from knowledge of patterns to wisdom. If you truly want to get machines to become more intelligent, you need to bridge that gap with at least some understanding of those patterns. Below is a visual presentation of the DIKUW pyramid.

In this episode we explain what each level is, and how you move up to the next level. Machine learning, which is what many of our current AI applications are today, is at the “K” knowledge level. However, we need more than machine learning – we need machine reasoning. Machine reason is the concept of giving machines the power to make connections between facts, observations, and all the magical things that we can train machines to do with machine learning. And, we explain why we aren’t yet here. Indeed, we’re rapidly facing the reality that we’re going to soon hit the wall on the current edge of capabilities with machine learning-focused AI. This is where the understanding layer comes into play.

The current wave of interest and investment in AI doesn’t show any signs of slowing or stopping any time soon, but it’s inevitable it will slow at some point for one simple reason: we still don’t understand intelligence and how it works.

Show Notes:

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