Jorge Garcia of Wired recently wrote, “IBM’s recent announcements of three new services based in Watson technology make it clear that there is pressure in the enterprise software space to incorporate new technologies, both in hardware and software, in order to keep pace with modern business. It seems we are approaching another turning point in technology where many concepts that were previously limited to academic research or very narrow industry niches are now being considered for mainstream enterprise software applications. Machine learning, along with many other disciplines within the field of artificial intelligence and cognitive systems, is gaining popularity, and it may in the not so distant future have a colossal impact on the software industry. This first part of my series on machine learning explores some basic concepts of the discipline and its potential for transforming the business intelligence and analytics space.”
Garcia goes on, “One of the more important applications of machine learning is to automate the acquisition of knowledge bases used by so-called expert systems, systems that aims to emulate the decision making process of human expertise in a field. But the scope of its application has been growing. In Applications of Machine Learning and Rule Induction, Langley and Simon review some major paradigms for machine learning scenarios, all based on a very important premise: ‘To improve performance on some task, and the general approach involves finding and exploiting regularities in training data.’ The major approaches include using neural networks, case-based learning, genetic algorithms, rule induction, and analytic learning. While in the past they were applied independently, in recent times these paradigms or models are being used in a hybrid fashion, closing the boundaries between them and enabling the development of more effective models.”
Want to learn more? Among the speakers at the inaugural Cognitive Computing Forum next month will be Chris Welty from IBM’s T.J. Watson Research Center.