Machine Learning Shaping the New World of Cognitive Computing
“Predictive analytics use a combination of statistical and machine learning algorithms to build predictive models,” Gualtieri said. “There is tremendous interest in using machine learning but most enterprises are still trying to understand what the heck it means.” For now, he said “machine learning is mostly for data scientists, but a few vendors are trying to democratize machine learning for business people and developers. IBM's Watson question answering system and Skytree are examples.” Getting "Cognitive" With Watson There is perhaps no more prominent example of cognitive computing than IBM’s Watson, where machine learning is but one component of the system’s overall cognitive package.“The breakthrough that we had when we formulated Watson for Jeopardy was to avoid depending on rules and ontologies," said Rob High, IBM’s CTO of Watson. “We completely divorced ourselves of that and looked at the problem entirely as a signal processing problem,” High said. Instead Watson works by measuring signal patterns—the patterns of signals and linguistics—and using the results to predict the meaning of that pattern, a process that depends heavily on machine learning, he explained. Cognitive computing is in turn rapidly becoming a cornerstone in IBM's future. Last month, IBM CEO Ginni Rometty announced that IBM is becoming a cognitive business. High said this means that IBM's focus "will be fundamentally based on tapping into the vast quantities of information out there that previously could not be processed with traditional computing techniques, but rather could only really be understood using techniques that come closer to recognizing the attention that humans would normally ascribe to that information.” “Developers and organizations are just beginning to understand and utilize the power of machine learning,” said IDC's Schubmehl. “A number of organizations have decided to use machine learning inside of cognitive system platforms like IBM Watson.” High said there are about 80,000 developers now using Watson cognitive computing APIs to build apps featuring cognitive technology. Doug Schaedler, CEO of Inno360, an IBM Watson ecosystem partner, told eWEEK his company is currently using seven Watson APIs and is planning to use seven more to support its cognitive solutions aimed at researchers working on specialized projects. The seven Watson APIs Inno360 is using include: Keyword Extraction, Concept Tagging, Taxonomy, Sentiment Analysis, Relationship Extraction, Linked Data and Entity Extraction. Schaedler said Inno360 came into being because Proctor & Gamble needed software to better connect its thousands of researchers and make them more effective. Inno360 started developing a system to serve that need by integrating Watson APIs in July 2014 and now relies on it. “Watson provides deep analysis and cognitive capability in the background,” he said. “It helps the whole enterprise get smarter.’ That element of adding “smarts” to systems and applications is essential to machine learning. “Pretty much any application that you’re building can get some intelligence,” Microsoft’s Oberoi said. “Most apps can benefit from that kind of intelligence introduced by machine learning.” Microsoft is investing in machine learning in a number of different ways. The company uses machine learning in most of its products—from Xbox to Bing and even in Outlook, Oberoi said. At the broadest level, the company has released its Cortana Analytics Suite, which is an end-to-end big data and advanced analytics platform. The company also is integrating that suite with its Cortana personal digital assistant which uses machine learning. In conjunction with Cortana Analytics Suite, Microsoft also offers its Azure Machine Learning Studio, a collaborative, drag-and-drop tool to build, test and deploy predictive analytics applications.
The original system underlying Watson was built on one API covering five technologies: natural language processing, machine learning, question analysis, feature engineering and ontology analysis. Yet now the Watson platform and ecosystem continues to grow with more than 25 APIs and services underpinned by more than 50 technologies in four key areas: language, speech, vision and data insights.