Neural Networks and the Future of Machine Learning

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In this special guest feature, Gary Baum, Vice President of Marketing at MyScript, talks about how handwriting recognition is enhancing machine (and human) learning. As an input method, handwriting recognition teaches machines to adapt to the user, adding in another layer to their evolving skill set. Those users can program systems simply by jotting down notes and in turn, build platforms most reflective of the human experience. Gary is a tech industry veteran with more than 20 years of executive marketing and product management experience. At MyScript, he oversees global marketing activities and educational efforts to build MyScript brand awareness and drive technology adoption, expand and commercialize digital ink technology offerings, and nurture strategic collaborations and partnerships within the digital writing ecosystem. He holds a Bachelor of Science in Electrical Engineering (BSEE) and graduate studies in computer architecture and advanced control theory. Gary holds six patents in the field of CPU and system design.

Not long ago, many would scoff at the notion that a machine is “learning,” “doing” or “knowing.” But neural networks and artificial intelligence (AI) technologies are layering those skillsets together to perform increasingly complicated, human-like functions. Google DeepMind, for example, is one of few very advanced neural networks that are driving the future of machine learning.

While machines have previously been able to read and answer our questions about news articles, for example, their knowledge was often limited by the length of a piece or driven to brute force computation. Newly-developed algorithms enable those systems to learn from experience and online data – leading to a more sophisticated understanding of topics and language. Researchers put this theory to the test by inputting hundreds of thousands of Daily Mail and CNN articles into a system with the goal of accurately detecting missing words or predicting a headline. The neural network correctly answered more than half of all queries, struggling only with those that featured more complex grammatical structures.

Read the source article at insideBIGDATA