Unstructured data is all around us: in news stories, web pages, journal articles, social media posts, patents, research reports, presentations, and a variety of other sources. These items are unstructured in that they don’t start out with a predefined, explicit schema or structure. Historically, these documents have been read by humans looking to find information relevant to their particular tasks or roles. In today's deluge, however, the need for scalable reading, repeatability, traceability, and speed has driven the advent of text analytics platforms.
Data lakes are no longer anomalies. Consolidating all of an organization’s data—unstructured, semi-structured, and structured—into a single repository for integration, access, and analytics purposes is rapidly emerging as the preferred way to manage big data initiatives.
Richard Mallah, our Director of Advanced Analytics recently participated at the The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), focused on machine learning and computational neuroscience. Richard was a panelist at the symposium higlighting The Societal Impacts of Machine Learning. This symposium aimed to turn the attention of Machine Learning researchers to the present and future consequences of their work, particularly in the areas of privacy, military robotics, employment, and liability.