by Angela Guess
Gary Baum, VP of Marketing at MyScript, recently wrote in InsideBigData, “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.”
Baum goes on, “Countless researchers agree on the benefits of deep neural networks for applications ranging from acoustic modeling to speech recognition, and more recently even facial recognition. Deep neural networks with new training methods are even claimed to outperform the older Gaussian mixture models in audio representations. More recent applications are using the technology to predict human emotions by monitoring input methods. Neural networks continue to push the boundaries of machine learning. While blockbuster hits such as ‘I, Robot’ have made many consumers weary of artificial intelligence, these technologies help simplify our lives. From suggesting future vacations destinations based on a user’s travel history, to outlining steps a consumer can take to meet their weight loss goals, neural networks enable machines to be more helpful than ever before.”
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