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Why Scrum is the Best Way to Create AI

There is no broad agreement on what constitutes artificial intelligence (AI). Still, this much is clear, we have already entered the age of AI and business. Just ask Amazon, Google, Apple, Facebook, really any company that is looking to replace algorithms with something much more complex, responsive, even predictive of customer and market wants and demands.

As more and more organizations look to machine learning to gain a competitive advantage, they’re also looking at the best ways for their AI labs to operate. And no, we’re not just talking about their technology stack.  Scrum is the best way to create AI.

In fact, machine learning systems may be the optimal platform to achieve all the benefits of Scrum.

 

Scrum Thrives In Complex Systems

Scrum operates best in complex environments where small changes to the system can create surprising and unknown behaviors. Just such environments are at the heart of any machine learning project.

A small problem with the code or even a poorly tagged data set can cause the AI to learn a bad pattern, or go entirely in the wrong direction.

So it's actually extremely important, when both computation time and cost are high, to identify mistakes early because even a small problem left unaddressed may compound and invalidate months of lab time. That is a very expensive problem to have.

Machine Learning and Rapid Inspect and Adapt Cycles

Whether lab teams are using Keras, Tensorflow, CUDA, or something else, they all face a similar set of problems not addressed by their technology stack. Are they prioritizing development? Are they testing their AI and delivering updates continuously. In short, are they and their AI continuously delivering, learning and improving?

These are the same set of issues regular software teams face. And Scrum has been empirically proven to address these issues.

In fact, a machine learning lab may be the optimal place to gain all of the boosted productivity associated with Scrum teams.

AI projects inherently have a wide range of test set data which the lab can observe in real time. Scrum’s focus on rapid inspect and adapt cycles means AI Scrum teams can observe how their AI is progressing. More importantly, because AI Scrum teams can inspect, iterate and adapt extremely quickly and at a regular cadence, AI Scrum teams quickly find errors, make changes and rapidly adapt their machine learning system to ensure it is always improving.

Scrum and AI was the subject a conversation between Joe Justice, President of Scrum@Hardware and Alex Sutherland, Scrum Inc.’s Chief Technology Officer. They were interviewed by Scrum Inc.’s Tom Bullock.

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