Many organizations today have a supermassive black hole at the center of their Digital Transformation strategy: Around 90% of the data they hold about their customers, employees, and business processes persists as "dark data" -- data that is locked away as ordinary human language written in reports, e-mails, internal documents, and customer service tickets, none of which can be easily understood by a machine. While people can read, and make sense of small batches of such data, the amount of data most organizations now hold has become too massive, and in-depth analysis of them goes beyond their resources. Fortunately, the dawn of a new era is upon us: A branch of artificial intelligence known as "Human Capacity Cognitive Computing" is able to provide human-level understanding of this dark data, giving organizations valuable insights into the contents of their dark data. Moreover, it can connect disparate pieces of the big data puzzle, providing a unified "big data big picture" of an organization's past, present, and future role in the marketplace.
Patrick Ehlen is Chief Scientist at Loop AI Labs. He has pursued artificial intelligence since 3rd grade, after reading Arthur C. Clarke's novel 2001: A Space Odyssey. In 1977, before most people had computers, his econometrician father bought a DEC PDP-11 to use in their home, and Patrick became obsessed with figuring out how to make the machine talk.
Fifteen years later, in 1992, inspired by the Parallel Distributed Processing volumes, he trained his first neural network to learn distributed representations of concepts.
Patrick earned a PhD in Cognitive Psychology from the New School for Social Research in 2005. While completing his PhD, he interned at speech recognition pioneer Dragon Systems (acquired by Nuance) and then at AT&T Labs-Research, trailblazing innovations in speech and multimodal interface technology.
He then went to the Center for the Study of Language and Information (CSLI), an AI mecca at Stanford University. As a research scientist in the Stanford's Computational Semantics Lab, he worked on the DARPA CALO AI project and devised machine learning methods to extract concepts and topics from ordinary, spontaneous conversations.
When approached by his old CALO colleague Bart Peintner about the work they were doing at Loop AI Labs, Patrick saw an opportunity to advance the state-of-the-art using techniques from Deep Learning that were just beginning to emerge.
Patrick has been awarded four U.S. patents and produced 45 research publications in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, human concept learning, and artificial intelligence. His work is cited in over 430 scientific papers.