Buy Dideo Subscription
Abstract: There is growing interest in brain-like computing, but can machines think like humans? Associative memories learn by example like humans. We present the world's fastest triple store -Saffron Memory Base- for just in time machine learning. Saffron Memory Base uncovers connections, counts and context in the raw data. It builds a semantic graph out of the box from hybrid data sources. Saffron stores the graph and its statistics in matrices that can be queried in real time even for Big Data. Connecting the Dots. We demonstrate the power of entity rank for real time search by the example of the London Bomber and Twitter sentiment analysis. Illuminating the Dots. We show the power of Saffron's model-free approach for pattern recognition and prediction on a couple of real world examples like Boeing's use case of predictive maintenance for aircraft. Speaker Bio: Dr. Paul Hofmann is an expert in AI, computer simulations, and graphics. He is CTO of Saffron Technology, a Big Data software analytics firm named top 5 coolest vendors in Enterprise Information Management by Gartner. Before joining Saffron, Paul was VP of Research at SAP Labs in Silicon Valley. Paul received his Ph.D. in Physics at the Darmstadt University of Technology. Paul's background is entrenched in research as Senior Scientist and Assistant Professor at outstanding European and American Universities (Northwestern University, U.S.; Munich University of Technology and Darmstadt University of Technology, Germany). He is an expert in computational chemistry (Ph.D., research and teaching in Nonlinear Dynamics and Chaos Theory), authoring numerous publications and books. Paul was visiting scientist at MIT, Cambridge in 2009.
