Dan Woods of Forbes reports, “It’s easy to get caught up in the mystique of machine learning. After all, what’s not to like about the idea of algorithms sucking up and sorting through the data detritus of our companies’ back alleys like a Roomba Sweeper Vac? The reality is a bit more complex. Like any ‘breakthrough’ technology, machine learning involves some forethought and discipline before being let loose in the enterprise. I recently spoke with Bob Tennant, CEO of Recommind, a San Francisco-based unstructured information management and analysis company, seeking a few points of advice about where to use machine learning most effectively.”

He goes on, “Recommind has developed a platform that automates discovery, search, and categorization of data. With user input, the software adjusts its behavior to predict which data will be most relevant for the question at hand. By using Probabilistic Latent Semantic Analysis (PLSA), a machine-learning technique developed by Recommind cofounder and CTO Jan Puzicha at University of California, Berkeley, Recommind can help sort out whether thousands of documents that contain the word “java” are about programming or coffee, for instance.”

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Image: Courtesy Recommind