The explosion in the development of artificial intelligence (AI) over the past ten years has been driven by a specific discipline of AI called Machine Learning. In a nutshell, machines can develop intelligence by learning. How do they learn? Algorithms are written to look for patterns in large amounts of data. These patterns can then be used to predict behaviour or outcomes.
One of the early examples of this was an experiment by Google. They fed, frame-by-frame, 10 million cat videos to an algorithm running inside a software application. These videos are what we call training data. Once their algorithm was finished learning it could identify, with a high level of accuracy, a video that included a cat.
Machine Learning makes it possible to implement automated solutions for problems that would otherwise be too difficult or cumbersome to solve. As the data changes, the models are updated, and the machines continue to learn.
At the heart of any Machine Learning program is problem solving. We identify problems that need solutions. For example, non-profit fundraisers try to increase the retention of donors. To frame this as a Machine Learning question, you could ask: “What is the most effective way to communicate with donors to keep them giving?”. We have come up with a lot of challenges that we know non-profits would love to tackle more efficiently and effectively. Our data science models provide the automated solutions and better outcomes.
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