Machine learning may seem like a mysterious creation to the average consumer, but the truth is we’re surrounded by it every day. ML algorithms power search results, monitor medical data, and impact our admission to schools, jobs, and even jail. Despite our proximity to machine learning algorithms, explaining how they work can be a difficult task, even for the experts who designed them.
In the early days of machine learning, algorithms were relatively straightforward, and not always as accurate as we’d like them to be. As research into machine learning progressed over the decades, the accuracy increased, and so did the complexity. Since the techniques were largely confined to academic research and some areas of industrial automation, it didn’t impact the average Joe very much.
Read the full article in datanami on “Making ML Explainable Again” featuring Ryohei Fujimaki, dotData CEO and founder.
