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Data without analysis is not enough. But even analysis isn’t enough—we use machine learning to focus on continuous improvement at scale. This is what drives doing well and doing good.

First, some terms and meanings: Machine learning is a type of artificial intelligence in which the outcome of the algorithm is exactly the input for the next iteration of that algorithm. In this way, the predicted outcome improves through the process of refining the algorithm itself. An algorithm is a replicable sequence of steps to solve a problem. Machine learning means we use continuously improving algorithms to improve prediction of the outcome. This simple concept is profound because recommendations for improvement are made with ever-increasing certainty and effectiveness. 

Green River has spent the last two decades building machine learning-powered software that increases outcomes for individuals and organizations. More importantly, however, we are improving industries, sectors, and global commerce. It is not an exaggeration that our focus is now, and will continue to be, a more just world through continuous improvement in businesses, human services, and government. 

Machine learning is the intelligence behind everything from movie recommendations and search engines to self-driving cars. The learning, however, is only as useful and essential as the software in which it is brought to implementation. Green River is at the forefront of innovation by being at the nexus of software development, machine learning, and applied data.