Machine Learning for Predicting The Unknown

An interview with data scientist, Courtenay Cotton.

“Where Is the Future?” is a series of interviews with industry leaders considering the potential and complexity of technology on the horizon.

Two summers ago, Courtenay Cotton led a workshop on machine learning that I attended with a New York–based group called the Women and Surveillance Initiative. It was a welcome introduction to the subject and a rare opportunity to cut through the hype to understand both the value of machine learning and the complications of this field of research. In our recent interview, Cotton, who now works as lead data scientist at n-Join, once again offered her clear thinking on machine learning and where it is headed.

What kind of problems is machine learning designed to solve? And are there times when machine learning isn’t the right method for prediction?

Courtenay Cotton: Machine learning is used for prediction. It could also be used for different types of problems. I think the main criteria about what type of problem you can easily solve with it has to do with what kind of data you have around that problem. Any problem where there’s really clear output that you want, that you know what it is, it’s well-defined, and you feel like you have enough inputs of whatever sort to help…