How do you manage the decision making risk with machine learning?
Conversations with Experts
Professor and Chief Scientist, Northwestern University, Narrative Science
Kris Hammond is Chief Scientist at Narrative Science and a professor of Computer Science and Journalism at Northwestern University. Prior to joining the faculty at Northwestern, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has been primarily focused on artificial intelligence, machine-generated content and context-driven information systems. Kris currently sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris received his PhD from Yale.
Kris was also named 2014 Innovator of the Year by the Best in Biz Awards and is author of the book, “Practical Artificial Intelligence for Dummies”.
Professor and Entrepreneur, Illinois Institute of Technology
Machine Humanity: How the Machine Learning of Today is Driving the Artificial Intelligence of Tomorrow
Machine learning is hot and for good reason. The components — big data, computing power, analytical methods — are in place, and compelling applications are multiplying. To capitalize on the technology, organizations must build experience. They must also proceed pragmatically with one eye on the business and the other on the ethical implications of the algorithms deployed and the decisions automatically made. To explore the opportunities, challenges, and success factors of machine learning today and tomorrow, IIA spoke with Andrew Pease, Principal Business Solutions Manager, Global Technology Practice at SAS Institute and Josefin Rosén, Principal Advisor Analytics, Nordic Government at SAS Institute.
Amazon launched Amazon Web Services (AWS) 10 years ago with a similar customer-centric, expansion minded approach to information technology. Today, there is no greater force in the technology industry than AWS. AWS will collect over $13B in revenue in 2016, a 55% increase over 2015. It contributes more than 100% of the operating profits of its parent company, as the other business units operate at a loss. Well known for providing the underlying infrastructure for digital native companies like Netflix, Uber and Airbnb, AWS is now heavily targeting the enterprise IT market – specifically Big Data, BI and analytics.
Machine learning is rapidly coming of age. Three forces – highly scalable computing, the ability to handle vast amounts of structured and unstructured data, and sophisticated algorithms and methods – combine to advance the power of machine learning and put it to work in a growing array of business applications. We discussed the opportunities with two of Intel’s leading experts, Bob Rogers, Chief Data Scientist for Big Data Solutions, and Kathleen Crowe, Director of Data Science.
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