MACHINE LEARNING
Machine learning broadly describes a field of study within computer science that tackles problems iteratively. Writing algorithms to solve some problems can be difficult or impossible, but it can be possible to provide feedback to a system when it does a better or worse job solving the problem. Using this feedback loop, machine learning systems develop the ability to analyze data in ways they were not directly programmed to do. Over time, these evolutions can become quite complex to the point of closely modeling organic learning.
PREDICTIVE ANALYTICS
Predictive analytics may use machine learning or other techniques to recognize patterns in data. Sometimes data is analyzed to search for expected patterns or to answer well understood questions; sometimes data is analyzed to determine whether there is useful information in a complex, difficult to understand data set. Predictive analytics can be used to forecast problems or better plan strategies to gain a competitive advantage. Predictive analytics can transform your historical data into a crystal ball that will help you succeed in digital product development.
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Predicting Weather in Space
Our scientists developed a system for predicting dangerous conditions in space, like solar winds, to help protect satellites from harm. The available data came from different sources, contained numerous variables, and were based on events that ranged in frequency from a few seconds to several hours. The data was noisy, poorly aligned and categorized, and contained numerous gaps. In short: a hard problem. Harder still was the requirement for 90% accuracy in predictions. Nonetheless, our engineers were able to apply proprietary time series analysis techniques to create a working model of predictions with the required accuracy.
