3 Reasons Evolutionary Machine Learning Drives Digital Transformation

Bill Porto | August 2, 2018

Machine learning systems are among the most useful technologies on the market today. This is especially true for brands seeking to optimize their digital transformation; a well-deployed machine learning solution can dramatically streamline operations and allow employees to focus more intently on tasks that require deeper thinking. Many brands recognize this value of machine learning, which is why worldwide spending on cognitive and artificial intelligence (AI) systems is predicted to reach $19.1 billion in 2018, an increase of 54.2 percent over spending in 2017.

All machine learning algorithms are valuable at various scales, but the technologies called “evolutionary machine learning” are demonstrably more powerful and efficient. Evolutionary machine learning borrows part of its definition from the biological sciences: machine learning that optimizes using simulated evolution and applies continual adaptation in these efforts. In essence, evolutionary machine learning leverages the idea of “survival of the fittest,” but applies it to optimizing complex tasks (e.g., data modeling and business processes) instead of biological organisms. Evolutionary machine learning allows marketers to test multiple kinds of different models and algorithms in order to find the one that fits best for each individual context. There are three major reasons that this form of machine learning is so powerful for a brand’s digital transformation efforts. These include:

  • Getting the right data model for every possible situation – Anyone who tells marketers that one type of model or one type of algorithm is the best for every situation is trying to sell snake oil. The reality is that no single algorithm and no single model are correct for every possible situation, much less every data set. The ability of evolutionary machine learning to efficiently search for, refine, and adapt data models over time is enormously important for digital transformation. What worked one day may not work the next, so the ability to adaptively tweak models for changing situations is key to long-term success.
  • Continual adaptation in decision making – Evolutionary machine learning allows brands to test and retest models faster and more efficiently than any human possibly can. Machine learning algorithms run 24 hours a day, seven days a week and, as a result, can test vastly more assumptions about which models (and parameterizations thereof) best fit data. This continual adaptation is enormously powerful; brands with this capability can be confident that they will always have the best possible data model in hand to make the best possible decision. The best part is that the testing and adaptation never has to stop. Brands can run evolutionary machine learning algorithms in perpetuity.
  • Applied optimization for business processes – The Greek philosopher Heraclitus once wrote that “everything changes and nothing stands still.” The ability of evolutionary machine learning to continually adapt over time means that brands gain the operational flexibility to adapt to changing business environments. More than that, they can apply the same evolutionary search technique to optimizing their customer engagement and broader business processes. This translates into greater accuracy as well, with 69 percent of CIOs in one recent study saying that machines will make more accurate decisions than humans.

Evolutionary machine learning is hugely valuable for brands seeking to effect a digital transformation. The continual adaptation and applied optimization of an evolutionary approach to machine learning means that brands can be confident in their models and their organizational flexibility over the long term. With evolutionary machine learning in place, brands of any size can and will be ready for almost any business shift.


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Bill Porto
Bill Porto

Bill Porto is an expert on computational intelligence and the application of it to real-world problems across a variety of domains. As a senior engineering analyst at Redpoint Global, Bill leverages this expertise to apply machine learning techniques to complex business problems for the company’s clients.