Deep learning has shown significant promise in common knowledge extraction tasks. However, the reputation of neural networks for being black-box learners can retard adoption in enterprise businesses.
An interactive system that feeds back results in real time is critical. A human-in-the-loop business process is superior to a completely black-box approach in several ways. Gains include greater trust in the results, higher overall accuracy, and a deeper understanding of the error modes of the learning algorithms employed.
Martin Goodson gives a tell-all account of an ultimately successful installation of a deep learning system in an enterprise environment. Andy Crisp then shares insights into the challenges of integrating artificial intelligence systems into real-world business processes.
Martin Goodson is the chief scientist and CEO of Evolution AI, where he specializes in large-scale statistical computing and natural language processing. Martin has designed data science products that are in use at companies like Time Inc., Hearst, John Lewis, Condé Nast, and Buzzfeed. Previously, Martin worked as a statistician at the University of Oxford, where he conducted research on statistical matching problems for DNA sequences.
Andy Crisp is leader for the EU and Asia Data Engineering team at Dun & Bradstreet, where his remit ensures that he keeps more than an eye on innovation. Andy started his career at Dun & Bradstreet in sales, which offered a way into the world of big data. He has since tirelessly led innovative and creative thinking, particularly in terms of how to build and improve the D&B global data asset. Andy was recognized by DataIQ as one of the 100 most influential people in data and in 2015 was on Information Age’s shortlist for the UK’s top 50 data leaders.
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